From abdb3c609d9d31c4e6bc372b4d2011b45b8aa1b8 Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Wed, 27 Apr 2022 12:52:42 +0530 Subject: [PATCH 01/57] converted diagonal guassian distribution to .hpp --- src/mlpack/core/dists/CMakeLists.txt | 2 +- .../dists/diagonal_gaussian_distribution.hpp | 3 +++ ...> diagonal_gaussian_distribution_impl.hpp} | 22 +++++++++++++------ 3 files changed, 19 insertions(+), 8 deletions(-) rename src/mlpack/core/dists/{diagonal_gaussian_distribution.cpp => diagonal_gaussian_distribution_impl.hpp} (90%) diff --git a/src/mlpack/core/dists/CMakeLists.txt b/src/mlpack/core/dists/CMakeLists.txt index c8365faac3..a11654f746 100644 --- a/src/mlpack/core/dists/CMakeLists.txt +++ b/src/mlpack/core/dists/CMakeLists.txt @@ -12,7 +12,7 @@ set(SOURCES gamma_distribution.hpp gamma_distribution.cpp diagonal_gaussian_distribution.hpp - diagonal_gaussian_distribution.cpp + diagonal_gaussian_distribution_impl.hpp ) # add directory name to sources diff --git a/src/mlpack/core/dists/diagonal_gaussian_distribution.hpp b/src/mlpack/core/dists/diagonal_gaussian_distribution.hpp index 05c5e54e2c..b9b606c293 100644 --- a/src/mlpack/core/dists/diagonal_gaussian_distribution.hpp +++ b/src/mlpack/core/dists/diagonal_gaussian_distribution.hpp @@ -153,4 +153,7 @@ class DiagonalGaussianDistribution } // namespace distribution } // namespace mlpack +// Include implementation. +#include "diagonal_gaussian_distribution_impl.hpp" + #endif diff --git a/src/mlpack/core/dists/diagonal_gaussian_distribution.cpp b/src/mlpack/core/dists/diagonal_gaussian_distribution_impl.hpp similarity index 90% rename from src/mlpack/core/dists/diagonal_gaussian_distribution.cpp rename to src/mlpack/core/dists/diagonal_gaussian_distribution_impl.hpp index bb58ea7931..463453297d 100644 --- a/src/mlpack/core/dists/diagonal_gaussian_distribution.cpp +++ b/src/mlpack/core/dists/diagonal_gaussian_distribution_impl.hpp @@ -1,5 +1,5 @@ /** - * @file core/dists/diagonal_gaussian_distribution.cpp + * @file core/dists/diagonal_gaussian_distribution_impl.hpp * @author Kim SangYeon * * Implementation of Gaussian distribution class with diagonal covariance. @@ -9,11 +9,14 @@ * 3-clause BSD license along with mlpack. If not, see * http://www.opensource.org/licenses/BSD-3-Clause for more information. */ +#ifndef MLPACK_CORE_DISTRIBUTIONS_DIAGONAL_GAUSSIAN_DISTRIBUTION_IMPL_HPP +#define MLPACK_CORE_DISTRIBUTIONS_DIAGONAL_GAUSSIAN_DISTRIBUTION_IMPL_HPP + #include "diagonal_gaussian_distribution.hpp" #include -using namespace mlpack; -using namespace mlpack::distribution; +namespace mlpack { +namespace distribution { DiagonalGaussianDistribution::DiagonalGaussianDistribution( const arma::vec& mean, @@ -25,15 +28,15 @@ DiagonalGaussianDistribution::DiagonalGaussianDistribution( void DiagonalGaussianDistribution::Covariance(const arma::vec& covariance) { - this->invCov = 1 / covariance; - this->logDetCov = arma::accu(log(covariance)); + invCov = 1 / covariance; + logDetCov = arma::accu(log(covariance)); this->covariance = covariance; } void DiagonalGaussianDistribution::Covariance(arma::vec&& covariance) { - this->invCov = 1 / covariance; - this->logDetCov = arma::accu(log(covariance)); + invCov = 1 / covariance; + logDetCov = arma::accu(log(covariance)); this->covariance = std::move(covariance); } @@ -146,3 +149,8 @@ void DiagonalGaussianDistribution::Train(const arma::mat& observations, invCov = 1 / covariance; logDetCov = arma::accu(log(covariance)); } + +} // namespace distribution +} // namespace mlpack + +#endif From e7201e27eca5a670db682e70384eda8e8df46ae8 Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Wed, 27 Apr 2022 13:04:12 +0530 Subject: [PATCH 02/57] forgot to add inline :) --- .../diagonal_gaussian_distribution_impl.hpp | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/src/mlpack/core/dists/diagonal_gaussian_distribution_impl.hpp b/src/mlpack/core/dists/diagonal_gaussian_distribution_impl.hpp index 463453297d..b7329a7daa 100644 --- a/src/mlpack/core/dists/diagonal_gaussian_distribution_impl.hpp +++ b/src/mlpack/core/dists/diagonal_gaussian_distribution_impl.hpp @@ -18,7 +18,7 @@ namespace mlpack { namespace distribution { -DiagonalGaussianDistribution::DiagonalGaussianDistribution( +inline DiagonalGaussianDistribution::DiagonalGaussianDistribution( const arma::vec& mean, const arma::vec& covariance) : mean(mean) @@ -26,21 +26,21 @@ DiagonalGaussianDistribution::DiagonalGaussianDistribution( Covariance(covariance); } -void DiagonalGaussianDistribution::Covariance(const arma::vec& covariance) +inline void DiagonalGaussianDistribution::Covariance(const arma::vec& covariance) { invCov = 1 / covariance; logDetCov = arma::accu(log(covariance)); this->covariance = covariance; } -void DiagonalGaussianDistribution::Covariance(arma::vec&& covariance) +inline void DiagonalGaussianDistribution::Covariance(arma::vec&& covariance) { invCov = 1 / covariance; logDetCov = arma::accu(log(covariance)); this->covariance = std::move(covariance); } -double DiagonalGaussianDistribution::LogProbability( +inline double DiagonalGaussianDistribution::LogProbability( const arma::vec& observation) const { const size_t k = observation.n_elem; @@ -49,7 +49,7 @@ double DiagonalGaussianDistribution::LogProbability( return -0.5 * k * log2pi - 0.5 * logDetCov - 0.5 * logExponent(0); } -void DiagonalGaussianDistribution::LogProbability( +inline void DiagonalGaussianDistribution::LogProbability( const arma::mat& observations, arma::vec& logProbabilities) const { @@ -66,12 +66,12 @@ void DiagonalGaussianDistribution::LogProbability( logProbabilities = -0.5 * k * log2pi - 0.5 * logDetCov + logExponents; } -arma::vec DiagonalGaussianDistribution::Random() const +inline arma::vec DiagonalGaussianDistribution::Random() const { return (arma::sqrt(covariance) % arma::randn(mean.n_elem)) + mean; } -void DiagonalGaussianDistribution::Train(const arma::mat& observations) +inline void DiagonalGaussianDistribution::Train(const arma::mat& observations) { if (observations.n_cols > 1) { @@ -98,7 +98,7 @@ void DiagonalGaussianDistribution::Train(const arma::mat& observations) logDetCov = arma::accu(log(covariance)); } -void DiagonalGaussianDistribution::Train(const arma::mat& observations, +inline void DiagonalGaussianDistribution::Train(const arma::mat& observations, const arma::vec& probabilities) { if (observations.n_cols > 0) From 36f76a996a05fda46d333cf56ad4cdd9f0fee051 Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Fri, 29 Apr 2022 11:35:08 +0530 Subject: [PATCH 03/57] converted discrete distribution to .hpp --- src/mlpack/core/dists/CMakeLists.txt | 2 +- .../dists/diagonal_gaussian_distribution.hpp | 2 +- .../diagonal_gaussian_distribution_impl.hpp | 2 +- .../core/dists/discrete_distribution.hpp | 2 ++ ...ion.cpp => discrete_distribution_impl.hpp} | 20 +++++++++++++------ 5 files changed, 19 insertions(+), 9 deletions(-) rename src/mlpack/core/dists/{discrete_distribution.cpp => discrete_distribution_impl.hpp} (90%) diff --git a/src/mlpack/core/dists/CMakeLists.txt b/src/mlpack/core/dists/CMakeLists.txt index a11654f746..d66dd44d0c 100644 --- a/src/mlpack/core/dists/CMakeLists.txt +++ b/src/mlpack/core/dists/CMakeLists.txt @@ -2,7 +2,7 @@ # Anything not in this list will not be compiled into mlpack. set(SOURCES discrete_distribution.hpp - discrete_distribution.cpp + discrete_distribution_impl.hpp gaussian_distribution.hpp gaussian_distribution.cpp laplace_distribution.hpp diff --git a/src/mlpack/core/dists/diagonal_gaussian_distribution.hpp b/src/mlpack/core/dists/diagonal_gaussian_distribution.hpp index b9b606c293..76515cd3ec 100644 --- a/src/mlpack/core/dists/diagonal_gaussian_distribution.hpp +++ b/src/mlpack/core/dists/diagonal_gaussian_distribution.hpp @@ -15,7 +15,7 @@ #include namespace mlpack { -namespace distribution { +namespace distribution /** Probability distributions. */ { //! A single multivariate Gaussian distribution with diagonal covariance. class DiagonalGaussianDistribution diff --git a/src/mlpack/core/dists/diagonal_gaussian_distribution_impl.hpp b/src/mlpack/core/dists/diagonal_gaussian_distribution_impl.hpp index b7329a7daa..c3e03ac689 100644 --- a/src/mlpack/core/dists/diagonal_gaussian_distribution_impl.hpp +++ b/src/mlpack/core/dists/diagonal_gaussian_distribution_impl.hpp @@ -16,7 +16,7 @@ #include namespace mlpack { -namespace distribution { +namespace distribution /** Probability distributions. */ { inline DiagonalGaussianDistribution::DiagonalGaussianDistribution( const arma::vec& mean, diff --git a/src/mlpack/core/dists/discrete_distribution.hpp b/src/mlpack/core/dists/discrete_distribution.hpp index bf209bb3d0..16d6092e29 100644 --- a/src/mlpack/core/dists/discrete_distribution.hpp +++ b/src/mlpack/core/dists/discrete_distribution.hpp @@ -252,4 +252,6 @@ class DiscreteDistribution } // namespace distribution } // namespace mlpack +#include "discrete_distribution_impl.hpp" + #endif diff --git a/src/mlpack/core/dists/discrete_distribution.cpp b/src/mlpack/core/dists/discrete_distribution_impl.hpp similarity index 90% rename from src/mlpack/core/dists/discrete_distribution.cpp rename to src/mlpack/core/dists/discrete_distribution_impl.hpp index 2fd64aea73..09533ae170 100644 --- a/src/mlpack/core/dists/discrete_distribution.cpp +++ b/src/mlpack/core/dists/discrete_distribution_impl.hpp @@ -1,5 +1,5 @@ /** - * @file core/dists/discrete_distribution.cpp + * @file core/dists/discrete_distribution_impl.hpp * @author Ryan Curtin * @author Rohan Raj * @@ -10,16 +10,19 @@ * 3-clause BSD license along with mlpack. If not, see * http://www.opensource.org/licenses/BSD-3-Clause for more information. */ +#ifndef MLPACK_CORE_DISTRIBUTIONS_DISCRETE_DISTRIBUTION_IMPL_HPP +#define MLPACK_CORE_DISTRIBUTIONS_DISCRETE_DISTRIBUTION_IMPL_HPP + #include "discrete_distribution.hpp" -using namespace mlpack; -using namespace mlpack::distribution; +namespace mlpack { +namespace distribution /** Probability distributions. */ { /** * Return a randomly generated observation according to the probability * distribution defined by this object. */ -arma::vec DiscreteDistribution::Random() const +inline arma::vec DiscreteDistribution::Random() const { size_t dimension = probabilities.size(); arma::vec result(dimension); @@ -53,7 +56,7 @@ arma::vec DiscreteDistribution::Random() const /** * Estimate the probability distribution directly from the given observations. */ -void DiscreteDistribution::Train(const arma::mat& observations) +inline void DiscreteDistribution::Train(const arma::mat& observations) { // Make sure the observations have same dimension as the probabilities. if (observations.n_rows != probabilities.size()) @@ -107,7 +110,7 @@ void DiscreteDistribution::Train(const arma::mat& observations) * Estimate the probability distribution from the given observations when also * given probabilities that each observation is from this distribution. */ -void DiscreteDistribution::Train(const arma::mat& observations, +inline void DiscreteDistribution::Train(const arma::mat& observations, const arma::vec& probObs) { // Make sure the observations have same dimension as the probabilities. @@ -158,3 +161,8 @@ void DiscreteDistribution::Train(const arma::mat& observations, probabilities[i].fill(1.0 / probabilities[i].n_elem); } } + +} // namespace distribution +} // namespace mlpack + +#endif From 3d18d41a5e40191ce7d790d3cf52310499061a2c Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Fri, 29 Apr 2022 11:56:33 +0530 Subject: [PATCH 04/57] converted gamma distribution to .hpp --- src/mlpack/core/dists/CMakeLists.txt | 2 +- .../core/dists/discrete_distribution.hpp | 1 + src/mlpack/core/dists/gamma_distribution.hpp | 5 ++- ...bution.cpp => gamma_distribution_impl.hpp} | 34 ++++++++++++------- 4 files changed, 27 insertions(+), 15 deletions(-) rename src/mlpack/core/dists/{gamma_distribution.cpp => gamma_distribution_impl.hpp} (87%) diff --git a/src/mlpack/core/dists/CMakeLists.txt b/src/mlpack/core/dists/CMakeLists.txt index d66dd44d0c..f75d27b632 100644 --- a/src/mlpack/core/dists/CMakeLists.txt +++ b/src/mlpack/core/dists/CMakeLists.txt @@ -10,7 +10,7 @@ set(SOURCES regression_distribution.hpp regression_distribution.cpp gamma_distribution.hpp - gamma_distribution.cpp + gamma_distribution_impl.hpp diagonal_gaussian_distribution.hpp diagonal_gaussian_distribution_impl.hpp ) diff --git a/src/mlpack/core/dists/discrete_distribution.hpp b/src/mlpack/core/dists/discrete_distribution.hpp index 16d6092e29..1321ee49ab 100644 --- a/src/mlpack/core/dists/discrete_distribution.hpp +++ b/src/mlpack/core/dists/discrete_distribution.hpp @@ -252,6 +252,7 @@ class DiscreteDistribution } // namespace distribution } // namespace mlpack +// Include implementation. #include "discrete_distribution_impl.hpp" #endif diff --git a/src/mlpack/core/dists/gamma_distribution.hpp b/src/mlpack/core/dists/gamma_distribution.hpp index ef8b43a1f0..631178ad6a 100644 --- a/src/mlpack/core/dists/gamma_distribution.hpp +++ b/src/mlpack/core/dists/gamma_distribution.hpp @@ -25,7 +25,7 @@ #include namespace mlpack { -namespace distribution { +namespace distribution /** Probability distributions. */ { /** * This class represents the Gamma distribution. It supports training a Gamma @@ -232,4 +232,7 @@ class GammaDistribution } // namespace distribution } // namespace mlpack +// Include implementation. +#include "gamma_distribution_impl.hpp" + #endif diff --git a/src/mlpack/core/dists/gamma_distribution.cpp b/src/mlpack/core/dists/gamma_distribution_impl.hpp similarity index 87% rename from src/mlpack/core/dists/gamma_distribution.cpp rename to src/mlpack/core/dists/gamma_distribution_impl.hpp index 80d0d0f6c0..9053c45508 100644 --- a/src/mlpack/core/dists/gamma_distribution.cpp +++ b/src/mlpack/core/dists/gamma_distribution_impl.hpp @@ -1,5 +1,5 @@ /** - * @file core/dists/gamma_distribution.cpp + * @file core/dists/gamma_distribution_impl.hpp * @author Yannis Mentekidis * @author Rohan Raj * @@ -10,25 +10,28 @@ * 3-clause BSD license along with mlpack. If not, see * http://www.opensource.org/licenses/BSD-3-Clause for more information. */ +#ifndef _MLPACK_CORE_DISTRIBUTIONS_GAMMA_DISTRIBUTION_IMPL_HPP +#define _MLPACK_CORE_DISTRIBUTIONS_GAMMA_DISTRIBUTION_IMPL_HPP + #include "gamma_distribution.hpp" -using namespace mlpack; -using namespace mlpack::distribution; +namespace mlpack { +namespace distribution /** Probability distributions. */ { -GammaDistribution::GammaDistribution(const size_t dimensionality) +inline GammaDistribution::GammaDistribution(const size_t dimensionality) { // Initialize distribution. alpha.zeros(dimensionality); beta.zeros(dimensionality); } -GammaDistribution::GammaDistribution(const arma::mat& data, +inline GammaDistribution::GammaDistribution(const arma::mat& data, const double tol) { Train(data, tol); } -GammaDistribution::GammaDistribution(const arma::vec& alpha, +inline GammaDistribution::GammaDistribution(const arma::vec& alpha, const arma::vec& beta) { if (beta.n_elem != alpha.n_elem) @@ -47,7 +50,7 @@ inline bool GammaDistribution::Converged(const double aOld, } // Fits an alpha and beta parameter to each dimension of the data. -void GammaDistribution::Train(const arma::mat& rdata, const double tol) +inline void GammaDistribution::Train(const arma::mat& rdata, const double tol) { // If fittingSet is empty, nothing to do. if (arma::size(rdata) == arma::size(arma::mat())) @@ -64,7 +67,7 @@ void GammaDistribution::Train(const arma::mat& rdata, const double tol) } // Fits an alpha and beta parameter according to observation probabilities. -void GammaDistribution::Train(const arma::mat& rdata, +inline void GammaDistribution::Train(const arma::mat& rdata, const arma::vec& probabilities, const double tol) { @@ -94,7 +97,7 @@ void GammaDistribution::Train(const arma::mat& rdata, } // Fits an alpha and beta parameter to each dimension of the data. -void GammaDistribution::Train(const arma::vec& logMeanxVec, +inline void GammaDistribution::Train(const arma::vec& logMeanxVec, const arma::vec& meanLogxVec, const arma::vec& meanxVec, const double tol) @@ -155,7 +158,7 @@ void GammaDistribution::Train(const arma::vec& logMeanxVec, } // Returns the probability of the provided observations. -void GammaDistribution::Probability(const arma::mat& observations, +inline void GammaDistribution::Probability(const arma::mat& observations, arma::vec& probabilities) const { size_t numObs = observations.n_cols; @@ -184,14 +187,14 @@ void GammaDistribution::Probability(const arma::mat& observations, // Returns the probability of one observation (x) for one of the Gamma's // dimensions. -double GammaDistribution::Probability(double x, size_t dim) const +inline double GammaDistribution::Probability(double x, size_t dim) const { return std::pow(x, alpha(dim) - 1) * std::exp(-x / beta(dim)) / (std::tgamma(alpha(dim)) * std::pow(beta(dim), alpha(dim))); } // Returns the log probability of the provided observations. -void GammaDistribution::LogProbability(const arma::mat& observations, +inline void GammaDistribution::LogProbability(const arma::mat& observations, arma::vec& logProbabilities) const { size_t numObs = observations.n_cols; @@ -221,7 +224,7 @@ void GammaDistribution::LogProbability(const arma::mat& observations, // Returns the log probability of one observation (x) for one of the Gamma's // dimensions. -double GammaDistribution::LogProbability(double x, size_t dim) const +inline double GammaDistribution::LogProbability(double x, size_t dim) const { return std::log(std::pow(x, alpha(dim) - 1) * std::exp(-x / beta(dim)) / (std::tgamma(alpha(dim)) * std::pow(beta(dim), alpha(dim)))); @@ -241,3 +244,8 @@ arma::vec GammaDistribution::Random() const return randVec; } + +} // namespace distribution +} // namespace mlpack + +#endif From 2a451e304780f6ccd58ff4fe075b4dbc082ce669 Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Fri, 29 Apr 2022 12:10:55 +0530 Subject: [PATCH 05/57] converted guassian to .hpp --- src/mlpack/core/dists/CMakeLists.txt | 2 +- .../core/dists/gaussian_distribution.hpp | 5 ++- ...ion.cpp => gaussian_distribution_impl.hpp} | 41 ++++++++++--------- 3 files changed, 26 insertions(+), 22 deletions(-) rename src/mlpack/core/dists/{gaussian_distribution.cpp => gaussian_distribution_impl.hpp} (82%) diff --git a/src/mlpack/core/dists/CMakeLists.txt b/src/mlpack/core/dists/CMakeLists.txt index f75d27b632..341a1d0200 100644 --- a/src/mlpack/core/dists/CMakeLists.txt +++ b/src/mlpack/core/dists/CMakeLists.txt @@ -4,7 +4,7 @@ set(SOURCES discrete_distribution.hpp discrete_distribution_impl.hpp gaussian_distribution.hpp - gaussian_distribution.cpp + gaussian_distribution_impl.hpp laplace_distribution.hpp laplace_distribution.cpp regression_distribution.hpp diff --git a/src/mlpack/core/dists/gaussian_distribution.hpp b/src/mlpack/core/dists/gaussian_distribution.hpp index 2d47f7d686..6bd1119254 100644 --- a/src/mlpack/core/dists/gaussian_distribution.hpp +++ b/src/mlpack/core/dists/gaussian_distribution.hpp @@ -16,7 +16,7 @@ #include namespace mlpack { -namespace distribution { +namespace distribution /** Probability distributions. */ { /** * A single multivariate Gaussian distribution. @@ -194,4 +194,7 @@ class GaussianDistribution } // namespace distribution } // namespace mlpack +// Include implementation. +#include "gaussian_distribution_impl.hpp" + #endif diff --git a/src/mlpack/core/dists/gaussian_distribution.cpp b/src/mlpack/core/dists/gaussian_distribution_impl.hpp similarity index 82% rename from src/mlpack/core/dists/gaussian_distribution.cpp rename to src/mlpack/core/dists/gaussian_distribution_impl.hpp index 8d4f496012..54ee51b259 100644 --- a/src/mlpack/core/dists/gaussian_distribution.cpp +++ b/src/mlpack/core/dists/gaussian_distribution_impl.hpp @@ -1,5 +1,5 @@ /** - * @file core/dists/gaussian_distribution.cpp + * @file core/dists/gaussian_distribution_impl.hpp * @author Ryan Curtin * @author Michael Fox * @@ -10,33 +10,35 @@ * 3-clause BSD license along with mlpack. If not, see * http://www.opensource.org/licenses/BSD-3-Clause for more information. */ +#ifndef MLPACK_CORE_DISTRIBUTIONS_GAUSSIAN_DISTRIBUTION_IMPL_HPP +#define MLPACK_CORE_DISTRIBUTIONS_GAUSSIAN_DISTRIBUTION_IMPL_HPP + #include "gaussian_distribution.hpp" #include -using namespace mlpack; -using namespace mlpack::distribution; +namespace mlpack { +namespace distribution /** Probability distributions. */ { - -GaussianDistribution::GaussianDistribution(const arma::vec& mean, +inline GaussianDistribution::GaussianDistribution(const arma::vec& mean, const arma::mat& covariance) : mean(mean), logDetCov(0.0) { Covariance(covariance); } -void GaussianDistribution::Covariance(const arma::mat& covariance) +inline void GaussianDistribution::Covariance(const arma::mat& covariance) { this->covariance = covariance; FactorCovariance(); } -void GaussianDistribution::Covariance(arma::mat&& covariance) +inline void GaussianDistribution::Covariance(arma::mat&& covariance) { this->covariance = std::move(covariance); FactorCovariance(); } -void GaussianDistribution::FactorCovariance() +inline void GaussianDistribution::FactorCovariance() { // On Armadillo < 4.500, the "lower" option isn't available. @@ -68,7 +70,7 @@ void GaussianDistribution::FactorCovariance() logDetCov *= 2; } -double GaussianDistribution::LogProbability(const arma::vec& observation) const +inline double GaussianDistribution::LogProbability(const arma::vec& observation) const { const size_t k = observation.n_elem; const arma::vec diff = mean - observation; @@ -76,7 +78,7 @@ double GaussianDistribution::LogProbability(const arma::vec& observation) const return -0.5 * k * log2pi - 0.5 * logDetCov - 0.5 * v(0); } -arma::vec GaussianDistribution::Random() const +inline arma::vec GaussianDistribution::Random() const { return covLower * arma::randn(mean.n_elem) + mean; } @@ -86,7 +88,7 @@ arma::vec GaussianDistribution::Random() const * * @param observations List of observations. */ -void GaussianDistribution::Train(const arma::mat& observations) +inline void GaussianDistribution::Train(const arma::mat& observations) { if (observations.n_cols > 0) { @@ -95,10 +97,7 @@ void GaussianDistribution::Train(const arma::mat& observations) } else // This will end up just being empty. { - // TODO(stephentu): why do we allow this case? why not throw an error? - mean.zeros(0); - covariance.zeros(0); - return; + Log::Fatal << "Observation columns equal to 0." << std::endl; } // Calculate the mean. @@ -130,7 +129,7 @@ void GaussianDistribution::Train(const arma::mat& observations) * account the probability of each observation actually being from this * distribution. */ -void GaussianDistribution::Train(const arma::mat& observations, +inline void GaussianDistribution::Train(const arma::mat& observations, const arma::vec& probabilities) { if (observations.n_cols > 0) @@ -140,10 +139,7 @@ void GaussianDistribution::Train(const arma::mat& observations, } else // This will end up just being empty. { - // TODO(stephentu): same as above - mean.zeros(0); - covariance.zeros(0); - return; + Log::Fatal << "Observation columns equal to 0." << std::endl; } double sumProb = 0; @@ -185,3 +181,8 @@ void GaussianDistribution::Train(const arma::mat& observations, FactorCovariance(); } + +} // namespace distribution +} // namespace mlpack + +#endif From 623717a68ed7e53658c40714064cd3a334b7c001 Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Fri, 29 Apr 2022 12:28:58 +0530 Subject: [PATCH 06/57] changed laplace distribution to .hpp --- src/mlpack/core/dists/CMakeLists.txt | 2 +- .../core/dists/gamma_distribution_impl.hpp | 2 +- .../core/dists/laplace_distribution.hpp | 6 ++++- ...tion.cpp => laplace_distribution_impl.hpp} | 22 ++++++++++++------- 4 files changed, 21 insertions(+), 11 deletions(-) rename src/mlpack/core/dists/{laplace_distribution.cpp => laplace_distribution_impl.hpp} (84%) diff --git a/src/mlpack/core/dists/CMakeLists.txt b/src/mlpack/core/dists/CMakeLists.txt index 341a1d0200..c9bd6ecac6 100644 --- a/src/mlpack/core/dists/CMakeLists.txt +++ b/src/mlpack/core/dists/CMakeLists.txt @@ -6,7 +6,7 @@ set(SOURCES gaussian_distribution.hpp gaussian_distribution_impl.hpp laplace_distribution.hpp - laplace_distribution.cpp + laplace_distribution_impl.hpp regression_distribution.hpp regression_distribution.cpp gamma_distribution.hpp diff --git a/src/mlpack/core/dists/gamma_distribution_impl.hpp b/src/mlpack/core/dists/gamma_distribution_impl.hpp index 9053c45508..66301587a9 100644 --- a/src/mlpack/core/dists/gamma_distribution_impl.hpp +++ b/src/mlpack/core/dists/gamma_distribution_impl.hpp @@ -231,7 +231,7 @@ inline double GammaDistribution::LogProbability(double x, size_t dim) const } // Returns a gamma-random d-dimensional vector. -arma::vec GammaDistribution::Random() const +inline arma::vec GammaDistribution::Random() const { arma::vec randVec(alpha.n_elem); diff --git a/src/mlpack/core/dists/laplace_distribution.hpp b/src/mlpack/core/dists/laplace_distribution.hpp index 991865f4dd..9af82652eb 100644 --- a/src/mlpack/core/dists/laplace_distribution.hpp +++ b/src/mlpack/core/dists/laplace_distribution.hpp @@ -14,8 +14,9 @@ #ifndef MLPACK_CORE_DISTRIBUTIONS_LAPLACE_DISTRIBUTION_HPP #define MLPACK_CORE_DISTRIBUTIONS_LAPLACE_DISTRIBUTION_HPP +#include namespace mlpack { -namespace distribution { +namespace distribution /** Probability distributions. */ { /** * The multivariate Laplace distribution centered at 0 has pdf @@ -189,4 +190,7 @@ class LaplaceDistribution } // namespace distribution } // namespace mlpack +// Include implementation. +#include "laplace_distribution_impl.hpp" + #endif diff --git a/src/mlpack/core/dists/laplace_distribution.cpp b/src/mlpack/core/dists/laplace_distribution_impl.hpp similarity index 84% rename from src/mlpack/core/dists/laplace_distribution.cpp rename to src/mlpack/core/dists/laplace_distribution_impl.hpp index 58ef9ba842..beb3ebcb2c 100644 --- a/src/mlpack/core/dists/laplace_distribution.cpp +++ b/src/mlpack/core/dists/laplace_distribution_impl.hpp @@ -1,5 +1,5 @@ /* - * @file core/dists/laplace_distribution.cpp + * @file core/dists/laplace_distribution_impl.hpp * @author Zhihao Lou * @author Rohan Raj * @@ -10,17 +10,18 @@ * 3-clause BSD license along with mlpack. If not, see * http://www.opensource.org/licenses/BSD-3-Clause for more information. */ -#include +#ifndef MLPACK_CORE_DISTRIBUTIONS_LAPLACE_DISTRIBUTION_IMPL_HPP +#define MLPACK_CORE_DISTRIBUTIONS_LAPLACE_DISTRIBUTION_IMPL_HPP #include "laplace_distribution.hpp" -using namespace mlpack; -using namespace mlpack::distribution; +namespace mlpack { +namespace distribution /** Probability distributions. */ { /** * Return the log probability of the given observation. */ -double LaplaceDistribution::LogProbability(const arma::vec& observation) const +inline double LaplaceDistribution::LogProbability(const arma::vec& observation) const { // Evaluate the PDF of the Laplace distribution to determine // the log probability. @@ -33,7 +34,7 @@ double LaplaceDistribution::LogProbability(const arma::vec& observation) const * @param x List of observations. * @param probabilities Output probabilities for each input observation. */ -void LaplaceDistribution::Probability(const arma::mat& x, +inline void LaplaceDistribution::Probability(const arma::mat& x, arma::vec& probabilities) const { probabilities.set_size(x.n_cols); @@ -48,7 +49,7 @@ void LaplaceDistribution::Probability(const arma::mat& x, * * @param observations List of observations. */ -void LaplaceDistribution::Estimate(const arma::mat& observations) +inline void LaplaceDistribution::Estimate(const arma::mat& observations) { // The maximum likelihood estimate of the mean is the median of the data for // the univariate case. See the short note "The Double Exponential @@ -81,7 +82,7 @@ void LaplaceDistribution::Estimate(const arma::mat& observations) * taking into account the probability of each observation actually being from * this distribution. */ -void LaplaceDistribution::Estimate(const arma::mat& observations, +inline void LaplaceDistribution::Estimate(const arma::mat& observations, const arma::vec& probabilities) { // I am not completely sure that this change results in a valid maximum @@ -99,3 +100,8 @@ void LaplaceDistribution::Estimate(const arma::mat& observations, scale += probabilities(i) * arma::norm(observations.col(i) - mean, 2); scale /= arma::accu(probabilities); } + +} // namespace distribution +} // namespace mlpack + +#endif From 8b4e4c37c7fe9c99a580b31a9a5b6414e932ff29 Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Fri, 29 Apr 2022 12:41:16 +0530 Subject: [PATCH 07/57] converted regression distribution to .hpp --- src/mlpack/core/dists/CMakeLists.txt | 2 +- .../core/dists/regression_distribution.hpp | 3 +++ ...n.cpp => regression_distribution_impl.hpp} | 26 ++++++++++++------- 3 files changed, 21 insertions(+), 10 deletions(-) rename src/mlpack/core/dists/{regression_distribution.cpp => regression_distribution_impl.hpp} (72%) diff --git a/src/mlpack/core/dists/CMakeLists.txt b/src/mlpack/core/dists/CMakeLists.txt index c9bd6ecac6..5368470db7 100644 --- a/src/mlpack/core/dists/CMakeLists.txt +++ b/src/mlpack/core/dists/CMakeLists.txt @@ -8,7 +8,7 @@ set(SOURCES laplace_distribution.hpp laplace_distribution_impl.hpp regression_distribution.hpp - regression_distribution.cpp + regression_distribution_impl.hpp gamma_distribution.hpp gamma_distribution_impl.hpp diagonal_gaussian_distribution.hpp diff --git a/src/mlpack/core/dists/regression_distribution.hpp b/src/mlpack/core/dists/regression_distribution.hpp index 03e909d2b5..c306b030eb 100644 --- a/src/mlpack/core/dists/regression_distribution.hpp +++ b/src/mlpack/core/dists/regression_distribution.hpp @@ -160,4 +160,7 @@ class RegressionDistribution } // namespace distribution } // namespace mlpack +// Include implementation. +#include "regression_distribution_impl.hpp" + #endif diff --git a/src/mlpack/core/dists/regression_distribution.cpp b/src/mlpack/core/dists/regression_distribution_impl.hpp similarity index 72% rename from src/mlpack/core/dists/regression_distribution.cpp rename to src/mlpack/core/dists/regression_distribution_impl.hpp index c44ba41200..6130ec66e4 100644 --- a/src/mlpack/core/dists/regression_distribution.cpp +++ b/src/mlpack/core/dists/regression_distribution_impl.hpp @@ -1,5 +1,5 @@ /** - * @file core/dists/regression_distribution.cpp + * @file core/dists/regression_distribution_impl.hpp * @author Michael Fox * * Implementation of conditional Gaussian distribution for HMM regression @@ -11,17 +11,20 @@ * http://www.opensource.org/licenses/BSD-3-Clause for more information. */ +#ifndef MLPACK_CORE_DISTRIBUTIONS_REGRESSION_DISTRIBUTION_IMPL_HPP +#define MLPACK_CORE_DISTRIBUTIONS_REGRESSION_DISTRIBUTION_IMPL_HPP + #include "regression_distribution.hpp" -using namespace mlpack; -using namespace mlpack::distribution; +namespace mlpack { +namespace distribution /** Probability distributions. */ { /** * Estimate parameters using provided observation weights. * * @param observations List of observations. */ -void RegressionDistribution::Train(const arma::mat& observations) +inline void RegressionDistribution::Train(const arma::mat& observations) { regression::LinearRegression lr(observations.rows(1, observations.n_rows - 1), arma::rowvec(observations.row(0)), 0, true); @@ -36,13 +39,13 @@ void RegressionDistribution::Train(const arma::mat& observations) * * @param weights Probability that given observation is from distribution. */ -void RegressionDistribution::Train(const arma::mat& observations, +inline void RegressionDistribution::Train(const arma::mat& observations, const arma::vec& weights) { Train(observations, arma::rowvec(weights.t())); } -void RegressionDistribution::Train(const arma::mat& observations, +inline void RegressionDistribution::Train(const arma::mat& observations, const arma::rowvec& weights) { regression::LinearRegression lr(observations.rows(1, observations.n_rows - 1), @@ -58,14 +61,14 @@ void RegressionDistribution::Train(const arma::mat& observations, * * @param observation Point to evaluate probability at. */ -double RegressionDistribution::Probability(const arma::vec& observation) const +inline double RegressionDistribution::Probability(const arma::vec& observation) const { arma::rowvec fitted; rf.Predict(observation.rows(1, observation.n_rows-1), fitted); return err.Probability(observation(0)-fitted.t()); } -void RegressionDistribution::Predict(const arma::mat& points, +inline void RegressionDistribution::Predict(const arma::mat& points, arma::vec& predictions) const { arma::rowvec rowPredictions; @@ -73,8 +76,13 @@ void RegressionDistribution::Predict(const arma::mat& points, predictions = rowPredictions.t(); } -void RegressionDistribution::Predict(const arma::mat& points, +inline void RegressionDistribution::Predict(const arma::mat& points, arma::rowvec& predictions) const { rf.Predict(points, predictions); } + +} // namespace distribution +} // namespace mlpack + +#endif From 626f034ce516804aee9c2087f70a5bf992d18ae2 Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Fri, 29 Apr 2022 12:43:08 +0530 Subject: [PATCH 08/57] added small comment --- src/mlpack/core/dists/regression_distribution.hpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/mlpack/core/dists/regression_distribution.hpp b/src/mlpack/core/dists/regression_distribution.hpp index c306b030eb..e109a142fb 100644 --- a/src/mlpack/core/dists/regression_distribution.hpp +++ b/src/mlpack/core/dists/regression_distribution.hpp @@ -18,7 +18,7 @@ #include namespace mlpack { -namespace distribution { +namespace distribution /** Probability distributions. */ { /** * A class that represents a univariate conditionally Gaussian distribution. From 947c73653f209d6996ab7f4a44bf272830abe2ee Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Fri, 29 Apr 2022 12:52:49 +0530 Subject: [PATCH 09/57] converted ColumnsToBlocks to .hpp --- src/mlpack/core/math/CMakeLists.txt | 2 +- src/mlpack/core/math/columns_to_blocks.hpp | 3 +++ ...ns_to_blocks.cpp => columns_to_blocks_impl.hpp} | 14 ++++++++++---- 3 files changed, 14 insertions(+), 5 deletions(-) rename src/mlpack/core/math/{columns_to_blocks.cpp => columns_to_blocks_impl.hpp} (85%) diff --git a/src/mlpack/core/math/CMakeLists.txt b/src/mlpack/core/math/CMakeLists.txt index 26c1e32fed..69e6e62bdd 100644 --- a/src/mlpack/core/math/CMakeLists.txt +++ b/src/mlpack/core/math/CMakeLists.txt @@ -3,7 +3,7 @@ set(SOURCES clamp.hpp columns_to_blocks.hpp - columns_to_blocks.cpp + columns_to_blocks_impl.hpp digamma.hpp lin_alg.hpp lin_alg_impl.hpp diff --git a/src/mlpack/core/math/columns_to_blocks.hpp b/src/mlpack/core/math/columns_to_blocks.hpp index a6d7e3a391..5f66643b0a 100644 --- a/src/mlpack/core/math/columns_to_blocks.hpp +++ b/src/mlpack/core/math/columns_to_blocks.hpp @@ -224,4 +224,7 @@ class ColumnsToBlocks } // namespace math } // namespace mlpack +// Include implementation. +#include "columns_to_blocks_impl.hpp" + #endif diff --git a/src/mlpack/core/math/columns_to_blocks.cpp b/src/mlpack/core/math/columns_to_blocks_impl.hpp similarity index 85% rename from src/mlpack/core/math/columns_to_blocks.cpp rename to src/mlpack/core/math/columns_to_blocks_impl.hpp index 6e98f0dbda..cc4fd2d95c 100644 --- a/src/mlpack/core/math/columns_to_blocks.cpp +++ b/src/mlpack/core/math/columns_to_blocks_impl.hpp @@ -1,5 +1,5 @@ /** - * @file core/math/columns_to_blocks.cpp + * @file core/math/columns_to_blocks_impl.hpp * @author Tham Ngap Wei * * Implementation of the ColumnsToBlocks class. @@ -9,12 +9,15 @@ * 3-clause BSD license along with mlpack. If not, see * http://www.opensource.org/licenses/BSD-3-Clause for more information. */ +#ifndef MLPACK_METHODS_NN_COLUMNS_TO_BLOCKS_IMPL_HPP +#define MLPACK_METHODS_NN_COLUMNS_TO_BLOCKS_IMPL_HPP + #include "columns_to_blocks.hpp" namespace mlpack { namespace math { -ColumnsToBlocks::ColumnsToBlocks(const size_t rows, +inline ColumnsToBlocks::ColumnsToBlocks(const size_t rows, const size_t cols, const size_t blockHeight, const size_t blockWidth) : @@ -30,15 +33,16 @@ ColumnsToBlocks::ColumnsToBlocks(const size_t rows, { } -bool ColumnsToBlocks::IsPerfectSquare(const size_t value) const +inline bool ColumnsToBlocks::IsPerfectSquare(const size_t value) const { const size_t root = (size_t) std::round(std::sqrt(value)); return (value == root * root); } -void ColumnsToBlocks::Transform(const arma::mat& maximalInputs, +inline void ColumnsToBlocks::Transform(const arma::mat& maximalInputs, arma::mat& output) { + //! TODO: Maybe replace std::runtime_error with Log::Fatal. if (!IsPerfectSquare(maximalInputs.n_rows)) { throw std::runtime_error("maximalInputs.n_rows should be perfect square"); @@ -97,3 +101,5 @@ void ColumnsToBlocks::Transform(const arma::mat& maximalInputs, } // namespace math } // namespace mlpack + +#endif From 1e96d07af7ecf7c883e460c78cd7be97e7f93c77 Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Sat, 30 Apr 2022 11:42:28 +0530 Subject: [PATCH 10/57] experimenting ways to export global variable --- src/mlpack/core/math/CMakeLists.txt | 1 - src/mlpack/core/math/random.cpp | 25 ------------------------ src/mlpack/core/math/random.hpp | 30 +++++++++++++++++++++++++++++ 3 files changed, 30 insertions(+), 26 deletions(-) delete mode 100644 src/mlpack/core/math/random.cpp diff --git a/src/mlpack/core/math/CMakeLists.txt b/src/mlpack/core/math/CMakeLists.txt index 69e6e62bdd..c001868938 100644 --- a/src/mlpack/core/math/CMakeLists.txt +++ b/src/mlpack/core/math/CMakeLists.txt @@ -14,7 +14,6 @@ set(SOURCES multiply_slices.hpp quantile.hpp random.hpp - random.cpp random_basis.hpp random_basis_impl.hpp range.hpp diff --git a/src/mlpack/core/math/random.cpp b/src/mlpack/core/math/random.cpp deleted file mode 100644 index d2049ea80b..0000000000 --- a/src/mlpack/core/math/random.cpp +++ /dev/null @@ -1,25 +0,0 @@ -/** - * @file core/math/random.cpp - * - * Declarations of global random number generators. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#include -#include - -namespace mlpack { -namespace math { - -// Global random object. -MLPACK_EXPORT std::mt19937 randGen; -// Global uniform distribution. -MLPACK_EXPORT std::uniform_real_distribution<> randUniformDist(0.0, 1.0); -// Global normal distribution. -MLPACK_EXPORT std::normal_distribution<> randNormalDist(0.0, 1.0); - -} // namespace math -} // namespace mlpack diff --git a/src/mlpack/core/math/random.hpp b/src/mlpack/core/math/random.hpp index 5091a08ff3..90de16ad91 100644 --- a/src/mlpack/core/math/random.hpp +++ b/src/mlpack/core/math/random.hpp @@ -23,6 +23,36 @@ namespace math /** Miscellaneous math routines. */ { * correctly on Windows. */ +#if __cplusplus < 201703L + namespace rand { + template + struct GlobalRandomVariables + { + static std::mt19937 randGen; + static std::uniform_real_distribution<> randUniformDist; + static std::normal_distribution<> randNormalDist; + }; + template<> + std::uniform_real_distribution<> GlobalRandomVariables<>::randUniformDist(0.0, 1.0); + template<> + std::normal_distribution<> GlobalRandomVariables<>::randNormalDist(0.0, 1.0); + } + + // Global random object. + static MLPACK_EXPORT std::mt19937& randGen = rand::GlobalRandomVariables<>::randGen; + // Global uniform distribution. + static MLPACK_EXPORT std::uniform_real_distribution<>& randUniformDist = rand::GlobalRandomVariables<>::randUniformDist; + // Global normal distribution. + static MLPACK_EXPORT std::normal_distribution<>& randNormalDist = rand::GlobalRandomVariables<>::randNormalDist; +#else + // Global random object. + inline MLPACK_EXPORT std::mt19937 randGen; + // Global uniform distribution. + inline MLPACK_EXPORT std::uniform_real_distribution<> randUniformDist(0.0, 1.0); + // Global normal distribution. + inline MLPACK_EXPORT std::normal_distribution<> randNormalDist(0.0, 1.0); +#endif + // Global random object. extern MLPACK_EXPORT std::mt19937 randGen; // Global uniform distribution. From 1b49b18cbd2f431d0d6fb85a00b35ffd2a149c06 Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Sat, 30 Apr 2022 11:43:31 +0530 Subject: [PATCH 11/57] forgot to remove extern --- src/mlpack/core/math/random.hpp | 7 ------- 1 file changed, 7 deletions(-) diff --git a/src/mlpack/core/math/random.hpp b/src/mlpack/core/math/random.hpp index 90de16ad91..464bcd588c 100644 --- a/src/mlpack/core/math/random.hpp +++ b/src/mlpack/core/math/random.hpp @@ -53,13 +53,6 @@ namespace math /** Miscellaneous math routines. */ { inline MLPACK_EXPORT std::normal_distribution<> randNormalDist(0.0, 1.0); #endif -// Global random object. -extern MLPACK_EXPORT std::mt19937 randGen; -// Global uniform distribution. -extern MLPACK_EXPORT std::uniform_real_distribution<> randUniformDist; -// Global normal distribution. -extern MLPACK_EXPORT std::normal_distribution<> randNormalDist; - /** * Set the random seed used by the random functions (Random() and RandInt()). * The seed is casted to a 32-bit integer before being given to the random From af6f1104842fd6fb0b29e7615982af82922f6c8f Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Sat, 30 Apr 2022 11:50:27 +0530 Subject: [PATCH 12/57] correction --- src/mlpack/core/math/random.hpp | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/src/mlpack/core/math/random.hpp b/src/mlpack/core/math/random.hpp index 464bcd588c..2c411b7b1b 100644 --- a/src/mlpack/core/math/random.hpp +++ b/src/mlpack/core/math/random.hpp @@ -24,7 +24,7 @@ namespace math /** Miscellaneous math routines. */ { */ #if __cplusplus < 201703L - namespace rand { + namespace rand_mlpack { template struct GlobalRandomVariables { @@ -39,18 +39,18 @@ namespace math /** Miscellaneous math routines. */ { } // Global random object. - static MLPACK_EXPORT std::mt19937& randGen = rand::GlobalRandomVariables<>::randGen; + static std::mt19937& randGen = rand_mlpack::GlobalRandomVariables<>::randGen; // Global uniform distribution. - static MLPACK_EXPORT std::uniform_real_distribution<>& randUniformDist = rand::GlobalRandomVariables<>::randUniformDist; + static std::uniform_real_distribution<>& randUniformDist = rand_mlpack::GlobalRandomVariables<>::randUniformDist; // Global normal distribution. - static MLPACK_EXPORT std::normal_distribution<>& randNormalDist = rand::GlobalRandomVariables<>::randNormalDist; + static std::normal_distribution<>& randNormalDist = rand_mlpack::GlobalRandomVariables<>::randNormalDist; #else // Global random object. - inline MLPACK_EXPORT std::mt19937 randGen; + inline std::mt19937 randGen; // Global uniform distribution. - inline MLPACK_EXPORT std::uniform_real_distribution<> randUniformDist(0.0, 1.0); + inline std::uniform_real_distribution<> randUniformDist(0.0, 1.0); // Global normal distribution. - inline MLPACK_EXPORT std::normal_distribution<> randNormalDist(0.0, 1.0); + inline std::normal_distribution<> randNormalDist(0.0, 1.0); #endif /** From 087c58dffb252fce5807136314ae95fa2fce6d52 Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Sat, 30 Apr 2022 12:03:14 +0530 Subject: [PATCH 13/57] trying extern --- src/mlpack/core/math/random.hpp | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/src/mlpack/core/math/random.hpp b/src/mlpack/core/math/random.hpp index 2c411b7b1b..3da1b73cbd 100644 --- a/src/mlpack/core/math/random.hpp +++ b/src/mlpack/core/math/random.hpp @@ -33,6 +33,8 @@ namespace math /** Miscellaneous math routines. */ { static std::normal_distribution<> randNormalDist; }; template<> + std::mt19937 rand_mlpack::GlobalRandomVariables<>::randGen; + template<> std::uniform_real_distribution<> GlobalRandomVariables<>::randUniformDist(0.0, 1.0); template<> std::normal_distribution<> GlobalRandomVariables<>::randNormalDist(0.0, 1.0); @@ -51,7 +53,14 @@ namespace math /** Miscellaneous math routines. */ { inline std::uniform_real_distribution<> randUniformDist(0.0, 1.0); // Global normal distribution. inline std::normal_distribution<> randNormalDist(0.0, 1.0); -#endif +#endif + +// Global random object. +extern MLPACK_EXPORT std::mt19937 randGen; +// Global uniform distribution. +extern MLPACK_EXPORT std::uniform_real_distribution<> randUniformDist; +// Global normal distribution. +extern MLPACK_EXPORT std::normal_distribution<> randNormalDist; /** * Set the random seed used by the random functions (Random() and RandInt()). From 4ab0f48e9b5089e2acf91de3b57ce615fd5bcf88 Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Sat, 30 Apr 2022 12:08:21 +0530 Subject: [PATCH 14/57] adding mlpack export in static var --- src/mlpack/core/math/random.hpp | 13 +++---------- 1 file changed, 3 insertions(+), 10 deletions(-) diff --git a/src/mlpack/core/math/random.hpp b/src/mlpack/core/math/random.hpp index 3da1b73cbd..6f8606acae 100644 --- a/src/mlpack/core/math/random.hpp +++ b/src/mlpack/core/math/random.hpp @@ -41,11 +41,11 @@ namespace math /** Miscellaneous math routines. */ { } // Global random object. - static std::mt19937& randGen = rand_mlpack::GlobalRandomVariables<>::randGen; + static MLPACK_EXPORT std::mt19937& randGen = rand_mlpack::GlobalRandomVariables<>::randGen; // Global uniform distribution. - static std::uniform_real_distribution<>& randUniformDist = rand_mlpack::GlobalRandomVariables<>::randUniformDist; + static MLPACK_EXPORT std::uniform_real_distribution<>& randUniformDist = rand_mlpack::GlobalRandomVariables<>::randUniformDist; // Global normal distribution. - static std::normal_distribution<>& randNormalDist = rand_mlpack::GlobalRandomVariables<>::randNormalDist; + static MLPACK_EXPORT std::normal_distribution<>& randNormalDist = rand_mlpack::GlobalRandomVariables<>::randNormalDist; #else // Global random object. inline std::mt19937 randGen; @@ -55,13 +55,6 @@ namespace math /** Miscellaneous math routines. */ { inline std::normal_distribution<> randNormalDist(0.0, 1.0); #endif -// Global random object. -extern MLPACK_EXPORT std::mt19937 randGen; -// Global uniform distribution. -extern MLPACK_EXPORT std::uniform_real_distribution<> randUniformDist; -// Global normal distribution. -extern MLPACK_EXPORT std::normal_distribution<> randNormalDist; - /** * Set the random seed used by the random functions (Random() and RandInt()). * The seed is casted to a 32-bit integer before being given to the random From a8a43970cf5e9e10f7ab7a84f67ec9c83326f4da Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Sat, 30 Apr 2022 12:43:59 +0530 Subject: [PATCH 15/57] trying to fix multiple declaration --- src/mlpack/core/math/random.hpp | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/src/mlpack/core/math/random.hpp b/src/mlpack/core/math/random.hpp index 6f8606acae..e1b417dbb1 100644 --- a/src/mlpack/core/math/random.hpp +++ b/src/mlpack/core/math/random.hpp @@ -23,9 +23,12 @@ namespace math /** Miscellaneous math routines. */ { * correctly on Windows. */ +#ifndef MLPACK_CORE_MATH_RANDOM_GLOBAL +#define MLPACK_CORE_MATH_RANDOM_GLOBAL + #if __cplusplus < 201703L namespace rand_mlpack { - template + template struct GlobalRandomVariables { static std::mt19937 randGen; @@ -55,6 +58,8 @@ namespace math /** Miscellaneous math routines. */ { inline std::normal_distribution<> randNormalDist(0.0, 1.0); #endif +#endif + /** * Set the random seed used by the random functions (Random() and RandInt()). * The seed is casted to a 32-bit integer before being given to the random From 059c0dcc3335f80e9877b1d10f8b7d0b241e5b3e Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Sat, 30 Apr 2022 17:55:50 +0530 Subject: [PATCH 16/57] idk :( --- src/mlpack/core/math/random.hpp | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/mlpack/core/math/random.hpp b/src/mlpack/core/math/random.hpp index e1b417dbb1..79f2a8daa5 100644 --- a/src/mlpack/core/math/random.hpp +++ b/src/mlpack/core/math/random.hpp @@ -44,11 +44,11 @@ namespace math /** Miscellaneous math routines. */ { } // Global random object. - static MLPACK_EXPORT std::mt19937& randGen = rand_mlpack::GlobalRandomVariables<>::randGen; + MLPACK_EXPORT std::mt19937& randGen = rand_mlpack::GlobalRandomVariables<>::randGen; // Global uniform distribution. - static MLPACK_EXPORT std::uniform_real_distribution<>& randUniformDist = rand_mlpack::GlobalRandomVariables<>::randUniformDist; + MLPACK_EXPORT std::uniform_real_distribution<>& randUniformDist = rand_mlpack::GlobalRandomVariables<>::randUniformDist; // Global normal distribution. - static MLPACK_EXPORT std::normal_distribution<>& randNormalDist = rand_mlpack::GlobalRandomVariables<>::randNormalDist; + MLPACK_EXPORT std::normal_distribution<>& randNormalDist = rand_mlpack::GlobalRandomVariables<>::randNormalDist; #else // Global random object. inline std::mt19937 randGen; From f99fd3585db8f011be06aebc1f3c642012116628 Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Sat, 30 Apr 2022 18:07:40 +0530 Subject: [PATCH 17/57] adding extern again :( --- src/mlpack/core/math/random.hpp | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/mlpack/core/math/random.hpp b/src/mlpack/core/math/random.hpp index 79f2a8daa5..6e9cb3e63e 100644 --- a/src/mlpack/core/math/random.hpp +++ b/src/mlpack/core/math/random.hpp @@ -44,11 +44,11 @@ namespace math /** Miscellaneous math routines. */ { } // Global random object. - MLPACK_EXPORT std::mt19937& randGen = rand_mlpack::GlobalRandomVariables<>::randGen; + extern MLPACK_EXPORT std::mt19937& randGen = rand_mlpack::GlobalRandomVariables<>::randGen; // Global uniform distribution. - MLPACK_EXPORT std::uniform_real_distribution<>& randUniformDist = rand_mlpack::GlobalRandomVariables<>::randUniformDist; + extern MLPACK_EXPORT std::uniform_real_distribution<>& randUniformDist = rand_mlpack::GlobalRandomVariables<>::randUniformDist; // Global normal distribution. - MLPACK_EXPORT std::normal_distribution<>& randNormalDist = rand_mlpack::GlobalRandomVariables<>::randNormalDist; + extern MLPACK_EXPORT std::normal_distribution<>& randNormalDist = rand_mlpack::GlobalRandomVariables<>::randNormalDist; #else // Global random object. inline std::mt19937 randGen; From c5bb70f77f22caa19f33083eaf6b7414c919d4e5 Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Sat, 30 Apr 2022 19:40:06 +0530 Subject: [PATCH 18/57] trying different approach --- .../core/dists/gamma_distribution_impl.hpp | 2 +- src/mlpack/core/math/random.hpp | 64 +++++++++---------- .../bias_svd/bias_svd_function_impl.hpp | 2 +- .../regularized_svd_function_impl.hpp | 2 +- .../svdplusplus/svdplusplus_function_impl.hpp | 2 +- src/mlpack/tests/distribution_test.cpp | 16 ++--- 6 files changed, 44 insertions(+), 44 deletions(-) diff --git a/src/mlpack/core/dists/gamma_distribution_impl.hpp b/src/mlpack/core/dists/gamma_distribution_impl.hpp index 66301587a9..e42a73f309 100644 --- a/src/mlpack/core/dists/gamma_distribution_impl.hpp +++ b/src/mlpack/core/dists/gamma_distribution_impl.hpp @@ -239,7 +239,7 @@ inline arma::vec GammaDistribution::Random() const { std::gamma_distribution dist(alpha(d), beta(d)); // Use the mlpack random object. - randVec(d) = dist(mlpack::math::randGen); + randVec(d) = dist(mlpack::math::GlobalRandomVariables::randGen); } return randVec; diff --git a/src/mlpack/core/math/random.hpp b/src/mlpack/core/math/random.hpp index 6e9cb3e63e..78adc7f4bf 100644 --- a/src/mlpack/core/math/random.hpp +++ b/src/mlpack/core/math/random.hpp @@ -27,35 +27,35 @@ namespace math /** Miscellaneous math routines. */ { #define MLPACK_CORE_MATH_RANDOM_GLOBAL #if __cplusplus < 201703L - namespace rand_mlpack { - template - struct GlobalRandomVariables + template + struct GlobalRandomVariables_ { + // Global random object. static std::mt19937 randGen; + // Global uniform distribution. static std::uniform_real_distribution<> randUniformDist; + // Global normal distribution. static std::normal_distribution<> randNormalDist; }; - template<> - std::mt19937 rand_mlpack::GlobalRandomVariables<>::randGen; - template<> - std::uniform_real_distribution<> GlobalRandomVariables<>::randUniformDist(0.0, 1.0); - template<> - std::normal_distribution<> GlobalRandomVariables<>::randNormalDist(0.0, 1.0); - } - - // Global random object. - extern MLPACK_EXPORT std::mt19937& randGen = rand_mlpack::GlobalRandomVariables<>::randGen; - // Global uniform distribution. - extern MLPACK_EXPORT std::uniform_real_distribution<>& randUniformDist = rand_mlpack::GlobalRandomVariables<>::randUniformDist; - // Global normal distribution. - extern MLPACK_EXPORT std::normal_distribution<>& randNormalDist = rand_mlpack::GlobalRandomVariables<>::randNormalDist; + template + // Global random object. + std::mt19937 GlobalRandomVariables_::randGen; + template + // Global uniform distribution. + std::uniform_real_distribution<> GlobalRandomVariables_::randUniformDist(0.0, 1.0); + template + // Global normal distribution. + std::normal_distribution<> GlobalRandomVariables_::randNormalDist(0.0, 1.0); + using GlobalRandomVariables = GlobalRandomVariables_; #else - // Global random object. - inline std::mt19937 randGen; - // Global uniform distribution. - inline std::uniform_real_distribution<> randUniformDist(0.0, 1.0); - // Global normal distribution. - inline std::normal_distribution<> randNormalDist(0.0, 1.0); + namespace GlobalRandomVariables { + // Global random object. + inline std::mt19937 randGen; + // Global uniform distribution. + inline std::uniform_real_distribution<> randUniformDist(0.0, 1.0); + // Global normal distribution. + inline std::normal_distribution<> randNormalDist(0.0, 1.0); + } #endif #endif @@ -70,7 +70,7 @@ namespace math /** Miscellaneous math routines. */ { inline void RandomSeed(const size_t seed) { #if (!defined(BINDING_TYPE) || BINDING_TYPE != BINDING_TYPE_TEST) - randGen.seed((uint32_t) seed); + GlobalRandomVariables::randGen.seed((uint32_t) seed); #if (BINDING_TYPE == BINDING_TYPE_R) // To suppress Found 'srand', possibly from 'srand' (C). (void) seed; @@ -94,14 +94,14 @@ inline void RandomSeed(const size_t seed) inline void FixedRandomSeed() { const static size_t seed = rand(); - randGen.seed((uint32_t) seed); + GlobalRandomVariables::randGen.seed((uint32_t) seed); srand((unsigned int) seed); arma::arma_rng::set_seed(seed); } inline void CustomRandomSeed(const size_t seed) { - randGen.seed((uint32_t) seed); + GlobalRandomVariables::randGen.seed((uint32_t) seed); srand((unsigned int) seed); arma::arma_rng::set_seed(seed); } @@ -112,7 +112,7 @@ inline void CustomRandomSeed(const size_t seed) */ inline double Random() { - return randUniformDist(randGen); + return GlobalRandomVariables::randUniformDist(GlobalRandomVariables::randGen); } /** @@ -120,7 +120,7 @@ inline double Random() */ inline double Random(const double lo, const double hi) { - return lo + (hi - lo) * randUniformDist(randGen); + return lo + (hi - lo) * GlobalRandomVariables::randUniformDist(GlobalRandomVariables::randGen); } /** @@ -139,7 +139,7 @@ inline double RandBernoulli(const double input) */ inline int RandInt(const int hiExclusive) { - return (int) std::floor((double) hiExclusive * randUniformDist(randGen)); + return (int) std::floor((double) hiExclusive * GlobalRandomVariables::randUniformDist(GlobalRandomVariables::randGen)); } /** @@ -148,7 +148,7 @@ inline int RandInt(const int hiExclusive) inline int RandInt(const int lo, const int hiExclusive) { return lo + (int) std::floor((double) (hiExclusive - lo) - * randUniformDist(randGen)); + * GlobalRandomVariables::randUniformDist(GlobalRandomVariables::randGen)); } /** @@ -156,7 +156,7 @@ inline int RandInt(const int lo, const int hiExclusive) */ inline double RandNormal() { - return randNormalDist(randGen); + return GlobalRandomVariables::randNormalDist(GlobalRandomVariables::randGen); } /** @@ -168,7 +168,7 @@ inline double RandNormal() */ inline double RandNormal(const double mean, const double variance) { - return variance * randNormalDist(randGen) + mean; + return variance * GlobalRandomVariables::randNormalDist(GlobalRandomVariables::randGen) + mean; } /** diff --git a/src/mlpack/methods/bias_svd/bias_svd_function_impl.hpp b/src/mlpack/methods/bias_svd/bias_svd_function_impl.hpp index e18e7393ad..fcd85a8140 100644 --- a/src/mlpack/methods/bias_svd/bias_svd_function_impl.hpp +++ b/src/mlpack/methods/bias_svd/bias_svd_function_impl.hpp @@ -317,7 +317,7 @@ inline double ParallelSGD::Optimize( if (shuffle) // Determine order of visitation. std::shuffle(visitationOrder.begin(), visitationOrder.end(), - mlpack::math::randGen); + mlpack::math::GlobalRandomVariables::randGen); #pragma omp parallel { diff --git a/src/mlpack/methods/regularized_svd/regularized_svd_function_impl.hpp b/src/mlpack/methods/regularized_svd/regularized_svd_function_impl.hpp index 6dc2510e27..36a3d87297 100644 --- a/src/mlpack/methods/regularized_svd/regularized_svd_function_impl.hpp +++ b/src/mlpack/methods/regularized_svd/regularized_svd_function_impl.hpp @@ -272,7 +272,7 @@ inline double ParallelSGD::Optimize( if (shuffle) // Determine order of visitation. std::shuffle(visitationOrder.begin(), visitationOrder.end(), - mlpack::math::randGen); + mlpack::math::GlobalRandomVariables::randGen); #pragma omp parallel { diff --git a/src/mlpack/methods/svdplusplus/svdplusplus_function_impl.hpp b/src/mlpack/methods/svdplusplus/svdplusplus_function_impl.hpp index e5e0a3ce10..867090fa00 100644 --- a/src/mlpack/methods/svdplusplus/svdplusplus_function_impl.hpp +++ b/src/mlpack/methods/svdplusplus/svdplusplus_function_impl.hpp @@ -454,7 +454,7 @@ inline double ParallelSGD::Optimize( if (shuffle) // Determine order of visitation. std::shuffle(visitationOrder.begin(), visitationOrder.end(), - mlpack::math::randGen); + mlpack::math::GlobalRandomVariables::randGen); #pragma omp parallel { diff --git a/src/mlpack/tests/distribution_test.cpp b/src/mlpack/tests/distribution_test.cpp index 99282e07ce..4df4630313 100644 --- a/src/mlpack/tests/distribution_test.cpp +++ b/src/mlpack/tests/distribution_test.cpp @@ -669,7 +669,7 @@ TEST_CASE("GammaDistributionTrainTest", "[DistributionTest]") // Random generation of gamma-like points. for (size_t j = 0; j < d; ++j) for (size_t i = 0; i < N; ++i) - rdata(j, i) = dist(math::randGen); + rdata(j, i) = dist(math::GlobalRandomVariables::randGen); // Create Gamma object and call Train() on reference set. GammaDistribution gDist; @@ -687,7 +687,7 @@ TEST_CASE("GammaDistributionTrainTest", "[DistributionTest]") // Random generation of gamma-like points. for (size_t j = 0; j < d2; ++j) for (size_t i = 0; i < N2; ++i) - rdata2(j, i) = dist(math::randGen); + rdata2(j, i) = dist(math::GlobalRandomVariables::randGen); // Fit results using old object. gDist.Train(rdata2); @@ -715,7 +715,7 @@ TEST_CASE("GammaDistributionTrainWithProbabilitiesTest", "[DistributionTest]") for (size_t j = 0; j < d; ++j) for (size_t i = 0; i < N; ++i) - rdata(j, i) = dist(math::randGen); + rdata(j, i) = dist(math::GlobalRandomVariables::randGen); // Fill the probabilities randomly. arma::vec probabilities(N, arma::fill::randu); @@ -759,7 +759,7 @@ TEST_CASE("GammaDistributionTrainAllProbabilities1Test", "[DistributionTest]") for (size_t j = 0; j < d; ++j) for (size_t i = 0; i < N; ++i) - rdata(j, i) = dist(math::randGen); + rdata(j, i) = dist(math::GlobalRandomVariables::randGen); // Fit results with only data. GammaDistribution gDist; @@ -808,9 +808,9 @@ TEST_CASE("GammaDistributionTrainTwoDistProbabilities1Test", for (size_t i = 0; i < N; ++i) { if (i % 2 == 0) - rdata(j, i) = dist(math::randGen); + rdata(j, i) = dist(math::GlobalRandomVariables::randGen); else - rdata(j, i) = dist2(math::randGen); + rdata(j, i) = dist2(math::GlobalRandomVariables::randGen); } } @@ -859,7 +859,7 @@ TEST_CASE("GammaDistributionFittingTest", "[DistributionTest]") arma::mat rdata(d, N); for (size_t j = 0; j < d; ++j) for (size_t i = 0; i < N; ++i) - rdata(j, i) = dist(math::randGen); + rdata(j, i) = dist(math::GlobalRandomVariables::randGen); // Create Gamma object and call Train() on reference set. GammaDistribution gDist; @@ -880,7 +880,7 @@ TEST_CASE("GammaDistributionFittingTest", "[DistributionTest]") arma::mat rdata2(d, N); for (size_t j = 0; j < d; ++j) for (size_t i = 0; i < N; ++i) - rdata2(j, i) = dist2(math::randGen); + rdata2(j, i) = dist2(math::GlobalRandomVariables::randGen); // Create Gamma object and call Train() on reference set. GammaDistribution gDist2; From 75d3333b1fbfa23a1e8b1ef6b5b75ed65a8a70d9 Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Sun, 1 May 2022 12:05:19 +0530 Subject: [PATCH 19/57] converted cosine tree to .hpp --- src/mlpack/core/tree/CMakeLists.txt | 2 +- .../core/tree/cosine_tree/cosine_tree.hpp | 5 ++ .../{cosine_tree.cpp => cosine_tree_impl.hpp} | 64 ++++++++++--------- 3 files changed, 39 insertions(+), 32 deletions(-) rename src/mlpack/core/tree/cosine_tree/{cosine_tree.cpp => cosine_tree_impl.hpp} (91%) diff --git a/src/mlpack/core/tree/CMakeLists.txt b/src/mlpack/core/tree/CMakeLists.txt index ebba35b1cc..d5e49d08a4 100644 --- a/src/mlpack/core/tree/CMakeLists.txt +++ b/src/mlpack/core/tree/CMakeLists.txt @@ -32,7 +32,7 @@ set(SOURCES cellbound.hpp cellbound_impl.hpp cosine_tree/cosine_tree.hpp - cosine_tree/cosine_tree.cpp + cosine_tree/cosine_tree_impl.hpp cover_tree.hpp cover_tree/cover_tree.hpp cover_tree/cover_tree_impl.hpp diff --git a/src/mlpack/core/tree/cosine_tree/cosine_tree.hpp b/src/mlpack/core/tree/cosine_tree/cosine_tree.hpp index 9a295ad025..b1ba2ab1b6 100644 --- a/src/mlpack/core/tree/cosine_tree/cosine_tree.hpp +++ b/src/mlpack/core/tree/cosine_tree/cosine_tree.hpp @@ -13,6 +13,8 @@ #define MLPACK_CORE_TREE_COSINE_TREE_COSINE_TREE_HPP #include +#include +#include namespace mlpack { namespace tree { @@ -285,4 +287,7 @@ class CompareCosineNode } // namespace tree } // namespace mlpack +// Include implementation. +#include "cosine_tree_impl.hpp" + #endif diff --git a/src/mlpack/core/tree/cosine_tree/cosine_tree.cpp b/src/mlpack/core/tree/cosine_tree/cosine_tree_impl.hpp similarity index 91% rename from src/mlpack/core/tree/cosine_tree/cosine_tree.cpp rename to src/mlpack/core/tree/cosine_tree/cosine_tree_impl.hpp index a857bca177..b3d28d72b3 100644 --- a/src/mlpack/core/tree/cosine_tree/cosine_tree.cpp +++ b/src/mlpack/core/tree/cosine_tree/cosine_tree_impl.hpp @@ -1,5 +1,5 @@ /** - * @file core/tree/cosine_tree/cosine_tree.cpp + * @file core/tree/cosine_tree/cosine_tree_impl.hpp * @author Siddharth Agrawal * * Implementation of cosine tree. @@ -9,15 +9,15 @@ * 3-clause BSD license along with mlpack. If not, see * http://www.opensource.org/licenses/BSD-3-Clause for more information. */ -#include "cosine_tree.hpp" -#include +#ifndef MLPACK_CORE_TREE_COSINE_TREE_COSINE_TREE_IMPL_HPP +#define MLPACK_CORE_TREE_COSINE_TREE_COSINE_TREE_IMPL_HPP -#include +#include "cosine_tree.hpp" namespace mlpack { namespace tree { -CosineTree::CosineTree(const arma::mat& dataset) : +inline CosineTree::CosineTree(const arma::mat& dataset) : dataset(&dataset), parent(NULL), left(NULL), @@ -46,8 +46,8 @@ CosineTree::CosineTree(const arma::mat& dataset) : splitPointIndex = ColumnSampleLS(); } -CosineTree::CosineTree(CosineTree& parentNode, - const std::vector& subIndices) : +inline CosineTree::CosineTree(CosineTree& parentNode, + const std::vector& subIndices) : dataset(&parentNode.GetDataset()), parent(&parentNode), left(NULL), @@ -75,9 +75,9 @@ CosineTree::CosineTree(CosineTree& parentNode, splitPointIndex = ColumnSampleLS(); } -CosineTree::CosineTree(const arma::mat& dataset, - const double epsilon, - const double delta) : +inline CosineTree::CosineTree(const arma::mat& dataset, + const double epsilon, + const double delta) : dataset(&dataset), delta(delta), left(NULL), @@ -158,7 +158,7 @@ CosineTree::CosineTree(const arma::mat& dataset, } //! Copy the given tree. -CosineTree::CosineTree(const CosineTree& other) : +inline CosineTree::CosineTree(const CosineTree& other) : // Copy matrix, but only if we are the root. dataset((other.parent == NULL) ? new arma::mat(*other.dataset) : NULL), delta(other.delta), @@ -211,7 +211,7 @@ CosineTree::CosineTree(const CosineTree& other) : } //! Copy assignment operator: copy the given other tree. -CosineTree& CosineTree::operator=(const CosineTree& other) +inline CosineTree& CosineTree::operator=(const CosineTree& other) { // Return if it's the same tree. if (this == &other) @@ -279,7 +279,7 @@ CosineTree& CosineTree::operator=(const CosineTree& other) } //! Move the given tree. -CosineTree::CosineTree(CosineTree&& other) : +inline CosineTree::CosineTree(CosineTree&& other) : dataset(other.dataset), delta(std::move(other.delta)), parent(other.parent), @@ -314,7 +314,7 @@ CosineTree::CosineTree(CosineTree&& other) : } //! Move assignment operator: take ownership of the given tree. -CosineTree& CosineTree::operator=(CosineTree&& other) +inline CosineTree& CosineTree::operator=(CosineTree&& other) { // Return if it's the same tree. if (this == &other) @@ -361,7 +361,7 @@ CosineTree& CosineTree::operator=(CosineTree&& other) return *this; } -CosineTree::~CosineTree() +inline CosineTree::~CosineTree() { if (localDataset) delete dataset; @@ -405,10 +405,10 @@ void CosineTree::ModifiedGramSchmidt(CosineNodeQueue& treeQueue, newBasisVector /= arma::norm(newBasisVector, 2); } -double CosineTree::MonteCarloError(CosineTree* node, - CosineNodeQueue& treeQueue, - arma::vec* addBasisVector1, - arma::vec* addBasisVector2) +inline double CosineTree::MonteCarloError(CosineTree* node, + CosineNodeQueue& treeQueue, + arma::vec* addBasisVector1, + arma::vec* addBasisVector2) { std::vector sampledIndices; arma::vec probabilities; @@ -489,7 +489,7 @@ double CosineTree::MonteCarloError(CosineTree* node, return (node->FrobNormSquared() - lowerBound); } -void CosineTree::ConstructBasis(CosineNodeQueue& treeQueue) +inline void CosineTree::ConstructBasis(CosineNodeQueue& treeQueue) { // Initialize basis as matrix of zeros. basis.zeros(dataset->n_rows, treeQueue.size()); @@ -507,7 +507,7 @@ void CosineTree::ConstructBasis(CosineNodeQueue& treeQueue) } } -void CosineTree::CosineNodeSplit() +inline void CosineTree::CosineNodeSplit() { // If less than two points, splitting does not make sense---there is nothing // to split. @@ -544,9 +544,9 @@ void CosineTree::CosineNodeSplit() right = new CosineTree(*this, rightIndices); } -void CosineTree::ColumnSamplesLS(std::vector& sampledIndices, - arma::vec& probabilities, - size_t numSamples) +inline void CosineTree::ColumnSamplesLS(std::vector& sampledIndices, + arma::vec& probabilities, + size_t numSamples) { // Initialize the cumulative distribution vector size. arma::vec cDistribution; @@ -576,7 +576,7 @@ void CosineTree::ColumnSamplesLS(std::vector& sampledIndices, } } -size_t CosineTree::ColumnSampleLS() +inline size_t CosineTree::ColumnSampleLS() { // If only one element is present, there can only be one sample. if (numColumns < 2) @@ -603,10 +603,10 @@ size_t CosineTree::ColumnSampleLS() return BinarySearch(cDistribution, randValue, start, end); } -size_t CosineTree::BinarySearch(arma::vec& cDistribution, - double value, - size_t start, - size_t end) +inline size_t CosineTree::BinarySearch(arma::vec& cDistribution, + double value, + size_t start, + size_t end) { size_t pivot = (start + end) / 2; @@ -631,7 +631,7 @@ size_t CosineTree::BinarySearch(arma::vec& cDistribution, } } -void CosineTree::CalculateCosines(arma::vec& cosines) +inline void CosineTree::CalculateCosines(arma::vec& cosines) { // Initialize cosine vector as a vector of zeros. cosines.zeros(numColumns); @@ -653,7 +653,7 @@ void CosineTree::CalculateCosines(arma::vec& cosines) } } -void CosineTree::CalculateCentroid() +inline void CosineTree::CalculateCentroid() { // Initialize centroid as vector of zeros. centroid.zeros(dataset->n_rows); @@ -668,3 +668,5 @@ void CosineTree::CalculateCentroid() } // namespace tree } // namespace mlpack + +#endif From 6a63ba1d4f8589b1da21e10b5201d563bf6ffdd1 Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Sun, 1 May 2022 12:41:36 +0530 Subject: [PATCH 20/57] fix missing inline --- src/mlpack/core/tree/cosine_tree/cosine_tree_impl.hpp | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/src/mlpack/core/tree/cosine_tree/cosine_tree_impl.hpp b/src/mlpack/core/tree/cosine_tree/cosine_tree_impl.hpp index b3d28d72b3..ce02357576 100644 --- a/src/mlpack/core/tree/cosine_tree/cosine_tree_impl.hpp +++ b/src/mlpack/core/tree/cosine_tree/cosine_tree_impl.hpp @@ -371,10 +371,10 @@ inline CosineTree::~CosineTree() delete right; } -void CosineTree::ModifiedGramSchmidt(CosineNodeQueue& treeQueue, - arma::vec& centroid, - arma::vec& newBasisVector, - arma::vec* addBasisVector) +inline void CosineTree::ModifiedGramSchmidt(CosineNodeQueue& treeQueue, + arma::vec& centroid, + arma::vec& newBasisVector, + arma::vec* addBasisVector) { // Set new basis vector to centroid. newBasisVector = centroid; From 4f19adc1ad903f921e05f08f02a3f9e1f7a572b3 Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Mon, 9 May 2022 12:30:09 +0530 Subject: [PATCH 21/57] converted softmax regression to .hpp --- .../methods/softmax_regression/CMakeLists.txt | 3 +- .../softmax_regression/softmax_regression.cpp | 140 ------------------ .../softmax_regression_function.hpp | 4 + ...p => softmax_regression_function_impl.hpp} | 58 +++++--- .../softmax_regression_impl.hpp | 121 +++++++++++++++ 5 files changed, 160 insertions(+), 166 deletions(-) delete mode 100644 src/mlpack/methods/softmax_regression/softmax_regression.cpp rename src/mlpack/methods/softmax_regression/{softmax_regression_function.cpp => softmax_regression_function_impl.hpp} (84%) diff --git a/src/mlpack/methods/softmax_regression/CMakeLists.txt b/src/mlpack/methods/softmax_regression/CMakeLists.txt index e0aa0d14f9..fa8ea108b5 100644 --- a/src/mlpack/methods/softmax_regression/CMakeLists.txt +++ b/src/mlpack/methods/softmax_regression/CMakeLists.txt @@ -2,10 +2,9 @@ # Anything not in this list will not be compiled into mlpack. set(SOURCES softmax_regression.hpp - softmax_regression.cpp softmax_regression_impl.hpp softmax_regression_function.hpp - softmax_regression_function.cpp + softmax_regression_function_impl.hpp ) # Add directory name to sources. diff --git a/src/mlpack/methods/softmax_regression/softmax_regression.cpp b/src/mlpack/methods/softmax_regression/softmax_regression.cpp deleted file mode 100644 index 567241b35a..0000000000 --- a/src/mlpack/methods/softmax_regression/softmax_regression.cpp +++ /dev/null @@ -1,140 +0,0 @@ -/** - * @file methods/softmax_regression/softmax_regression.cpp - * @author Siddharth Agrawal - * - * Implementation of softmax regression. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ - -#include "softmax_regression.hpp" - -namespace mlpack { -namespace regression { - -SoftmaxRegression:: -SoftmaxRegression(const size_t inputSize, - const size_t numClasses, - const bool fitIntercept) : - numClasses(numClasses), - lambda(0.0001), - fitIntercept(fitIntercept) -{ - SoftmaxRegressionFunction::InitializeWeights( - parameters, inputSize, numClasses, fitIntercept); -} - -void SoftmaxRegression::Classify(const arma::mat& dataset, - arma::Row& labels) - const -{ - arma::mat probabilities; - Classify(dataset, probabilities); - - // Prepare necessary data. - labels.zeros(dataset.n_cols); - double maxProbability = 0; - - // For each test input. - for (size_t i = 0; i < dataset.n_cols; ++i) - { - // For each class. - for (size_t j = 0; j < numClasses; ++j) - { - // If a higher class probability is encountered, change prediction. - if (probabilities(j, i) > maxProbability) - { - maxProbability = probabilities(j, i); - labels(i) = j; - } - } - - // Set maximum probability to zero for the next input. - maxProbability = 0; - } -} - -void SoftmaxRegression::Classify(const arma::mat& dataset, - arma::Row& labels, - arma::mat& probabilities) - const -{ - Classify(dataset, probabilities); - - // Prepare necessary data. - labels.zeros(dataset.n_cols); - double maxProbability = 0; - - // For each test input. - for (size_t i = 0; i < dataset.n_cols; ++i) - { - // For each class. - for (size_t j = 0; j < numClasses; ++j) - { - // If a higher class probability is encountered, change prediction. - if (probabilities(j, i) > maxProbability) - { - maxProbability = probabilities(j, i); - labels(i) = j; - } - } - - // Set maximum probability to zero for the next input. - maxProbability = 0; - } -} - -void SoftmaxRegression::Classify(const arma::mat& dataset, - arma::mat& probabilities) - const -{ - util::CheckSameDimensionality(dataset, FeatureSize(), - "SoftmaxRegression::Classify()"); - - // Calculate the probabilities for each test input. - arma::mat hypothesis; - if (fitIntercept) - { - // In order to add the intercept term, we should compute following matrix: - // [1; data] = arma::join_cols(ones(1, data.n_cols), data) - // hypothesis = arma::exp(parameters * [1; data]). - // - // Since the cost of join maybe high due to the copy of original data, - // split the hypothesis computation to two components. - hypothesis = arma::exp( - arma::repmat(parameters.col(0), 1, dataset.n_cols) + - parameters.cols(1, parameters.n_cols - 1) * dataset); - } - else - { - hypothesis = arma::exp(parameters * dataset); - } - - probabilities = hypothesis / arma::repmat(arma::sum(hypothesis, 0), - numClasses, 1); -} - -double SoftmaxRegression::ComputeAccuracy( - const arma::mat& testData, - const arma::Row& labels) const -{ - arma::Row predictions; - - // Get predictions for the provided data. - Classify(testData, predictions); - - // Increment count for every correctly predicted label. - size_t count = 0; - for (size_t i = 0; i < predictions.n_elem; ++i) - if (predictions(i) == labels(i)) - count++; - - // Return percentage accuracy. - return (count * 100.0) / predictions.n_elem; -} - -} // namespace regression -} // namespace mlpack diff --git a/src/mlpack/methods/softmax_regression/softmax_regression_function.hpp b/src/mlpack/methods/softmax_regression/softmax_regression_function.hpp index 952d81b6a9..181e035bed 100644 --- a/src/mlpack/methods/softmax_regression/softmax_regression_function.hpp +++ b/src/mlpack/methods/softmax_regression/softmax_regression_function.hpp @@ -14,6 +14,7 @@ #define MLPACK_METHODS_SOFTMAX_REGRESSION_SOFTMAX_REGRESSION_FUNCTION_HPP #include +#include namespace mlpack { namespace regression { @@ -205,4 +206,7 @@ class SoftmaxRegressionFunction } // namespace regression } // namespace mlpack +// Include implementation. +#include "softmax_regression_function_impl.hpp" + #endif diff --git a/src/mlpack/methods/softmax_regression/softmax_regression_function.cpp b/src/mlpack/methods/softmax_regression/softmax_regression_function_impl.hpp similarity index 84% rename from src/mlpack/methods/softmax_regression/softmax_regression_function.cpp rename to src/mlpack/methods/softmax_regression/softmax_regression_function_impl.hpp index 2e8a93b79c..e91205552e 100644 --- a/src/mlpack/methods/softmax_regression/softmax_regression_function.cpp +++ b/src/mlpack/methods/softmax_regression/softmax_regression_function_impl.hpp @@ -1,5 +1,5 @@ /** - * @file methods/softmax_regression/softmax_regression_function.cpp + * @file methods/softmax_regression/softmax_regression_function_impl.hpp * @author Siddharth Agrawal * * Implementation of function to be optimized for softmax regression. @@ -9,13 +9,15 @@ * 3-clause BSD license along with mlpack. If not, see * http://www.opensource.org/licenses/BSD-3-Clause for more information. */ +#ifndef MLPACK_METHODS_SOFTMAX_REGRESSION_SOFTMAX_REGRESSION_FUNCTION_IMPL_HPP +#define MLPACK_METHODS_SOFTMAX_REGRESSION_SOFTMAX_REGRESSION_FUNCTION_IMPL_HPP + #include "softmax_regression_function.hpp" -#include -using namespace mlpack; -using namespace mlpack::regression; +namespace mlpack { +namespace regression { -SoftmaxRegressionFunction::SoftmaxRegressionFunction( +inline SoftmaxRegressionFunction::SoftmaxRegressionFunction( const arma::mat& data, const arma::Row& labels, const size_t numClasses, @@ -36,7 +38,7 @@ SoftmaxRegressionFunction::SoftmaxRegressionFunction( /** * Shuffle the data. */ -void SoftmaxRegressionFunction::Shuffle() +inline void SoftmaxRegressionFunction::Shuffle() { // Determine new ordering. arma::uvec ordering = arma::shuffle(arma::linspace(0, @@ -75,12 +77,12 @@ void SoftmaxRegressionFunction::Shuffle() * normal distribution. The weights cannot be initialized to zero, as that will * lead to each class output being the same. */ -const arma::mat SoftmaxRegressionFunction::InitializeWeights() +inline const arma::mat SoftmaxRegressionFunction::InitializeWeights() { return InitializeWeights(data.n_rows, numClasses, fitIntercept); } -const arma::mat SoftmaxRegressionFunction::InitializeWeights( +inline const arma::mat SoftmaxRegressionFunction::InitializeWeights( const size_t featureSize, const size_t numClasses, const bool fitIntercept) @@ -90,7 +92,7 @@ const arma::mat SoftmaxRegressionFunction::InitializeWeights( return parameters; } -void SoftmaxRegressionFunction::InitializeWeights( +inline void SoftmaxRegressionFunction::InitializeWeights( arma::mat &weights, const size_t featureSize, const size_t numClasses, @@ -111,7 +113,7 @@ void SoftmaxRegressionFunction::InitializeWeights( * labels. The output is in the form of a matrix, which leads to simpler * calculations in the Evaluate() and Gradient() methods. */ -void SoftmaxRegressionFunction::GetGroundTruthMatrix( +inline void SoftmaxRegressionFunction::GetGroundTruthMatrix( const arma::Row& labels, arma::sp_mat& groundTruth) { // Calculate the ground truth matrix according to the labels passed. The @@ -147,7 +149,7 @@ void SoftmaxRegressionFunction::GetGroundTruthMatrix( * Evaluate the probabilities matrix. If fitIntercept flag is true, * it should consider the parameters.cols(0) intercept term. */ -void SoftmaxRegressionFunction::GetProbabilitiesMatrix( +inline void SoftmaxRegressionFunction::GetProbabilitiesMatrix( const arma::mat& parameters, arma::mat& probabilities, const size_t start, @@ -181,7 +183,8 @@ void SoftmaxRegressionFunction::GetProbabilitiesMatrix( /** * Evaluates the objective function given the parameters. */ -double SoftmaxRegressionFunction::Evaluate(const arma::mat& parameters) const +inline double SoftmaxRegressionFunction::Evaluate(const arma::mat& parameters) + const { // The objective function is the negative log likelihood of the model // calculated over all the training examples. Mathematically it is as follows: @@ -219,9 +222,9 @@ double SoftmaxRegressionFunction::Evaluate(const arma::mat& parameters) const /** * Evaluate the objective function for the given points given the parameters. */ -double SoftmaxRegressionFunction::Evaluate(const arma::mat& parameters, - const size_t start, - const size_t batchSize) const +inline double SoftmaxRegressionFunction::Evaluate(const arma::mat& parameters, + const size_t start, + const size_t batchSize) const { arma::mat probabilities; GetProbabilitiesMatrix(parameters, probabilities, start, batchSize); @@ -239,8 +242,8 @@ double SoftmaxRegressionFunction::Evaluate(const arma::mat& parameters, /** * Calculates and stores the gradient values given a set of parameters. */ -void SoftmaxRegressionFunction::Gradient(const arma::mat& parameters, - arma::mat& gradient) const +inline void SoftmaxRegressionFunction::Gradient(const arma::mat& parameters, + arma::mat& gradient) const { // Calculate the class probabilities for each training example. The // probabilities for each of the classes are given by: @@ -272,10 +275,11 @@ void SoftmaxRegressionFunction::Gradient(const arma::mat& parameters, } } -void SoftmaxRegressionFunction::Gradient(const arma::mat& parameters, - const size_t start, - arma::mat& gradient, - const size_t batchSize) const +inline void SoftmaxRegressionFunction::Gradient(const arma::mat& parameters, + const size_t start, + arma::mat& gradient, + const size_t batchSize) + const { arma::mat probabilities; GetProbabilitiesMatrix(parameters, probabilities, start, batchSize); @@ -301,9 +305,10 @@ void SoftmaxRegressionFunction::Gradient(const arma::mat& parameters, } } -void SoftmaxRegressionFunction::PartialGradient(const arma::mat& parameters, - const size_t j, - arma::sp_mat& gradient) const +inline void SoftmaxRegressionFunction::PartialGradient(const arma::mat& parameters, + const size_t j, + arma::sp_mat& gradient) + const { gradient.zeros(arma::size(parameters)); @@ -332,3 +337,8 @@ void SoftmaxRegressionFunction::PartialGradient(const arma::mat& parameters, parameters.col(j); } } + +} // namespace regression +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/softmax_regression/softmax_regression_impl.hpp b/src/mlpack/methods/softmax_regression/softmax_regression_impl.hpp index 69b2a35621..c46abcedaf 100644 --- a/src/mlpack/methods/softmax_regression/softmax_regression_impl.hpp +++ b/src/mlpack/methods/softmax_regression/softmax_regression_impl.hpp @@ -49,6 +49,108 @@ SoftmaxRegression::SoftmaxRegression( Train(data, labels, numClasses, optimizer, callbacks...); } +inline SoftmaxRegression::SoftmaxRegression( + const size_t inputSize, + const size_t numClasses, + const bool fitIntercept) : + numClasses(numClasses), + lambda(0.0001), + fitIntercept(fitIntercept) +{ + SoftmaxRegressionFunction::InitializeWeights( + parameters, inputSize, numClasses, fitIntercept); +} + +inline void SoftmaxRegression::Classify(const arma::mat& dataset, + arma::Row& labels) + const +{ + arma::mat probabilities; + Classify(dataset, probabilities); + + // Prepare necessary data. + labels.zeros(dataset.n_cols); + double maxProbability = 0; + + // For each test input. + for (size_t i = 0; i < dataset.n_cols; ++i) + { + // For each class. + for (size_t j = 0; j < numClasses; ++j) + { + // If a higher class probability is encountered, change prediction. + if (probabilities(j, i) > maxProbability) + { + maxProbability = probabilities(j, i); + labels(i) = j; + } + } + + // Set maximum probability to zero for the next input. + maxProbability = 0; + } +} + +inline void SoftmaxRegression::Classify(const arma::mat& dataset, + arma::Row& labels, + arma::mat& probabilities) + const +{ + Classify(dataset, probabilities); + + // Prepare necessary data. + labels.zeros(dataset.n_cols); + double maxProbability = 0; + + // For each test input. + for (size_t i = 0; i < dataset.n_cols; ++i) + { + // For each class. + for (size_t j = 0; j < numClasses; ++j) + { + // If a higher class probability is encountered, change prediction. + if (probabilities(j, i) > maxProbability) + { + maxProbability = probabilities(j, i); + labels(i) = j; + } + } + + // Set maximum probability to zero for the next input. + maxProbability = 0; + } +} + +inline void SoftmaxRegression::Classify(const arma::mat& dataset, + arma::mat& probabilities) + const +{ + util::CheckSameDimensionality(dataset, FeatureSize(), + "SoftmaxRegression::Classify()"); + + // Calculate the probabilities for each test input. + arma::mat hypothesis; + if (fitIntercept) + { + // In order to add the intercept term, we should compute following matrix: + // [1; data] = arma::join_cols(ones(1, data.n_cols), data) + // hypothesis = arma::exp(parameters * [1; data]). + // + // Since the cost of join maybe high due to the copy of original data, + // split the hypothesis computation to two components. + hypothesis = arma::exp( + arma::repmat(parameters.col(0), 1, dataset.n_cols) + + parameters.cols(1, parameters.n_cols - 1) * dataset); + } + else + { + hypothesis = arma::exp(parameters * dataset); + } + + probabilities = hypothesis / arma::repmat(arma::sum(hypothesis, 0), + numClasses, 1); +} + template size_t SoftmaxRegression::Classify(const VecType& point) const { @@ -57,6 +159,25 @@ size_t SoftmaxRegression::Classify(const VecType& point) const return size_t(label(0)); } +inline double SoftmaxRegression::ComputeAccuracy( + const arma::mat& testData, + const arma::Row& labels) const +{ + arma::Row predictions; + + // Get predictions for the provided data. + Classify(testData, predictions); + + // Increment count for every correctly predicted label. + size_t count = 0; + for (size_t i = 0; i < predictions.n_elem; ++i) + if (predictions(i) == labels(i)) + count++; + + // Return percentage accuracy. + return (count * 100.0) / predictions.n_elem; +} + template double SoftmaxRegression::Train(const arma::mat& data, const arma::Row& labels, From 0bef9de3836934426621824b590ac72400a30bcf Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Mon, 9 May 2022 13:30:06 +0530 Subject: [PATCH 22/57] converted randomized svd to .hpp --- .../methods/randomized_svd/CMakeLists.txt | 2 +- .../methods/randomized_svd/randomized_svd.hpp | 3 ++ ...omized_svd.cpp => randomized_svd_impl.hpp} | 51 +++++++++++-------- 3 files changed, 33 insertions(+), 23 deletions(-) rename src/mlpack/methods/randomized_svd/{randomized_svd.cpp => randomized_svd_impl.hpp} (51%) diff --git a/src/mlpack/methods/randomized_svd/CMakeLists.txt b/src/mlpack/methods/randomized_svd/CMakeLists.txt index ad0e5b34fa..542b55e255 100644 --- a/src/mlpack/methods/randomized_svd/CMakeLists.txt +++ b/src/mlpack/methods/randomized_svd/CMakeLists.txt @@ -2,7 +2,7 @@ # Anything not in this list will not be compiled into mlpack. set(SOURCES randomized_svd.hpp - randomized_svd.cpp + randomized_svd_impl.hpp ) # Add directory name to sources. diff --git a/src/mlpack/methods/randomized_svd/randomized_svd.hpp b/src/mlpack/methods/randomized_svd/randomized_svd.hpp index 574ba1cd66..5949bb1487 100644 --- a/src/mlpack/methods/randomized_svd/randomized_svd.hpp +++ b/src/mlpack/methods/randomized_svd/randomized_svd.hpp @@ -260,4 +260,7 @@ class RandomizedSVD } // namespace svd } // namespace mlpack +// Include implementation. +#include "randomized_svd_impl.hpp" + #endif diff --git a/src/mlpack/methods/randomized_svd/randomized_svd.cpp b/src/mlpack/methods/randomized_svd/randomized_svd_impl.hpp similarity index 51% rename from src/mlpack/methods/randomized_svd/randomized_svd.cpp rename to src/mlpack/methods/randomized_svd/randomized_svd_impl.hpp index f812277cf8..ce813353d0 100644 --- a/src/mlpack/methods/randomized_svd/randomized_svd.cpp +++ b/src/mlpack/methods/randomized_svd/randomized_svd_impl.hpp @@ -1,5 +1,5 @@ /** - * @file methods/randomized_svd/randomized_svd.cpp + * @file methods/randomized_svd/randomized_svd_impl.hpp * @author Marcus Edel * * Implementation of the randomized SVD method. @@ -10,19 +10,23 @@ * http://www.opensource.org/licenses/BSD-3-Clause for more information. */ +#ifndef MLPACK_METHODS_RANDOMIZED_SVD_RANDOMIZED_SVD_IMPL_HPP +#define MLPACK_METHODS_RANDOMIZED_SVD_RANDOMIZED_SVD_IMPL_HPP + #include "randomized_svd.hpp" namespace mlpack { namespace svd { -RandomizedSVD::RandomizedSVD(const arma::mat& data, - arma::mat& u, - arma::vec& s, - arma::mat& v, - const size_t iteratedPower, - const size_t maxIterations, - const size_t rank, - const double eps) : +inline RandomizedSVD::RandomizedSVD( + const arma::mat& data, + arma::mat& u, + arma::vec& s, + arma::mat& v, + const size_t iteratedPower, + const size_t maxIterations, + const size_t rank, + const double eps) : iteratedPower(iteratedPower), maxIterations(maxIterations), eps(eps) @@ -37,9 +41,10 @@ RandomizedSVD::RandomizedSVD(const arma::mat& data, } } -RandomizedSVD::RandomizedSVD(const size_t iteratedPower, - const size_t maxIterations, - const double eps) : +inline RandomizedSVD::RandomizedSVD( + const size_t iteratedPower, + const size_t maxIterations, + const double eps) : iteratedPower(iteratedPower), maxIterations(maxIterations), eps(eps) @@ -48,11 +53,11 @@ RandomizedSVD::RandomizedSVD(const size_t iteratedPower, } -void RandomizedSVD::Apply(const arma::sp_mat& data, - arma::mat& u, - arma::vec& s, - arma::mat& v, - const size_t rank) +inline void RandomizedSVD::Apply(const arma::sp_mat& data, + arma::mat& u, + arma::vec& s, + arma::mat& v, + const size_t rank) { // Center the data into a temporary matrix for sparse matrix. arma::sp_mat rowMean = arma::sum(data, 1) / data.n_cols; @@ -60,11 +65,11 @@ void RandomizedSVD::Apply(const arma::sp_mat& data, Apply(data, u, s, v, rank, rowMean); } -void RandomizedSVD::Apply(const arma::mat& data, - arma::mat& u, - arma::vec& s, - arma::mat& v, - const size_t rank) +inline void RandomizedSVD::Apply(const arma::mat& data, + arma::mat& u, + arma::vec& s, + arma::mat& v, + const size_t rank) { // Center the data into a temporary matrix. arma::mat rowMean = arma::sum(data, 1) / data.n_cols + eps; @@ -74,3 +79,5 @@ void RandomizedSVD::Apply(const arma::mat& data, } // namespace svd } // namespace mlpack + +#endif From a13ab0a8e30ac8d49a18b113728d5010963778b5 Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Mon, 9 May 2022 13:51:07 +0530 Subject: [PATCH 23/57] converted radical to .hpp --- src/mlpack/methods/radical/CMakeLists.txt | 2 +- src/mlpack/methods/radical/radical.hpp | 5 ++ .../radical/{radical.cpp => radical_impl.hpp} | 82 ++++++++++--------- 3 files changed, 50 insertions(+), 39 deletions(-) rename src/mlpack/methods/radical/{radical.cpp => radical_impl.hpp} (71%) diff --git a/src/mlpack/methods/radical/CMakeLists.txt b/src/mlpack/methods/radical/CMakeLists.txt index f51f2c377d..79968372cd 100644 --- a/src/mlpack/methods/radical/CMakeLists.txt +++ b/src/mlpack/methods/radical/CMakeLists.txt @@ -2,7 +2,7 @@ # Anything not in this list will not be compiled into the output library set(SOURCES radical.hpp - radical.cpp + radical_impl.hpp ) # add directory name to sources diff --git a/src/mlpack/methods/radical/radical.hpp b/src/mlpack/methods/radical/radical.hpp index 94d30faee2..5d79b3daad 100644 --- a/src/mlpack/methods/radical/radical.hpp +++ b/src/mlpack/methods/radical/radical.hpp @@ -16,6 +16,8 @@ #include #include +#include +#include namespace mlpack { namespace radical { @@ -148,4 +150,7 @@ void WhitenFeatureMajorMatrix(const arma::mat& matX, } // namespace radical } // namespace mlpack +// Include implementation. +#include "radical_impl.hpp" + #endif diff --git a/src/mlpack/methods/radical/radical.cpp b/src/mlpack/methods/radical/radical_impl.hpp similarity index 71% rename from src/mlpack/methods/radical/radical.cpp rename to src/mlpack/methods/radical/radical_impl.hpp index 481fb0f0fc..8914c11000 100644 --- a/src/mlpack/methods/radical/radical.cpp +++ b/src/mlpack/methods/radical/radical_impl.hpp @@ -1,5 +1,5 @@ /** - * @file methods/radical/radical.cpp + * @file methods/radical/radical_impl.hpp * @author Nishant Mehta * * Implementation of Radical class @@ -9,22 +9,21 @@ * 3-clause BSD license along with mlpack. If not, see * http://www.opensource.org/licenses/BSD-3-Clause for more information. */ +#ifndef MLPACK_METHODS_RADICAL_RADICAL_IMPL_HPP +#define MLPACK_METHODS_RADICAL_RADICAL_IMPL_HPP #include "radical.hpp" -#include -#include -using namespace std; -using namespace arma; -using namespace mlpack; -using namespace mlpack::radical; +namespace mlpack { +namespace radical { // Set the parameters to RADICAL. -Radical::Radical(const double noiseStdDev, - const size_t replicates, - const size_t angles, - const size_t sweeps, - const size_t m) : +inline Radical::Radical( + const double noiseStdDev, + const size_t replicates, + const size_t angles, + const size_t sweeps, + const size_t m) : noiseStdDev(noiseStdDev), replicates(replicates), angles(angles), @@ -34,14 +33,15 @@ Radical::Radical(const double noiseStdDev, // Nothing to do here. } -void Radical::CopyAndPerturb(mat& xNew, const mat& x) const +inline void Radical::CopyAndPerturb(arma::mat& xNew, + const arma::mat& x) const { - xNew = repmat(x, replicates, 1) + noiseStdDev * randn(replicates * x.n_rows, + xNew = arma::repmat(x, replicates, 1) + noiseStdDev * arma::randn(replicates * x.n_rows, x.n_cols); } -double Radical::Vasicek(vec& z) const +inline double Radical::Vasicek(arma::vec& z) const { z = sort(z); @@ -54,25 +54,26 @@ double Radical::Vasicek(vec& z) const // Apparently faster. double sum = 0; - uword range = z.n_elem - m; - for (uword i = 0; i < range; ++i) + arma::uword range = z.n_elem - m; + for (arma::uword i = 0; i < range; ++i) { - sum += log(max(z(i + m) - z(i), DBL_MIN)); + sum += log(std::max(z(i + m) - z(i), DBL_MIN)); } return sum; } -double Radical::DoRadical2D(const mat& matX, util::Timers& timers) +inline double Radical::DoRadical2D(const arma::mat& matX, + util::Timers& timers) { timers.Start("radical_copy_and_perturb"); CopyAndPerturb(perturbed, matX); timers.Stop("radical_copy_and_perturb"); - mat::fixed<2, 2> matJacobi; + arma::mat::fixed<2, 2> matJacobi; - vec values(angles); + arma::vec values(angles); for (size_t i = 0; i < angles; ++i) { @@ -86,29 +87,29 @@ double Radical::DoRadical2D(const mat& matX, util::Timers& timers) matJacobi(1, 1) = cosTheta; candidate = perturbed * matJacobi; - vec candidateY1 = candidate.unsafe_col(0); - vec candidateY2 = candidate.unsafe_col(1); + arma::vec candidateY1 = candidate.unsafe_col(0); + arma::vec candidateY2 = candidate.unsafe_col(1); values(i) = Vasicek(candidateY1) + Vasicek(candidateY2); } - uword indOpt = 0; + arma::uword indOpt = 0; values.min(indOpt); // we ignore the return value; we don't care about it return (indOpt / (double) angles) * M_PI / 2.0; } -void Radical::DoRadical(const mat& matXT, - mat& matY, - mat& matW, - util::Timers& timers) +inline void Radical::DoRadical(const arma::mat& matXT, + arma::mat& matY, + arma::mat& matW, + util::Timers& timers) { // matX is nPoints by nDims (although less intuitive than columns being // points, and although this is the transpose of the ICA literature, this // choice is for computational efficiency when repeatedly generating // two-dimensional coordinate projections for Radical2D). timers.Start("radical_transpose_data"); - mat matX = trans(matXT); + arma::mat matX = trans(matXT); timers.Stop("radical_transpose_data"); // If m was not specified, initialize m as recommended in @@ -120,8 +121,8 @@ void Radical::DoRadical(const mat& matXT, const size_t nPoints = matX.n_rows; timers.Start("radical_whiten_data"); - mat matXWhitened; - mat matWhitening; + arma::mat matXWhitened; + arma::mat matWhitening; WhitenFeatureMajorMatrix(matX, matY, matWhitening); timers.Stop("radical_whiten_data"); // matY is now the whitened form of matX. @@ -135,9 +136,9 @@ void Radical::DoRadical(const mat& matXT, timers.Start("radical_do_radical"); matW = matWhitening; - mat matYSubspace(nPoints, 2); + arma::mat matYSubspace(nPoints, 2); - mat matJ = eye(nDims, nDims); + arma::mat matJ = arma::eye(nDims, nDims); for (size_t sweepNum = 0; sweepNum < sweeps; sweepNum++) { @@ -184,13 +185,18 @@ void Radical::DoRadical(const mat& matXT, timers.Stop("radical_transpose_data"); } -void mlpack::radical::WhitenFeatureMajorMatrix(const mat& matX, - mat& matXWhitened, - mat& matWhitening) +inline void WhitenFeatureMajorMatrix(const arma::mat& matX, + arma::mat& matXWhitened, + arma::mat& matWhitening) { - mat matU, matV; - vec s; + arma::mat matU, matV; + arma::vec s; arma::svd(matU, s, matV, cov(matX)); matWhitening = matU * diagmat(1 / sqrt(s)) * trans(matV); matXWhitened = matX * matWhitening; } + +} // namespace radical +} // namespace mlpack + +#endif From d8f2ea43c5993c2fe7156eddaa8e6c1a251f3412 Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Mon, 9 May 2022 19:38:05 +0530 Subject: [PATCH 24/57] converted sparse coding to .hpp --- .../methods/sparse_coding/CMakeLists.txt | 1 - .../methods/sparse_coding/sparse_coding.cpp | 277 ------------------ .../methods/sparse_coding/sparse_coding.hpp | 1 + .../sparse_coding/sparse_coding_impl.hpp | 259 ++++++++++++++++ 4 files changed, 260 insertions(+), 278 deletions(-) delete mode 100644 src/mlpack/methods/sparse_coding/sparse_coding.cpp diff --git a/src/mlpack/methods/sparse_coding/CMakeLists.txt b/src/mlpack/methods/sparse_coding/CMakeLists.txt index ed72066c8e..73345260d5 100644 --- a/src/mlpack/methods/sparse_coding/CMakeLists.txt +++ b/src/mlpack/methods/sparse_coding/CMakeLists.txt @@ -5,7 +5,6 @@ set(SOURCES nothing_initializer.hpp random_initializer.hpp sparse_coding.hpp - sparse_coding.cpp sparse_coding_impl.hpp ) diff --git a/src/mlpack/methods/sparse_coding/sparse_coding.cpp b/src/mlpack/methods/sparse_coding/sparse_coding.cpp deleted file mode 100644 index c0b69082fe..0000000000 --- a/src/mlpack/methods/sparse_coding/sparse_coding.cpp +++ /dev/null @@ -1,277 +0,0 @@ -/** - * @file methods/sparse_coding/sparse_coding.cpp - * @author Nishant Mehta - * - * Implementation of Sparse Coding with Dictionary Learning using l1 (LASSO) or - * l1+l2 (Elastic Net) regularization. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#include "sparse_coding.hpp" -#include - -namespace mlpack { -namespace sparse_coding { - -SparseCoding::SparseCoding( - const size_t atoms, - const double lambda1, - const double lambda2, - const size_t maxIterations, - const double objTolerance, - const double newtonTolerance) : - atoms(atoms), - lambda1(lambda1), - lambda2(lambda2), - maxIterations(maxIterations), - objTolerance(objTolerance), - newtonTolerance(newtonTolerance) -{ - // Nothing to do. -} - -void SparseCoding::Encode(const arma::mat& data, arma::mat& codes) -{ - // When using the Cholesky version of LARS, this is correct even if - // lambda2 > 0. - arma::mat matGram = trans(dictionary) * dictionary; - - codes.set_size(atoms, data.n_cols); - for (size_t i = 0; i < data.n_cols; ++i) - { - // Report progress. - if ((i % 100) == 0) - Log::Debug << "Optimization at point " << i << "." << std::endl; - - bool useCholesky = true; - regression::LARS lars(useCholesky, matGram, lambda1, lambda2); - - // Create an alias of the code (using the same memory), and then LARS will - // place the result directly into that; then we will not need to have an - // extra copy. - arma::vec code = codes.unsafe_col(i); - arma::rowvec responses = data.unsafe_col(i).t(); - lars.Train(dictionary, responses, code, false); - } -} - -// Dictionary step for optimization. -double SparseCoding::OptimizeDictionary(const arma::mat& data, - const arma::mat& codes, - const arma::uvec& adjacencies) -{ - // Count the number of atomic neighbors for each point x^i. - arma::uvec neighborCounts = arma::zeros(data.n_cols, 1); - - if (adjacencies.n_elem > 0) - { - // This gets the column index. Intentional integer division. - size_t curPointInd = (size_t) (adjacencies(0) / atoms); - - size_t nextColIndex = (curPointInd + 1) * atoms; - for (size_t l = 1; l < adjacencies.n_elem; ++l) - { - // If l no longer refers to an element in this column, advance the column - // number accordingly. - if (adjacencies(l) >= nextColIndex) - { - curPointInd = (size_t) (adjacencies(l) / atoms); - nextColIndex = (curPointInd + 1) * atoms; - } - - ++neighborCounts(curPointInd); - } - } - - // Handle the case of inactive atoms (atoms not used in the given coding). - std::vector inactiveAtoms; - - for (size_t j = 0; j < atoms; ++j) - { - if (arma::accu(codes.row(j) != 0) == 0) - inactiveAtoms.push_back(j); - } - - const size_t nInactiveAtoms = inactiveAtoms.size(); - const size_t nActiveAtoms = atoms - nInactiveAtoms; - - // Efficient construction of Z restricted to active atoms. - arma::mat matActiveZ; - if (nInactiveAtoms > 0) - { - math::RemoveRows(codes, inactiveAtoms, matActiveZ); - } - - if (nInactiveAtoms > 0) - { - Log::Warn << "There are " << nInactiveAtoms - << " inactive atoms. They will be re-initialized randomly.\n"; - } - - Log::Debug << "Solving Dual via Newton's Method.\n"; - - // Solve using Newton's method in the dual - note that the final dot - // multiplication with inv(A) seems to be unavoidable. Although more - // expensive, the code written this way (we use solve()) should be more - // numerically stable than just using inv(A) for everything. - arma::vec dualVars = arma::zeros(nActiveAtoms); - - // vec dualVars = 1e-14 * ones(nActiveAtoms); - - // Method used by feature sign code - fails miserably here. Perhaps the - // MATLAB optimizer fmincon does something clever? - // vec dualVars = 10.0 * randu(nActiveAtoms, 1); - - // vec dualVars = diagvec(solve(dictionary, data * trans(codes)) - // - codes * trans(codes)); - // for (size_t i = 0; i < dualVars.n_elem; ++i) - // if (dualVars(i) < 0) - // dualVars(i) = 0; - - bool converged = false; - - // If we have any inactive atoms, we must construct these differently. - arma::mat codesXT; - arma::mat codesZT; - - if (inactiveAtoms.empty()) - { - codesXT = codes * trans(data); - codesZT = codes * trans(codes); - } - else - { - codesXT = matActiveZ * trans(data); - codesZT = matActiveZ * trans(matActiveZ); - } - - double normGradient = 0; - double improvement = 0; - for (size_t t = 1; (t != maxIterations) && !converged; ++t) - { - arma::mat A = codesZT + diagmat(dualVars); - - arma::mat matAInvZXT = solve(A, codesXT); - - arma::vec gradient = -arma::sum(arma::square(matAInvZXT), 1); - gradient += 1; - - arma::mat hessian = -(-2 * (matAInvZXT * trans(matAInvZXT)) % inv(A)); - - arma::vec searchDirection = -solve(hessian, gradient); - - // Armijo line search. - const double c = 1e-4; - double alpha = 1.0; - const double rho = 0.9; - double sufficientDecrease = c * dot(gradient, searchDirection); - - // A maxIterations parameter for the Armijo line search may be a good idea, - // but it doesn't seem to be causing any problems for now. - while (true) - { - // Calculate objective. - double sumDualVars = arma::sum(dualVars); - double fOld = -(-trace(trans(codesXT) * matAInvZXT) - sumDualVars); - double fNew = -(-trace(trans(codesXT) * solve(codesZT + - diagmat(dualVars + alpha * searchDirection), codesXT)) - - (sumDualVars + alpha * arma::sum(searchDirection))); - - if (fNew <= fOld + alpha * sufficientDecrease) - { - searchDirection = alpha * searchDirection; - improvement = fOld - fNew; - break; - } - - alpha *= rho; - } - - // Take step and print useful information. - dualVars += searchDirection; - normGradient = arma::norm(gradient, 2); - Log::Debug << "Newton Method iteration " << t << ":" << std::endl; - Log::Debug << " Gradient norm: " << std::scientific << normGradient - << "." << std::endl; - Log::Debug << " Improvement: " << std::scientific << improvement << ".\n"; - - if (normGradient < newtonTolerance) - converged = true; - } - - if (inactiveAtoms.empty()) - { - // Directly update dictionary. - dictionary = trans(solve(codesZT + diagmat(dualVars), codesXT)); - } - else - { - arma::mat activeDictionary = trans(solve(codesZT + - diagmat(dualVars), codesXT)); - - // Update all atoms. - size_t currentInactiveIndex = 0; - for (size_t i = 0; i < atoms; ++i) - { - if (inactiveAtoms[currentInactiveIndex] == i) - { - // This atom is inactive. Reinitialize it randomly. - dictionary.col(i) = (data.col(math::RandInt(data.n_cols)) + - data.col(math::RandInt(data.n_cols)) + - data.col(math::RandInt(data.n_cols))); - - dictionary.col(i) /= arma::norm(dictionary.col(i), 2); - - // Increment inactive index counter. - ++currentInactiveIndex; - } - else - { - // Update estimate. - dictionary.col(i) = activeDictionary.col(i - currentInactiveIndex); - } - } - } - - return normGradient; -} - -// Project each atom of the dictionary back into the unit ball (if necessary). -void SparseCoding::ProjectDictionary() -{ - for (size_t j = 0; j < atoms; ++j) - { - double atomNorm = arma::norm(dictionary.col(j), 2); - if (atomNorm > 1) - { - Log::Info << "Norm of atom " << j << " exceeds 1 (" << std::scientific - << atomNorm << "). Shrinking...\n"; - dictionary.col(j) /= atomNorm; - } - } -} - -// Compute the objective function. -double SparseCoding::Objective(const arma::mat& data, const arma::mat& codes) - const -{ - double l11NormZ = arma::sum(arma::sum(arma::abs(codes))); - double froNormResidual = arma::norm(data - (dictionary * codes), "fro"); - - if (lambda2 > 0) - { - double froNormZ = arma::norm(codes, "fro"); - return 0.5 * (std::pow(froNormResidual, 2.0) + (lambda2 * - std::pow(froNormZ, 2.0))) + (lambda1 * l11NormZ); - } - else // It can be simpler. - { - return 0.5 * std::pow(froNormResidual, 2.0) + lambda1 * l11NormZ; - } -} - -} // namespace sparse_coding -} // namespace mlpack diff --git a/src/mlpack/methods/sparse_coding/sparse_coding.hpp b/src/mlpack/methods/sparse_coding/sparse_coding.hpp index 308d91a3f9..7ea6604568 100644 --- a/src/mlpack/methods/sparse_coding/sparse_coding.hpp +++ b/src/mlpack/methods/sparse_coding/sparse_coding.hpp @@ -15,6 +15,7 @@ #include #include +#include // Include our three simple dictionary initializers. #include "nothing_initializer.hpp" diff --git a/src/mlpack/methods/sparse_coding/sparse_coding_impl.hpp b/src/mlpack/methods/sparse_coding/sparse_coding_impl.hpp index 13ea3ebc19..7f9e29b10f 100644 --- a/src/mlpack/methods/sparse_coding/sparse_coding_impl.hpp +++ b/src/mlpack/methods/sparse_coding/sparse_coding_impl.hpp @@ -39,6 +39,265 @@ SparseCoding::SparseCoding( Train(data, initializer); } +inline SparseCoding::SparseCoding( + const size_t atoms, + const double lambda1, + const double lambda2, + const size_t maxIterations, + const double objTolerance, + const double newtonTolerance) : + atoms(atoms), + lambda1(lambda1), + lambda2(lambda2), + maxIterations(maxIterations), + objTolerance(objTolerance), + newtonTolerance(newtonTolerance) +{ + // Nothing to do. +} + +inline void SparseCoding::Encode(const arma::mat& data, + arma::mat& codes) +{ + // When using the Cholesky version of LARS, this is correct even if + // lambda2 > 0. + arma::mat matGram = trans(dictionary) * dictionary; + + codes.set_size(atoms, data.n_cols); + for (size_t i = 0; i < data.n_cols; ++i) + { + // Report progress. + if ((i % 100) == 0) + Log::Debug << "Optimization at point " << i << "." << std::endl; + + bool useCholesky = true; + regression::LARS lars(useCholesky, matGram, lambda1, lambda2); + + // Create an alias of the code (using the same memory), and then LARS will + // place the result directly into that; then we will not need to have an + // extra copy. + arma::vec code = codes.unsafe_col(i); + arma::rowvec responses = data.unsafe_col(i).t(); + lars.Train(dictionary, responses, code, false); + } +} + +// Dictionary step for optimization. +inline double SparseCoding::OptimizeDictionary(const arma::mat& data, + const arma::mat& codes, + const arma::uvec& adjacencies) +{ + // Count the number of atomic neighbors for each point x^i. + arma::uvec neighborCounts = arma::zeros(data.n_cols, 1); + + if (adjacencies.n_elem > 0) + { + // This gets the column index. Intentional integer division. + size_t curPointInd = (size_t) (adjacencies(0) / atoms); + + size_t nextColIndex = (curPointInd + 1) * atoms; + for (size_t l = 1; l < adjacencies.n_elem; ++l) + { + // If l no longer refers to an element in this column, advance the column + // number accordingly. + if (adjacencies(l) >= nextColIndex) + { + curPointInd = (size_t) (adjacencies(l) / atoms); + nextColIndex = (curPointInd + 1) * atoms; + } + + ++neighborCounts(curPointInd); + } + } + + // Handle the case of inactive atoms (atoms not used in the given coding). + std::vector inactiveAtoms; + + for (size_t j = 0; j < atoms; ++j) + { + if (arma::accu(codes.row(j) != 0) == 0) + inactiveAtoms.push_back(j); + } + + const size_t nInactiveAtoms = inactiveAtoms.size(); + const size_t nActiveAtoms = atoms - nInactiveAtoms; + + // Efficient construction of Z restricted to active atoms. + arma::mat matActiveZ; + if (nInactiveAtoms > 0) + { + math::RemoveRows(codes, inactiveAtoms, matActiveZ); + } + + if (nInactiveAtoms > 0) + { + Log::Warn << "There are " << nInactiveAtoms + << " inactive atoms. They will be re-initialized randomly.\n"; + } + + Log::Debug << "Solving Dual via Newton's Method.\n"; + + // Solve using Newton's method in the dual - note that the final dot + // multiplication with inv(A) seems to be unavoidable. Although more + // expensive, the code written this way (we use solve()) should be more + // numerically stable than just using inv(A) for everything. + arma::vec dualVars = arma::zeros(nActiveAtoms); + + // vec dualVars = 1e-14 * ones(nActiveAtoms); + + // Method used by feature sign code - fails miserably here. Perhaps the + // MATLAB optimizer fmincon does something clever? + // vec dualVars = 10.0 * randu(nActiveAtoms, 1); + + // vec dualVars = diagvec(solve(dictionary, data * trans(codes)) + // - codes * trans(codes)); + // for (size_t i = 0; i < dualVars.n_elem; ++i) + // if (dualVars(i) < 0) + // dualVars(i) = 0; + + bool converged = false; + + // If we have any inactive atoms, we must construct these differently. + arma::mat codesXT; + arma::mat codesZT; + + if (inactiveAtoms.empty()) + { + codesXT = codes * trans(data); + codesZT = codes * trans(codes); + } + else + { + codesXT = matActiveZ * trans(data); + codesZT = matActiveZ * trans(matActiveZ); + } + + double normGradient = 0; + double improvement = 0; + for (size_t t = 1; (t != maxIterations) && !converged; ++t) + { + arma::mat A = codesZT + diagmat(dualVars); + + arma::mat matAInvZXT = solve(A, codesXT); + + arma::vec gradient = -arma::sum(arma::square(matAInvZXT), 1); + gradient += 1; + + arma::mat hessian = -(-2 * (matAInvZXT * trans(matAInvZXT)) % inv(A)); + + arma::vec searchDirection = -solve(hessian, gradient); + + // Armijo line search. + const double c = 1e-4; + double alpha = 1.0; + const double rho = 0.9; + double sufficientDecrease = c * dot(gradient, searchDirection); + + // A maxIterations parameter for the Armijo line search may be a good idea, + // but it doesn't seem to be causing any problems for now. + while (true) + { + // Calculate objective. + double sumDualVars = arma::sum(dualVars); + double fOld = -(-trace(trans(codesXT) * matAInvZXT) - sumDualVars); + double fNew = -(-trace(trans(codesXT) * solve(codesZT + + diagmat(dualVars + alpha * searchDirection), codesXT)) - + (sumDualVars + alpha * arma::sum(searchDirection))); + + if (fNew <= fOld + alpha * sufficientDecrease) + { + searchDirection = alpha * searchDirection; + improvement = fOld - fNew; + break; + } + + alpha *= rho; + } + + // Take step and print useful information. + dualVars += searchDirection; + normGradient = arma::norm(gradient, 2); + Log::Debug << "Newton Method iteration " << t << ":" << std::endl; + Log::Debug << " Gradient norm: " << std::scientific << normGradient + << "." << std::endl; + Log::Debug << " Improvement: " << std::scientific << improvement << ".\n"; + + if (normGradient < newtonTolerance) + converged = true; + } + + if (inactiveAtoms.empty()) + { + // Directly update dictionary. + dictionary = trans(solve(codesZT + diagmat(dualVars), codesXT)); + } + else + { + arma::mat activeDictionary = trans(solve(codesZT + + diagmat(dualVars), codesXT)); + + // Update all atoms. + size_t currentInactiveIndex = 0; + for (size_t i = 0; i < atoms; ++i) + { + if (inactiveAtoms[currentInactiveIndex] == i) + { + // This atom is inactive. Reinitialize it randomly. + dictionary.col(i) = (data.col(math::RandInt(data.n_cols)) + + data.col(math::RandInt(data.n_cols)) + + data.col(math::RandInt(data.n_cols))); + + dictionary.col(i) /= arma::norm(dictionary.col(i), 2); + + // Increment inactive index counter. + ++currentInactiveIndex; + } + else + { + // Update estimate. + dictionary.col(i) = activeDictionary.col(i - currentInactiveIndex); + } + } + } + + return normGradient; +} + +// Project each atom of the dictionary back into the unit ball (if necessary). +inline void SparseCoding::ProjectDictionary() +{ + for (size_t j = 0; j < atoms; ++j) + { + double atomNorm = arma::norm(dictionary.col(j), 2); + if (atomNorm > 1) + { + Log::Info << "Norm of atom " << j << " exceeds 1 (" << std::scientific + << atomNorm << "). Shrinking...\n"; + dictionary.col(j) /= atomNorm; + } + } +} + +// Compute the objective function. +inline double SparseCoding::Objective(const arma::mat& data, + const arma::mat& codes) + const +{ + double l11NormZ = arma::sum(arma::sum(arma::abs(codes))); + double froNormResidual = arma::norm(data - (dictionary * codes), "fro"); + + if (lambda2 > 0) + { + double froNormZ = arma::norm(codes, "fro"); + return 0.5 * (std::pow(froNormResidual, 2.0) + (lambda2 * + std::pow(froNormZ, 2.0))) + (lambda1 * l11NormZ); + } + else // It can be simpler. + { + return 0.5 * std::pow(froNormResidual, 2.0) + lambda1 * l11NormZ; + } +} + template double SparseCoding::Train( const arma::mat& data, From 61550e06783aa6ec4c95c62e91b947d0bd31a477 Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Mon, 9 May 2022 20:48:19 +0530 Subject: [PATCH 25/57] converted sparse autoencoder to .hpp --- .../methods/sparse_autoencoder/CMakeLists.txt | 5 +-- .../sparse_autoencoder/maximal_inputs.hpp | 3 ++ ...mal_inputs.cpp => maximal_inputs_impl.hpp} | 13 +++++-- .../sparse_autoencoder/sparse_autoencoder.cpp | 30 --------------- .../sparse_autoencoder_function.hpp | 3 ++ ...p => sparse_autoencoder_function_impl.hpp} | 37 ++++++++++++------- .../sparse_autoencoder_impl.hpp | 11 ++++++ 7 files changed, 51 insertions(+), 51 deletions(-) rename src/mlpack/methods/sparse_autoencoder/{maximal_inputs.cpp => maximal_inputs_impl.hpp} (75%) delete mode 100644 src/mlpack/methods/sparse_autoencoder/sparse_autoencoder.cpp rename src/mlpack/methods/sparse_autoencoder/{sparse_autoencoder_function.cpp => sparse_autoencoder_function_impl.hpp} (90%) diff --git a/src/mlpack/methods/sparse_autoencoder/CMakeLists.txt b/src/mlpack/methods/sparse_autoencoder/CMakeLists.txt index 799d9cafcd..db505bb46d 100644 --- a/src/mlpack/methods/sparse_autoencoder/CMakeLists.txt +++ b/src/mlpack/methods/sparse_autoencoder/CMakeLists.txt @@ -2,12 +2,11 @@ # Anything not in this list will not be compiled into mlpack. set(SOURCES sparse_autoencoder.hpp - sparse_autoencoder.cpp sparse_autoencoder_impl.hpp sparse_autoencoder_function.hpp - sparse_autoencoder_function.cpp + sparse_autoencoder_function_impl.hpp maximal_inputs.hpp - maximal_inputs.cpp + maximal_inputs_impl.hpp ) # Add directory name to sources. diff --git a/src/mlpack/methods/sparse_autoencoder/maximal_inputs.hpp b/src/mlpack/methods/sparse_autoencoder/maximal_inputs.hpp index 887f5f0e15..2b9a6eec10 100644 --- a/src/mlpack/methods/sparse_autoencoder/maximal_inputs.hpp +++ b/src/mlpack/methods/sparse_autoencoder/maximal_inputs.hpp @@ -93,4 +93,7 @@ void NormalizeColByMax(const arma::mat& input, arma::mat& output); } // namespace nn } // namespace mlpack +// Include implementation. +#include "maximal_inputs_impl.hpp" + #endif diff --git a/src/mlpack/methods/sparse_autoencoder/maximal_inputs.cpp b/src/mlpack/methods/sparse_autoencoder/maximal_inputs_impl.hpp similarity index 75% rename from src/mlpack/methods/sparse_autoencoder/maximal_inputs.cpp rename to src/mlpack/methods/sparse_autoencoder/maximal_inputs_impl.hpp index 742cfecae2..d6ed1c497c 100644 --- a/src/mlpack/methods/sparse_autoencoder/maximal_inputs.cpp +++ b/src/mlpack/methods/sparse_autoencoder/maximal_inputs_impl.hpp @@ -1,5 +1,5 @@ /** - * @file methods/sparse_autoencoder/maximal_inputs.cpp + * @file methods/sparse_autoencoder/maximal_inputs_impl.hpp * @author Tham Ngap Wei * * Implementation of MaximalInputs(). @@ -9,12 +9,15 @@ * 3-clause BSD license along with mlpack. If not, see * http://www.opensource.org/licenses/BSD-3-Clause for more information. */ +#ifndef MLPACK_METHODS_NN_MAXIMAL_INPUTS_IMPL_HPP +#define MLPACK_METHODS_NN_MAXIMAL_INPUTS_IMPL_HPP + #include "maximal_inputs.hpp" namespace mlpack { namespace nn { -void MaximalInputs(const arma::mat& parameters, arma::mat& output) +inline void MaximalInputs(const arma::mat& parameters, arma::mat& output) { arma::mat paramTemp(parameters.submat(0, 0, (parameters.n_rows - 1) / 2 - 1, parameters.n_cols - 2).t()); @@ -24,8 +27,8 @@ void MaximalInputs(const arma::mat& parameters, arma::mat& output) NormalizeColByMax(paramTemp, output); } -void NormalizeColByMax(const arma::mat &input, - arma::mat &output) +inline void NormalizeColByMax(const arma::mat &input, + arma::mat &output) { output.set_size(input.n_rows, input.n_cols); for (arma::uword i = 0; i != input.n_cols; ++i) @@ -44,3 +47,5 @@ void NormalizeColByMax(const arma::mat &input, } // namespace nn } // namespace mlpack + +#endif diff --git a/src/mlpack/methods/sparse_autoencoder/sparse_autoencoder.cpp b/src/mlpack/methods/sparse_autoencoder/sparse_autoencoder.cpp deleted file mode 100644 index 734b45998a..0000000000 --- a/src/mlpack/methods/sparse_autoencoder/sparse_autoencoder.cpp +++ /dev/null @@ -1,30 +0,0 @@ -/** - * @file methods/sparse_autoencoder/sparse_autoencoder.cpp - * @author Siddharth Agrawal - * - * Implementation of sparse autoencoders. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ - -#include "sparse_autoencoder.hpp" - -namespace mlpack { -namespace nn { - -void SparseAutoencoder::GetNewFeatures(arma::mat& data, - arma::mat& features) -{ - const size_t l1 = hiddenSize; - const size_t l2 = visibleSize; - - Sigmoid(parameters.submat(0, 0, l1 - 1, l2 - 1) * data + - arma::repmat(parameters.submat(0, l2, l1 - 1, l2), 1, data.n_cols), - features); -} - -} // namespace nn -} // namespace mlpack diff --git a/src/mlpack/methods/sparse_autoencoder/sparse_autoencoder_function.hpp b/src/mlpack/methods/sparse_autoencoder/sparse_autoencoder_function.hpp index dcfea042d7..904343e09e 100644 --- a/src/mlpack/methods/sparse_autoencoder/sparse_autoencoder_function.hpp +++ b/src/mlpack/methods/sparse_autoencoder/sparse_autoencoder_function.hpp @@ -165,4 +165,7 @@ class SparseAutoencoderFunction } // namespace nn } // namespace mlpack +// Include implementation. +#include "sparse_autoencoder_function_impl.hpp" + #endif diff --git a/src/mlpack/methods/sparse_autoencoder/sparse_autoencoder_function.cpp b/src/mlpack/methods/sparse_autoencoder/sparse_autoencoder_function_impl.hpp similarity index 90% rename from src/mlpack/methods/sparse_autoencoder/sparse_autoencoder_function.cpp rename to src/mlpack/methods/sparse_autoencoder/sparse_autoencoder_function_impl.hpp index 4916c021fd..5829f99b40 100644 --- a/src/mlpack/methods/sparse_autoencoder/sparse_autoencoder_function.cpp +++ b/src/mlpack/methods/sparse_autoencoder/sparse_autoencoder_function_impl.hpp @@ -1,5 +1,5 @@ /** - * @file methods/sparse_autoencoder/sparse_autoencoder_function.cpp + * @file methods/sparse_autoencoder/sparse_autoencoder_function_impl.hpp * @author Siddharth Agrawal * * Implementation of function to be optimized for sparse autoencoders. @@ -9,18 +9,21 @@ * 3-clause BSD license along with mlpack. If not, see * http://www.opensource.org/licenses/BSD-3-Clause for more information. */ +#ifndef MLPACK_METHODS_SPARSE_AUTOENCODER_SPARSE_AUTOENCODER_FUNCTION_IMPL_HPP +#define MLPACK_METHODS_SPARSE_AUTOENCODER_SPARSE_AUTOENCODER_FUNCTION_IMPL_HPP + #include "sparse_autoencoder_function.hpp" -using namespace mlpack; -using namespace mlpack::nn; -using namespace std; +namespace mlpack { +namespace nn { -SparseAutoencoderFunction::SparseAutoencoderFunction(const arma::mat& data, - const size_t visibleSize, - const size_t hiddenSize, - const double lambda, - const double beta, - const double rho) : +inline SparseAutoencoderFunction::SparseAutoencoderFunction( + const arma::mat& data, + const size_t visibleSize, + const size_t hiddenSize, + const double lambda, + const double beta, + const double rho) : data(data), visibleSize(visibleSize), hiddenSize(hiddenSize), @@ -37,7 +40,7 @@ SparseAutoencoderFunction::SparseAutoencoderFunction(const arma::mat& data, * [-r, r] where 'r' is decided using the sizes of the visible and hidden * layers. The biases b1, b2 are initialized to 0. */ -const arma::mat SparseAutoencoderFunction::InitializeWeights() +inline const arma::mat SparseAutoencoderFunction::InitializeWeights() { // The module uses a matrix to store the parameters, its structure looks like: // vSize 1 @@ -73,7 +76,8 @@ const arma::mat SparseAutoencoderFunction::InitializeWeights() /** Evaluates the objective function given the parameters. */ -double SparseAutoencoderFunction::Evaluate(const arma::mat& parameters) const +inline double SparseAutoencoderFunction::Evaluate(const arma::mat& parameters) + const { // The objective function is the average squared reconstruction error of the // network. w1 and b1 are the weights and biases associated with the hidden @@ -141,8 +145,8 @@ double SparseAutoencoderFunction::Evaluate(const arma::mat& parameters) const /** Calculates and stores the gradient values given a set of parameters. */ -void SparseAutoencoderFunction::Gradient(const arma::mat& parameters, - arma::mat& gradient) const +inline void SparseAutoencoderFunction::Gradient(const arma::mat& parameters, + arma::mat& gradient) const { // Performs a feedforward pass of the neural network, and computes the // activations of the output layer as in the Evaluate() method. It uses the @@ -208,3 +212,8 @@ void SparseAutoencoderFunction::Gradient(const arma::mat& parameters, gradient.submat(0, l2, l1 - 1, l2) = arma::sum(delHid, 1) / data.n_cols; gradient.submat(l3, 0, l3, l2 - 1) = (arma::sum(delOut, 1) / data.n_cols).t(); } + +} // namespace nn +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/sparse_autoencoder/sparse_autoencoder_impl.hpp b/src/mlpack/methods/sparse_autoencoder/sparse_autoencoder_impl.hpp index ecf788c86c..d21e554e9f 100644 --- a/src/mlpack/methods/sparse_autoencoder/sparse_autoencoder_impl.hpp +++ b/src/mlpack/methods/sparse_autoencoder/sparse_autoencoder_impl.hpp @@ -72,6 +72,17 @@ SparseAutoencoder::SparseAutoencoder(const arma::mat& data, << "trained model is " << out << "." << std::endl; } +inline void SparseAutoencoder::GetNewFeatures(arma::mat& data, + arma::mat& features) +{ + const size_t l1 = hiddenSize; + const size_t l2 = visibleSize; + + Sigmoid(parameters.submat(0, 0, l1 - 1, l2 - 1) * data + + arma::repmat(parameters.submat(0, l2, l1 - 1, l2), 1, data.n_cols), + features); +} + } // namespace nn } // namespace mlpack From e19c00a00838e6f8f315bd636ceafccfa7bef478 Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Mon, 9 May 2022 21:00:51 +0530 Subject: [PATCH 26/57] trying to initialize env_type var in same header --- .../environment/CMakeLists.txt | 1 - .../environment/env_type.cpp | 29 ------------------- .../environment/env_type.hpp | 13 +++++++++ 3 files changed, 13 insertions(+), 30 deletions(-) delete mode 100644 src/mlpack/methods/reinforcement_learning/environment/env_type.cpp diff --git a/src/mlpack/methods/reinforcement_learning/environment/CMakeLists.txt b/src/mlpack/methods/reinforcement_learning/environment/CMakeLists.txt index be5d1f3486..77d822aced 100644 --- a/src/mlpack/methods/reinforcement_learning/environment/CMakeLists.txt +++ b/src/mlpack/methods/reinforcement_learning/environment/CMakeLists.txt @@ -2,7 +2,6 @@ # Anything not in this list will not be compiled into mlpack. set(SOURCES env_type.hpp - env_type.cpp mountain_car.hpp cart_pole.hpp continuous_mountain_car.hpp diff --git a/src/mlpack/methods/reinforcement_learning/environment/env_type.cpp b/src/mlpack/methods/reinforcement_learning/environment/env_type.cpp deleted file mode 100644 index 98f0799199..0000000000 --- a/src/mlpack/methods/reinforcement_learning/environment/env_type.cpp +++ /dev/null @@ -1,29 +0,0 @@ -/** - * @file methods/reinforcement_learning/environment/env_type.cpp - * @author Nishant Kumar - * - * This file defines the static variables used by the discrete and continuous - * environments. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#include "env_type.hpp" - -namespace mlpack { -namespace rl { - -// Instantiate static members. - -size_t DiscreteActionEnv::State::dimension = 0; -size_t DiscreteActionEnv::Action::size = 0; -size_t DiscreteActionEnv::rewardSize = 0; - -size_t ContinuousActionEnv::State::dimension = 0; -size_t ContinuousActionEnv::Action::size = 0; -size_t ContinuousActionEnv::rewardSize = 0; - -} // namespace rl -} // namespace mlpack diff --git a/src/mlpack/methods/reinforcement_learning/environment/env_type.hpp b/src/mlpack/methods/reinforcement_learning/environment/env_type.hpp index 46a4c746bf..98bd8963b9 100644 --- a/src/mlpack/methods/reinforcement_learning/environment/env_type.hpp +++ b/src/mlpack/methods/reinforcement_learning/environment/env_type.hpp @@ -206,6 +206,19 @@ class ContinuousActionEnv static size_t rewardSize; }; +#ifndef MLPACK_METHODS_RL_ENVIRONMENT_ENV_TYPE_VARIABLES +#define MLPACK_METHODS_RL_ENVIRONMENT_ENV_TYPE_VARIABLES + +size_t DiscreteActionEnv::State::dimension = 0; +size_t DiscreteActionEnv::Action::size = 0; +size_t DiscreteActionEnv::rewardSize = 0; + +size_t ContinuousActionEnv::State::dimension = 0; +size_t ContinuousActionEnv::Action::size = 0; +size_t ContinuousActionEnv::rewardSize = 0; + +#endif + } // namespace rl } // namespace mlpack From b76c33d7a8a53f8bc11fae6291d88d1a2ec1dde9 Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Tue, 10 May 2022 12:33:52 +0530 Subject: [PATCH 27/57] converted rann to .hpp --- src/mlpack/methods/rann/CMakeLists.txt | 3 +- src/mlpack/methods/rann/ra_model.cpp | 238 ------------------ src/mlpack/methods/rann/ra_model_impl.hpp | 220 ++++++++++++++++ src/mlpack/methods/rann/ra_util.hpp | 16 +- .../rann/{ra_util.cpp => ra_util_impl.hpp} | 30 ++- 5 files changed, 243 insertions(+), 264 deletions(-) delete mode 100644 src/mlpack/methods/rann/ra_model.cpp rename src/mlpack/methods/rann/{ra_util.cpp => ra_util_impl.hpp} (83%) diff --git a/src/mlpack/methods/rann/CMakeLists.txt b/src/mlpack/methods/rann/CMakeLists.txt index a3eb4ff2e2..592da747e7 100644 --- a/src/mlpack/methods/rann/CMakeLists.txt +++ b/src/mlpack/methods/rann/CMakeLists.txt @@ -18,12 +18,11 @@ set(SOURCES # utilities ra_util.hpp - ra_util.cpp + ra_util_impl.hpp # model ra_model.hpp ra_model_impl.hpp - ra_model.cpp ) # add directory name to sources diff --git a/src/mlpack/methods/rann/ra_model.cpp b/src/mlpack/methods/rann/ra_model.cpp deleted file mode 100644 index 061bece78a..0000000000 --- a/src/mlpack/methods/rann/ra_model.cpp +++ /dev/null @@ -1,238 +0,0 @@ -/** - * @file methods/rann/ra_model.cpp - * @author Ryan Curtin - * - * Implementation of the RAModel class. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#include "ra_model.hpp" -#include - -namespace mlpack { -namespace neighbor { - -RAModel::RAModel(const TreeTypes treeType, const bool randomBasis) : - treeType(treeType), - leafSize(20), - randomBasis(randomBasis), - raSearch(NULL) -{ - // Nothing to do. -} - -// Copy constructor. -RAModel::RAModel(const RAModel& other) : - treeType(other.treeType), - leafSize(other.leafSize), - randomBasis(other.randomBasis), - q(other.q), - raSearch(other.raSearch->Clone()) -{ - // Nothing to do. -} - -// Move constructor. -RAModel::RAModel(RAModel&& other) : - treeType(other.treeType), - leafSize(other.leafSize), - randomBasis(other.randomBasis), - q(std::move(other.q)), - raSearch(std::move(other.raSearch)) -{ - // Clear other model. - other.treeType = TreeTypes::KD_TREE; - other.leafSize = 20; - other.randomBasis = false; -} - -// Copy operator. -RAModel& RAModel::operator=(const RAModel& other) -{ - if (this != &other) - { - // Clear current model. - delete raSearch; - - treeType = other.treeType; - leafSize = other.leafSize; - randomBasis = other.randomBasis; - q = other.q; - raSearch = other.raSearch->Clone(); - } - - return *this; -} - -RAModel& RAModel::operator=(RAModel&& other) -{ - if (this != &other) - { - // Clear current model. - delete raSearch; - - treeType = other.treeType; - leafSize = other.leafSize; - randomBasis = other.randomBasis; - q = std::move(other.q); - raSearch = std::move(other.raSearch); - - // Reset other model. - other.treeType = TreeTypes::KD_TREE; - other.leafSize = 20; - other.randomBasis = false; - } - - return *this; -} - -// Clean memory, if necessary -RAModel::~RAModel() -{ - delete raSearch; -} - -void RAModel::InitializeModel(const bool naive, const bool singleMode) -{ - // Clean memory, if necessary. - delete raSearch; - - switch (treeType) - { - case KD_TREE: - raSearch = new LeafSizeRAWrapper(naive, singleMode); - break; - case COVER_TREE: - raSearch = new RAWrapper(naive, singleMode); - break; - case R_TREE: - raSearch = new RAWrapper(naive, singleMode); - break; - case R_STAR_TREE: - raSearch = new RAWrapper(naive, singleMode); - break; - case X_TREE: - raSearch = new RAWrapper(naive, singleMode); - break; - case HILBERT_R_TREE: - raSearch = new RAWrapper(naive, singleMode); - break; - case R_PLUS_TREE: - raSearch = new RAWrapper(naive, singleMode); - break; - case R_PLUS_PLUS_TREE: - raSearch = new RAWrapper(naive, singleMode); - break; - case UB_TREE: - raSearch = new LeafSizeRAWrapper(naive, singleMode); - break; - case OCTREE: - raSearch = new LeafSizeRAWrapper(naive, singleMode); - break; - } -} - -void RAModel::BuildModel(util::Timers& timers, - arma::mat&& referenceSet, - const size_t leafSize, - const bool naive, - const bool singleMode) -{ - // Initialize random basis, if necessary. - if (randomBasis) - { - timers.Start("computing_random_basis"); - Log::Info << "Creating random basis..." << std::endl; - math::RandomBasis(q, referenceSet.n_rows); - - referenceSet = q * referenceSet; - timers.Stop("computing_random_basis"); - } - - this->leafSize = leafSize; - - if (!naive) - Log::Info << "Building reference tree..." << std::endl; - - InitializeModel(naive, singleMode); - - raSearch->Train(timers, std::move(referenceSet), leafSize); - - if (!naive) - Log::Info << "Tree built." << std::endl; -} - -void RAModel::Search(util::Timers& timers, - arma::mat&& querySet, - const size_t k, - arma::Mat& neighbors, - arma::mat& distances) -{ - // Apply the random basis if necessary. - if (randomBasis) - querySet = q * querySet; - - Log::Info << "Searching for " << k << " approximate nearest neighbors with "; - if (!Naive() && !SingleMode()) - Log::Info << "dual-tree rank-approximate " << TreeName() << " search..."; - else if (!Naive()) - Log::Info << "single-tree rank-approximate " << TreeName() << " search..."; - else - Log::Info << "brute-force (naive) rank-approximate search..."; - Log::Info << std::endl; - - raSearch->Search(timers, std::move(querySet), k, neighbors, distances, - leafSize); -} - -void RAModel::Search(util::Timers& timers, - const size_t k, - arma::Mat& neighbors, - arma::mat& distances) -{ - Log::Info << "Searching for " << k << " approximate nearest neighbors with "; - if (!Naive() && !SingleMode()) - Log::Info << "dual-tree rank-approximate " << TreeName() << " search..."; - else if (!Naive()) - Log::Info << "single-tree rank-approximate " << TreeName() << " search..."; - else - Log::Info << "brute-force (naive) rank-approximate search..."; - Log::Info << std::endl; - - raSearch->Search(timers, k, neighbors, distances); -} - -std::string RAModel::TreeName() const -{ - switch (treeType) - { - case KD_TREE: - return "kd-tree"; - case COVER_TREE: - return "cover tree"; - case R_TREE: - return "R tree"; - case R_STAR_TREE: - return "R* tree"; - case X_TREE: - return "X tree"; - case HILBERT_R_TREE: - return "Hilbert R tree"; - case R_PLUS_TREE: - return "R+ tree"; - case R_PLUS_PLUS_TREE: - return "R++ tree"; - case UB_TREE: - return "UB tree"; - case OCTREE: - return "octree"; - default: - return "unknown tree"; - } -} - -} // namespace neighbor -} // namespace mlpack diff --git a/src/mlpack/methods/rann/ra_model_impl.hpp b/src/mlpack/methods/rann/ra_model_impl.hpp index 088f181138..a5c38004a7 100644 --- a/src/mlpack/methods/rann/ra_model_impl.hpp +++ b/src/mlpack/methods/rann/ra_model_impl.hpp @@ -19,6 +19,226 @@ namespace mlpack { namespace neighbor { +inline RAModel::RAModel(const TreeTypes treeType, const bool randomBasis) : + treeType(treeType), + leafSize(20), + randomBasis(randomBasis), + raSearch(NULL) +{ + // Nothing to do. +} + +// Copy constructor. +inline RAModel::RAModel(const RAModel& other) : + treeType(other.treeType), + leafSize(other.leafSize), + randomBasis(other.randomBasis), + q(other.q), + raSearch(other.raSearch->Clone()) +{ + // Nothing to do. +} + +// Move constructor. +inline RAModel::RAModel(RAModel&& other) : + treeType(other.treeType), + leafSize(other.leafSize), + randomBasis(other.randomBasis), + q(std::move(other.q)), + raSearch(std::move(other.raSearch)) +{ + // Clear other model. + other.treeType = TreeTypes::KD_TREE; + other.leafSize = 20; + other.randomBasis = false; +} + +// Copy operator. +inline RAModel& RAModel::operator=(const RAModel& other) +{ + if (this != &other) + { + // Clear current model. + delete raSearch; + + treeType = other.treeType; + leafSize = other.leafSize; + randomBasis = other.randomBasis; + q = other.q; + raSearch = other.raSearch->Clone(); + } + + return *this; +} + +inline RAModel& RAModel::operator=(RAModel&& other) +{ + if (this != &other) + { + // Clear current model. + delete raSearch; + + treeType = other.treeType; + leafSize = other.leafSize; + randomBasis = other.randomBasis; + q = std::move(other.q); + raSearch = std::move(other.raSearch); + + // Reset other model. + other.treeType = TreeTypes::KD_TREE; + other.leafSize = 20; + other.randomBasis = false; + } + + return *this; +} + +// Clean memory, if necessary +inline RAModel::~RAModel() +{ + delete raSearch; +} + +inline void RAModel::InitializeModel(const bool naive, + const bool singleMode) +{ + // Clean memory, if necessary. + delete raSearch; + + switch (treeType) + { + case KD_TREE: + raSearch = new LeafSizeRAWrapper(naive, singleMode); + break; + case COVER_TREE: + raSearch = new RAWrapper(naive, singleMode); + break; + case R_TREE: + raSearch = new RAWrapper(naive, singleMode); + break; + case R_STAR_TREE: + raSearch = new RAWrapper(naive, singleMode); + break; + case X_TREE: + raSearch = new RAWrapper(naive, singleMode); + break; + case HILBERT_R_TREE: + raSearch = new RAWrapper(naive, singleMode); + break; + case R_PLUS_TREE: + raSearch = new RAWrapper(naive, singleMode); + break; + case R_PLUS_PLUS_TREE: + raSearch = new RAWrapper(naive, singleMode); + break; + case UB_TREE: + raSearch = new LeafSizeRAWrapper(naive, singleMode); + break; + case OCTREE: + raSearch = new LeafSizeRAWrapper(naive, singleMode); + break; + } +} + +inline void RAModel::BuildModel(util::Timers& timers, + arma::mat&& referenceSet, + const size_t leafSize, + const bool naive, + const bool singleMode) +{ + // Initialize random basis, if necessary. + if (randomBasis) + { + timers.Start("computing_random_basis"); + Log::Info << "Creating random basis..." << std::endl; + math::RandomBasis(q, referenceSet.n_rows); + + referenceSet = q * referenceSet; + timers.Stop("computing_random_basis"); + } + + this->leafSize = leafSize; + + if (!naive) + Log::Info << "Building reference tree..." << std::endl; + + InitializeModel(naive, singleMode); + + raSearch->Train(timers, std::move(referenceSet), leafSize); + + if (!naive) + Log::Info << "Tree built." << std::endl; +} + +inline void RAModel::Search(util::Timers& timers, + arma::mat&& querySet, + const size_t k, + arma::Mat& neighbors, + arma::mat& distances) +{ + // Apply the random basis if necessary. + if (randomBasis) + querySet = q * querySet; + + Log::Info << "Searching for " << k << " approximate nearest neighbors with "; + if (!Naive() && !SingleMode()) + Log::Info << "dual-tree rank-approximate " << TreeName() << " search..."; + else if (!Naive()) + Log::Info << "single-tree rank-approximate " << TreeName() << " search..."; + else + Log::Info << "brute-force (naive) rank-approximate search..."; + Log::Info << std::endl; + + raSearch->Search(timers, std::move(querySet), k, neighbors, distances, + leafSize); +} + +inline void RAModel::Search(util::Timers& timers, + const size_t k, + arma::Mat& neighbors, + arma::mat& distances) +{ + Log::Info << "Searching for " << k << " approximate nearest neighbors with "; + if (!Naive() && !SingleMode()) + Log::Info << "dual-tree rank-approximate " << TreeName() << " search..."; + else if (!Naive()) + Log::Info << "single-tree rank-approximate " << TreeName() << " search..."; + else + Log::Info << "brute-force (naive) rank-approximate search..."; + Log::Info << std::endl; + + raSearch->Search(timers, k, neighbors, distances); +} + +inline std::string RAModel::TreeName() const +{ + switch (treeType) + { + case KD_TREE: + return "kd-tree"; + case COVER_TREE: + return "cover tree"; + case R_TREE: + return "R tree"; + case R_STAR_TREE: + return "R* tree"; + case X_TREE: + return "X tree"; + case HILBERT_R_TREE: + return "Hilbert R tree"; + case R_PLUS_TREE: + return "R+ tree"; + case R_PLUS_PLUS_TREE: + return "R++ tree"; + case UB_TREE: + return "UB tree"; + case OCTREE: + return "octree"; + default: + return "unknown tree"; + } +} + template class TreeType> diff --git a/src/mlpack/methods/rann/ra_util.hpp b/src/mlpack/methods/rann/ra_util.hpp index a4ccc87d36..a2ef07f763 100644 --- a/src/mlpack/methods/rann/ra_util.hpp +++ b/src/mlpack/methods/rann/ra_util.hpp @@ -48,22 +48,12 @@ class RAUtil const size_t k, const size_t m, const size_t t); - - /** - * Pick up desired number of samples (with replacement) from a given range - * of integers so that only the distinct samples are returned from - * the range [0 - specified upper bound) - * - * @param numSamples Number of random samples. - * @param rangeUpperBound The upper bound on the range of integers. - * @param distinctSamples The list of the distinct samples. - */ - static void ObtainDistinctSamples(const size_t numSamples, - const size_t rangeUpperBound, - arma::uvec& distinctSamples); }; } // namespace neighbor } // namespace mlpack +// Include implementation. +#include "ra_util_impl.hpp" + #endif diff --git a/src/mlpack/methods/rann/ra_util.cpp b/src/mlpack/methods/rann/ra_util_impl.hpp similarity index 83% rename from src/mlpack/methods/rann/ra_util.cpp rename to src/mlpack/methods/rann/ra_util_impl.hpp index 50aceed04e..1804aeb01a 100644 --- a/src/mlpack/methods/rann/ra_util.cpp +++ b/src/mlpack/methods/rann/ra_util_impl.hpp @@ -1,5 +1,5 @@ /** - * @file methods/rann/ra_util.cpp + * @file methods/rann/ra_util_impl.hpp * @author Parikshit Ram * @author Ryan Curtin * @@ -10,15 +10,18 @@ * 3-clause BSD license along with mlpack. If not, see * http://www.opensource.org/licenses/BSD-3-Clause for more information. */ +#ifndef MLPACK_METHODS_RANN_RA_UTIL_IMPL_HPP +#define MLPACK_METHODS_RANN_RA_UTIL_IMPL_HPP + #include "ra_util.hpp" -using namespace mlpack; -using namespace mlpack::neighbor; +namespace mlpack { +namespace neighbor { -size_t mlpack::neighbor::RAUtil::MinimumSamplesReqd(const size_t n, - const size_t k, - const double tau, - const double alpha) +inline size_t RAUtil::MinimumSamplesReqd(const size_t n, + const size_t k, + const double tau, + const double alpha) { size_t ub = n; // The upper bound on the binary search. size_t lb = k; // The lower bound on the binary search. @@ -69,10 +72,10 @@ size_t mlpack::neighbor::RAUtil::MinimumSamplesReqd(const size_t n, return (std::min(m + 1, n)); } -double mlpack::neighbor::RAUtil::SuccessProbability(const size_t n, - const size_t k, - const size_t m, - const size_t t) +inline double RAUtil::SuccessProbability(const size_t n, + const size_t k, + const size_t m, + const size_t t) { if (k == 1) { @@ -161,3 +164,8 @@ double mlpack::neighbor::RAUtil::SuccessProbability(const size_t n, return sum; } // For k > 1. } + +} // namespace neighbor +} // namespace mlpack + +#endif From 83965a2a48b11772cfa61837d54aeb74e1c6aafe Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Tue, 10 May 2022 12:38:50 +0530 Subject: [PATCH 28/57] converted range search to .hpp --- .../methods/range_search/CMakeLists.txt | 1 - src/mlpack/methods/range_search/rs_model.cpp | 289 ------------------ .../methods/range_search/rs_model_impl.hpp | 270 ++++++++++++++++ 3 files changed, 270 insertions(+), 290 deletions(-) delete mode 100644 src/mlpack/methods/range_search/rs_model.cpp diff --git a/src/mlpack/methods/range_search/CMakeLists.txt b/src/mlpack/methods/range_search/CMakeLists.txt index 7bff7bf0e4..c7f8f8ecef 100644 --- a/src/mlpack/methods/range_search/CMakeLists.txt +++ b/src/mlpack/methods/range_search/CMakeLists.txt @@ -8,7 +8,6 @@ set(SOURCES range_search_stat.hpp rs_model.hpp rs_model_impl.hpp - rs_model.cpp ) # Add directory name to sources. diff --git a/src/mlpack/methods/range_search/rs_model.cpp b/src/mlpack/methods/range_search/rs_model.cpp deleted file mode 100644 index 52fe3bea62..0000000000 --- a/src/mlpack/methods/range_search/rs_model.cpp +++ /dev/null @@ -1,289 +0,0 @@ -/** - * @file methods/range_search/rs_model.cpp - * @author Ryan Curtin - * - * Implementation of serialize() and inline functions for RSModel. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#include "rs_model.hpp" - -#include - -namespace mlpack { -namespace range { - -/** - * Initialize the RSModel with the given tree type and whether or not a random - * basis should be used. - */ -RSModel::RSModel(TreeTypes treeType, bool randomBasis) : - treeType(treeType), - leafSize(0), - randomBasis(randomBasis), - rSearch(NULL) -{ - // Nothing to do. -} - -// Copy constructor. -RSModel::RSModel(const RSModel& other) : - treeType(other.treeType), - leafSize(other.leafSize), - randomBasis(other.randomBasis), - q(other.q), - rSearch(other.rSearch->Clone()) -{ - // Nothing to do. -} - -// Move constructor. -RSModel::RSModel(RSModel&& other) : - treeType(other.treeType), - leafSize(other.leafSize), - randomBasis(other.randomBasis), - q(std::move(other.q)), - rSearch(std::move(other.rSearch)) -{ - // Reset other model. - other.treeType = TreeTypes::KD_TREE; - other.leafSize = 0; - other.randomBasis = false; -} - -// Copy operator. -RSModel& RSModel::operator=(const RSModel& other) -{ - if (this != &other) - { - delete rSearch; - - treeType = other.treeType; - leafSize = other.leafSize; - randomBasis = other.randomBasis; - q = other.q; - rSearch = other.rSearch->Clone(); - } - - return *this; -} - -// Move operator. -RSModel& RSModel::operator=(RSModel&& other) -{ - if (this != &other) - { - delete rSearch; - - treeType = other.treeType; - leafSize = other.leafSize; - randomBasis = other.randomBasis; - q = std::move(other.q); - rSearch = std::move(other.rSearch); - - other.treeType = TreeTypes::KD_TREE; - other.leafSize = 0; - other.randomBasis = false; - } - - return *this; -} - -// Clean memory, if necessary. -RSModel::~RSModel() -{ - delete rSearch; -} - -void RSModel::InitializeModel(const bool naive, const bool singleMode) -{ - // Clean memory, if necessary. - delete rSearch; - - switch (treeType) - { - case KD_TREE: - rSearch = new LeafSizeRSWrapper(naive, singleMode); - break; - - case COVER_TREE: - rSearch = new RSWrapper(naive, singleMode); - break; - - case R_TREE: - rSearch = new RSWrapper(naive, singleMode); - break; - - case R_STAR_TREE: - rSearch = new RSWrapper(naive, singleMode); - break; - - case BALL_TREE: - rSearch = new LeafSizeRSWrapper(naive, singleMode); - break; - - case X_TREE: - rSearch = new RSWrapper(naive, singleMode); - break; - - case HILBERT_R_TREE: - rSearch = new RSWrapper(naive, singleMode); - break; - - case R_PLUS_TREE: - rSearch = new RSWrapper(naive, singleMode); - break; - - case R_PLUS_PLUS_TREE: - rSearch = new RSWrapper(naive, singleMode); - break; - - case VP_TREE: - rSearch = new LeafSizeRSWrapper(naive, singleMode); - break; - - case RP_TREE: - rSearch = new LeafSizeRSWrapper(naive, singleMode); - break; - - case MAX_RP_TREE: - rSearch = new LeafSizeRSWrapper(naive, singleMode); - break; - - case UB_TREE: - rSearch = new LeafSizeRSWrapper(naive, singleMode); - break; - - case OCTREE: - rSearch = new LeafSizeRSWrapper(naive, singleMode); - break; - } -} - -void RSModel::BuildModel(util::Timers& timers, - arma::mat&& referenceSet, - const size_t leafSize, - const bool naive, - const bool singleMode) -{ - // Initialize random basis if necessary. - if (randomBasis) - { - timers.Start("computing_random_basis"); - Log::Info << "Creating random basis..." << std::endl; - math::RandomBasis(q, referenceSet.n_rows); - - // Do we need to modify the reference set? - if (randomBasis) - referenceSet = q * referenceSet; - timers.Stop("computing_random_basis"); - } - - this->leafSize = leafSize; - - if (!naive) - Log::Info << "Building reference tree..." << std::endl; - - InitializeModel(naive, singleMode); - - rSearch->Train(timers, std::move(referenceSet), leafSize); - - if (!naive) - Log::Info << "Tree built." << std::endl; -} - -// Perform range search. -void RSModel::Search(util::Timers& timers, - arma::mat&& querySet, - const math::Range& range, - std::vector>& neighbors, - std::vector>& distances) -{ - // We may need to map the query set randomly. - if (randomBasis) - { - timers.Start("applying_random_basis"); - querySet = q * querySet; - timers.Stop("applying_random_basis"); - } - - Log::Info << "Search for points in the range [" << range.Lo() << ", " - << range.Hi() << "] with "; - if (!Naive() && !SingleMode()) - Log::Info << "dual-tree " << TreeName() << " search..." << std::endl; - else if (!Naive()) - Log::Info << "single-tree " << TreeName() << " search..." << std::endl; - else - Log::Info << "brute-force (naive) search..." << std::endl; - - rSearch->Search(timers, std::move(querySet), range, neighbors, distances, - leafSize); -} - -// Perform range search (monochromatic case). -void RSModel::Search(util::Timers& timers, - const math::Range& range, - std::vector>& neighbors, - std::vector>& distances) -{ - Log::Info << "Search for points in the range [" << range.Lo() << ", " - << range.Hi() << "] with "; - if (!Naive() && !SingleMode()) - Log::Info << "dual-tree " << TreeName() << " search..." << std::endl; - else if (!Naive()) - Log::Info << "single-tree " << TreeName() << " search..." << std::endl; - else - Log::Info << "brute-force (naive) search..." << std::endl; - - rSearch->Search(timers, range, neighbors, distances); -} - -// Get the name of the tree type. -std::string RSModel::TreeName() const -{ - switch (treeType) - { - case KD_TREE: - return "kd-tree"; - case COVER_TREE: - return "cover tree"; - case R_TREE: - return "R tree"; - case R_STAR_TREE: - return "R* tree"; - case BALL_TREE: - return "ball tree"; - case X_TREE: - return "X tree"; - case HILBERT_R_TREE: - return "Hilbert R tree"; - case R_PLUS_TREE: - return "R+ tree"; - case R_PLUS_PLUS_TREE: - return "R++ tree"; - case VP_TREE: - return "vantage point tree"; - case RP_TREE: - return "random projection tree (mean split)"; - case MAX_RP_TREE: - return "random projection tree (max split)"; - case UB_TREE: - return "UB tree"; - case OCTREE: - return "octree"; - default: - return "unknown tree"; - } -} - -// Clean memory. -void RSModel::CleanMemory() -{ - delete rSearch; -} - -} // namespace range -} // namespace mlpack diff --git a/src/mlpack/methods/range_search/rs_model_impl.hpp b/src/mlpack/methods/range_search/rs_model_impl.hpp index 8eaedf4e36..c55cf0fee6 100644 --- a/src/mlpack/methods/range_search/rs_model_impl.hpp +++ b/src/mlpack/methods/range_search/rs_model_impl.hpp @@ -20,6 +20,276 @@ namespace mlpack { namespace range { +/** + * Initialize the RSModel with the given tree type and whether or not a random + * basis should be used. + */ +inline RSModel::RSModel(TreeTypes treeType, bool randomBasis) : + treeType(treeType), + leafSize(0), + randomBasis(randomBasis), + rSearch(NULL) +{ + // Nothing to do. +} + +// Copy constructor. +inline RSModel::RSModel(const RSModel& other) : + treeType(other.treeType), + leafSize(other.leafSize), + randomBasis(other.randomBasis), + q(other.q), + rSearch(other.rSearch->Clone()) +{ + // Nothing to do. +} + +// Move constructor. +inline RSModel::RSModel(RSModel&& other) : + treeType(other.treeType), + leafSize(other.leafSize), + randomBasis(other.randomBasis), + q(std::move(other.q)), + rSearch(std::move(other.rSearch)) +{ + // Reset other model. + other.treeType = TreeTypes::KD_TREE; + other.leafSize = 0; + other.randomBasis = false; +} + +// Copy operator. +inline RSModel& RSModel::operator=(const RSModel& other) +{ + if (this != &other) + { + delete rSearch; + + treeType = other.treeType; + leafSize = other.leafSize; + randomBasis = other.randomBasis; + q = other.q; + rSearch = other.rSearch->Clone(); + } + + return *this; +} + +// Move operator. +inline RSModel& RSModel::operator=(RSModel&& other) +{ + if (this != &other) + { + delete rSearch; + + treeType = other.treeType; + leafSize = other.leafSize; + randomBasis = other.randomBasis; + q = std::move(other.q); + rSearch = std::move(other.rSearch); + + other.treeType = TreeTypes::KD_TREE; + other.leafSize = 0; + other.randomBasis = false; + } + + return *this; +} + +// Clean memory, if necessary. +inline RSModel::~RSModel() +{ + delete rSearch; +} + +inline void RSModel::InitializeModel(const bool naive, + const bool singleMode) +{ + // Clean memory, if necessary. + delete rSearch; + + switch (treeType) + { + case KD_TREE: + rSearch = new LeafSizeRSWrapper(naive, singleMode); + break; + + case COVER_TREE: + rSearch = new RSWrapper(naive, singleMode); + break; + + case R_TREE: + rSearch = new RSWrapper(naive, singleMode); + break; + + case R_STAR_TREE: + rSearch = new RSWrapper(naive, singleMode); + break; + + case BALL_TREE: + rSearch = new LeafSizeRSWrapper(naive, singleMode); + break; + + case X_TREE: + rSearch = new RSWrapper(naive, singleMode); + break; + + case HILBERT_R_TREE: + rSearch = new RSWrapper(naive, singleMode); + break; + + case R_PLUS_TREE: + rSearch = new RSWrapper(naive, singleMode); + break; + + case R_PLUS_PLUS_TREE: + rSearch = new RSWrapper(naive, singleMode); + break; + + case VP_TREE: + rSearch = new LeafSizeRSWrapper(naive, singleMode); + break; + + case RP_TREE: + rSearch = new LeafSizeRSWrapper(naive, singleMode); + break; + + case MAX_RP_TREE: + rSearch = new LeafSizeRSWrapper(naive, singleMode); + break; + + case UB_TREE: + rSearch = new LeafSizeRSWrapper(naive, singleMode); + break; + + case OCTREE: + rSearch = new LeafSizeRSWrapper(naive, singleMode); + break; + } +} + +inline void RSModel::BuildModel(util::Timers& timers, + arma::mat&& referenceSet, + const size_t leafSize, + const bool naive, + const bool singleMode) +{ + // Initialize random basis if necessary. + if (randomBasis) + { + timers.Start("computing_random_basis"); + Log::Info << "Creating random basis..." << std::endl; + math::RandomBasis(q, referenceSet.n_rows); + + // Do we need to modify the reference set? + if (randomBasis) + referenceSet = q * referenceSet; + timers.Stop("computing_random_basis"); + } + + this->leafSize = leafSize; + + if (!naive) + Log::Info << "Building reference tree..." << std::endl; + + InitializeModel(naive, singleMode); + + rSearch->Train(timers, std::move(referenceSet), leafSize); + + if (!naive) + Log::Info << "Tree built." << std::endl; +} + +// Perform range search. +inline void RSModel::Search(util::Timers& timers, + arma::mat&& querySet, + const math::Range& range, + std::vector>& neighbors, + std::vector>& distances) +{ + // We may need to map the query set randomly. + if (randomBasis) + { + timers.Start("applying_random_basis"); + querySet = q * querySet; + timers.Stop("applying_random_basis"); + } + + Log::Info << "Search for points in the range [" << range.Lo() << ", " + << range.Hi() << "] with "; + if (!Naive() && !SingleMode()) + Log::Info << "dual-tree " << TreeName() << " search..." << std::endl; + else if (!Naive()) + Log::Info << "single-tree " << TreeName() << " search..." << std::endl; + else + Log::Info << "brute-force (naive) search..." << std::endl; + + rSearch->Search(timers, std::move(querySet), range, neighbors, distances, + leafSize); +} + +// Perform range search (monochromatic case). +inline void RSModel::Search(util::Timers& timers, + const math::Range& range, + std::vector>& neighbors, + std::vector>& distances) +{ + Log::Info << "Search for points in the range [" << range.Lo() << ", " + << range.Hi() << "] with "; + if (!Naive() && !SingleMode()) + Log::Info << "dual-tree " << TreeName() << " search..." << std::endl; + else if (!Naive()) + Log::Info << "single-tree " << TreeName() << " search..." << std::endl; + else + Log::Info << "brute-force (naive) search..." << std::endl; + + rSearch->Search(timers, range, neighbors, distances); +} + +// Get the name of the tree type. +inline std::string RSModel::TreeName() const +{ + switch (treeType) + { + case KD_TREE: + return "kd-tree"; + case COVER_TREE: + return "cover tree"; + case R_TREE: + return "R tree"; + case R_STAR_TREE: + return "R* tree"; + case BALL_TREE: + return "ball tree"; + case X_TREE: + return "X tree"; + case HILBERT_R_TREE: + return "Hilbert R tree"; + case R_PLUS_TREE: + return "R+ tree"; + case R_PLUS_PLUS_TREE: + return "R++ tree"; + case VP_TREE: + return "vantage point tree"; + case RP_TREE: + return "random projection tree (mean split)"; + case MAX_RP_TREE: + return "random projection tree (max split)"; + case UB_TREE: + return "UB tree"; + case OCTREE: + return "octree"; + default: + return "unknown tree"; + } +} + +// Clean memory. +inline void RSModel::CleanMemory() +{ + delete rSearch; +} + template class TreeType> From 6dea51dfeef439d3205d40ccb7996dfb79774b75 Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Tue, 10 May 2022 12:45:26 +0530 Subject: [PATCH 29/57] converted quic svd to .hpp --- src/mlpack/methods/quic_svd/CMakeLists.txt | 2 +- src/mlpack/methods/quic_svd/quic_svd.hpp | 3 ++ .../{quic_svd.cpp => quic_svd_impl.hpp} | 33 ++++++++++--------- 3 files changed, 22 insertions(+), 16 deletions(-) rename src/mlpack/methods/quic_svd/{quic_svd.cpp => quic_svd_impl.hpp} (75%) diff --git a/src/mlpack/methods/quic_svd/CMakeLists.txt b/src/mlpack/methods/quic_svd/CMakeLists.txt index ef7def2f08..ae7a36adc8 100644 --- a/src/mlpack/methods/quic_svd/CMakeLists.txt +++ b/src/mlpack/methods/quic_svd/CMakeLists.txt @@ -2,7 +2,7 @@ # Anything not in this list will not be compiled into mlpack. set(SOURCES quic_svd.hpp - quic_svd.cpp + quic_svd_impl.hpp ) # Add directory name to sources. diff --git a/src/mlpack/methods/quic_svd/quic_svd.hpp b/src/mlpack/methods/quic_svd/quic_svd.hpp index ce386881f8..7f7f262800 100644 --- a/src/mlpack/methods/quic_svd/quic_svd.hpp +++ b/src/mlpack/methods/quic_svd/quic_svd.hpp @@ -94,4 +94,7 @@ class QUIC_SVD } // namespace svd } // namespace mlpack +// Include implementation. +#include "quic_svd_impl.hpp" + #endif diff --git a/src/mlpack/methods/quic_svd/quic_svd.cpp b/src/mlpack/methods/quic_svd/quic_svd_impl.hpp similarity index 75% rename from src/mlpack/methods/quic_svd/quic_svd.cpp rename to src/mlpack/methods/quic_svd/quic_svd_impl.hpp index 4baf3a8282..b01bf06029 100644 --- a/src/mlpack/methods/quic_svd/quic_svd.cpp +++ b/src/mlpack/methods/quic_svd/quic_svd_impl.hpp @@ -1,5 +1,5 @@ /** - * @file methods/quic_svd/quic_svd.cpp + * @file methods/quic_svd/quic_svd_impl.hpp * @author Siddharth Agrawal * * An implementation of QUIC-SVD. @@ -9,30 +9,31 @@ * 3-clause BSD license along with mlpack. If not, see * http://www.opensource.org/licenses/BSD-3-Clause for more information. */ +#ifndef MLPACK_METHODS_QUIC_SVD_QUIC_SVD_IMPL_HPP +#define MLPACK_METHODS_QUIC_SVD_QUIC_SVD_IMPL_HPP // In case it hasn't been included yet. #include "quic_svd.hpp" -using namespace mlpack::tree; - namespace mlpack { namespace svd { -QUIC_SVD::QUIC_SVD(const arma::mat& dataset, - arma::mat& u, - arma::mat& v, - arma::mat& sigma, - const double epsilon, - const double delta) : +inline QUIC_SVD::QUIC_SVD( + const arma::mat& dataset, + arma::mat& u, + arma::mat& v, + arma::mat& sigma, + const double epsilon, + const double delta) : dataset(dataset) { // Since columns are sample in the implementation, the matrix is transposed if // necessary for maximum speedup. - CosineTree* ctree; + tree::CosineTree* ctree; if (dataset.n_cols > dataset.n_rows) - ctree = new CosineTree(dataset, epsilon, delta); + ctree = new tree::CosineTree(dataset, epsilon, delta); else - ctree = new CosineTree(dataset.t(), epsilon, delta); + ctree = new tree::CosineTree(dataset.t(), epsilon, delta); // Get subspace basis by creating the cosine tree. ctree->GetFinalBasis(basis); @@ -45,9 +46,9 @@ QUIC_SVD::QUIC_SVD(const arma::mat& dataset, ExtractSVD(u, v, sigma); } -void QUIC_SVD::ExtractSVD(arma::mat& u, - arma::mat& v, - arma::mat& sigma) +inline void QUIC_SVD::ExtractSVD(arma::mat& u, + arma::mat& v, + arma::mat& sigma) { // Calculate A * V_hat, necessary for further calculations. arma::mat projectedMat; @@ -82,3 +83,5 @@ void QUIC_SVD::ExtractSVD(arma::mat& u, } // namespace svd } // namespace mlpack + +#endif From 1e6ee1197eda9028afda82d84135050854ffcc97 Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Tue, 10 May 2022 12:49:42 +0530 Subject: [PATCH 30/57] converted unmap to .hpp --- .../methods/neighbor_search/CMakeLists.txt | 2 +- src/mlpack/methods/neighbor_search/unmap.hpp | 3 ++ .../{unmap.cpp => unmap_impl.hpp} | 33 +++++++++++-------- 3 files changed, 23 insertions(+), 15 deletions(-) rename src/mlpack/methods/neighbor_search/{unmap.cpp => unmap_impl.hpp} (66%) diff --git a/src/mlpack/methods/neighbor_search/CMakeLists.txt b/src/mlpack/methods/neighbor_search/CMakeLists.txt index b20b562d9c..1d7b76a452 100644 --- a/src/mlpack/methods/neighbor_search/CMakeLists.txt +++ b/src/mlpack/methods/neighbor_search/CMakeLists.txt @@ -14,7 +14,7 @@ set(SOURCES sort_policies/furthest_neighbor_sort_impl.hpp typedef.hpp unmap.hpp - unmap.cpp + unmap_impl.hpp ) # Add directory name to sources. diff --git a/src/mlpack/methods/neighbor_search/unmap.hpp b/src/mlpack/methods/neighbor_search/unmap.hpp index d0165e9005..7b0ec77780 100644 --- a/src/mlpack/methods/neighbor_search/unmap.hpp +++ b/src/mlpack/methods/neighbor_search/unmap.hpp @@ -63,4 +63,7 @@ void Unmap(const arma::Mat& neighbors, } // namespace neighbor } // namespace mlpack +// Include implementation. +#include "unmap_impl.hpp" + #endif diff --git a/src/mlpack/methods/neighbor_search/unmap.cpp b/src/mlpack/methods/neighbor_search/unmap_impl.hpp similarity index 66% rename from src/mlpack/methods/neighbor_search/unmap.cpp rename to src/mlpack/methods/neighbor_search/unmap_impl.hpp index a8efba8386..6d3b52b057 100644 --- a/src/mlpack/methods/neighbor_search/unmap.cpp +++ b/src/mlpack/methods/neighbor_search/unmap_impl.hpp @@ -1,5 +1,5 @@ /** - * @file methods/neighbor_search/unmap.cpp + * @file methods/neighbor_search/unmap_impl.hpp * @author Ryan Curtin * * Auxiliary function to unmap neighbor search results. @@ -9,19 +9,22 @@ * 3-clause BSD license along with mlpack. If not, see * http://www.opensource.org/licenses/BSD-3-Clause for more information. */ +#ifndef MLPACK_METHODS_NEIGHBOR_SEARCH_UNMAP_IMPL_HPP +#define MLPACK_METHODS_NEIGHBOR_SEARCH_UNMAP_IMPL_HPP + #include "unmap.hpp" namespace mlpack { namespace neighbor { // Useful in the dual-tree setting. -void Unmap(const arma::Mat& neighbors, - const arma::mat& distances, - const std::vector& referenceMap, - const std::vector& queryMap, - arma::Mat& neighborsOut, - arma::mat& distancesOut, - const bool squareRoot) +inline void Unmap(const arma::Mat& neighbors, + const arma::mat& distances, + const std::vector& referenceMap, + const std::vector& queryMap, + arma::Mat& neighborsOut, + arma::mat& distancesOut, + const bool squareRoot) { // Set matrices to correct size. neighborsOut.set_size(neighbors.n_rows, neighbors.n_cols); @@ -44,12 +47,12 @@ void Unmap(const arma::Mat& neighbors, } // Useful in the single-tree setting. -void Unmap(const arma::Mat& neighbors, - const arma::mat& distances, - const std::vector& referenceMap, - arma::Mat& neighborsOut, - arma::mat& distancesOut, - const bool squareRoot) +inline void Unmap(const arma::Mat& neighbors, + const arma::mat& distances, + const std::vector& referenceMap, + arma::Mat& neighborsOut, + arma::mat& distancesOut, + const bool squareRoot) { // Set matrices to correct size. neighborsOut.set_size(neighbors.n_rows, neighbors.n_cols); @@ -67,3 +70,5 @@ void Unmap(const arma::Mat& neighbors, } // namespace neighbor } // namespace mlpack + +#endif From c010b4d510d34d4d9851031d6771311ce950c41a Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Tue, 10 May 2022 13:26:17 +0530 Subject: [PATCH 31/57] converted matrix completion to .hpp --- .../methods/matrix_completion/CMakeLists.txt | 2 +- .../matrix_completion/matrix_completion.hpp | 3 + ...pletion.cpp => matrix_completion_impl.hpp} | 60 ++++++++++++------- 3 files changed, 42 insertions(+), 23 deletions(-) rename src/mlpack/methods/matrix_completion/{matrix_completion.cpp => matrix_completion_impl.hpp} (69%) diff --git a/src/mlpack/methods/matrix_completion/CMakeLists.txt b/src/mlpack/methods/matrix_completion/CMakeLists.txt index 818ec34c80..968ce9240f 100644 --- a/src/mlpack/methods/matrix_completion/CMakeLists.txt +++ b/src/mlpack/methods/matrix_completion/CMakeLists.txt @@ -2,7 +2,7 @@ # Anything not in this list will not be compiled into mlpack. set(SOURCES matrix_completion.hpp - matrix_completion.cpp + matrix_completion_impl.hpp ) # Add directory name to sources. diff --git a/src/mlpack/methods/matrix_completion/matrix_completion.hpp b/src/mlpack/methods/matrix_completion/matrix_completion.hpp index 8023822958..3eae08260e 100644 --- a/src/mlpack/methods/matrix_completion/matrix_completion.hpp +++ b/src/mlpack/methods/matrix_completion/matrix_completion.hpp @@ -145,4 +145,7 @@ class MatrixCompletion } // namespace matrix_completion } // namespace mlpack +// Include implementation. +#include "matrix_completion_impl.hpp" + #endif diff --git a/src/mlpack/methods/matrix_completion/matrix_completion.cpp b/src/mlpack/methods/matrix_completion/matrix_completion_impl.hpp similarity index 69% rename from src/mlpack/methods/matrix_completion/matrix_completion.cpp rename to src/mlpack/methods/matrix_completion/matrix_completion_impl.hpp index 6210b69453..68927307df 100644 --- a/src/mlpack/methods/matrix_completion/matrix_completion.cpp +++ b/src/mlpack/methods/matrix_completion/matrix_completion_impl.hpp @@ -1,5 +1,5 @@ /** - * @file methods/matrix_completion/matrix_completion.cpp + * @file methods/matrix_completion/matrix_completion_impl.hpp * @author Stephen Tu * * Implementation of MatrixCompletion class. @@ -9,41 +9,55 @@ * 3-clause BSD license along with mlpack. If not, see * http://www.opensource.org/licenses/BSD-3-Clause for more information. */ +#ifndef MLPACK_METHODS_MATRIX_COMPLETION_MATRIX_COMPLETION_IMPL_HPP +#define MLPACK_METHODS_MATRIX_COMPLETION_MATRIX_COMPLETION_IMPL_HPP #include "matrix_completion.hpp" namespace mlpack { namespace matrix_completion { -MatrixCompletion::MatrixCompletion(const size_t m, - const size_t n, - const arma::umat& indices, - const arma::vec& values, - const size_t r) : - m(m), n(n), indices(indices), values(values), +inline MatrixCompletion::MatrixCompletion( + const size_t m, + const size_t n, + const arma::umat& indices, + const arma::vec& values, + const size_t r) : + m(m), + n(n), + indices(indices), + values(values), sdp(indices.n_cols, 0, arma::randu(m + n, r)) { CheckValues(); InitSDP(); } -MatrixCompletion::MatrixCompletion(const size_t m, - const size_t n, - const arma::umat& indices, - const arma::vec& values, - const arma::mat& initialPoint) : - m(m), n(n), indices(indices), values(values), +inline MatrixCompletion::MatrixCompletion( + const size_t m, + const size_t n, + const arma::umat& indices, + const arma::vec& values, + const arma::mat& initialPoint) : + m(m), + n(n), + indices(indices), + values(values), sdp(indices.n_cols, 0, initialPoint) { CheckValues(); InitSDP(); } -MatrixCompletion::MatrixCompletion(const size_t m, - const size_t n, - const arma::umat& indices, - const arma::vec& values) : - m(m), n(n), indices(indices), values(values), +inline MatrixCompletion::MatrixCompletion( + const size_t m, + const size_t n, + const arma::umat& indices, + const arma::vec& values) : + m(m), + n(n), + indices(indices), + values(values), sdp(indices.n_cols, 0, arma::randu(m + n, DefaultRank(m, n, indices.n_cols))) { @@ -51,7 +65,7 @@ MatrixCompletion::MatrixCompletion(const size_t m, InitSDP(); } -void MatrixCompletion::CheckValues() +inline void MatrixCompletion::CheckValues() { if (indices.n_rows != 2) { @@ -77,7 +91,7 @@ void MatrixCompletion::CheckValues() } } -void MatrixCompletion::InitSDP() +inline void MatrixCompletion::InitSDP() { sdp.SDP().C().eye(m + n, m + n); sdp.SDP().SparseB() = 2. * values; @@ -90,7 +104,7 @@ void MatrixCompletion::InitSDP() } } -void MatrixCompletion::Recover(arma::mat& recovered) +inline void MatrixCompletion::Recover(arma::mat& recovered) { recovered = sdp.Function().GetInitialPoint(); sdp.Optimize(recovered); @@ -98,7 +112,7 @@ void MatrixCompletion::Recover(arma::mat& recovered) recovered = recovered(arma::span(0, m - 1), arma::span(m, m + n - 1)); } -size_t MatrixCompletion::DefaultRank(const size_t m, +inline size_t MatrixCompletion::DefaultRank(const size_t m, const size_t n, const size_t p) { @@ -118,3 +132,5 @@ size_t MatrixCompletion::DefaultRank(const size_t m, } // namespace matrix_completion } // namespace mlpack + +#endif From 833a73242352cf561859a8b838f79ec21485fe0a Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Tue, 10 May 2022 13:35:11 +0530 Subject: [PATCH 32/57] converted lcc to .hpp --- .../local_coordinate_coding/CMakeLists.txt | 1 - .../methods/local_coordinate_coding/lcc.cpp | 247 ------------------ .../methods/local_coordinate_coding/lcc.hpp | 6 +- .../local_coordinate_coding/lcc_impl.hpp | 231 ++++++++++++++++ 4 files changed, 234 insertions(+), 251 deletions(-) delete mode 100644 src/mlpack/methods/local_coordinate_coding/lcc.cpp diff --git a/src/mlpack/methods/local_coordinate_coding/CMakeLists.txt b/src/mlpack/methods/local_coordinate_coding/CMakeLists.txt index 04d8db4479..cedb019596 100644 --- a/src/mlpack/methods/local_coordinate_coding/CMakeLists.txt +++ b/src/mlpack/methods/local_coordinate_coding/CMakeLists.txt @@ -5,7 +5,6 @@ # that you have files in both sections set(SOURCES lcc.hpp - lcc.cpp lcc_impl.hpp ) diff --git a/src/mlpack/methods/local_coordinate_coding/lcc.cpp b/src/mlpack/methods/local_coordinate_coding/lcc.cpp deleted file mode 100644 index c97aca4ed1..0000000000 --- a/src/mlpack/methods/local_coordinate_coding/lcc.cpp +++ /dev/null @@ -1,247 +0,0 @@ -/** - * @file methods/local_coordinate_coding/lcc.cpp - * @author Nishant Mehta - * - * Implementation of Local Coordinate Coding. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#include "lcc.hpp" -#include - -namespace mlpack { -namespace lcc { - -LocalCoordinateCoding::LocalCoordinateCoding( - const size_t atoms, - const double lambda, - const size_t maxIterations, - const double tolerance) : - atoms(atoms), - lambda(lambda), - maxIterations(maxIterations), - tolerance(tolerance) -{ - // Nothing to do. -} - -void LocalCoordinateCoding::Encode(const arma::mat& data, arma::mat& codes) -{ - arma::mat invSqDists = 1.0 / (repmat(trans(sum(square(dictionary))), 1, - data.n_cols) + repmat(sum(square(data)), atoms, 1) - 2 * trans(dictionary) - * data); - - arma::mat dictGram = trans(dictionary) * dictionary; - arma::mat dictGramTD(dictGram.n_rows, dictGram.n_cols); - - codes.set_size(atoms, data.n_cols); - for (size_t i = 0; i < data.n_cols; ++i) - { - // Report progress. - if ((i % 100) == 0) - { - Log::Debug << "Optimization at point " << i << "." << std::endl; - } - - arma::vec invW = invSqDists.unsafe_col(i); - arma::mat dictPrime = dictionary * diagmat(invW); - - arma::mat dictGramTD = diagmat(invW) * dictGram * diagmat(invW); - - bool useCholesky = false; - regression::LARS lars(useCholesky, dictGramTD, 0.5 * lambda); - - // Run LARS for this point, by making an alias of the point and passing - // that. - arma::vec beta = codes.unsafe_col(i); - arma::rowvec responses = data.unsafe_col(i).t(); - lars.Train(dictPrime, responses, beta, false); - beta %= invW; // Remember, beta is an alias of codes.col(i). - } -} - -void LocalCoordinateCoding::OptimizeDictionary(const arma::mat& data, - const arma::mat& codes, - const arma::uvec& adjacencies) -{ - // Count number of atomic neighbors for each point x^i. - arma::uvec neighborCounts = arma::zeros(data.n_cols, 1); - if (adjacencies.n_elem > 0) - { - // This gets the column index. Intentional integer division. - size_t curPointInd = (size_t) (adjacencies(0) / atoms); - ++neighborCounts(curPointInd); - - size_t nextColIndex = (curPointInd + 1) * atoms; - for (size_t l = 1; l < adjacencies.n_elem; l++) - { - // If l no longer refers to an element in this column, advance the column - // number accordingly. - if (adjacencies(l) >= nextColIndex) - { - curPointInd = (size_t) (adjacencies(l) / atoms); - nextColIndex = (curPointInd + 1) * atoms; - } - - ++neighborCounts(curPointInd); - } - } - - // Build dataPrime := [X x^1 ... x^1 ... x^n ... x^n] - // where each x^i is repeated for the number of neighbors x^i has. - arma::mat dataPrime = arma::zeros(data.n_rows, - data.n_cols + adjacencies.n_elem); - - dataPrime(arma::span::all, arma::span(0, data.n_cols - 1)) = data; - - size_t curCol = data.n_cols; - for (size_t i = 0; i < data.n_cols; ++i) - { - if (neighborCounts(i) > 0) - { - dataPrime(arma::span::all, arma::span(curCol, curCol + neighborCounts(i) - - 1)) = repmat(data.col(i), 1, neighborCounts(i)); - } - curCol += neighborCounts(i); - } - - // Handle the case of inactive atoms (atoms not used in the given coding). - std::vector inactiveAtoms; - for (size_t j = 0; j < atoms; ++j) - if (accu(codes.row(j) != 0) == 0) - inactiveAtoms.push_back(j); - - const size_t nInactiveAtoms = inactiveAtoms.size(); - const size_t nActiveAtoms = atoms - nInactiveAtoms; - - // Efficient construction of codes restricted to active atoms. - arma::mat codesPrime = arma::zeros(nActiveAtoms, data.n_cols + - adjacencies.n_elem); - arma::vec wSquared = arma::ones(data.n_cols + adjacencies.n_elem, 1); - - if (nInactiveAtoms > 0) - { - Log::Warn << "There are " << nInactiveAtoms - << " inactive atoms. They will be re-initialized randomly.\n"; - - // Create matrix holding only active codes. - arma::mat activeCodes; - math::RemoveRows(codes, inactiveAtoms, activeCodes); - - // Create reverse atom lookup for active atoms. - arma::uvec atomReverseLookup(atoms); - size_t inactiveOffset = 0; - for (size_t i = 0; i < atoms; ++i) - { - if (inactiveAtoms[inactiveOffset] == i) - ++inactiveOffset; - else - atomReverseLookup(i - inactiveOffset) = i; - } - - codesPrime(arma::span::all, arma::span(0, data.n_cols - 1)) = activeCodes; - - // Fill the rest of codesPrime. - for (size_t l = 0; l < adjacencies.n_elem; ++l) - { - // Recover the location in the codes matrix that this adjacency refers to. - size_t atomInd = adjacencies(l) % atoms; - size_t pointInd = (size_t) (adjacencies(l) / atoms); - - // Fill matrix. - codesPrime(atomReverseLookup(atomInd), data.n_cols + l) = 1.0; - wSquared(data.n_cols + l) = codes(atomInd, pointInd); - } - } - else - { - // All atoms are active. - codesPrime(arma::span::all, arma::span(0, data.n_cols - 1)) = codes; - - for (size_t l = 0; l < adjacencies.n_elem; ++l) - { - // Recover the location in the codes matrix that this adjacency refers to. - size_t atomInd = adjacencies(l) % atoms; - size_t pointInd = (size_t) (adjacencies(l) / atoms); - - // Fill matrix. - codesPrime(atomInd, data.n_cols + l) = 1.0; - wSquared(data.n_cols + l) = codes(atomInd, pointInd); - } - } - - wSquared.subvec(data.n_cols, wSquared.n_elem - 1) = lambda * - abs(wSquared.subvec(data.n_cols, wSquared.n_elem - 1)); - - // Solve system. - if (nInactiveAtoms == 0) - { - // No inactive atoms. We can solve directly. - arma::mat A = codesPrime * diagmat(wSquared) * trans(codesPrime); - arma::mat B = codesPrime * diagmat(wSquared) * trans(dataPrime); - - dictionary = trans(solve(A, B)); - /* - dictionary = trans(solve(codesPrime * diagmat(wSquared) * trans(codesPrime), - codesPrime * diagmat(wSquared) * trans(dataPrime))); - */ - } - else - { - // Inactive atoms must be reinitialized randomly, so we cannot solve - // directly for the entire dictionary estimate. - arma::mat dictionaryActive = - trans(solve(codesPrime * diagmat(wSquared) * trans(codesPrime), - codesPrime * diagmat(wSquared) * trans(dataPrime))); - - // Update all atoms. - size_t currentInactiveIndex = 0; - for (size_t i = 0; i < atoms; ++i) - { - if (inactiveAtoms[currentInactiveIndex] == i) - { - // This atom is inactive. Reinitialize it randomly. - dictionary.col(i) = (data.col(math::RandInt(data.n_cols)) + - data.col(math::RandInt(data.n_cols)) + - data.col(math::RandInt(data.n_cols))); - - // Now normalize the atom. - dictionary.col(i) /= norm(dictionary.col(i), 2); - - // Increment inactive atom counter. - ++currentInactiveIndex; - } - else - { - // Update estimate. - dictionary.col(i) = dictionaryActive.col(i - currentInactiveIndex); - } - } - } -} - -double LocalCoordinateCoding::Objective(const arma::mat& data, - const arma::mat& codes, - const arma::uvec& adjacencies) const -{ - double weightedL1NormZ = 0; - - for (size_t l = 0; l < adjacencies.n_elem; l++) - { - // Map adjacency back to its location in the codes matrix. - const size_t atomInd = adjacencies(l) % atoms; - const size_t pointInd = (size_t) (adjacencies(l) / atoms); - - weightedL1NormZ += fabs(codes(atomInd, pointInd)) * arma::as_scalar( - arma::sum(arma::square(dictionary.col(atomInd) - data.col(pointInd)))); - } - - double froNormResidual = norm(data - dictionary * codes, "fro"); - return std::pow(froNormResidual, 2.0) + lambda * weightedL1NormZ; -} - -} // namespace lcc -} // namespace mlpack diff --git a/src/mlpack/methods/local_coordinate_coding/lcc.hpp b/src/mlpack/methods/local_coordinate_coding/lcc.hpp index 681253897e..373907586b 100644 --- a/src/mlpack/methods/local_coordinate_coding/lcc.hpp +++ b/src/mlpack/methods/local_coordinate_coding/lcc.hpp @@ -17,9 +17,9 @@ #include // Include three simple dictionary initializers from sparse coding. -#include "../sparse_coding/nothing_initializer.hpp" -#include "../sparse_coding/data_dependent_random_initializer.hpp" -#include "../sparse_coding/random_initializer.hpp" +#include +#include +#include namespace mlpack { namespace lcc { diff --git a/src/mlpack/methods/local_coordinate_coding/lcc_impl.hpp b/src/mlpack/methods/local_coordinate_coding/lcc_impl.hpp index a0b02dd49a..54c903db36 100644 --- a/src/mlpack/methods/local_coordinate_coding/lcc_impl.hpp +++ b/src/mlpack/methods/local_coordinate_coding/lcc_impl.hpp @@ -35,6 +35,19 @@ LocalCoordinateCoding::LocalCoordinateCoding( Train(data, initializer); } +inline LocalCoordinateCoding::LocalCoordinateCoding( + const size_t atoms, + const double lambda, + const size_t maxIterations, + const double tolerance) : + atoms(atoms), + lambda(lambda), + maxIterations(maxIterations), + tolerance(tolerance) +{ + // Nothing to do. +} + template double LocalCoordinateCoding::Train( const arma::mat& data, @@ -103,6 +116,224 @@ double LocalCoordinateCoding::Train( return lastObjVal; } +inline void LocalCoordinateCoding::Encode(const arma::mat& data, + arma::mat& codes) +{ + arma::mat invSqDists = 1.0 / (repmat(trans(sum(square(dictionary))), 1, + data.n_cols) + repmat(sum(square(data)), atoms, 1) - 2 * trans(dictionary) + * data); + + arma::mat dictGram = trans(dictionary) * dictionary; + arma::mat dictGramTD(dictGram.n_rows, dictGram.n_cols); + + codes.set_size(atoms, data.n_cols); + for (size_t i = 0; i < data.n_cols; ++i) + { + // Report progress. + if ((i % 100) == 0) + { + Log::Debug << "Optimization at point " << i << "." << std::endl; + } + + arma::vec invW = invSqDists.unsafe_col(i); + arma::mat dictPrime = dictionary * diagmat(invW); + + arma::mat dictGramTD = diagmat(invW) * dictGram * diagmat(invW); + + bool useCholesky = false; + regression::LARS lars(useCholesky, dictGramTD, 0.5 * lambda); + + // Run LARS for this point, by making an alias of the point and passing + // that. + arma::vec beta = codes.unsafe_col(i); + arma::rowvec responses = data.unsafe_col(i).t(); + lars.Train(dictPrime, responses, beta, false); + beta %= invW; // Remember, beta is an alias of codes.col(i). + } +} + +inline void LocalCoordinateCoding::OptimizeDictionary( + const arma::mat& data, + const arma::mat& codes, + const arma::uvec& adjacencies) +{ + // Count number of atomic neighbors for each point x^i. + arma::uvec neighborCounts = arma::zeros(data.n_cols, 1); + if (adjacencies.n_elem > 0) + { + // This gets the column index. Intentional integer division. + size_t curPointInd = (size_t) (adjacencies(0) / atoms); + ++neighborCounts(curPointInd); + + size_t nextColIndex = (curPointInd + 1) * atoms; + for (size_t l = 1; l < adjacencies.n_elem; l++) + { + // If l no longer refers to an element in this column, advance the column + // number accordingly. + if (adjacencies(l) >= nextColIndex) + { + curPointInd = (size_t) (adjacencies(l) / atoms); + nextColIndex = (curPointInd + 1) * atoms; + } + + ++neighborCounts(curPointInd); + } + } + + // Build dataPrime := [X x^1 ... x^1 ... x^n ... x^n] + // where each x^i is repeated for the number of neighbors x^i has. + arma::mat dataPrime = arma::zeros(data.n_rows, + data.n_cols + adjacencies.n_elem); + + dataPrime(arma::span::all, arma::span(0, data.n_cols - 1)) = data; + + size_t curCol = data.n_cols; + for (size_t i = 0; i < data.n_cols; ++i) + { + if (neighborCounts(i) > 0) + { + dataPrime(arma::span::all, arma::span(curCol, curCol + neighborCounts(i) + - 1)) = repmat(data.col(i), 1, neighborCounts(i)); + } + curCol += neighborCounts(i); + } + + // Handle the case of inactive atoms (atoms not used in the given coding). + std::vector inactiveAtoms; + for (size_t j = 0; j < atoms; ++j) + if (accu(codes.row(j) != 0) == 0) + inactiveAtoms.push_back(j); + + const size_t nInactiveAtoms = inactiveAtoms.size(); + const size_t nActiveAtoms = atoms - nInactiveAtoms; + + // Efficient construction of codes restricted to active atoms. + arma::mat codesPrime = arma::zeros(nActiveAtoms, data.n_cols + + adjacencies.n_elem); + arma::vec wSquared = arma::ones(data.n_cols + adjacencies.n_elem, 1); + + if (nInactiveAtoms > 0) + { + Log::Warn << "There are " << nInactiveAtoms + << " inactive atoms. They will be re-initialized randomly.\n"; + + // Create matrix holding only active codes. + arma::mat activeCodes; + math::RemoveRows(codes, inactiveAtoms, activeCodes); + + // Create reverse atom lookup for active atoms. + arma::uvec atomReverseLookup(atoms); + size_t inactiveOffset = 0; + for (size_t i = 0; i < atoms; ++i) + { + if (inactiveAtoms[inactiveOffset] == i) + ++inactiveOffset; + else + atomReverseLookup(i - inactiveOffset) = i; + } + + codesPrime(arma::span::all, arma::span(0, data.n_cols - 1)) = activeCodes; + + // Fill the rest of codesPrime. + for (size_t l = 0; l < adjacencies.n_elem; ++l) + { + // Recover the location in the codes matrix that this adjacency refers to. + size_t atomInd = adjacencies(l) % atoms; + size_t pointInd = (size_t) (adjacencies(l) / atoms); + + // Fill matrix. + codesPrime(atomReverseLookup(atomInd), data.n_cols + l) = 1.0; + wSquared(data.n_cols + l) = codes(atomInd, pointInd); + } + } + else + { + // All atoms are active. + codesPrime(arma::span::all, arma::span(0, data.n_cols - 1)) = codes; + + for (size_t l = 0; l < adjacencies.n_elem; ++l) + { + // Recover the location in the codes matrix that this adjacency refers to. + size_t atomInd = adjacencies(l) % atoms; + size_t pointInd = (size_t) (adjacencies(l) / atoms); + + // Fill matrix. + codesPrime(atomInd, data.n_cols + l) = 1.0; + wSquared(data.n_cols + l) = codes(atomInd, pointInd); + } + } + + wSquared.subvec(data.n_cols, wSquared.n_elem - 1) = lambda * + abs(wSquared.subvec(data.n_cols, wSquared.n_elem - 1)); + + // Solve system. + if (nInactiveAtoms == 0) + { + // No inactive atoms. We can solve directly. + arma::mat A = codesPrime * diagmat(wSquared) * trans(codesPrime); + arma::mat B = codesPrime * diagmat(wSquared) * trans(dataPrime); + + dictionary = trans(solve(A, B)); + /* + dictionary = trans(solve(codesPrime * diagmat(wSquared) * trans(codesPrime), + codesPrime * diagmat(wSquared) * trans(dataPrime))); + */ + } + else + { + // Inactive atoms must be reinitialized randomly, so we cannot solve + // directly for the entire dictionary estimate. + arma::mat dictionaryActive = + trans(solve(codesPrime * diagmat(wSquared) * trans(codesPrime), + codesPrime * diagmat(wSquared) * trans(dataPrime))); + + // Update all atoms. + size_t currentInactiveIndex = 0; + for (size_t i = 0; i < atoms; ++i) + { + if (inactiveAtoms[currentInactiveIndex] == i) + { + // This atom is inactive. Reinitialize it randomly. + dictionary.col(i) = (data.col(math::RandInt(data.n_cols)) + + data.col(math::RandInt(data.n_cols)) + + data.col(math::RandInt(data.n_cols))); + + // Now normalize the atom. + dictionary.col(i) /= norm(dictionary.col(i), 2); + + // Increment inactive atom counter. + ++currentInactiveIndex; + } + else + { + // Update estimate. + dictionary.col(i) = dictionaryActive.col(i - currentInactiveIndex); + } + } + } +} + +inline double LocalCoordinateCoding::Objective( + const arma::mat& data, + const arma::mat& codes, + const arma::uvec& adjacencies) const +{ + double weightedL1NormZ = 0; + + for (size_t l = 0; l < adjacencies.n_elem; l++) + { + // Map adjacency back to its location in the codes matrix. + const size_t atomInd = adjacencies(l) % atoms; + const size_t pointInd = (size_t) (adjacencies(l) / atoms); + + weightedL1NormZ += fabs(codes(atomInd, pointInd)) * arma::as_scalar( + arma::sum(arma::square(dictionary.col(atomInd) - data.col(pointInd)))); + } + + double froNormResidual = norm(data - dictionary * codes, "fro"); + return std::pow(froNormResidual, 2.0) + lambda * weightedL1NormZ; +} + template void LocalCoordinateCoding::serialize(Archive& ar, const uint32_t /* version */) From 0023ded6a80a9a765d746cb94d9f1114bf023cd4 Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Tue, 10 May 2022 13:44:34 +0530 Subject: [PATCH 33/57] trying to resolve merge conflict --- .../matrix_completion/matrix_completion_impl.hpp | 14 +++++--------- 1 file changed, 5 insertions(+), 9 deletions(-) diff --git a/src/mlpack/methods/matrix_completion/matrix_completion_impl.hpp b/src/mlpack/methods/matrix_completion/matrix_completion_impl.hpp index 68927307df..d829788cef 100644 --- a/src/mlpack/methods/matrix_completion/matrix_completion_impl.hpp +++ b/src/mlpack/methods/matrix_completion/matrix_completion_impl.hpp @@ -13,6 +13,7 @@ #define MLPACK_METHODS_MATRIX_COMPLETION_MATRIX_COMPLETION_IMPL_HPP #include "matrix_completion.hpp" +#include namespace mlpack { namespace matrix_completion { @@ -73,13 +74,8 @@ inline void MatrixCompletion::CheckValues() << "indices does not have 2 rows!" << std::endl; } - if (indices.n_cols != values.n_elem) - { - Log::Fatal << "MatrixCompletion::CheckValues(): the number of constraint " - << "indices (columns of constraint indices matrix) does not match the " - << "number of constraint values (length of constraint value vector)!" - << std::endl; - } + util::CheckSameSizes(indices, values, + "MatrixCompletion::CheckValues()", "labels", false, true); for (size_t i = 0; i < values.n_elem; ++i) { @@ -113,8 +109,8 @@ inline void MatrixCompletion::Recover(arma::mat& recovered) } inline size_t MatrixCompletion::DefaultRank(const size_t m, - const size_t n, - const size_t p) + const size_t n, + const size_t p) { // If r = O(sqrt(p)), then we are guaranteed an exact solution. // For more details, see From 1ea72a60aba0045995fd651927f736334b910ad4 Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Tue, 10 May 2022 13:53:21 +0530 Subject: [PATCH 34/57] Squashed commit of the following: commit 54c6ebe03a07d7c32db46a6a06a03e8b821da4f2 Merge: 775a3b55f b406fc150 Author: Ryan Curtin Date: Sun May 1 13:24:24 2022 -0400 Merge pull request #3200 from shubham1206agra/go-cli-fix Go Build Fix commit 775a3b55f73eb595c03baf78e6901c9e21a59aaa Merge: 8e72ed698 54e291443 Author: Ryan Curtin Date: Sat Apr 30 10:41:45 2022 -0400 Merge pull request #3198 from shubham1206agra/py-cli-fix Python Build Fix commit 8e72ed6986b6898b8b452682aaab80694e22e61b Merge: f7cd03866 1c1182301 Author: Ryan Curtin Date: Sat Apr 30 10:40:59 2022 -0400 Merge pull request #3199 from shubham1206agra/r-cli-fix R Build Fix commit b406fc15069054fee73263c450da7cedf3ed0d2c Author: shubham1206agra Date: Fri Apr 29 10:42:32 2022 +0530 changes according to suggestion commit 54e2914430ed4862ecdbde557790e828d9e6a7af Author: Shubham Agrawal <58412969+shubham1206agra@users.noreply.github.com> Date: Fri Apr 29 10:20:14 2022 +0530 Update src/mlpack/bindings/python/copy_artifacts.py Co-authored-by: Ryan Curtin commit f7cd03866077b7813c5a2ba352cc86aab28e7806 Merge: 065fcee29 3eb8ae67e Author: Ryan Curtin Date: Thu Apr 28 19:00:58 2022 -0700 Merge pull request #2777 from zoq/ann-vtable Swap boost::variant with vtable. commit ad4569213a5d96625e0ced566451a8d64ea0c2bd Author: shubham1206agra Date: Thu Apr 28 19:32:14 2022 +0530 temp sol to version issue commit 1bfa385663f1f6f97cb8a17591b7f97a8c0b8829 Author: shubham1206agra Date: Thu Apr 28 18:48:03 2022 +0530 initial fix by disabling go modules commit 1c1182301e0e44421bd1588ae2e3d038bd34e25e Author: shubham1206agra Date: Thu Apr 28 12:46:18 2022 +0530 force install pkgbuild commit 3cfbb4e65d9822df8a25c5625d189ac734665c7b Author: shubham1206agra Date: Thu Apr 28 12:36:51 2022 +0530 cleanup commit feab906927dca5a88a19ec00dea281263e4be35d Author: shubham1206agra Date: Thu Apr 28 11:28:32 2022 +0530 missing '/' added commit 577cc2d6929d6b22d671f314a49756815d99b4c1 Author: shubham1206agra Date: Thu Apr 28 10:33:02 2022 +0530 new directory structure using glob commit 3eb8ae67eceeb1c1ad226687641b4748a573e3df Merge: 6f98ab7bc 065fcee29 Author: Ryan Curtin Date: Wed Apr 27 20:51:46 2022 -0400 Merge remote-tracking branch 'origin/master' into ann-vtable commit 6f98ab7bc9749b04565c512f7eb59c406beb7826 Author: Eshaan Agarwal Date: Wed Apr 13 20:13:06 2022 +0530 Fix style issues Co-authored-by: Ryan Curtin commit ac3097e875f42c95650634530026acde77dace63 Author: eshaanagarwal Date: Wed Apr 13 18:22:03 2022 +0530 fix error in size_t cast Signed-off-by: eshaanagarwal commit d03aafacf618977ed9de4ef2a2ad70e0394e4daf Author: eshaanagarwal Date: Tue Apr 5 13:02:00 2022 +0530 add parameter documentation in size checks commit e55609d5beeb626ec4d7cfe0f95e16059b050779 Author: eshaanagarwal Date: Tue Apr 5 00:51:27 2022 +0530 Add transpose parameter in size check commit a485da2b715bdd67e0ec7848f845be116db16a05 Author: eshaanagarwal Date: Thu Mar 31 04:22:53 2022 +0530 fixed issues in styling commit dff01492ed44f117ab1d7f0be518547da280dd44 Author: eshaanagarwal Date: Mon Mar 14 22:25:27 2022 +0530 fixed styling issues commit ca50361083791425908aea6606902ca7230413eb Author: eshaanagarwal Date: Fri Mar 11 22:50:03 2022 +0530 fix build issue by removing row vector assert condition commit 5b7d36db8586bdbf7e70b623906c86759867750a Author: eshaanagarwal Date: Fri Mar 11 20:15:07 2022 +0530 fix failed build commit 614f924a4ef0c67894dff5b1f41e37fda55c03e9 Author: eshaanagarwal Date: Fri Mar 11 19:50:54 2022 +0530 fixed redundancy in size-checks commit f67c566a5ffa21afb60d460fe813c363da67924a Author: eshaanagarwal Date: Fri Mar 11 19:30:22 2022 +0530 fix matrix completion size-checks commit e1e2b5308b4dbeaddb8838e20658b3bf63a66165 Author: eshaanagarwal Date: Fri Mar 11 12:19:37 2022 +0530 remove incorrect checks in adaboost commit e5d701df32f02289b9aea2209b5b1944bee9a2c8 Author: eshaanagarwal Date: Thu Mar 10 16:30:49 2022 +0530 fix size checks commit 2656b300f9ef1984d2a2b58add8ce4b8004908aa Author: eshaanagarwal Date: Thu Mar 10 12:24:15 2022 +0530 fix styling issue Signed-off-by: eshaanagarwal commit ef4d293fedd338f847483a2246462ec7a8ed62cb Author: eshaanagarwal Date: Wed Mar 9 02:33:08 2022 +0530 Add: size checks for kmeans and linear regression commit 640dd0cde815aa24a0ddf2f0ff7cccf6eec5c8f0 Author: eshaanagarwal Date: Tue Mar 8 02:27:38 2022 +0530 Add : Size checks for adaboost and matix completion Signed-off-by: eshaanagarwal commit 66bc9cbe008ded17638cec58c186ac0487980007 Author: Ryan Curtin Date: Fri Apr 15 21:29:30 2022 -0400 Huh, I guess it is a new year. commit 7b0c1e157bd413f1e58c40e7ad2f982d5561bba7 Author: Ryan Curtin Date: Fri Apr 15 21:29:15 2022 -0400 Update HISTORY. commit e4be17defe01e980f7ffe1859020fee021de7641 Author: Ryan Curtin Date: Fri Apr 15 21:26:42 2022 -0400 Add test for KFoldCV and Perceptron. commit a70437ffc30627cf69a981ae10618cd61fccc287 Author: Ryan Curtin Date: Fri Apr 15 21:26:28 2022 -0400 Add constructor to Perceptron for weighted data for KFoldCV. commit 9b86271002ba1c5ab2cc99694e83d54c369a2b07 Author: Ryan Curtin Date: Fri Apr 15 21:26:08 2022 -0400 Make Classify() set the output predictions' size. commit a35fc3b994de138b99b82453e98be84afedfd5a8 Author: Yashwants19 Date: Sun Apr 17 10:02:24 2022 +0000 Upgrade Catch to 2.13.9 commit a1bb763729f00475a3904db4d798c2a81767c19a Author: Omar Shrit Date: Sat Apr 16 21:40:15 2022 +0200 Update src/mlpack/core/data/save_image.hpp Co-authored-by: Marcus Edel commit 57cec6f081c16be729d9a35e302847bd5d18e743 Author: Omar Shrit Date: Sat Apr 16 16:20:14 2022 +0100 Apply @rcurtin modification to check STB version Signed-off-by: Omar Shrit commit 8e34862cf62c5c610d98dd1ac9768307e11830b2 Author: Omar Shrit Date: Sun Mar 13 21:28:32 2022 +0000 Let us see inital try for STB test Signed-off-by: Omar Shrit commit afcc862ced00728ec0a27251f07a51e1cd08adb6 Author: Omar Shrit Date: Thu Feb 3 21:46:40 2022 +0000 Adding the missing starts, shitty regexp Signed-off-by: Omar Shrit commit 0c076cca19e7e03e5aacd3cf199cec349f66407d Author: Omar Shrit Date: Tue Jan 25 12:09:08 2022 +0000 Finish this PR Signed-off-by: Omar Shrit commit b26f2d2a15d652427dfd6ef1542343ecbfe6f620 Author: Omar Shrit Date: Mon Jan 24 22:15:55 2022 +0000 Refactor save_image into save_image_impl finally done!! Signed-off-by: Omar Shrit commit 8fffbd55fa5b92425a3e202c3bf7faeadcb69d0f Author: Omar Shrit Date: Mon Jan 24 21:20:12 2022 +0000 Adjust namespace of mlpack::Log Signed-off-by: Omar Shrit commit 37367c7ec5514ffd423317ddd5ddeca250761680 Author: Omar Shrit Date: Mon Jan 24 19:51:55 2022 +0100 Update src/mlpack/core/data/image_info_impl.hpp Co-authored-by: Ryan Curtin commit 3cc2d250b588aed384881ae01d0ef36aaf57d07a Author: Omar Shrit Date: Mon Jan 24 19:51:49 2022 +0100 Update src/mlpack/core/data/image_info_impl.hpp Co-authored-by: Ryan Curtin commit 55c6cfd7df62646e29bd73102af533b127738586 Author: Omar Shrit Date: Sun Jan 23 19:38:29 2022 +0000 Apply @rcurtin patch to fix the STB issue. Signed-off-by: Omar Shrit commit 8cdb53a313af3b8aaddc891ebf514445d7ec58ee Author: Omar Shrit Date: Sun Jan 16 18:20:04 2022 +0000 Commenting all #defines that are causing the problems Signed-off-by: Omar Shrit commit e2e000b06f8c08937aac345700f0e0927b4e59a1 Author: Omar Shrit Date: Sun Jan 16 16:21:27 2022 +0000 Move constructor to implementation Signed-off-by: Omar Shrit commit 981f57c4be46a855eaef81a38ac513ead28931ce Author: Omar Shrit Date: Sun Jan 16 16:03:30 2022 +0000 Compiling locally, adding all modifications Signed-off-by: Omar Shrit commit 4d29f5f9c1a85e2808bf0526e3386e665a184c64 Author: Omar Shrit Date: Sun Jan 16 12:59:31 2022 +0000 Provide more details for namespace in src/mlpack/methods/det/dtree_impl.hpp Co-authored-by: Ryan Curtin commit e8a76f8f354eb03d2a8b8658dbc5b756baf0f941 Author: Omar Shrit Date: Sun Jan 16 12:59:03 2022 +0000 Remove mlpack namspace from src/mlpack/methods/hmm/hmm_util_impl.hpp Co-authored-by: Ryan Curtin commit 9dd15d08655e0c65755912725652271c82f4b182 Author: Omar Shrit Date: Sun Jan 16 12:58:50 2022 +0000 remove mlpack namespace from src/mlpack/methods/reinforcement_learning/q_networks/categorical_dqn.hpp Co-authored-by: Ryan Curtin commit aa057ccb8b519c110a1c0a43a9576da65df9b266 Author: Omar Shrit Date: Sun Jan 16 12:58:38 2022 +0000 Remove mlpack namespace from src/mlpack/methods/hmm/hmm_util_impl.hpp Co-authored-by: Ryan Curtin commit 61cc69b1a5db9e39d9f87af984166e0ec7961ead Author: Omar Shrit Date: Sun Jan 16 12:58:22 2022 +0000 Remove mlpack namespace from src/mlpack/methods/reinforcement_learning/q_networks/dueling_dqn.hpp Co-authored-by: Ryan Curtin commit 31f9545685df3492b1bdfafee70a226c29e082af Author: Omar Shrit Date: Sun Jan 16 12:58:06 2022 +0000 Remove mlpack namespace from src/mlpack/methods/reinforcement_learning/q_networks/simple_dqn.hpp Co-authored-by: Ryan Curtin commit 665711b9cee4f3ea4186ae4c71230d18c8f2c872 Author: Omar Shrit Date: Thu Dec 16 15:33:51 2021 +0100 Remove additional line in src/mlpack/core/kernels/pspectrum_string_kernel.hpp Co-authored-by: Ryan Curtin commit 895c885b44fbcca2f07353ec0284d506688759fb Author: Omar Shrit Date: Thu Dec 16 15:33:08 2021 +0100 Fix indentation in src/mlpack/core/data/detect_file_type.hpp Co-authored-by: Ryan Curtin commit cf6f60aee899e924718265756a2f46cf4c5b6412 Author: Omar Shrit Date: Thu Dec 16 15:32:55 2021 +0100 Add forgetten dot in src/mlpack/core/math/random_basis.hpp Co-authored-by: Marcus Edel commit 21e045928a384ec9a02455eac66b5c6f49aec1ff Author: Omar Shrit Date: Thu Dec 16 15:32:38 2021 +0100 Add spaces in src/mlpack/core/math/lin_alg_impl.hpp Co-authored-by: Marcus Edel commit 1f79e4fc8346d88db22895653c36f5c892bc5fe6 Author: Omar Shrit Date: Thu Dec 16 15:32:16 2021 +0100 Remove additional line in src/mlpack/core/math/lin_alg_impl.hpp Co-authored-by: Marcus Edel commit 6dc2d63af66cd3c13d75456c4c9f369ccadc2c30 Author: Omar Shrit Date: Thu Dec 16 15:32:00 2021 +0100 Fix indentation src/mlpack/core/math/lin_alg_impl.hpp Co-authored-by: Marcus Edel commit ed07d9f05afbfa1bc9b5a145fca092cd9aba0933 Author: Omar Shrit Date: Thu Dec 16 15:31:41 2021 +0100 Fix indentation in src/mlpack/core/math/lin_alg_impl.hpp Co-authored-by: Marcus Edel commit 047dc47b3b44a819df0d4b430cf81ed3d20ecfa1 Author: Omar Shrit Date: Thu Dec 16 15:31:24 2021 +0100 Add parantheses in src/mlpack/core/math/lin_alg_impl.hpp Co-authored-by: Marcus Edel commit 2dae84947a81412a2f51efc333a4e29f1d063fa0 Author: Omar Shrit Date: Thu Dec 16 15:31:00 2021 +0100 Fix style in src/mlpack/core/kernels/epanechnikov_kernel_impl.hpp Co-authored-by: Marcus Edel commit 8666165293d5e5e5a54872954a52147ceb43da50 Author: Omar Shrit Date: Thu Dec 16 15:30:43 2021 +0100 Update style in src/mlpack/core/kernels/epanechnikov_kernel_impl.hpp Co-authored-by: Marcus Edel commit 6254b4b49a93c6e14b63d6943bfa198fb1a1135a Author: Omar Shrit Date: Thu Dec 2 23:54:27 2021 +0000 Add forgetten layer name Signed-off-by: Omar Shrit commit 407b563330cd1f81020f85198f14d7a3a4e5cccd Author: Omar Shrit Date: Thu Dec 2 22:54:06 2021 +0000 Clean all using namespaces from the headers. Signed-off-by: Omar Shrit commit 4a713818abe2a891f4118d67f70f3c7cb2462acd Author: Omar Shrit Date: Thu Dec 2 19:40:02 2021 +0000 Fix the random_basis_impl in addition Signed-off-by: Omar Shrit commit 99a485b6be4cbe9662398ab23f507923cd041fe3 Author: Omar Shrit Date: Thu Dec 2 19:39:05 2021 +0000 Clean partly the namespace. Let us see if this resolve the macOS issues Signed-off-by: Omar Shrit commit 986bdb6bdd5b07aa5b9e209b498a89ecd386dc1b Author: Omar Shrit Date: Sat Nov 20 22:03:31 2021 +0000 Adjust namespace in preprocess Signed-off-by: Omar Shrit commit c3691bdf657281f2574e875feb751aaad1712ec5 Author: Omar Shrit Date: Sat Nov 20 20:33:55 2021 +0000 Remove the mlpack:: namespace Signed-off-by: Omar Shrit commit adbedb857bed36229d6637aea0499a844afa455a Author: Omar Shrit Date: Sat Nov 20 19:56:14 2021 +0000 Adding missing math/random headers Signed-off-by: Omar Shrit commit d246f942863aed5899234857e49d4f9d444edbab Author: Omar Shrit Date: Sat Nov 20 19:29:07 2021 +0000 Fix the gmm error Signed-off-by: Omar Shrit commit 10ed5c1c965cd09c83ebcf25c5ecf57fd6bcecce Author: Omar Shrit Date: Sat Nov 20 17:06:11 2021 +0000 Let us if this resolves the binding issues Signed-off-by: Omar Shrit commit 8b0c8038dd4d45dd2efd3d28f515e8bcc674987c Author: Omar Shrit Date: Sat Nov 13 19:52:19 2021 +0000 Adding a missing std string Signed-off-by: Omar Shrit commit ff2f37c4e7a37121bf74d94b78a169c11c905a63 Author: Omar Shrit Date: Sat Nov 13 19:40:23 2021 +0000 Finish the pspectrum_string_kernel Signed-off-by: Omar Shrit commit c6bf80178a260a63d0a0ddcb20a6d312f434e2af Author: Omar Shrit Date: Sat Nov 13 19:39:32 2021 +0000 Deleting epanechnikov_kernel and adding pspectrum_string_kernel Signed-off-by: Omar Shrit commit 09f508c6de1641aa047eec5e584d7c09a7237a5d Author: Omar Shrit Date: Sat Nov 13 19:33:05 2021 +0000 Finishing the epanechnikov_kernel Signed-off-by: Omar Shrit commit 61d01a39491d487196373a67358d0b6c7a519b40 Author: Omar Shrit Date: Sat Nov 13 19:23:57 2021 +0000 remove epanechnikov_kernel Signed-off-by: Omar Shrit commit bd84bb2d0dd13728a20be5315e9a28ac46af607c Author: Omar Shrit Date: Sat Nov 13 19:15:58 2021 +0000 inline random_basis Signed-off-by: Omar Shrit commit 90b80ea766b80849599ed7a18c0c87e2aaf1e3d2 Author: Omar Shrit Date: Sat Nov 13 19:11:32 2021 +0000 Adding missing headers Signed-off-by: Omar Shrit commit e0c5ac34fbc43ba246eeb77a92dd08a528381906 Author: Omar Shrit Date: Sat Nov 13 18:56:00 2021 +0000 move the impl from .cpp to .impl Signed-off-by: Omar Shrit commit 85a53b486f45ecc9caf5f5f2e30cf8df402d6ebd Author: Omar Shrit Date: Sat Nov 13 18:43:17 2021 +0000 Fix the compilation warning related to include implementations Signed-off-by: Omar Shrit commit c1d3833ff155c289fddaf45195ee92ed971b4ad7 Author: Omar Shrit Date: Sat Nov 13 18:24:15 2021 +0000 Fix comments and compilation bugs Signed-off-by: Omar Shrit commit b037f4f69fff9c40454859a245a743680ab0ff65 Author: Omar Shrit Date: Sat Nov 13 18:12:02 2021 +0000 Finish inlining the data dir Signed-off-by: Omar Shrit commit 0941f650383a1e710152459d21f32a6abcf2d233 Author: Omar Shrit Date: Sat Nov 13 18:04:01 2021 +0000 Make save image header only Signed-off-by: Omar Shrit commit ba84188163d891764a84551b5c2827708875d484 Author: Marcus Edel Date: Wed Mar 16 23:23:01 2022 -0400 Prettify C++ code used to check for atomic linkage. commit f9cc2f6bfa65454f49b82fa16c21c94eb7d085cb Author: Marcus Edel Date: Tue Mar 15 22:14:12 2022 -0400 If we use MSVC no need to check for atomic. commit 119da780ed487cd10f6ab0872d2b88bae8edc169 Author: Marcus Edel Date: Sun Feb 20 21:15:50 2022 -0500 Check if libatomic is bundled. commit 96f4d9aea6c4b43d20c4302df650a4ac70767ea8 Author: Marcus Edel Date: Wed Feb 16 21:42:44 2022 -0500 Check if atomics need -latomic linking. commit 47f94fbaee9e67ed5871774ce0df953345b115c0 Author: zoq Date: Thu Apr 14 10:05:15 2022 +0000 Upgrade Boost Version in CMake script. commit c31961bc46fb4fb0ac91221953620138168c2d8e Author: LiuZhuojin Date: Thu Apr 14 09:53:55 2022 +0800 Update contributor list commit 2d77be64d9504d8d54058b43455935d1e7ce6fcc Author: LiuZhuojin Date: Wed Apr 13 09:33:03 2022 +0800 Replace boost::heap::priority_queue with std::vector commit aa4a8b3922420a8cef248e4392b0a5d09af8a3e6 Author: LiuZhuojin Date: Wed Apr 13 00:40:08 2022 +0800 Add vector and queue to standard includes commit d9145891a7bee9147004cc92efe3756422662f32 Author: LiuZhuojin Date: Wed Apr 13 00:38:40 2022 +0800 Replace boost::heap::priority_queue with std::vector commit 58518ab811c144bb4e0f4edb5601c352df9bfba9 Author: LiuZhuojin Date: Wed Apr 13 00:27:28 2022 +0800 Replace boost::heap::priority_queue with std::vector commit bc30907f5a57a3652bd5778bb1604cf27482af63 Author: LiuZhuojin Date: Sat Apr 9 15:04:31 2022 +0800 Replace boost::heap::priority_queue with std::vector commit e117627c7f70f024576389fa372c1b1d6866c7aa Author: LiuZhuojin Date: Sat Apr 9 14:58:28 2022 +0800 Replace boost::heap::priority_queue with std::vector commit 4d962ebab6242b117eac634d81bb446d538952f7 Author: LiuZhuojin Date: Fri Apr 8 13:53:10 2022 +0800 Replace boost::heap::priority_queue with std::vector commit 65afadc4afbcd8ac054f19de48ca6978d5855058 Author: Yashwants19 Date: Fri Apr 1 10:00:54 2022 +0000 Upgrade CLI11 to 2.2.0 commit 5875d3625adeb460568d23d3ce9d29cb53ad0a3c Author: Ryan Curtin Date: Wed Apr 27 16:10:15 2022 -0400 Apply suggestions from code review Co-authored-by: Marcus Edel commit fef34ea8db04060479255608d2e226468d8b5b13 Author: shubham1206agra Date: Wed Apr 27 19:25:31 2022 +0530 trying something else commit 64e1f75b65f8710a1f293c473470ab0991107c69 Author: shubham1206agra Date: Wed Apr 27 19:11:02 2022 +0530 trying config file commit 065fcee296caa289af68d59b0eafec6658bc38ce Merge: 56d3f1636 01fa1241c Author: Ryan Curtin Date: Mon Apr 18 15:43:03 2022 -0700 Merge pull request #3164 from eshaanagarwal/size-checks Added Size checks for Matrix Completion, Kmeans and Linear Regression commit 56d3f16368b62cbe174062d0445f524cebf0c54a Merge: 2994570a0 152094dbb Author: Ryan Curtin Date: Mon Apr 18 06:01:10 2022 -0700 Merge pull request #3190 from mlpack/fix-perceptron-cv Add weighted data constructor to `Perceptron` commit 2994570a05997fa1607d5702196fdd48be266af7 Merge: fdc7af7a6 c47cebdd8 Author: Ryan Curtin Date: Mon Apr 18 05:55:12 2022 -0700 Merge pull request #3191 from mlpack/catch-header-updates-2.13.9 Upgrade Catch to 2.13.9 commit c47cebdd822490b90f8aa88c0cfbb80a4c14c237 Author: Yashwants19 Date: Sun Apr 17 10:02:24 2022 +0000 Upgrade Catch to 2.13.9 commit 1645bb22eba65ba22b3ea5d4ce5769397d41aa9d Author: Ryan Curtin Date: Sat Apr 16 21:05:15 2022 -0400 Use `typename` instead of `class` for consistency. commit d8a1c27b7f9efd7844f0dec0519d2f16fb595615 Author: Ryan Curtin Date: Sat Apr 16 20:55:50 2022 -0400 Add serialization to GlorotInit. commit 152094dbbea68d59ee3491ce0b60e9218896c93f Author: Ryan Curtin Date: Fri Apr 15 21:29:30 2022 -0400 Huh, I guess it is a new year. commit c17e9c2f0f0efbb3ff9729907d1ac3ca6809da06 Author: Ryan Curtin Date: Fri Apr 15 21:29:15 2022 -0400 Update HISTORY. commit 9a6551a1b5aab9d4e82a52b861438901c085616d Author: Ryan Curtin Date: Fri Apr 15 21:26:42 2022 -0400 Add test for KFoldCV and Perceptron. commit 3133a4abbded4be9b440cdb315c802bb17c60d07 Author: Ryan Curtin Date: Fri Apr 15 21:26:28 2022 -0400 Add constructor to Perceptron for weighted data for KFoldCV. commit 80e65d8a5d35780dfba21690c891c1258c89265a Author: Ryan Curtin Date: Fri Apr 15 21:26:08 2022 -0400 Make Classify() set the output predictions' size. commit 01fa1241cee30b9e2cd47d61137dc733994a74af Author: Eshaan Agarwal Date: Wed Apr 13 20:13:06 2022 +0530 Fix style issues Co-authored-by: Ryan Curtin commit cb35eba204c328cda8864aa742f2d3ea6c6b294d Author: eshaanagarwal Date: Wed Apr 13 18:22:03 2022 +0530 fix error in size_t cast Signed-off-by: eshaanagarwal commit f12d714c50f124045173d9c5544a35aa5e052f02 Author: Ryan Curtin Date: Mon Apr 11 13:34:43 2022 -0400 Comment on the weightsPtr parameter. commit 44b30019f4c32555311fd1a3d4f3213fa7e1cb8d Author: Ryan Curtin Date: Mon Apr 11 13:32:39 2022 -0400 Add another padding test for Convolution. commit 1a9f7ccd5bfd82b1653d30e23cb95c9fd6fc84d0 Author: Ryan Curtin Date: Mon Apr 11 11:56:39 2022 -0400 Return correct type of Weights(). commit bacd18db556064061c8c68faa68e1e73cdfd0f31 Author: Ryan Curtin Date: Mon Apr 11 11:53:54 2022 -0400 Update src/mlpack/methods/ann/layer/concatenate_impl.hpp Co-authored-by: Marcus Edel commit 3dfe5a000d8f774f0dc63936269e871875f4ddea Author: Ryan Curtin Date: Mon Apr 11 11:53:40 2022 -0400 Update src/mlpack/methods/ann/layer/concatenate.hpp Co-authored-by: Marcus Edel commit 6b47a9cd6752fa6b7ef4e4e319ebdc31e756b5b3 Author: Ryan Curtin Date: Mon Apr 11 11:53:33 2022 -0400 Update src/mlpack/methods/ann/layer/alpha_dropout.hpp Co-authored-by: Marcus Edel commit 77203b84cf26d85a6cff79bc8af57f84a0014871 Author: Ryan Curtin Date: Mon Apr 11 11:53:25 2022 -0400 Update src/mlpack/methods/ann/layer/alpha_dropout.hpp Co-authored-by: Marcus Edel commit 845a5a0aed34910f8d7ef147ca8a726218963a16 Author: eshaanagarwal Date: Tue Apr 5 13:02:00 2022 +0530 add parameter documentation in size checks commit 07be7313a75d0c0676ec8cb7a845a51d2fbdc837 Author: Ryan Curtin Date: Mon Apr 4 22:23:36 2022 -0400 Use network.Forward() to avoid the extra output copy. commit 0710d9f1113074fdb7d000aae3d1dcb82f325e27 Author: Ryan Curtin Date: Mon Apr 4 22:05:35 2022 -0400 Update src/mlpack/methods/ann/ffn.hpp Co-authored-by: Marcus Edel commit 0e9f05adbc9ec75c5604050b6012ba690f4d9ba8 Author: eshaanagarwal Date: Tue Apr 5 00:51:27 2022 +0530 Add transpose parameter in size check commit 2cd38b3c9e5ecdf9438cb4c88850efc236868b76 Author: Ryan Curtin Date: Sun Apr 3 22:02:45 2022 -0400 Some additional cleanups. commit c38625f8d46db5b6adce8895e57e8103685ffc7f Author: Ryan Curtin Date: Sun Apr 3 22:01:13 2022 -0400 Change 'rho' to 'bpttSteps' for clarity. commit 6eecba768ae6abb6f2a37c78f5cca185c7b1a990 Author: Ryan Curtin Date: Sun Apr 3 21:55:50 2022 -0400 Include numeric header for std::accumulate. commit 12287174e463226075f2a9eddd0cd3c7995bd3a1 Author: Ryan Curtin Date: Sun Apr 3 21:29:51 2022 -0400 Fix TODOs in LSTM layer. commit 71ecddf8f644a675e0666d2f870e8b4ed82c3e61 Author: Ryan Curtin Date: Sun Apr 3 20:49:03 2022 -0400 Some cleanups of the RNN class. commit 83a178dffa17c76d43df42ca7e1ba3e12c0a9213 Author: Ryan Curtin Date: Sun Apr 3 16:53:25 2022 -0400 Some additional type fixes. commit f5b3885998501c0ba406c8cd851805c3c34bdbfd Author: Ryan Curtin Date: Sun Apr 3 16:30:02 2022 -0400 Remove unused typedef. commit eae99cfacaec22b7c743034664d96b12e56f269d Author: Ryan Curtin Date: Sun Apr 3 16:27:29 2022 -0400 Simplify Shuffle() implementation. commit 250ab5cdd0400525cbfe9f5edb1724821ed6bcdb Author: Ryan Curtin Date: Sun Apr 3 16:24:42 2022 -0400 Remove unused Swap() function declaration. commit 14324209171a8fb919731199e78d93e27cfa3dea Author: Ryan Curtin Date: Sun Apr 3 16:23:28 2022 -0400 Use MakeAlias() in the RNN implementation. commit 03045b023569c2ca106a08bda97130754c905e92 Author: Ryan Curtin Date: Sun Apr 3 16:08:59 2022 -0400 Fix line wrap. commit fe8a288613a19542f924e56c9d2a12cce9873a38 Author: Ryan Curtin Date: Sun Apr 3 16:07:01 2022 -0400 Minor fixes to tests. commit 44a18de28cb71b07b3f042520eb49cdadcd7abed Author: Ryan Curtin Date: Sun Apr 3 15:58:36 2022 -0400 Clarify RNN comment. commit 10d04c4153b8cf25eec88e8e005b95b35e5ff128 Author: Ryan Curtin Date: Sun Apr 3 15:57:02 2022 -0400 Add clarifying comment about where MakeAlias() is used. commit 19c60244a798658515ce8e22a5c4babf6dd761e9 Author: Ryan Curtin Date: Sun Apr 3 15:56:06 2022 -0400 More CEREAL_NVP() fixes. commit d45d8787b93f8cac374972104f3e513363369030 Author: Ryan Curtin Date: Sun Apr 3 15:54:58 2022 -0400 Fix serialization: use CEREAL_NVP(). commit 194211feec5b5d81eaa34a18ae84088b3462a439 Author: Ryan Curtin Date: Sun Apr 3 15:53:45 2022 -0400 Fixes for RBFType implementation comments. commit 9c33d6b780cdc7e42db6f92d2d478deff3a118d9 Author: Ryan Curtin Date: Sun Apr 3 15:52:02 2022 -0400 Fix header guard name. commit 880c95bee5f7ea7086a344b946b36c4b8d4cdfb0 Author: Ryan Curtin Date: Sun Apr 3 15:50:50 2022 -0400 Fix typo. commit 2fdd4b823b8133f006749339817d9697393aded5 Author: Ryan Curtin Date: Sun Apr 3 15:50:27 2022 -0400 Add explanatory comment. commit 416dfc7c4aa18a4a605352b0fd54337f23eb1b7d Author: Ryan Curtin Date: Sun Apr 3 15:48:36 2022 -0400 Fix inaccurate comment. commit 19563e3f63f584ab7ba57afab6a041914a02465e Author: Ryan Curtin Date: Sun Apr 3 14:49:37 2022 -0400 Update comment. commit ff3717bc53e9675f604c3c90be1eb0f0dc7de1ab Author: Ryan Curtin Date: Sun Apr 3 14:41:01 2022 -0400 Fix typo in comment. commit ac42ea450b8e5730c05e2c16102729b0b302a08d Author: Ryan Curtin Date: Sun Apr 3 14:40:39 2022 -0400 Clarify implementations. commit 20c7b5a836cd1ea88d18ec03ef0fc1b46b5843a7 Author: Ryan Curtin Date: Sun Apr 3 14:37:01 2022 -0400 Remove unused method. commit 4fb0dd49659c5b08261cc56d0fb73114dc5bcc35 Author: Ryan Curtin Date: Sun Apr 3 14:36:36 2022 -0400 Fix includes. commit 5253b1427a5b4e72ed0416e28e82a1082cdbade9 Author: Ryan Curtin Date: Sun Apr 3 14:28:42 2022 -0400 Clarify comment. commit f0aceda809a00a34df32a107090181ad91c8ee32 Author: Ryan Curtin Date: Sun Apr 3 14:27:38 2022 -0400 Fix serialization for base layer. commit 2413a1cdf51c195e42691a344266f9c1bf5daf5f Author: Ryan Curtin Date: Sun Apr 3 14:26:38 2022 -0400 Add a comment. commit 2f0a9419eff2113fb657823359cce4e772056846 Author: Ryan Curtin Date: Sun Apr 3 14:26:05 2022 -0400 Use MakeAlias() instead. commit c599ecfd1ac633860437155db4f099560a183fd9 Author: Ryan Curtin Date: Sun Apr 3 13:23:32 2022 -0400 Change BaseLayer names for consistency. commit 6d69ea45f10f6724561d2dd2a20d58e8d32ba29a Author: Ryan Curtin Date: Sun Apr 3 13:18:45 2022 -0400 Clear member that is not serialized on loading. commit 8f52887fb5b48b6c061fcd602db97aefaa44b424 Author: Ryan Curtin Date: Sun Apr 3 13:16:29 2022 -0400 Remove inaccurate comments (we did not drop these). commit cb952537e2d00a8cf7145d411ff640d836d1f631 Author: Ryan Curtin Date: Sun Apr 3 13:15:56 2022 -0400 Re-add removed files from CMake. commit c75d03ff6fe60370096a79cdfd1be2f360b67e1a Author: Ryan Curtin Date: Sun Apr 3 13:14:53 2022 -0400 Fix line spacing. commit 8b2e57d32e8d6c107b073e8f46bb5f174732198e Author: Ryan Curtin Date: Sun Apr 3 13:13:46 2022 -0400 Add a clarifying comment. commit 454c746491b62741a548022bda86ebedaf7abb29 Author: Ryan Curtin Date: Sun Apr 3 13:12:52 2022 -0400 Remove duplicated `training` member. commit c6443a43d830b601fe3d6f35ad25d7ffa3cba7a5 Author: Ryan Curtin Date: Sun Apr 3 13:05:37 2022 -0400 Add clarifying comment. commit 6258a8a9fc69772d5c095aef0ca5689636709461 Author: Ryan Curtin Date: Sun Apr 3 13:02:25 2022 -0400 Clean up copy and move operators. commit 07fc52fba3ef21aed85a45df78cfe32da1a2a2b8 Author: Ryan Curtin Date: Sun Apr 3 12:51:28 2022 -0400 Some clarifying comments. commit 0ed1971ce042368414d8e135d77dad257d0fa27f Author: Ryan Curtin Date: Sun Apr 3 12:50:24 2022 -0400 Some cleanups of Forward() and Backward(). commit cade46d605d9337fc35896f6423a66c193ec245a Author: Ryan Curtin Date: Sun Apr 3 12:31:27 2022 -0400 Some extra paranoia about `Network()`. commit 20bf26651c844115369954eee3315521c627bedd Author: Ryan Curtin Date: Sun Apr 3 12:18:34 2022 -0400 Use a separate file for forward declarations. commit 56ef5ded8a693829f55aaaa993969245dfe969c5 Author: Ryan Curtin Date: Sat Apr 2 21:38:09 2022 -0400 Oops, revert unintentional CMake changes. commit a1e3a8dbef1366ba7dbd59f16065406d13e3cc9c Author: Ryan Curtin Date: Sat Apr 2 21:28:20 2022 -0400 Fix minor merge issues. commit f52a977dacec04fb3ae13ac6cf973a618bd614bd Merge: 835499fdb c4bb721ef Author: Ryan Curtin Date: Sat Apr 2 19:15:59 2022 -0400 Merge remote-tracking branch 'origin/master' into ann-vtable commit 835499fdb458b9fe1640dfee56964a29b61e55a9 Author: Ryan Curtin Date: Sat Apr 2 18:39:16 2022 -0400 Fix comments and remove working comments. commit 2950e1ff9db3764c60c4df374a10458b75a0bf00 Author: Ryan Curtin Date: Sat Apr 2 18:36:29 2022 -0400 Add comments to RecurrentLayer implementation. commit 23d27c9b5dd7b37bd658cbf05f57ced99d99553d Author: Ryan Curtin Date: Sat Apr 2 18:20:59 2022 -0400 Minor style fixes. commit e542fd8404457bd27e12a79137c4185fe9fa01d1 Author: Ryan Curtin Date: Sat Apr 2 18:19:44 2022 -0400 Comment default typedefs. commit 59ca9428c6955a18fbfbcdebb2769b544b67d337 Author: Ryan Curtin Date: Sat Apr 2 18:16:28 2022 -0400 Add standardized comment about MatType. commit cc4e3aec28d8cfa76ad0fe5dc3e36f5d4fd5b8fb Author: Ryan Curtin Date: Sat Apr 2 17:52:48 2022 -0400 Use 'Type' and typedef conventions for loss functions. commit 232ce2a3425cebd672c64bb43cfff0d5f84ece28 Author: Ryan Curtin Date: Sat Apr 2 15:37:55 2022 -0400 Adapt CustomLayer for testing to have only one template parameter. commit 9a9b6334a6915844a35418c67e2dad5488c6bd0d Author: Ryan Curtin Date: Sat Apr 2 15:37:41 2022 -0400 Adapt tests for FFNs and RNNs having only one template parameter for type. commit 9fe42a2d7b94b04c3f6760ad06ce03371957d14a Author: Ryan Curtin Date: Sat Apr 2 15:37:21 2022 -0400 Adapt to use only one template parameter. commit 6ff20e912b2358cb715f3438baf7325e5c8a4e35 Author: Ryan Curtin Date: Sat Apr 2 15:36:52 2022 -0400 Adapt to use only one template parameter for types. commit 48685d34830a25d14e2f7cb46451a48f0d593e7d Author: Ryan Curtin Date: Sat Apr 2 15:36:34 2022 -0400 Adapt to use only one template parameter. commit d79df403822b57c5b7e7fc1bf83673ad48011404 Author: Ryan Curtin Date: Fri Apr 1 19:42:59 2022 -0400 Don't allow calling Parameters() on a layer with no weights. commit 6cdd49ab8882800ab0c54b785067d1dca8945598 Author: Ryan Curtin Date: Fri Apr 1 18:21:50 2022 -0400 Some early attempts to adapt some tests. commit 0d73c5e1e24adbf255d2586c7b08cf549920239a Author: Ryan Curtin Date: Fri Apr 1 18:20:50 2022 -0400 Early attempts at refactoring Q-learning code. commit a446c67f5a66108ff0a7ff1af544ab9f61c692a2 Author: Ryan Curtin Date: Fri Apr 1 17:53:47 2022 -0400 Re-enable RLComponentsTest. commit 263d1ddd15b9d19d0b8b378764521a309deced44 Author: Ryan Curtin Date: Wed Mar 30 22:07:37 2022 -0400 Allow the Rho parameter to be reset (it should also be renamed). commit bcbeebcf27fe7371b0c4de756fc1881967c8eba0 Author: Ryan Curtin Date: Wed Mar 30 22:07:27 2022 -0400 Adapt CustomLayer. commit 53d903d1c43620fbe1a94ba8d6d07d4c4565d0f6 Author: Ryan Curtin Date: Wed Mar 30 21:17:29 2022 -0400 Re-add the callback tests. commit f34513341fc2ba6a6929235346ee91006d82a449 Author: Ryan Curtin Date: Wed Mar 30 21:15:36 2022 -0400 We don't have an arbitrary type; move() should be used here. commit 5a2f58f5598ac64c8f1e7d4ae0a81640995d3287 Author: Ryan Curtin Date: Wed Mar 30 20:03:55 2022 -0400 Re-enable some of the activation function tests. commit 0155643e72931e6b59d47a17a9791a63a89d1b34 Author: Ryan Curtin Date: Wed Mar 30 19:36:28 2022 -0400 Forward() should not worry about whether we are in single mode (that's only relevant for training). commit b3b5c6827a685262ea12897564668dd356042c46 Author: eshaanagarwal Date: Thu Mar 31 04:22:53 2022 +0530 fixed issues in styling commit ed424ae4a1670379803672f3aa130da207ff2fb3 Author: Ryan Curtin Date: Tue Mar 29 21:23:37 2022 -0400 Remove other bits of boost::visitor things. commit be9a3882ad568ef55ef49a3979226a65ccd2978e Author: Ryan Curtin Date: Tue Mar 29 21:23:28 2022 -0400 Comment out RBMNetworkTests. commit f1f5fc7fa280574aee3f3daedbf4b042675542c7 Author: Ryan Curtin Date: Tue Mar 29 21:23:22 2022 -0400 Fix includes for moved files. commit 112016ccf18d116dc901825c247cb41995c8a09d Author: Ryan Curtin Date: Tue Mar 29 21:22:59 2022 -0400 Remove boost usage. commit 2da71b1f36ecbbda72a9531607d0d2af76c62038 Author: Ryan Curtin Date: Tue Mar 29 21:22:46 2022 -0400 Move some files that aren't yet adapted or are unneeded. commit 1998227d9ce759d477155f875b7d8bcba56f0507 Author: Ryan Curtin Date: Tue Mar 29 19:58:28 2022 -0400 Fix header guard name. commit f2a1b79e993c3d9c8ec529f06e304532487b13bb Author: Ryan Curtin Date: Sun Mar 27 14:16:58 2022 -0400 Fix incorrect step reference. commit 7fa7eb15f0306c2709a5a403c5b9d5838763af2c Author: Ryan Curtin Date: Sun Mar 27 12:16:33 2022 -0400 Don't forget virtual destructor for LSTMType. commit a501389238b455bf0b51b114acf20124896d06f9 Author: Ryan Curtin Date: Sun Mar 27 12:16:14 2022 -0400 Make sure to be able to serialize RecurrentLayer. commit 4c8364c16962208d8f8d0d16db0a002b529a13b3 Author: Ryan Curtin Date: Sun Mar 27 12:15:52 2022 -0400 No need to serialize the training-time-only predictors and responses. commit c219a57da34952dc33629834c99825eb16a0889a Author: Ryan Curtin Date: Sun Mar 27 12:15:38 2022 -0400 Reference local `results` instead of class-wide `responses`. commit 9b7b7f875d50e1e54e86bc8e305e3f5facfba7fe Author: Ryan Curtin Date: Sun Mar 27 12:15:20 2022 -0400 Add copy and move operator implementation. commit accce42b35996d1a3dcc5fa239d12d5df8c93cf1 Author: Ryan Curtin Date: Sun Mar 27 12:14:55 2022 -0400 Oops, add RecurrentLayer to the repository. commit df613499f3cb3238c2aba204a20fa32c732aa714 Author: Ryan Curtin Date: Fri Mar 25 11:59:49 2022 -0400 Adapt test to use multiple epochs. commit aa0ce85656745bd6d9a59a24302b1b2985b84eb6 Author: Ryan Curtin Date: Fri Mar 25 11:58:43 2022 -0400 Fix alias computation. commit 20b109fe42ae46cb4117962050075cf55737603d Author: Ryan Curtin Date: Fri Mar 25 11:57:57 2022 -0400 Set step correctly for forward pass. commit 9ff1fb11f5e1539bcee2e51036ff6b6152e0ba33 Author: Ryan Curtin Date: Wed Mar 23 22:40:52 2022 -0400 Test that the RNN and FFN give the same output for only one time step. commit 66d929f92380644fd826c200e04177c0eec31fa9 Author: Ryan Curtin Date: Wed Mar 23 22:40:42 2022 -0400 Handle series with only one time step. commit 0c97b42d807d5739cd9a791833347d446a544d98 Author: Ryan Curtin Date: Wed Mar 23 17:58:28 2022 -0400 Be sure to serialize rho and single too. commit 76992e86d6be1ff91170a73c9489d845d358967f Author: Ryan Curtin Date: Tue Mar 22 21:26:19 2022 -0400 Add serialization for some more initializations. commit d6e200ff37014105af2c9b09d2821f2bc11ace2f Author: Ryan Curtin Date: Tue Mar 22 21:26:09 2022 -0400 Fix include issue (this may not be the right fix). commit 080dcfccb470251badda157887873d97d1b3405f Author: Ryan Curtin Date: Tue Mar 22 21:25:59 2022 -0400 Correct NumFunctions() implementation. commit 4816d7ccfe1a7b36cd76c60db19a635678cc1c6a Author: Ryan Curtin Date: Sat Mar 19 23:48:10 2022 -0400 Some other minor bugfixes; now the tests pass. commit 80efe31e5bb42f3f50ee33aa4d40364ba25215ad Author: Ryan Curtin Date: Thu Mar 17 22:06:01 2022 -0400 Fix a few bugs---now LSTMBatchSizeTest works! (That doesn't mean that RNNs actually work...) commit eab019541d44a23c6dd4dbb123cc3d463759dd05 Author: Ryan Curtin Date: Thu Mar 17 21:12:19 2022 -0400 Comment out tests that don't work yet. commit 9ff8fd401d4fada458313152c55e041622d37fc6 Author: Ryan Curtin Date: Thu Mar 17 21:11:47 2022 -0400 Clean up (in some places) RNN implementation. commit 5683066a660c948ef7b5363467191693ea3739ce Author: eshaanagarwal Date: Mon Mar 14 22:25:27 2022 +0530 fixed styling issues commit 68e40a6b23b4fcea525f4b7692c28195ac1aa67d Author: eshaanagarwal Date: Fri Mar 11 22:50:03 2022 +0530 fix build issue by removing row vector assert condition commit ca353778a444bc826daed20c10438d4fe4277c89 Author: eshaanagarwal Date: Fri Mar 11 20:15:07 2022 +0530 fix failed build commit 6883c30cb263f3226f664c58cebe70f124e64e97 Author: eshaanagarwal Date: Fri Mar 11 19:50:54 2022 +0530 fixed redundancy in size-checks commit 2685d6909743c650137b1e31558c5b818e0d6b01 Author: eshaanagarwal Date: Fri Mar 11 19:30:22 2022 +0530 fix matrix completion size-checks commit f4db66192bf2d9210265f02fcce3b0b0b27653ac Author: eshaanagarwal Date: Fri Mar 11 12:19:37 2022 +0530 remove incorrect checks in adaboost commit 05d74baffd841e09a607bf3b03e721d442d29e59 Author: eshaanagarwal Date: Thu Mar 10 16:30:49 2022 +0530 fix size checks commit 54f65060158fd883bb43fcb72d7062ce60c26cd3 Author: eshaanagarwal Date: Thu Mar 10 12:24:15 2022 +0530 fix styling issue Signed-off-by: eshaanagarwal commit 8714c77a959e708a6100d6c1a4813c086185bcff Author: eshaanagarwal Date: Wed Mar 9 02:33:08 2022 +0530 Add: size checks for kmeans and linear regression commit 0a0681f2a578188f2b0ce0b878a3923e7accfcb6 Author: eshaanagarwal Date: Tue Mar 8 02:27:38 2022 +0530 Add : Size checks for adaboost and matix completion Signed-off-by: eshaanagarwal commit bd08f9a92ec85a01b422d6c2e82329e5c3a4dc48 Author: Ryan Curtin Date: Wed Feb 16 22:18:54 2022 -0500 Remove debugging output. commit d8b72a862bbda1853613f3cd3e0a1a041a6fc27e Author: Marcus Edel Date: Wed Feb 9 22:14:32 2022 -0500 Some RNN refactoring. commit 93fecce9c80aa13e4e940e6e94069923681dded0 Author: Ryan Curtin Date: Tue Feb 15 23:00:08 2022 -0500 Significant cleanup of all layers. The ones not in not_adapted/ (other than the LSTM) are ready for review. commit ce12cd3e25a9f53aec04f5c1f2161b9b42a57bdc Author: Ryan Curtin Date: Wed Feb 9 22:44:07 2022 -0500 Remove files from CMakeLists.txt. commit ee117f355b491cbcfe4cb3b27ca18fe6a079b0a3 Author: Ryan Curtin Date: Wed Feb 9 22:43:38 2022 -0500 Remove layer_traits.hpp since it's no longer needed. commit 7b969f3014d8c575decf2436a99052c75fd06837 Author: Ryan Curtin Date: Wed Feb 9 22:41:54 2022 -0500 Move two layers that got missed. commit e2502cbb9c9b42a6787c9d24fdfc4f5063e26258 Author: Ryan Curtin Date: Wed Feb 9 22:41:09 2022 -0500 Move unadapted layers into a separate directory (for organization). commit f72340106ce78ec632925f8198fc7d6102b78f54 Author: Ryan Curtin Date: Wed Feb 9 22:09:08 2022 -0500 Add some documentation about what will happen. commit 637b93ddc048e23c74500fe62bc2e7ba7ef7a9bd Merge: 2065e17df cf190e11f Author: Ryan Curtin Date: Wed Feb 9 22:05:56 2022 -0500 Merge remote-tracking branch 'origin/master' into ann-vtable commit 2065e17df8467d83b91295b623a8b8810a14c2a3 Author: Ryan Curtin Date: Wed Feb 9 21:45:29 2022 -0500 Add a test for uneven stride. commit c532632ce970c6294ec151cd29d9cf6ae015788a Author: Ryan Curtin Date: Sat Feb 5 20:41:43 2022 -0500 Remove comments that turn out to be unnecessary to address. commit 5e09ed16715e4b914e6dd172f093523ae1ca2033 Author: Ryan Curtin Date: Sat Feb 5 18:08:06 2022 -0500 Fix implementations of NaiveConvolution for stride and dilation. commit 5cd0e38eb6cbf5f0316e0f6ee7376b1ea783ad30 Author: Ryan Curtin Date: Sat Feb 5 18:07:39 2022 -0500 Add tests for different strides and dilations. commit 8c1a8185408328a0ab474c0dd9dc69186cfe5878 Author: Ryan Curtin Date: Wed Feb 2 22:55:35 2022 -0500 Fix stride usage for backwards pass. commit 110ed75b6f81022b94ea0f29db42b5bf9be881e7 Author: Ryan Curtin Date: Tue Feb 1 18:06:55 2022 -0500 Fix some minor issues and merge problems. commit a77d29021436e956eb6cd2e106e6a546f7172fdf Author: Ryan Curtin Date: Thu Jan 27 16:28:22 2022 -0500 Add/fix final set of copy/move constructors/operators. commit 11cc9a122b55095446662ad598617dba8356a2e1 Author: Ryan Curtin Date: Wed Jan 26 22:29:45 2022 -0500 Add and fix a bunch more copy/move constructors/operators. commit ec06bcbbffc4f6cdb49af7c269b5acd85db5db87 Author: Ryan Curtin Date: Tue Jan 25 22:44:34 2022 -0500 Start implementing copy and move constructors correctly. commit f8421110394df1b8f1abdbf3826de683f5990cde Author: Ryan Curtin Date: Tue Jan 25 22:44:25 2022 -0500 Set sizes correctly for test. commit 3f5e06e9c5fbe2b5c3d00f968f2a930bebbfd2e8 Author: Ryan Curtin Date: Sat Jan 22 11:20:35 2022 -0500 Restructure PaddingTest for slight behavior changes. commit abb1450102bdd748c229eedc15f6887dcb86d429 Author: Ryan Curtin Date: Fri Jan 21 23:32:35 2022 -0500 Fix minor bugs in shape computation. commit d600ece415fc5462956670462971d6c2b60e0164 Author: Ryan Curtin Date: Fri Jan 21 23:03:24 2022 -0500 Fix failing padding tests. commit 94b3294a5d250eb65ec175d4e09741bc5b31683b Author: Ryan Curtin Date: Fri Jan 21 14:40:53 2022 -0500 Fix some merge issues. commit c3caed04907c0938d0e7e8becede9af7357a97f2 Merge: 09a28caa9 3264cd87e Author: Ryan Curtin Date: Thu Jan 20 20:23:26 2022 -0500 Merge remote-tracking branch 'origin/master' into ann-vtable commit 09a28caa902b5e38010f6bd103363cd28317b51e Author: Ryan Curtin Date: Wed Jan 19 17:41:18 2022 -0500 Some minor bugfixes to FFN. Some need further cleanup. commit 96f97421f2e3cf938b6fadc9726ff25c2a0fd822 Author: Ryan Curtin Date: Wed Jan 19 17:16:57 2022 -0500 Fix bug in Gradient() implementation. commit 6dc0596ad6b58204857c16a2eb04851ed76f0d0b Author: Ryan Curtin Date: Wed Jan 19 17:16:44 2022 -0500 Consider bias term in gradient. commit 9a71a61aac3e960a79481128ac96bdba82a5b085 Author: Ryan Curtin Date: Fri Jan 7 18:16:18 2022 -0500 Fix setting of inputDimensionsAreSet. commit a4ee2545f6f915634bb738b6051870afd88e8032 Author: Ryan Curtin Date: Tue Jan 4 20:12:34 2022 -0500 Add a sanity check test. It passes. commit f5c4b9586070497008ce5e47a3ec8d6034558e17 Author: Ryan Curtin Date: Sun Nov 28 21:47:40 2021 -0500 Remove debugging output. commit d8fef7eed6b09594261f2505e0e2f5a0b356cfc0 Author: Ryan Curtin Date: Sun Nov 28 21:47:17 2021 -0500 Adapt to new mlpack 4 conventions. commit bda6e0969f3ee43dffb9e48735655b5af914c7d7 Author: Ryan Curtin Date: Sun Nov 28 21:46:59 2021 -0500 Fix failing tests. commit 8d1c05c36f1ec99f2ae37e2d7d2fc6cb94166f12 Author: Ryan Curtin Date: Sun Nov 28 21:46:44 2021 -0500 Update to mlpack 4 conventions. commit ead043c9b88945213ae408848c2b78189d28d13e Author: Ryan Curtin Date: Sun Nov 28 21:45:41 2021 -0500 Oops, make sure to add the bias in the forward pass. commit eae19f7c28b11c63a360856463b5b71f16dda839 Author: Omar Shrit Date: Tue Nov 16 17:43:09 2021 +0000 Finishing the Alphadroput layer, tests are passing Signed-off-by: Omar Shrit commit c19e39a152f36beea3b322c32327ec764c18897f Author: Ryan Curtin Date: Sun Nov 14 22:30:39 2021 -0500 Turns out the MaxPooling adaptation I did was wrong---this seems more correct. commit a1fd4829a167591a35ea489ba4c7f67308977044 Author: Ryan Curtin Date: Sun Nov 14 22:30:15 2021 -0500 Redo Convolution layer implementation. I think this is right but not 100% sure. commit ca010221e9432ba8f9aedb8b221e48dfae7a60c7 Author: Ryan Curtin Date: Sun Nov 14 22:29:26 2021 -0500 Enable LeakyReLU layer. commit 49954574e998ee136d5541d58e98436a3bcd8d35 Author: Ryan Curtin Date: Mon Nov 1 19:17:24 2021 -0400 Add gradient test for Convolution layer... but it seems to work? commit be5b4e5e016a23afe7d1856dee500710c04084e2 Author: Ryan Curtin Date: Wed Oct 20 17:46:03 2021 -0400 Fix shape of input to reflect the number of input maps. commit 19f06709d47ec9f2023649260337cf77a21dd640 Author: Ryan Curtin Date: Tue Oct 19 10:18:14 2021 -0400 Use MakeAlias() to avoid accidental copies. commit 081c32593d7e97b07439b7fb3f8eac0d3d007031 Author: Ryan Curtin Date: Tue Sep 28 11:35:41 2021 -0400 Bias should be one per output map. commit 58f1718be1c7893c9e23ee592c42d70268f655d6 Author: Ryan Curtin Date: Tue Sep 28 11:22:07 2021 -0400 Huh, it seems like this fixes ConvolutionLayerPaddingTest. commit 6fff4457c6f724bb60f1b2141fe2f20efd02c58c Author: Ryan Curtin Date: Mon Sep 20 11:09:36 2021 -0400 Fix two more tests by making sure the inputs and outputs are right. commit ad2cb74d0553affb513ebe23be2d77f105fb02c7 Author: Ryan Curtin Date: Sun Sep 19 00:10:20 2021 -0400 Fix some tests. commit 051205d78f8a82ff90cba7b693050aac84d08ff5 Author: Ryan Curtin Date: Wed Sep 15 22:12:20 2021 -0400 Now at least the tests don't segfault. :) commit 1152c75d2341c21ccce9a69fe9eaf0807370291e Author: Ryan Curtin Date: Wed Sep 15 13:59:43 2021 -0400 Update some ann_layer tests. commit f05590b8785da7c173bc08ca18ecd79f0b04b2ab Author: Ryan Curtin Date: Wed Sep 15 13:59:29 2021 -0400 Fix convolution. commit 1cbdaa8ac5fb9b4067b64838e7b3eda8d27f4564 Author: Ryan Curtin Date: Wed Sep 15 13:58:09 2021 -0400 Fix bug for uninitialized output. commit 3a00d82a23608980312d21b6a803e75bef665ee9 Author: Ryan Curtin Date: Thu Sep 9 12:02:31 2021 -0400 Some cleanups for the convolution layer. commit 72c0f58a27dc8160391407bc8324ea28d7145ad6 Author: Ryan Curtin Date: Thu Sep 9 12:02:06 2021 -0400 Fix some incorrect dimension usages. commit a46542b25f0b8af1dd87cf3a13812554fbe16f51 Author: Ryan Curtin Date: Tue Sep 7 18:05:04 2021 -0400 Too much writing Julia... commit 06f7b34d7374acad4cbc4cf0432f35172beff5d2 Author: Ryan Curtin Date: Fri Sep 3 18:43:21 2021 -0400 Fix max pooling bug. commit c9df713091ee2f51f211a56e8d49ec5a7f2a8f29 Author: Ryan Curtin Date: Sat Aug 21 10:44:43 2021 -0400 First attempt at refactoring Padding, MaxPooling, and Convolution. commit 498afa0ab77f00f5d802e2d1d72bd5486bf682c0 Author: Ryan Curtin Date: Sat Aug 7 11:15:14 2021 -0400 Fix various bugs in the MultiLayer implementation. commit 8105bb8583a3050174f22fb29f16ca36e81a256a Author: Ryan Curtin Date: Wed Aug 4 21:19:54 2021 -0400 Refactor to use MultiLayer inside an FFN. commit 5f7a9cd684d9a1fd3b48ad8662655c92f60d2e82 Author: Marcus Edel Date: Mon Aug 2 18:19:58 2021 +0200 Filter some 'unused' layers. commit 8a4e5b9bdfe1554ccb95d6983d5bbd64f76b1ad9 Author: Ryan Curtin Date: Sun Aug 1 21:42:09 2021 -0400 Refactor Highway (and fix MultiLayer). commit 0375e7d57562f912c569c1130f5e0b079c6d8474 Author: Ryan Curtin Date: Sun Aug 1 21:42:00 2021 -0400 Extra paranoia to avoid include boost::visitor... commit d3972c16da12291b85954e7105e1ee5c23186959 Author: Ryan Curtin Date: Sun Aug 1 21:41:49 2021 -0400 Just make sure boost isn't included... commit 57884df9f6423a3b7bf2be7ba9b6c432e142f800 Author: Ryan Curtin Date: Sun Aug 1 21:41:33 2021 -0400 Split out into convenience function. commit d136ac3df302151a4a73c658252365ddd454fa56 Author: Ryan Curtin Date: Sun Aug 1 21:41:00 2021 -0400 Hey, this is no longer needed! :) commit be7eeb134d0cc4fd6e5ee74e3c736e6a2c4aa777 Merge: ce3c3bcee e3f4654a8 Author: Ryan Curtin Date: Fri Jul 23 16:45:08 2021 -0400 Merge branch 'ann-vtable-attempt' into HEAD commit e3f4654a8ab4c885d232ad35d15e3e3ca1a5243b Author: Ryan Curtin Date: Fri Jul 23 16:43:46 2021 -0400 Update RBF<> layer so tests pass. commit ce3c3bcee6b08c9c3d79fed1c31b367897c37775 Merge: 34cf419bb 927fabff8 Author: Marcus Edel Date: Tue Jul 20 08:56:37 2021 -0400 Merge pull request #2 from rcurtin/ann-vtable-attempt Further refactoring of ANN to remove boost::visitor. commit 927fabff8afdc2480180905546dbe5ae038fc316 Author: Ryan Curtin Date: Tue Jul 13 17:53:33 2021 -0400 Adapt the last commented test in FeedforwardNetworkTest. commit ed8881d2b6200e54828f0efbd2b7b2840838ee71 Author: Ryan Curtin Date: Tue Jul 13 17:48:46 2021 -0400 Uncomment another test. commit b05736d42ccc7b99f33e9fee8f9157a18d71e09f Author: Ryan Curtin Date: Tue Jul 13 17:48:34 2021 -0400 Make sure Parameters() returns the correct thing. commit 522ebd11fe8734dfdeae63902275215fc9f894ed Author: Ryan Curtin Date: Tue Jul 13 17:48:27 2021 -0400 Adapt AddType<>. commit c8c0797c9d26f5edda44007b3da247ebbf1f3582 Author: Ryan Curtin Date: Tue Jul 13 17:19:57 2021 -0400 Adapt a few more layers, and uncomment some more tests. commit 2b7429594f523588763a4cfbee75eea109ac78a1 Author: Ryan Curtin Date: Tue Jul 13 16:47:06 2021 -0400 Oops, I didn't really need to refactor this, but it might work. commit ab67249f9fc04be089bead0e064f6462d328c653 Author: Ryan Curtin Date: Thu Jul 8 17:49:57 2021 -0400 Fix additional warnings. commit a5bb31b82421d9481ea254b754e11a79aedf9eb0 Author: Ryan Curtin Date: Thu Jul 8 17:38:04 2021 -0400 Remove debugging output. commit 188759042cc83e26429014387273a982dac3a3d0 Author: Ryan Curtin Date: Thu Jul 8 17:37:42 2021 -0400 Fix a compilation warning. commit 130890c85d3c178d6fd119d10046fe84a01a6735 Author: Ryan Curtin Date: Thu Jul 8 17:36:09 2021 -0400 Some additional refactoring and cleanups. Notably, the adapted layers no longer need an input size. commit 32b19eb7efe99daf2b783f0aeec9e5432521a341 Author: Ryan Curtin Date: Wed Jul 7 19:08:32 2021 -0400 Refactor Reparametrization layer. commit d180cc36393c1bfea4bd66d8a9df0efb05c8e082 Author: Ryan Curtin Date: Wed Jul 7 19:08:12 2021 -0400 Serialize output dimensions also. commit a87672665caaf8d04ce4e8a95b8c799035cff049 Author: Ryan Curtin Date: Wed Jul 7 18:24:15 2021 -0400 Remove unnecessary copy/move constructor/operators. commit af68997a6cdf0fcdea18db218fbd0a98ddf77b3f Author: Ryan Curtin Date: Wed Jul 7 18:22:30 2021 -0400 This function should be const. commit 60fe292da9f0f2e328d8c2110b27799cf843510d Author: Ryan Curtin Date: Wed Jul 7 18:22:10 2021 -0400 These are all the default versions anyway (but don't consider inheritance...). commit 30e4ff741b6bd16cb756d5205d744f36d0a11f44 Author: Ryan Curtin Date: Tue Jul 6 16:22:37 2021 -0400 Update comments. commit 7f7f56481f951383edff9cc1c8097fc710e7096c Author: Ryan Curtin Date: Mon Jul 5 21:53:33 2021 -0400 Remove unnecessary functions. commit 30aca349cff4054bf66671882297e000ba585c5c Author: Ryan Curtin Date: Mon Jul 5 19:26:55 2021 -0400 Fix train/test modes. commit ffba7a966c069aef7f6675d4abc971a4636d7a35 Author: Ryan Curtin Date: Mon Jul 5 18:52:20 2021 -0400 Remove unnecessary utilities. commit a3dd3739c631c9a9ce6a2ab91e74b9231ae90d46 Author: Ryan Curtin Date: Mon Jul 5 18:51:18 2021 -0400 Change 'deterministic' to 'training'. commit afa76d6ae8c503ce5d823fc17251bc015f20f653 Author: Ryan Curtin Date: Sun Jul 4 16:05:47 2021 -0400 Make sure that we save layerOutputs.back() in case we need it later... commit 2875194721172dfdd0f7ac4f1a746fb85b3dc750 Author: Ryan Curtin Date: Wed Jun 23 19:58:38 2021 -0400 Fix FFNReturnModel test. commit 292bfbef9ca1e6b6d0b5ff26b8afaba435d9f28e Author: Ryan Curtin Date: Wed Jun 23 19:32:34 2021 -0400 Serialize deterministic in whatever state it is currently in---no assumptions. commit a30e15fca7afef8b1eac736cc8531a744297430b Author: Ryan Curtin Date: Wed Jun 23 19:32:20 2021 -0400 Remove unnecessary output. commit 5d27a4d43ed4399247075891c36bd026a2dbb71c Author: Ryan Curtin Date: Wed Jun 23 19:24:36 2021 -0400 Add the 'MultiLayer', although maybe we can just use the FFN class itself? commit c2b54b7af68eb38eac11f08bedffd6342dbf7ef0 Author: Ryan Curtin Date: Wed Jun 23 19:24:21 2021 -0400 Set the size correctly in Predict() and fix a few other errors. commit 0c307e89dee16541a828756967e646daebe74dcd Author: Ryan Curtin Date: Fri Jun 18 14:11:47 2021 -0400 Initialize totalInputSize and totalOutputSize in the right place. commit d2fd462a8c8f52c3a89098173860f30c6dd058f7 Author: Ryan Curtin Date: Fri Jun 18 13:51:46 2021 -0400 Use aliases for layer outputs and deltas. commit 064cb7b2966e0072f795c06fe7799bfcdd9e772e Author: Ryan Curtin Date: Fri Jun 18 12:42:29 2021 -0400 Okay, this passes FFVanillaNetworkTest! commit 9a521c8080d6bdaddd4674b7c74052b5b3c66403 Author: Ryan Curtin Date: Wed Jun 16 14:07:23 2021 -0400 Step 1: something compiles at all. commit 5220de7d143e911bd5dc5c91e78ba80d9c22bdfc Author: Ryan Curtin Date: Fri May 28 12:52:21 2021 -0400 Fix some minor compilation issues. commit f8123469e9e1aa82e25c4b3a692944a72c662df4 Author: Ryan Curtin Date: Sat May 22 05:27:37 2021 -0400 Add serialization file. commit 1b3ea01b45f5059df3dfffbe009d588a8f3adab9 Author: Ryan Curtin Date: Sat May 22 05:19:41 2021 -0400 In-progress, does not quite compile yet. commit 34cf419bb86658f137ee2414879e4be9e514d9f3 Author: Marcus Edel Date: Sun Jan 31 19:05:00 2021 +0100 Update FFN tests to use the base layer class. commit b700f8d311df2e04a3646cf4b752f16185da939b Author: Marcus Edel Date: Sun Jan 31 04:36:35 2021 +0100 Update FFN copy/move constructor tests to use the layer base class. commit eafac8609db48d6b4e2002add74cae1255e58c3d Author: Marcus Edel Date: Sat Jan 30 04:39:08 2021 +0100 Add Clone() function which handles polymorphism correctly. commit 0c4db57106e72bee2bfb9e392e744073fda79c6e Author: Mrityunjay Tripathi Date: Fri Jan 29 10:14:53 2021 +0530 typo fix commit 21057324f3f001b5a3fb633d3ce8b0e5d519d478 Author: Mrityunjay Tripathi Date: Thu Jan 28 16:21:27 2021 +0530 add ResetCell and Reward methods to base class and add method to push layer to ffn model commit 43e7639e614ffada15121ae3f2f9d09590f0f6c7 Author: Marcus Edel Date: Tue Jan 26 00:47:55 2021 +0100 Use layer base class for the network initialization. commit fadaaa59a27188377122959b651bdd1a14e41b49 Author: Marcus Edel Date: Mon Jan 25 00:04:26 2021 +0100 Update layer to use updated layer base class interface. commit 3cdd972be8ecf413eb40ceef9a526d676d984382 Author: Marcus Edel Date: Sun Jan 24 23:17:26 2021 +0100 Adjust determenistic parameter interface. commit 5b453b712a193fca5dc8161b3891a4bc1bc78c33 Author: Marcus Edel Date: Sun Jan 24 23:14:04 2021 +0100 Add utiliy functions to update layer parameters and states. commit c641bc527c1034a54a6574e5d56b621083d36c7c Author: Marcus Edel Date: Sun Jan 24 23:13:09 2021 +0100 Restructure FFN class to use the layer base class. commit 9c29e5b6031b41a796ede96bad2852021afa3caa Author: Mrityunjay Tripathi Date: Tue Jan 19 21:17:32 2021 +0530 update weight_norm layer to use abstract class, and some other fixes commit 5d62339e02e48440124776ae7d300a3b4715ca92 Author: Mrityunjay Tripathi Date: Sun Jan 17 18:04:32 2021 +0530 update fast_lstm to use abstract class (without unit test) commit f2f84991ce434bf9a45aa82b1f84533409102631 Author: Mrityunjay Tripathi Date: Sun Jan 17 17:30:36 2021 +0530 migrate vr_class_reward to loss_functions commit ecd2fae814a59bd23fe615037c7bd696c590ddc9 Author: Mrityunjay Tripathi Date: Sat Jan 16 10:32:18 2021 +0530 update reinforce_normal and reparametrization layer to use abstract class commit 826be5b404357521ea3c1643d94eba96cf12b383 Author: Mrityunjay Tripathi Date: Fri Jan 15 23:29:21 2021 +0530 update virtual_batch_norm to support abstract class commit f694114805e295bcb3a2358322e5f1875e7d68e5 Author: Mrityunjay Tripathi Date: Fri Jan 15 23:14:22 2021 +0530 update select, subview, padding and transposed convolution to use abstract class commit dfec0f787d90c70c9a64e61dc0f2caf09fb1e365 Author: Mrityunjay Tripathi Date: Fri Jan 15 18:28:43 2021 +0530 updated sequential layer to use abstract class commit c6675f4ba33272d389719a6d26f14443cae0bbb8 Author: Mrityunjay Tripathi Date: Fri Jan 15 16:34:30 2021 +0530 update concat and highway layers to use abstract class commit 0da7330f012525585502d7962c4ff0c51b2dd2b3 Author: Mrityunjay Tripathi Date: Fri Jan 15 09:11:57 2021 +0530 update positional_encoding and multiply_merge layers to use abstract class commit c30d9ed85c98063885acc8200907e5243d1dba7b Author: Mrityunjay Tripathi Date: Fri Jan 15 07:18:52 2021 +0530 slight style fixes and lexicographical ordering of layer_types commit cfdb84173aa6a2eedaf1f105f9a96db90d0ca2d4 Author: Mrityunjay Tripathi Date: Thu Jan 14 11:22:58 2021 +0530 some corrections and updating layer_norm, max_pooling and mean_pooling commit 97d865df79f7dad9d0bcd97480af2e6e7f477040 Author: Mrityunjay Tripathi Date: Thu Jan 14 10:25:47 2021 +0530 correction in convolution and update join and glimpse layer to use abstract class commit 270aab791eee2efc58e27c17ad06d81982764278 Author: Mrityunjay Tripathi Date: Thu Jan 14 10:05:39 2021 +0530 update glimpse layer to use abstract class method commit 28d56b1693d8245c241b3dfe9fcf4a8fb7595988 Author: Mrityunjay Tripathi Date: Wed Jan 13 10:52:22 2021 +0530 corrected documentation and updated convolution layer commit 09e299534615628b02e79aa92fe18ce022bb684a Author: Mrityunjay Tripathi Date: Wed Jan 13 09:19:09 2021 +0530 update multihead attention layer commit dd6b70c2f22b7f94b0fb5ff826e14de21214b123 Author: Mrityunjay Tripathi Date: Tue Jan 12 09:34:23 2021 +0530 update concatenate layer commit 004818cca231d57b37b356793a720a2c8ebcd51f Author: Mrityunjay Tripathi Date: Mon Jan 11 23:26:42 2021 +0530 update bilinear_interpolation, linear3d, lookup and base_layer commit aef1158187ddac5388574f693129d7d4e43093cc Author: Marcus Edel Date: Sun Jan 3 23:31:22 2021 +0100 Add Deterministic() to the abstract layer class. commit 3f4d24f7f59f66d3e9539916910a8876eda565c7 Author: Marcus Edel Date: Sun Jan 3 23:06:40 2021 +0100 Update SpatialDropout layer to use the abstract layer class and add typedef. commit 5193102f09954806470e997fb7392a39847ba765 Author: Marcus Edel Date: Sat Jan 2 20:57:20 2021 +0100 Update PReLU layer to use the abstract layer class and add typedef. commit a57d13f8388c9b73cd111ef6939f4be56eeaee92 Author: Marcus Edel Date: Sat Jan 2 20:40:07 2021 +0100 Update CELU layer to use the abstract layer class and add typedef. commit 47f35337f6f7df3e20e5bcd1a152d48b54c04015 Author: Marcus Edel Date: Sat Jan 2 20:25:57 2021 +0100 Update CReLU layer to use the abstract layer class and add typedef. commit cd05cbecd42c679c8df68cbfa4f66c90b831612d Author: Marcus Edel Date: Sat Jan 2 18:54:42 2021 +0100 Update MultiplyConstant layer to use the abstract layer class and add typedef. commit 8b0abc219ab3862e46d3dbef3da697d108c46f24 Author: Marcus Edel Date: Sun Dec 27 22:57:13 2020 +0100 Update Softshrink layer to use the abstract layer class and add typedef. commit 8cf621f4fdc86728516232e92869e552b3da10e1 Author: Marcus Edel Date: Sun Dec 27 19:57:37 2020 +0100 Update Softmin layer to use the abstract layer class and add typedef. commit 3b0318a50da15cccdf5053fe26892c2098eb9b81 Author: Marcus Edel Date: Sun Dec 27 17:44:04 2020 +0100 Update Softmax layer to use the abstract layer class and add typedef. commit f4f66af580d95214c22e35b2b5062f6d1ebbd23f Author: Marcus Edel Date: Sun Dec 27 17:23:13 2020 +0100 Update Constant layer to use the abstract layer class and add typedef. commit cebce07398a5b717680e3aa98cd6de85a3c01451 Author: Marcus Edel Date: Sun Dec 27 16:25:25 2020 +0100 Update LeakyReLU layer to use the abstract layer class and add typedef. commit 0217a19b2571659c74d10c9f41bafe0377cbd753 Author: Marcus Edel Date: Sun Dec 27 14:39:47 2020 +0100 Update HardShrink layer to use the abstract layer class and add typedef. commit 69f0d5b56a252ba1503212107933c9a1a951ba3a Author: Marcus Edel Date: Sun Dec 27 14:24:29 2020 +0100 Update NoisyLinear layer to use the abstract layer class and add typedef. commit c927971363af28e135231b87a6b4dc3ecccf5fe1 Author: Marcus Edel Date: Sun Dec 27 13:43:57 2020 +0100 Update LinearNoBias layer to use the abstract layer class and add typedef. commit dea01f7215b4e881351d3087d8fa912e991b0fdc Author: Aakash kaushik Date: Fri Dec 25 22:41:46 2020 +0530 tests pass commit 09d905f2e52c2643763d25ec750e34b36eebcbd7 Author: Aakash kaushik Date: Fri Dec 25 17:28:02 2020 +0530 update adaptive_mean_pooling_layer to use the abstract class and added typedef commit a893867f37b2ba78970185e0a5a1da13c2db1820 Author: Aakash kaushik Date: Fri Dec 25 17:27:30 2020 +0530 update adaptive_mean_pooling_layer to use the abstract class and added typedef commit 66853d1a5bb4af399b2b84793aded51d3988884e Author: Aakash kaushik Date: Fri Dec 25 16:57:51 2020 +0530 update adaptive_max_pooling_layer to use the abstract class and added typedef commit 60afb1e1bcbae865dddc8abc9fbb7702c981a266 Author: Marcus Edel Date: Sat Dec 26 01:13:17 2020 +0100 Update Dropconnect layer to use the abstract layer class and add typedef. commit 322753be752fdafd6d2f279ecaa912d804e75c6f Author: Marcus Edel Date: Thu Dec 24 11:57:47 2020 +0100 Update HardTanH layer to use the abstract layer class and add typedef. commit 80ecca13564973eadbfc56a022baa8af63008d37 Author: Marcus Edel Date: Thu Dec 24 11:41:37 2020 +0100 Update Dropout layer to use the abstract layer class and add typedef. commit 9acac6838d08b85f7da4062f04040ac135c03568 Author: Marcus Edel Date: Thu Dec 24 01:23:32 2020 +0100 Update ELU/SELU layer to use the abstract layer class and add typedefs. commit 5ea2b93618e8329584eae2159f7a557d2565c80e Author: Marcus Edel Date: Thu Dec 24 00:59:45 2020 +0100 Update SoftMax layer to use the abstract layer class and add typedef. commit 2232915792e19e23522c6104242ff8e7f923a382 Author: Marcus Edel Date: Thu Dec 24 00:39:44 2020 +0100 Update FlexibleReLU layer to use the abstract layer class, add documentation and typedef. commit 4fd7ae9da7e72399c2dbb53250c5e02a39994f08 Author: Marcus Edel Date: Thu Dec 24 00:38:25 2020 +0100 Remove functions that are already part of the base class. commit a6995f83ee04d941b1c4b4a8c9198d7257fce521 Author: Marcus Edel Date: Wed Dec 23 17:14:07 2020 +0100 Update Linear layer to use the abstract Layer class and add documentation and typedef. commit a3f746580b2ab350c404e1c1a9f7f49a9c9c3a1b Author: Marcus Edel Date: Wed Dec 23 17:12:32 2020 +0100 Clean-up abstract layer class and add some further documentation. commit aa6d2b1aad1261874bea888e8841de4fd31b3bf6 Author: Marcus Edel Date: Mon Dec 21 17:23:08 2020 +0100 Swap boost::variant with vtable. --- .ci/linux-steps.yaml | 1 + .ci/macos-steps.yaml | 1 + .github/workflows/main.yml | 1 + CMake/FindGo.cmake | 4 +- CMake/FindGonum.cmake | 11 +- COPYRIGHT.txt | 2 +- HISTORY.md | 2 + src/mlpack/bindings/python/copy_artifacts.py | 12 +- .../bindings/python/is_serializable.hpp | 4 +- src/mlpack/core/cereal/CMakeLists.txt | 2 - .../core/cereal/pointer_variant_wrapper.hpp | 159 - .../cereal/pointer_vector_variant_wrapper.hpp | 97 - src/mlpack/core/util/size_checks.hpp | 19 +- src/mlpack/methods/ann/CMakeLists.txt | 11 +- .../convolution_rules/naive_convolution.hpp | 51 +- src/mlpack/methods/ann/ffn.hpp | 686 +- src/mlpack/methods/ann/ffn_impl.hpp | 1162 ++-- src/mlpack/methods/ann/forward_decls.hpp | 29 + .../methods/ann/init_rules/const_init.hpp | 8 +- .../methods/ann/init_rules/glorot_init.hpp | 18 +- src/mlpack/methods/ann/init_rules/he_init.hpp | 6 + .../methods/ann/init_rules/network_init.hpp | 47 +- .../methods/ann/init_rules/oivs_init.hpp | 10 +- .../methods/ann/init_rules/random_init.hpp | 7 + src/mlpack/methods/ann/layer/CMakeLists.txt | 112 +- src/mlpack/methods/ann/layer/add.hpp | 116 +- src/mlpack/methods/ann/layer/add_impl.hpp | 110 +- .../methods/ann/layer/add_merge_impl.hpp | 168 - .../methods/ann/layer/alpha_dropout.hpp | 79 +- .../methods/ann/layer/alpha_dropout_impl.hpp | 96 +- src/mlpack/methods/ann/layer/base_layer.hpp | 267 +- src/mlpack/methods/ann/layer/concat.hpp | 263 - src/mlpack/methods/ann/layer/concat_impl.hpp | 293 - src/mlpack/methods/ann/layer/concatenate.hpp | 102 +- .../methods/ann/layer/concatenate_impl.hpp | 123 +- .../methods/ann/layer/constant_impl.hpp | 65 - src/mlpack/methods/ann/layer/convolution.hpp | 289 +- .../methods/ann/layer/convolution_impl.hpp | 931 +-- src/mlpack/methods/ann/layer/dropconnect.hpp | 149 +- .../methods/ann/layer/dropconnect_impl.hpp | 166 +- src/mlpack/methods/ann/layer/dropout.hpp | 83 +- src/mlpack/methods/ann/layer/dropout_impl.hpp | 96 +- src/mlpack/methods/ann/layer/gru_impl.hpp | 411 -- 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.../methods/ann/layer/mean_pooling_impl.hpp | 134 - .../layer/minibatch_discrimination_impl.hpp | 147 - src/mlpack/methods/ann/layer/multi_layer.hpp | 252 + .../methods/ann/layer/multi_layer_impl.hpp | 414 ++ .../ann/layer/multiply_constant_impl.hpp | 96 - .../methods/ann/layer/multiply_merge_impl.hpp | 190 - src/mlpack/methods/ann/layer/noisylinear.hpp | 161 +- .../methods/ann/layer/noisylinear_impl.hpp | 175 +- .../methods/ann/layer/not_adapted/README.md | 9 + .../adaptive_max_pooling.hpp | 90 +- .../adaptive_max_pooling_impl.hpp | 42 +- .../adaptive_mean_pooling.hpp | 91 +- .../adaptive_mean_pooling_impl.hpp | 42 +- .../ann/layer/{ => not_adapted}/add_merge.hpp | 138 +- .../ann/layer/not_adapted/add_merge_impl.hpp | 145 + .../{ => not_adapted}/atrous_convolution.hpp | 117 +- .../atrous_convolution_impl.hpp | 149 +- .../layer/{ => not_adapted}/batch_norm.hpp | 92 +- .../{ => not_adapted}/batch_norm_impl.hpp | 122 +- .../bicubic_interpolation.hpp | 0 .../bicubic_interpolation_impl.hpp | 0 .../bilinear_interpolation.hpp | 96 +- .../bilinear_interpolation_impl.hpp | 127 +- .../ann/layer/{ => not_adapted}/c_relu.hpp | 60 +- .../layer/{ => not_adapted}/c_relu_impl.hpp | 25 +- .../ann/layer/{ => not_adapted}/celu.hpp | 67 +- .../ann/layer/{ => not_adapted}/celu_impl.hpp | 31 +- .../{ => not_adapted}/channel_shuffle.hpp | 0 .../channel_shuffle_impl.hpp | 0 .../methods/ann/layer/not_adapted/concat.hpp | 222 + .../ann/layer/not_adapted/concat_impl.hpp | 267 + .../{ => not_adapted}/concat_performance.hpp | 45 +- .../concat_performance_impl.hpp | 69 +- .../ann/layer/{ => not_adapted}/constant.hpp | 82 +- .../ann/layer/not_adapted/constant_impl.hpp | 118 + .../ann/layer/{ => not_adapted}/elu.hpp | 76 +- .../ann/layer/{ => not_adapted}/elu_impl.hpp | 77 +- .../ann/layer/{ => not_adapted}/fast_lstm.hpp | 133 +- .../{ => not_adapted}/fast_lstm_impl.hpp | 86 +- .../{ => not_adapted}/flatten_t_swish.hpp | 0 .../flatten_t_swish_impl.hpp | 0 .../layer/{ => 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0 .../{ => not_adapted}/pixel_shuffle_impl.hpp | 0 .../{ => not_adapted}/positional_encoding.hpp | 61 +- .../positional_encoding_impl.hpp | 38 +- .../ann/layer/{ => not_adapted}/recurrent.hpp | 112 +- .../{ => not_adapted}/recurrent_attention.hpp | 128 +- .../not_adapted/recurrent_attention_impl.hpp | 237 + .../ann/layer/not_adapted/recurrent_impl.hpp | 284 + .../{ => not_adapted}/reinforce_normal.hpp | 56 +- .../reinforce_normal_impl.hpp | 25 +- .../ann/layer/{ => not_adapted}/relu6.hpp | 0 .../layer/{ => not_adapted}/relu6_impl.hpp | 0 .../{ => not_adapted}/reparametrization.hpp | 143 +- .../not_adapted/reparametrization_impl.hpp | 152 + .../ann/layer/{ => not_adapted}/select.hpp | 76 +- .../layer/{ => not_adapted}/select_impl.hpp | 35 +- .../ann/layer/not_adapted/sequential.hpp | 150 + .../ann/layer/not_adapted/sequential_impl.hpp | 163 + .../ann/layer/{ => not_adapted}/softmax.hpp | 62 +- .../layer/{ => not_adapted}/softmax_impl.hpp | 26 +- .../ann/layer/{ => 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.../ann/layer/recurrent_layer_impl.hpp | 84 + .../ann/layer/reparametrization_impl.hpp | 156 - src/mlpack/methods/ann/layer/sequential.hpp | 267 - .../methods/ann/layer/sequential_impl.hpp | 270 - .../methods/ann/layer/serialization.hpp | 53 + .../ann/layer/vr_class_reward_impl.hpp | 107 - .../methods/ann/layer/weight_norm_impl.hpp | 165 - .../methods/ann/loss_functions/CMakeLists.txt | 2 + .../binary_cross_entropy_loss.hpp | 56 +- .../binary_cross_entropy_loss_impl.hpp | 31 +- .../loss_functions/cosine_embedding_loss.hpp | 60 +- .../cosine_embedding_loss_impl.hpp | 43 +- .../methods/ann/loss_functions/dice_loss.hpp | 42 +- .../ann/loss_functions/dice_loss_impl.hpp | 41 +- .../loss_functions/earth_mover_distance.hpp | 42 +- .../earth_mover_distance_impl.hpp | 34 +- .../methods/ann/loss_functions/empty_loss.hpp | 36 +- .../ann/loss_functions/empty_loss_impl.hpp | 22 +- .../loss_functions/hinge_embedding_loss.hpp | 43 +- .../hinge_embedding_loss_impl.hpp | 35 +- .../methods/ann/loss_functions/hinge_loss.hpp | 44 +- .../ann/loss_functions/hinge_loss_impl.hpp | 39 +- .../methods/ann/loss_functions/huber_loss.hpp | 42 +- .../ann/loss_functions/huber_loss_impl.hpp | 47 +- .../ann/loss_functions/kl_divergence.hpp | 43 +- .../ann/loss_functions/kl_divergence_impl.hpp | 35 +- .../methods/ann/loss_functions/l1_loss.hpp | 42 +- .../ann/loss_functions/l1_loss_impl.hpp | 37 +- .../ann/loss_functions/log_cosh_loss.hpp | 40 +- .../ann/loss_functions/log_cosh_loss_impl.hpp | 37 +- .../loss_functions/margin_ranking_loss.hpp | 51 +- .../margin_ranking_loss_impl.hpp | 50 +- .../mean_absolute_percentage_error.hpp | 47 +- .../mean_absolute_percentage_error_impl.hpp | 37 +- .../ann/loss_functions/mean_bias_error.hpp | 46 +- .../loss_functions/mean_bias_error_impl.hpp | 35 +- .../ann/loss_functions/mean_squared_error.hpp | 43 +- .../mean_squared_error_impl.hpp | 33 +- .../mean_squared_logarithmic_error.hpp | 46 +- .../mean_squared_logarithmic_error_impl.hpp | 34 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89 - .../ann/visitor/gradient_visitor_impl.hpp | 90 - .../ann/visitor/gradient_zero_visitor.hpp | 60 - .../visitor/gradient_zero_visitor_impl.hpp | 57 - .../ann/visitor/input_shape_visitor.hpp | 58 - .../ann/visitor/input_shape_visitor_impl.hpp | 53 - .../visitor/load_output_parameter_visitor.hpp | 65 - .../load_output_parameter_visitor_impl.hpp | 66 - .../methods/ann/visitor/loss_visitor.hpp | 71 - .../methods/ann/visitor/loss_visitor_impl.hpp | 99 - .../ann/visitor/output_height_visitor.hpp | 75 - .../visitor/output_height_visitor_impl.hpp | 99 - .../ann/visitor/output_parameter_visitor.hpp | 43 - .../visitor/output_parameter_visitor_impl.hpp | 36 - .../ann/visitor/output_width_visitor.hpp | 75 - .../ann/visitor/output_width_visitor_impl.hpp | 99 - .../ann/visitor/parameters_set_visitor.hpp | 64 - .../visitor/parameters_set_visitor_impl.hpp | 58 - .../ann/visitor/parameters_visitor.hpp | 64 - .../ann/visitor/parameters_visitor_impl.hpp | 58 - .../ann/visitor/reset_cell_visitor.hpp | 62 - .../ann/visitor/reset_cell_visitor_impl.hpp | 58 - .../methods/ann/visitor/reset_visitor.hpp | 75 - .../ann/visitor/reset_visitor_impl.hpp | 80 - .../ann/visitor/reward_set_visitor.hpp | 81 - .../ann/visitor/reward_set_visitor_impl.hpp | 87 - .../methods/ann/visitor/run_set_visitor.hpp | 83 - .../ann/visitor/run_set_visitor_impl.hpp | 88 - .../visitor/save_output_parameter_visitor.hpp | 64 - .../save_output_parameter_visitor_impl.hpp | 64 - .../ann/visitor/set_input_height_visitor.hpp | 84 - .../visitor/set_input_height_visitor_impl.hpp | 102 - .../ann/visitor/set_input_width_visitor.hpp | 83 - .../visitor/set_input_width_visitor_impl.hpp | 102 - .../ann/visitor/weight_set_visitor.hpp | 82 - .../ann/visitor/weight_set_visitor_impl.hpp | 100 - .../ann/visitor/weight_size_visitor.hpp | 76 - .../ann/visitor/weight_size_visitor_impl.hpp | 84 - src/mlpack/methods/kmeans/kmeans_impl.hpp | 17 +- .../linear_regression/linear_regression.cpp | 17 +- src/mlpack/methods/perceptron/perceptron.hpp | 23 + .../methods/perceptron/perceptron_impl.hpp | 29 +- .../q_learning_impl.hpp | 4 +- .../q_networks/dueling_dqn.hpp | 2 +- .../q_networks/simple_dqn.hpp | 34 +- .../methods/reinforcement_learning/sac.hpp | 1 - src/mlpack/prereqs.hpp | 12 +- src/mlpack/tests/CMakeLists.txt | 18 +- .../tests/activation_functions_test.cpp | 146 +- src/mlpack/tests/ann_layer_test.cpp | 5544 ++++++++++------- src/mlpack/tests/ann_test_tools.hpp | 41 +- src/mlpack/tests/ann_visitor_test.cpp | 235 - src/mlpack/tests/async_learning_test.cpp | 32 +- src/mlpack/tests/callback_test.cpp | 53 +- src/mlpack/tests/catch.hpp | 14 +- src/mlpack/tests/convolution_test.cpp | 200 + .../tests/convolutional_network_test.cpp | 353 +- src/mlpack/tests/custom_layer.hpp | 24 +- src/mlpack/tests/cv_test.cpp | 36 +- .../tests/feedforward_network_2_test.cpp | 13 +- src/mlpack/tests/feedforward_network_test.cpp | 713 +-- src/mlpack/tests/init_rules_test.cpp | 58 +- src/mlpack/tests/ksinit_test.cpp | 12 +- src/mlpack/tests/layer_names_test.cpp | 160 - src/mlpack/tests/loss_functions_test.cpp | 173 +- .../tests/{ => not_adapted}/gan_test.cpp | 0 .../{ => not_adapted}/rbm_network_test.cpp | 0 .../tests/{ => not_adapted}/wgan_test.cpp | 0 src/mlpack/tests/perceptron_test.cpp | 34 +- src/mlpack/tests/recurrent_network_test.cpp | 318 +- src/mlpack/tests/reward_clipping_test.cpp | 4 +- src/mlpack/tests/serialization_test.cpp | 8 +- 356 files changed, 16844 insertions(+), 22263 deletions(-) delete mode 100644 src/mlpack/core/cereal/pointer_variant_wrapper.hpp delete mode 100644 src/mlpack/core/cereal/pointer_vector_variant_wrapper.hpp create mode 100644 src/mlpack/methods/ann/forward_decls.hpp delete mode 100644 src/mlpack/methods/ann/layer/add_merge_impl.hpp delete mode 100644 src/mlpack/methods/ann/layer/concat.hpp delete mode 100644 src/mlpack/methods/ann/layer/concat_impl.hpp delete mode 100644 src/mlpack/methods/ann/layer/constant_impl.hpp delete mode 100644 src/mlpack/methods/ann/layer/gru_impl.hpp delete mode 100644 src/mlpack/methods/ann/layer/highway.hpp delete mode 100644 src/mlpack/methods/ann/layer/highway_impl.hpp delete mode 100644 src/mlpack/methods/ann/layer/layer_traits.hpp delete mode 100644 src/mlpack/methods/ann/layer/mean_pooling_impl.hpp delete mode 100644 src/mlpack/methods/ann/layer/minibatch_discrimination_impl.hpp create mode 100644 src/mlpack/methods/ann/layer/multi_layer.hpp create mode 100644 src/mlpack/methods/ann/layer/multi_layer_impl.hpp delete mode 100644 src/mlpack/methods/ann/layer/multiply_constant_impl.hpp delete mode 100644 src/mlpack/methods/ann/layer/multiply_merge_impl.hpp create mode 100644 src/mlpack/methods/ann/layer/not_adapted/README.md rename src/mlpack/methods/ann/layer/{ => not_adapted}/adaptive_max_pooling.hpp (62%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/adaptive_max_pooling_impl.hpp (55%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/adaptive_mean_pooling.hpp (62%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/adaptive_mean_pooling_impl.hpp (55%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/add_merge.hpp (52%) create mode 100644 src/mlpack/methods/ann/layer/not_adapted/add_merge_impl.hpp rename src/mlpack/methods/ann/layer/{ => not_adapted}/atrous_convolution.hpp (82%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/atrous_convolution_impl.hpp (81%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/batch_norm.hpp (69%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/batch_norm_impl.hpp (65%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/bicubic_interpolation.hpp (100%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/bicubic_interpolation_impl.hpp (100%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/bilinear_interpolation.hpp (55%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/bilinear_interpolation_impl.hpp (51%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/c_relu.hpp (65%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/c_relu_impl.hpp (63%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/celu.hpp (64%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/celu_impl.hpp (66%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/channel_shuffle.hpp (100%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/channel_shuffle_impl.hpp (100%) create mode 100644 src/mlpack/methods/ann/layer/not_adapted/concat.hpp create mode 100644 src/mlpack/methods/ann/layer/not_adapted/concat_impl.hpp rename src/mlpack/methods/ann/layer/{ => not_adapted}/concat_performance.hpp (67%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/concat_performance_impl.hpp (61%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/constant.hpp (56%) create mode 100644 src/mlpack/methods/ann/layer/not_adapted/constant_impl.hpp rename src/mlpack/methods/ann/layer/{ => not_adapted}/elu.hpp (70%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/elu_impl.hpp (53%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/fast_lstm.hpp (72%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/fast_lstm_impl.hpp (81%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/flatten_t_swish.hpp (100%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/flatten_t_swish_impl.hpp (100%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/flexible_relu.hpp (50%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/flexible_relu_impl.hpp (51%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/glimpse.hpp (80%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/glimpse_impl.hpp (76%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/group_norm.hpp (100%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/group_norm_impl.hpp (100%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/gru.hpp (67%) create mode 100644 src/mlpack/methods/ann/layer/not_adapted/gru_impl.hpp rename src/mlpack/methods/ann/layer/{ => not_adapted}/hard_tanh.hpp (71%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/hard_tanh_impl.hpp (71%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/hardshrink.hpp (67%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/hardshrink_impl.hpp (63%) create mode 100644 src/mlpack/methods/ann/layer/not_adapted/highway.hpp create mode 100644 src/mlpack/methods/ann/layer/not_adapted/highway_impl.hpp rename src/mlpack/methods/ann/layer/{ => not_adapted}/instance_norm.hpp (100%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/instance_norm_impl.hpp (100%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/isrlu.hpp (100%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/isrlu_impl.hpp (100%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/join.hpp (61%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/join_impl.hpp (63%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/layer_norm.hpp (67%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/layer_norm_impl.hpp (60%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/lookup.hpp (64%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/lookup_impl.hpp (58%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/lp_pooling.hpp (100%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/lp_pooling_impl.hpp (100%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/mean_pooling.hpp (73%) create mode 100644 src/mlpack/methods/ann/layer/not_adapted/mean_pooling_impl.hpp rename src/mlpack/methods/ann/layer/{ => not_adapted}/minibatch_discrimination.hpp (61%) create mode 100644 src/mlpack/methods/ann/layer/not_adapted/minibatch_discrimination_impl.hpp rename src/mlpack/methods/ann/layer/{ => not_adapted}/multihead_attention.hpp (70%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/multihead_attention_impl.hpp (79%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/multiply_constant.hpp (64%) create mode 100644 src/mlpack/methods/ann/layer/not_adapted/multiply_constant_impl.hpp rename src/mlpack/methods/ann/layer/{ => not_adapted}/multiply_merge.hpp (51%) create mode 100644 src/mlpack/methods/ann/layer/not_adapted/multiply_merge_impl.hpp rename src/mlpack/methods/ann/layer/{ => not_adapted}/nearest_interpolation.hpp (100%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/nearest_interpolation_impl.hpp (100%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/parametric_relu.hpp (64%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/parametric_relu_impl.hpp (51%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/pixel_shuffle.hpp (100%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/pixel_shuffle_impl.hpp (100%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/positional_encoding.hpp (63%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/positional_encoding_impl.hpp (59%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/recurrent.hpp (54%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/recurrent_attention.hpp (57%) create mode 100644 src/mlpack/methods/ann/layer/not_adapted/recurrent_attention_impl.hpp create mode 100644 src/mlpack/methods/ann/layer/not_adapted/recurrent_impl.hpp rename src/mlpack/methods/ann/layer/{ => not_adapted}/reinforce_normal.hpp (64%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/reinforce_normal_impl.hpp (68%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/relu6.hpp (100%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/relu6_impl.hpp (100%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/reparametrization.hpp (54%) create mode 100644 src/mlpack/methods/ann/layer/not_adapted/reparametrization_impl.hpp rename src/mlpack/methods/ann/layer/{ => not_adapted}/select.hpp (53%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/select_impl.hpp (58%) create mode 100644 src/mlpack/methods/ann/layer/not_adapted/sequential.hpp create mode 100644 src/mlpack/methods/ann/layer/not_adapted/sequential_impl.hpp rename src/mlpack/methods/ann/layer/{ => not_adapted}/softmax.hpp (58%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/softmax_impl.hpp (65%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/softmin.hpp (57%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/softmin_impl.hpp (65%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/softshrink.hpp (66%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/softshrink_impl.hpp (59%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/spatial_dropout.hpp (66%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/spatial_dropout_impl.hpp (57%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/subview.hpp (69%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/transposed_convolution.hpp (72%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/transposed_convolution_impl.hpp (79%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/virtual_batch_norm.hpp (60%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/virtual_batch_norm_impl.hpp (61%) rename src/mlpack/methods/ann/layer/{ => not_adapted}/weight_norm.hpp (58%) create mode 100644 src/mlpack/methods/ann/layer/not_adapted/weight_norm_impl.hpp delete mode 100644 src/mlpack/methods/ann/layer/recurrent_attention_impl.hpp delete mode 100644 src/mlpack/methods/ann/layer/recurrent_impl.hpp create mode 100644 src/mlpack/methods/ann/layer/recurrent_layer.hpp create mode 100644 src/mlpack/methods/ann/layer/recurrent_layer_impl.hpp delete mode 100644 src/mlpack/methods/ann/layer/reparametrization_impl.hpp delete mode 100644 src/mlpack/methods/ann/layer/sequential.hpp delete mode 100644 src/mlpack/methods/ann/layer/sequential_impl.hpp create mode 100644 src/mlpack/methods/ann/layer/serialization.hpp delete mode 100644 src/mlpack/methods/ann/layer/vr_class_reward_impl.hpp delete mode 100644 src/mlpack/methods/ann/layer/weight_norm_impl.hpp rename src/mlpack/methods/ann/{layer => loss_functions}/vr_class_reward.hpp (58%) create mode 100644 src/mlpack/methods/ann/loss_functions/vr_class_reward_impl.hpp create mode 100644 src/mlpack/methods/ann/make_alias.hpp rename src/mlpack/methods/ann/{ => not_adapted}/brnn.hpp (99%) rename src/mlpack/methods/ann/{ => not_adapted}/brnn_impl.hpp (100%) rename src/mlpack/methods/ann/{ => not_adapted}/gan/CMakeLists.txt (100%) rename src/mlpack/methods/ann/{ => not_adapted}/gan/gan.hpp (100%) rename src/mlpack/methods/ann/{ => not_adapted}/gan/gan_impl.hpp (100%) rename src/mlpack/methods/ann/{ => not_adapted}/gan/gan_policies.hpp (100%) rename src/mlpack/methods/ann/{ => not_adapted}/gan/metrics/CMakeLists.txt (100%) rename src/mlpack/methods/ann/{ => not_adapted}/gan/metrics/inception_score.hpp (100%) rename src/mlpack/methods/ann/{ => not_adapted}/gan/metrics/inception_score_impl.hpp (100%) rename src/mlpack/methods/ann/{ => not_adapted}/gan/wgan_impl.hpp (100%) rename src/mlpack/methods/ann/{ => not_adapted}/gan/wgangp_impl.hpp (100%) rename src/mlpack/methods/ann/{ => not_adapted}/rbm/CMakeLists.txt (100%) rename src/mlpack/methods/ann/{ => not_adapted}/rbm/rbm.hpp (100%) rename src/mlpack/methods/ann/{ => not_adapted}/rbm/rbm_impl.hpp (100%) rename src/mlpack/methods/ann/{ => not_adapted}/rbm/rbm_policies.hpp (100%) rename src/mlpack/methods/ann/{ => not_adapted}/rbm/spike_slab_rbm_impl.hpp (100%) delete mode 100644 src/mlpack/methods/ann/util/CMakeLists.txt delete mode 100644 src/mlpack/methods/ann/util/check_input_shape.hpp delete mode 100644 src/mlpack/methods/ann/visitor/CMakeLists.txt delete mode 100644 src/mlpack/methods/ann/visitor/add_visitor.hpp delete mode 100644 src/mlpack/methods/ann/visitor/add_visitor_impl.hpp delete mode 100644 src/mlpack/methods/ann/visitor/backward_visitor.hpp delete mode 100644 src/mlpack/methods/ann/visitor/backward_visitor_impl.hpp delete mode 100644 src/mlpack/methods/ann/visitor/bias_set_visitor.hpp delete mode 100644 src/mlpack/methods/ann/visitor/bias_set_visitor_impl.hpp delete mode 100644 src/mlpack/methods/ann/visitor/copy_visitor.hpp delete mode 100644 src/mlpack/methods/ann/visitor/copy_visitor_impl.hpp delete mode 100644 src/mlpack/methods/ann/visitor/delete_visitor.hpp delete mode 100644 src/mlpack/methods/ann/visitor/delete_visitor_impl.hpp delete mode 100644 src/mlpack/methods/ann/visitor/delta_visitor.hpp delete mode 100644 src/mlpack/methods/ann/visitor/delta_visitor_impl.hpp delete mode 100644 src/mlpack/methods/ann/visitor/deterministic_set_visitor.hpp delete mode 100644 src/mlpack/methods/ann/visitor/deterministic_set_visitor_impl.hpp delete mode 100644 src/mlpack/methods/ann/visitor/forward_visitor.hpp delete mode 100644 src/mlpack/methods/ann/visitor/forward_visitor_impl.hpp delete mode 100644 src/mlpack/methods/ann/visitor/gradient_set_visitor.hpp delete mode 100644 src/mlpack/methods/ann/visitor/gradient_set_visitor_impl.hpp delete mode 100644 src/mlpack/methods/ann/visitor/gradient_update_visitor.hpp delete mode 100644 src/mlpack/methods/ann/visitor/gradient_update_visitor_impl.hpp delete mode 100644 src/mlpack/methods/ann/visitor/gradient_visitor.hpp delete mode 100644 src/mlpack/methods/ann/visitor/gradient_visitor_impl.hpp delete mode 100644 src/mlpack/methods/ann/visitor/gradient_zero_visitor.hpp delete mode 100644 src/mlpack/methods/ann/visitor/gradient_zero_visitor_impl.hpp delete mode 100644 src/mlpack/methods/ann/visitor/input_shape_visitor.hpp delete mode 100644 src/mlpack/methods/ann/visitor/input_shape_visitor_impl.hpp delete mode 100644 src/mlpack/methods/ann/visitor/load_output_parameter_visitor.hpp delete mode 100644 src/mlpack/methods/ann/visitor/load_output_parameter_visitor_impl.hpp delete mode 100644 src/mlpack/methods/ann/visitor/loss_visitor.hpp delete mode 100644 src/mlpack/methods/ann/visitor/loss_visitor_impl.hpp delete mode 100644 src/mlpack/methods/ann/visitor/output_height_visitor.hpp delete mode 100644 src/mlpack/methods/ann/visitor/output_height_visitor_impl.hpp delete mode 100644 src/mlpack/methods/ann/visitor/output_parameter_visitor.hpp delete mode 100644 src/mlpack/methods/ann/visitor/output_parameter_visitor_impl.hpp delete mode 100644 src/mlpack/methods/ann/visitor/output_width_visitor.hpp delete mode 100644 src/mlpack/methods/ann/visitor/output_width_visitor_impl.hpp delete mode 100644 src/mlpack/methods/ann/visitor/parameters_set_visitor.hpp delete mode 100644 src/mlpack/methods/ann/visitor/parameters_set_visitor_impl.hpp delete mode 100644 src/mlpack/methods/ann/visitor/parameters_visitor.hpp delete mode 100644 src/mlpack/methods/ann/visitor/parameters_visitor_impl.hpp delete mode 100644 src/mlpack/methods/ann/visitor/reset_cell_visitor.hpp delete mode 100644 src/mlpack/methods/ann/visitor/reset_cell_visitor_impl.hpp delete mode 100644 src/mlpack/methods/ann/visitor/reset_visitor.hpp delete mode 100644 src/mlpack/methods/ann/visitor/reset_visitor_impl.hpp delete mode 100644 src/mlpack/methods/ann/visitor/reward_set_visitor.hpp delete mode 100644 src/mlpack/methods/ann/visitor/reward_set_visitor_impl.hpp delete mode 100644 src/mlpack/methods/ann/visitor/run_set_visitor.hpp delete mode 100644 src/mlpack/methods/ann/visitor/run_set_visitor_impl.hpp delete mode 100644 src/mlpack/methods/ann/visitor/save_output_parameter_visitor.hpp delete mode 100644 src/mlpack/methods/ann/visitor/save_output_parameter_visitor_impl.hpp delete mode 100644 src/mlpack/methods/ann/visitor/set_input_height_visitor.hpp delete mode 100644 src/mlpack/methods/ann/visitor/set_input_height_visitor_impl.hpp delete mode 100644 src/mlpack/methods/ann/visitor/set_input_width_visitor.hpp delete mode 100644 src/mlpack/methods/ann/visitor/set_input_width_visitor_impl.hpp delete mode 100644 src/mlpack/methods/ann/visitor/weight_set_visitor.hpp delete mode 100644 src/mlpack/methods/ann/visitor/weight_set_visitor_impl.hpp delete mode 100644 src/mlpack/methods/ann/visitor/weight_size_visitor.hpp delete mode 100644 src/mlpack/methods/ann/visitor/weight_size_visitor_impl.hpp delete mode 100644 src/mlpack/tests/ann_visitor_test.cpp delete mode 100644 src/mlpack/tests/layer_names_test.cpp rename src/mlpack/tests/{ => not_adapted}/gan_test.cpp (100%) rename src/mlpack/tests/{ => not_adapted}/rbm_network_test.cpp (100%) rename src/mlpack/tests/{ => not_adapted}/wgan_test.cpp (100%) diff --git a/.ci/linux-steps.yaml b/.ci/linux-steps.yaml index 5e87b118a5..a04e305bc3 100644 --- a/.ci/linux-steps.yaml +++ b/.ci/linux-steps.yaml @@ -80,6 +80,7 @@ steps: mkdir build && cd build if [ "$(binding)" == "go" ]; then export GOPATH=$PWD/src/mlpack/bindings/go + export GO111MODULE=off go get -u -t gonum.org/v1/gonum/... fi cmake $(CMakeArgs) -DPYTHON_EXECUTABLE=`which python` -DCEREAL_INCLUDE_DIR=/usr/include/ .. diff --git a/.ci/macos-steps.yaml b/.ci/macos-steps.yaml index 60f5e4d3b6..ef9a736fac 100644 --- a/.ci/macos-steps.yaml +++ b/.ci/macos-steps.yaml @@ -33,6 +33,7 @@ steps: mkdir build && cd build if [ "$(binding)" == "go" ]; then export GOPATH=$PWD/src/mlpack/bindings/go + export GO111MODULE=off go get -u -t gonum.org/v1/gonum/... fi if [ "$(binding)" == "python" ]; then diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 99e4039bfc..8b6d7a7ffb 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -57,6 +57,7 @@ jobs: run: | remotes::install_deps(dependencies = TRUE) remotes::install_cran("roxygen2") + remotes::install_cran("pkgbuild") shell: Rscript {0} - name: CMake diff --git a/CMake/FindGo.cmake b/CMake/FindGo.cmake index 39b93f69aa..6d5011f1c8 100644 --- a/CMake/FindGo.cmake +++ b/CMake/FindGo.cmake @@ -14,8 +14,8 @@ if (GO_EXECUTABLE) RESULT_VARIABLE RESULT ) if (RESULT EQUAL 0) - string(REGEX REPLACE ".*([0-9]+\\.[0-9]+\(\\.[0-9]+\)?).*" "\\1" - GO_VERSION_STRING ${GO_VERSION_STRING}) + string(REGEX MATCH "([0-9]+\\.[0-9]+\(\\.[0-9]+\)?)" + GO_VERSION_STRING "${GO_VERSION_STRING}") endif() endif() diff --git a/CMake/FindGonum.cmake b/CMake/FindGonum.cmake index eb18819a85..3667625915 100644 --- a/CMake/FindGonum.cmake +++ b/CMake/FindGonum.cmake @@ -4,20 +4,21 @@ if (GO_EXECUTABLE) execute_process( COMMAND ${GO_EXECUTABLE} list gonum.org/v1/gonum/mat - OUTPUT_VARIABLE GONUM_VERSION_STRING + OUTPUT_VARIABLE GONUM_RAW_STRING RESULT_VARIABLE RESULT ) if (RESULT EQUAL 0) - string(REGEX REPLACE ".*([0-9]+\\.[0-9]+\\.[0-9]+[\n]+).*" "\\1" - GONUM_VERSION_STRING ${GONUM_VERSION_STRING}) string(REGEX REPLACE "\n$" "" - GONUM_VERSION_STRING ${GONUM_VERSION_STRING}) + GONUM_RAW_STRING ${GONUM_RAW_STRING}) + if ("${GONUM_RAW_STRING}" STREQUAL "gonum.org/v1/gonum/mat") + set(GONUM_FOUND 1) + endif() endif() endif() include(FindPackageHandleStandardArgs) find_package_handle_standard_args( Gonum - REQUIRED_VARS GONUM_VERSION_STRING + REQUIRED_VARS GONUM_FOUND FAIL_MESSAGE "Gonum not found" ) diff --git a/COPYRIGHT.txt b/COPYRIGHT.txt index b9b43bdc6d..8bd3ffc06b 100644 --- a/COPYRIGHT.txt +++ b/COPYRIGHT.txt @@ -7,7 +7,7 @@ Source: Files: * Copyright: - Copyright 2008-2021, Ryan Curtin + Copyright 2008-2022, Ryan Curtin Copyright 2008-2013, Bill March Copyright 2008-2012, Dongryeol Lee Copyright 2008-2013, Nishant Mehta diff --git a/HISTORY.md b/HISTORY.md index c5c0146a13..605aaec568 100644 --- a/HISTORY.md +++ b/HISTORY.md @@ -1,5 +1,7 @@ ### mlpack ?.?.? ###### ????-??-?? + * Fix `Perceptron` to work with cross-validation framework (#3190). + * Migrate from boost tests to Catch2 framework (#2523), (#2584). * Bump minimum armadillo version from 8.400 to 9.800 (#3043), (#3048). diff --git a/src/mlpack/bindings/python/copy_artifacts.py b/src/mlpack/bindings/python/copy_artifacts.py index ab62a403c5..969be65451 100644 --- a/src/mlpack/bindings/python/copy_artifacts.py +++ b/src/mlpack/bindings/python/copy_artifacts.py @@ -6,18 +6,14 @@ # terms of the 3-clause BSD license. You should have received a copy of the # 3-clause BSD license along with mlpack. If not, see # http://www.opensource.org/licenses/BSD-3-Clause for more information. -import sys import sysconfig import shutil import os +import glob -directory = 'build/lib.' + \ - sysconfig.get_platform() + \ - '-' + \ - str(sys.version_info[0]) + \ - '.' + \ - str(sys.version_info[1]) + \ - '/mlpack/' +# Match any lib.$platform*/mlpack/ directory. +directory = glob.glob('build/lib.' + sysconfig.get_platform() + '*/mlpack/')[0] +directory = directory.replace('\\', '/') # Now copy all the files from the directory to the desired location. for f in os.listdir(directory): diff --git a/src/mlpack/bindings/python/is_serializable.hpp b/src/mlpack/bindings/python/is_serializable.hpp index dcec2986f5..f57553939a 100644 --- a/src/mlpack/bindings/python/is_serializable.hpp +++ b/src/mlpack/bindings/python/is_serializable.hpp @@ -21,7 +21,7 @@ namespace python { template inline bool IsSerializable( util::ParamData& /* d */, - const typename boost::disable_if>::type* = 0) + const typename std::enable_if::value>::type* = 0) { return false; } @@ -29,7 +29,7 @@ inline bool IsSerializable( template inline bool IsSerializable( util::ParamData& /* d */, - const typename boost::enable_if>::type* = 0) + const typename std::enable_if::value>::type* = 0) { return true; } diff --git a/src/mlpack/core/cereal/CMakeLists.txt b/src/mlpack/core/cereal/CMakeLists.txt index e6acb18e3f..cad97c39ea 100644 --- a/src/mlpack/core/cereal/CMakeLists.txt +++ b/src/mlpack/core/cereal/CMakeLists.txt @@ -7,8 +7,6 @@ set(SOURCES pair_associative_container.hpp pointer_wrapper.hpp pointer_vector_wrapper.hpp - pointer_variant_wrapper.hpp - pointer_vector_variant_wrapper.hpp unordered_map.hpp ) diff --git a/src/mlpack/core/cereal/pointer_variant_wrapper.hpp b/src/mlpack/core/cereal/pointer_variant_wrapper.hpp deleted file mode 100644 index 92ac65f7fc..0000000000 --- a/src/mlpack/core/cereal/pointer_variant_wrapper.hpp +++ /dev/null @@ -1,159 +0,0 @@ -/** - * @file core/cereal/pointer_variant_wrapper.hpp - * @author Omar Shrit - * - * Implementation of a boost::variant wrapper to enable the serialization of - * the pointers inside boost variant in cereal - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_CORE_CEREAL_POINTER_VARIANT_WRAPPER_HPP -#define MLPACK_CORE_CEREAL_POINTER_VARIANT_WRAPPER_HPP - -#include -#include -#include -#include - -#include -#include -#include - -#include "pointer_wrapper.hpp" - -namespace cereal { - -// Forward declaration. -template -class PointerVariantWrapper; - -/** - * Serialize a boost variant in which the variant it self is a raw pointer. - * This wrapper will wrap each variant independently by encapsulating each variant - * into the PoninterWrapper we have created already. - * - * @param t A reference to boost variant that holds raw pointer. - */ -template -inline PointerVariantWrapper -make_pointer_variant(boost::variant& t) -{ - return PointerVariantWrapper(t); -} - -template -struct save_visitor : public boost::static_visitor -{ - save_visitor(Archive& ar) : ar(ar) {} - - template - void operator()(const T* value) const - { - ar(CEREAL_POINTER(value)); - } - - template - void operator()(boost::variant& value) const - { - ar(make_pointer_variant(value)); - } - - Archive& ar; -}; - -template -struct load_visitor : public boost::static_visitor -{ - template - static void load_impl(Archive& ar, VariantType& variant, std::true_type) - { - // Note that T will be a pointer type. - T loadVariant; - ar(CEREAL_POINTER(loadVariant)); - variant = loadVariant; - } - - template - static void load_impl(Archive& ar, VariantType& value, std::false_type) - { - // This must be a nested boost::variant. - T loadVariant; - ar(make_pointer_variant(loadVariant)); - value = loadVariant; - } - - template - static void load(Archive& ar, VariantType& variant) - { - // Delegate to the proper load_impl() overload depending on whether T is a - // pointer type. If T is not a pointer type, then we expect it to be a - // nested boost::variant. - load_impl(ar, variant, typename std::is_pointer::type()); - } -}; - -/** - * The objective of this class is to create a wrapper for - * boost::variant. - * Cereal supports the serialization of boost::variant, but - * we need to serialize it if it holds a raw pointers. - * This class depeds on the PointerWrapper we have already created in which it is - * used to serialize each variant independently - */ -template -class PointerVariantWrapper -{ - public: - PointerVariantWrapper(boost::variant& pointerVar) : - pointerVariant(pointerVar) - {} - - template - void save(Archive& ar) const - { - // which represents the index in std::variant. - int which = pointerVariant.which(); - ar(CEREAL_NVP(which)); - save_visitor s(ar); - boost::apply_visitor(s, pointerVariant); - } - - template - void load(Archive& ar) - { - // Load the size of the serialized type. - int which; - ar(CEREAL_NVP(which)); - - // Create function pointers to each overload of load_visitor::load, for - // all T in VariantTypes. - using LoadFuncType = void(*)(Archive&, boost::variant&); - LoadFuncType loadFuncArray[] = { &load_visitor::load... }; - - if (which >= int(sizeof(loadFuncArray)/sizeof(loadFuncArray[0]))) - throw std::runtime_error("Invalid 'which' selector when" - "deserializing boost::variant"); - - loadFuncArray[which](ar, pointerVariant); - } - - private: - boost::variant& pointerVariant; -}; - -/** - * Cereal does not support the serialization of raw pointer. - * This macro enable developers to serialize boost::variant that holds raw - * pointers by using the above PointerVariantWrapper class which replace the - * internal raw pointers by smart pointer internally. - * - * @param T boost::variant that holds raw pointer to be serialized. - */ -#define CEREAL_VARIANT_POINTER(T) cereal::make_pointer_variant(T) - -} // namespace cereal - -#endif // CEREAL_POINTER_VARIANT_WRAPPER_HPP diff --git a/src/mlpack/core/cereal/pointer_vector_variant_wrapper.hpp b/src/mlpack/core/cereal/pointer_vector_variant_wrapper.hpp deleted file mode 100644 index 76f035e7b5..0000000000 --- a/src/mlpack/core/cereal/pointer_vector_variant_wrapper.hpp +++ /dev/null @@ -1,97 +0,0 @@ -/** - * @file core/cereal/pointer_vector_variant_wrapper.hpp - * @author Omar Shrit - * - * Implementation of a boost::variant wrapper to enable the serialization of - * the pointers inside boost variant in cereal - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_CORE_CEREAL_POINTER_VECTOR_VARIANT_WRAPPER_HPP -#define MLPACK_CORE_CEREAL_POINTER_VECTOR_VARIANT_WRAPPER_HPP - -#include "pointer_wrapper.hpp" -#include "pointer_variant_wrapper.hpp" -#include "pointer_vector_wrapper.hpp" - -namespace cereal { - -// Forward declaration -template -class PointerVectorVariantWrapper; - -/** - * Serialize a std::vector of boost variants in which the variant in each boost - * variant is a raw pointer. - * This wrapper will wrap each boost variant independently by encapsulating each - * boost variant into the PoninterVariantWrapper we have created already. - * - * @param t A reference to a vector of boost variants that holds raw pointer. - */ -template -inline PointerVectorVariantWrapper -make_vector_pointer_variant(std::vector>& t) -{ - return PointerVectorVariantWrapper(t); -} - -/** - * The objective of this class is to create a wrapper for - * a vector of boost::variant that holds pointer. - * Cereal supports the serialization of boost::variant, but - * we need to serialize it if it holds a vector of boost::variant that holds a - * pointers. - */ -template -class PointerVectorVariantWrapper -{ - public: - PointerVectorVariantWrapper( - std::vector>& vecPointerVar) - : vectorPointerVariant(vecPointerVar) - {} - - template - void save(Archive& ar) const - { - size_t vecSize = vectorPointerVariant.size(); - ar(CEREAL_NVP(vecSize)); - for (size_t i = 0; i < vectorPointerVariant.size(); ++i) - { - ar(CEREAL_VARIANT_POINTER(vectorPointerVariant.at(i))); - } - } - - template - void load(Archive& ar) - { - size_t vecSize = 0; - ar(CEREAL_NVP(vecSize)); - vectorPointerVariant.resize(vecSize); - for (size_t i = 0; i < vectorPointerVariant.size(); ++i) - { - ar(CEREAL_VARIANT_POINTER(vectorPointerVariant.at(i))); - } - } - - private: - std::vector>& vectorPointerVariant; -}; - -/** - * Cereal does not support the serialization of raw pointer. - * This macro enable developers to serialize a std vector that holds boost::variants - * that holds raw pointers by using the above PointerVectorVariantWrapper class - * which replace the internal raw pointers by smart pointer internally. - * - * @param T std::vector that holds raw pointer to be serialized. - */ -#define CEREAL_VECTOR_VARIANT_POINTER(T) cereal::make_vector_pointer_variant(T) - -} // namespace cereal - -#endif // CEREAL_POINTER_VECTOR_VARIANT_WRAPPER_HPP - diff --git a/src/mlpack/core/util/size_checks.hpp b/src/mlpack/core/util/size_checks.hpp index c2d60459b8..f5125a591b 100644 --- a/src/mlpack/core/util/size_checks.hpp +++ b/src/mlpack/core/util/size_checks.hpp @@ -26,18 +26,27 @@ namespace util { * error generation. * @param addInfo Name to use for labels for precise error generation. Default * is "labels"; for example, "weights" could also be used. + * @param isDataTranspose Bool parameter which can be set true to transpose data + * before size-check. Default is false. + * @param isLabelTranspose Bool parameter which can be set true to transpose label + * before size-check. Default is false. */ template inline void CheckSameSizes(const DataType& data, const LabelsType& label, const std::string& callerDescription, - const std::string& addInfo = "labels") -{ - if (data.n_cols != label.n_cols) + const std::string& addInfo = "labels", + const bool& isDataTranspose = false, + const bool& isLabelTranspose = false) +{ + const size_t dataPoints = (isDataTranspose == true) ? data.n_rows : data.n_cols; + const size_t labelPoints = (isLabelTranspose == true) ? label.n_rows : label.n_cols; + + if (dataPoints != labelPoints) { std::ostringstream oss; - oss << callerDescription << ": number of points (" << data.n_cols << ") " - << "does not match number of " << addInfo << " (" << label.n_cols + oss << callerDescription << ": number of points (" << dataPoints << ") " + << "does not match number of " << addInfo << " (" << labelPoints << ")!" << std::endl; throw std::invalid_argument(oss.str()); } diff --git a/src/mlpack/methods/ann/CMakeLists.txt b/src/mlpack/methods/ann/CMakeLists.txt index 8888113548..fb1101ec11 100644 --- a/src/mlpack/methods/ann/CMakeLists.txt +++ b/src/mlpack/methods/ann/CMakeLists.txt @@ -3,24 +3,17 @@ set(SOURCES ffn.hpp ffn_impl.hpp + forward_decls.hpp + make_alias.hpp rnn.hpp rnn_impl.hpp - brnn.hpp - brnn_impl.hpp - layer_names.hpp ) -add_subdirectory(visitor) -add_subdirectory(activation_functions) add_subdirectory(init_rules) add_subdirectory(layer) add_subdirectory(loss_functions) add_subdirectory(convolution_rules) -add_subdirectory(gan) -add_subdirectory(rbm) -add_subdirectory(augmented) add_subdirectory(regularizer) -add_subdirectory(util) # Add directory name to sources. set(DIR_SRCS) diff --git a/src/mlpack/methods/ann/convolution_rules/naive_convolution.hpp b/src/mlpack/methods/ann/convolution_rules/naive_convolution.hpp index d0bf1cadfd..c01fd6d749 100644 --- a/src/mlpack/methods/ann/convolution_rules/naive_convolution.hpp +++ b/src/mlpack/methods/ann/convolution_rules/naive_convolution.hpp @@ -57,9 +57,15 @@ class NaiveConvolution const size_t dilationW = 1, const size_t dilationH = 1) { - output = arma::zeros >( - (input.n_rows - (filter.n_rows - 1) * dilationW - 1) / dW + 1, - (input.n_cols - (filter.n_cols - 1) * dilationH - 1) / dH + 1); + // Compute the output size. The filterRows and filterCols computation must + // take into account the fact that dilation only adds rows or columns + // *between* filter elements. So, e.g., a dilation of 2 on a kernel size of + // 3x3 means an effective kernel size of 5x5, *not* 6x6. + const size_t filterRows = filter.n_rows * dilationH - (dilationH - 1); + const size_t filterCols = filter.n_cols * dilationW - (dilationW - 1); + const size_t outputRows = (input.n_rows - filterRows + dH) / dH; + const size_t outputCols = (input.n_cols - filterCols + dW) / dW; + output.zeros(outputRows, outputCols); // It seems to be about 3.5 times faster to use pointers instead of // filter(ki, kj) * input(leftInput + ki, topInput + kj) and output(i, j). @@ -103,37 +109,22 @@ class NaiveConvolution const size_t dilationW = 1, const size_t dilationH = 1) { - size_t outputRows = (input.n_rows - 1) * dW + 2 * (filter.n_rows - 1) - * dilationW + 1; - size_t outputCols = (input.n_cols - 1) * dH + 2 * (filter.n_cols - 1) - * dilationH + 1; - - for (size_t i = 0; i < dW; ++i) - { - if (((((i + outputRows - 2 * (filter.n_rows - 1) * dilationW - 1) % dW) - + dW) % dW) == i){ - outputRows += i; - break; - } - } - for (size_t i = 0; i < dH; ++i) - { - if (((((i + outputCols - 2 * (filter.n_cols - 1) * dilationH - 1) % dH) - + dH) % dH) == i){ - outputCols += i; - break; - } - } + // First, compute the necessary padding for the full convolution. It is + // possible that this might be an overestimate. Note that these variables + // only hold the padding on one side of the input. + const size_t filterRows = filter.n_rows * dilationH - (dilationH - 1); + const size_t filterCols = filter.n_cols * dilationW - (dilationW - 1); + const size_t paddingRows = filterRows - 1; + const size_t paddingCols = filterCols - 1; // Pad filter and input to the working output shape. - arma::Mat inputPadded = arma::zeros >(outputRows, - outputCols); - inputPadded.submat((filter.n_rows - 1) * dilationW, (filter.n_cols - 1) - * dilationH, (filter.n_rows - 1) * dilationW + input.n_rows - 1, - (filter.n_cols - 1) * dilationH + input.n_cols - 1) = input; + arma::Mat inputPadded(input.n_rows + 2 * paddingRows, + input.n_cols + 2 * paddingCols, arma::fill::zeros); + inputPadded.submat(paddingRows, paddingCols, paddingRows + input.n_rows - 1, + paddingCols + input.n_cols - 1) = input; NaiveConvolution::Convolution(inputPadded, filter, - output, 1, 1, dilationW, dilationH); + output, dW, dH, dilationW, dilationH); } /* diff --git a/src/mlpack/methods/ann/ffn.hpp b/src/mlpack/methods/ann/ffn.hpp index 1c65bbb749..0b6bde3648 100644 --- a/src/mlpack/methods/ann/ffn.hpp +++ b/src/mlpack/methods/ann/ffn.hpp @@ -15,46 +15,49 @@ #include -#include "visitor/delete_visitor.hpp" -#include "visitor/delta_visitor.hpp" -#include "visitor/output_height_visitor.hpp" -#include "visitor/output_parameter_visitor.hpp" -#include "visitor/output_width_visitor.hpp" -#include "visitor/reset_visitor.hpp" -#include "visitor/weight_size_visitor.hpp" -#include "visitor/copy_visitor.hpp" -#include "visitor/loss_visitor.hpp" - +#include "forward_decls.hpp" #include "init_rules/network_init.hpp" -#include #include +#include #include -#include +#include #include namespace mlpack { namespace ann /** Artificial Neural Network. */ { /** - * Implementation of a standard feed forward network. + * Implementation of a standard feed forward network. Any layer that inherits + * from the base `Layer` class can be added to this model. For recursive neural + * networks, see the `RNN` class. + * + * In general, a network can be created by using the `Add()` method to add + * layers to the network. Then, training can be performed with `Train()`, and + * data points can be passed through the trained network with `Predict()`. + * + * Although the actual types passed as input will be matrix objects with one + * data point per column, each data point can be a tensor of arbitrary shape. + * If data points are not 1-dimensional vectors, then set the shape of the input + * with `InputDimensions()` before calling `Train()`. + * + * More granular functionality is available with `Forward()`, Backward()`, and + * `Evaluate()`, or even by accessing the individual layers directly with + * `Network()`. * * @tparam OutputLayerType The output layer type used to evaluate the network. * @tparam InitializationRuleType Rule used to initialize the weight matrix. - * @tparam CustomLayers Any set of custom layers that could be a part of the - * feed forward network. + * @tparam MatType Type of matrix to be given as input to the network. + * @tparam MatType Type of matrix to be produced as output from the last + * layer. */ template< - typename OutputLayerType = NegativeLogLikelihood<>, - typename InitializationRuleType = RandomInitialization, - typename... CustomLayers -> + typename OutputLayerType = NegativeLogLikelihood, + typename InitializationRuleType = RandomInitialization, + typename MatType = arma::mat> class FFN { public: - //! Convenience typedef for the internal model construction. - using NetworkType = FFN; - /** * Create the FFN object. * @@ -72,56 +75,73 @@ class FFN InitializationRuleType initializeRule = InitializationRuleType()); //! Copy constructor. - FFN(const FFN&); - + FFN(const FFN& other); //! Move constructor. - FFN(FFN&&); - - //! Copy/move assignment operator. - FFN& operator = (FFN); - - //! Destructor to release allocated memory. - ~FFN(); + FFN(FFN&& other); + //! Copy operator. + FFN& operator=(const FFN& other); + //! Move assignment operator. + FFN& operator=(FFN&& other); /** - * Check if the optimizer has MaxIterations() parameter, if it does - * then check if it's value is less than the number of datapoints - * in the dataset. + * Add a new layer to the model. * - * @tparam OptimizerType Type of optimizer to use to train the model. - * @param optimizer optimizer used in the training process. - * @param samples Number of datapoints in the dataset. + * @param args The layer parameter. */ - template - typename std::enable_if< - HasMaxIterations - ::value, void>::type - WarnMessageMaxIterations(OptimizerType& optimizer, size_t samples) const; + template + void Add(Args... args) + { + network.template Add(args...); + inputDimensionsAreSet = false; + } /** - * Check if the optimizer has MaxIterations() parameter, if it - * doesn't then simply return from the function. + * Add a new layer to the model. Note that any trainable weights of this + * layer will be reset! (Any constant parameters are kept.) * - * @tparam OptimizerType Type of optimizer to use to train the model. - * @param optimizer optimizer used in the training process. - * @param samples Number of datapoints in the dataset. + * @param layer The Layer to be added to the model. */ - template - typename std::enable_if< - !HasMaxIterations - ::value, void>::type - WarnMessageMaxIterations(OptimizerType& optimizer, size_t samples) const; + void Add(Layer* layer) + { + network.Add(layer); + inputDimensionsAreSet = false; + } + + //! Get the layers of the network. + const std::vector*>& Network() const + { + return network.Network(); + } + + /** + * Modify the network model. Be careful! If you change the structure of the + * network or parameters for layers, its state may become invalid, and the + * next time it is used for any operation the parameters will be reset. + * + * Don't add any layers like this; use `Add()` instead. + */ + std::vector*>& Network() + { + // We can no longer make any assumptions... the user may change anything. + inputDimensionsAreSet = false; + layerMemoryIsSet = false; + + return network.Network(); + } /** * Train the feedforward network on the given input data using the given * optimizer. * - * This will use the existing model parameters as a starting point for the - * optimization. If this is not what you want, then you should access the - * parameters vector directly with Parameters() and modify it as desired. + * If no parameters have ever been set (e.g. if `Parameters()` is an empty + * matrix), or if the parameters' size does not match the number of weights + * needed for the current input size (as given by `predictors` and optionally + * set further by `InputDimensions()`), then the network will be initialized + * using `InitializeRuleType`. * - * If you want to pass in a parameter and discard the original parameter - * object, be sure to use std::move to avoid unnecessary copy. + * If parameters are the right size for the given `predictors` and + * `InputDimensions()`, then the existing parameters will be used as a + * starting point. (If you want to reinitialize, first call `Reset()`.) * * @tparam OptimizerType Type of optimizer to use to train the model. * @tparam CallbackTypes Types of Callback Functions. @@ -133,22 +153,25 @@ class FFN * @return The final objective of the trained model (NaN or Inf on error). */ template - double Train(arma::mat predictors, - arma::mat responses, - OptimizerType& optimizer, - CallbackTypes&&... callbacks); + typename MatType::elem_type Train(MatType predictors, + MatType responses, + OptimizerType& optimizer, + CallbackTypes&&... callbacks); /** * Train the feedforward network on the given input data. By default, the * RMSProp optimization algorithm is used, but others can be specified * (such as ens::SGD). * - * This will use the existing model parameters as a starting point for the - * optimization. If this is not what you want, then you should access the - * parameters vector directly with Parameters() and modify it as desired. + * If no parameters have ever been set (e.g. if `Parameters()` is an empty + * matrix), or if the parameters' size does not match the number of weights + * needed for the current input size (as given by `predictors` and optionally + * set further by `InputDimensions()`), then the network will be initialized + * using `InitializeRuleType`. * - * If you want to pass in a parameter and discard the original parameter - * object, be sure to use std::move to avoid unnecessary copy. + * If parameters are the right size for the given `predictors` and + * `InputDimensions()`, then the existing parameters will be used as a + * starting point. (If you want to reinitialize, first call `Reset()`.) * * @tparam OptimizerType Type of optimizer to use to train the model. * @param predictors Input training variables. @@ -159,22 +182,123 @@ class FFN * @return The final objective of the trained model (NaN or Inf on error). */ template - double Train(arma::mat predictors, - arma::mat responses, - CallbackTypes&&... callbacks); + typename MatType::elem_type Train(MatType predictors, + MatType responses, + CallbackTypes&&... callbacks); /** - * Predict the responses to a given set of predictors. The responses will - * reflect the output of the given output layer as returned by the - * output layer function. - * - * If you want to pass in a parameter and discard the original parameter - * object, be sure to use std::move to avoid unnecessary copy. + * Predict the responses to a given set of predictors. The responses will be + * the output of the output layer when `predictors` is passed through the + * whole network (`OutputLayerType`). * * @param predictors Input predictors. * @param results Matrix to put output predictions of responses into. + * @param batchSize Batch size to use for prediction. */ - void Predict(arma::mat predictors, arma::mat& results); + void Predict(MatType predictors, + MatType& results, + const size_t batchSize = 128); + + // Return the number of weights in the model. + size_t WeightSize(); + + /** + * Set the logical dimensions of the input. `Train()` and `Predict()` expect + * data to be passed such that one point corresponds to one column, but this + * data is allowed to be an arbitrary higher-order tensor. + * + * So, if the input is meant to be 28x28x3 images, then the + * input data to `Train()` or `Predict()` should have 28*28*3 = 2352 rows, and + * `InputDimensions()` should be set to `{ 28, 28, 3 }`. Then, the layers of + * the network will interpret each input point as a 3-dimensional image + * instead of a 1-dimensional vector. + * + * If `InputDimensions()` is left unset before training, the data will be + * assumed to be a 1-dimensional vector. + */ + std::vector& InputDimensions() + { + // The user may change the input dimensions, so we will have to propagate + // these changes to the network. + inputDimensionsAreSet = false; + return inputDimensions; + } + //! Get the logical dimensions of the input. + const std::vector& InputDimensions() const { return inputDimensions; } + + //! Return the current set of weights. These are linearized: this contains + //! the weights of every layer. + const MatType& Parameters() const { return parameters; } + //! Modify the current set of weights. These are linearized: this contains + //! the weights of every layer. Be careful! If you change the shape of + //! `parameters` to something incorrect, it may be re-initialized the next + //! time a forward pass is done. + MatType& Parameters() { return parameters; } + + /** + * Reset the stored data of the network entirely. This resets all weights of + * each layer using `InitializationRuleType`, and prepares the network to + * accept a (flat 1-d) input size of `inputDimensionality` (if passed), or + * whatever input size has been set with `InputDimensions()`. + * + * This also resets the mode of the network to prediction mode (not training + * mode). See `SetNetworkMode()` for more information. + */ + void Reset(const size_t inputDimensionality = 0); + + /** + * Set all the layers in the network to training mode, if `training` is + * `true`, or set all the layers in the network to testing mode, if `training` + * is `false`. + */ + void SetNetworkMode(const bool training); + + /** + * Perform a manual forward pass of the data. + * + * `Forward()` and `Backward()` should be used as a pair, and they are + * designed mainly for advanced users. You should try to use `Predict()` and + * `Train()`, if you can. + * + * @param inputs The input data. + * @param results The predicted results. + */ + void Forward(const MatType& inputs, MatType& results); + + /** + * Perform a manual partial forward pass of the data. + * + * This function is meant for the cases when users require a forward pass only + * through certain layers and not the entire network. `Forward()` and + * `Backward()` should be used as a pair, and they are designed mainly for + * advanced users. You should try to use `Predict()` and `Train()`, if you + * can. + * + * @param inputs The input data for the specified first layer. + * @param results The predicted results from the specified last layer. + * @param begin The index of the first layer. + * @param end The index of the last layer. + */ + void Forward(const MatType& inputs, + MatType& results, + const size_t begin, + const size_t end); + + /** + * Perform a manual backward pass of the data. + * + * `Forward()` and `Backward()` should be used as a pair, and they are + * designed mainly for advanced users. You should try to use `Predict()` and + * `Train()` instead, if you can. + * + * @param inputs Inputs of current pass. + * @param targets The training target. + * @param gradients Computed gradients. + * @return Training error of the current pass. + */ + typename MatType::elem_type Backward(const MatType& inputs, + const MatType& targets, + MatType& gradients); /** * Evaluate the feedforward network with the given predictors and responses. @@ -183,41 +307,38 @@ class FFN * @param predictors Input variables. * @param responses Target outputs for input variables. */ - template - double Evaluate(const PredictorsType& predictors, - const ResponsesType& responses); + typename MatType::elem_type Evaluate(const MatType& predictors, + const MatType& responses); + + //! Serialize the model. + template + void serialize(Archive& ar, const uint32_t /* version */); + + // + // Only ensmallen utility functions for training are found below here. + // They aren't generally useful otherwise. + // /** - * Evaluate the feedforward network with the given parameters. This function - * is usually called by the optimizer to train the model. + * Note: this function is implemented so that it can be used by ensmallen's + * optimizers. It's not generally meant to be used otherwise. + * + * Evaluate the feedforward network with the given parameters. * * @param parameters Matrix model parameters. */ - double Evaluate(const arma::mat& parameters); + typename MatType::elem_type Evaluate(const MatType& parameters); - /** + /** + * Note: this function is implemented so that it can be used by ensmallen's + * optimizers. It's not generally meant to be used otherwise. + * * Evaluate the feedforward network with the given parameters, but using only * a number of data points. This is useful for optimizers such as SGD, which * require a separable objective function. * - * @param parameters Matrix model parameters. - * @param begin Index of the starting point to use for objective function - * evaluation. - * @param batchSize Number of points to be passed at a time to use for - * objective function evaluation. - * @param deterministic Whether or not to train or test the model. Note some - * layer act differently in training or testing mode. - */ - double Evaluate(const arma::mat& parameters, - const size_t begin, - const size_t batchSize, - const bool deterministic); - - /** - * Evaluate the feedforward network with the given parameters, but using only - * a number of data points. This is useful for optimizers such as SGD, which - * require a separable objective function. This just calls the overload of - * Evaluate() with deterministic = true. + * Note that the network may return different results depending on the mode it + * is in (see `SetNetworkMode()`). * * @param parameters Matrix model parameters. * @param begin Index of the starting point to use for objective function @@ -225,11 +346,14 @@ class FFN * @param batchSize Number of points to be passed at a time to use for * objective function evaluation. */ - double Evaluate(const arma::mat& parameters, - const size_t begin, - const size_t batchSize); + typename MatType::elem_type Evaluate(const MatType& parameters, + const size_t begin, + const size_t batchSize); /** + * Note: this function is implemented so that it can be used by ensmallen's + * optimizers. It's not generally meant to be used otherwise. + * * Evaluate the feedforward network with the given parameters. * This function is usually called by the optimizer to train the model. * This just calls the overload of EvaluateWithGradient() with batchSize = 1. @@ -237,10 +361,13 @@ class FFN * @param parameters Matrix model parameters. * @param gradient Matrix to output gradient into. */ - template - double EvaluateWithGradient(const arma::mat& parameters, GradType& gradient); + typename MatType::elem_type EvaluateWithGradient(const MatType& parameters, + MatType& gradient); - /** + /** + * Note: this function is implemented so that it can be used by ensmallen's + * optimizers. It's not generally meant to be used otherwise. + * * Evaluate the feedforward network with the given parameters, but using only * a number of data points. This is useful for optimizers such as SGD, which * require a separable objective function. @@ -252,13 +379,15 @@ class FFN * @param batchSize Number of points to be passed at a time to use for * objective function evaluation. */ - template - double EvaluateWithGradient(const arma::mat& parameters, - const size_t begin, - GradType& gradient, - const size_t batchSize); + typename MatType::elem_type EvaluateWithGradient(const MatType& parameters, + const size_t begin, + MatType& gradient, + const size_t batchSize); /** + * Note: this function is implemented so that it can be used by ensmallen's + * optimizers. It's not generally meant to be used otherwise. + * * Evaluate the gradient of the feedforward network with the given parameters, * and with respect to only a number of points in the dataset. This is useful * for optimizers such as SGD, which require a separable objective function. @@ -270,253 +399,156 @@ class FFN * @param batchSize Number of points to be processed as a batch for objective * function gradient evaluation. */ - void Gradient(const arma::mat& parameters, + void Gradient(const MatType& parameters, const size_t begin, - arma::mat& gradient, + MatType& gradient, const size_t batchSize); /** - * Shuffle the order of function visitation. This may be called by the - * optimizer. + * Note: this function is implemented so that it can be used by ensmallen's + * optimizers. It's not generally meant to be used otherwise. + * + * Return the number of separable functions (the number of predictor points). + */ + size_t NumFunctions() const { return responses.n_cols; } + + /** + * Note: this function is implemented so that it can be used by ensmallen's + * optimizers. It's not generally meant to be used otherwise. + * + * Shuffle the order of function visitation. (This is equivalent to shuffling + * the dataset during training.) */ void Shuffle(); - /* - * Add a new module to the model. - * - * @param args The layer parameter. - */ - template - void Add(Args... args) { network.push_back(new LayerType(args...)); } - - /* - * Add a new module to the model. - * - * @param layer The Layer to be added to the model. - */ - void Add(LayerTypes layer) { network.push_back(layer); } - - //! Get the network model. - const std::vector >& Model() const - { - return network; - } - //! Modify the network model. Be careful! If you change the structure of the - //! network or parameters for layers, its state may become invalid, so be sure - //! to call ResetParameters() afterwards. - std::vector >& Model() { return network; } - - //! Return the number of separable functions (the number of predictor points). - size_t NumFunctions() const { return numFunctions; } - - //! Return the initial point for the optimization. - const arma::mat& Parameters() const { return parameter; } - //! Modify the initial point for the optimization. - arma::mat& Parameters() { return parameter; } - - //! Get the matrix of responses to the input data points. - const arma::mat& Responses() const { return responses; } - //! Modify the matrix of responses to the input data points. - arma::mat& Responses() { return responses; } - - //! Get the matrix of data points (predictors). - const arma::mat& Predictors() const { return predictors; } - //! Modify the matrix of data points (predictors). - arma::mat& Predictors() { return predictors; } - /** - * Reset the module infomration (weights/parameters). - */ - void ResetParameters(); - - //! Serialize the model. - template - void serialize(Archive& ar, const uint32_t /* version */); - - /** - * Perform the forward pass of the data in real batch mode. + * Prepare the network for training on the given data. * - * Forward and Backward should be used as a pair, and they are designed mainly - * for advanced users. User should try to use Predict and Train unless those - * two functions can't satisfy some special requirements. - * - * @param inputs The input data. - * @param results The predicted results. - */ - template - void Forward(const PredictorsType& inputs, ResponsesType& results); - - /** - * Perform a partial forward pass of the data. - * - * This function is meant for the cases when users require a forward pass only - * through certain layers and not the entire network. - * - * @param inputs The input data for the specified first layer. - * @param results The predicted results from the specified last layer. - * @param begin The index of the first layer. - * @param end The index of the last layer. - */ - template - void Forward(const PredictorsType& inputs , - ResponsesType& results, - const size_t begin, - const size_t end); - - /** - * Perform the backward pass of the data in real batch mode. - * - * Forward and Backward should be used as a pair, and they are designed mainly - * for advanced users. User should try to use Predict and Train unless those - * two functions can't satisfy some special requirements. - * - * @param inputs Inputs of current pass. - * @param targets The training target. - * @param gradients Computed gradients. - * @return Training error of the current pass. - */ - template - double Backward(const PredictorsType& inputs, - const TargetsType& targets, - GradientsType& gradients); - - private: - // Helper functions. - /** - * The Forward algorithm (part of the Forward-Backward algorithm). Computes - * forward probabilities for each module. - * - * @param input Data sequence to compute probabilities for. - */ - template - void Forward(const InputType& input); - - /** - * Prepare the network for the given data. - * This function won't actually trigger training process. + * This function won't actually trigger the training process, and is + * generally only useful internally. * * @param predictors Input data variables. * @param responses Outputs results from input data variables. */ - void ResetData(arma::mat predictors, arma::mat responses); + void ResetData(MatType predictors, MatType responses); + + private: + // Helper functions. + + //! Use the InitializationPolicy to initialize all the weights in the network. + void InitializeWeights(); + + //! Make the memory of each layer point to the right place, by calling + //! SetWeightPtr() on each layer. + void SetLayerMemory(); /** - * The Backward algorithm (part of the Forward-Backward algorithm). Computes - * backward pass for module. - */ - void Backward(); - - /** - * Iterate through all layer modules and update the the gradient using the - * layer defined optimizer. - */ - template - void Gradient(const InputType& input); - - /** - * Reset the module status by setting the current deterministic parameter - * for all modules that implement the Deterministic function. - */ - void ResetDeterministic(); - - /** - * Reset the gradient for all modules that implement the Gradient function. - */ - void ResetGradients(arma::mat& gradient); - - /** - * Swap the content of this network with given network. + * Ensure that all the locally-cached information about the network is valid, + * all parameter memory is initialized, and we can make forward and backward + * passes. * - * @param network Desired source network. + * @param functionName Name of function to use if an exception is thrown. + * @param inputDimensionality Given dimensionality of the input data. + * @param setMode If true, the mode of the network will be set to the + * parameter given in `training`. Otherwise the mode of the network is + * left unmodified. + * @param training Mode to set the network to; `true` indicates the network + * should be set to training mode; `false` indicates testing mode. */ - void Swap(FFN& network); + void CheckNetwork(const std::string& functionName, + const size_t inputDimensionality, + const bool setMode = false, + const bool training = false); - //! Instantiated outputlayer used to evaluate the network. + /** + * Set the input and output dimensions of each layer in the network correctly. + * The size of the input is taken, in case `inputDimensions` has not been set + * otherwise (e.g. via `InputDimensions()`). If `InputDimensions()` is not + * empty, then `inputDimensionality` is ignored. + */ + void UpdateDimensions(const std::string& functionName, + const size_t inputDimensionality = 0); + + /** + * Check if the optimizer has MaxIterations() parameter, if it does then check + * if its value is less than the number of datapoints in the dataset. + * + * @tparam OptimizerType Type of optimizer to use to train the model. + * @param optimizer optimizer used in the training process. + * @param samples Number of datapoints in the dataset. + */ + template + typename std::enable_if< + ens::traits::HasMaxIterationsSignature::value, void + >::type + WarnMessageMaxIterations(OptimizerType& optimizer, size_t samples) const; + + /** + * Check if the optimizer has MaxIterations() parameter; if it doesn't then + * simply return from the function. + * + * @tparam OptimizerType Type of optimizer to use to train the model. + * @param optimizer optimizer used in the training process. + * @param samples Number of datapoints in the dataset. + */ + template + typename std::enable_if< + !ens::traits::HasMaxIterationsSignature::value, void + >::type + WarnMessageMaxIterations(OptimizerType& optimizer, size_t samples) const; + + //! Instantiated output layer used to evaluate the network. OutputLayerType outputLayer; //! Instantiated InitializationRule object for initializing the network //! parameter. InitializationRuleType initializeRule; - //! The input width. - size_t width; + //! All of the network is stored inside this multilayer. + MultiLayer network; - //! The input height. - size_t height; + /** + * Matrix of (trainable) parameters. Each weight here corresponds to a layer, + * and each layer's `parameters` member is an alias pointing to parameters in + * this matrix. + * + * Note: although each layer may have its own MatType and MatType, + * ensmallen optimization requires everything to be stored in one matrix + * object, so we have chosen MatType. This could be made more flexible + * with a "wrapper" class implementing the Armadillo API. + */ + MatType parameters; - //! Indicator if we already trained the model. - bool reset; + //! Dimensions of input data. + std::vector inputDimensions; - //! Locally-stored model modules. - std::vector > network; + //! The matrix of data points (predictors). This member is empty, except + //! during training---we must store a local copy of the training data since + //! the ensmallen optimizer will not provide training data. + MatType predictors; - //! The matrix of data points (predictors). - arma::mat predictors; + //! The matrix of responses to the input data points. This member is empty, + //! except during training. + MatType responses; - //! The matrix of responses to the input data points. - arma::mat responses; + //! Locally-stored output of the network from a forward pass; used by the + //! backward pass. + MatType networkOutput; + //! Locally-stored output of the backward pass; used by the gradient pass. + MatType networkDelta; + //! Locally-stored error of the backward pass; used by the gradient pass. + MatType error; - //! Matrix of (trained) parameters. - arma::mat parameter; + //! If true, each layer has its memory properly set for a forward/backward + //! pass. + bool layerMemoryIsSet; - //! The number of separable functions (the number of predictor points). - size_t numFunctions; + //! If true, each layer has its inputDimensions properly set, and + //! `totalInputSize` and `totalOutputSize` are valid. + bool inputDimensionsAreSet; - //! The current error for the backward pass. - arma::mat error; - - //! Locally-stored delta visitor. - DeltaVisitor deltaVisitor; - - //! Locally-stored output parameter visitor. - OutputParameterVisitor outputParameterVisitor; - - //! Locally-stored weight size visitor. - WeightSizeVisitor weightSizeVisitor; - - //! Locally-stored output width visitor. - OutputWidthVisitor outputWidthVisitor; - - //! Locally-stored output height visitor. - OutputHeightVisitor outputHeightVisitor; - - //! Locally-stored loss visitor - LossVisitor lossVisitor; - - //! Locally-stored reset visitor. - ResetVisitor resetVisitor; - - //! Locally-stored delete visitor. - DeleteVisitor deleteVisitor; - - //! The current evaluation mode (training or testing). - bool deterministic; - - //! Locally-stored delta object. - arma::mat delta; - - //! Locally-stored input parameter object. - arma::mat inputParameter; - - //! Locally-stored output parameter object. - arma::mat outputParameter; - - //! Locally-stored gradient parameter. - arma::mat gradient; - - //! Locally-stored copy visitor - CopyVisitor copyVisitor; - - // The GAN class should have access to internal members. - template< - typename Model, - typename InitializerType, - typename NoiseType, - typename PolicyType - > - friend class GAN; + // RNN will call `CheckNetwork()`, which is private. + friend class RNN; }; // class FFN } // namespace ann diff --git a/src/mlpack/methods/ann/ffn_impl.hpp b/src/mlpack/methods/ann/ffn_impl.hpp index c30573ea9b..734b961301 100644 --- a/src/mlpack/methods/ann/ffn_impl.hpp +++ b/src/mlpack/methods/ann/ffn_impl.hpp @@ -15,661 +15,693 @@ // In case it hasn't been included yet. #include "ffn.hpp" -#include "visitor/forward_visitor.hpp" -#include "visitor/backward_visitor.hpp" -#include "visitor/deterministic_set_visitor.hpp" -#include "visitor/gradient_set_visitor.hpp" -#include "visitor/gradient_visitor.hpp" -#include "visitor/set_input_height_visitor.hpp" -#include "visitor/set_input_width_visitor.hpp" - -#include "util/check_input_shape.hpp" +#include "make_alias.hpp" namespace mlpack { namespace ann /** Artificial Neural Network. */ { - -template -FFN::FFN( - OutputLayerType outputLayer, InitializationRuleType initializeRule) : +template +FFN< + OutputLayerType, + InitializationRuleType, + MatType +>::FFN(OutputLayerType outputLayer, InitializationRuleType initializeRule) : outputLayer(std::move(outputLayer)), initializeRule(std::move(initializeRule)), - width(0), - height(0), - reset(false), - numFunctions(0), - deterministic(false) + layerMemoryIsSet(false), + inputDimensionsAreSet(false) { /* Nothing to do here. */ } -template -FFN::~FFN() +template +FFN< + OutputLayerType, + InitializationRuleType, + MatType +>::FFN(const FFN& network): + outputLayer(network.outputLayer), + initializeRule(network.initializeRule), + network(network.network), + parameters(network.parameters), + inputDimensions(network.inputDimensions), + predictors(network.predictors), + responses(network.responses), + // These will be set correctly in the first Forward() call. + layerMemoryIsSet(false), + inputDimensionsAreSet(false) { - std::for_each(network.begin(), network.end(), - boost::apply_visitor(deleteVisitor)); -} + // Nothing to do. +}; -template -void FFN::ResetData( - arma::mat predictors, arma::mat responses) +template +FFN< + OutputLayerType, + InitializationRuleType, + MatType +>::FFN(FFN&& network): + outputLayer(std::move(network.outputLayer)), + initializeRule(std::move(network.initializeRule)), + network(std::move(network.network)), + parameters(std::move(network.parameters)), + inputDimensions(std::move(network.inputDimensions)), + predictors(std::move(network.predictors)), + responses(std::move(network.responses)), + // Aliases will not be correct after a std::move(), so we will manually + // reset them. + layerMemoryIsSet(false), + inputDimensionsAreSet(std::move(network.inputDimensionsAreSet)) { - numFunctions = responses.n_cols; - this->predictors = std::move(predictors); - this->responses = std::move(responses); - this->deterministic = false; - ResetDeterministic(); + // Nothing to do. +}; - if (!reset) - ResetParameters(); -} - -template -template -typename std::enable_if< - HasMaxIterations - ::value, void>::type -FFN:: -WarnMessageMaxIterations(OptimizerType& optimizer, size_t samples) const +template +FFN& FFN< + OutputLayerType, + InitializationRuleType, + MatType +>::operator=(const FFN& other) { - if (optimizer.MaxIterations() < samples && - optimizer.MaxIterations() != 0) + if (this != &other) { - Log::Warn << "The optimizer's maximum number of iterations " - << "is less than the size of the dataset; the " - << "optimizer will not pass over the entire " - << "dataset. To fix this, modify the maximum " - << "number of iterations to be at least equal " - << "to the number of points of your dataset " - << "(" << samples << ")." << std::endl; + outputLayer = other.outputLayer; + initializeRule = other.initializeRule; + network = other.network; + parameters = other.parameters; + inputDimensions = other.inputDimensions; + predictors = other.predictors; + responses = other.responses; + networkOutput = other.networkOutput; + networkDelta = other.networkDelta; + error = other.error; + inputDimensionsAreSet = other.inputDimensionsAreSet; + + // Copying will not preserve Armadillo aliases correctly, so we will reset + // those. + layerMemoryIsSet = false; } + + return *this; } -template -template -typename std::enable_if< - !HasMaxIterations - ::value, void>::type -FFN:: -WarnMessageMaxIterations(OptimizerType& /* optimizer */, size_t /* samples */) - const +template +FFN& FFN< + OutputLayerType, + InitializationRuleType, + MatType +>::operator=(FFN&& other) { - return; + if (this != &other) + { + outputLayer = std::move(other.outputLayer); + initializeRule = std::move(other.initializeRule); + network = std::move(other.network); + parameters = std::move(other.parameters); + inputDimensions = std::move(other.inputDimensions); + predictors = std::move(other.predictors); + responses = std::move(other.responses); + networkOutput = std::move(other.networkOutput); + networkDelta = std::move(other.networkDelta); + error = std::move(other.error); + inputDimensionsAreSet = std::move(other.inputDimensionsAreSet); + layerMemoryIsSet = std::move(other.layerMemoryIsSet); + } + + return *this; } -template +template template -double FFN::Train( - arma::mat predictors, - arma::mat responses, - OptimizerType& optimizer, - CallbackTypes&&... callbacks) +typename MatType::elem_type FFN< + OutputLayerType, + InitializationRuleType, + MatType +>::Train(MatType predictors, + MatType responses, + OptimizerType& optimizer, + CallbackTypes&&... callbacks) { - CheckInputShape > >(network, - predictors.n_rows, - "FFN<>::Train()"); - ResetData(std::move(predictors), std::move(responses)); WarnMessageMaxIterations(optimizer, this->predictors.n_cols); - // Train the model. - const double out = optimizer.Optimize(*this, parameter, callbacks...); + // Ensure that the network can be used. + CheckNetwork("FFN::Train()", this->predictors.n_rows, true, true); - Log::Info << "FFN::FFN(): final objective of trained model is " << out + // Train the model. + Timer::Start("ffn_optimization"); + const typename MatType::elem_type out = + optimizer.Optimize(*this, parameters, callbacks...); + Timer::Stop("ffn_optimization"); + + Log::Info << "FFN::Train(): final objective of trained model is " << out << "." << std::endl; return out; } -template +template template -double FFN::Train( - arma::mat predictors, - arma::mat responses, - CallbackTypes&&... callbacks) +typename MatType::elem_type FFN< + OutputLayerType, + InitializationRuleType, + MatType +>::Train(MatType predictors, + MatType responses, + CallbackTypes&&... callbacks) { - CheckInputShape > >(network, - predictors.n_rows, - "FFN<>::Train()"); - - ResetData(std::move(predictors), std::move(responses)); - OptimizerType optimizer; - - WarnMessageMaxIterations(optimizer, this->predictors.n_cols); - - // Train the model. - const double out = optimizer.Optimize(*this, parameter, callbacks...); - - Log::Info << "FFN::FFN(): final objective of trained model is " << out - << "." << std::endl; - return out; + return Train(std::move(predictors), std::move(responses), optimizer, + callbacks...); } -template -template -void FFN::Forward( - const PredictorsType& inputs, ResponsesType& results) +template +void FFN< + OutputLayerType, + InitializationRuleType, + MatType +>::Predict(MatType predictors, MatType& results, const size_t batchSize) { - if (parameter.is_empty()) - ResetParameters(); + // Ensure that the network is configured correctly. + CheckNetwork("FFN::Predict()", predictors.n_rows, true, false); - Forward(inputs); - results = boost::apply_visitor(outputParameterVisitor, network.back()); -} + results.set_size(network.OutputSize(), predictors.n_cols); -template -template -void FFN::Forward( - const PredictorsType& inputs, - ResponsesType& results, - const size_t begin, - const size_t end) -{ - boost::apply_visitor(ForwardVisitor(inputs, - boost::apply_visitor(outputParameterVisitor, network[begin])), - network[begin]); - - for (size_t i = 1; i < end - begin + 1; ++i) + for (size_t i = 0; i < predictors.n_cols; i += batchSize) { - boost::apply_visitor(ForwardVisitor(boost::apply_visitor( - outputParameterVisitor, network[begin + i - 1]), - boost::apply_visitor(outputParameterVisitor, network[begin + i])), - network[begin + i]); - } + const size_t effectiveBatchSize = std::min(batchSize, + size_t(predictors.n_cols) - i); - results = boost::apply_visitor(outputParameterVisitor, network[end]); -} + MatType predictorAlias(predictors.colptr(i), predictors.n_rows, + effectiveBatchSize, false, true); + MatType resultAlias(results.colptr(i), results.n_rows, + effectiveBatchSize, false, true); -template -template -double FFN::Backward( - const PredictorsType& inputs, - const TargetsType& targets, - GradientsType& gradients) -{ - double res = outputLayer.Forward(boost::apply_visitor( - outputParameterVisitor, network.back()), targets); - - for (size_t i = 0; i < network.size(); ++i) - { - res += boost::apply_visitor(lossVisitor, network[i]); - } - - outputLayer.Backward(boost::apply_visitor(outputParameterVisitor, - network.back()), targets, error); - - gradients = arma::zeros(parameter.n_rows, parameter.n_cols); - - Backward(); - ResetGradients(gradients); - Gradient(inputs); - - return res; -} - -template -void FFN::Predict( - arma::mat predictors, arma::mat& results) -{ - CheckInputShape > >( - network, predictors.n_rows, "FFN<>::Predict()"); - - if (parameter.is_empty()) - ResetParameters(); - - if (!deterministic) - { - deterministic = true; - ResetDeterministic(); - } - - arma::mat resultsTemp; - Forward(arma::mat(predictors.colptr(0), predictors.n_rows, 1, false, true)); - resultsTemp = boost::apply_visitor(outputParameterVisitor, - network.back()).col(0); - - results = arma::mat(resultsTemp.n_elem, predictors.n_cols); - results.col(0) = resultsTemp.col(0); - - for (size_t i = 1; i < predictors.n_cols; ++i) - { - Forward(arma::mat(predictors.colptr(i), predictors.n_rows, 1, false, true)); - - resultsTemp = boost::apply_visitor(outputParameterVisitor, - network.back()); - results.col(i) = resultsTemp.col(0); + network.Forward(predictorAlias, resultAlias); } } -template -template -double FFN::Evaluate( - const PredictorsType& predictors, const ResponsesType& responses) +template +size_t FFN< + OutputLayerType, + InitializationRuleType, + MatType +>::WeightSize() { - CheckInputShape > >( - network, predictors.n_rows, "FFN<>::Evaluate()"); - - if (parameter.is_empty()) - ResetParameters(); - - if (!deterministic) - { - deterministic = true; - ResetDeterministic(); - } - - Forward(predictors); - - double res = outputLayer.Forward(boost::apply_visitor( - outputParameterVisitor, network.back()), responses); - - for (size_t i = 0; i < network.size(); ++i) - { - res += boost::apply_visitor(lossVisitor, network[i]); - } - - return res; + // If the input dimensions have not yet been propagated to the network, we + // must do that now. + UpdateDimensions("FFN::WeightSize()"); + return network.WeightSize(); } -template -double FFN::Evaluate( - const arma::mat& parameters) +template +void FFN< + OutputLayerType, + InitializationRuleType, + MatType +>::Reset(const size_t inputDimensionality) { - double res = 0; - for (size_t i = 0; i < predictors.n_cols; ++i) - res += Evaluate(parameters, i, 1, true); + parameters.clear(); - return res; -} - -template -double FFN::Evaluate( - const arma::mat& /* parameters */, - const size_t begin, - const size_t batchSize, - const bool deterministic) -{ - if (parameter.is_empty()) - ResetParameters(); - - if (deterministic != this->deterministic) + // If the user provided an input dimensionality, then we will take that as the + // new input size. Otherwise, whatever is currently specified in + // `InputDimensions()` will be used. + if (inputDimensionality != 0) { - this->deterministic = deterministic; - ResetDeterministic(); - } - - Forward(predictors.cols(begin, begin + batchSize - 1)); - double res = outputLayer.Forward( - boost::apply_visitor(outputParameterVisitor, network.back()), - responses.cols(begin, begin + batchSize - 1)); - - for (size_t i = 0; i < network.size(); ++i) - { - res += boost::apply_visitor(lossVisitor, network[i]); - } - - return res; -} - -template -double FFN::Evaluate( - const arma::mat& parameters, const size_t begin, const size_t batchSize) -{ - return Evaluate(parameters, begin, batchSize, true); -} - -template -template -double FFN:: -EvaluateWithGradient(const arma::mat& parameters, GradType& gradient) -{ - double res = 0; - for (size_t i = 0; i < predictors.n_cols; ++i) - res += EvaluateWithGradient(parameters, i, gradient, 1); - - return res; -} - -template -template -double FFN:: -EvaluateWithGradient(const arma::mat& /* parameters */, - const size_t begin, - GradType& gradient, - const size_t batchSize) -{ - if (gradient.is_empty()) - { - if (parameter.is_empty()) - ResetParameters(); - - gradient = arma::zeros(parameter.n_rows, parameter.n_cols); + CheckNetwork("FFN::Reset()", inputDimensionality, true, false); } else { - gradient.zeros(); + const size_t inputDims = std::accumulate(inputDimensions.begin(), + inputDimensions.end(), 0); + CheckNetwork("FFN::Reset()", inputDims, true, false); } +} - if (this->deterministic) - { - this->deterministic = false; - ResetDeterministic(); - } +template +void FFN< + OutputLayerType, + InitializationRuleType, + MatType +>::SetNetworkMode(const bool training) +{ + network.Training() = training; +} - Forward(predictors.cols(begin, begin + batchSize - 1)); - double res = outputLayer.Forward( - boost::apply_visitor(outputParameterVisitor, network.back()), - responses.cols(begin, begin + batchSize - 1)); +template +void FFN< + OutputLayerType, + InitializationRuleType, + MatType +>::Forward(const MatType& inputs, MatType& results) +{ + Forward(inputs, results, 0, network.Network().size() - 1); +} - for (size_t i = 0; i < network.size(); ++i) - { - res += boost::apply_visitor(lossVisitor, network[i]); - } +template +void FFN< + OutputLayerType, + InitializationRuleType, + MatType +>::Forward(const MatType& inputs, + MatType& results, + const size_t begin, + const size_t end) +{ + // Sanity checking... + if (end < begin) + return; - outputLayer.Backward( - boost::apply_visitor(outputParameterVisitor, network.back()), - responses.cols(begin, begin + batchSize - 1), - error); + // Ensure the network is valid. + CheckNetwork("FFN::Forward()", inputs.n_rows); - Backward(); - ResetGradients(gradient); - Gradient(predictors.cols(begin, begin + batchSize - 1)); + // We must always store a copy of the forward pass in `networkOutputs` in case + // we do a backward pass. + networkOutput.set_size(network.OutputSize(), inputs.n_cols); + network.Forward(inputs, networkOutput, begin, end); + + // It's possible the user passed `networkOutput` as `results`; in this case, + // we don't need to create an alias. + if (&results != &networkOutput) + results = networkOutput; +} + +template +typename MatType::elem_type FFN< + OutputLayerType, + InitializationRuleType, + MatType +>::Backward(const MatType& inputs, + const MatType& targets, + MatType& gradients) +{ + const typename MatType::elem_type res = + outputLayer.Forward(networkOutput, targets) + network.Loss(); + + // Compute the error of the output layer. + outputLayer.Backward(networkOutput, targets, error); + + // Perform the backward pass. + network.Backward(networkOutput, error, networkDelta); + + // Now compute the gradients. + // The gradient should have the same size as the parameters. + gradients.set_size(parameters.n_rows, parameters.n_cols); + network.Gradient(inputs, error, gradients); return res; } -template -void FFN::Gradient( - const arma::mat& parameters, - const size_t begin, - arma::mat& gradient, - const size_t batchSize) +template +typename MatType::elem_type FFN< + OutputLayerType, + InitializationRuleType, + MatType +>::Evaluate(const MatType& predictors, const MatType& responses) { - this->EvaluateWithGradient(parameters, begin, gradient, batchSize); + // Sanity check: ensure network is valid. + CheckNetwork("FFN::Evaluate()", predictors.n_rows); + + // Set networkOutput to the right size if needed, then perform the forward + // pass. + network.Forward(predictors, networkOutput); + + return outputLayer.Forward(networkOutput, responses) + network.Loss(); } -template -void FFN::Shuffle() -{ - math::ShuffleData(predictors, responses, predictors, responses); -} - -template -void FFN::ResetParameters() -{ - ResetDeterministic(); - - // Reset the network parameter with the given initialization rule. - NetworkInitialization networkInit(initializeRule); - networkInit.Initialize(network, parameter); -} - -template -void FFN::ResetDeterministic() -{ - DeterministicSetVisitor deterministicSetVisitor(deterministic); - std::for_each(network.begin(), network.end(), - boost::apply_visitor(deterministicSetVisitor)); -} - -template -void FFN::ResetGradients(arma::mat& gradient) -{ - size_t offset = 0; - for (size_t i = 0; i < network.size(); ++i) - { - offset += boost::apply_visitor(GradientSetVisitor(gradient, offset), - network[i]); - } -} - -template -template -void FFN::Forward(const InputType& input) -{ - boost::apply_visitor(ForwardVisitor(input, - boost::apply_visitor(outputParameterVisitor, network.front())), - network.front()); - - if (!reset) - { - if (boost::apply_visitor(outputWidthVisitor, network.front()) != 0) - { - width = boost::apply_visitor(outputWidthVisitor, network.front()); - } - - if (boost::apply_visitor(outputHeightVisitor, network.front()) != 0) - { - height = boost::apply_visitor(outputHeightVisitor, network.front()); - } - } - - for (size_t i = 1; i < network.size(); ++i) - { - if (!reset) - { - // Set the input width. - boost::apply_visitor(SetInputWidthVisitor(width), network[i]); - - // Set the input height. - boost::apply_visitor(SetInputHeightVisitor(height), network[i]); - } - - boost::apply_visitor(ForwardVisitor(boost::apply_visitor( - outputParameterVisitor, network[i - 1]), - boost::apply_visitor(outputParameterVisitor, network[i])), network[i]); - - if (!reset) - { - // Get the output width. - if (boost::apply_visitor(outputWidthVisitor, network[i]) != 0) - { - width = boost::apply_visitor(outputWidthVisitor, network[i]); - } - - // Get the output height. - if (boost::apply_visitor(outputHeightVisitor, network[i]) != 0) - { - height = boost::apply_visitor(outputHeightVisitor, network[i]); - } - } - } - - if (!reset) - reset = true; -} - -template -void FFN::Backward() -{ - boost::apply_visitor(BackwardVisitor(boost::apply_visitor( - outputParameterVisitor, network.back()), error, - boost::apply_visitor(deltaVisitor, network.back())), network.back()); - - for (size_t i = 2; i < network.size(); ++i) - { - boost::apply_visitor(BackwardVisitor(boost::apply_visitor( - outputParameterVisitor, network[network.size() - i]), - boost::apply_visitor(deltaVisitor, network[network.size() - i + 1]), - boost::apply_visitor(deltaVisitor, network[network.size() - i])), - network[network.size() - i]); - } -} - -template -template -void FFN::Gradient(const InputType& input) -{ - boost::apply_visitor(GradientVisitor(input, - boost::apply_visitor(deltaVisitor, network[1])), network.front()); - - for (size_t i = 1; i < network.size() - 1; ++i) - { - boost::apply_visitor(GradientVisitor(boost::apply_visitor( - outputParameterVisitor, network[i - 1]), - boost::apply_visitor(deltaVisitor, network[i + 1])), network[i]); - } - - boost::apply_visitor(GradientVisitor(boost::apply_visitor( - outputParameterVisitor, network[network.size() - 2]), error), - network[network.size() - 1]); -} - -template +template template -void FFN::serialize( - Archive& ar, const uint32_t /* version */) +void FFN< + OutputLayerType, + InitializationRuleType, + MatType +>::serialize(Archive& ar, const uint32_t /* version */) { - ar(CEREAL_NVP(parameter)); - ar(CEREAL_NVP(width)); - ar(CEREAL_NVP(height)); + // Serialize the output layer and initialization rule. + ar(CEREAL_NVP(outputLayer)); + ar(CEREAL_NVP(initializeRule)); - ar(CEREAL_NVP(reset)); + // Serialize the network itself. + ar(CEREAL_NVP(network)); + ar(CEREAL_NVP(parameters)); - // Be sure to clear other layers before loading. - if (cereal::is_loading()) - { - std::for_each(network.begin(), network.end(), - boost::apply_visitor(deleteVisitor)); - network.clear(); - } - - ar(CEREAL_VECTOR_VARIANT_POINTER(network)); + // Serialize the expected input size. + ar(CEREAL_NVP(inputDimensions)); // If we are loading, we need to initialize the weights. if (cereal::is_loading()) { - size_t offset = 0; - for (size_t i = 0; i < network.size(); ++i) - { - offset += boost::apply_visitor(WeightSetVisitor(parameter, offset), - network[i]); + // We can clear these members, since it's not possible to serialize in the + // middle of training and resume. + predictors.clear(); + responses.clear(); - boost::apply_visitor(resetVisitor, network[i]); - } + networkOutput.clear(); + networkDelta.clear(); - deterministic = true; - ResetDeterministic(); + layerMemoryIsSet = false; + inputDimensionsAreSet = false; + + // The weights in `parameters` will be correctly set for each layer in the + // first call to Forward(). } } -template -void FFN::Swap(FFN& network) +template +typename MatType::elem_type FFN< + OutputLayerType, + InitializationRuleType, + MatType +>::Evaluate(const MatType& parameters) { - std::swap(outputLayer, network.outputLayer); - std::swap(initializeRule, network.initializeRule); - std::swap(width, network.width); - std::swap(height, network.height); - std::swap(reset, network.reset); - std::swap(this->network, network.network); - std::swap(predictors, network.predictors); - std::swap(responses, network.responses); - std::swap(parameter, network.parameter); - std::swap(numFunctions, network.numFunctions); - std::swap(error, network.error); - std::swap(deterministic, network.deterministic); - std::swap(delta, network.delta); - std::swap(inputParameter, network.inputParameter); - std::swap(outputParameter, network.outputParameter); - std::swap(gradient, network.gradient); -}; + typename MatType::elem_type res = 0; + for (size_t i = 0; i < predictors.n_cols; ++i) + res += Evaluate(parameters, i, 1); -template -FFN::FFN( - const FFN& network): - outputLayer(network.outputLayer), - initializeRule(network.initializeRule), - width(network.width), - height(network.height), - reset(network.reset), - predictors(network.predictors), - responses(network.responses), - parameter(network.parameter), - numFunctions(network.numFunctions), - error(network.error), - deterministic(network.deterministic), - delta(network.delta), - inputParameter(network.inputParameter), - outputParameter(network.outputParameter), - gradient(network.gradient) + return res; +} + +template +typename MatType::elem_type FFN< + OutputLayerType, + InitializationRuleType, + MatType +>::Evaluate(const MatType& /* parameters */, + const size_t begin, + const size_t batchSize) { - // Build new layers according to source network - for (size_t i = 0; i < network.network.size(); ++i) + CheckNetwork("FFN::Evaluate()", predictors.n_rows); + + // Set networkOutput to the right size if needed, then perform the forward + // pass. + networkOutput.set_size(network.OutputSize(), batchSize); + network.Forward(predictors.cols(begin, begin + batchSize - 1), networkOutput); + + return outputLayer.Forward(networkOutput, + responses.cols(begin, begin + batchSize - 1)) + network.Loss(); +} + +template +typename MatType::elem_type FFN< + OutputLayerType, + InitializationRuleType, + MatType +>::EvaluateWithGradient(const MatType& parameters, MatType& gradient) +{ + typename MatType::elem_type res = 0; + res += EvaluateWithGradient(parameters, 0, gradient, 1); + for (size_t i = 1; i < predictors.n_cols; ++i) { - this->network.push_back(boost::apply_visitor(copyVisitor, - network.network[i])); - boost::apply_visitor(resetVisitor, this->network.back()); + arma::mat tmpGradient(gradient.n_rows, gradient.n_cols); + res += EvaluateWithGradient(parameters, i, tmpGradient, 1); + gradient += tmpGradient; } -}; -template -FFN::FFN( - FFN&& network): - outputLayer(std::move(network.outputLayer)), - initializeRule(std::move(network.initializeRule)), - width(network.width), - height(network.height), - reset(network.reset), - predictors(std::move(network.predictors)), - responses(std::move(network.responses)), - parameter(std::move(network.parameter)), - numFunctions(network.numFunctions), - error(std::move(network.error)), - deterministic(network.deterministic), - delta(std::move(network.delta)), - inputParameter(std::move(network.inputParameter)), - outputParameter(std::move(network.outputParameter)), - gradient(std::move(network.gradient)) -{ - this->network = std::move(network.network); -}; + return res; +} -template -FFN& -FFN::operator = (FFN network) +template +typename MatType::elem_type FFN< + OutputLayerType, + InitializationRuleType, + MatType +>::EvaluateWithGradient(const MatType& parameters, + const size_t begin, + MatType& gradient, + const size_t batchSize) { - Swap(network); - return *this; -}; + CheckNetwork("FFN::EvaluateWithGradient()", predictors.n_rows); + + // Set networkOutput to the right size if needed, then perform the forward + // pass. + networkOutput.set_size(network.OutputSize(), batchSize); + + network.Forward(predictors.cols(begin, begin + batchSize - 1), networkOutput); + + const typename MatType::elem_type obj = outputLayer.Forward(networkOutput, + responses.cols(begin, begin + batchSize - 1)) + network.Loss(); + + // Now perform the backward pass. + outputLayer.Backward(networkOutput, + responses.cols(begin, begin + batchSize - 1), error); + + // The delta should have the same size as the input. + networkDelta.set_size(predictors.n_rows, batchSize); + network.Backward(networkOutput, error, networkDelta); + + // Now compute the gradients. + // The gradient should have the same size as the parameters. + gradient.set_size(parameters.n_rows, parameters.n_cols); + network.Gradient(predictors.cols(begin, begin + batchSize - 1), error, + gradient); + + return obj; +} + +template +void FFN< + OutputLayerType, + InitializationRuleType, + MatType +>::Gradient(const MatType& parameters, + const size_t begin, + MatType& gradient, + const size_t batchSize) +{ + this->EvaluateWithGradient(parameters, begin, gradient, batchSize); +} + +template +void FFN< + OutputLayerType, + InitializationRuleType, + MatType +>::Shuffle() +{ + math::ShuffleData(predictors, responses, predictors, responses); +} + +template +void FFN< + OutputLayerType, + InitializationRuleType, + MatType +>::ResetData(MatType predictors, MatType responses) +{ + this->predictors = std::move(predictors); + this->responses = std::move(responses); + + // Set the network to training mode. + SetNetworkMode(true); +} + +template +void FFN< + OutputLayerType, + InitializationRuleType, + MatType +>::InitializeWeights() +{ + // Set the network to testing mode. + SetNetworkMode(false); + + // Reset the network parameters with the given initialization rule. + NetworkInitialization networkInit(initializeRule); + networkInit.Initialize(network.Network(), parameters); +} + +template +void FFN< + OutputLayerType, + InitializationRuleType, + MatType +>::SetLayerMemory() +{ + size_t totalWeightSize = network.WeightSize(); + + Log::Assert(totalWeightSize == parameters.n_elem, + "FFN::SetLayerMemory(): total layer weight size does not match parameter " + "size!"); + + network.SetWeights(parameters.memptr()); + layerMemoryIsSet = true; +} + +template +void FFN< + OutputLayerType, + InitializationRuleType, + MatType +>::CheckNetwork(const std::string& functionName, + const size_t inputDimensionality, + const bool setMode, + const bool training) +{ + // If the network is empty, we can't do anything. + if (network.Network().size() == 0) + { + throw std::invalid_argument(functionName + ": cannot use network with no " + "layers!"); + } + + // Next, check that the input dimensions for each layer are correct. Note + // that this will throw an exception if the user has passed data that does not + // match this->inputDimensions. + if (!inputDimensionsAreSet) + UpdateDimensions(functionName, inputDimensionality); + + // We may need to initialize the `parameters` matrix if it is empty or the + // wrong size. + if (parameters.is_empty()) + { + InitializeWeights(); + } + else if (parameters.n_elem != network.WeightSize()) + { + parameters.clear(); + InitializeWeights(); + } + + // Make sure each layer is pointing at the right memory. + if (!layerMemoryIsSet) + SetLayerMemory(); + + // Finally, set the layers of the network to the right mode if the user + // requested it. + if (setMode) + SetNetworkMode(training); +} + +template +void FFN< + OutputLayerType, + InitializationRuleType, + MatType +>::UpdateDimensions(const std::string& functionName, + const size_t inputDimensionality) +{ + // If the input dimensions are completely unset, then assume our input is + // flat. + if (inputDimensions.size() == 0) + inputDimensions = { inputDimensionality }; + + size_t totalInputSize = 1; + for (size_t i = 0; i < inputDimensions.size(); ++i) + totalInputSize *= inputDimensions[i]; + + if (totalInputSize != inputDimensionality && inputDimensionality != 0) + { + throw std::logic_error(functionName + ": input size does not match expected" + " size set with InputDimensions()!"); + } + + // If the input dimensions have not changed from what has been computed + // before, we can terminate early---the network already has its dimensions + // set. + if (inputDimensions == network.InputDimensions()) + { + inputDimensionsAreSet = true; + return; + } + + network.InputDimensions() = inputDimensions; + network.ComputeOutputDimensions(); + inputDimensionsAreSet = true; +} + +template +template +typename std::enable_if< + ens::traits::HasMaxIterationsSignature::value, void +>::type +FFN< + OutputLayerType, + InitializationRuleType, + MatType +>::WarnMessageMaxIterations(OptimizerType& optimizer, size_t samples) const +{ + if (optimizer.MaxIterations() < samples && + optimizer.MaxIterations() != 0) + { + Log::Warn << "The optimizer's maximum number of iterations is less than the" + << " size of the dataset; the optimizer will not pass over the entire " + << "dataset. To fix this, modify the maximum number of iterations to be" + << " at least equal to the number of points of your dataset (" + << samples << ")." << std::endl; + } +} + +template +template +typename std::enable_if< + !ens::traits::HasMaxIterationsSignature::value, void +>::type +FFN< + OutputLayerType, + InitializationRuleType, + MatType +>::WarnMessageMaxIterations(OptimizerType& /* optimizer */, + size_t /* samples */) const +{ + // Nothing to do here. +} } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/forward_decls.hpp b/src/mlpack/methods/ann/forward_decls.hpp new file mode 100644 index 0000000000..e3bfc62d82 --- /dev/null +++ b/src/mlpack/methods/ann/forward_decls.hpp @@ -0,0 +1,29 @@ +/** + * @file forward_decls.hpp + * @author Ryan Curtin + * + * Forward declarations of network types. This is needed for some `friend` + * functionality. + */ +#ifndef MLPACK_METHODS_ANN_FORWARD_DECLS_HPP +#define MLPACK_METHODS_ANN_FORWARD_DECLS_HPP + +namespace mlpack { +namespace ann { + +// See ffn.hpp. +template +class FFN; + +// See rnn.hpp. +template +class RNN; + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/init_rules/const_init.hpp b/src/mlpack/methods/ann/init_rules/const_init.hpp index ec1f24deea..3005da003c 100644 --- a/src/mlpack/methods/ann/init_rules/const_init.hpp +++ b/src/mlpack/methods/ann/init_rules/const_init.hpp @@ -98,7 +98,13 @@ class ConstInitialization //! Get the initialization value. double const& InitValue() const { return initVal; } //! Modify the initialization value. - double& initValue() { return initVal; } + double& InitValue() { return initVal; } + + template + void serialize(Archive& ar, const uint32_t /* version */) + { + ar(CEREAL_NVP(initVal)); + } private: //! Value to be initialized with diff --git a/src/mlpack/methods/ann/init_rules/glorot_init.hpp b/src/mlpack/methods/ann/init_rules/glorot_init.hpp index f2d02bbb1c..8f1c9d51b6 100644 --- a/src/mlpack/methods/ann/init_rules/glorot_init.hpp +++ b/src/mlpack/methods/ann/init_rules/glorot_init.hpp @@ -104,13 +104,19 @@ class GlorotInitializationType */ template void Initialize(arma::Cube& W); + + /** + * Serialize the initialization. (Nothing to serialize for this one.) + */ + template + void serialize(Archive& /* ar */, const uint32_t /* version */) { } }; // class GlorotInitializationType -template <> +template<> template inline void GlorotInitializationType::Initialize(arma::Mat& W, - const size_t rows, - const size_t cols) + const size_t rows, + const size_t cols) { if (W.is_empty()) W.set_size(rows, cols); @@ -120,7 +126,7 @@ inline void GlorotInitializationType::Initialize(arma::Mat& W, normalInit.Initialize(W, rows, cols); } -template <> +template<> template inline void GlorotInitializationType::Initialize(arma::Mat& W) { @@ -132,7 +138,7 @@ inline void GlorotInitializationType::Initialize(arma::Mat& W) normalInit.Initialize(W); } -template <> +template<> template inline void GlorotInitializationType::Initialize(arma::Mat& W, const size_t rows, @@ -147,7 +153,7 @@ inline void GlorotInitializationType::Initialize(arma::Mat& W, randomInit.Initialize(W, rows, cols); } -template <> +template<> template inline void GlorotInitializationType::Initialize(arma::Mat& W) { diff --git a/src/mlpack/methods/ann/init_rules/he_init.hpp b/src/mlpack/methods/ann/init_rules/he_init.hpp index c82ecaec6c..2608b1740f 100644 --- a/src/mlpack/methods/ann/init_rules/he_init.hpp +++ b/src/mlpack/methods/ann/init_rules/he_init.hpp @@ -136,6 +136,12 @@ class HeInitialization for (size_t i = 0; i < W.n_slices; ++i) Initialize(W.slice(i)); } + + template + void serialize(Archive& /* ar */, const uint32_t /* version */) + { + // Nothing to do. + } }; // class HeInitialization } // namespace ann diff --git a/src/mlpack/methods/ann/init_rules/network_init.hpp b/src/mlpack/methods/ann/init_rules/network_init.hpp index 5e811ef3a4..8f51d243bc 100644 --- a/src/mlpack/methods/ann/init_rules/network_init.hpp +++ b/src/mlpack/methods/ann/init_rules/network_init.hpp @@ -14,14 +14,10 @@ #define MLPACK_METHODS_ANN_INIT_RULES_NETWORK_INIT_HPP #include +#include -#include "../visitor/reset_visitor.hpp" -#include "../visitor/weight_size_visitor.hpp" -#include "../visitor/weight_set_visitor.hpp" #include "init_rules_traits.hpp" -#include - namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -29,7 +25,7 @@ namespace ann /** Artificial Neural Network. */ { * This class is used to initialize the network with the given initialization * rule. */ -template +template class NetworkInitialization { public: @@ -54,16 +50,18 @@ class NetworkInitialization * @param parameterOffset Offset for network paramater, default 0. */ template - void Initialize(const std::vector >& network, - arma::Mat& parameter, size_t parameterOffset = 0) + void Initialize(const std::vector>*>& network, + arma::Mat& parameters, + size_t parameterOffset = 0) { - // Determine the number of parameter/weights of the given network. - if (parameter.is_empty()) + // Determine the total number of parameters/weights of the given network. + if (parameters.is_empty()) { size_t weights = 0; for (size_t i = 0; i < network.size(); ++i) - weights += boost::apply_visitor(weightSizeVisitor, network[i]); - parameter.set_size(weights, 1); + weights += network[i]->WeightSize(); + + parameters.set_size(weights, 1); } // Initialize the network layer by layer or the complete network. @@ -73,9 +71,8 @@ class NetworkInitialization { // Initialize the layer with the specified parameter/weight // initialization rule. - const size_t weight = boost::apply_visitor(weightSizeVisitor, - network[i]); - arma::Mat tmp = arma::mat(parameter.memptr() + offset, + const size_t weight = network[i]->WeightSize(); + arma::Mat tmp = arma::mat(parameters.memptr() + offset, weight, 1, false, false); initializeRule.Initialize(tmp, tmp.n_elem, 1); @@ -85,19 +82,7 @@ class NetworkInitialization } else { - initializeRule.Initialize(parameter, parameter.n_elem, 1); - } - - // Note: We can't merge the for loop into the for loop above because - // WeightSetVisitor also sets the parameter/weights of the inner modules. - // Inner Modules are held by the parent module e.g. the concat module can - // hold various other modules. - for (size_t i = 0, offset = parameterOffset; i < network.size(); ++i) - { - offset += boost::apply_visitor(WeightSetVisitor(parameter, offset), - network[i]); - - boost::apply_visitor(resetVisitor, network[i]); + initializeRule.Initialize(parameters, parameters.n_elem, 1); } } @@ -105,12 +90,6 @@ class NetworkInitialization //! Instantiated InitializationRule object for initializing the network //! parameter. InitializationRuleType initializeRule; - - //! Locally-stored reset visitor. - ResetVisitor resetVisitor; - - //! Locally-stored weight size visitor. - WeightSizeVisitor weightSizeVisitor; }; // class NetworkInitialization } // namespace ann diff --git a/src/mlpack/methods/ann/init_rules/oivs_init.hpp b/src/mlpack/methods/ann/init_rules/oivs_init.hpp index 1c34fbc80f..314eabf46f 100644 --- a/src/mlpack/methods/ann/init_rules/oivs_init.hpp +++ b/src/mlpack/methods/ann/init_rules/oivs_init.hpp @@ -47,15 +47,13 @@ namespace ann /** Artificial Neural Network. */ { * w_i &=& \hat{w} \cdot \sqrt{a_i + 1} * @f} * - * Where f is the transfer function epsilon, k custom parameters, n the number of - * neurons in the outgoing layer and gamma a parameter that defines the random - * interval. + * Where f is the transfer function epsilon, k custom parameters, n the number + * of neurons in the outgoing layer and gamma a parameter that defines the + * random interval. * * @tparam ActivationFunction The activation function used for the oivs method. */ -template< - class ActivationFunction = LogisticFunction -> +template class OivsInitialization { public: diff --git a/src/mlpack/methods/ann/init_rules/random_init.hpp b/src/mlpack/methods/ann/init_rules/random_init.hpp index e48615f4ff..22095d33a3 100644 --- a/src/mlpack/methods/ann/init_rules/random_init.hpp +++ b/src/mlpack/methods/ann/init_rules/random_init.hpp @@ -115,6 +115,13 @@ class RandomInitialization Initialize(W.slice(i)); } + template + void serialize(Archive& ar, const uint32_t /* version */) + { + ar(CEREAL_NVP(lowerBound)); + ar(CEREAL_NVP(upperBound)); + } + private: //! The number used as lower bound. double lowerBound; diff --git a/src/mlpack/methods/ann/layer/CMakeLists.txt b/src/mlpack/methods/ann/layer/CMakeLists.txt index c96c48799c..e69e65e23d 100644 --- a/src/mlpack/methods/ann/layer/CMakeLists.txt +++ b/src/mlpack/methods/ann/layer/CMakeLists.txt @@ -3,67 +3,18 @@ set(SOURCES add.hpp add_impl.hpp - add_merge.hpp - add_merge_impl.hpp - adaptive_max_pooling.hpp - adaptive_max_pooling_impl.hpp - adaptive_mean_pooling.hpp - adaptive_mean_pooling_impl.hpp alpha_dropout.hpp alpha_dropout_impl.hpp - atrous_convolution.hpp - atrous_convolution_impl.hpp base_layer.hpp - batch_norm.hpp - batch_norm_impl.hpp - bicubic_interpolation.hpp - bicubic_interpolation_impl.hpp - bilinear_interpolation.hpp - bilinear_interpolation_impl.hpp - channel_shuffle.hpp - channel_shuffle_impl.hpp - concat.hpp - concat_impl.hpp - concat_performance.hpp - concat_performance_impl.hpp concatenate.hpp concatenate_impl.hpp - constant.hpp - constant_impl.hpp convolution.hpp convolution_impl.hpp dropconnect.hpp dropconnect_impl.hpp dropout.hpp dropout_impl.hpp - elu.hpp - elu_impl.hpp - fast_lstm.hpp - fast_lstm_impl.hpp - flatten_t_swish.hpp - flatten_t_swish_impl.hpp - flexible_relu.hpp - flexible_relu_impl.hpp - glimpse.hpp - glimpse_impl.hpp - group_norm.hpp - group_norm_impl.hpp - gru.hpp - gru_impl.hpp - hard_tanh.hpp - hard_tanh_impl.hpp - highway.hpp - highway_impl.hpp - instance_norm.hpp - instance_norm_impl.hpp - isrlu.hpp - isrlu_impl.hpp - join.hpp - join_impl.hpp layer.hpp - layer_norm.hpp - layer_norm_impl.hpp - layer_traits.hpp layer_types.hpp leaky_relu.hpp leaky_relu_impl.hpp @@ -71,73 +22,22 @@ set(SOURCES linear_impl.hpp linear_no_bias.hpp linear_no_bias_impl.hpp + linear3d.hpp + linear3d_impl.hpp log_softmax.hpp log_softmax_impl.hpp - lookup.hpp - lookup_impl.hpp - lp_pooling.hpp - lp_pooling_impl.hpp lstm.hpp lstm_impl.hpp max_pooling.hpp max_pooling_impl.hpp - mean_pooling.hpp - mean_pooling_impl.hpp - minibatch_discrimination.hpp - minibatch_discrimination_impl.hpp - multihead_attention_impl.hpp - multihead_attention.hpp - multiply_constant.hpp - multiply_constant_impl.hpp - multiply_merge.hpp - multiply_merge_impl.hpp - nearest_interpolation.hpp - nearest_interpolation_impl.hpp + multi_layer.hpp + multi_layer_impl.hpp noisylinear.hpp noisylinear_impl.hpp - parametric_relu.hpp - parametric_relu_impl.hpp - pixel_shuffle.hpp - pixel_shuffle_impl.hpp - positional_encoding.hpp - positional_encoding_impl.hpp - recurrent.hpp - recurrent_impl.hpp - recurrent_attention.hpp - recurrent_attention_impl.hpp - reinforce_normal.hpp - reinforce_normal_impl.hpp - relu6.hpp - relu6_impl.hpp - reparametrization.hpp - reparametrization_impl.hpp + padding.hpp radial_basis_function.hpp radial_basis_function_impl.hpp - select.hpp - select_impl.hpp - sequential.hpp - sequential_impl.hpp - softmax_impl.hpp - softmax.hpp - spatial_dropout.hpp - spatial_dropout_impl.hpp - subview.hpp - transposed_convolution.hpp - transposed_convolution_impl.hpp - vr_class_reward.hpp - vr_class_reward_impl.hpp - c_relu.hpp - c_relu_impl.hpp - weight_norm.hpp - weight_norm_impl.hpp - hardshrink.hpp - hardshrink_impl.hpp - celu.hpp - celu_impl.hpp - softshrink.hpp - softshrink_impl.hpp - softmin.hpp - softmin_impl.hpp + serialization.hpp ) # Add directory name to sources. diff --git a/src/mlpack/methods/ann/layer/add.hpp b/src/mlpack/methods/ann/layer/add.hpp index 5fd498e704..8645f38612 100644 --- a/src/mlpack/methods/ann/layer/add.hpp +++ b/src/mlpack/methods/ann/layer/add.hpp @@ -13,98 +13,90 @@ #define MLPACK_METHODS_ANN_LAYER_ADD_HPP #include -#include +#include "layer.hpp" namespace mlpack { namespace ann /** Artificial Neural Network. */ { /** - * Implementation of the Add module class. The Add module applies a bias term - * to the incoming data. + * Implementation of the Add layer. The Add module applies a bias term to the + * incoming data. * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam MatType Matrix representation to accept as input and use for + * computation. */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class Add +template +class AddType : public Layer { public: /** - * Create the Add object using the specified number of output units. - * - * @param outSize The number of output units. + * Create the AddType object. The output size of the layer will be the same + * as the input size. */ - Add(const size_t outSize = 0); + AddType(); + + //! Clone the AddType object. This handles polymorphism correctly. + AddType* Clone() const { return new AddType(*this); } + + // Virtual destructor. + virtual ~AddType() { } + + //! Copy the given AddType layer. + AddType(const AddType& other); + //! Take ownership of the given AddType layer. + AddType(AddType&& other); + //! Copy the given AddType layer. + AddType& operator=(const AddType& other); + //! Take ownership of the given AddType layer. + AddType& operator=(AddType&& other); /** - * Ordinary feed forward pass of a neural network, evaluating the function - * f(x) by propagating the activity forward through f. + * Forward pass: add the bias to the input. * * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template - void Forward(const arma::Mat& input, arma::Mat& output); + void Forward(const MatType& input, MatType& output); /** - * Ordinary feed backward pass of a neural network, calculating the function - * f(x) by propagating x backwards trough f. Using the results from the feed - * forward pass. + * Backward pass: send weights backwards (the bias does not affect anything). * * @param * (input) The propagated input activation. * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g); + void Backward(const MatType& /* input */, + const MatType& gy, + MatType& g); /** - * Calculate the gradient using the output delta and the input activation. + * Calculate the gradient using the output and the input activation. * * @param * (input) The propagated input. * @param error The calculated error. * @param gradient The calculated gradient. */ - template - void Gradient(const arma::Mat& /* input */, - const arma::Mat& error, - arma::Mat& gradient); + void Gradient(const MatType& /* input */, + const MatType& error, + MatType& gradient); - //! Get the parameters. - OutputDataType const& Parameters() const { return weights; } - //! Modify the parameters. - OutputDataType& Parameters() { return weights; } - - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } - - //! Get the gradient. - OutputDataType const& Gradient() const { return gradient; } - //! Modify the gradient. - OutputDataType& Gradient() { return gradient; } - - //! Get the output size. - size_t OutputSize() const { return outSize; } + //! Return the weights of the network. + const MatType& Parameters() const { return weights; } + //! Modify the weights of the network. + MatType& Parameters() { return weights; } //! Get the size of weights. size_t WeightSize() const { return outSize; } + //! Compute the output dimensions of the layer, based on the internal values + //! of `InputDimensions()`. + void ComputeOutputDimensions(); + + //! Set the weights of the layer to use the given memory. + void SetWeights(typename MatType::elem_type* weightPtr); + /** - * Serialize the layer + * Serialize the layer. */ template void serialize(Archive& ar, const uint32_t /* version */); @@ -114,18 +106,12 @@ class Add size_t outSize; //! Locally-stored weight object. - OutputDataType weights; - - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored gradient object. - OutputDataType gradient; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; + MatType weights; }; // class Add +// Standard Add layer. +typedef AddType Add; + } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/add_impl.hpp b/src/mlpack/methods/ann/layer/add_impl.hpp index a268956fe8..8cd582c5ab 100644 --- a/src/mlpack/methods/ann/layer/add_impl.hpp +++ b/src/mlpack/methods/ann/layer/add_impl.hpp @@ -19,51 +19,103 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -Add::Add(const size_t outSize) : - outSize(outSize) +template +AddType::AddType() : outSize(0) { - weights.set_size(WeightSize(), 1); + // Nothing to do. } -template -template -void Add::Forward( - const arma::Mat& input, arma::Mat& output) +template +AddType::AddType(const AddType& other) : + Layer(other), + outSize(other.outSize) { - output = input; - output.each_col() += weights; + // Nothing to do. } -template -template -void Add::Backward( - const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g) +template +AddType::AddType(AddType&& other) : + Layer(std::move(other)), + outSize(std::move(other.outSize)) +{ + // Nothing to do. +} + +template +AddType& +AddType::operator=(const AddType& other) +{ + if (&other != this) + { + Layer::operator=(other); + outSize = other.outSize; + } + + return *this; +} + +template +AddType& +AddType::operator=(AddType&& other) +{ + if (&other != this) + { + Layer::operator=(std::move(other)); + outSize = std::move(other.outSize); + } + + return *this; +} + +template +void AddType::Forward(const MatType& input, MatType& output) +{ + output = input + arma::repmat(arma::vectorise(weights), 1, input.n_cols); +} + +template +void AddType::Backward( + const MatType& /* input */, + const MatType& gy, + MatType& g) { g = gy; } -template -template -void Add::Gradient( - const arma::Mat& /* input */, - const arma::Mat& error, - arma::Mat& gradient) +template +void AddType::Gradient( + const MatType& /* input */, + const MatType& error, + MatType& gradient) { gradient = error; } -template -template -void Add::serialize( - Archive& ar, const uint32_t /* version */) +template +void AddType::SetWeights(typename MatType::elem_type* weightPtr) { - ar(CEREAL_NVP(outSize)); + // Set the weights to wrap the given memory. + MakeAlias(weights, weightPtr, 1, outSize); +} - if (cereal::is_loading()) - weights.set_size(outSize, 1); +template +void AddType::ComputeOutputDimensions() +{ + this->outputDimensions = this->inputDimensions; + + outSize = this->outputDimensions[0]; + for (size_t i = 1; i < this->outputDimensions.size(); ++i) + outSize *= this->outputDimensions[i]; +} + +template +template +void AddType::serialize(Archive& ar, const uint32_t /* version */) +{ + ar(cereal::base_class>(this)); + + ar(CEREAL_NVP(outSize)); + ar(CEREAL_NVP(weights)); } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/add_merge_impl.hpp b/src/mlpack/methods/ann/layer/add_merge_impl.hpp deleted file mode 100644 index cd891088a4..0000000000 --- a/src/mlpack/methods/ann/layer/add_merge_impl.hpp +++ /dev/null @@ -1,168 +0,0 @@ -/** - * @file methods/ann/layer/add_merge_impl.hpp - * @author Marcus Edel - * - * Definition of the AddMerge module which accumulates the output of the given - * modules. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_LAYER_ADD_MERGE_IMPL_HPP -#define MLPACK_METHODS_ANN_LAYER_ADD_MERGE_IMPL_HPP - -// In case it hasn't yet been included. -#include "add_merge.hpp" - -#include "../visitor/forward_visitor.hpp" -#include "../visitor/backward_visitor.hpp" -#include "../visitor/gradient_visitor.hpp" - -namespace mlpack { -namespace ann /** Artificial Neural Network. */ { - -template -AddMerge::AddMerge( - const bool model, const bool run) : - model(model), run(run), ownsLayers(!model) -{ - // Nothing to do here. -} - -template -AddMerge::AddMerge( - const bool model, const bool run, const bool ownsLayers) : - model(model), run(run), ownsLayers(ownsLayers) -{ - // Nothing to do here. -} - -template -AddMerge::~AddMerge() -{ - if (!model && ownsLayers) - { - std::for_each(network.begin(), network.end(), - boost::apply_visitor(deleteVisitor)); - } -} - -template -template -void AddMerge::Forward( - const InputType& input, OutputType& output) -{ - if (run) - { - for (size_t i = 0; i < network.size(); ++i) - { - boost::apply_visitor(ForwardVisitor(input, - boost::apply_visitor(outputParameterVisitor, network[i])), - network[i]); - } - } - - output = boost::apply_visitor(outputParameterVisitor, network.front()); - for (size_t i = 1; i < network.size(); ++i) - { - output += boost::apply_visitor(outputParameterVisitor, network[i]); - } -} - -template -template -void AddMerge::Backward( - const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g) -{ - if (run) - { - for (size_t i = 0; i < network.size(); ++i) - { - boost::apply_visitor(BackwardVisitor(boost::apply_visitor( - outputParameterVisitor, network[i]), gy, - boost::apply_visitor(deltaVisitor, network[i])), network[i]); - } - - g = boost::apply_visitor(deltaVisitor, network[0]); - for (size_t i = 1; i < network.size(); ++i) - { - g += boost::apply_visitor(deltaVisitor, network[i]); - } - } - else - g = gy; -} - -template -template -void AddMerge::Backward( - const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g, - const size_t index) -{ - boost::apply_visitor(BackwardVisitor(boost::apply_visitor( - outputParameterVisitor, network[index]), gy, - boost::apply_visitor(deltaVisitor, network[index])), network[index]); - g = boost::apply_visitor(deltaVisitor, network[index]); -} - -template -template -void AddMerge::Gradient( - const arma::Mat& input, - const arma::Mat& error, - arma::Mat& /* gradient */ ) -{ - if (run) - { - for (size_t i = 0; i < network.size(); ++i) - { - boost::apply_visitor(GradientVisitor(input, error), network[i]); - } - } -} - -template -template -void AddMerge::Gradient( - const arma::Mat& input, - const arma::Mat& error, - arma::Mat& /* gradient */, - const size_t index) -{ - boost::apply_visitor(GradientVisitor(input, error), network[index]); -} - -template -template -void AddMerge::serialize( - Archive& ar, const uint32_t /* version */) -{ - // Be sure to clear other layers before loading. - if (cereal::is_loading()) - network.clear(); - - ar(CEREAL_VECTOR_VARIANT_POINTER(network)); - ar(CEREAL_NVP(model)); - ar(CEREAL_NVP(run)); - ar(CEREAL_NVP(ownsLayers)); -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/layer/alpha_dropout.hpp b/src/mlpack/methods/ann/layer/alpha_dropout.hpp index abf75d87b8..b1e63870f6 100644 --- a/src/mlpack/methods/ann/layer/alpha_dropout.hpp +++ b/src/mlpack/methods/ann/layer/alpha_dropout.hpp @@ -17,6 +17,7 @@ #define MLPACK_METHODS_ANN_LAYER_ALPHA_DROPOUT_HPP #include +#include "layer.hpp" namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -40,14 +41,11 @@ namespace ann /** Artificial Neural Network. */ { * } * @endcode * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam MatType Matrix representation to accept as input and use for + * computation. */ -template -class AlphaDropout +template +class AlphaDropoutType : public Layer { public: /** @@ -56,17 +54,33 @@ class AlphaDropout * @param ratio The probability of setting a value to alphaDash. * @param alphaDash The dropout scaling parameter. */ - AlphaDropout(const double ratio = 0.5, - const double alphaDash = -alpha * lambda); + AlphaDropoutType(const double ratio = 0.5, + const double alphaDash = -alpha * lambda); /** - * Ordinary feed forward pass of the alpha_dropout layer. + * Clone the AlphaDropoutType object. This handles polymorphism correctly. + */ + AlphaDropoutType* Clone() const { return new AlphaDropoutType(*this); } + + // Virtual destructor. + virtual ~AlphaDropoutType() { } + + //! Copy the given AlphaDropoutType layer. + AlphaDropoutType(const AlphaDropoutType& other); + //! Take ownership of the given AlphaDropoutType layer. + AlphaDropoutType(AlphaDropoutType&& other); + //! Copy the given AlphaDropoutType layer. + AlphaDropoutType& operator=(const AlphaDropoutType& other); + //! Take ownership of the given AlphaDropoutType layer. + AlphaDropoutType& operator=(AlphaDropoutType&& other); + + /** + * Ordinary feed forward pass of the AlphaDropout layer. * * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template - void Forward(const arma::Mat& input, arma::Mat& output); + void Forward(const MatType& input, MatType& output); /** * Ordinary feed backward pass of the alpha_dropout layer. @@ -75,25 +89,7 @@ class AlphaDropout * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g); - - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the detla. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } - - //! The value of the deterministic parameter. - bool Deterministic() const { return deterministic; } - //! Modify the value of the deterministic parameter. - bool& Deterministic() { return deterministic; } + void Backward(const MatType& /* input */, const MatType& gy, MatType& g); //! The probability of setting a value to alphaDash. double Ratio() const { return ratio; } @@ -105,10 +101,10 @@ class AlphaDropout double B() const { return b; } //! Value of alphaDash. - double AlphaDash() const {return alphaDash; } + double AlphaDash() const { return alphaDash; } //! Get the mask. - OutputDataType const& Mask() const {return mask;} + const MatType& Mask() const { return mask; } //! Modify the probability of setting a value to alphaDash. As //! 'a' and 'b' depend on 'ratio', modify them as well. @@ -126,14 +122,8 @@ class AlphaDropout void serialize(Archive& ar, const uint32_t /* version */); private: - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; - - //! Locally-stored mast object. - OutputDataType mask; + //! Locally-stored mask object. + MatType mask; //! The probability of setting a value to aplhaDash. double ratio; @@ -141,9 +131,6 @@ class AlphaDropout //! The low variance value of SELU activation function. double alphaDash; - //! If true dropout and scaling is disabled, see notes above. - bool deterministic; - //! Value of alpha for normalized inputs (taken from SELU). static constexpr double alpha = 1.6732632423543772848170429916717; @@ -155,7 +142,9 @@ class AlphaDropout //! Value to be added to a*x for affine transformation. double b; -}; // class AlphaDropout +}; // class AlphaDropoutType + +typedef AlphaDropoutType AlphaDropout; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/alpha_dropout_impl.hpp b/src/mlpack/methods/ann/layer/alpha_dropout_impl.hpp index 34a1b44544..91a150f410 100644 --- a/src/mlpack/methods/ann/layer/alpha_dropout_impl.hpp +++ b/src/mlpack/methods/ann/layer/alpha_dropout_impl.hpp @@ -22,25 +22,79 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -AlphaDropout::AlphaDropout( +template +AlphaDropoutType::AlphaDropoutType( const double ratio, const double alphaDash) : ratio(ratio), - alphaDash(alphaDash), - deterministic(false) + alphaDash(alphaDash) { Ratio(ratio); } -template -template -void AlphaDropout::Forward( - const arma::Mat& input, arma::Mat& output) +template +AlphaDropoutType::AlphaDropoutType(const AlphaDropoutType& other) : + Layer(other), + mask(other.mask), + ratio(other.ratio), + alphaDash(other.alphaDash), + a(other.a), + b(other.b) { - // The dropout mask will not be multiplied in the deterministic mode - // (during testing). - if (deterministic) + // Nothing to do. +} + +template +AlphaDropoutType::AlphaDropoutType(AlphaDropoutType&& other) : + Layer(std::move(other)), + mask(std::move(other.mask)), + ratio(std::move(other.ratio)), + alphaDash(std::move(other.alphaDash)), + a(std::move(other.a)), + b(std::move(other.b)) +{ + // Nothing to do. +} + +template +AlphaDropoutType& +AlphaDropoutType::operator=(const AlphaDropoutType& other) +{ + if (&other != this) + { + Layer::operator=(other); + mask = other.mask; + ratio = other.ratio; + alphaDash = other.alphaDash; + a = other.a; + b = other.b; + } + + return *this; +} + +template +AlphaDropoutType& +AlphaDropoutType::operator=(AlphaDropoutType&& other) +{ + if (&other != this) + { + Layer::operator=(std::move(other)); + mask = std::move(other.mask); + ratio = std::move(other.ratio); + alphaDash = std::move(other.alphaDash); + a = std::move(other.a); + b = std::move(other.b); + } + + return *this; +} + +template +void AlphaDropoutType::Forward(const MatType& input, MatType& output) +{ + // The dropout mask will not be multiplied during testing. + if (!this->training) { output = input; } @@ -49,29 +103,35 @@ void AlphaDropout::Forward( // Set values to alphaDash with probability ratio. Then apply affine // transformation so as to keep mean and variance of outputs to their // original values. - mask = arma::randu< arma::Mat >(input.n_rows, input.n_cols); + mask = arma::randu(input.n_rows, input.n_cols); mask.transform( [&](double val) { return (val > ratio); } ); output = (input % mask + alphaDash * (1 - mask)) * a + b; } } -template -template -void AlphaDropout::Backward( - const arma::Mat& /* input */, const arma::Mat& gy, arma::Mat& g) +template +void AlphaDropoutType::Backward( + const MatType& /* input */, const MatType& gy, MatType& g) { g = gy % mask * a; } -template +template template -void AlphaDropout::serialize( +void AlphaDropoutType::serialize( Archive& ar, const uint32_t /* version */) { + ar(cereal::base_class>(this)); + ar(CEREAL_NVP(ratio)); ar(CEREAL_NVP(alphaDash)); ar(CEREAL_NVP(a)); ar(CEREAL_NVP(b)); + + // No need to serialize the mask, since it will be recomputed on the next + // forward pass. But we should clear it if we are loading. + if (Archive::is_loading::value) + mask.clear(); } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/base_layer.hpp b/src/mlpack/methods/ann/layer/base_layer.hpp index e2d7aaf809..4762ee0b59 100644 --- a/src/mlpack/methods/ann/layer/base_layer.hpp +++ b/src/mlpack/methods/ann/layer/base_layer.hpp @@ -30,6 +30,7 @@ #include #include #include +#include "layer.hpp" namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -40,102 +41,83 @@ namespace ann /** Artificial Neural Network. */ { * * A few convenience typedefs are given: * - * - SigmoidLayer - * - IdentityLayer - * - ReLULayer - * - TanHLayer - * - SoftplusLayer - * - HardSigmoidLayer - * - SwishLayer - * - MishLayer - * - LiSHTLayer - * - GELULayer - * - ELiSHLayer - * - ElliotLayer - * - GaussianLayer - * - HardSwishLayer - * - TanhExpLayer - * - SILULayer + * - Sigmoid + * - ReLU + * - TanH + * - Softplus + * - HardSigmoid + * - Swish + * - Mish + * - LiSHT + * - GELU + * - ELiSH + * - Elliot + * - Gaussian + * - HardSwish + * - TanhExp + * - SILU * * @tparam ActivationFunction Activation function used for the embedding layer. - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). */ template < class ActivationFunction = LogisticFunction, - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat + typename MatType = arma::mat > -class BaseLayer +class BaseLayer : public Layer { public: /** * Create the BaseLayer object. */ - BaseLayer() + BaseLayer() : Layer() { // Nothing to do here. } + // Virtual destructor. + virtual ~BaseLayer() { } + + // No copy constructor or operators needed here, since the class has no + // members. + + //! Clone the BaseLayer object. This handles polymorphism correctly. + BaseLayer* Clone() const { return new BaseLayer(*this); } + /** - * Ordinary feed forward pass of a neural network, evaluating the function - * f(x) by propagating the activity forward through f. + * Forward pass: apply the activation to the inputs. * * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template - void Forward(const InputType& input, OutputType& output) + void Forward(const MatType& input, MatType& output) { ActivationFunction::Fn(input, output); } /** - * Ordinary feed backward pass of a neural network, calculating the function - * f(x) by propagating x backwards trough f. Using the results from the feed - * forward pass. + * Backward pass: compute the function f(x) by propagating x backwards through + * f, using the results from the forward pass. * * @param input The propagated input activation. * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const arma::Mat& input, - const arma::Mat& gy, - arma::Mat& g) + void Backward(const MatType& input, const MatType& gy, MatType& g) { - arma::Mat derivative; + MatType derivative; ActivationFunction::Deriv(input, derivative); g = gy % derivative; } - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } - /** * Serialize the layer. */ template - void serialize(Archive& /* ar */, const uint32_t /* version */) + void serialize(Archive& ar, const uint32_t /* version */) { - /* Nothing to do here */ + ar(cereal::base_class>(this)); + // Nothing to serialize. } - - private: - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; }; // class BaseLayer // Convenience typedefs. @@ -143,179 +125,122 @@ class BaseLayer /** * Standard Sigmoid-Layer using the logistic activation function. */ -template < - class ActivationFunction = LogisticFunction, - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -using SigmoidLayer = BaseLayer< - ActivationFunction, InputDataType, OutputDataType>; +typedef BaseLayer Sigmoid; -/** - * Standard Identity-Layer using the identity activation function. - */ -template < - class ActivationFunction = IdentityFunction, - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -using IdentityLayer = BaseLayer< - ActivationFunction, InputDataType, OutputDataType>; +template +using SigmoidType = BaseLayer; /** * Standard rectified linear unit non-linearity layer. */ -template < - class ActivationFunction = RectifierFunction, - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -using ReLULayer = BaseLayer< - ActivationFunction, InputDataType, OutputDataType>; +typedef BaseLayer ReLU; + +template +using ReLUType = BaseLayer; /** * Standard hyperbolic tangent layer. */ -template < - class ActivationFunction = TanhFunction, - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -using TanHLayer = BaseLayer< - ActivationFunction, InputDataType, OutputDataType>; +typedef BaseLayer TanH; + +template +using TanHType = BaseLayer; /** * Standard Softplus-Layer using the Softplus activation function. */ -template < - class ActivationFunction = SoftplusFunction, - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -using SoftPlusLayer = BaseLayer< - ActivationFunction, InputDataType, OutputDataType>; +typedef BaseLayer SoftPlus; + +template +using SoftPlusType = BaseLayer; /** * Standard HardSigmoid-Layer using the HardSigmoid activation function. */ -template < - class ActivationFunction = HardSigmoidFunction, - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -using HardSigmoidLayer = BaseLayer< - ActivationFunction, InputDataType, OutputDataType>; +typedef BaseLayer HardSigmoid; + +template +using HardSigmoidType = BaseLayer; /** * Standard Swish-Layer using the Swish activation function. */ -template < - class ActivationFunction = SwishFunction, - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -using SwishFunctionLayer = BaseLayer< - ActivationFunction, InputDataType, OutputDataType>; +typedef BaseLayer Swish; + +template +using SwishType = BaseLayer; /** * Standard Mish-Layer using the Mish activation function. */ -template < - class ActivationFunction = MishFunction, - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -using MishFunctionLayer = BaseLayer< - ActivationFunction, InputDataType, OutputDataType>; +typedef BaseLayer Mish; + +template +using MishType = BaseLayer; /** * Standard LiSHT-Layer using the LiSHT activation function. */ -template < - class ActivationFunction = LiSHTFunction, - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -using LiSHTFunctionLayer = BaseLayer< - ActivationFunction, InputDataType, OutputDataType>; +typedef BaseLayer LiSHT; + +template +using LiSHTType = BaseLayer; /** * Standard GELU-Layer using the GELU activation function. */ -template < - class ActivationFunction = GELUFunction, - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -using GELUFunctionLayer = BaseLayer< - ActivationFunction, InputDataType, OutputDataType>; +typedef BaseLayer GELU; + +template +using GELUType = BaseLayer; /** * Standard Elliot-Layer using the Elliot activation function. */ -template < - class ActivationFunction = ElliotFunction, - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -using ElliotFunctionLayer = BaseLayer< - ActivationFunction, InputDataType, OutputDataType>; +typedef BaseLayer Elliot; + +template +using ElliotType = BaseLayer; /** * Standard ELiSH-Layer using the ELiSH activation function. */ -template < - class ActivationFunction = ElishFunction, - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -using ElishFunctionLayer = BaseLayer< - ActivationFunction, InputDataType, OutputDataType>; +typedef BaseLayer Elish; + +template +using ElishType = BaseLayer; /** * Standard Gaussian-Layer using the Gaussian activation function. */ -template < - class ActivationFunction = GaussianFunction, - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -using GaussianFunctionLayer = BaseLayer< - ActivationFunction, InputDataType, OutputDataType>; +typedef BaseLayer Gaussian; + +template +using GaussianType = BaseLayer; /** * Standard HardSwish-Layer using the HardSwish activation function. */ -template < - class ActivationFunction = HardSwishFunction, - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -using HardSwishFunctionLayer = BaseLayer< - ActivationFunction, InputDataType, OutputDataType>; +typedef BaseLayer HardSwish; + +template +using HardSwishType = BaseLayer; /** * Standard TanhExp-Layer using the TanhExp activation function. */ -template < - class ActivationFunction = TanhExpFunction, - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -using TanhExpFunctionLayer = BaseLayer< - ActivationFunction, InputDataType, OutputDataType>; +typedef BaseLayer TanhExp; + +template +using TanhExpType = BaseLayer; /** * Standard SILU-Layer using the SILU activation function. */ -template < - class ActivationFunction = SILUFunction, - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -using SILUFunctionLayer = BaseLayer< - ActivationFunction, InputDataType, OutputDataType ->; +typedef BaseLayer SILU; + +template +using SILUType = BaseLayer; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/concat.hpp b/src/mlpack/methods/ann/layer/concat.hpp deleted file mode 100644 index e693234f3e..0000000000 --- a/src/mlpack/methods/ann/layer/concat.hpp +++ /dev/null @@ -1,263 +0,0 @@ -/** - * @file methods/ann/layer/concat.hpp - * @author Marcus Edel - * @author Mehul Kumar Nirala - * - * Definition of the Concat class, which acts as a concatenation container. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_LAYER_CONCAT_HPP -#define MLPACK_METHODS_ANN_LAYER_CONCAT_HPP - -#include - -#include "../visitor/delete_visitor.hpp" -#include "../visitor/delta_visitor.hpp" -#include "../visitor/output_parameter_visitor.hpp" - -#include "layer_types.hpp" - -namespace mlpack { -namespace ann /** Artificial Neural Network. */ { - -/** - * Implementation of the Concat class. The Concat class works as a - * feed-forward fully connected network container which plugs various layers - * together. - * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam CustomLayers Additional custom layers if required. - */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat, - typename... CustomLayers -> -class Concat -{ - public: - /** - * Create the Concat object using the specified parameters. - * - * @param model Expose all network modules. - * @param run Call the Forward/Backward method before the output is merged. - */ - Concat(const bool model = false, - const bool run = true); - - /** - * Create the Concat object using the specified parameters. - * - * @param inputSize A vector denoting input size of each layer added. - * @param axis Concat axis. - * @param model Expose all network modules. - * @param run Call the Forward/Backward method before the output is merged. - */ - Concat(arma::Row& inputSize, - const size_t axis, - const bool model = false, - const bool run = true); - - /** - * Destroy the layers held by the model. - */ - ~Concat(); - - /** - * Ordinary feed forward pass of a neural network, evaluating the function - * f(x) by propagating the activity forward through f. - * - * @param input Input data used for evaluating the specified function. - * @param output Resulting output activation. - */ - template - void Forward(const arma::Mat& input, arma::Mat& output); - - /** - * Ordinary feed backward pass of a neural network, using 3rd-order tensors as - * input, calculating the function f(x) by propagating x backwards through f. - * Using the results from the feed forward pass. - * - * @param * (input) The propagated input activation. - * @param gy The backpropagated error. - * @param g The calculated gradient. - */ - template - void Backward(const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g); - - /** - * This is the overload of Backward() that runs only a specific layer with - * the given input. - * - * @param * (input) The propagated input activation. - * @param gy The backpropagated error. - * @param g The calculated gradient. - * @param index The index of the layer to run. - */ - template - void Backward(const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g, - const size_t index); - - /* - * Calculate the gradient using the output delta and the input activation. - * - * @param input The input parameter used for calculating the gradient. - * @param error The calculated error. - * @param gradient The calculated gradient. - */ - template - void Gradient(const arma::Mat& /* input */, - const arma::Mat& error, - arma::Mat& /* gradient */); - - /* - * This is the overload of Gradient() that runs a specific layer with the - * given input. - * - * @param input The input parameter used for calculating the gradient. - * @param error The calculated error. - * @param gradient The calculated gradient. - * @param The index of the layer to run. - */ - template - void Gradient(const arma::Mat& input, - const arma::Mat& error, - arma::Mat& gradient, - const size_t index); - - /* - * Add a new module to the model. - * - * @param args The layer parameter. - */ - template - void Add(Args... args) { network.push_back(new LayerType(args...)); } - - /* - * Add a new module to the model. - * - * @param layer The Layer to be added to the model. - */ - void Add(LayerTypes layer) { network.push_back(layer); } - - //! Return the model modules. - std::vector >& Model() - { - if (model) - { - return network; - } - - return empty; - } - - //! Return the initial point for the optimization. - const arma::mat& Parameters() const { return weights; } - //! Modify the initial point for the optimization. - arma::mat& Parameters() { return weights; } - - //! Get the value of run parameter. - bool Run() const { return run; } - //! Modify the value of run parameter. - bool& Run() { return run; } - - arma::mat const& InputParameter() const { return inputParameter; } - //! Modify the input parameter. - arma::mat& InputParameter() { return inputParameter; } - - //! Get the output parameter. - arma::mat const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - arma::mat& OutputParameter() { return outputParameter; } - - //! Get the delta.e - arma::mat const& Delta() const { return delta; } - //! Modify the delta. - arma::mat& Delta() { return delta; } - - //! Get the gradient. - arma::mat const& Gradient() const { return gradient; } - //! Modify the gradient. - arma::mat& Gradient() { return gradient; } - - //! Get the axis of concatenation. - size_t const& ConcatAxis() const { return axis; } - - //! Get the size of the weight matrix. - size_t WeightSize() const { return 0; } - - /** - * Serialize the layer - */ - template - void serialize(Archive& ar, const uint32_t /* version */); - - private: - //! Parameter which indicates the input size of modules. - arma::Row inputSize; - - //! Parameter which indicates the axis of concatenation. - size_t axis; - - //! Parameter which indicates whether to use the axis of concatenation. - bool useAxis; - - //! Parameter which indicates if the modules should be exposed. - bool model; - - //! Parameter which indicates if the Forward/Backward method should be called - //! before merging the output. - bool run; - - //! Parameter to store channels. - size_t channels; - - //! Locally-stored network modules. - std::vector > network; - - //! Locally-stored model weights. - OutputDataType weights; - - //! Locally-stored delta visitor. - DeltaVisitor deltaVisitor; - - //! Locally-stored output parameter visitor. - OutputParameterVisitor outputParameterVisitor; - - //! Locally-stored delete visitor. - DeleteVisitor deleteVisitor; - - //! Locally-stored empty list of modules. - std::vector > empty; - - //! Locally-stored delta object. - arma::mat delta; - - //! Locally-stored input parameter object. - arma::mat inputParameter; - - //! Locally-stored output parameter object. - arma::mat outputParameter; - - //! Locally-stored gradient object. - arma::mat gradient; -}; // class Concat - -} // namespace ann -} // namespace mlpack - -// Include implementation. -#include "concat_impl.hpp" - -#endif diff --git a/src/mlpack/methods/ann/layer/concat_impl.hpp b/src/mlpack/methods/ann/layer/concat_impl.hpp deleted file mode 100644 index b410361295..0000000000 --- a/src/mlpack/methods/ann/layer/concat_impl.hpp +++ /dev/null @@ -1,293 +0,0 @@ -/** - * @file methods/ann/layer/concat_impl.hpp - * @author Marcus Edel - * @author Mehul Kumar Nirala - * - * Implementation of the Concat class, which acts as a concatenation contain. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_LAYER_CONCAT_IMPL_HPP -#define MLPACK_METHODS_ANN_LAYER_CONCAT_IMPL_HPP - -// In case it hasn't yet been included. -#include "concat.hpp" - -#include "../visitor/forward_visitor.hpp" -#include "../visitor/backward_visitor.hpp" -#include "../visitor/gradient_visitor.hpp" - -namespace mlpack { -namespace ann /** Artificial Neural Network. */ { - -template -Concat::Concat( - const bool model, const bool run) : - axis(0), - useAxis(false), - model(model), - run(run), - channels(1) -{ - weights.set_size(0, 0); -} - -template -Concat::Concat( - arma::Row& inputSize, - const size_t axis, - const bool model, - const bool run) : - inputSize(inputSize), - axis(axis), - useAxis(true), - model(model), - run(run) -{ - weights.set_size(0, 0); - - // Parameters to help calculate the number of channels. - size_t oldColSize = 1, newColSize = 1; - // Axis is specified and useAxis is true. - if (useAxis) - { - // Axis is specified without input dimension. - // Throw an error. - if (inputSize.n_elem > 0) - { - // Calculate rowSize, newColSize based on the axis - // of concatenation. Finally concat along cols and - // reshape to original format i.e. (input, batch_size). - size_t i = std::min(axis + 1, (size_t) inputSize.n_elem); - for (; i < inputSize.n_elem; ++i) - { - newColSize *= inputSize[i]; - } - } - else - { - throw std::logic_error("Input dimensions not specified."); - } - } - else - { - channels = 1; - } - if (newColSize <= 0) - { - throw std::logic_error("Col size is zero."); - } - channels = newColSize / oldColSize; - inputSize.clear(); -} - -template -Concat::~Concat() -{ - if (!model) - { - // Clear memory. - std::for_each(network.begin(), network.end(), - boost::apply_visitor(deleteVisitor)); - } -} - -template -template -void Concat::Forward( - const arma::Mat& input, arma::Mat& output) -{ - if (run) - { - for (size_t i = 0; i < network.size(); ++i) - { - boost::apply_visitor(ForwardVisitor(input, - boost::apply_visitor(outputParameterVisitor, network[i])), - network[i]); - } - } - - output = boost::apply_visitor(outputParameterVisitor, network.front()); - - // Reshape output to incorporate the channels. - output.reshape(output.n_rows / channels, output.n_cols * channels); - - for (size_t i = 1; i < network.size(); ++i) - { - arma::Mat out = boost::apply_visitor(outputParameterVisitor, - network[i]); - - out.reshape(out.n_rows / channels, out.n_cols * channels); - - // Vertically concatentate output from each layer. - output = arma::join_cols(output, out); - } - // Reshape output to its original shape. - output.reshape(output.n_rows * channels, output.n_cols / channels); -} - -template -template -void Concat::Backward( - const arma::Mat& /* input */, const arma::Mat& gy, arma::Mat& g) -{ - size_t rowCount = 0; - if (run) - { - arma::Mat delta; - arma::Mat gyTmp(((arma::Mat&) gy).memptr(), gy.n_rows / channels, - gy.n_cols * channels, false, false); - for (size_t i = 0; i < network.size(); ++i) - { - // Use rows from the error corresponding to the output from each layer. - size_t rows = boost::apply_visitor( - outputParameterVisitor, network[i]).n_rows; - - // Extract from gy the parameters for the i-th network. - delta = gyTmp.rows(rowCount / channels, (rowCount + rows) / channels - 1); - delta.reshape(delta.n_rows * channels, delta.n_cols / channels); - - boost::apply_visitor(BackwardVisitor( - boost::apply_visitor(outputParameterVisitor, - network[i]), delta, - boost::apply_visitor(deltaVisitor, network[i])), network[i]); - rowCount += rows; - } - - g = boost::apply_visitor(deltaVisitor, network[0]); - for (size_t i = 1; i < network.size(); ++i) - { - g += boost::apply_visitor(deltaVisitor, network[i]); - } - } - else - { - g = gy; - } -} - -template -template -void Concat::Backward( - const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g, - const size_t index) -{ - size_t rowCount = 0, rows = 0; - - for (size_t i = 0; i < index; ++i) - { - rowCount += boost::apply_visitor( - outputParameterVisitor, network[i]).n_rows; - } - rows = boost::apply_visitor(outputParameterVisitor, network[index]).n_rows; - - // Reshape gy to extract the i-th layer gy. - arma::Mat gyTmp(((arma::Mat&) gy).memptr(), gy.n_rows / channels, - gy.n_cols * channels, false, false); - - arma::Mat delta = gyTmp.rows(rowCount / channels, (rowCount + rows) / - channels - 1); - delta.reshape(delta.n_rows * channels, delta.n_cols / channels); - - boost::apply_visitor(BackwardVisitor(boost::apply_visitor( - outputParameterVisitor, network[index]), delta, - boost::apply_visitor(deltaVisitor, network[index])), network[index]); - - g = boost::apply_visitor(deltaVisitor, network[index]); -} - -template -template -void Concat::Gradient( - const arma::Mat& input, - const arma::Mat& error, - arma::Mat& /* gradient */) -{ - if (run) - { - size_t rowCount = 0; - // Reshape error to extract the i-th layer error. - arma::Mat errorTmp(((arma::Mat&) error).memptr(), - error.n_rows / channels, error.n_cols * channels, false, false); - for (size_t i = 0; i < network.size(); ++i) - { - size_t rows = boost::apply_visitor( - outputParameterVisitor, network[i]).n_rows; - - // Extract from error the parameters for the i-th network. - arma::Mat err = errorTmp.rows(rowCount / channels, (rowCount + rows) / - channels - 1); - err.reshape(err.n_rows * channels, err.n_cols / channels); - - boost::apply_visitor(GradientVisitor(input, err), network[i]); - rowCount += rows; - } - } -} - -template -template -void Concat::Gradient( - const arma::Mat& input, - const arma::Mat& error, - arma::Mat& /* gradient */, - const size_t index) -{ - size_t rowCount = 0; - for (size_t i = 0; i < index; ++i) - { - rowCount += boost::apply_visitor(outputParameterVisitor, - network[i]).n_rows; - } - size_t rows = boost::apply_visitor( - outputParameterVisitor, network[index]).n_rows; - - arma::Mat errorTmp(((arma::Mat&) error).memptr(), - error.n_rows / channels, error.n_cols * channels, false, false); - arma::Mat err = errorTmp.rows(rowCount / channels, (rowCount + rows) / - channels - 1); - err.reshape(err.n_rows * channels, err.n_cols / channels); - - boost::apply_visitor(GradientVisitor(input, err), network[index]); -} - -template -template -void Concat::serialize( - Archive& ar, const uint32_t /* version */) -{ - ar(CEREAL_NVP(model)); - ar(CEREAL_NVP(run)); - - // Do we have to load or save a model? - if (model) - { - // Clear memory first, if needed. - if (cereal::is_loading()) - { - std::for_each(network.begin(), network.end(), - boost::apply_visitor(deleteVisitor)); - } - ar(CEREAL_VECTOR_VARIANT_POINTER(network)); - } -} - -} // namespace ann -} // namespace mlpack - - -#endif diff --git a/src/mlpack/methods/ann/layer/concatenate.hpp b/src/mlpack/methods/ann/layer/concatenate.hpp index 561ecf2595..7c21560465 100644 --- a/src/mlpack/methods/ann/layer/concatenate.hpp +++ b/src/mlpack/methods/ann/layer/concatenate.hpp @@ -14,7 +14,7 @@ #define MLPACK_METHODS_ANN_LAYER_CONCATENATE_HPP #include -#include +#include "layer.hpp" namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -22,36 +22,39 @@ namespace ann /** Artificial Neural Network. */ { /** * Implementation of the Concatenate module class. The Concatenate module * concatenates a constant given matrix to the incoming data. - * Note: Users need to use the Concat() function to provide the concat matrix. * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * The Concat() function provides the concat matrix, or it can be passed to + * the constructor. + * + * After this layer is applied, the shape of the data will be a vector. + * + * @tparam MatType Matrix representation to accept as input and use for + * computation. */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class Concatenate +template +class ConcatenateType : public Layer { public: /** - * Create the Concatenate object using the specified number of output units. + * Create the ConcatenateType object using the given constant matrix as the + * data to be concatenated to the output of the forward pass. */ - Concatenate(); + ConcatenateType(const MatType& concat = MatType()); - //! Copy constructor. - Concatenate(const Concatenate& layer); + //! Clone the ConcatenateType object. This handles polymorphism correctly. + ConcatenateType* Clone() const { return new ConcatenateType(*this); } - //! Move constructor. - Concatenate(Concatenate&& layer); + // Virtual destructor. + virtual ~ConcatenateType() { } - //! Operator= copy constructor. - Concatenate& operator=(const Concatenate& layer); - - //! Operator= move constructor. - Concatenate& operator=(Concatenate&& layer); + //! Copy the given ConcatenateType layer. + ConcatenateType(const ConcatenateType& other); + //! Take ownership of the given ConcatenateType layer. + ConcatenateType(ConcatenateType&& other); + //! Copy the given ConcatenateType layer. + ConcatenateType& operator=(const ConcatenateType& other); + //! Take ownership of the given ConcatenateType layer. + ConcatenateType& operator=(ConcatenateType&& other); /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -60,8 +63,7 @@ class Concatenate * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template - void Forward(const arma::Mat& input, arma::Mat& output); + void Forward(const MatType& input, MatType& output); /** * Ordinary feed backward pass of a neural network, calculating the function @@ -72,57 +74,31 @@ class Concatenate * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g); - - //! Get the parameters. - OutputDataType const& Parameters() const { return weights; } - //! Modify the parameters. - OutputDataType& Parameters() { return weights; } - - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } + void Backward(const MatType& /* input */, const MatType& gy, MatType& g); //! Get the concat matrix. - OutputDataType const& Concat() const { return concat; } + MatType const& Concat() const { return concat; } //! Modify the concat. - OutputDataType& Concat() { return concat; } + MatType& Concat() { return concat; } + + //! Compute the output dimensions of the layer based on `InputDimensions()`. + void ComputeOutputDimensions(); /** - * Serialize the layer + * Serialize the layer. */ template - void serialize(Archive& /* ar */, const uint32_t /* version */) - { - // Nothing to do here. - } + void serialize(Archive& ar, const uint32_t /* version */); private: - //! Locally-stored number of input rows. - size_t inRows; + //! Matrix to be concatenated to input. + MatType concat; - //! Locally-stored weight object. - OutputDataType weights; - - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; - - //! Locally-stored matrix to be concatenated to input. - OutputDataType concat; }; // class Concatenate +// Standard Concatenate layer. +typedef ConcatenateType Concatenate; + } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/concatenate_impl.hpp b/src/mlpack/methods/ann/layer/concatenate_impl.hpp index 54b53471c8..8980e0c606 100644 --- a/src/mlpack/methods/ann/layer/concatenate_impl.hpp +++ b/src/mlpack/methods/ann/layer/concatenate_impl.hpp @@ -19,91 +19,106 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -Concatenate::Concatenate() : - inRows(0) +template +ConcatenateType:: +ConcatenateType(const MatType& concat) : + concat(concat) { // Nothing to do here. } -template -Concatenate::Concatenate( - const Concatenate& layer) : - inRows(layer.inRows), - weights(layer.weights), - delta(layer.delta), - concat(layer.concat) +template +ConcatenateType:: +ConcatenateType(const ConcatenateType& other) : + Layer(other), + concat(other.concat) { - // Nothing to to here. + // Nothing to do. } -template -Concatenate::Concatenate(Concatenate&& layer) : - inRows(layer.inRows), - weights(std::move(layer.weights)), - delta(std::move(layer.delta)), - concat(std::move(layer.concat)) +template +ConcatenateType:: +ConcatenateType(ConcatenateType&& other) : + Layer(std::move(other)), + concat(other.concat) { - // Nothing to do here. + // Nothing to do. } -template -Concatenate& -Concatenate:: -operator=(const Concatenate& layer) +template +ConcatenateType& +ConcatenateType::operator=(const ConcatenateType& other) { - if (this != &layer) + if (&other != this) { - inRows = layer.inRows; - weights = layer.weights; - delta = layer.delta; - concat = layer.concat; + Layer::operator=(other); + concat = other.concat; } return *this; } -template -Concatenate& -Concatenate:: -operator=(Concatenate&& layer) +template +ConcatenateType& +ConcatenateType::operator=(ConcatenateType&& other) { - if (this != &layer) + if (&other != this) { - inRows = layer.inRows; - weights = std::move(layer.weights); - delta = std::move(layer.delta); - concat = std::move(layer.concat); + Layer::operator=(std::move(other)); + concat = std::move(other.concat); } + return *this; } -template -template -void Concatenate::Forward( - const arma::Mat& input, arma::Mat& output) +template +void ConcatenateType::Forward(const MatType& input, MatType& output) { if (concat.is_empty()) - Log::Warn << "The concat matrix has not been provided." << std::endl; - - if (input.n_cols != concat.n_cols) { - Log::Fatal << "The number of columns of the concat matrix should be equal " - << "to the number of columns of input matrix." << std::endl; + Log::Warn << "Concatenate::Forward(): the concat matrix is empty or was " + << "not provided." << std::endl; } - inRows = input.n_rows; - output = arma::join_cols(input, concat); + output.submat(0, 0, input.n_rows - 1, input.n_cols - 1) = input; + output.submat(input.n_rows, 0, output.n_rows - 1, input.n_cols - 1) = + arma::repmat(arma::vectorise(concat), 1, input.n_cols); } -template -template -void Concatenate::Backward( - const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g) +template +void ConcatenateType::Backward( + const MatType& /* input */, + const MatType& gy, + MatType& g) { - g = gy.submat(0, 0, inRows - 1, concat.n_cols - 1); + // Pass back the non-concatenated part. + g = gy.submat(0, 0, gy.n_rows - 1 - concat.n_elem, gy.n_cols - 1); +} + +template +void ConcatenateType::ComputeOutputDimensions() +{ + // This flattens the input. + size_t inSize = this->inputDimensions[0]; + for (size_t i = 1; i < this->inputDimensions.size(); ++i) + inSize *= this->inputDimensions[i]; + + this->outputDimensions = std::vector(this->inputDimensions.size(), + 1); + this->outputDimensions[0] = inSize + concat.n_elem; +} + +/** + * Serialize the layer. + */ +template +template +void ConcatenateType::serialize( + Archive& ar, const uint32_t /* version */) +{ + ar(cereal::base_class>(this)); + + ar(CEREAL_NVP(concat)); } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/constant_impl.hpp b/src/mlpack/methods/ann/layer/constant_impl.hpp deleted file mode 100644 index 3f16311e46..0000000000 --- a/src/mlpack/methods/ann/layer/constant_impl.hpp +++ /dev/null @@ -1,65 +0,0 @@ -/** - * @file methods/ann/layer/constant_impl.hpp - * @author Marcus Edel - * - * Implementation of the Constant class, which outputs a constant value given - * any input. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_LAYER_CONSTANT_IMPL_HPP -#define MLPACK_METHODS_ANN_LAYER_CONSTANT_IMPL_HPP - -// In case it hasn't yet been included. -#include "constant.hpp" - -namespace mlpack { -namespace ann /** Artificial Neural Network. */ { - -template -Constant::Constant( - const size_t outSize, - const double scalar) : - inSize(0), - outSize(outSize) -{ - constantOutput = OutputDataType(outSize, 1); - constantOutput.fill(scalar); -} - -template -template -void Constant::Forward( - const InputType& input, OutputType& output) -{ - if (inSize == 0) - { - inSize = input.n_elem; - } - - output = constantOutput; -} - -template -template -void Constant::Backward( - const DataType& /* input */, const DataType& /* gy */, DataType& g) -{ - g = arma::zeros(inSize, 1); -} - -template -template -void Constant::serialize( - Archive& ar, const uint32_t /* version */) -{ - ar(CEREAL_NVP(constantOutput)); -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/layer/convolution.hpp b/src/mlpack/methods/ann/layer/convolution.hpp index c325eba95d..d6597edadb 100644 --- a/src/mlpack/methods/ann/layer/convolution.hpp +++ b/src/mlpack/methods/ann/layer/convolution.hpp @@ -20,7 +20,7 @@ #include #include -#include "layer_types.hpp" +#include "layer.hpp" #include "padding.hpp" namespace mlpack { @@ -30,7 +30,7 @@ namespace ann /** Artificial Neural Network. */ { * Implementation of the Convolution class. The Convolution class represents a * single layer of a neural network. * Example usage: - * + * * Suppose we want to pass a matrix M (2744x100) to a `Convolution` layer; * in this example, `M` was obtained from "flattening" 100 images (or Mel * cepstral coefficients, if we talk about speech, or whatever you like) of @@ -62,100 +62,95 @@ namespace ann /** Artificial Neural Network. */ { * @tparam ForwardConvolutionRule Convolution to perform forward process. * @tparam BackwardConvolutionRule Convolution to perform backward process. * @tparam GradientConvolutionRule Convolution to calculate gradient. - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam MatType Matrix representation to accept as input and use for + * computation. */ template < typename ForwardConvolutionRule = NaiveConvolution, typename BackwardConvolutionRule = NaiveConvolution, typename GradientConvolutionRule = NaiveConvolution, - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat + typename MatType = arma::mat > -class Convolution +class ConvolutionType : public Layer { public: - //! Create the Convolution object. - Convolution(); + //! Create the ConvolutionType object. + ConvolutionType(); /** - * Create the Convolution object using the specified number of input maps, - * output maps, filter size, stride and padding parameter. + * Create the ConvolutionType object using the specified number of output + * maps, filter size, stride and padding parameter. * - * @param inSize The number of input maps. - * @param outSize The number of output maps. + * @param maps The number of output maps. * @param kernelWidth Width of the filter/kernel. * @param kernelHeight Height of the filter/kernel. * @param strideWidth Stride of filter application in the x direction. * @param strideHeight Stride of filter application in the y direction. * @param padW Padding width of the input. * @param padH Padding height of the input. - * @param inputWidth The width of the input data. - * @param inputHeight The height of the input data. - * @param paddingType The type of padding (Valid or Same). Defaults to None. + * @param paddingType The type of padding ("valid" or "same"). Defaults to + * "none". If not specified or "none", the values for `padW` and `padH` + * will be used. */ - Convolution(const size_t inSize, - const size_t outSize, - const size_t kernelWidth, - const size_t kernelHeight, - const size_t strideWidth = 1, - const size_t strideHeight = 1, - const size_t padW = 0, - const size_t padH = 0, - const size_t inputWidth = 0, - const size_t inputHeight = 0, - const std::string& paddingType = "None"); + ConvolutionType(const size_t maps, + const size_t kernelWidth, + const size_t kernelHeight, + const size_t strideWidth = 1, + const size_t strideHeight = 1, + const size_t padW = 0, + const size_t padH = 0, + const std::string& paddingType = "none"); /** * Create the Convolution object using the specified number of input maps, * output maps, filter size, stride and padding parameter. * - * @param inSize The number of input maps. - * @param outSize The number of output maps. + * @param maps The number of output maps. * @param kernelWidth Width of the filter/kernel. * @param kernelHeight Height of the filter/kernel. * @param strideWidth Stride of filter application in the x direction. * @param strideHeight Stride of filter application in the y direction. - * @param padW A two-value tuple indicating padding widths of the input. - * First value is padding at left side. Second value is padding on - * right side. - * @param padH A two-value tuple indicating padding heights of the input. - * First value is padding at top. Second value is padding on - * bottom. - * @param inputWidth The width of the input data. - * @param inputHeight The height of the input data. - * @param paddingType The type of padding (Valid or Same). Defaults to None. + * @param padW A two-value tuple indicating padding widths of the input. The + * first value is the padding for the left side; the second value is the + * padding on the right side. + * @param padH A two-value tuple indicating padding heights of the input. The + * first value is the padding for the top; the second value is the + * padding on the bottom. + * @param paddingType The type of padding ("valid" or "same"). Defaults to + * "none". If not specified or "none", the values for `padW` and `padH` + * will be used. */ - Convolution(const size_t inSize, - const size_t outSize, - const size_t kernelWidth, - const size_t kernelHeight, - const size_t strideWidth, - const size_t strideHeight, - const std::tuple& padW, - const std::tuple& padH, - const size_t inputWidth = 0, - const size_t inputHeight = 0, - const std::string& paddingType = "None"); + ConvolutionType(const size_t maps, + const size_t kernelWidth, + const size_t kernelHeight, + const size_t strideWidth, + const size_t strideHeight, + const std::tuple& padW, + const std::tuple& padH, + const std::string& paddingType = "none"); - //! Copy constructor. - Convolution(const Convolution& layer); + //! Clone the ConvolutionType object. This handles polymorphism correctly. + ConvolutionType* Clone() const { return new ConvolutionType(*this); } - //! Move constructor. - Convolution(Convolution&&); + //! Copy the given ConvolutionType (but not weights). + ConvolutionType(const ConvolutionType& layer); - //! Copy assignment operator. - Convolution& operator=(const Convolution& layer); + //! Take ownership of the given ConvolutionType (but not weights). + ConvolutionType(ConvolutionType&&); - //! Move assignment operator. - Convolution& operator=(Convolution&& layer); + //! Copy the given ConvolutionType (but not weights). + ConvolutionType& operator=(const ConvolutionType& layer); + + //! Take ownership of the given ConvolutionType (but not weights). + ConvolutionType& operator=(ConvolutionType&& layer); + + // Virtual destructor. + virtual ~ConvolutionType() { } /* * Set the weight and bias term. */ - void Reset(); + void SetWeights(typename MatType::elem_type* weightsPtr); /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -164,8 +159,7 @@ class Convolution * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template - void Forward(const arma::Mat& input, arma::Mat& output); + void Forward(const MatType& input, MatType& output); /** * Ordinary feed backward pass of a neural network, calculating the function @@ -176,135 +170,91 @@ class Convolution * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g); + void Backward(const MatType& /* input */, + const MatType& gy, + MatType& g); - /* + /** * Calculate the gradient using the output delta and the input activation. * * @param input The input parameter used for calculating the gradient. * @param error The calculated error. * @param gradient The calculated gradient. */ - template - void Gradient(const arma::Mat& /* input */, - const arma::Mat& error, - arma::Mat& gradient); + void Gradient(const MatType& /* input */, + const MatType& error, + MatType& gradient); //! Get the parameters. - OutputDataType const& Parameters() const { return weights; } + MatType const& Parameters() const { return weights; } //! Modify the parameters. - OutputDataType& Parameters() { return weights; } + MatType& Parameters() { return weights; } - //! Get the weight of the layer. - arma::cube const& Weight() const { return weight; } - //! Modify the weight of the layer. - arma::cube& Weight() { return weight; } + //! Get the weight of the layer as a cube. + arma::Cube const& Weight() const + { + return weight; + } + //! Modify the weight of the layer as a cube. + arma::Cube& Weight() { return weight; } //! Get the bias of the layer. - arma::mat const& Bias() const { return bias; } + MatType const& Bias() const { return bias; } //! Modify the bias of the layer. - arma::mat& Bias() { return bias; } - - //! Get the input parameter. - InputDataType const& InputParameter() const { return inputParameter; } - //! Modify the input parameter. - InputDataType& InputParameter() { return inputParameter; } - - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } - - //! Get the gradient. - OutputDataType const& Gradient() const { return gradient; } - //! Modify the gradient. - OutputDataType& Gradient() { return gradient; } - - //! Get the input width. - size_t InputWidth() const { return inputWidth; } - //! Modify input the width. - size_t& InputWidth() { return inputWidth; } - - //! Get the input height. - size_t InputHeight() const { return inputHeight; } - //! Modify the input height. - size_t& InputHeight() { return inputHeight; } - - //! Get the output width. - size_t OutputWidth() const { return outputWidth; } - //! Modify the output width. - size_t& OutputWidth() { return outputWidth; } - - //! Get the output height. - size_t OutputHeight() const { return outputHeight; } - //! Modify the output height. - size_t& OutputHeight() { return outputHeight; } - - //! Get the number of input maps. - size_t InputSize() const { return inSize; } + MatType& Bias() { return bias; } //! Get the number of output maps. - size_t OutputSize() const { return outSize; } + size_t const& Maps() const { return maps; } //! Get the kernel width. - size_t KernelWidth() const { return kernelWidth; } + size_t const& KernelWidth() const { return kernelWidth; } //! Modify the kernel width. size_t& KernelWidth() { return kernelWidth; } //! Get the kernel height. - size_t KernelHeight() const { return kernelHeight; } + size_t const& KernelHeight() const { return kernelHeight; } //! Modify the kernel height. size_t& KernelHeight() { return kernelHeight; } //! Get the stride width. - size_t StrideWidth() const { return strideWidth; } + size_t const& StrideWidth() const { return strideWidth; } //! Modify the stride width. size_t& StrideWidth() { return strideWidth; } //! Get the stride height. - size_t StrideHeight() const { return strideHeight; } + size_t const& StrideHeight() const { return strideHeight; } //! Modify the stride height. size_t& StrideHeight() { return strideHeight; } //! Get the top padding height. - size_t PadHTop() const { return padHTop; } + size_t const& PadHTop() const { return padHTop; } //! Modify the top padding height. size_t& PadHTop() { return padHTop; } //! Get the bottom padding height. - size_t PadHBottom() const { return padHBottom; } + size_t const& PadHBottom() const { return padHBottom; } //! Modify the bottom padding height. size_t& PadHBottom() { return padHBottom; } //! Get the left padding width. - size_t PadWLeft() const { return padWLeft; } + size_t const& PadWLeft() const { return padWLeft; } //! Modify the left padding width. size_t& PadWLeft() { return padWLeft; } //! Get the right padding width. - size_t PadWRight() const { return padWRight; } + size_t const& PadWRight() const { return padWRight; } //! Modify the right padding width. size_t& PadWRight() { return padWRight; } //! Get size of weights for the layer. size_t WeightSize() const { - return (outSize * inSize * kernelWidth * kernelHeight) + outSize; + return (maps * inMaps * higherInDimensions * kernelWidth * kernelHeight) + + maps; } - //! Get the shape of the input. - size_t InputShape() const - { - return inputHeight * inputWidth * inSize; - } + //! Compute the output dimensions of the layer based on `InputDimensions()`. + void ComputeOutputDimensions(); /** * Serialize the layer. @@ -313,7 +263,7 @@ class Convolution void serialize(Archive& ar, const uint32_t /* version */); private: - /* + /** * Return the convolution output size. * * @param size The size of the input (row or column). @@ -332,12 +282,12 @@ class Convolution return std::floor(size + pSideOne + pSideTwo - k) / s + 1; } - /* + /** * Function to assign padding such that output size is same as input size. */ void InitializeSamePadding(); - /* + /** * Rotates a 3rd-order tensor counterclockwise by 180 degrees. * * @param input The input data to be rotated. @@ -353,7 +303,7 @@ class Convolution output.slice(s) = arma::fliplr(arma::flipud(input.slice(s))); } - /* + /** * Rotates a dense matrix counterclockwise by 180 degrees. * * @param input The input data to be rotated. @@ -366,11 +316,8 @@ class Convolution output = arma::fliplr(arma::flipud(input)); } - //! Locally-stored number of input channels. - size_t inSize; - //! Locally-stored number of output channels. - size_t outSize; + size_t maps; //! Locally-stored number of input units. size_t batchSize; @@ -400,54 +347,46 @@ class Convolution size_t padHTop; //! Locally-stored weight object. - OutputDataType weights; + MatType weights; //! Locally-stored weight object. - arma::cube weight; + arma::Cube weight; //! Locally-stored bias term object. - arma::mat bias; - - //! Locally-stored input width. - size_t inputWidth; - - //! Locally-stored input height. - size_t inputHeight; - - //! Locally-stored output width. - size_t outputWidth; - - //! Locally-stored output height. - size_t outputHeight; + MatType bias; //! Locally-stored transformed output parameter. - arma::cube outputTemp; + arma::Cube outputTemp; //! Locally-stored transformed padded input parameter. - arma::cube inputPaddedTemp; + MatType inputPadded; //! Locally-stored transformed error parameter. - arma::cube gTemp; + arma::Cube gTemp; //! Locally-stored transformed gradient parameter. - arma::cube gradientTemp; + arma::Cube gradientTemp; //! Locally-stored padding layer. - ann::Padding<> padding; + ann::Padding padding; - //! Locally-stored delta object. - OutputDataType delta; + //! Type of padding. + std::string paddingType; - //! Locally-stored gradient object. - OutputDataType gradient; - - //! Locally-stored input parameter object. - InputDataType inputParameter; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; + //! Locally-cached number of input maps. + size_t inMaps; + //! Locally-cached higher-order input dimensions. + size_t higherInDimensions; }; // class Convolution +// Standard Convolution layer. +typedef ConvolutionType< + NaiveConvolution, + NaiveConvolution, + NaiveConvolution, + arma::mat +> Convolution; + } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/convolution_impl.hpp b/src/mlpack/methods/ann/layer/convolution_impl.hpp index ba957b6ead..5e4f2c618e 100644 --- a/src/mlpack/methods/ann/layer/convolution_impl.hpp +++ b/src/mlpack/methods/ann/layer/convolution_impl.hpp @@ -22,16 +22,14 @@ template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename InputDataType, - typename OutputDataType + typename MatType > -Convolution< +ConvolutionType< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - InputDataType, - OutputDataType ->::Convolution() + MatType +>::ConvolutionType() { // Nothing to do here. } @@ -40,38 +38,30 @@ template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename InputDataType, - typename OutputDataType + typename MatType > -Convolution< +ConvolutionType< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - InputDataType, - OutputDataType ->::Convolution( - const size_t inSize, - const size_t outSize, + MatType +>::ConvolutionType( + const size_t maps, const size_t kernelWidth, const size_t kernelHeight, const size_t strideWidth, const size_t strideHeight, const size_t padW, const size_t padH, - const size_t inputWidth, - const size_t inputHeight, const std::string& paddingType) : - Convolution( - inSize, - outSize, + ConvolutionType( + maps, kernelWidth, kernelHeight, strideWidth, strideHeight, std::tuple(padW, padW), std::tuple(padH, padH), - inputWidth, - inputHeight, paddingType) { // Nothing to do here. @@ -81,29 +71,23 @@ template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename InputDataType, - typename OutputDataType + typename MatType > -Convolution< +ConvolutionType< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - InputDataType, - OutputDataType ->::Convolution( - const size_t inSize, - const size_t outSize, + MatType +>::ConvolutionType( + const size_t maps, const size_t kernelWidth, const size_t kernelHeight, const size_t strideWidth, const size_t strideHeight, const std::tuple& padW, const std::tuple& padH, - const size_t inputWidth, - const size_t inputHeight, - const std::string& paddingType) : - inSize(inSize), - outSize(outSize), + const std::string& paddingTypeIn) : + maps(maps), kernelWidth(kernelWidth), kernelHeight(kernelHeight), strideWidth(strideWidth), @@ -111,443 +95,484 @@ Convolution< padWLeft(std::get<0>(padW)), padWRight(std::get<1>(padW)), padHBottom(std::get<1>(padH)), - padHTop(std::get<0>(padH)), - inputWidth(inputWidth), - inputHeight(inputHeight), - outputWidth(0), - outputHeight(0) + padHTop(std::get<0>(padH)) { - weights.set_size(WeightSize(), 1); - // Transform paddingType to lowercase. - const std::string paddingTypeLow = util::ToLower(paddingType); + this->paddingType = util::ToLower(paddingTypeIn); +} - if (paddingTypeLow == "valid") +template< + typename ForwardConvolutionRule, + typename BackwardConvolutionRule, + typename GradientConvolutionRule, + typename MatType +> +ConvolutionType< + ForwardConvolutionRule, + BackwardConvolutionRule, + GradientConvolutionRule, + MatType +>::ConvolutionType(const ConvolutionType& other) : + Layer(other), + maps(other.maps), + kernelWidth(other.kernelWidth), + kernelHeight(other.kernelHeight), + strideWidth(other.strideWidth), + strideHeight(other.strideHeight), + padWLeft(other.padWLeft), + padWRight(other.padWRight), + padHBottom(other.padHBottom), + padHTop(other.padHTop), + padding(other.padding), + paddingType(other.paddingType), + inMaps(other.inMaps), + higherInDimensions(other.higherInDimensions) +{ + // Nothing to do. +} + +template< + typename ForwardConvolutionRule, + typename BackwardConvolutionRule, + typename GradientConvolutionRule, + typename MatType +> +ConvolutionType< + ForwardConvolutionRule, + BackwardConvolutionRule, + GradientConvolutionRule, + MatType +>::ConvolutionType(ConvolutionType&& other) : + Layer(std::move(other)), + maps(std::move(other.maps)), + kernelWidth(std::move(other.kernelWidth)), + kernelHeight(std::move(other.kernelHeight)), + strideWidth(std::move(other.strideWidth)), + strideHeight(std::move(other.strideHeight)), + padWLeft(std::move(other.padWLeft)), + padWRight(std::move(other.padWRight)), + padHBottom(std::move(other.padHBottom)), + padHTop(std::move(other.padHTop)), + padding(std::move(other.padding)), + paddingType(std::move(other.paddingType)), + inMaps(std::move(other.inMaps)), + higherInDimensions(std::move(other.higherInDimensions)) +{ + // Nothing to do. +} + +template< + typename ForwardConvolutionRule, + typename BackwardConvolutionRule, + typename GradientConvolutionRule, + typename MatType +> +ConvolutionType< + ForwardConvolutionRule, + BackwardConvolutionRule, + GradientConvolutionRule, + MatType +>& +ConvolutionType< + ForwardConvolutionRule, + BackwardConvolutionRule, + GradientConvolutionRule, + MatType +>::operator=(const ConvolutionType& other) +{ + if (&other != this) + { + Layer::operator=(other); + maps = other.maps; + kernelWidth = other.kernelWidth; + kernelHeight = other.kernelHeight; + strideWidth = other.strideWidth; + strideHeight = other.strideHeight; + padWLeft = other.padWLeft; + padWRight = other.padWRight; + padHBottom = other.padHBottom; + padHTop = other.padHTop; + padding = other.padding; + paddingType = other.paddingType; + inMaps = other.inMaps; + higherInDimensions = other.higherInDimensions; + } + + return *this; +} + +template< + typename ForwardConvolutionRule, + typename BackwardConvolutionRule, + typename GradientConvolutionRule, + typename MatType +> +ConvolutionType< + ForwardConvolutionRule, + BackwardConvolutionRule, + GradientConvolutionRule, + MatType +>& +ConvolutionType< + ForwardConvolutionRule, + BackwardConvolutionRule, + GradientConvolutionRule, + MatType +>::operator=(ConvolutionType&& other) +{ + if (&other != this) + { + Layer::operator=(std::move(other)); + maps = std::move(other.maps); + kernelWidth = std::move(other.kernelWidth); + kernelHeight = std::move(other.kernelHeight); + strideWidth = std::move(other.strideWidth); + strideHeight = std::move(other.strideHeight); + padWLeft = std::move(other.padWLeft); + padWRight = std::move(other.padWRight); + padHBottom = std::move(other.padHBottom); + padHTop = std::move(other.padHTop); + padding = std::move(other.padding); + paddingType = std::move(other.paddingType); + inMaps = std::move(other.inMaps); + higherInDimensions = std::move(other.higherInDimensions); + } + + return *this; +} + +template< + typename ForwardConvolutionRule, + typename BackwardConvolutionRule, + typename GradientConvolutionRule, + typename MatType +> +void ConvolutionType< + ForwardConvolutionRule, + BackwardConvolutionRule, + GradientConvolutionRule, + MatType +>::SetWeights(typename MatType::elem_type* weightPtr) +{ + MakeAlias(weight, weightPtr, kernelWidth, kernelHeight, maps * inMaps); + MakeAlias(bias, weightPtr + weight.n_elem, maps, 1); + MakeAlias(weights, weightPtr, weight.n_elem + bias.n_elem, 1); +} + +template< + typename ForwardConvolutionRule, + typename BackwardConvolutionRule, + typename GradientConvolutionRule, + typename MatType +> +void ConvolutionType< + ForwardConvolutionRule, + BackwardConvolutionRule, + GradientConvolutionRule, + MatType +>::Forward(const MatType& input, MatType& output) +{ + batchSize = input.n_cols; + + // First, perform any padding if necessary. + const bool usingPadding = + (padWLeft != 0 || padWRight != 0 || padHTop != 0 || padHBottom != 0); + const size_t paddedRows = this->inputDimensions[0] + padWLeft + padWRight; + const size_t paddedCols = this->inputDimensions[1] + padHTop + padHBottom; + if (usingPadding) + { + inputPadded.set_size(paddedRows * paddedCols * inMaps * higherInDimensions, + input.n_cols); + padding.Forward(input, inputPadded); + } + + arma::Cube inputTemp; + MakeAlias(inputTemp, + const_cast(usingPadding ? inputPadded : input).memptr(), + paddedRows, paddedCols, inMaps * higherInDimensions * batchSize); + + MakeAlias(outputTemp, output.memptr(), this->outputDimensions[0], + this->outputDimensions[1], maps * higherInDimensions * batchSize); + outputTemp.zeros(); + + // We "ignore" dimensions higher than the third---that means that we just pass + // them through and treat them like different input points. + // + // If we eventually have a way to do convolutions for a single kernel + // in-batch, then this strategy may not be the most efficient solution. + for (size_t offset = 0; offset < (higherInDimensions * batchSize); ++offset) + { + const size_t fullInputOffset = offset * inMaps; + const size_t fullOutputOffset = offset * maps; + + // Iterate over output maps. + for (size_t outMap = 0; outMap < maps; ++outMap) + { + // Iterate over input maps (we will apply the filter and sum). + for (size_t inMap = 0; inMap < inMaps; ++inMap) + { + MatType convOutput; + + ForwardConvolutionRule::Convolution( + inputTemp.slice(inMap + fullInputOffset), + weight.slice(outMap), + convOutput, + strideWidth, + strideHeight); + + outputTemp.slice(outMap + fullOutputOffset) += convOutput; + } + + // Make sure to add the bias. + outputTemp.slice(outMap + fullOutputOffset) += bias(outMap); + } + } +} + +template< + typename ForwardConvolutionRule, + typename BackwardConvolutionRule, + typename GradientConvolutionRule, + typename MatType +> +void ConvolutionType< + ForwardConvolutionRule, + BackwardConvolutionRule, + GradientConvolutionRule, + MatType +>::Backward( + const MatType& /* input */, const MatType& gy, MatType& g) +{ + arma::Cube mappedError; + MakeAlias(mappedError, ((MatType&) gy).memptr(), this->outputDimensions[0], + this->outputDimensions[1], higherInDimensions * maps * batchSize); + + MakeAlias(gTemp, g.memptr(), this->inputDimensions[0], + this->inputDimensions[1], inMaps * higherInDimensions * batchSize); + gTemp.zeros(); + + const bool usingPadding = + (padWLeft != 0 || padWRight != 0 || padHTop != 0 || padHBottom != 0); + + // To perform the backward pass, we need to rotate all the filters. + arma::Cube rotatedFilters(weight.n_cols, + weight.n_rows, weight.n_slices); + for (size_t map = 0; map < maps; ++map) + { + Rotate180(weight.slice(map), rotatedFilters.slice(map)); + } + + // See Forward() for the overall iteration strategy. + for (size_t offset = 0; offset < (higherInDimensions * batchSize); ++offset) + { + const size_t fullInputOffset = offset * inMaps; + const size_t fullOutputOffset = offset * maps; + + // Iterate over input maps. + for (size_t inMap = 0; inMap < inMaps; ++inMap) + { + // Iterate over output maps. + for (size_t outMap = 0; outMap < maps; ++outMap) + { + MatType output; + + BackwardConvolutionRule::Convolution( + mappedError.slice(outMap + fullOutputOffset), + rotatedFilters.slice(outMap), + output, + strideHeight, + strideWidth); + + // If the stride width or height is greater than 1, then we have to + // insert columns and rows into the convolution output. + if (strideWidth == 1 && strideHeight == 1) + { + if (usingPadding) + { + gTemp.slice(inMap + fullInputOffset) += output.submat( + padWLeft, + padHTop, + padWLeft + gTemp.n_rows - 1, + padHTop + gTemp.n_cols - 1); + } + else + { + gTemp.slice(inMap + fullInputOffset) += output; + } + } + else + { + // We must iterate over each element of the output and manually + // re-insert the stride. + size_t col = padWLeft; + for (size_t i = 0; i < output.n_cols; ++i) + { + size_t row = padHTop; + for (size_t j = 0; j < output.n_rows; ++j) + { + gTemp(row, col, inMap + fullInputOffset) += output(j, i); + row += strideHeight; + } + col += strideWidth; + } + } + } + } + } +} + +template< + typename ForwardConvolutionRule, + typename BackwardConvolutionRule, + typename GradientConvolutionRule, + typename MatType +> +void ConvolutionType< + ForwardConvolutionRule, + BackwardConvolutionRule, + GradientConvolutionRule, + MatType +>::Gradient( + const MatType& input, + const MatType& error, + MatType& gradient) +{ + arma::Cube mappedError; + MakeAlias(mappedError, ((MatType&) error).memptr(), + this->outputDimensions[0], this->outputDimensions[1], + higherInDimensions * maps * batchSize); + + // We are depending here on `inputPadded` being properly set from a call to + // Forward(). + const bool usingPadding = + (padWLeft != 0 || padWRight != 0 || padHTop != 0 || padHBottom != 0); + const size_t paddedRows = this->inputDimensions[0] + padWLeft + padWRight; + const size_t paddedCols = this->inputDimensions[1] + padHTop + padHBottom; + + arma::Cube inputTemp( + const_cast(usingPadding ? inputPadded : input).memptr(), + paddedRows, paddedCols, inMaps * batchSize, false, false); + + // We will make an alias for the gradient, but note that this is only for the + // convolution map weights! The bias will be handled by direct accesses into + // `gradient`. + gradient.zeros(); + MakeAlias(gradientTemp, gradient.memptr(), weight.n_rows, weight.n_cols, + weight.n_slices); + + // See Forward() for our iteration strategy. + for (size_t offset = 0; offset < higherInDimensions * batchSize; ++offset) + { + const size_t fullInputOffset = offset * inMaps; + const size_t fullOutputOffset = offset * maps; + + for (size_t outMap = 0; outMap < maps; ++outMap) + { + for (size_t inMap = 0; inMap < inMaps; ++inMap) + { + MatType output; + GradientConvolutionRule::Convolution( + inputTemp.slice(inMap + fullInputOffset), + mappedError.slice(outMap + fullOutputOffset), + output, + strideWidth, + strideHeight); + + // TODO: understand this conditional. Is it needed? + if (gradientTemp.n_rows < output.n_rows || + gradientTemp.n_cols < output.n_cols) + { + gradientTemp.slice(outMap) += output.submat(0, 0, + gradientTemp.n_rows - 1, gradientTemp.n_cols - 1); + } + else if (gradientTemp.n_rows > output.n_rows || + gradientTemp.n_cols > output.n_cols) + { + gradientTemp.slice(outMap).submat(0, 0, output.n_rows - 1, + output.n_cols - 1) += output; + } + else + { + gradientTemp.slice(outMap) += output; + } + } + + gradient[weight.n_elem + outMap] += arma::accu(mappedError.slice(outMap + + fullOutputOffset)); + } + } +} + +template< + typename ForwardConvolutionRule, + typename BackwardConvolutionRule, + typename GradientConvolutionRule, + typename MatType +> +void ConvolutionType< + ForwardConvolutionRule, + BackwardConvolutionRule, + GradientConvolutionRule, + MatType +>::ComputeOutputDimensions() +{ + // First, we must make sure the padding sizes are up to date, which we can + // now do since inputDimensions is set correctly. + if (paddingType == "valid") { padWLeft = 0; padWRight = 0; padHTop = 0; padHBottom = 0; } - else if (paddingTypeLow == "same") + else if (paddingType == "same") { InitializeSamePadding(); } - padding = ann::Padding<>(padWLeft, padWRight, padHTop, padHBottom); -} + padding = ann::Padding(padWLeft, padWRight, padHTop, padHBottom); + padding.InputDimensions() = this->inputDimensions; + padding.ComputeOutputDimensions(); -template< - typename ForwardConvolutionRule, - typename BackwardConvolutionRule, - typename GradientConvolutionRule, - typename InputDataType, - typename OutputDataType -> -Convolution< - ForwardConvolutionRule, - BackwardConvolutionRule, - GradientConvolutionRule, - InputDataType, - OutputDataType ->::Convolution( - const Convolution& layer) : - inSize(layer.inSize), - outSize(layer.outSize), - kernelWidth(layer.kernelWidth), - kernelHeight(layer.kernelHeight), - strideWidth(layer.strideWidth), - strideHeight(layer.strideHeight), - padWLeft(layer.padWLeft), - padWRight(layer.padWRight), - padHBottom(layer.padHBottom), - padHTop(layer.padHTop), - weights(layer.weights), - inputWidth(layer.inputWidth), - inputHeight(layer.inputHeight), - outputWidth(layer.outputWidth), - outputHeight(layer.outputHeight), - padding(layer.padding) -{ - // Nothing to do here. -} + // We must ensure that the output has at least 3 dimensions, since we will + // be adding some number of maps to the output. + this->outputDimensions = std::vector( + std::max(this->inputDimensions.size(), size_t(3)), 1); + this->outputDimensions[0] = ConvOutSize(this->inputDimensions[0], + kernelWidth, strideWidth, padWLeft, padWRight); + this->outputDimensions[1] = ConvOutSize(this->inputDimensions[1], + kernelHeight, strideHeight, padHTop, padHBottom); -template< - typename ForwardConvolutionRule, - typename BackwardConvolutionRule, - typename GradientConvolutionRule, - typename InputDataType, - typename OutputDataType -> -Convolution< - ForwardConvolutionRule, - BackwardConvolutionRule, - GradientConvolutionRule, - InputDataType, - OutputDataType ->::Convolution( - Convolution&& layer) : - inSize(0), - outSize(0), - kernelWidth(layer.kernelWidth), - kernelHeight(layer.kernelHeight), - strideWidth(layer.strideWidth), - strideHeight(layer.strideHeight), - padWLeft(layer.padWLeft), - padWRight(layer.padWRight), - padHBottom(layer.padHBottom), - padHTop(layer.padHTop), - weights(std::move(layer.weights)), - inputWidth(layer.inputWidth), - inputHeight(layer.inputHeight), - outputWidth(layer.outputWidth), - outputHeight(layer.outputHeight), - padding(std::move(layer.padding)) -{ - // Nothing to do here. -} + inMaps = (this->inputDimensions.size() >= 3) ? this->inputDimensions[2] : 1; -template< - typename ForwardConvolutionRule, - typename BackwardConvolutionRule, - typename GradientConvolutionRule, - typename InputDataType, - typename OutputDataType -> -Convolution< - ForwardConvolutionRule, - BackwardConvolutionRule, - GradientConvolutionRule, - InputDataType, - OutputDataType ->& -Convolution< - ForwardConvolutionRule, - BackwardConvolutionRule, - GradientConvolutionRule, - InputDataType, - OutputDataType ->:: -operator=(const Convolution& layer) -{ - if (this != &layer) + // Compute and cache the total number of input maps. + higherInDimensions = 1; + for (size_t i = 3; i < this->inputDimensions.size(); ++i) { - inSize = layer.inSize; - outSize = layer.outSize; - kernelWidth = layer.kernelWidth; - kernelHeight = layer.kernelHeight; - strideWidth = layer.strideWidth; - strideHeight = layer.strideHeight; - padWLeft = layer.padWLeft; - padWRight = layer.padWRight; - padHBottom = layer.padHBottom; - padHTop = layer.padHTop; - inputWidth = layer.inputWidth; - inputHeight = layer.inputHeight; - outputWidth = layer.outputWidth; - outputHeight = layer.outputHeight; - padding = layer.padding; - weights = layer.weights; + higherInDimensions *= this->inputDimensions[i]; + this->outputDimensions[i] = this->inputDimensions[i]; } - return *this; -} -template< - typename ForwardConvolutionRule, - typename BackwardConvolutionRule, - typename GradientConvolutionRule, - typename InputDataType, - typename OutputDataType -> -Convolution< - ForwardConvolutionRule, - BackwardConvolutionRule, - GradientConvolutionRule, - InputDataType, - OutputDataType ->& -Convolution< - ForwardConvolutionRule, - BackwardConvolutionRule, - GradientConvolutionRule, - InputDataType, - OutputDataType ->:: -operator=(Convolution&& layer) -{ - if (this != &layer) - { - inSize = layer.inSize; - outSize = layer.outSize; - kernelWidth = layer.kernelWidth; - kernelHeight = layer.kernelHeight; - strideWidth = layer.strideWidth; - strideHeight = layer.strideHeight; - padWLeft = layer.padWLeft; - padWRight = layer.padWRight; - padHBottom = layer.padHBottom; - padHTop = layer.padHTop; - inputWidth = layer.inputWidth; - inputHeight = layer.inputHeight; - outputWidth = layer.outputWidth; - outputHeight = layer.outputHeight; - padding = std::move(layer.padding); - weights = std::move(layer.weights); - } - - return *this; + this->outputDimensions[2] = maps; } template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename InputDataType, - typename OutputDataType -> -void Convolution< - ForwardConvolutionRule, - BackwardConvolutionRule, - GradientConvolutionRule, - InputDataType, - OutputDataType ->::Reset() -{ - weight = arma::cube(weights.memptr(), kernelWidth, kernelHeight, - outSize * inSize, false, false); - bias = arma::mat(weights.memptr() + weight.n_elem, - outSize, 1, false, false); -} - -template< - typename ForwardConvolutionRule, - typename BackwardConvolutionRule, - typename GradientConvolutionRule, - typename InputDataType, - typename OutputDataType -> -template -void Convolution< - ForwardConvolutionRule, - BackwardConvolutionRule, - GradientConvolutionRule, - InputDataType, - OutputDataType ->::Forward(const arma::Mat& input, arma::Mat& output) -{ - batchSize = input.n_cols; - arma::cube inputTemp(const_cast&>(input).memptr(), - inputWidth, inputHeight, inSize * batchSize, false, false); - - if (padWLeft != 0 || padWRight != 0 || padHTop != 0 || padHBottom != 0) - { - inputPaddedTemp.set_size(inputTemp.n_rows + padWLeft + padWRight, - inputTemp.n_cols + padHTop + padHBottom, inputTemp.n_slices); - - for (size_t i = 0; i < inputTemp.n_slices; ++i) - { - padding.Forward(inputTemp.slice(i), inputPaddedTemp.slice(i)); - } - } - - size_t wConv = ConvOutSize(inputWidth, kernelWidth, strideWidth, padWLeft, - padWRight); - size_t hConv = ConvOutSize(inputHeight, kernelHeight, strideHeight, padHTop, - padHBottom); - - output.set_size(wConv * hConv * outSize, batchSize); - outputTemp = arma::Cube(output.memptr(), wConv, hConv, - outSize * batchSize, false, false); - outputTemp.zeros(); - - for (size_t outMap = 0, outMapIdx = 0, batchCount = 0; outMap < - outSize * batchSize; outMap++) - { - if (outMap != 0 && outMap % outSize == 0) - { - batchCount++; - outMapIdx = 0; - } - - for (size_t inMap = 0; inMap < inSize; inMap++, outMapIdx++) - { - arma::Mat convOutput; - - if (padWLeft != 0 || padWRight != 0 || padHTop != 0 || padHBottom != 0) - { - ForwardConvolutionRule::Convolution(inputPaddedTemp.slice(inMap + - batchCount * inSize), weight.slice(outMapIdx), convOutput, - strideWidth, strideHeight); - } - else - { - ForwardConvolutionRule::Convolution(inputTemp.slice(inMap + - batchCount * inSize), weight.slice(outMapIdx), convOutput, - strideWidth, strideHeight); - } - - outputTemp.slice(outMap) += convOutput; - } - - outputTemp.slice(outMap) += bias(outMap % outSize); - } - - outputWidth = outputTemp.n_rows; - outputHeight = outputTemp.n_cols; -} - -template< - typename ForwardConvolutionRule, - typename BackwardConvolutionRule, - typename GradientConvolutionRule, - typename InputDataType, - typename OutputDataType -> -template -void Convolution< - ForwardConvolutionRule, - BackwardConvolutionRule, - GradientConvolutionRule, - InputDataType, - OutputDataType ->::Backward( - const arma::Mat& /* input */, const arma::Mat& gy, arma::Mat& g) -{ - arma::cube mappedError(((arma::Mat&) gy).memptr(), outputWidth, - outputHeight, outSize * batchSize, false, false); - - g.set_size(inputWidth * inputHeight * inSize, batchSize); - gTemp = arma::Cube(g.memptr(), inputWidth, inputHeight, - inSize * batchSize, false, false); - gTemp.zeros(); - - for (size_t outMap = 0, outMapIdx = 0, batchCount = 0; outMap < - outSize * batchSize; outMap++) - { - if (outMap != 0 && outMap % outSize == 0) - { - batchCount++; - outMapIdx = 0; - } - - for (size_t inMap = 0; inMap < inSize; inMap++, outMapIdx++) - { - arma::Mat output, rotatedFilter; - Rotate180(weight.slice(outMapIdx), rotatedFilter); - - BackwardConvolutionRule::Convolution(mappedError.slice(outMap), - rotatedFilter, output, strideWidth, strideHeight); - - if (padWLeft != 0 || padWRight != 0 || padHTop != 0 || padHBottom != 0) - { - gTemp.slice(inMap + batchCount * inSize) += output.submat(padWLeft, - padHTop, padWLeft + gTemp.n_rows - 1, padHTop + gTemp.n_cols - 1); - } - else - { - gTemp.slice(inMap + batchCount * inSize) += output; - } - } - } -} - -template< - typename ForwardConvolutionRule, - typename BackwardConvolutionRule, - typename GradientConvolutionRule, - typename InputDataType, - typename OutputDataType -> -template -void Convolution< - ForwardConvolutionRule, - BackwardConvolutionRule, - GradientConvolutionRule, - InputDataType, - OutputDataType ->::Gradient( - const arma::Mat& input, - const arma::Mat& error, - arma::Mat& gradient) -{ - arma::cube mappedError(((arma::Mat&) error).memptr(), outputWidth, - outputHeight, outSize * batchSize, false, false); - arma::cube inputTemp(((arma::Mat&) input).memptr(), inputWidth, - inputHeight, inSize * batchSize, false, false); - - gradient.set_size(weights.n_elem, 1); - gradientTemp = arma::Cube(gradient.memptr(), weight.n_rows, - weight.n_cols, weight.n_slices, false, false); - gradientTemp.zeros(); - - for (size_t outMap = 0, outMapIdx = 0, batchCount = 0; outMap < - outSize * batchSize; outMap++) - { - if (outMap != 0 && outMap % outSize == 0) - { - batchCount++; - outMapIdx = 0; - } - - for (size_t inMap = 0; inMap < inSize; inMap++, outMapIdx++) - { - arma::Mat inputSlice; - if (padWLeft != 0 || padWRight != 0 || padHTop != 0 || padHBottom != 0) - { - inputSlice = inputPaddedTemp.slice(inMap + batchCount * inSize); - } - else - { - inputSlice = inputTemp.slice(inMap + batchCount * inSize); - } - - arma::Mat deltaSlice = mappedError.slice(outMap); - - arma::Mat output; - GradientConvolutionRule::Convolution(inputSlice, deltaSlice, - output, strideWidth, strideHeight); - - if (gradientTemp.n_rows < output.n_rows || - gradientTemp.n_cols < output.n_cols) - { - gradientTemp.slice(outMapIdx) += output.submat(0, 0, - gradientTemp.n_rows - 1, gradientTemp.n_cols - 1); - } - else if (gradientTemp.n_rows > output.n_rows || - gradientTemp.n_cols > output.n_cols) - { - gradientTemp.slice(outMapIdx).submat(0, 0, output.n_rows - 1, - output.n_cols - 1) += output; - } - else - { - gradientTemp.slice(outMapIdx) += output; - } - } - - gradient.submat(weight.n_elem + (outMap % outSize), 0, weight.n_elem + - (outMap % outSize), 0) = arma::accu(mappedError.slice(outMap)); - } -} - -template< - typename ForwardConvolutionRule, - typename BackwardConvolutionRule, - typename GradientConvolutionRule, - typename InputDataType, - typename OutputDataType + typename MatType > template -void Convolution< +void ConvolutionType< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - InputDataType, - OutputDataType + MatType >::serialize(Archive& ar, const uint32_t /* version*/) { - ar(CEREAL_NVP(inSize)); - ar(CEREAL_NVP(outSize)); + ar(cereal::base_class>(this)); + + ar(CEREAL_NVP(maps)); ar(CEREAL_NVP(batchSize)); ar(CEREAL_NVP(kernelWidth)); ar(CEREAL_NVP(kernelHeight)); @@ -557,41 +582,31 @@ void Convolution< ar(CEREAL_NVP(padWRight)); ar(CEREAL_NVP(padHBottom)); ar(CEREAL_NVP(padHTop)); - ar(CEREAL_NVP(inputWidth)); - ar(CEREAL_NVP(inputHeight)); - ar(CEREAL_NVP(outputWidth)); - ar(CEREAL_NVP(outputHeight)); ar(CEREAL_NVP(padding)); - - if (cereal::is_loading()) - { - weights.set_size((outSize * inSize * kernelWidth * kernelHeight) + outSize, - 1); - } + ar(CEREAL_NVP(inMaps)); + ar(CEREAL_NVP(higherInDimensions)); } template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename InputDataType, - typename OutputDataType + typename MatType > -void Convolution< +void ConvolutionType< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - InputDataType, - OutputDataType + MatType >::InitializeSamePadding() { /* * Using O = (W - F + 2P) / s + 1; */ - size_t totalVerticalPadding = (strideWidth - 1) * inputWidth + kernelWidth - - strideWidth; - size_t totalHorizontalPadding = (strideHeight - 1) * inputHeight + - kernelHeight - strideHeight; + size_t totalVerticalPadding = (strideWidth - 1) * this->inputDimensions[0] + + kernelWidth - strideWidth; + size_t totalHorizontalPadding = (strideHeight - 1) * this->inputDimensions[1] + + kernelHeight - strideHeight; padWLeft = totalVerticalPadding / 2; padWRight = totalVerticalPadding - totalVerticalPadding / 2; diff --git a/src/mlpack/methods/ann/layer/dropconnect.hpp b/src/mlpack/methods/ann/layer/dropconnect.hpp index db8d76aa4b..c00a2d2129 100644 --- a/src/mlpack/methods/ann/layer/dropconnect.hpp +++ b/src/mlpack/methods/ann/layer/dropconnect.hpp @@ -16,10 +16,7 @@ #include -#include "layer_types.hpp" -#include "add_merge.hpp" -#include "linear.hpp" -#include "sequential.hpp" +#include "layer.hpp" namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -28,55 +25,58 @@ namespace ann /** Artificial Neural Network. */ { * The DropConnect layer is a regularizer that randomly with probability * ratio sets the connection values to zero and scales the remaining * elements by factor 1 /(1 - ratio). The output is scaled with 1 / (1 - p) - * when deterministic is false. In the deterministic mode(during testing), - * the layer just computes the output. The output is computed according - * to the input layer. If no input layer is given, it will take a linear layer - * as default. + * when in training mode. During testing, the layer just computes the output. + * The output is computed according to the input layer. If no input layer is + * given, it will take a linear layer as default. * - * Note: - * During training you should set deterministic to false and during testing - * you should set deterministic to true. - * - * For more information, see the following. + * For more information, see the following. * * @code * @inproceedings{WanICML2013, - * title={Regularization of Neural Networks using DropConnect}, + * title = {Regularization of Neural Networks using DropConnect}, * booktitle = {Proceedings of the 30th International Conference on Machine * Learning(ICML - 13)}, - * author = {Li Wan and Matthew Zeiler and Sixin Zhang and Yann L. Cun and - * Rob Fergus}, - * year = {2013}, - * url = {http://proceedings.mlr.press/v28/wan13.pdf} + * author = {Li Wan and Matthew Zeiler and Sixin Zhang and Yann L. Cun and + * Rob Fergus}, + * year = {2013}, + * url = {http://proceedings.mlr.press/v28/wan13.pdf} * } * @endcode * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam MatType Matrix representation to accept as input and use for + * computation. */ -template< - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class DropConnect +template +class DropConnectType : public Layer { public: //! Create the DropConnect object. - DropConnect(); + DropConnectType(); /** - * Creates the DropConnect Layer as a Linear Object that takes input size, - * output size and ratio as parameter. + * Creates the DropConnect Layer as a Linear Object that takes the number of + * output units and a ratio as parameter. * - * @param inSize The number of input units. * @param outSize The number of output units. * @param ratio The probability of setting a value to zero. */ - DropConnect(const size_t inSize, - const size_t outSize, - const double ratio = 0.5); + DropConnectType(const size_t outSize, + const double ratio = 0.5); + + //! Clone the DropConnectType object. This handles polymorphism correctly. + DropConnectType* Clone() const { return new DropConnectType(*this); } + + // Virtual destructor. + virtual ~DropConnectType(); + + //! Copy the given DropConnectType (except for weights). + DropConnectType(const DropConnectType& other); + //! Take ownership of the given DropConnectType (except for weights). + DropConnectType(DropConnectType&& other); + //! Copy the given DropConnectType (except for weights). + DropConnectType& operator=(const DropConnectType& other); + //! Take ownership of the given DropConnectType (except for weights). + DropConnectType& operator=(DropConnectType&& other); /** * Ordinary feed forward pass of the DropConnect layer. @@ -84,8 +84,7 @@ class DropConnect * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template - void Forward(const arma::Mat& input, arma::Mat& output); + void Forward(const MatType& input, MatType& output); /** * Ordinary feed backward pass of the DropConnect layer. @@ -94,10 +93,7 @@ class DropConnect * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const arma::Mat& input, - const arma::Mat& gy, - arma::Mat& g); + void Backward(const MatType& input, const MatType& gy, MatType& g); /** * Calculate the gradient using the output delta and the input activation. @@ -106,39 +102,7 @@ class DropConnect * @param error The calculated error. * @param * (gradient) The calculated gradient. */ - template - void Gradient(const arma::Mat& input, - const arma::Mat& error, - arma::Mat& /* gradient */); - - //! Get the model modules. - std::vector >& Model() { return network; } - - //! Get the parameters. - OutputDataType const& Parameters() const { return weights; } - //! Modify the parameters. - OutputDataType& Parameters() { return weights; } - - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } - - //! Get the gradient. - OutputDataType const& Gradient() const { return gradient; } - //! Modify the gradient. - OutputDataType& Gradient() { return gradient; } - - //! The value of the deterministic parameter. - bool Deterministic() const { return deterministic; } - - //! Modify the value of the deterministic parameter. - bool &Deterministic() { return deterministic; } + void Gradient(const MatType& input, const MatType& error, MatType& gradient); //! The probability of setting a value to zero. double Ratio() const { return ratio; } @@ -150,8 +114,14 @@ class DropConnect scale = 1.0 / (1.0 - ratio); } - //! Return the size of the weight matrix. - size_t WeightSize() const { return 0; } + //! Compute the output dimensions of the layer based on `InputDimensions()`. + void ComputeOutputDimensions(); + + //! Return the size of the weights. + size_t WeightSize() const { return baseLayer->WeightSize(); } + + // Set the weights to use the given memory `weightsPtr`. + void SetWeights(typename MatType::elem_type* weightsPtr); /** * Serialize the layer. @@ -166,34 +136,21 @@ class DropConnect //! The scale fraction. double scale; - //! Locally-stored weight object. - OutputDataType weights; - - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored gradient object. - OutputDataType gradient; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; - //! Locally-stored mask object. - OutputDataType mask; - - //! If true dropout and scaling is disabled, see notes above. - bool deterministic; + MatType mask; //! Denoise mask for the weights. - OutputDataType denoise; + MatType denoise; //! Locally-stored layer module. - LayerTypes<> baseLayer; - - //! Locally-stored network modules. - std::vector > network; + Layer* baseLayer; }; // class DropConnect. +// Convenience typedefs. + +// Standard DropConnect layer. +typedef DropConnectType DropConnect; + } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/dropconnect_impl.hpp b/src/mlpack/methods/ann/layer/dropconnect_impl.hpp index 9b22c91951..9eb8bb91a4 100644 --- a/src/mlpack/methods/ann/layer/dropconnect_impl.hpp +++ b/src/mlpack/methods/ann/layer/dropconnect_impl.hpp @@ -17,112 +17,160 @@ // In case it hasn't yet been included. #include "dropconnect.hpp" -#include "../visitor/delete_visitor.hpp" -#include "../visitor/forward_visitor.hpp" -#include "../visitor/backward_visitor.hpp" -#include "../visitor/gradient_visitor.hpp" -#include "../visitor/parameters_set_visitor.hpp" -#include "../visitor/parameters_visitor.hpp" +#include "linear.hpp" namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -DropConnect::DropConnect() : +template +DropConnectType::DropConnectType() : + Layer(), ratio(0.5), scale(2.0), - deterministic(true) + baseLayer(new LinearType(0)) { // Nothing to do here. } -template -DropConnect::DropConnect( - const size_t inSize, +template +DropConnectType::DropConnectType( const size_t outSize, const double ratio) : + Layer(), ratio(ratio), scale(1.0 / (1 - ratio)), - baseLayer(new Linear(inSize, outSize)) + baseLayer(new LinearType(outSize)) { - network.push_back(baseLayer); + // Nothing to do. } -template -template -void DropConnect::Forward( - const arma::Mat& input, - arma::Mat& output) +template +DropConnectType::~DropConnectType() { - // The DropConnect mask will not be multiplied in the deterministic mode - // (during testing). - if (deterministic) + delete baseLayer; +} + +template +DropConnectType::DropConnectType(const DropConnectType& other) : + Layer(other), + ratio(other.ratio), + scale(other.scale), + baseLayer(other.baseLayer->Clone()) +{ + // Nothing to do. +} + +template +DropConnectType::DropConnectType(DropConnectType&& other) : + Layer(std::move(other)), + ratio(std::move(other.ratio)), + scale(std::move(other.scale)), + baseLayer(std::move(other.baseLayer)) +{ + // Nothing to do. +} + +template +DropConnectType& +DropConnectType::operator=(const DropConnectType& other) +{ + if (&other != this) { - boost::apply_visitor(ForwardVisitor(input, output), baseLayer); + Layer::operator=(other); + ratio = other.ratio; + scale = other.scale; + baseLayer = other.baseLayer->Clone(); + } + + return *this; +} + +template +DropConnectType& +DropConnectType::operator=(DropConnectType&& other) +{ + if (&other != this) + { + Layer::operator=(std::move(other)); + ratio = std::move(other.ratio); + scale = std::move(other.scale); + baseLayer = std::move(other.baseLayer); + } + + return *this; +} + +template +void DropConnectType::Forward(const MatType& input, MatType& output) +{ + // The DropConnect mask will not be multiplied in testing mode. + if (!this->training) + { + baseLayer->Forward(input, output); } else { // Save weights for denoising. - boost::apply_visitor(ParametersVisitor(denoise), baseLayer); + denoise = baseLayer->Parameters(); // Scale with input / (1 - ratio) and set values to zero with // probability ratio. - mask = arma::randu >(denoise.n_rows, denoise.n_cols); + mask = arma::randu(denoise.n_rows, denoise.n_cols); mask.transform([&](double val) { return (val > ratio); }); - arma::mat tmp = denoise % mask; - boost::apply_visitor(ParametersSetVisitor(tmp), baseLayer); - - boost::apply_visitor(ForwardVisitor(input, output), baseLayer); + baseLayer->Parameters() = denoise % mask; + baseLayer->Forward(input, output); output = output * scale; } } -template -template -void DropConnect::Backward( - const arma::Mat& input, - const arma::Mat& gy, - arma::Mat& g) +template +void DropConnectType::Backward( + const MatType& input, + const MatType& gy, + MatType& g) { - boost::apply_visitor(BackwardVisitor(input, gy, g), baseLayer); + baseLayer->Backward(input, gy, g); } -template -template -void DropConnect::Gradient( - const arma::Mat& input, - const arma::Mat& error, - arma::Mat& /* gradient */) +template +void DropConnectType::Gradient( + const MatType& input, + const MatType& error, + MatType& gradient) { - boost::apply_visitor(GradientVisitor(input, error), - baseLayer); + baseLayer->Gradient(input, error, gradient); // Denoise the weights. - boost::apply_visitor(ParametersSetVisitor(denoise), baseLayer); + baseLayer->Parameters() = denoise; } -template +template +void DropConnectType::ComputeOutputDimensions() +{ + // Propagate input dimensions to the base layer. + baseLayer->InputDimensions() = this->inputDimensions; + this->outputDimensions = baseLayer->OutputDimensions(); +} + +template +void DropConnectType::SetWeights( + typename MatType::elem_type* weightsPtr) +{ + baseLayer->SetWeights(weightsPtr); +} + +template template -void DropConnect::serialize( +void DropConnectType::serialize( Archive& ar, const uint32_t /* version */) { - // Delete the old network first, if needed. - if (cereal::is_loading()) - { - boost::apply_visitor(DeleteVisitor(), baseLayer); - } + ar(cereal::base_class>(this)); ar(CEREAL_NVP(ratio)); ar(CEREAL_NVP(scale)); - ar(CEREAL_VARIANT_POINTER(baseLayer)); - - if (cereal::is_loading()) - { - network.clear(); - network.push_back(baseLayer); - } + ar(CEREAL_POINTER(baseLayer)); } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/dropout.hpp b/src/mlpack/methods/ann/layer/dropout.hpp index f3fb8f18b4..f03250145f 100644 --- a/src/mlpack/methods/ann/layer/dropout.hpp +++ b/src/mlpack/methods/ann/layer/dropout.hpp @@ -15,18 +15,16 @@ #include +#include "layer.hpp" + namespace mlpack { namespace ann /** Artificial Neural Network. */ { - /** * The dropout layer is a regularizer that randomly with probability 'ratio' * sets input values to zero and scales the remaining elements by factor 1 / * (1 - ratio) rather than during test time so as to keep the expected sum same. - * In the deterministic mode (during testing), there is no change in the input. - * - * Note: During training you should set deterministic to false and during - * testing you should set deterministic to true. + * When the layer is in testing mode, there is no change in the input. * * For more information, see the following. * @@ -43,14 +41,11 @@ namespace ann /** Artificial Neural Network. */ { * } * @endcode * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam MatType Matrix representation to accept as input and use for + * computation. */ -template -class Dropout +template +class DropoutType : public Layer { public: /** @@ -58,19 +53,22 @@ class Dropout * * @param ratio The probability of setting a value to zero. */ - Dropout(const double ratio = 0.5); + DropoutType(const double ratio = 0.5); - //! Copy Constructor - Dropout(const Dropout& layer); + //! Clone the DropoutType object. This handles polymorphism correctly. + DropoutType* Clone() const { return new DropoutType(*this); } - //! Move Constructor - Dropout(const Dropout&&); + // Virtual destructor. + virtual ~DropoutType() { } - //! Copy assignment operator - Dropout& operator=(const Dropout& layer); - - //! Move assignment operator - Dropout& operator=(Dropout&& layer); + //! Copy the given DropoutType. + DropoutType(const DropoutType& other); + //! Take ownership of the given DropoutType. + DropoutType(DropoutType&& other); + //! Copy the given DropoutType. + DropoutType& operator=(const DropoutType& other); + //! Take ownership of the given DropoutType. + DropoutType& operator=(DropoutType&& other); /** * Ordinary feed forward pass of the dropout layer. @@ -78,8 +76,7 @@ class Dropout * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template - void Forward(const arma::Mat& input, arma::Mat& output); + void Forward(const MatType& input, MatType& output); /** * Ordinary feed backward pass of the dropout layer. @@ -88,25 +85,7 @@ class Dropout * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g); - - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the detla. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } - - //! The value of the deterministic parameter. - bool Deterministic() const { return deterministic; } - //! Modify the value of the deterministic parameter. - bool& Deterministic() { return deterministic; } + void Backward(const MatType& /* input */, const MatType& gy, MatType& g); //! The probability of setting a value to zero. double Ratio() const { return ratio; } @@ -125,24 +104,20 @@ class Dropout void serialize(Archive& ar, const uint32_t /* version */); private: - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; - - //! Locally-stored mast object. - OutputDataType mask; + //! Locally-stored mask object. + MatType mask; //! The probability of setting a value to zero. double ratio; //! The scale fraction. double scale; +}; // class DropoutType - //! If true dropout and scaling is disabled, see notes above. - bool deterministic; -}; // class Dropout +// Convenience typedefs. + +// Standard Dropout layer. +typedef DropoutType Dropout; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/dropout_impl.hpp b/src/mlpack/methods/ann/layer/dropout_impl.hpp index 80f0d81fec..012de36f62 100644 --- a/src/mlpack/methods/ann/layer/dropout_impl.hpp +++ b/src/mlpack/methods/ann/layer/dropout_impl.hpp @@ -19,73 +19,66 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -Dropout::Dropout( +template +DropoutType::DropoutType( const double ratio) : ratio(ratio), - scale(1.0 / (1.0 - ratio)), - deterministic(false) + scale(1.0 / (1.0 - ratio)) { // Nothing to do here. } -template -Dropout::Dropout( - const Dropout& layer) : - ratio(layer.ratio), - scale(layer.scale), - deterministic(layer.deterministic) +template +DropoutType::DropoutType(const DropoutType& other) : + Layer(other), + ratio(other.ratio), + scale(other.scale) { - // Nothing to do here. + // Nothing to do. } -template -Dropout::Dropout( - const Dropout&& layer) : - ratio(std::move(layer.ratio)), - scale(std::move(scale)), - deterministic(std::move(deterministic)) +template +DropoutType::DropoutType(DropoutType&& other) : + Layer(std::move(other)), + ratio(std::move(other.ratio)), + scale(std::move(other.scale)) { - // Nothing to do here. + // Nothing to do. } -template -Dropout& -Dropout:: -operator=(const Dropout& layer) +template +DropoutType& +DropoutType::operator=(const DropoutType& other) { - if (this != &layer) + if (&other != this) { - ratio = layer.ratio; - scale = layer.scale; - deterministic = layer.deterministic; + Layer::operator=(other); + ratio = other.ratio; + scale = other.scale; } + return *this; } -template -Dropout& -Dropout:: -operator=(Dropout&& layer) +template +DropoutType& +DropoutType::operator=(DropoutType&& other) { - if (this != &layer) + if (&other != this) { - ratio = std::move(layer.ratio); - scale = std::move(layer.scale); - deterministic = std::move(layer.deterministic); + Layer::operator=(std::move(other)); + ratio = std::move(other.ratio); + scale = std::move(other.scale); } + return *this; } -template -template -void Dropout::Forward( - const arma::Mat& input, - arma::Mat& output) +template +void DropoutType::Forward(const MatType& input, MatType& output) { - // The dropout mask will not be multiplied in the deterministic mode - // (during testing). - if (deterministic) + // The dropout mask will not be multiplied in testing mode. + if (!this->training) { output = input; } @@ -93,28 +86,29 @@ void Dropout::Forward( { // Scale with input / (1 - ratio) and set values to zero with probability // 'ratio'. - mask = arma::randu >(input.n_rows, input.n_cols); + mask = arma::randu(input.n_rows, input.n_cols); mask.transform([&](double val) { return (val > ratio); }); output = input % mask * scale; } } -template -template -void Dropout::Backward( - const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g) +template +void DropoutType::Backward( + const MatType& /* input */, + const MatType& gy, + MatType& g) { g = gy % mask * scale; } -template +template template -void Dropout::serialize( +void DropoutType::serialize( Archive& ar, const uint32_t /* version */) { + ar(cereal::base_class>(this)); + ar(CEREAL_NVP(ratio)); // Reset scale. diff --git a/src/mlpack/methods/ann/layer/gru_impl.hpp b/src/mlpack/methods/ann/layer/gru_impl.hpp deleted file mode 100644 index e9b71ebb97..0000000000 --- a/src/mlpack/methods/ann/layer/gru_impl.hpp +++ /dev/null @@ -1,411 +0,0 @@ -/** - * @file methods/ann/layer/gru_impl.hpp - * @author Sumedh Ghaisas - * - * Implementation of the GRU class, which implements a gru network - * layer. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_LAYER_GRU_IMPL_HPP -#define MLPACK_METHODS_ANN_LAYER_GRU_IMPL_HPP - -// In case it hasn't yet been included. -#include "gru.hpp" - -#include "../visitor/forward_visitor.hpp" -#include "../visitor/backward_visitor.hpp" -#include "../visitor/gradient_visitor.hpp" - -namespace mlpack { -namespace ann /** Artificial Neural Network. */ { - -template -GRU::GRU() -{ - // Nothing to do here. -} - -template -GRU::GRU( - const size_t inSize, - const size_t outSize, - const size_t rho) : - inSize(inSize), - outSize(outSize), - rho(rho), - batchSize(1), - forwardStep(0), - backwardStep(0), - gradientStep(0), - deterministic(false) -{ - // Input specific linear layers(for zt, rt, ot). - input2GateModule = new Linear<>(inSize, 3 * outSize); - - // Previous output gates (for zt and rt). - output2GateModule = new LinearNoBias<>(outSize, 2 * outSize); - - // Previous output gate for ot. - outputHidden2GateModule = new LinearNoBias<>(outSize, outSize); - - network.push_back(input2GateModule); - network.push_back(output2GateModule); - network.push_back(outputHidden2GateModule); - - inputGateModule = new SigmoidLayer<>(); - forgetGateModule = new SigmoidLayer<>(); - hiddenStateModule = new TanHLayer<>(); - - network.push_back(inputGateModule); - network.push_back(hiddenStateModule); - network.push_back(forgetGateModule); - - prevError = arma::zeros(3 * outSize, batchSize); - - allZeros = arma::zeros(outSize, batchSize); - - outParameter.emplace_back(allZeros.memptr(), - allZeros.n_rows, allZeros.n_cols, false, true); - - prevOutput = outParameter.begin(); - backIterator = outParameter.end(); - gradIterator = outParameter.end(); -} - -template -template -void GRU::Forward( - const arma::Mat& input, arma::Mat& output) -{ - if (input.n_cols != batchSize) - { - batchSize = input.n_cols; - prevError.resize(3 * outSize, batchSize); - allZeros.zeros(outSize, batchSize); - // Batch size better not change during an iteration... - if (outParameter.size() > 1) - { - Log::Fatal << "GRU<>::Forward(): batch size cannot change during a " - << "forward pass!" << std::endl; - } - - outParameter.clear(); - outParameter.emplace_back(allZeros.memptr(), - allZeros.n_rows, allZeros.n_cols, false, true); - - prevOutput = outParameter.begin(); - backIterator = outParameter.end(); - gradIterator = outParameter.end(); - } - - // Process the input linearly(zt, rt, ot). - boost::apply_visitor(ForwardVisitor(input, - boost::apply_visitor(outputParameterVisitor, input2GateModule)), - input2GateModule); - - // Process the output(zt, rt) linearly. - boost::apply_visitor(ForwardVisitor(*prevOutput, - boost::apply_visitor(outputParameterVisitor, output2GateModule)), - output2GateModule); - - // Merge the outputs(zt and rt). - output = (boost::apply_visitor(outputParameterVisitor, - input2GateModule).submat(0, 0, 2 * outSize - 1, batchSize - 1) + - boost::apply_visitor(outputParameterVisitor, output2GateModule)); - - // Pass the first outSize through inputGate(it). - boost::apply_visitor(ForwardVisitor(output.submat( - 0, 0, 1 * outSize - 1, batchSize - 1), boost::apply_visitor( - outputParameterVisitor, inputGateModule)), inputGateModule); - - // Pass the second through forgetGate. - boost::apply_visitor(ForwardVisitor(output.submat( - 1 * outSize, 0, 2 * outSize - 1, batchSize - 1), - boost::apply_visitor(outputParameterVisitor, forgetGateModule)), - forgetGateModule); - - arma::mat modInput = (boost::apply_visitor(outputParameterVisitor, - forgetGateModule) % *prevOutput); - - // Pass that through the outputHidden2GateModule. - boost::apply_visitor(ForwardVisitor(modInput, - boost::apply_visitor(outputParameterVisitor, outputHidden2GateModule)), - outputHidden2GateModule); - - // Merge for ot. - arma::mat outputH = boost::apply_visitor(outputParameterVisitor, - input2GateModule).submat(2 * outSize, 0, 3 * outSize - 1, batchSize - 1) + - boost::apply_visitor(outputParameterVisitor, outputHidden2GateModule); - - // Pass it through hiddenGate. - boost::apply_visitor(ForwardVisitor(outputH, - boost::apply_visitor(outputParameterVisitor, hiddenStateModule)), - hiddenStateModule); - - // Update the output (nextOutput): cmul1 + cmul2 - // Where cmul1 is input gate * prevOutput and - // cmul2 is (1 - input gate) * hidden gate. - output = (boost::apply_visitor(outputParameterVisitor, inputGateModule) - % (*prevOutput - boost::apply_visitor(outputParameterVisitor, - hiddenStateModule))) + boost::apply_visitor(outputParameterVisitor, - hiddenStateModule); - - forwardStep++; - if (forwardStep == rho) - { - forwardStep = 0; - if (!deterministic) - { - outParameter.emplace_back(allZeros.memptr(), - allZeros.n_rows, allZeros.n_cols, false, true); - prevOutput = --outParameter.end(); - } - else - { - *prevOutput = arma::mat(allZeros.memptr(), - allZeros.n_rows, allZeros.n_cols, false, true); - } - } - else if (!deterministic) - { - outParameter.push_back(output); - prevOutput = --outParameter.end(); - } - else - { - if (forwardStep == 1) - { - outParameter.clear(); - outParameter.push_back(output); - - prevOutput = outParameter.begin(); - } - else - { - *prevOutput = output; - } - } -} - -template -template -void GRU::Backward( - const arma::Mat& input, const arma::Mat& gy, arma::Mat& g) -{ - if (input.n_cols != batchSize) - { - batchSize = input.n_cols; - prevError.resize(3 * outSize, batchSize); - allZeros.zeros(outSize, batchSize); - // Batch size better not change during an iteration... - if (outParameter.size() > 1) - { - Log::Fatal << "GRU<>::Forward(): batch size cannot change during a " - << "forward pass!" << std::endl; - } - - outParameter.clear(); - outParameter.emplace_back(allZeros.memptr(), - allZeros.n_rows, allZeros.n_cols, false, true); - - prevOutput = outParameter.begin(); - backIterator = outParameter.end(); - gradIterator = outParameter.end(); - } - - arma::Mat gyLocal; - if ((outParameter.size() - backwardStep - 1) % rho != 0 && backwardStep != 0) - { - gyLocal = gy + boost::apply_visitor(deltaVisitor, output2GateModule); - } - else - { - gyLocal = arma::Mat(((arma::Mat&) gy).memptr(), gy.n_rows, - gy.n_cols, false, false); - } - - if (backIterator == outParameter.end()) - { - backIterator = --(--outParameter.end()); - } - - // Delta zt. - arma::mat dZt = gyLocal % (*backIterator - - boost::apply_visitor(outputParameterVisitor, - hiddenStateModule)); - - // Delta ot. - arma::mat dOt = gyLocal % (arma::ones(outSize, batchSize) - - boost::apply_visitor(outputParameterVisitor, inputGateModule)); - - // Delta of input gate. - boost::apply_visitor(BackwardVisitor(boost::apply_visitor( - outputParameterVisitor, inputGateModule), dZt, - boost::apply_visitor(deltaVisitor, inputGateModule)), - inputGateModule); - - // Delta of hidden gate. - boost::apply_visitor(BackwardVisitor(boost::apply_visitor( - outputParameterVisitor, hiddenStateModule), dOt, - boost::apply_visitor(deltaVisitor, hiddenStateModule)), - hiddenStateModule); - - // Delta of outputHidden2GateModule. - boost::apply_visitor(BackwardVisitor(boost::apply_visitor( - outputParameterVisitor, outputHidden2GateModule), - boost::apply_visitor(deltaVisitor, hiddenStateModule), - boost::apply_visitor(deltaVisitor, outputHidden2GateModule)), - outputHidden2GateModule); - - // Delta rt. - arma::mat dRt = boost::apply_visitor(deltaVisitor, outputHidden2GateModule) % - *backIterator; - - // Delta of forget gate. - boost::apply_visitor(BackwardVisitor(boost::apply_visitor( - outputParameterVisitor, forgetGateModule), dRt, - boost::apply_visitor(deltaVisitor, forgetGateModule)), - forgetGateModule); - - // Put delta zt. - prevError.submat(0, 0, 1 * outSize - 1, batchSize - 1) = boost::apply_visitor( - deltaVisitor, inputGateModule); - - // Put delta rt. - prevError.submat(1 * outSize, 0, 2 * outSize - 1, batchSize - 1) = - boost::apply_visitor(deltaVisitor, forgetGateModule); - - // Put delta ot. - prevError.submat(2 * outSize, 0, 3 * outSize - 1, batchSize - 1) = - boost::apply_visitor(deltaVisitor, hiddenStateModule); - - // Get delta ht - 1 for input gate and forget gate. - arma::mat prevErrorSubview = prevError.submat(0, 0, 2 * outSize - 1, - batchSize - 1); - boost::apply_visitor(BackwardVisitor(boost::apply_visitor( - outputParameterVisitor, input2GateModule), - prevErrorSubview, - boost::apply_visitor(deltaVisitor, output2GateModule)), - output2GateModule); - - // Add delta ht - 1 from hidden state. - boost::apply_visitor(deltaVisitor, output2GateModule) += - boost::apply_visitor(deltaVisitor, outputHidden2GateModule) % - boost::apply_visitor(outputParameterVisitor, forgetGateModule); - - // Add delta ht - 1 from ht. - boost::apply_visitor(deltaVisitor, output2GateModule) += gyLocal % - boost::apply_visitor(outputParameterVisitor, inputGateModule); - - // Get delta input. - boost::apply_visitor(BackwardVisitor(boost::apply_visitor( - outputParameterVisitor, input2GateModule), prevError, - boost::apply_visitor(deltaVisitor, input2GateModule)), - input2GateModule); - - backwardStep++; - backIterator--; - - g = boost::apply_visitor(deltaVisitor, input2GateModule); -} - -template -template -void GRU::Gradient( - const arma::Mat& input, - const arma::Mat& /* error */, - arma::Mat& /* gradient */) -{ - if (input.n_cols != batchSize) - { - batchSize = input.n_cols; - prevError.resize(3 * outSize, batchSize); - allZeros.zeros(outSize, batchSize); - // Batch size better not change during an iteration... - if (outParameter.size() > 1) - { - Log::Fatal << "GRU<>::Forward(): batch size cannot change during a " - << "forward pass!" << std::endl; - } - - outParameter.clear(); - outParameter.emplace_back(allZeros.memptr(), - allZeros.n_rows, allZeros.n_cols, false, true); - - prevOutput = outParameter.begin(); - backIterator = outParameter.end(); - gradIterator = outParameter.end(); - } - - if (gradIterator == outParameter.end()) - { - gradIterator = --(--outParameter.end()); - } - - boost::apply_visitor(GradientVisitor(input, prevError), input2GateModule); - - boost::apply_visitor(GradientVisitor( - *gradIterator, - prevError.submat(0, 0, 2 * outSize - 1, batchSize - 1)), - output2GateModule); - - boost::apply_visitor(GradientVisitor( - *gradIterator % boost::apply_visitor(outputParameterVisitor, - forgetGateModule), - prevError.submat(2 * outSize, 0, 3 * outSize - 1, batchSize - 1)), - outputHidden2GateModule); - - gradIterator--; -} - -template -void GRU::ResetCell(const size_t /* size */) -{ - outParameter.clear(); - outParameter.emplace_back(allZeros.memptr(), - allZeros.n_rows, allZeros.n_cols, false, true); - - prevOutput = outParameter.begin(); - backIterator = outParameter.end(); - gradIterator = outParameter.end(); - - forwardStep = 0; - backwardStep = 0; -} - -template -template -void GRU::serialize( - Archive& ar, const uint32_t /* version */) -{ - // If necessary, clean memory from the old model. - if (cereal::is_loading()) - { - boost::apply_visitor(deleteVisitor, input2GateModule); - boost::apply_visitor(deleteVisitor, output2GateModule); - boost::apply_visitor(deleteVisitor, outputHidden2GateModule); - boost::apply_visitor(deleteVisitor, inputGateModule); - boost::apply_visitor(deleteVisitor, forgetGateModule); - boost::apply_visitor(deleteVisitor, hiddenStateModule); - } - - ar(CEREAL_NVP(inSize)); - ar(CEREAL_NVP(outSize)); - ar(CEREAL_NVP(rho)); - - ar(CEREAL_VARIANT_POINTER(input2GateModule)); - ar(CEREAL_VARIANT_POINTER(output2GateModule)); - ar(CEREAL_VARIANT_POINTER(outputHidden2GateModule)); - ar(CEREAL_VARIANT_POINTER(inputGateModule)); - ar(CEREAL_VARIANT_POINTER(forgetGateModule)); - ar(CEREAL_VARIANT_POINTER(hiddenStateModule)); -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/layer/highway.hpp b/src/mlpack/methods/ann/layer/highway.hpp deleted file mode 100644 index 526a434d23..0000000000 --- a/src/mlpack/methods/ann/layer/highway.hpp +++ /dev/null @@ -1,270 +0,0 @@ -/** - * @file methods/ann/layer/highway.hpp - * @author Konstantin Sidorov - * @author Saksham Bansal - * - * Definition of the Highway layer. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_LAYER_HIGHWAY_HPP -#define MLPACK_METHODS_ANN_LAYER_HIGHWAY_HPP - -#include - -#include "../visitor/delete_visitor.hpp" -#include "../visitor/delta_visitor.hpp" -#include "../visitor/output_height_visitor.hpp" -#include "../visitor/output_parameter_visitor.hpp" -#include "../visitor/output_width_visitor.hpp" - -#include "layer_types.hpp" -#include "add_merge.hpp" - -namespace mlpack { -namespace ann /** Artificial Neural Network. */ { - -/** - * Implementation of the Highway layer. The Highway class can vary its behavior - * between that of feed-forward fully connected network container and that - * of a layer which simply passes its inputs through depending on the transform - * gate. Note that the size of the input and output matrices of this class - * should be equal. - * - * For more information, refer the following paper. - * - * @code - * @article{Srivastava2015, - * author = {Rupesh Kumar Srivastava, Klaus Greff, Jurgen Schmidhuber}, - * title = {Training Very Deep Networks}, - * journal = {Advances in Neural Information Processing Systems}, - * year = {2015}, - * url = {https://arxiv.org/abs/1507.06228}, - * } - * @endcode - * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat, - typename... CustomLayers> -class Highway -{ - public: - //! Create the Highway object. - Highway(); - - /** - * Create the Highway object. - * - * @param inSize The number of input units. - * @param model Expose all the network modules. - */ - Highway(const size_t inSize, const bool model = true); - - //! Destroy the Highway object. - ~Highway(); - - /** - * Reset the layer parameter. - */ - void Reset(); - - /** - * Ordinary feed-forward pass of a neural network, evaluating the function - * f(x) by propagating the activity forward through f. - * - * @param input Input data used for evaluating the specified function. - * @param output Resulting output activation. - */ - template - void Forward(const arma::Mat& input, arma::Mat& output); - - /** - * Ordinary feed-backward pass of a neural network, calculating the function - * f(x) by propagating x backwards through f. Using the results from the - * feed-forward pass. - * - * @param * (input) The propagated input activation. - * @param gy The backpropagated error. - * @param g The calculated gradient. - */ - template - void Backward(const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g); - - /** - * Calculate the gradient using the output delta and the input activation. - * - * @param input The input parameter used for calculating the gradient. - * @param error The calculated error. - * @param gradient The calculated gradient. - */ - template - void Gradient(const arma::Mat& input, - const arma::Mat& error, - arma::Mat& gradient); - - /** - * Add a new module to the model. - * - * @param args The layer parameter. - */ - template - void Add(Args... args) - { - network.push_back(new LayerType(args...)); - networkOwnerships.push_back(true); - } - - /** - * Add a new module to the model. - * - * @param layer The Layer to be added to the model. - */ - void Add(LayerTypes layer) - { - network.push_back(layer); - networkOwnerships.push_back(false); - } - - //! Return the modules of the model. - std::vector >& Model() - { - if (model) - { - return network; - } - - return empty; - } - - //! Get the parameters. - OutputDataType const& Parameters() const { return weights; } - //! Modify the parameters. - OutputDataType& Parameters() { return weights; } - - //! Get the input parameter. - InputDataType const& InputParameter() const { return inputParameter; } - //! Modify the input parameter. - InputDataType& InputParameter() { return inputParameter; } - - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } - - //! Get the gradient. - OutputDataType const& Gradient() const { return gradient; } - //! Modify the gradient. - OutputDataType& Gradient() { return gradient; } - - //! Get the number of input units. - size_t InSize() const { return inSize; } - - //! Get the shape of the input. - size_t InputShape() const - { - return inSize; - } - - /** - * Serialize the layer. - */ - template - void serialize(Archive& ar, const uint32_t /* version */); - - private: - //! Locally-stored number of input units. - size_t inSize; - - //! Parameter which indicates if the modules should be exposed. - bool model; - - //! Indicator if we already initialized the model. - bool reset; - - //! Locally-stored network modules. - std::vector > network; - - //! The list of network modules we are responsible for. - std::vector networkOwnerships; - - //! Locally-stored empty list of modules. - std::vector > empty; - - //! Locally-stored weight object. - OutputDataType weights; - - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored gradient object. - OutputDataType gradient; - - //! Weights for transformation of output. - OutputDataType transformWeight; - - //! Bias for transformation of output. - OutputDataType transformBias; - - //! Locally-stored transform gate parameters. - OutputDataType transformGate; - - //! Locally-stored transform gate activation. - OutputDataType transformGateActivation; - - //! Locally-stored transform gate error. - OutputDataType transformGateError; - - //! Locally-stored input parameter object. - InputDataType inputParameter; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; - - //! The input width. - size_t width; - - //! The input height. - size_t height; - - //! The normal output without highway network. - OutputDataType networkOutput; - - //! Locally-stored delta visitor. - DeltaVisitor deltaVisitor; - - //! Locally-stored output parameter visitor. - OutputParameterVisitor outputParameterVisitor; - - //! Locally-stored delete visitor. - DeleteVisitor deleteVisitor; - - //! Locally-stored output width visitor. - OutputWidthVisitor outputWidthVisitor; - - //! Locally-stored output height visitor. - OutputHeightVisitor outputHeightVisitor; -}; // class Highway - -} // namespace ann -} // namespace mlpack - -// Include implementation. -#include "highway_impl.hpp" - -#endif diff --git a/src/mlpack/methods/ann/layer/highway_impl.hpp b/src/mlpack/methods/ann/layer/highway_impl.hpp deleted file mode 100644 index 00418e03a3..0000000000 --- a/src/mlpack/methods/ann/layer/highway_impl.hpp +++ /dev/null @@ -1,238 +0,0 @@ -/** - * @file methods/ann/layer/highway_impl.hpp - * @author Konstantin Sidorov - * @author Saksham Bansal - * - * Implementation of Highway layer. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_LAYER_HIGHWAY_IMPL_HPP -#define MLPACK_METHODS_ANN_LAYER_HIGHWAY_IMPL_HPP - -// In case it hasn't yet been included. -#include "highway.hpp" - -#include "../visitor/forward_visitor.hpp" -#include "../visitor/backward_visitor.hpp" -#include "../visitor/gradient_visitor.hpp" -#include "../visitor/set_input_height_visitor.hpp" -#include "../visitor/set_input_width_visitor.hpp" - -namespace mlpack { -namespace ann /** Artificial Neural Network. */ { - -template -Highway::Highway() : - inSize(0), - model(true), - reset(false), - width(0), - height(0) -{ - // Nothing to do here. -} - -template< - typename InputDataType, typename OutputDataType, typename... CustomLayers> -Highway::Highway( - const size_t inSize, - const bool model) : - inSize(inSize), - model(model), - reset(false), - width(0), - height(0) -{ - weights.set_size(inSize * inSize + inSize, 1); -} - -template -Highway::~Highway() -{ - if (!model) - { - for (size_t i = 0; i < network.size(); ++i) - { - if (networkOwnerships[i]) - boost::apply_visitor(deleteVisitor, network[i]); - } - } -} - -template -void Highway::Reset() -{ - transformWeight = arma::mat(weights.memptr(), inSize, inSize, false, false); - transformBias = arma::mat(weights.memptr() + transformWeight.n_elem, - inSize, 1, false, false); -} - -template -template -void Highway::Forward( - const arma::Mat& input, arma::Mat& output) -{ - boost::apply_visitor(ForwardVisitor(input, - boost::apply_visitor(outputParameterVisitor, network.front())), - network.front()); - - if (!reset) - { - if (boost::apply_visitor(outputWidthVisitor, network.front()) != 0) - { - width = boost::apply_visitor(outputWidthVisitor, network.front()); - } - - if (boost::apply_visitor(outputHeightVisitor, network.front()) != 0) - { - height = boost::apply_visitor(outputHeightVisitor, network.front()); - } - } - - for (size_t i = 1; i < network.size(); ++i) - { - if (!reset) - { - // Set the input width. - boost::apply_visitor(SetInputWidthVisitor(width), network[i]); - - // Set the input height. - boost::apply_visitor(SetInputHeightVisitor(height), network[i]); - } - - boost::apply_visitor(ForwardVisitor(boost::apply_visitor( - outputParameterVisitor, network[i - 1]), - boost::apply_visitor(outputParameterVisitor, network[i])), - network[i]); - - if (!reset) - { - // Get the output width. - if (boost::apply_visitor(outputWidthVisitor, network[i]) != 0) - { - width = boost::apply_visitor(outputWidthVisitor, network[i]); - } - - // Get the output height. - if (boost::apply_visitor(outputHeightVisitor, network[i]) != 0) - { - height = boost::apply_visitor(outputHeightVisitor, network[i]); - } - } - } - if (!reset) - { - reset = true; - } - - output = boost::apply_visitor(outputParameterVisitor, network.back()); - - if (arma::size(output) != arma::size(input)) - { - Log::Fatal << "The sizes of the output and input matrices of the Highway" - << " network should be equal. Please examine the network layers."; - } - - transformGate = transformWeight * input; - transformGate.each_col() += transformBias; - transformGateActivation = 1.0 /(1 + arma::exp(-transformGate)); - inputParameter = input; - networkOutput = output; - output = (output % transformGateActivation) + - (input % (1 - transformGateActivation)); -} - -template -template -void Highway::Backward( - const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g) -{ - arma::Mat gyTransform = gy % transformGateActivation; - boost::apply_visitor(BackwardVisitor(boost::apply_visitor( - outputParameterVisitor, network.back()), - gyTransform, - boost::apply_visitor(deltaVisitor, network.back())), - network.back()); - - for (size_t i = 2; i < network.size() + 1; ++i) - { - boost::apply_visitor(BackwardVisitor(boost::apply_visitor( - outputParameterVisitor, network[network.size() - i]), - boost::apply_visitor(deltaVisitor, network[network.size() - i + 1]), - boost::apply_visitor(deltaVisitor, - network[network.size() - i])), network[network.size() - i]); - } - - g = boost::apply_visitor(deltaVisitor, network.front()); - - transformGateError = gy % (networkOutput - inputParameter) % - transformGateActivation % (1.0 - transformGateActivation); - g += transformWeight.t() * transformGateError; - g += gy % (1 - transformGateActivation); -} - -template -template -void Highway::Gradient( - const arma::Mat& input, - const arma::Mat& error, - arma::Mat& gradient) -{ - arma::Mat errorTransform = error % transformGateActivation; - boost::apply_visitor(GradientVisitor(boost::apply_visitor( - outputParameterVisitor, network[network.size() - 2]), - errorTransform), network.back()); - - for (size_t i = 2; i < network.size(); ++i) - { - boost::apply_visitor(GradientVisitor(boost::apply_visitor( - outputParameterVisitor, network[network.size() - i - 1]), - boost::apply_visitor(deltaVisitor, network[network.size() - i + 1])), - network[network.size() - i]); - } - - boost::apply_visitor(GradientVisitor(input, - boost::apply_visitor(deltaVisitor, network[1])), network.front()); - - gradient.submat(0, 0, transformWeight.n_elem - 1, 0) = arma::vectorise( - transformGateError * input.t()); - gradient.submat(transformWeight.n_elem, 0, gradient.n_elem - 1, 0) = - arma::sum(transformGateError, 1); -} - -template -template -void Highway::serialize( - Archive& ar, const uint32_t /* version */) -{ - // If loading, delete the old layers and set size for weights. - if (cereal::is_loading()) - { - for (LayerTypes& layer : network) - { - boost::apply_visitor(deleteVisitor, layer); - } - weights.set_size(inSize * inSize + inSize, 1); - } - - ar(CEREAL_NVP(model)); - ar(CEREAL_VECTOR_VARIANT_POINTER(network)); -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/layer/layer.hpp b/src/mlpack/methods/ann/layer/layer.hpp index 8aca0ad270..c3bfc6a593 100644 --- a/src/mlpack/methods/ann/layer/layer.hpp +++ b/src/mlpack/methods/ann/layer/layer.hpp @@ -2,7 +2,7 @@ * @file methods/ann/layer/layer.hpp * @author Marcus Edel * - * This includes various layers to construct a model. + * Base class for neural network layers. * * mlpack is free software; you may redistribute it and/or modify it under the * terms of the 3-clause BSD license. You should have received a copy of the @@ -12,73 +12,312 @@ #ifndef MLPACK_METHODS_ANN_LAYER_LAYER_HPP #define MLPACK_METHODS_ANN_LAYER_LAYER_HPP -#include "add.hpp" -#include "adaptive_max_pooling.hpp" -#include "adaptive_mean_pooling.hpp" -#include "add_merge.hpp" -#include "alpha_dropout.hpp" -#include "atrous_convolution.hpp" -#include "base_layer.hpp" -#include "batch_norm.hpp" -#include "bicubic_interpolation.hpp" -#include "bilinear_interpolation.hpp" -#include "c_relu.hpp" -#include "celu.hpp" -#include "concat_performance.hpp" -#include "concat.hpp" -#include "concatenate.hpp" -#include "constant.hpp" -#include "convolution.hpp" -#include "dropconnect.hpp" -#include "dropout.hpp" -#include "elu.hpp" -#include "fast_lstm.hpp" -#include "flatten_t_swish.hpp" -#include "flexible_relu.hpp" -#include "glimpse.hpp" -#include "gru.hpp" -#include "hard_tanh.hpp" -#include "hardshrink.hpp" -#include "highway.hpp" -#include "instance_norm.hpp" -#include "join.hpp" -#include "layer_norm.hpp" -#include "layer_types.hpp" -#include "leaky_relu.hpp" -#include "linear.hpp" -#include "linear_no_bias.hpp" -#include "linear3d.hpp" -#include "log_softmax.hpp" -#include "lookup.hpp" -#include "lp_pooling.hpp" -#include "lstm.hpp" -#include "max_pooling.hpp" -#include "mean_pooling.hpp" -#include "minibatch_discrimination.hpp" -#include "multihead_attention.hpp" -#include "multiply_constant.hpp" -#include "multiply_merge.hpp" -#include "nearest_interpolation.hpp" -#include "noisylinear.hpp" -#include "padding.hpp" -#include "parametric_relu.hpp" -#include "pixel_shuffle.hpp" -#include "positional_encoding.hpp" -#include "recurrent_attention.hpp" -#include "recurrent.hpp" -#include "reinforce_normal.hpp" -#include "relu6.hpp" -#include "reparametrization.hpp" -#include "select.hpp" -#include "sequential.hpp" -#include "softshrink.hpp" -#include "softmax.hpp" -#include "softmin.hpp" -#include "spatial_dropout.hpp" -#include "subview.hpp" -#include "transposed_convolution.hpp" -#include "virtual_batch_norm.hpp" -#include "vr_class_reward.hpp" -#include "weight_norm.hpp" +namespace mlpack { +namespace ann { + +/** + * A layer is an abstract class implementing common neural networks operations, + * such as convolution, batch norm, etc. These operations require managing + * weights, losses, updates, and inter-layer connectivity. + * + * Users will just instantiate a layer by inherited from the abstract class and + * implement the layer specific methods. It is recommend that descendants of + * Layer implement the following methods: + * + * - Constructor: Defines custom layer attributes, and creates layer state + * variables. + * + * - Forward(input, output): Performs the forward logic of applying the layer + * to the input object and storing the result in the output object. + * + * - Backward(input, gy, g): Performs a backpropagation step through the layer, + * with respect to the given input. + * + * - Gradient(input, error, gradient): Computing the gradient of the layer with + * respect to its own input. + * + * The memory for the layer's parameters (weights and biases) is not allocated + * by the layer itself, instead it is allocated by the network that the layer + * belongs to, and passed to the layer when it needs to use it. + * + * See the linear layer implementation for a basic example. It's a layer with + * two variables, w and b, that returns y = w * x + b. It shows how to implement + * Forward(), Backward() and Gradient(). The weights of the layers are tracked + * in layer.Parameters(). + * + * @tparam MatType Matrix representation to accept as input and use for + * computation. + */ +template +class Layer +{ + public: + //! Default constructor. + Layer() : validOutputDimensions(false), training(false) + { /* Nothing to do here */ } + + //! Default deconstructor. + virtual ~Layer() { /* Nothing to do here */ } + + //! Copy constructor. This is not responsible for copying weights! + Layer(const Layer& layer) : + inputDimensions(layer.inputDimensions), + outputDimensions(layer.outputDimensions), + validOutputDimensions(layer.validOutputDimensions), + training(layer.training) + { /* Nothing to do here */ } + + //! Make a copy of the object. + virtual Layer* Clone() const = 0; + + //! Move constructor. This is not responsible for moving weights! + Layer(Layer&& layer) : + inputDimensions(std::move(layer.inputDimensions)), + outputDimensions(std::move(layer.outputDimensions)), + validOutputDimensions(std::move(layer.validOutputDimensions)), + training(std::move(layer.training)) + { /* Nothing to do here */ } + + //! Copy assignment operator. This is not responsible for copying weights! + virtual Layer& operator=(const Layer& layer) + { + if (&layer != this) + { + inputDimensions = layer.inputDimensions; + outputDimensions = layer.outputDimensions; + validOutputDimensions = layer.validOutputDimensions; + training = layer.training; + } + + return *this; + } + + //! Move assignment operator. This is not responsible for moving weights! + virtual Layer& operator=(Layer&& layer) + { + if (&layer != this) + { + inputDimensions = std::move(layer.inputDimensions); + outputDimensions = std::move(layer.outputDimensions); + validOutputDimensions = std::move(layer.validOutputDimensions); + training = std::move(layer.training); + } + + return *this; + } + + /** + * Takes an input object, and computes the corresponding output of the layer. + * In general input and output are matrices. However, some special layers like + * table layers might expect something else. Please, refer to each layer + * specification for further information. + * + * @param * (input) Input data used for evaluating the specified layer. + * @param * (output) Resulting output. + */ + virtual void Forward(const MatType& /* input */, + MatType& /* output */) + { /* Nothing to do here */ } + + /** + * Takes an input and output object, and computes the corresponding loss of + * the layer. In general input and output are matrices. However, some special + * layers like table layers might expect something else. Please, refer to each + * layer specification for further information. + * + * @param * (input) Input data used for evaluating the specified layer. + * @param * (output) Resulting output. + */ + virtual void Forward(const MatType& /* input */, + const MatType& /* output */) + { /* Nothing to do here */ } + + /** + * Performs a backpropagation step through the layer, with respect to the + * given input. In general this method makes the assumption Forward(input, + * output) has been called before, with the same input. If you do not respect + * this rule, Backward(input, gy, g) might compute incorrect results. + * + * In general input and gy and g are matrices. However, some special + * sub-classes like table layers might expect something else. Please, refer to + * each module specification for further information. + * + * A backpropagation step consist of computing of computing the gradient + * output input with respect to the output of the layer and given error. + * + * During the backward pass our goal is to use 'gy' in order to compute the + * downstream gradients (g). We assume that the upstream gradient (gy) has + * already been computed and is passed to the layer. + * + * @param * (input) The propagated input activation. + * @param * (gy) The backpropagated error. + * @param * (g) The calculated gradient. + */ + virtual void Backward(const MatType& /* input */, + const MatType& /* gy */, + MatType& /* g */) + { /* Nothing to do here */ } + + /** + * Computing the gradient of the layer with respect to its own input. This is + * returned in gradient. + * + * The layer parameters (weights and biases) are updated accordingly using the + * computed gradient not by the layer itself, instead they are updated by the + * network that holds the instantiated layer. + * + * @param * (input) The input parameter used for calculating the gradient. + * @param * (error) The calculated error. + * @param * (gradient) The calculated gradient. + */ + virtual void Gradient(const MatType& /* input */, + const MatType& /* error */, + MatType& /* gradient */) + { /* Nothing to do here */ } + + /** + * Reset the layer parameter. The method is called to assigned the allocated + * memory to the internal layer parameters like weights and biases. The method + * should be called before the first call of Forward(input, output). If you + * do not respect this rule, Forward(input, output) and Backward(input, gy, g) + * might compute incorrect results. + * + * @param weightsPtr This pointer should be used as the first element of the + * memory that is allocated for this layer. In general, SetWeights() + * implementations should use MakeAlias() with weightsPtr to wrap the + * weights of a layer. + */ + virtual void SetWeights(typename MatType::elem_type* /* weightsPtr */) { } + + /** + * Get the total number of trainable weights in the layer. + */ + virtual size_t WeightSize() const { return 0; } + + /** + * Get whether the layer is currently in training mode. + * + * @note During network training, this should be set to `true` for each layer + * in the network, and when predicting/testing the network, this should be set + * to `false`. (This is handled automatically by the `FFN` class and other + * related classes.) + */ + virtual bool const& Training() const { return training; } + + /** + * Modify whether the layer is currently in training mode. + * + * @note During network training, this should be set to `true` for each layer + * in the network, and when predicting/testing the network, this should be set + * to `false`. (This is handled automatically by the `FFN` class and other + * related classes.) + */ + virtual bool& Training() { return training; } + + //! Get the layer loss. Overload this if the layer should add any extra loss + //! to the loss function when computing the objective. (TODO: better comment) + virtual double Loss() { return 0; } + + //! Get the input dimensions. + const std::vector& InputDimensions() const { return inputDimensions; } + //! Modify the input dimensions. + std::vector& InputDimensions() + { + validOutputDimensions = false; + return inputDimensions; + } + + //! Get the output dimensions. + const std::vector& OutputDimensions() + { + if (!validOutputDimensions) + { + this->ComputeOutputDimensions(); + validOutputDimensions = true; + } + + return outputDimensions; + } + + //! Get the parameters. + virtual const MatType& Parameters() const + { + throw std::invalid_argument("Layer::Parameters(): cannot access parameters " + "of a layer with no weights!"); + } + //! Set the parameters. + virtual MatType& Parameters() + { + throw std::invalid_argument("Layer::Parameters(): cannot modify parameters " + "of a layer with no weights!"); + } + + //! Compute the output dimensions. This should be overloaded if the layer is + //! meant to work on higher-dimensional objects. When this is called, it is a + //! safe assumption that InputDimensions() is correct. + virtual void ComputeOutputDimensions() + { + // The default implementation is to assume that the output size is the same + // as the input. + outputDimensions = inputDimensions; + } + + //! Get the number of elements in the output from this layer. This cannot be + //! overloaded! Overload `ComputeOutputDimensions()` instead. + virtual size_t OutputSize() final + { + if (!validOutputDimensions) + { + this->ComputeOutputDimensions(); + validOutputDimensions = true; + } + + size_t outputSize = 1; + for (size_t i = 0; i < this->outputDimensions.size(); ++i) + outputSize *= this->outputDimensions[i]; + return outputSize; + } + + //! Serialize the layer. + template + void serialize(Archive& ar, const uint32_t /* version */) + { + ar(CEREAL_NVP(inputDimensions)); + ar(CEREAL_NVP(outputDimensions)); + ar(CEREAL_NVP(validOutputDimensions)); + ar(CEREAL_NVP(training)); + + // Note that layer weights are serialized by the FFN! + } + + protected: + /** + * Logical input dimensions of each point. Although each point given to ! + * `Forward()` will be represented as a column in a matrix, logically + * speaking it can be a higher-order tensor. So, for instance, if the point + * is 2-dimensional images of size 10x10, `Forward()` will contain columns + * with 100 rows, and `inputDimensions` will be `{10, 10}`. This generalizes + * to higher dimensions. + */ + std::vector inputDimensions; + + /** + * Logical output dimensions of each point. If the layer only performs + * elementwise operations, this is most likely equal to `inputDimensions`; but + * if the layer performs more complicated transformations, it may be + * different. + */ + std::vector outputDimensions; + + //! This is `true` if `ComputeOutputDimensions()` has been called, and + //! `outputDimensions` can be considered to be up-to-date. + bool validOutputDimensions; + + //! If true, the layer is in training mode; otherwise, it is in testing mode. + bool training; +}; + +} // namespace ann +} // namespace mlpack #endif diff --git a/src/mlpack/methods/ann/layer/layer_traits.hpp b/src/mlpack/methods/ann/layer/layer_traits.hpp deleted file mode 100644 index a6a447f43d..0000000000 --- a/src/mlpack/methods/ann/layer/layer_traits.hpp +++ /dev/null @@ -1,130 +0,0 @@ -/** - * @file methods/ann/layer/layer_traits.hpp - * @author Marcus Edel - * - * This provides the LayerTraits class, a template class to get information - * about various layers. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_LAYER_LAYER_TRAITS_HPP -#define MLPACK_METHODS_ANN_LAYER_LAYER_TRAITS_HPP - -#include - -namespace mlpack { -namespace ann { - -/** - * This is a template class that can provide information about various layers. - * By default, this class will provide the weakest possible assumptions on - * layer, and each layer should override values as necessary. If a layer - * doesn't need to override a value, then there's no need to write a LayerTraits - * specialization for that class. - */ -template -class LayerTraits -{ - public: - /** - * This is true if the layer is a binary layer. - */ - static const bool IsBinary = false; - - /** - * This is true if the layer is an output layer. - */ - static const bool IsOutputLayer = false; - - /** - * This is true if the layer is a bias layer. - */ - static const bool IsBiasLayer = false; - - /* - * This is true if the layer is a LSTM layer. - **/ - static const bool IsLSTMLayer = false; - - /* - * This is true if the layer is a connection layer. - **/ - static const bool IsConnection = false; -}; - -// This gives us a HasGradientCheck type (where U is a function pointer) -// we can use with SFINAE to catch when a type has a Gradient(...) function. -HAS_MEM_FUNC(Gradient, HasGradientCheck); - -// This gives us a HasDeterministicCheck type (where U is a function -// pointer) we can use with SFINAE to catch when a type has a Deterministic() -// function. -HAS_MEM_FUNC(Deterministic, HasDeterministicCheck); - -// This gives us a HasParametersCheck type (where U is a function pointer) -// we can use with SFINAE to catch when a type has a Parameters() function. -HAS_MEM_FUNC(Parameters, HasParametersCheck); - -// This gives us a HasAddCheck type (where U is a function pointer) we -// can use with SFINAE to catch when a type has a Add() function. -HAS_MEM_FUNC(Add, HasAddCheck); - -// This gives us a HasModelCheck type we can use with SFINAE to catch when -// a type has a function named Model. -HAS_ANY_METHOD_FORM(Model, HasModelCheck); - -// This gives us a HasLocationCheck type (where U is a function pointer) -// we can use with SFINAE to catch when a type has a Location() function. -HAS_MEM_FUNC(Location, HasLocationCheck); - -// This gives us a HasResetCheck type (where U is a function pointer) -// we can use with SFINAE to catch when a type has a Reset() function. -HAS_MEM_FUNC(Reset, HasResetCheck); - -// This gives us a HasResetCheck type (where U is a function pointer) -// we can use with SFINAE to catch when a type has a ResetCell() function. -HAS_MEM_FUNC(ResetCell, HasResetCellCheck); - -// This gives us a HasRewardCheck type (where U is a function pointer) we -// can use with SFINAE to catch when a type has a Reward() function. -HAS_MEM_FUNC(Reward, HasRewardCheck); - -// This gives us a HasInputWidth type (where U is a function pointer) we -// can use with SFINAE to catch when a type has a InputWidth() function. -HAS_MEM_FUNC(InputWidth, HasInputWidth); - -// This gives us a HasInputHeight type (where U is a function pointer) we -// can use with SFINAE to catch when a type has a InputHeight() function. -HAS_MEM_FUNC(InputHeight, HasInputHeight); - -// This gives us a HasRho type (where U is a function pointer) we -// can use with SFINAE to catch when a type has a Rho() function. -HAS_MEM_FUNC(Rho, HasRho); - -// This gives us a HasLoss type (where U is a function pointer) we -// can use with SFINAE to catch when a type has a Loss() function. -HAS_MEM_FUNC(Loss, HasLoss); - -// This gives us a HasRunCheck type (where U is a function pointer) we -// can use with SFINAE to catch when a type has a Run() function. -HAS_MEM_FUNC(Run, HasRunCheck); - -// This gives us a HasBiasCheck type (where U is a function pointer) we -// can use with SFINAE to catch when a type has a Bias() function. -HAS_MEM_FUNC(Bias, HasBiasCheck); - -// This gives us a HasMaxIterationsC type (where U is a function pointer) -// we can use with SFINAE to catch when a type has a MaxIterations() function. -HAS_MEM_FUNC(MaxIterations, HasMaxIterations); - -// This gives us a HasInShapeCheck type we can use with SFINAE to catch when -// a type has a function named InputShape. -HAS_ANY_METHOD_FORM(InputShape, HasInputShapeCheck); - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/layer/layer_types.hpp b/src/mlpack/methods/ann/layer/layer_types.hpp index a1970bd6e1..fb179b6457 100644 --- a/src/mlpack/methods/ann/layer/layer_types.hpp +++ b/src/mlpack/methods/ann/layer/layer_types.hpp @@ -12,62 +12,33 @@ #ifndef MLPACK_METHODS_ANN_LAYER_LAYER_TYPES_HPP #define MLPACK_METHODS_ANN_LAYER_LAYER_TYPES_HPP -#include +#include +#include +#include -// Layer modules. +// Include each layer. #include #include #include -#include -#include -#include -#include -#include #include +#include +#include #include -#include -#include -#include -#include -#include -#include #include -#include -#include +#include #include #include #include -#include -#include -#include +#include #include -#include -#include -#include #include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include +#include #include -// Convolution modules. +// Convolution modes. #include -#include #include +#include // Regularizers. #include @@ -75,250 +46,7 @@ // Loss function modules. #include -namespace mlpack { -namespace ann { - -template class BatchNorm; -template class DropConnect; -template class Glimpse; -template class LayerNorm; -template class LSTM; -template class GRU; -template class FastLSTM; -template class VRClassReward; -template class Concatenate; -template class Padding; -template class ReLU6; - -template -class Linear; - -template -class RBF; - -template -class LinearNoBias; - -template -class NoisyLinear; - -template -class Linear3D; - -template -class VirtualBatchNorm; - -template -class MiniBatchDiscrimination; - -template -class MultiheadAttention; - -template -class Reparametrization; - -template -class AddMerge; - -template -class Sequential; - -template -class Highway; - -template -class Recurrent; - -template -class Concat; - -template< - typename OutputLayerType, - typename InputDataType, - typename OutputDataType -> -class ConcatPerformance; - -template< - typename ForwardConvolutionRule, - typename BackwardConvolutionRule, - typename GradientConvolutionRule, - typename InputDataType, - typename OutputDataType -> -class Convolution; - -template< - typename ForwardConvolutionRule, - typename BackwardConvolutionRule, - typename GradientConvolutionRule, - typename InputDataType, - typename OutputDataType -> -class TransposedConvolution; - -template< - typename ForwardConvolutionRule, - typename BackwardConvolutionRule, - typename GradientConvolutionRule, - typename InputDataType, - typename OutputDataType -> -class AtrousConvolution; - -template< - typename InputDataType, - typename OutputDataType -> -class RecurrentAttention; - -template -class MultiplyMerge; - -template -class WeightNorm; - -template -class AdaptiveMaxPooling; - -template -class AdaptiveMeanPooling; - -using MoreTypes = boost::variant< - FlexibleReLU*, - Linear3D*, - LpPooling*, - PixelShuffle*, - ChannelShuffle*, - Glimpse*, - Highway*, - MultiheadAttention*, - Recurrent*, - RecurrentAttention*, - ReinforceNormal*, - ReLU6*, - Reparametrization*, - Select*, - SpatialDropout*, - Subview*, - VRClassReward*, - VirtualBatchNorm*, - RBF*, - BaseLayer*, - PositionalEncoding*, - ISRLU*, - BicubicInterpolation*, - NearestInterpolation*, - GroupNorm*, - InstanceNorm* ->; - -template -using LayerTypes = boost::variant< - AdaptiveMaxPooling*, - AdaptiveMeanPooling*, - Add*, - AddMerge*, - AlphaDropout*, - AtrousConvolution, - NaiveConvolution, - NaiveConvolution, - arma::mat, arma::mat>*, - BaseLayer*, - BaseLayer*, - BaseLayer*, - BaseLayer*, - BaseLayer*, - BatchNorm*, - BilinearInterpolation*, - CELU*, - Concat*, - Concatenate*, - ConcatPerformance, - arma::mat, arma::mat>*, - Constant*, - Convolution, - NaiveConvolution, - NaiveConvolution, arma::mat, arma::mat>*, - CReLU*, - DropConnect*, - Dropout*, - ELU*, - FastLSTM*, - GRU*, - HardTanH*, - Join*, - LayerNorm*, - LeakyReLU*, - Linear*, - LinearNoBias*, - LogSoftMax*, - Lookup*, - LSTM*, - MaxPooling*, - MeanPooling*, - MiniBatchDiscrimination*, - MultiplyConstant*, - MultiplyMerge*, - NegativeLogLikelihood*, - NoisyLinear*, - Padding*, - PReLU*, - Sequential*, - Sequential*, - Softmax*, - TransposedConvolution, - NaiveConvolution, - NaiveConvolution, arma::mat, arma::mat>*, - WeightNorm*, - MoreTypes, - CustomLayers*... ->; - -} // namespace ann -} // namespace mlpack +// Include definitions for polymorphic serialization. +#include #endif diff --git a/src/mlpack/methods/ann/layer/leaky_relu.hpp b/src/mlpack/methods/ann/layer/leaky_relu.hpp index 52b2896fca..b275371de6 100644 --- a/src/mlpack/methods/ann/layer/leaky_relu.hpp +++ b/src/mlpack/methods/ann/layer/leaky_relu.hpp @@ -16,6 +16,8 @@ #include +#include "layer.hpp" + namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -32,16 +34,11 @@ namespace ann /** Artificial Neural Network. */ { * \right. * @f} * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam MatType Matrix representation to accept as input and use for + * computation. */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class LeakyReLU +template +class LeakyReLUType : public Layer { public: /** @@ -49,9 +46,24 @@ class LeakyReLU * The non zero gradient can be adjusted by specifying the parameter * alpha in the range 0 to 1. Default (alpha = 0.03) * - * @param alpha Non zero gradient + * @param alpha Non zero gradient. */ - LeakyReLU(const double alpha = 0.03); + LeakyReLUType(const double alpha = 0.03); + + //! Clone the LeakyReLUType object. This handles polymorphism correctly. + LeakyReLUType* Clone() const { return new LeakyReLUType(*this); } + + // Virtual destructor. + virtual ~LeakyReLUType() { } + + //! Copy the given LeakyReLUType. + LeakyReLUType(const LeakyReLUType& other); + //! Take ownership of the given LeakyReLUType. + LeakyReLUType(LeakyReLUType&& other); + //! Copy the given LeakyReLUType. + LeakyReLUType& operator=(const LeakyReLUType& other); + //! Take ownership of the given LeakyReLUType. + LeakyReLUType& operator=(LeakyReLUType&& other); /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -60,8 +72,7 @@ class LeakyReLU * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template - void Forward(const InputType& input, OutputType& output); + void Forward(const MatType& input, MatType& output); /** * Ordinary feed backward pass of a neural network, calculating the function @@ -72,43 +83,27 @@ class LeakyReLU * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const DataType& input, const DataType& gy, DataType& g); - - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } + void Backward(const MatType& input, const MatType& gy, MatType& g); //! Get the non zero gradient. double const& Alpha() const { return alpha; } //! Modify the non zero gradient. double& Alpha() { return alpha; } - //! Get size of weights. - size_t WeightSize() const { return 0; } - - /** - * Serialize the layer. - */ + //! Serialize the layer. template void serialize(Archive& ar, const uint32_t /* version */); private: - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; - //! Leakyness Parameter in the range 0 LeakyReLU; + } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/leaky_relu_impl.hpp b/src/mlpack/methods/ann/layer/leaky_relu_impl.hpp index d03009f348..7a6e8dce3a 100644 --- a/src/mlpack/methods/ann/layer/leaky_relu_impl.hpp +++ b/src/mlpack/methods/ann/layer/leaky_relu_impl.hpp @@ -20,27 +20,68 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -LeakyReLU::LeakyReLU( - const double alpha) : alpha(alpha) +template +LeakyReLUType::LeakyReLUType(const double alpha) : + Layer(), + alpha(alpha) { // Nothing to do here. } -template -template -void LeakyReLU::Forward( - const InputType& input, OutputType& output) +template +LeakyReLUType::LeakyReLUType(const LeakyReLUType& other) : + Layer(other), + alpha(other.alpha) +{ + // Nothing to do. +} + +template +LeakyReLUType::LeakyReLUType( + LeakyReLUType&& other) : + Layer(std::move(other)), + alpha(std::move(other.alpha)) +{ + // Nothing to do. +} + +template +LeakyReLUType& +LeakyReLUType::operator=(const LeakyReLUType& other) +{ + if (&other != this) + { + Layer::operator=(other); + alpha = other.alpha; + } + + return *this; +} + +template +LeakyReLUType& +LeakyReLUType::operator=(LeakyReLUType&& other) +{ + if (&other != this) + { + Layer::operator=(std::move(other)); + alpha = std::move(other.alpha); + } + + return *this; +} + +template +void LeakyReLUType::Forward(const MatType& input, MatType& output) { output = arma::max(input, alpha * input); } -template -template -void LeakyReLU::Backward( - const DataType& input, const DataType& gy, DataType& g) +template +void LeakyReLUType::Backward( + const MatType& input, const MatType& gy, MatType& g) { - DataType derivative; + MatType derivative; derivative.set_size(arma::size(input)); for (size_t i = 0; i < input.n_elem; ++i) derivative(i) = (input(i) >= 0) ? 1 : alpha; @@ -48,12 +89,14 @@ void LeakyReLU::Backward( g = gy % derivative; } -template +template template -void LeakyReLU::serialize( +void LeakyReLUType::serialize( Archive& ar, const uint32_t /* version */) { + ar(cereal::base_class>(this)); + ar(CEREAL_NVP(alpha)); } diff --git a/src/mlpack/methods/ann/layer/linear.hpp b/src/mlpack/methods/ann/layer/linear.hpp index 5b9d23bd87..7dca66ea01 100644 --- a/src/mlpack/methods/ann/layer/linear.hpp +++ b/src/mlpack/methods/ann/layer/linear.hpp @@ -16,7 +16,7 @@ #include #include -#include "layer_types.hpp" +#include "layer.hpp" namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -25,142 +25,120 @@ namespace ann /** Artificial Neural Network. */ { * Implementation of the Linear layer class. The Linear class represents a * single layer of a neural network. * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * The linear layer applies a linear transformation to the incoming data + * (input), i.e. y = Ax + b. The input matrix given in Forward(input, output) + * must be either a vector or matrix. If the input is a matrix, then each column + * is assumed to be an input sample of given batch. + * + * @tparam MatType Matrix representation to accept as input and use for + * computation. + * @tparam RegularizerType Type of the regularizer to be used (Default no + * regularizer). */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat, +template< + typename MatType = arma::mat, typename RegularizerType = NoRegularizer > -class Linear +class LinearType : public Layer { public: //! Create the Linear object. - Linear(); + LinearType(); /** - * Create the Linear layer object using the specified number of units. + * Create the Linear layer object with the specified number of output + * dimensions. * - * @param inSize The number of input units. - * @param outSize The number of output units. - * @param regularizer The regularizer to use, optional. + * @param outSize The output dimension. + * @param regularizer The regularizer to use, optional (default: no + * regularizer). */ - Linear(const size_t inSize, - const size_t outSize, - RegularizerType regularizer = RegularizerType()); + LinearType(const size_t outSize, + RegularizerType regularizer = RegularizerType()); - //! Copy constructor. - Linear(const Linear& layer); + virtual ~LinearType() { } - //! Move constructor. - Linear(Linear&&); + //! Clone the LinearType object. This handles polymorphism correctly. + LinearType* Clone() const { return new LinearType(*this); } - //! Copy assignment operator. - Linear& operator=(const Linear& layer); + //! Copy the other Linear layer (but not weights). + LinearType(const LinearType& layer); - //! Move assignment operator. - Linear& operator=(Linear&& layer); + //! Take ownership of the members of the other Linear layer (but not weights). + LinearType(LinearType&& layer); - /* - * Reset the layer parameter. + //! Copy the other Linear layer (but not weights). + LinearType& operator=(const LinearType& layer); + + //! Take ownership of the members of the other Linear layer (but not weights). + LinearType& operator=(LinearType&& layer); + + /** + * Reset the layer parameter (weights and bias). The method is called to + * assign the allocated memory to the internal learnable parameters. */ - void Reset(); + void SetWeights(typename MatType::elem_type* weightsPtr); /** * Ordinary feed forward pass of a neural network, evaluating the function * f(x) by propagating the activity forward through f. * + * f(x) is a linear transformation: Ax + b, where x is the given input, x are + * the layer weights and b is the layer bias. + * * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template - void Forward(const arma::Mat& input, arma::Mat& output); + void Forward(const MatType& input, MatType& output); /** * Ordinary feed backward pass of a neural network, calculating the function * f(x) by propagating x backwards trough f. Using the results from the feed * forward pass. * + * To compute the downstream gradient (g) the chain rule is used. + * * @param * (input) The propagated input activation. * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g); + void Backward(const MatType& /* input */, + const MatType& gy, + MatType& g); - /* + /** * Calculate the gradient using the output delta and the input activation. * * @param input The input parameter used for calculating the gradient. * @param error The calculated error. * @param gradient The calculated gradient. */ - template - void Gradient(const arma::Mat& input, - const arma::Mat& error, - arma::Mat& gradient); + void Gradient(const MatType& input, + const MatType& error, + MatType& gradient); //! Get the parameters. - OutputDataType const& Parameters() const { return weights; } + const MatType& Parameters() const { return weights; } //! Modify the parameters. - OutputDataType& Parameters() { return weights; } - - //! Get the input parameter. - InputDataType const& InputParameter() const { return inputParameter; } - //! Modify the input parameter. - InputDataType& InputParameter() { return inputParameter; } - - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } - - //! Get the input size. - size_t InputSize() const { return inSize; } - - //! Get the output size. - size_t OutputSize() const { return outSize; } - - //! Get the gradient. - OutputDataType const& Gradient() const { return gradient; } - //! Modify the gradient. - OutputDataType& Gradient() { return gradient; } + MatType& Parameters() { return weights; } //! Get the weight of the layer. - OutputDataType const& Weight() const { return weight; } + MatType const& Weight() const { return weight; } //! Modify the weight of the layer. - OutputDataType& Weight() { return weight; } + MatType& Weight() { return weight; } //! Get the bias of the layer. - OutputDataType const& Bias() const { return bias; } + MatType const& Bias() const { return bias; } //! Modify the bias weights of the layer. - OutputDataType& Bias() { return bias; } + MatType& Bias() { return bias; } //! Get the size of the weights. - size_t WeightSize() const - { - return (inSize * outSize) + outSize; - } + size_t WeightSize() const { return (inSize * outSize) + outSize; } - //! Get the shape of the input. - size_t InputShape() const - { - return inSize; - } + //! Compute the output dimensions of the layer given `InputDimensions()`. + void ComputeOutputDimensions(); - /** - * Serialize the layer - */ + //! Serialize the layer. template void serialize(Archive& ar, const uint32_t /* version */); @@ -171,30 +149,24 @@ class Linear //! Locally-stored number of output units. size_t outSize; - //! Locally-stored weight object. - OutputDataType weights; + //! Locally-stored weight object. This holds all the weights in a vectorized + //! form; i.e., the weights and the bias. + MatType weights; //! Locally-stored weight parameters. - OutputDataType weight; + MatType weight; //! Locally-stored bias term parameters. - OutputDataType bias; - - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored gradient object. - OutputDataType gradient; - - //! Locally-stored input parameter object. - InputDataType inputParameter; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; + MatType bias; //! Locally-stored regularizer object. RegularizerType regularizer; -}; // class Linear +}; // class LinearType + +// Convenience typedefs. + +// Standard Linear layer using no regularization. +typedef LinearType Linear; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/linear3d.hpp b/src/mlpack/methods/ann/layer/linear3d.hpp index 24e3de56e6..6b9b554ea7 100644 --- a/src/mlpack/methods/ann/layer/linear3d.hpp +++ b/src/mlpack/methods/ann/layer/linear3d.hpp @@ -14,9 +14,10 @@ #define MLPACK_METHODS_ANN_LAYER_LINEAR3D_HPP #include -#include #include +#include "layer.hpp" + namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -27,49 +28,48 @@ namespace ann /** Artificial Neural Network. */ { * Shape of input : (inSize * nPoints, batchSize) * Shape of output : (outSize * nPoints, batchSize) * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam MatType Matrix representation to accept as input and use for + * computation. */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat, +template< + typename MatType = arma::mat, typename RegularizerType = NoRegularizer > -class Linear3D +class Linear3DType : public Layer { public: //! Create the Linear3D object. - Linear3D(); + Linear3DType(); /** - * Create the Linear3D layer object using the specified number of units. + * Create the Linear3D layer object using the specified number of output + * units. * - * @param inSize The number of input units. * @param outSize The number of output units. * @param regularizer The regularizer to use, optional. */ - Linear3D(const size_t inSize, - const size_t outSize, - RegularizerType regularizer = RegularizerType()); + Linear3DType(const size_t outSize, + RegularizerType regularizer = RegularizerType()); - //! Copy constructor. - Linear3D(const Linear3D& layer); + //! Clone the Linear3DType object. This handles polymorphism correctly. + Linear3DType* Clone() const { return new Linear3DType(*this); } - //! Move constructor. - Linear3D(Linear3D&&); + // Virtual destructor. + virtual ~Linear3DType() { } - //! Copy assignment operator. - Linear3D& operator=(const Linear3D& layer); - - //! Move assignment operator. - Linear3D& operator=(Linear3D&& layer); + //! Copy the given Linear3DType (but not weights). + Linear3DType(const Linear3DType& other); + //! Take ownership of the given Linear3DType (but not weights). + Linear3DType(Linear3DType&& other); + //! Copy the given Linear3DType (but not weights). + Linear3DType& operator=(const Linear3DType& other); + //! Take ownership of the given Linear3DType (but not weights). + Linear3DType& operator=(Linear3DType&& other); /* * Reset the layer parameter. */ - void Reset(); + void SetWeights(typename MatType::elem_type* weightsPtr); /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -78,8 +78,7 @@ class Linear3D * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template - void Forward(const arma::Mat& input, arma::Mat& output); + void Forward(const MatType& input, MatType& output); /** * Ordinary feed backward pass of a neural network, calculating the function @@ -90,69 +89,41 @@ class Linear3D * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g); + void Backward(const MatType& /* input */, + const MatType& gy, + MatType& g); - /* + /** * Calculate the gradient using the output delta and the input activation. * * @param input The input parameter used for calculating the gradient. * @param error The calculated error. * @param gradient The calculated gradient. */ - template - void Gradient(const arma::Mat& input, - const arma::Mat& error, - arma::Mat& gradient); + void Gradient(const MatType& input, + const MatType& error, + MatType& gradient); //! Get the parameters. - OutputDataType const& Parameters() const { return weights; } + MatType const& Parameters() const { return weights; } //! Modify the parameters. - OutputDataType& Parameters() { return weights; } - - //! Get the input parameter. - InputDataType const& InputParameter() const { return inputParameter; } - //! Modify the input parameter. - InputDataType& InputParameter() { return inputParameter; } - - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } - - //! Get the input size. - size_t InputSize() const { return inSize; } - - //! Get the output size. - size_t OutputSize() const { return outSize; } - - //! Get the gradient. - OutputDataType const& Gradient() const { return gradient; } - //! Modify the gradient. - OutputDataType& Gradient() { return gradient; } + MatType& Parameters() { return weights; } //! Get the weight of the layer. - OutputDataType const& Weight() const { return weight; } + MatType const& Weight() const { return weight; } //! Modify the weight of the layer. - OutputDataType& Weight() { return weight; } + MatType& Weight() { return weight; } //! Get the bias of the layer. - OutputDataType const& Bias() const { return bias; } + MatType const& Bias() const { return bias; } //! Modify the bias weights of the layer. - OutputDataType& Bias() { return bias; } + MatType& Bias() { return bias; } - //! Get the shape of the input. - size_t InputShape() const - { - return inSize; - } + //! Return the number of weight elements. + size_t WeightSize() const { return outSize * (this->inputDimensions[0] + 1); } + + //! Compute the output dimensions for the layer, using `InputDimensions()`. + void ComputeOutputDimensions(); /** * Serialize the layer @@ -161,37 +132,25 @@ class Linear3D void serialize(Archive& ar, const uint32_t /* version */); private: - //! Locally-stored number of input units. - size_t inSize; - //! Locally-stored number of output units. size_t outSize; //! Locally-stored weight object. - OutputDataType weights; + MatType weights; //! Locally-stored weight parameters. - OutputDataType weight; + MatType weight; //! Locally-stored bias term parameters. - OutputDataType bias; - - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored gradient object. - OutputDataType gradient; - - //! Locally-stored input parameter object. - InputDataType inputParameter; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; + MatType bias; //! Locally-stored regularizer object. RegularizerType regularizer; }; // class Linear +// Standard Linear3D layer. +typedef Linear3DType Linear3D; + } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/linear3d_impl.hpp b/src/mlpack/methods/ann/layer/linear3d_impl.hpp index 37cc9e39e5..cc4ae74375 100644 --- a/src/mlpack/methods/ann/layer/linear3d_impl.hpp +++ b/src/mlpack/methods/ann/layer/linear3d_impl.hpp @@ -18,116 +18,94 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -Linear3D::Linear3D() : - inSize(0), +template +Linear3DType::Linear3DType() : + Layer(), outSize(0) { // Nothing to do here. } -template -Linear3D::Linear3D( - const size_t inSize, +template +Linear3DType::Linear3DType( const size_t outSize, RegularizerType regularizer) : - inSize(inSize), + Layer(), outSize(outSize), regularizer(regularizer) +{ } + +template +Linear3DType::Linear3DType( + const Linear3DType& other) : + Layer(other), + outSize(other.outSize), + regularizer(other.regularizer) { - weights.set_size(outSize * inSize + outSize, 1); + // Nothing to do. } -template -Linear3D::Linear3D( - const Linear3D& layer) : - inSize(layer.inSize), - outSize(layer.outSize), - weights(layer.weights), - regularizer(layer.regularizer) +template +Linear3DType::Linear3DType( + Linear3DType&& other) : + Layer(std::move(other)), + outSize(std::move(other.outSize)), + regularizer(std::move(other.regularizer)) { - // Nothing to do here. + // Nothing to do. } -template -Linear3D::Linear3D( - Linear3D&& layer) : - inSize(0), - outSize(0), - weights(std::move(layer.weights)), - regularizer(std::move(layer.regularizer)) +template +Linear3DType& +Linear3DType::operator=( + const Linear3DType& other) { - // Nothing to do here. -} - -template -Linear3D& -Linear3D:: -operator=(const Linear3D& layer) -{ - if (this != &layer) + if (&other != this) { - inSize = layer.inSize; - outSize = layer.outSize; - weights = layer.weights; - regularizer = layer.regularizer; + Layer::operator=(other); + outSize = other.outSize; + regularizer = other.regularizer; } + return *this; } -template -Linear3D& -Linear3D:: -operator=(Linear3D&& layer) +template +Linear3DType& +Linear3DType::operator=( + Linear3DType&& other) { - if (this != &layer) + if (&other != this) { - inSize = 0; - outSize = 0; - weights = std::move(layer.weights); - regularizer = std::move(layer.regularizer); + Layer::operator=(std::move(other)); + outSize = std::move(other.outSize); + regularizer = std::move(other.regularizer); } + return *this; } -template -void Linear3D::Reset() +template +void Linear3DType::SetWeights( + typename MatType::elem_type* weightsPtr) { - typedef typename arma::Mat MatType; - - weight = MatType(weights.memptr(), outSize, inSize, false, false); - bias = MatType(weights.memptr() + weight.n_elem, outSize, 1, false, false); + MakeAlias(weights, weightsPtr, outSize * this->inputDimensions[0] + outSize, + 1); + MakeAlias(weight, weightsPtr, outSize, this->inputDimensions[0]); + MakeAlias(bias, weightsPtr + weight.n_elem, outSize, 1); } -template -template -void Linear3D::Forward( - const arma::Mat& input, arma::Mat& output) +template +void Linear3DType::Forward( + const MatType& input, MatType& output) { - typedef typename arma::Mat MatType; - typedef typename arma::Cube CubeType; + typedef typename arma::Cube CubeType; - if (input.n_rows % inSize != 0) - { - Log::Fatal << "Number of features in the input must be divisible by inSize." - << std::endl; - } - - const size_t nPoints = input.n_rows / inSize; + const size_t nPoints = input.n_rows / this->inputDimensions[0]; const size_t batchSize = input.n_cols; - output.set_size(outSize * nPoints, batchSize); - - const CubeType inputTemp(const_cast(input).memptr(), inSize, - nPoints, batchSize, false, false); + const CubeType inputTemp(const_cast(input).memptr(), + this->inputDimensions[0], nPoints, batchSize, false, false); for (size_t i = 0; i < batchSize; ++i) { @@ -139,16 +117,13 @@ void Linear3D::Forward( } } -template -template -void Linear3D::Backward( - const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g) +template +void Linear3DType::Backward( + const MatType& /* input */, + const MatType& gy, + MatType& g) { - typedef typename arma::Mat MatType; - typedef typename arma::Cube CubeType; + typedef typename arma::Cube CubeType; if (gy.n_rows % outSize != 0) { @@ -162,8 +137,6 @@ void Linear3D::Backward( const CubeType gyTemp(const_cast(gy).memptr(), outSize, nPoints, batchSize, false, false); - g.set_size(inSize * nPoints, batchSize); - for (size_t i = 0; i < gyTemp.n_slices; ++i) { // Shape of weight : (outSize, inSize). @@ -172,29 +145,26 @@ void Linear3D::Backward( } } -template -template -void Linear3D::Gradient( - const arma::Mat& input, - const arma::Mat& error, - arma::Mat& gradient) +template +void Linear3DType::Gradient( + const MatType& input, + const MatType& error, + MatType& gradient) { - typedef typename arma::Mat MatType; - typedef typename arma::Cube CubeType; + typedef typename arma::Cube CubeType; if (error.n_rows % outSize != 0) Log::Fatal << "Propagated error matrix has invalid dimension!" << std::endl; - const size_t nPoints = input.n_rows / inSize; + const size_t nPoints = input.n_rows / this->inputDimensions[0]; const size_t batchSize = input.n_cols; - const CubeType inputTemp(const_cast(input).memptr(), inSize, - nPoints, batchSize, false, false); + const CubeType inputTemp(const_cast(input).memptr(), + this->inputDimensions[0], nPoints, batchSize, false, false); const CubeType errorTemp(const_cast(error).memptr(), outSize, nPoints, batchSize, false, false); - CubeType dW(outSize, inSize, batchSize); + CubeType dW(outSize, this->inputDimensions[0], batchSize); for (size_t i = 0; i < batchSize; ++i) { // Shape of errorTemp : (outSize, nPoints, batchSize). @@ -202,8 +172,6 @@ void Linear3D::Gradient( dW.slice(i) = errorTemp.slice(i) * inputTemp.slice(i).t(); } - gradient.set_size(arma::size(weights)); - gradient.submat(0, 0, weight.n_elem - 1, 0) = arma::vectorise(arma::sum(dW, 2)); @@ -213,19 +181,27 @@ void Linear3D::Gradient( regularizer.Evaluate(weights, gradient); } -template +template +void Linear3DType< + MatType, RegularizerType +>::ComputeOutputDimensions() +{ + // The Linear3D layer shares weights for each row of the input, and + // duplicates it across the columns. Thus, we only change the number of + // rows. + this->outputDimensions = this->inputDimensions; + this->outputDimensions[0] = outSize; +} + +template template -void Linear3D::serialize( +void Linear3DType::serialize( Archive& ar, const uint32_t /* version */) { - ar(CEREAL_NVP(inSize)); - ar(CEREAL_NVP(outSize)); + ar(cereal::base_class>(this)); - // This is inefficient, but we have to allocate this memory so that - // WeightSetVisitor gets the right size. - if (cereal::is_loading()) - weights.set_size(outSize * inSize + outSize, 1); + ar(CEREAL_NVP(outSize)); + ar(CEREAL_NVP(regularizer)); } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/linear_impl.hpp b/src/mlpack/methods/ann/layer/linear_impl.hpp index 2edfb4802c..fe7841e791 100644 --- a/src/mlpack/methods/ann/layer/linear_impl.hpp +++ b/src/mlpack/methods/ann/layer/linear_impl.hpp @@ -19,136 +19,141 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -Linear::Linear() : +template +LinearType::LinearType() : + Layer(), inSize(0), outSize(0) { // Nothing to do here. } -template -Linear::Linear( - const size_t inSize, +template +LinearType::LinearType( const size_t outSize, RegularizerType regularizer) : - inSize(inSize), + Layer(), + inSize(0), // This will be computed in ComputeOutputDimensions(). outSize(outSize), regularizer(regularizer) { weights.set_size(WeightSize(), 1); } -template -Linear::Linear( - const Linear& layer) : +// Copy constructor. +template +LinearType::LinearType(const LinearType& layer) : + Layer(layer), inSize(layer.inSize), outSize(layer.outSize), - weights(layer.weights), regularizer(layer.regularizer) { - // Nothing to do here. + // Nothing else to do. } -template -Linear::Linear( - Linear&& layer) : - inSize(0), - outSize(0), - weights(std::move(layer.weights)), +// Move constructor. +template +LinearType::LinearType(LinearType&& layer) : + Layer(std::move(layer)), + inSize(std::move(layer.inSize)), + outSize(std::move(layer.outSize)), regularizer(std::move(layer.regularizer)) { - // Nothing to do here. + // Nothing else to do. } -template -Linear& -Linear:: -operator=(const Linear& layer) +template +LinearType& +LinearType::operator=(const LinearType& layer) { - if (this != &layer) + if (&layer != this) { + Layer::operator=(layer); inSize = layer.inSize; outSize = layer.outSize; - weights = layer.weights; regularizer = layer.regularizer; } + return *this; } -template -Linear& -Linear:: -operator=(Linear&& layer) +template +LinearType& +LinearType::operator=( + LinearType&& layer) { - if (this != &layer) + if (&layer != this) { - inSize = layer.inSize; - outSize = layer.outSize; - weights = std::move(layer.weights); + Layer::operator=(std::move(layer)); + inSize = std::move(layer.inSize); + outSize = std::move(layer.outSize); regularizer = std::move(layer.regularizer); } + return *this; } -template -void Linear::Reset() +template +void LinearType::SetWeights( + typename MatType::elem_type* weightsPtr) { - weight = arma::mat(weights.memptr(), outSize, inSize, false, false); - bias = arma::mat(weights.memptr() + weight.n_elem, - outSize, 1, false, false); + MakeAlias(weights, weightsPtr, outSize * inSize + outSize, 1); + MakeAlias(weight, weightsPtr, outSize, inSize); + MakeAlias(bias, weightsPtr + weight.n_elem, outSize, 1); } -template -template -void Linear::Forward( - const arma::Mat& input, arma::Mat& output) +template +void LinearType::Forward( + const MatType& input, MatType& output) { output = weight * input; output.each_col() += bias; } -template -template -void Linear::Backward( - const arma::Mat& /* input */, const arma::Mat& gy, arma::Mat& g) +template +void LinearType::Backward( + const MatType& /* input */, const MatType& gy, MatType& g) { g = weight.t() * gy; } -template -template -void Linear::Gradient( - const arma::Mat& input, - const arma::Mat& error, - arma::Mat& gradient) +template +void LinearType::Gradient( + const MatType& input, + const MatType& error, + MatType& gradient) { gradient.submat(0, 0, weight.n_elem - 1, 0) = arma::vectorise( error * input.t()); gradient.submat(weight.n_elem, 0, gradient.n_elem - 1, 0) = arma::sum(error, 1); + regularizer.Evaluate(weights, gradient); } -template +template +void LinearType::ComputeOutputDimensions() +{ + inSize = this->inputDimensions[0]; + for (size_t i = 1; i < this->inputDimensions.size(); ++i) + inSize *= this->inputDimensions[i]; + this->outputDimensions = std::vector(this->inputDimensions.size(), + 1); + + // The Linear layer flattens its input. + this->outputDimensions[0] = outSize; +} + +template template -void Linear::serialize( +void LinearType::serialize( Archive& ar, const uint32_t /* version */) { + ar(cereal::base_class>(this)); + ar(CEREAL_NVP(inSize)); ar(CEREAL_NVP(outSize)); - ar(CEREAL_NVP(weights)); + ar(CEREAL_NVP(regularizer)); } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/linear_no_bias.hpp b/src/mlpack/methods/ann/layer/linear_no_bias.hpp index 1e85933135..00bf074d7a 100644 --- a/src/mlpack/methods/ann/layer/linear_no_bias.hpp +++ b/src/mlpack/methods/ann/layer/linear_no_bias.hpp @@ -16,7 +16,7 @@ #include #include -#include "layer_types.hpp" +#include "layer.hpp" namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -25,48 +25,50 @@ namespace ann /** Artificial Neural Network. */ { * Implementation of the LinearNoBias class. The LinearNoBias class represents a * single layer of a neural network. * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam MatType Matrix representation to accept as input and use for + * computation. + * @tparam RegularizerType Type of the regularizer to be used (Default no + * regularizer). */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat, +template< + typename MatType = arma::mat, typename RegularizerType = NoRegularizer > -class LinearNoBias +class LinearNoBiasType : public Layer { public: //! Create the LinearNoBias object. - LinearNoBias(); + LinearNoBiasType(); + /** * Create the LinearNoBias object using the specified number of units. * - * @param inSize The number of input units. * @param outSize The number of output units. * @param regularizer The regularizer to use, optional. */ - LinearNoBias(const size_t inSize, - const size_t outSize, - RegularizerType regularizer = RegularizerType()); + LinearNoBiasType(const size_t outSize, + RegularizerType regularizer = RegularizerType()); + + //! Clone the LinearNoBiasType object. This handles polymorphism correctly. + LinearNoBiasType* Clone() const { return new LinearNoBiasType(*this); } + + //! Reset the layer parameter. + void SetWeights(typename MatType::elem_type* weightsPtr); //! Copy constructor. - LinearNoBias(const LinearNoBias& layer); + LinearNoBiasType(const LinearNoBiasType& layer); //! Move constructor. - LinearNoBias(LinearNoBias&&); + LinearNoBiasType(LinearNoBiasType&&); //! Copy assignment operator. - LinearNoBias& operator=(const LinearNoBias& layer); + LinearNoBiasType& operator=(const LinearNoBiasType& layer); //! Move assignment operator. - LinearNoBias& operator=(LinearNoBias&& layer); + LinearNoBiasType& operator=(LinearNoBiasType&& layer); - /* - * Reset the layer parameter. - */ - void Reset(); + //! Virtual destructor. + virtual ~LinearNoBiasType() { } /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -75,8 +77,7 @@ class LinearNoBias * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template - void Forward(const arma::Mat& input, arma::Mat& output); + void Forward(const MatType& input, MatType& output); /** * Ordinary feed backward pass of a neural network, calculating the function @@ -87,69 +88,33 @@ class LinearNoBias * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g); + void Backward(const MatType& /* input */, + const MatType& gy, + MatType& g); - /* + /** * Calculate the gradient using the output delta and the input activation. * * @param input The input parameter used for calculating the gradient. * @param error The calculated error. * @param gradient The calculated gradient. */ - template - void Gradient(const arma::Mat& input, - const arma::Mat& error, - arma::Mat& gradient); + void Gradient(const MatType& input, + const MatType& error, + MatType& gradient); //! Get the parameters. - OutputDataType const& Parameters() const { return weights; } + const MatType& Parameters() const { return weight; } //! Modify the parameters. - OutputDataType& Parameters() { return weights; } + MatType& Parameters() { return weight; } - //! Get the input parameter. - InputDataType const& InputParameter() const { return inputParameter; } - //! Modify the input parameter. - InputDataType& InputParameter() { return inputParameter; } + //! Get the number of weights in the layer. + size_t WeightSize() const { return inSize * outSize; } - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } + //! Compute the output dimensions of the layer using `InputDimensions()`. + void ComputeOutputDimensions(); - //! Get the delta. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } - - //! Get the input size. - size_t InputSize() const { return inSize; } - - //! Get the output size. - size_t OutputSize() const { return outSize; } - - //! Get the gradient. - OutputDataType const& Gradient() const { return gradient; } - //! Modify the gradient. - OutputDataType& Gradient() { return gradient; } - - //! Get the size of the weights. - size_t WeightSize() const - { - return inSize * outSize; - } - - //! Get the shape of the input. - size_t InputShape() const - { - return inSize; - } - - /** - * Serialize the layer - */ + //! Serialize the layer. template void serialize(Archive& ar, const uint32_t /* version */); @@ -160,27 +125,17 @@ class LinearNoBias //! Locally-stored number of output units. size_t outSize; - //! Locally-stored weight object. - OutputDataType weights; - //! Locally-stored weight parameter. - OutputDataType weight; - - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored gradient object. - OutputDataType gradient; - - //! Locally-stored input parameter object. - InputDataType inputParameter; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; + MatType weight; //! Locally-stored regularizer object. RegularizerType regularizer; -}; // class LinearNoBias +}; // class LinearNoBiasType + +// Convenience typedefs. + +// Standard Linear without bias layer using no regularization. +typedef LinearNoBiasType LinearNoBias; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/linear_no_bias_impl.hpp b/src/mlpack/methods/ann/layer/linear_no_bias_impl.hpp index ed26cd59fa..bc2f18702c 100644 --- a/src/mlpack/methods/ann/layer/linear_no_bias_impl.hpp +++ b/src/mlpack/methods/ann/layer/linear_no_bias_impl.hpp @@ -19,135 +19,136 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -LinearNoBias::LinearNoBias() : +template +LinearNoBiasType::LinearNoBiasType() : + Layer(), inSize(0), outSize(0) { // Nothing to do here. } -template -LinearNoBias::LinearNoBias( - const size_t inSize, +template +LinearNoBiasType::LinearNoBiasType( const size_t outSize, RegularizerType regularizer) : - inSize(inSize), + Layer(), + inSize(0), // This will be set by ComputeOutputDimensions(). outSize(outSize), regularizer(regularizer) { - weights.set_size(WeightSize(), 1); + // Nothing to do. } -template -LinearNoBias::LinearNoBias( - const LinearNoBias& layer) : +template +LinearNoBiasType::LinearNoBiasType( + const LinearNoBiasType& layer) : + Layer(layer), inSize(layer.inSize), outSize(layer.outSize), - weights(layer.weights), regularizer(layer.regularizer) { // Nothing to do here. } -template -LinearNoBias::LinearNoBias( - LinearNoBias&& layer) : +template +LinearNoBiasType::LinearNoBiasType( + LinearNoBiasType&& layer) : + Layer(std::move(layer)), inSize(0), outSize(0), - weights(std::move(layer.weights)), regularizer(std::move(layer.regularizer)) { // Nothing to do here. } -template -LinearNoBias& -LinearNoBias:: -operator=(const LinearNoBias& layer) +template +LinearNoBiasType& +LinearNoBiasType::operator=( + const LinearNoBiasType& layer) { if (this != &layer) { + Layer::operator=(layer); inSize = layer.inSize; outSize = layer.outSize; - weights = layer.weights; regularizer = layer.regularizer; } + return *this; } -template -LinearNoBias& -LinearNoBias:: -operator=(LinearNoBias&& layer) +template +LinearNoBiasType& +LinearNoBiasType::operator=( + LinearNoBiasType&& layer) { if (this != &layer) { - inSize = layer.inSize; - outSize = layer.outSize; - weights = std::move(layer.weights); + Layer::operator=(std::move(layer)); + inSize = std::move(layer.inSize); + outSize = std::move(layer.outSize); regularizer = std::move(layer.regularizer); } + return *this; } -template -void LinearNoBias::Reset() +template +void LinearNoBiasType::SetWeights( + typename MatType::elem_type* weightsPtr) { - weight = arma::mat(weights.memptr(), outSize, inSize, false, false); + MakeAlias(weight, weightsPtr, outSize, inSize); } -template -template -void LinearNoBias::Forward( - const arma::Mat& input, arma::Mat& output) +template +void LinearNoBiasType::Forward( + const MatType& input, MatType& output) { output = weight * input; } -template -template -void LinearNoBias::Backward( - const arma::Mat& /* input */, const arma::Mat& gy, arma::Mat& g) +template +void LinearNoBiasType::Backward( + const MatType& /* input */, const MatType& gy, MatType& g) { g = weight.t() * gy; } -template -template -void LinearNoBias::Gradient( - const arma::Mat& input, - const arma::Mat& error, - arma::Mat& gradient) +template +void LinearNoBiasType::Gradient( + const MatType& input, + const MatType& error, + MatType& gradient) { gradient.submat(0, 0, weight.n_elem - 1, 0) = arma::vectorise( error * input.t()); - regularizer.Evaluate(weights, gradient); + regularizer.Evaluate(weight, gradient); } -template +template +void LinearNoBiasType::ComputeOutputDimensions() +{ + inSize = this->inputDimensions[0]; + for (size_t i = 1; i < this->inputDimensions.size(); ++i) + inSize *= this->inputDimensions[i]; + + this->outputDimensions = std::vector(this->inputDimensions.size(), + 1); + + this->outputDimensions[0] = outSize; +} + +template template -void LinearNoBias::serialize( +void LinearNoBiasType::serialize( Archive& ar, const uint32_t /* version */) { + ar(cereal::base_class>(this)); + ar(CEREAL_NVP(inSize)); ar(CEREAL_NVP(outSize)); - - // This is inefficient, but necessary so that WeightSetVisitor sets the right - // size. - if (cereal::is_loading()) - weights.set_size(outSize * inSize, 1); + ar(CEREAL_NVP(regularizer)); } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/log_softmax.hpp b/src/mlpack/methods/ann/layer/log_softmax.hpp index 8c62fd2d9e..a93edb6183 100644 --- a/src/mlpack/methods/ann/layer/log_softmax.hpp +++ b/src/mlpack/methods/ann/layer/log_softmax.hpp @@ -14,6 +14,8 @@ #include +#include "layer.hpp" + namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -24,22 +26,32 @@ namespace ann /** Artificial Neural Network. */ { * (NegativeLogLikelihoodLayer), which expects that the input contains * log-probabilities for each class. * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam MatType Matrix representation to accept as input and use for + * computation. */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class LogSoftMax +template +class LogSoftMaxType : public Layer { public: /** - * Create the LogSoftmax object. + * Create the LogSoftmax layer. */ - LogSoftMax(); + LogSoftMaxType(); + + //! Clone the LogSoftMaxType object. This handles polymorphism correctly. + LogSoftMaxType* Clone() const { return new LogSoftMaxType(*this); } + + // Virtual destructor. + virtual ~LogSoftMaxType() { } + + //! Copy the given LogSoftMaxType. + LogSoftMaxType(const LogSoftMaxType& other); + //! Take ownership of the given LogSoftMaxType. + LogSoftMaxType(LogSoftMaxType&& other); + //! Copy the given LogSoftMaxType. + LogSoftMaxType& operator=(const LogSoftMaxType& other); + //! Take ownership of the given LogSoftMaxType. + LogSoftMaxType& operator=(LogSoftMaxType&& other); /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -48,8 +60,7 @@ class LogSoftMax * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template - void Forward(const InputType& input, OutputType& output); + void Forward(const MatType& input, MatType& output); /** * Ordinary feed backward pass of a neural network, calculating the function @@ -60,34 +71,22 @@ class LogSoftMax * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const arma::Mat& input, - const arma::Mat& gy, - arma::Mat& g); + void Backward(const MatType& input, const MatType& gy, MatType& g); - //! Get the output parameter. - OutputDataType& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - InputDataType& Delta() const { return delta; } - //! Modify the delta. - InputDataType& Delta() { return delta; } - - /** - * Serialize the layer. - */ template - void serialize(Archive& /* ar */, const uint32_t /* version */); + void serialize(Archive& ar, const uint32_t /* version */) + { + ar(cereal::base_class>(this)); + // Nothing to do. + } private: - //! Locally-stored delta object. - OutputDataType delta; +}; // class LogSoftmaxType - //! Locally-stored output parameter object. - OutputDataType outputParameter; -}; // class LogSoftmax +// Convenience typedefs. + +// Standard Linear layer using no regularization. +typedef LogSoftMaxType LogSoftMax; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/log_softmax_impl.hpp b/src/mlpack/methods/ann/layer/log_softmax_impl.hpp index 4762662c2f..37b2e296bd 100644 --- a/src/mlpack/methods/ann/layer/log_softmax_impl.hpp +++ b/src/mlpack/methods/ann/layer/log_softmax_impl.hpp @@ -18,18 +18,54 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -LogSoftMax::LogSoftMax() +template +LogSoftMaxType::LogSoftMaxType() { // Nothing to do here. } -template -template -void LogSoftMax::Forward( - const InputType& input, OutputType& output) +template +LogSoftMaxType::LogSoftMaxType(const LogSoftMaxType& other) : + Layer(other) { - arma::mat maxInput = arma::repmat(arma::max(input), input.n_rows, 1); + // Nothing to do here. +} + +template +LogSoftMaxType::LogSoftMaxType(LogSoftMaxType&& other) : + Layer(std::move(other)) +{ + // Nothing to do here. +} + +template +LogSoftMaxType& +LogSoftMaxType::operator=(const LogSoftMaxType& other) +{ + if (&other != this) + { + Layer::operator=(other); + } + + return *this; +} + +template +LogSoftMaxType& +LogSoftMaxType::operator=(LogSoftMaxType&& other) +{ + if (&other != this) + { + Layer::operator=(std::move(other)); + } + + return *this; +} + +template +void LogSoftMaxType::Forward(const MatType& input, MatType& output) +{ + MatType maxInput = arma::repmat(arma::max(input), input.n_rows, 1); output = (maxInput - input); // Approximation of the base-e exponential function. The acuracy however is @@ -61,25 +97,15 @@ void LogSoftMax::Forward( output = input - maxInput; } -template -template -void LogSoftMax::Backward( - const arma::Mat& input, - const arma::Mat& gy, - arma::Mat& g) +template +void LogSoftMaxType::Backward( + const MatType& input, + const MatType& gy, + MatType& g) { g = arma::exp(input) + gy; } -template -template -void LogSoftMax::serialize( - Archive& /* ar */, - const uint32_t /* version */) -{ - // Nothing to do here. -} - } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/lstm.hpp b/src/mlpack/methods/ann/layer/lstm.hpp index effca7328e..e2e297b2f2 100644 --- a/src/mlpack/methods/ann/layer/lstm.hpp +++ b/src/mlpack/methods/ann/layer/lstm.hpp @@ -15,6 +15,8 @@ #include #include +#include "layer.hpp" + namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -50,43 +52,43 @@ namespace ann /** Artificial Neural Network. */ { * \see FastLSTM for a faster LSTM version which combines the calculation of the * input, forget, output gates and hidden state in a single step. * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam MatType Matrix representation to accept as input and use for + * computation. */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class LSTM +template +class LSTMType : public RecurrentLayer { public: //! Create the LSTM object. - LSTM(); + LSTMType(); /** * Create the LSTM layer object using the specified parameters. * - * @param inSize The number of input units. * @param outSize The number of output units. * @param rho Maximum number of steps to backpropagate through time (BPTT). */ - LSTM(const size_t inSize, - const size_t outSize, - const size_t rho = std::numeric_limits::max()); + LSTMType(const size_t outSize); - //! Copy constructor. - LSTM(const LSTM& layer); + //! Clone the LSTMType object. This handles polymorphism correctly. + LSTMType* Clone() const { return new LSTMType(*this); } - //! Move constructor. - LSTM(LSTM&&); + //! Copy the given LSTMType object. + LSTMType(const LSTMType& other); + //! Take ownership of the given LSTMType object's data. + LSTMType(LSTMType&& other); + //! Copy the given LSTMType object. + LSTMType& operator=(const LSTMType& other); + //! Take ownership of the given LSTMType object's data. + LSTMType& operator=(LSTMType&& other); - //! Copy assignment operator. - LSTM& operator=(const LSTM& layer); + virtual ~LSTMType() { } - //! Move assignment operator. - LSTM& operator=(LSTM&& layer); + /** + * Reset the layer parameter. The method is called to + * assign the allocated memory to the internal learnable parameters. + */ + void SetWeights(typename MatType::elem_type* weightsPtr); /** * Ordinary feed-forward pass of a neural network, evaluating the function @@ -95,23 +97,7 @@ class LSTM * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template - void Forward(const InputType& input, OutputType& output); - - /** - * Ordinary feed-forward pass of a neural network, evaluating the function - * f(x) by propagating the activity forward through f. - * - * @param input Input data used for evaluating the specified function. - * @param output Resulting output activation. - * @param cellState Cell state of the LSTM. - * @param useCellState Use the cellState passed in the LSTM cell. - */ - template - void Forward(const InputType& input, - OutputType& output, - OutputType& cellState, - bool useCellState = false); + void Forward(const MatType& input, MatType& output); /** * Ordinary feed backward pass of a neural network, calculating the function @@ -122,23 +108,7 @@ class LSTM * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const InputType& input, - const ErrorType& gy, - GradientType& g); - - /* - * Reset the layer parameter. - */ - void Reset(); - - /* - * Resets the cell to accept a new input. This breaks the BPTT chain starts a - * new one. - * - * @param size The current maximum number of steps through time. - */ - void ResetCell(const size_t size); + void Backward(const MatType& input, const MatType& gy, MatType& g); /* * Calculate the gradient using the output delta and the input activation. @@ -147,56 +117,44 @@ class LSTM * @param error The calculated error. * @param gradient The calculated gradient. */ - template - void Gradient(const InputType& input, - const ErrorType& error, - GradientType& gradient); + void Gradient(const MatType& input, + const MatType& error, + MatType& gradient); - //! Get the maximum number of steps to backpropagate through time (BPTT). - size_t Rho() const { return rho; } - //! Modify the maximum number of steps to backpropagate through time (BPTT). - size_t& Rho() { return rho; } + /** + * Reset the recurrent state of the LSTM layer, and allocate enough space to + * hold `bpttSteps` of previous passes with a batch size of `batchSize`. + * + * @param bpttSteps Number of steps of history to allocate space for. + * @param batchSize Batch size to prepare for. + */ + void ClearRecurrentState(const size_t bpttSteps, const size_t batchSize); //! Get the parameters. - OutputDataType const& Parameters() const { return weights; } + const MatType& Parameters() const { return weights; } //! Modify the parameters. - OutputDataType& Parameters() { return weights; } + MatType& Parameters() { return weights; } - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } - - //! Get the gradient. - OutputDataType const& Gradient() const { return grad; } - //! Modify the gradient. - OutputDataType& Gradient() { return grad; } - - //! Get the number of input units. - size_t InSize() const { return inSize; } - - //! Get the number of output units. - size_t OutSize() const { return outSize; } - - //! Get the size of the weights. + //! Get the total number of trainable parameters. size_t WeightSize() const { return (4 * outSize * inSize + 7 * outSize + 4 * outSize * outSize); } - //! Get the shape of the input. - size_t InputShape() const + //! Given a properly set InputDimensions(), compute the output dimensions. + void ComputeOutputDimensions() { - return inSize; + inSize = std::accumulate(this->inputDimensions.begin(), + this->inputDimensions.end(), 0); + this->outputDimensions = std::vector(this->inputDimensions.size(), + 1); + + // The LSTM layer flattens its input. + this->outputDimensions[0] = outSize; } /** - * Serialize the layer + * Serialize the layer. */ template void serialize(Archive& ar, const uint32_t /* version */); @@ -208,148 +166,111 @@ class LSTM //! Locally-stored number of output units. size_t outSize; - //! Number of steps to backpropagate through time (BPTT). - size_t rho; - - //! Locally-stored number of forward steps. - size_t forwardStep; - - //! Locally-stored number of backward steps. - size_t backwardStep; - - //! Locally-stored number of gradient steps. - size_t gradientStep; - //! Locally-stored weight object. - OutputDataType weights; - - //! Locally-stored previous output. - OutputDataType prevOutput; - - //! Locally-stored batch size. - size_t batchSize; - - //! Current batch step, alias for batchSize - 1. - size_t batchStep; - - //! Current gradient step to keep track of the backpropagate through time - //! step. - size_t gradientStepIdx; - - //! Locally-stored cell activation error. - OutputDataType cellActivationError; - - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored gradient object. - OutputDataType grad; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; + MatType weights; //! Weights between the output and input gate. - OutputDataType output2GateInputWeight; + MatType output2GateInputWeight; //! Weights between the input and gate. - OutputDataType input2GateInputWeight; + MatType input2GateInputWeight; //! Bias between the input and input gate. - OutputDataType input2GateInputBias; + MatType input2GateInputBias; //! Weights between the cell and input gate. - OutputDataType cell2GateInputWeight; + MatType cell2GateInputWeight; //! Weights between the output and forget gate. - OutputDataType output2GateForgetWeight; + MatType output2GateForgetWeight; //! Weights between the input and gate. - OutputDataType input2GateForgetWeight; + MatType input2GateForgetWeight; //! Bias between the input and gate. - OutputDataType input2GateForgetBias; + MatType input2GateForgetBias; //! Bias between the input and gate. - OutputDataType cell2GateForgetWeight; + MatType cell2GateForgetWeight; //! Weights between the output and gate. - OutputDataType output2GateOutputWeight; + MatType output2GateOutputWeight; //! Weights between the input and gate. - OutputDataType input2GateOutputWeight; + MatType input2GateOutputWeight; //! Bias between the input and gate. - OutputDataType input2GateOutputBias; + MatType input2GateOutputBias; //! Weights between cell and output gate. - OutputDataType cell2GateOutputWeight; + MatType cell2GateOutputWeight; + + // Below here are recurrent state matrices. //! Locally-stored input gate parameter. - OutputDataType inputGate; + MatType inputGate; //! Locally-stored forget gate parameter. - OutputDataType forgetGate; + MatType forgetGate; //! Locally-stored hidden layer parameter. - OutputDataType hiddenLayer; + MatType hiddenLayer; //! Locally-stored output gate parameter. - OutputDataType outputGate; - - //! Locally-stored input gate activation. - OutputDataType inputGateActivation; - - //! Locally-stored forget gate activation. - OutputDataType forgetGateActivation; - - //! Locally-stored output gate activation. - OutputDataType outputGateActivation; - - //! Locally-stored hidden layer activation. - OutputDataType hiddenLayerActivation; + MatType outputGate; //! Locally-stored input to hidden weight. - OutputDataType input2HiddenWeight; + MatType input2HiddenWeight; //! Locally-stored input to hidden bias. - OutputDataType input2HiddenBias; + MatType input2HiddenBias; //! Locally-stored output to hidden weight. - OutputDataType output2HiddenWeight; + MatType output2HiddenWeight; //! Locally-stored cell parameter. - OutputDataType cell; + arma::Cube cell; + + // These members store recurrent state. + + //! Locally-stored input gate activation. + arma::Cube inputGateActivation; + + //! Locally-stored forget gate activation. + arma::Cube forgetGateActivation; + + //! Locally-stored output gate activation. + arma::Cube outputGateActivation; + + //! Locally-stored hidden layer activation. + arma::Cube hiddenLayerActivation; //! Locally-stored cell activation error. - OutputDataType cellActivation; + arma::Cube cellActivation; //! Locally-stored forget gate error. - OutputDataType forgetGateError; + MatType forgetGateError; //! Locally-stored output gate error. - OutputDataType outputGateError; - - //! Locally-stored previous error. - OutputDataType prevError; + MatType outputGateError; //! Locally-stored output parameters. - OutputDataType outParameter; + arma::Cube outParameter; //! Locally-stored input cell error parameter. - OutputDataType inputCellError; + MatType inputCellError; //! Locally-stored input gate error. - OutputDataType inputGateError; + MatType inputGateError; //! Locally-stored hidden layer error. - OutputDataType hiddenError; + MatType hiddenError; +}; // class LSTMType - //! Locally-stored current rho size. - size_t rhoSize; +// Convenience typedefs. - //! Current backpropagate through time steps. - size_t bpttSteps; -}; // class LSTM +// Standard LSTM layer. +typedef LSTMType LSTM; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/lstm_impl.hpp b/src/mlpack/methods/ann/layer/lstm_impl.hpp index 2b298d18aa..078bcb1a1b 100644 --- a/src/mlpack/methods/ann/layer/lstm_impl.hpp +++ b/src/mlpack/methods/ann/layer/lstm_impl.hpp @@ -18,430 +18,296 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -LSTM::LSTM() +template +LSTMType::LSTMType() : + RecurrentLayer(), + outSize(0) { // Nothing to do here. } -template -LSTM::LSTM( - const LSTM& layer) : - inSize(layer.inSize), - outSize(layer.outSize), - rho(layer.rho), - forwardStep(layer.forwardStep), - backwardStep(layer.backwardStep), - gradientStep(layer.gradientStep), - weights(layer.weights), - batchSize(layer.batchSize), - batchStep(layer.batchStep), - gradientStepIdx(layer.gradientStepIdx), - rhoSize(layer.rho), - bpttSteps(layer.bpttSteps) +template +LSTMType::LSTMType(const size_t outSize) : + RecurrentLayer(), + outSize(outSize) { // Nothing to do here. } -template -LSTM::LSTM( - LSTM&& layer) : - inSize(std::move(layer.inSize)), - outSize(std::move(layer.outSize)), - rho(std::move(layer.rho)), - forwardStep(std::move(layer.forwardStep)), - backwardStep(std::move(layer.backwardStep)), - gradientStep(std::move(layer.gradientStep)), - weights(std::move(layer.weights)), - batchSize(std::move(layer.batchSize)), - batchStep(std::move(layer.batchStep)), - gradientStepIdx(std::move(layer.gradientStepIdx)), - rhoSize(std::move(layer.rho)), - bpttSteps(std::move(layer.bpttSteps)) +template +LSTMType::LSTMType(const LSTMType& layer) : + RecurrentLayer(layer) { // Nothing to do here. } -template -LSTM& -LSTM :: operator=(const LSTM& layer) +template +LSTMType::LSTMType(LSTMType&& layer) : + RecurrentLayer(std::move(layer)) +{ + // Nothing to do here. +} + +template +LSTMType& LSTMType::operator=(const LSTMType& layer) { if (this != &layer) { - inSize = layer.inSize; - outSize = layer.outSize; - rho = layer.rho; - forwardStep = layer.forwardStep; - backwardStep = layer.backwardStep; - gradientStep = layer.gradientStep; - weights = layer.weights; - batchSize = layer.batchSize; - batchStep = layer.batchStep; - gradientStepIdx = layer.gradientStepIdx; - grad = layer.grad; - rhoSize = layer.rho; - bpttSteps = layer.bpttSteps; + RecurrentLayer::operator=(layer); } + return *this; } -template -LSTM& -LSTM :: operator=(LSTM&& layer) +template +LSTMType& LSTMType::operator=(LSTMType&& layer) { if (this != &layer) { - inSize = std::move(layer.inSize); - outSize = std::move(layer.outSize); - rho = std::move(layer.rho); - forwardStep = std::move(layer.forwardStep); - backwardStep = std::move(layer.backwardStep); - gradientStep = std::move(layer.gradientStep); - weights = std::move(layer.weights); - batchSize = std::move(layer.batchSize); - batchStep = std::move(layer.batchStep); - gradientStepIdx = std::move(layer.gradientStepIdx); - grad = std::move(layer.grad); - rhoSize = std::move(layer.rho); - bpttSteps = std::move(layer.bpttSteps); + RecurrentLayer::operator=(std::move(layer)); } + return *this; } -template -LSTM::LSTM( - const size_t inSize, const size_t outSize, const size_t rho) : - inSize(inSize), - outSize(outSize), - rho(rho), - forwardStep(0), - backwardStep(0), - gradientStep(0), - batchSize(0), - batchStep(0), - gradientStepIdx(0), - rhoSize(rho), - bpttSteps(0) +template +void LSTMType::ClearRecurrentState( + const size_t bpttSteps, const size_t batchSize) { - weights.set_size(WeightSize(), 1); -} - -template -void LSTM::ResetCell(const size_t size) -{ - if (size == std::numeric_limits::max()) - return; - - rhoSize = size; - - if (batchSize == 0) - return; - - bpttSteps = std::min(rho, rhoSize); - forwardStep = 0; - gradientStepIdx = 0; - backwardStep = batchSize * size - 1; - gradientStep = batchSize * size - 1; - - const size_t rhoBatchSize = size * batchSize; - // Make sure all of the different matrices we will use to hold parameters are // at least as large as we need. - inputGate.set_size(outSize, rhoBatchSize); - forgetGate.set_size(outSize, rhoBatchSize); - hiddenLayer.set_size(outSize, rhoBatchSize); - outputGate.set_size(outSize, rhoBatchSize); + inputGate.set_size(outSize, batchSize); + forgetGate.set_size(outSize, batchSize); + hiddenLayer.set_size(outSize, batchSize); + outputGate.set_size(outSize, batchSize); - inputGateActivation.set_size(outSize, rhoBatchSize); - forgetGateActivation.set_size(outSize, rhoBatchSize); - outputGateActivation.set_size(outSize, rhoBatchSize); - hiddenLayerActivation.set_size(outSize, rhoBatchSize); + inputGateActivation.set_size(outSize, batchSize, bpttSteps); + forgetGateActivation.set_size(outSize, batchSize, bpttSteps); + outputGateActivation.set_size(outSize, batchSize, bpttSteps); + hiddenLayerActivation.set_size(outSize, batchSize, bpttSteps); - cellActivation.set_size(outSize, rhoBatchSize); - prevError.set_size(4 * outSize, batchSize); + cellActivation.set_size(outSize, batchSize, bpttSteps); + outParameter.set_size(outSize, batchSize, bpttSteps); // Now reset recurrent values to 0. - cell.zeros(outSize, size * batchSize); - outParameter.zeros(outSize, (size + 1) * batchSize); + cell.zeros(outSize, batchSize, bpttSteps); } -template -void LSTM::Reset() +template +void LSTMType::SetWeights( + typename MatType::elem_type* weightsPtr) { // Set the weight parameter for the output gate. - input2GateOutputWeight = OutputDataType(weights.memptr(), outSize, inSize, - false, false); - input2GateOutputBias = OutputDataType(weights.memptr() + - input2GateOutputWeight.n_elem, outSize, 1, false, false); - size_t offset = input2GateOutputWeight.n_elem + input2GateOutputBias.n_elem; + MakeAlias(input2GateOutputWeight, weightsPtr, outSize, inSize); + size_t offset = input2GateOutputWeight.n_elem; + MakeAlias(input2GateOutputBias, weightsPtr + offset, outSize, 1); + offset += input2GateOutputBias.n_elem; // Set the weight parameter for the forget gate. - input2GateForgetWeight = OutputDataType(weights.memptr() + offset, - outSize, inSize, false, false); - input2GateForgetBias = OutputDataType(weights.memptr() + - offset + input2GateForgetWeight.n_elem, outSize, 1, false, false); - offset += input2GateForgetWeight.n_elem + input2GateForgetBias.n_elem; + MakeAlias(input2GateForgetWeight, weightsPtr + offset, outSize, inSize); + offset += input2GateForgetWeight.n_elem; + MakeAlias(input2GateForgetBias, weightsPtr + offset, outSize, 1); + offset += input2GateForgetBias.n_elem; // Set the weight parameter for the input gate. - input2GateInputWeight = OutputDataType(weights.memptr() + - offset, outSize, inSize, false, false); - input2GateInputBias = OutputDataType(weights.memptr() + - offset + input2GateInputWeight.n_elem, outSize, 1, false, false); - offset += input2GateInputWeight.n_elem + input2GateInputBias.n_elem; + MakeAlias(input2GateInputWeight, weightsPtr + offset, outSize, inSize); + offset += input2GateInputWeight.n_elem; + MakeAlias(input2GateInputBias, weightsPtr + offset, outSize, 1); + offset += input2GateInputBias.n_elem; // Set the weight parameter for the hidden gate. - input2HiddenWeight = OutputDataType(weights.memptr() + - offset, outSize, inSize, false, false); - input2HiddenBias = OutputDataType(weights.memptr() + - offset + input2HiddenWeight.n_elem, outSize, 1, false, false); - offset += input2HiddenWeight.n_elem + input2HiddenBias.n_elem; + MakeAlias(input2HiddenWeight, weightsPtr + offset, outSize, inSize); + offset += input2HiddenWeight.n_elem; + MakeAlias(input2HiddenBias, weightsPtr + offset, outSize, 1); + offset += input2HiddenBias.n_elem; // Set the weight parameter for the output multiplication. - output2GateOutputWeight = OutputDataType(weights.memptr() + - offset, outSize, outSize, false, false); + MakeAlias(output2GateOutputWeight, weightsPtr + offset, outSize, outSize); offset += output2GateOutputWeight.n_elem; // Set the weight parameter for the output multiplication. - output2GateForgetWeight = OutputDataType(weights.memptr() + - offset, outSize, outSize, false, false); + MakeAlias(output2GateForgetWeight, weightsPtr + offset, outSize, outSize); offset += output2GateForgetWeight.n_elem; // Set the weight parameter for the input multiplication. - output2GateInputWeight = OutputDataType(weights.memptr() + - offset, outSize, outSize, false, false); + MakeAlias(output2GateInputWeight, weightsPtr + offset, outSize, outSize); offset += output2GateInputWeight.n_elem; // Set the weight parameter for the hidden multiplication. - output2HiddenWeight = OutputDataType(weights.memptr() + - offset, outSize, outSize, false, false); + MakeAlias(output2HiddenWeight, weightsPtr + offset, outSize, outSize); offset += output2HiddenWeight.n_elem; // Set the weight parameter for the cell multiplication. - cell2GateOutputWeight = OutputDataType(weights.memptr() + - offset, outSize, 1, false, false); + MakeAlias(cell2GateOutputWeight, weightsPtr + offset, outSize, 1); offset += cell2GateOutputWeight.n_elem; // Set the weight parameter for the cell - forget gate multiplication. - cell2GateForgetWeight = OutputDataType(weights.memptr() + - offset, outSize, 1, false, false); + MakeAlias(cell2GateForgetWeight, weightsPtr + offset, outSize, 1); offset += cell2GateOutputWeight.n_elem; // Set the weight parameter for the cell - input gate multiplication. - cell2GateInputWeight = OutputDataType(weights.memptr() + - offset, outSize, 1, false, false); + MakeAlias(cell2GateInputWeight, weightsPtr + offset, outSize, 1); } // Forward when cellState is not needed. -template -template -void LSTM::Forward( - const InputType& input, OutputType& output) +template +void LSTMType::Forward(const MatType& input, MatType& output) { - //! Locally-stored cellState. - OutputType cellState; - Forward(input, output, cellState, false); -} + // Convenience alias. + const size_t batchSize = input.n_cols; -// Forward when cellState is needed overloaded LSTM::Forward(). -template -template -void LSTM::Forward(const InputType& input, - OutputType& output, - OutputType& cellState, - bool useCellState) -{ - // Check if the batch size changed, the number of cols is defines the input - // batch size. - if (input.n_cols != batchSize) + inputGate = input2GateInputWeight * input; + if (this->HasPreviousStep()) { - batchSize = input.n_cols; - batchStep = batchSize - 1; - ResetCell(rhoSize); + inputGate += + output2GateInputWeight * outParameter.slice(this->PreviousStep()); + } + inputGate.each_col() += input2GateInputBias; + + forgetGate = input2GateForgetWeight * input; + if (this->HasPreviousStep()) + { + forgetGate += output2GateForgetWeight * outParameter.slice( + this->PreviousStep()); + } + forgetGate.each_col() += input2GateForgetBias; + + if (this->HasPreviousStep()) + { + inputGate += arma::repmat(cell2GateInputWeight, 1, batchSize) % + cell.slice(this->PreviousStep()); + + forgetGate += arma::repmat(cell2GateForgetWeight, 1, batchSize) % + cell.slice(this->PreviousStep()); } - inputGate.cols(forwardStep, forwardStep + batchStep) = input2GateInputWeight * - input + output2GateInputWeight * outParameter.cols(forwardStep, - forwardStep + batchStep); - inputGate.cols(forwardStep, forwardStep + batchStep).each_col() += - input2GateInputBias; + inputGateActivation.slice(this->CurrentStep()) = + 1.0 / (1.0 + arma::exp(-inputGate)); + forgetGateActivation.slice(this->CurrentStep()) = + 1.0 / (1.0 + arma::exp(-forgetGate)); - forgetGate.cols(forwardStep, forwardStep + batchStep) = input2GateForgetWeight - * input + output2GateForgetWeight * outParameter.cols( - forwardStep, forwardStep + batchStep); - forgetGate.cols(forwardStep, forwardStep + batchStep).each_col() += - input2GateForgetBias; - - if (forwardStep > 0) + hiddenLayer = input2HiddenWeight * input; + if (this->HasPreviousStep()) { - if (useCellState) - { - if (!cellState.is_empty()) - { - cell.cols(forwardStep - batchSize, - forwardStep - batchSize + batchStep) = cellState; - } - else - { - throw std::runtime_error("Cell parameter is empty."); - } - } - inputGate.cols(forwardStep, forwardStep + batchStep) += - arma::repmat(cell2GateInputWeight, 1, batchSize) % - cell.cols(forwardStep - batchSize, forwardStep - batchSize + batchStep); - - forgetGate.cols(forwardStep, forwardStep + batchStep) += - arma::repmat(cell2GateForgetWeight, 1, batchSize) % - cell.cols(forwardStep - batchSize, forwardStep - batchSize + batchStep); + hiddenLayer += output2HiddenWeight * + outParameter.slice(this->PreviousStep()); } + hiddenLayer.each_col() += input2HiddenBias; - inputGateActivation.cols(forwardStep, forwardStep + batchStep) = 1.0 / - (1 + arma::exp(-inputGate.cols(forwardStep, forwardStep + batchStep))); + hiddenLayerActivation.slice(this->CurrentStep()) = arma::tanh(hiddenLayer); - forgetGateActivation.cols(forwardStep, forwardStep + batchStep) = 1.0 / - (1 + arma::exp(-forgetGate.cols(forwardStep, forwardStep + batchStep))); - - hiddenLayer.cols(forwardStep, forwardStep + batchStep) = input2HiddenWeight * - input + output2HiddenWeight * outParameter.cols( - forwardStep, forwardStep + batchStep); - - hiddenLayer.cols(forwardStep, forwardStep + batchStep).each_col() += - input2HiddenBias; - - hiddenLayerActivation.cols(forwardStep, forwardStep + batchStep) = - arma::tanh(hiddenLayer.cols(forwardStep, forwardStep + batchStep)); - - if (forwardStep == 0) + if (!this->HasPreviousStep()) { - cell.cols(forwardStep, forwardStep + batchStep) = - inputGateActivation.cols(forwardStep, forwardStep + batchStep) % - hiddenLayerActivation.cols(forwardStep, forwardStep + batchStep); + cell.slice(this->CurrentStep()) = + inputGateActivation.slice(this->CurrentStep()) % + hiddenLayerActivation.slice(this->CurrentStep()); } else { - cell.cols(forwardStep, forwardStep + batchStep) = - forgetGateActivation.cols(forwardStep, forwardStep + batchStep) % - cell.cols(forwardStep - batchSize, forwardStep - batchSize + batchStep) - + inputGateActivation.cols(forwardStep, forwardStep + batchStep) % - hiddenLayerActivation.cols(forwardStep, forwardStep + batchStep); + cell.slice(this->CurrentStep()) = + forgetGateActivation.slice(this->CurrentStep()) % + cell.slice(this->PreviousStep()) + + inputGateActivation.slice(this->CurrentStep()) % + hiddenLayerActivation.slice(this->CurrentStep()); } - outputGate.cols(forwardStep, forwardStep + batchStep) = input2GateOutputWeight - * input + output2GateOutputWeight * outParameter.cols( - forwardStep, forwardStep + batchStep) + cell.cols(forwardStep, - forwardStep + batchStep).each_col() % cell2GateOutputWeight; - - outputGate.cols(forwardStep, forwardStep + batchStep).each_col() += - input2GateOutputBias; - - outputGateActivation.cols(forwardStep, forwardStep + batchStep) = 1.0 / - (1 + arma::exp(-outputGate.cols(forwardStep, forwardStep + batchStep))); - - cellActivation.cols(forwardStep, forwardStep + batchStep) = - arma::tanh(cell.cols(forwardStep, forwardStep + batchStep)); - - outParameter.cols(forwardStep + batchSize, - forwardStep + batchSize + batchStep) = - cellActivation.cols(forwardStep, forwardStep + batchStep) % - outputGateActivation.cols(forwardStep, forwardStep + batchStep); - - output = OutputType(outParameter.memptr() + - (forwardStep + batchSize) * outSize, outSize, batchSize, false, false); - - cellState = OutputType(cell.memptr() + - forwardStep * outSize, outSize, batchSize, false, false); - - forwardStep += batchSize; - if ((forwardStep / batchSize) == bpttSteps) + outputGate = input2GateOutputWeight * input + + cell.slice(this->CurrentStep()).each_col() % cell2GateOutputWeight; + if (this->HasPreviousStep()) { - forwardStep = 0; + outputGate += + output2GateOutputWeight * outParameter.slice(this->PreviousStep()); } + outputGate.each_col() += input2GateOutputBias; + + outputGateActivation.slice(this->CurrentStep()) = + 1.0 / (1.0 + arma::exp(-outputGate)); + + cellActivation.slice(this->CurrentStep()) = + arma::tanh(cell.slice(this->CurrentStep())); + + // There's a bit of an issue here: we need to preserve the output for the next + // time step, but we also need to set `output` to that. Unfortunately for now + // we make a copy, but it's possible that we could instead use an alias here, + // or have `outParameter` hold a collection of aliases. + outParameter.slice(this->CurrentStep()) = + cellActivation.slice(this->CurrentStep()) % + outputGateActivation.slice(this->CurrentStep()); + + output = outParameter.slice(this->CurrentStep()); } -template -template -void LSTM::Backward( - const InputType& /* input */, const ErrorType& gy, GradientType& g) +template +void LSTMType::Backward( + const MatType& /* input */, const MatType& gy, MatType& g) { - ErrorType gyLocal; - if (gradientStepIdx > 0) + MatType gyLocal; + if (this->HasPreviousStep()) { - gyLocal = gy + prevError; + gyLocal = gy + output2GateOutputWeight.t() * outputGateError + + output2GateForgetWeight.t() * forgetGateError + + output2GateInputWeight.t() * inputGateError + + output2HiddenWeight.t() * hiddenError; } else { // Make an alias. - gyLocal = ErrorType(((ErrorType&) gy).memptr(), gy.n_rows, gy.n_cols, false, - false); + gyLocal = MatType(((MatType&) gy).memptr(), gy.n_rows, gy.n_cols, + false, false); } - outputGateError = - gyLocal % cellActivation.cols(backwardStep - batchStep, backwardStep) % - (outputGateActivation.cols(backwardStep - batchStep, backwardStep) % - (1.0 - outputGateActivation.cols(backwardStep - batchStep, - backwardStep))); + outputGateError = gyLocal % cellActivation.slice(this->CurrentStep()) % + (outputGateActivation.slice(this->CurrentStep()) % + (1.0 - outputGateActivation.slice(this->CurrentStep()))); - OutputDataType cellError = gyLocal % - outputGateActivation.cols(backwardStep - batchStep, backwardStep) % - (1 - arma::pow(cellActivation.cols(backwardStep - - batchStep, backwardStep), 2)) + outputGateError.each_col() % - cell2GateOutputWeight; + MatType cellError = gyLocal % + outputGateActivation.slice(this->CurrentStep()) % + (1 - arma::pow(cellActivation.slice(this->CurrentStep()), 2)) + + outputGateError.each_col() % cell2GateOutputWeight; - if (gradientStepIdx > 0) + if (this->HasPreviousStep()) { cellError += inputCellError; } - if (backwardStep > batchStep) + if (this->HasPreviousStep()) { - forgetGateError = cell.cols((backwardStep - batchSize) - batchStep, - (backwardStep - batchSize)) % cellError % (forgetGateActivation.cols( - backwardStep - batchStep, backwardStep) % (1.0 - - forgetGateActivation.cols(backwardStep - batchStep, backwardStep))); + forgetGateError = cell.slice(this->PreviousStep()) % cellError % + (forgetGateActivation.slice(this->CurrentStep()) % + (1.0 - forgetGateActivation.slice(this->CurrentStep()))); } else { - forgetGateError.zeros(); + forgetGateError.zeros(forgetGateActivation.n_rows, + forgetGateActivation.n_cols); } - inputGateError = hiddenLayerActivation.cols(backwardStep - batchStep, - backwardStep) % cellError % - (inputGateActivation.cols(backwardStep - batchStep, backwardStep) % - (1.0 - inputGateActivation.cols(backwardStep - batchStep, backwardStep))); + inputGateError = hiddenLayerActivation.slice(this->CurrentStep()) % + cellError % (inputGateActivation.slice(this->CurrentStep()) % + (1.0 - inputGateActivation.slice(this->CurrentStep()))); - hiddenError = inputGateActivation.cols(backwardStep - batchStep, - backwardStep) % cellError % (1 - arma::pow(hiddenLayerActivation.cols( - backwardStep - batchStep, backwardStep), 2)); + hiddenError = inputGateActivation.slice(this->CurrentStep()) % cellError % + (1 - arma::pow(hiddenLayerActivation.slice(this->CurrentStep()), 2)); - inputCellError = forgetGateActivation.cols(backwardStep - batchStep, - backwardStep) % cellError + forgetGateError.each_col() % - cell2GateForgetWeight + inputGateError.each_col() % cell2GateInputWeight; + inputCellError = forgetGateActivation.slice(this->CurrentStep()) % cellError + + forgetGateError.each_col() % cell2GateForgetWeight + + inputGateError.each_col() % cell2GateInputWeight; g = input2GateInputWeight.t() * inputGateError + input2HiddenWeight.t() * hiddenError + input2GateForgetWeight.t() * forgetGateError + input2GateOutputWeight.t() * outputGateError; - - prevError = output2GateOutputWeight.t() * outputGateError + - output2GateForgetWeight.t() * forgetGateError + - output2GateInputWeight.t() * inputGateError + - output2HiddenWeight.t() * hiddenError; - - backwardStep -= batchSize; - gradientStepIdx++; - if (gradientStepIdx == bpttSteps) - { - backwardStep = bpttSteps - 1; - gradientStepIdx = 0; - } } -template -template -void LSTM::Gradient( - const InputType& input, - const ErrorType& /* error */, - GradientType& gradient) +template +void LSTMType::Gradient( + const MatType& input, + const MatType& /* error */, + MatType& gradient) { + // This implementation depends on Gradient() being called just after + // Backward(), which is something we can safely assume. + // Input2GateOutputWeight and input2GateOutputBias gradients. gradient.submat(0, 0, input2GateOutputWeight.n_elem - 1, 0) = arma::vectorise(outputGateError * input.t()); @@ -450,7 +316,7 @@ void LSTM::Gradient( arma::sum(outputGateError, 1); size_t offset = input2GateOutputWeight.n_elem + input2GateOutputBias.n_elem; - // Input2GateForgetWeight and input2GateForgetBias gradients. + // input2GateForgetWeight and input2GateForgetBias gradients. gradient.submat(offset, 0, offset + input2GateForgetWeight.n_elem - 1, 0) = arma::vectorise(forgetGateError * input.t()); gradient.submat(offset + input2GateForgetWeight.n_elem, 0, @@ -458,7 +324,7 @@ void LSTM::Gradient( input2GateForgetBias.n_elem - 1, 0) = arma::sum(forgetGateError, 1); offset += input2GateForgetWeight.n_elem + input2GateForgetBias.n_elem; - // Input2GateInputWeight and input2GateInputBias gradients. + // input2GateInputWeight and input2GateInputBias gradients. gradient.submat(offset, 0, offset + input2GateInputWeight.n_elem - 1, 0) = arma::vectorise(inputGateError * input.t()); gradient.submat(offset + input2GateInputWeight.n_elem, 0, @@ -466,7 +332,7 @@ void LSTM::Gradient( input2GateInputBias.n_elem - 1, 0) = arma::sum(inputGateError, 1); offset += input2GateInputWeight.n_elem + input2GateInputBias.n_elem; - // Input2HiddenWeight and input2HiddenBias gradients. + // input2HiddenWeight and input2HiddenBias gradients. gradient.submat(offset, 0, offset + input2HiddenWeight.n_elem - 1, 0) = arma::vectorise(hiddenError * input.t()); gradient.submat(offset + input2HiddenWeight.n_elem, 0, @@ -474,48 +340,43 @@ void LSTM::Gradient( arma::sum(hiddenError, 1); offset += input2HiddenWeight.n_elem + input2HiddenBias.n_elem; - // Output2GateOutputWeight gradients. + // output2GateOutputWeight gradients. gradient.submat(offset, 0, offset + output2GateOutputWeight.n_elem - 1, 0) = arma::vectorise(outputGateError * - outParameter.cols(gradientStep - batchStep, gradientStep).t()); + outParameter.slice(this->CurrentStep()).t()); offset += output2GateOutputWeight.n_elem; - // Output2GateForgetWeight gradients. + // output2GateForgetWeight gradients. gradient.submat(offset, 0, offset + output2GateForgetWeight.n_elem - 1, 0) = arma::vectorise(forgetGateError * - outParameter.cols(gradientStep - batchStep, gradientStep).t()); + outParameter.slice(this->CurrentStep()).t()); offset += output2GateForgetWeight.n_elem; - // Output2GateInputWeight gradients. + // output2GateInputWeight gradients. gradient.submat(offset, 0, offset + output2GateInputWeight.n_elem - 1, 0) = arma::vectorise(inputGateError * - outParameter.cols(gradientStep - batchStep, gradientStep).t()); + outParameter.slice(this->CurrentStep()).t()); offset += output2GateInputWeight.n_elem; - // Output2HiddenWeight gradients. + // output2HiddenWeight gradients. gradient.submat(offset, 0, offset + output2HiddenWeight.n_elem - 1, 0) = arma::vectorise(hiddenError * - outParameter.cols(gradientStep - batchStep, gradientStep).t()); + outParameter.slice(this->CurrentStep()).t()); offset += output2HiddenWeight.n_elem; - // Cell2GateOutputWeight gradients. + // cell2GateOutputWeight gradients. gradient.submat(offset, 0, offset + cell2GateOutputWeight.n_elem - 1, 0) = - arma::sum(outputGateError % - cell.cols(gradientStep - batchStep, gradientStep), 1); + arma::sum(outputGateError % cell.slice(this->CurrentStep()), 1); offset += cell2GateOutputWeight.n_elem; - // Cell2GateForgetWeight and cell2GateInputWeight gradients. - if (gradientStep > batchStep) + // cell2GateForgetWeight and cell2GateInputWeight gradients. + if (this->HasPreviousStep()) { gradient.submat(offset, 0, offset + cell2GateForgetWeight.n_elem - 1, 0) = - arma::sum(forgetGateError % - cell.cols((gradientStep - batchSize) - batchStep, - (gradientStep - batchSize)), 1); + arma::sum(forgetGateError % cell.slice(this->PreviousStep()), 1); gradient.submat(offset + cell2GateForgetWeight.n_elem, 0, offset + cell2GateForgetWeight.n_elem + cell2GateInputWeight.n_elem - 1, 0) = - arma::sum(inputGateError % - cell.cols((gradientStep - batchSize) - batchStep, - (gradientStep - batchSize)), 1); + arma::sum(inputGateError % cell.slice(this->PreviousStep()), 1); } else { @@ -525,41 +386,32 @@ void LSTM::Gradient( cell2GateForgetWeight.n_elem + cell2GateInputWeight.n_elem - 1, 0).zeros(); } - - if (gradientStep == 0) - { - gradientStep = batchSize * bpttSteps - 1; - } - else - { - gradientStep -= batchSize; - } } -template +template template -void LSTM::serialize( - Archive& ar, const uint32_t /* version */) +void LSTMType::serialize(Archive& ar, const uint32_t /* version */) { - ar(CEREAL_NVP(weights)); + ar(cereal::base_class>(this)); + ar(CEREAL_NVP(inSize)); ar(CEREAL_NVP(outSize)); - ar(CEREAL_NVP(rho)); - ar(CEREAL_NVP(bpttSteps)); - ar(CEREAL_NVP(batchSize)); - ar(CEREAL_NVP(batchStep)); - ar(CEREAL_NVP(forwardStep)); - ar(CEREAL_NVP(backwardStep)); - ar(CEREAL_NVP(gradientStep)); - ar(CEREAL_NVP(gradientStepIdx)); - ar(CEREAL_NVP(cell)); - ar(CEREAL_NVP(inputGateActivation)); - ar(CEREAL_NVP(forgetGateActivation)); - ar(CEREAL_NVP(outputGateActivation)); - ar(CEREAL_NVP(hiddenLayerActivation)); - ar(CEREAL_NVP(cellActivation)); - ar(CEREAL_NVP(prevError)); - ar(CEREAL_NVP(outParameter)); + + // Clear recurrent state if we are loading. + if (Archive::is_loading::value) + { + inputGateActivation.clear(); + forgetGateActivation.clear(); + outputGateActivation.clear(); + hiddenLayerActivation.clear(); + cellActivation.clear(); + forgetGateError.clear(); + outputGateError.clear(); + outParameter.clear(); + inputCellError.clear(); + inputGateError.clear(); + hiddenError.clear(); + } } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/max_pooling.hpp b/src/mlpack/methods/ann/layer/max_pooling.hpp index 2547c5596a..25d25509e4 100644 --- a/src/mlpack/methods/ann/layer/max_pooling.hpp +++ b/src/mlpack/methods/ann/layer/max_pooling.hpp @@ -15,6 +15,8 @@ #include +#include "layer.hpp" + namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -28,32 +30,39 @@ class MaxPoolingRule /* * Return the maximum value within the receptive block. * - * @param input Input used to perform the pooling operation. + * @param input Input used to perform the pooling operation. Could be an + * Armadillo subview. */ template - size_t Pooling(const MatType& input) + typename MatType::elem_type Pooling(const MatType& input) { - return arma::as_scalar(arma::find(input.max() == input, 1)); + return arma::max(arma::vectorise(input)); + } + + template + std::tuple PoolingWithIndex( + const MatType& input) + { + const typename MatType::elem_type maxVal = + arma::max(arma::vectorise(input)); + const size_t index = arma::as_scalar(arma::find(input == maxVal, 1)); + + return std::tuple(index, maxVal); } }; /** * Implementation of the MaxPooling layer. * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam MatType Matrix representation to accept as input and use for + * computation. */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class MaxPooling +template +class MaxPoolingType : public Layer { public: //! Create the MaxPooling object. - MaxPooling(); + MaxPoolingType(); /** * Create the MaxPooling object using the specified number of units. @@ -64,11 +73,25 @@ class MaxPooling * @param strideHeight Width of the stride operation. * @param floor Rounding operator (floor or ceil). */ - MaxPooling(const size_t kernelWidth, - const size_t kernelHeight, - const size_t strideWidth = 1, - const size_t strideHeight = 1, - const bool floor = true); + MaxPoolingType(const size_t kernelWidth, + const size_t kernelHeight, + const size_t strideWidth = 1, + const size_t strideHeight = 1, + const bool floor = true); + + // Virtual destructor. + virtual ~MaxPoolingType() { } + + //! Copy the given MaxPoolingType. + MaxPoolingType(const MaxPoolingType& other); + //! Take ownership of the given MaxPoolingType. + MaxPoolingType(MaxPoolingType&& other); + //! Copy the given MaxPoolingType. + MaxPoolingType& operator=(const MaxPoolingType& other); + //! Take ownership of the given MaxPoolingType. + MaxPoolingType& operator=(MaxPoolingType&& other); + + MaxPoolingType* Clone() const { return new MaxPoolingType(*this); } /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -77,8 +100,7 @@ class MaxPooling * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template - void Forward(const arma::Mat& input, arma::Mat& output); + void Forward(const MatType& input, MatType& output); /** * Ordinary feed backward pass of a neural network, using 3rd-order tensors as @@ -89,79 +111,37 @@ class MaxPooling * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g); - - //! Get the output parameter. - const OutputDataType& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - const OutputDataType& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } - - //! Get the input width. - size_t InputWidth() const { return inputWidth; } - //! Modify the input width. - size_t& InputWidth() { return inputWidth; } - - //! Get the input height. - size_t InputHeight() const { return inputHeight; } - //! Modify the input height. - size_t& InputHeight() { return inputHeight; } - - //! Get the output width. - size_t OutputWidth() const { return outputWidth; } - //! Modify the output width. - size_t& OutputWidth() { return outputWidth; } - - //! Get the output height. - size_t OutputHeight() const { return outputHeight; } - //! Modify the output height. - size_t& OutputHeight() { return outputHeight; } - - //! Get the input size. - size_t InputSize() const { return inSize; } - - //! Get the output size. - size_t OutputSize() const { return outSize; } + void Backward(const MatType& /* input */, + const MatType& gy, + MatType& g); //! Get the kernel width. - size_t KernelWidth() const { return kernelWidth; } + size_t const& KernelWidth() const { return kernelWidth; } //! Modify the kernel width. size_t& KernelWidth() { return kernelWidth; } //! Get the kernel height. - size_t KernelHeight() const { return kernelHeight; } + size_t const& KernelHeight() const { return kernelHeight; } //! Modify the kernel height. size_t& KernelHeight() { return kernelHeight; } //! Get the stride width. - size_t StrideWidth() const { return strideWidth; } + size_t const& StrideWidth() const { return strideWidth; } //! Modify the stride width. size_t& StrideWidth() { return strideWidth; } //! Get the stride height. - size_t StrideHeight() const { return strideHeight; } + size_t const& StrideHeight() const { return strideHeight; } //! Modify the stride height. size_t& StrideHeight() { return strideHeight; } //! Get the value of the rounding operation. - bool Floor() const { return floor; } + bool const& Floor() const { return floor; } //! Modify the value of the rounding operation. bool& Floor() { return floor; } - //! Get the value of the deterministic parameter. - bool Deterministic() const { return deterministic; } - //! Modify the value of the deterministic parameter. - bool& Deterministic() { return deterministic; } - - //! Get the size of the weights. - size_t WeightSize() const { return 0; } + //! Compute the size of the output given `InputDimensions()`. + void ComputeOutputDimensions(); /** * Serialize the layer. @@ -170,62 +150,94 @@ class MaxPooling void serialize(Archive& ar, const uint32_t /* version */); private: - /** + /** * Apply pooling to the input and store the results. * * @param input The input to be apply the pooling rule. * @param output The pooled result. * @param poolingIndices The pooled indices. */ - template - void PoolingOperation(const arma::Mat& input, - arma::Mat& output, - arma::Mat& poolingIndices) + void PoolingOperation( + const arma::Cube& input, + arma::Cube& output, + arma::Cube& poolingIndices) { - for (size_t j = 0, colidx = 0; j < output.n_cols; - ++j, colidx += strideHeight) + // Iterate over all slices individually. + for (size_t s = 0; s < input.n_slices; ++s) { - for (size_t i = 0, rowidx = 0; i < output.n_rows; - ++i, rowidx += strideWidth) + for (size_t j = 0, colidx = 0; j < output.n_cols; + ++j, colidx += strideHeight) { - size_t rowEnd = rowidx + kernelWidth - 1; - size_t colEnd = colidx + kernelHeight - 1; - - if (rowEnd > input.n_rows - 1) - rowEnd = input.n_rows - 1; - if (colEnd > input.n_cols - 1) - colEnd = input.n_cols - 1; - - arma::mat subInput = input( - arma::span(rowidx, rowEnd), - arma::span(colidx, colEnd)); - - const size_t idx = pooling.Pooling(subInput); - output(i, j) = subInput(idx); - - if (!deterministic) + for (size_t i = 0, rowidx = 0; i < output.n_rows; + ++i, rowidx += strideWidth) { - arma::Mat subIndices = indices(arma::span(rowidx, rowEnd), - arma::span(colidx, colEnd)); + const std::tuple poolResult = + pooling.PoolingWithIndex(input.slice(s).submat( + rowidx, + colidx, + rowidx + kernelWidth - 1 - offset, + colidx + kernelHeight - 1 - offset)); - poolingIndices(i, j) = subIndices(idx); + // Now map the returned pooling index, which corresponds to the + // submatrix we gave, back to its position in the (linearized) input. + const size_t poolIndex = std::get<0>(poolResult); + const size_t poolingCol = poolIndex / (kernelWidth - offset); + const size_t poolingRow = poolIndex % (kernelWidth - offset); + const size_t unmappedPoolingIndex = (rowidx + poolingRow) + + input.n_rows * (colidx + poolingCol) + + input.n_rows * input.n_cols * s; + + poolingIndices(i, j, s) = unmappedPoolingIndex; + output(i, j, s) = std::get<1>(poolResult); } } } } /** - * Apply unpooling to the input and store the results. + * Apply pooling to all slices of the input and store the results, but not the + * indices used. + * + * @param input The input to apply the pooling rule to. + * @param output The pooled result. + */ + void PoolingOperation( + const arma::Cube& input, + arma::Cube& output) + { + // Iterate over all slices individually. + for (size_t s = 0; s < input.n_slices; ++s) + { + for (size_t j = 0, colidx = 0; j < output.n_cols; + ++j, colidx += strideHeight) + { + for (size_t i = 0, rowidx = 0; i < output.n_rows; + ++i, rowidx += strideWidth) + { + output(i, j, s) = pooling.Pooling(input.slice(s).submat( + rowidx, + colidx, + rowidx + kernelWidth - 1 - offset, + colidx + kernelHeight - 1 - offset)); + } + } + } + } + + /** + * Apply unpooling to all slices of the input and store the results. * * @param error The backward error. * @param output The pooled result. - * @param poolingIndices The pooled indices. + * @param poolingIndices The pooled indices (from `PoolingOperation()`). */ - template - void Unpooling(const arma::Mat& error, - arma::Mat& output, - arma::Mat& poolingIndices) + void UnpoolingOperation( + const arma::Cube& error, + arma::Cube& output, + const arma::Cube& poolingIndices) { + output.zeros(); + for (size_t i = 0; i < poolingIndices.n_elem; ++i) { output(poolingIndices(i)) += error(i); @@ -247,64 +259,22 @@ class MaxPooling //! Rounding operation used. bool floor; - //! Locally-stored number of input channels. - size_t inSize; + //! Locally-stored number of channels. + size_t channels; - //! Locally-stored number of output channels. - size_t outSize; - - //! Locally-stored reset parameter used to initialize the module once. - bool reset; - - //! Locally-stored input width. - size_t inputWidth; - - //! Locally-stored input height. - size_t inputHeight; - - //! Locally-stored output width. - size_t outputWidth; - - //! Locally-stored output height. - size_t outputHeight; - - //! If true use maximum a posteriori during the forward pass. - bool deterministic; - - - //! Locally-stored number of input units. - size_t batchSize; - - //! Locally-stored output parameter. - arma::cube outputTemp; - - //! Locally-stored transformed input parameter. - arma::cube inputTemp; - - //! Locally-stored transformed output parameter. - arma::cube gTemp; + //! Locally-stored offset: indicates whether we take the first element or the + //! second element when pooling. Computed by `ComputeOutputDimensions()`. + size_t offset; //! Locally-stored pooling strategy. MaxPoolingRule pooling; - //! Locally-stored delta object. - OutputDataType delta; + //! Locally-stored pooling indices. + arma::Cube poolingIndices; +}; // class MaxPoolingType - //! Locally-stored gradient object. - OutputDataType gradient; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; - - //! Locally-stored indices matrix parameter. - arma::Mat indices; - - //! Locally-stored indices column parameter. - arma::Col indicesCol; - - //! Locally-stored pooling indicies. - std::vector poolingIndices; -}; // class MaxPooling +// Standard MaxPooling layer. +typedef MaxPoolingType MaxPooling; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/max_pooling_impl.hpp b/src/mlpack/methods/ann/layer/max_pooling_impl.hpp index 9650a5f2c3..29badb4e36 100644 --- a/src/mlpack/methods/ann/layer/max_pooling_impl.hpp +++ b/src/mlpack/methods/ann/layer/max_pooling_impl.hpp @@ -19,141 +19,201 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -MaxPooling::MaxPooling() +template +MaxPoolingType::MaxPoolingType() : + Layer() { // Nothing to do here. } -template -MaxPooling::MaxPooling( +template +MaxPoolingType::MaxPoolingType( const size_t kernelWidth, const size_t kernelHeight, const size_t strideWidth, const size_t strideHeight, const bool floor) : + Layer(), kernelWidth(kernelWidth), kernelHeight(kernelHeight), strideWidth(strideWidth), strideHeight(strideHeight), floor(floor), - inSize(0), - outSize(0), - reset(false), - inputWidth(0), - inputHeight(0), - outputWidth(0), - outputHeight(0), - deterministic(false), - batchSize(0) + channels(0), + offset(0) { // Nothing to do here. } -template -template -void MaxPooling::Forward( - const arma::Mat& input, arma::Mat& output) +template +MaxPoolingType::MaxPoolingType( + const MaxPoolingType& other) : + Layer(other), + kernelWidth(other.kernelWidth), + kernelHeight(other.kernelHeight), + strideWidth(other.strideWidth), + strideHeight(other.strideHeight), + floor(other.floor), + channels(other.channels), + offset(other.offset), + pooling(other.pooling) { - batchSize = input.n_cols; - inSize = input.n_elem / (inputWidth * inputHeight * batchSize); - inputTemp = arma::cube(const_cast&>(input).memptr(), - inputWidth, inputHeight, batchSize * inSize, false, false); + // Nothing to do here. +} - if (floor) +template +MaxPoolingType::MaxPoolingType( + MaxPoolingType&& other) : + Layer(std::move(other)), + kernelWidth(std::move(other.kernelWidth)), + kernelHeight(std::move(other.kernelHeight)), + strideWidth(std::move(other.strideWidth)), + strideHeight(std::move(other.strideHeight)), + floor(std::move(other.floor)), + channels(std::move(other.channels)), + offset(std::move(other.offset)), + pooling(std::move(other.pooling)) +{ + // Nothing to do here. +} + +template +MaxPoolingType& +MaxPoolingType::operator=(const MaxPoolingType& other) +{ + if (&other != this) { - outputWidth = std::floor((inputWidth - - (double) kernelWidth) / (double) strideWidth + 1); - outputHeight = std::floor((inputHeight - - (double) kernelHeight) / (double) strideHeight + 1); + Layer::operator=(other); + kernelWidth = other.kernelWidth; + kernelHeight = other.kernelHeight; + strideWidth = other.strideWidth; + strideHeight = other.strideHeight; + floor = other.floor; + channels = other.channels; + offset = other.offset; + pooling = other.pooling; + } + + return *this; +} + +template +MaxPoolingType& +MaxPoolingType::operator=(MaxPoolingType&& other) +{ + if (&other != this) + { + Layer::operator=(std::move(other)); + kernelWidth = std::move(other.kernelWidth); + kernelHeight = std::move(other.kernelHeight); + strideWidth = std::move(other.strideWidth); + strideHeight = std::move(other.strideHeight); + floor = std::move(other.floor); + channels = std::move(other.channels); + offset = std::move(other.offset); + pooling = std::move(other.pooling); + } + + return *this; +} + +template +void MaxPoolingType::Forward(const MatType& input, MatType& output) +{ + arma::Cube inputTemp( + const_cast(input).memptr(), this->inputDimensions[0], + this->inputDimensions[1], input.n_cols * channels, false, false); + + arma::Cube outputTemp(output.memptr(), + this->outputDimensions[0], this->outputDimensions[1], + input.n_cols * channels, false, true); + + if (this->training) + { + // If we are training, we'll do a backwards pass, so we need to ensure that + // we know what indices we used. + poolingIndices.set_size(this->outputDimensions[0], + this->outputDimensions[1], input.n_cols * channels); + + PoolingOperation(inputTemp, outputTemp, poolingIndices); } else { - outputWidth = std::ceil((inputWidth - - (double) kernelWidth) / (double) strideWidth + 1); - outputHeight = std::ceil((inputHeight - - (double) kernelHeight) / (double) strideHeight + 1); + PoolingOperation(inputTemp, outputTemp); } - - outputTemp = arma::zeros >(outputWidth, outputHeight, - batchSize * inSize); - - if (!deterministic) - { - poolingIndices.push_back(outputTemp); - } - - if (!reset) - { - size_t elements = inputWidth * inputHeight; - indicesCol = arma::linspace >(0, (elements - 1), - elements); - - indices = arma::Mat(indicesCol.memptr(), inputWidth, inputHeight); - - reset = true; - } - - for (size_t s = 0; s < inputTemp.n_slices; s++) - { - if (!deterministic) - { - PoolingOperation(inputTemp.slice(s), outputTemp.slice(s), - poolingIndices.back().slice(s)); - } - else - { - PoolingOperation(inputTemp.slice(s), outputTemp.slice(s), - inputTemp.slice(s)); - } - } - - output = arma::Mat(outputTemp.memptr(), outputTemp.n_elem / batchSize, - batchSize); - - outputWidth = outputTemp.n_rows; - outputHeight = outputTemp.n_cols; - outSize = batchSize * inSize; } -template -template -void MaxPooling::Backward( - const arma::Mat& /* input */, const arma::Mat& gy, arma::Mat& g) +template +void MaxPoolingType::Backward( + const MatType& input, const MatType& gy, MatType& g) { - arma::cube mappedError = arma::cube(((arma::Mat&) gy).memptr(), - outputWidth, outputHeight, outSize, false, false); + arma::Cube mappedError = + arma::Cube(((MatType&) gy).memptr(), + this->outputDimensions[0], this->outputDimensions[1], + channels * input.n_cols, false, false); - gTemp = arma::zeros(inputTemp.n_rows, - inputTemp.n_cols, inputTemp.n_slices); + arma::Cube gTemp(g.memptr(), + this->inputDimensions[0], this->inputDimensions[1], + channels * input.n_cols, false, true); - for (size_t s = 0; s < mappedError.n_slices; s++) - { - Unpooling(mappedError.slice(s), gTemp.slice(s), - poolingIndices.back().slice(s)); - } - - poolingIndices.pop_back(); - - g = arma::mat(gTemp.memptr(), gTemp.n_elem / batchSize, batchSize); + // There's no version of UnpoolingOperation without pooling indices, because + // if we call `Backward()`, we know for sure we are training. + UnpoolingOperation(mappedError, gTemp, poolingIndices); } -template +template +void MaxPoolingType::ComputeOutputDimensions() +{ + this->outputDimensions = this->inputDimensions; + + // Compute the size of the output. + if (floor) + { + this->outputDimensions[0] = std::floor((this->inputDimensions[0] - + (double) kernelWidth) / (double) strideWidth + 1); + this->outputDimensions[1] = std::floor((this->inputDimensions[1] - + (double) kernelHeight) / (double) strideHeight + 1); + offset = 0; + } + else + { + this->outputDimensions[0] = std::ceil((this->inputDimensions[0] - + (double) kernelWidth) / (double) strideWidth + 1); + this->outputDimensions[1] = std::ceil((this->inputDimensions[1] - + (double) kernelHeight) / (double) strideHeight + 1); + offset = 1; + } + + // Higher dimensions are not modified. + + // Cache input size and output size. + channels = 1; + for (size_t i = 2; i < this->inputDimensions.size(); ++i) + channels *= this->inputDimensions[i]; +} + +template template -void MaxPooling::serialize( +void MaxPoolingType::serialize( Archive& ar, const uint32_t /* version */) + { + ar(cereal::base_class>(this)); + ar(CEREAL_NVP(kernelWidth)); ar(CEREAL_NVP(kernelHeight)); ar(CEREAL_NVP(strideWidth)); ar(CEREAL_NVP(strideHeight)); - ar(CEREAL_NVP(batchSize)); + ar(CEREAL_NVP(channels)); ar(CEREAL_NVP(floor)); - ar(CEREAL_NVP(inputWidth)); - ar(CEREAL_NVP(inputHeight)); - ar(CEREAL_NVP(outputWidth)); - ar(CEREAL_NVP(outputHeight)); + ar(CEREAL_NVP(offset)); + + if (Archive::is_loading::value) + { + // Clear any memory used by `poolingIndices`. + poolingIndices.clear(); + } } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/mean_pooling_impl.hpp b/src/mlpack/methods/ann/layer/mean_pooling_impl.hpp deleted file mode 100644 index ad4a8e6943..0000000000 --- a/src/mlpack/methods/ann/layer/mean_pooling_impl.hpp +++ /dev/null @@ -1,134 +0,0 @@ -/** - * @file methods/ann/layer/mean_pooling_impl.hpp - * @author Marcus Edel - * @author Nilay Jain - * - * Implementation of the MeanPooling layer class. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_LAYER_MEAN_POOLING_IMPL_HPP -#define MLPACK_METHODS_ANN_LAYER_MEAN_POOLING_IMPL_HPP - -// In case it hasn't yet been included. -#include "mean_pooling.hpp" - -namespace mlpack { -namespace ann /** Artificial Neural Network. */ { - -template -MeanPooling::MeanPooling() -{ - // Nothing to do here. -} - -template -MeanPooling::MeanPooling( - const size_t kernelWidth, - const size_t kernelHeight, - const size_t strideWidth, - const size_t strideHeight, - const bool floor) : - kernelWidth(kernelWidth), - kernelHeight(kernelHeight), - strideWidth(strideWidth), - strideHeight(strideHeight), - floor(floor), - inSize(0), - outSize(0), - inputWidth(0), - inputHeight(0), - outputWidth(0), - outputHeight(0), - reset(false), - deterministic(false), - batchSize(0) -{ - // Nothing to do here. -} - -template -template -void MeanPooling::Forward( - const arma::Mat& input, arma::Mat& output) -{ - batchSize = input.n_cols; - inSize = input.n_elem / (inputWidth * inputHeight * batchSize); - inputTemp = arma::cube(const_cast&>(input).memptr(), - inputWidth, inputHeight, batchSize * inSize, false, false); - - if (floor) - { - outputWidth = std::floor((inputWidth - - (double) kernelWidth) / (double) strideWidth + 1); - outputHeight = std::floor((inputHeight - - (double) kernelHeight) / (double) strideHeight + 1); - } - else - { - outputWidth = std::ceil((inputWidth - - (double) kernelWidth) / (double) strideWidth + 1); - outputHeight = std::ceil((inputHeight - - (double) kernelHeight) / (double) strideHeight + 1); - } - - outputTemp = arma::zeros >(outputWidth, outputHeight, - batchSize * inSize); - - for (size_t s = 0; s < inputTemp.n_slices; s++) - Pooling(inputTemp.slice(s), outputTemp.slice(s)); - - output = arma::Mat(outputTemp.memptr(), outputTemp.n_elem / batchSize, - batchSize); - - outputWidth = outputTemp.n_rows; - outputHeight = outputTemp.n_cols; - outSize = batchSize * inSize; -} - -template -template -void MeanPooling::Backward( - const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g) -{ - arma::cube mappedError = arma::cube(((arma::Mat&) gy).memptr(), - outputWidth, outputHeight, outSize, false, false); - - gTemp = arma::zeros(inputTemp.n_rows, - inputTemp.n_cols, inputTemp.n_slices); - - for (size_t s = 0; s < mappedError.n_slices; s++) - { - Unpooling(inputTemp.slice(s), mappedError.slice(s), gTemp.slice(s)); - } - - g = arma::mat(gTemp.memptr(), gTemp.n_elem / batchSize, batchSize); -} - -template -template -void MeanPooling::serialize( - Archive& ar, - const uint32_t /* version */) -{ - ar(CEREAL_NVP(kernelWidth)); - ar(CEREAL_NVP(kernelHeight)); - ar(CEREAL_NVP(strideWidth)); - ar(CEREAL_NVP(strideHeight)); - ar(CEREAL_NVP(batchSize)); - ar(CEREAL_NVP(floor)); - ar(CEREAL_NVP(inputWidth)); - ar(CEREAL_NVP(inputHeight)); - ar(CEREAL_NVP(outputWidth)); - ar(CEREAL_NVP(outputHeight)); -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/layer/minibatch_discrimination_impl.hpp b/src/mlpack/methods/ann/layer/minibatch_discrimination_impl.hpp deleted file mode 100644 index e600b807d4..0000000000 --- a/src/mlpack/methods/ann/layer/minibatch_discrimination_impl.hpp +++ /dev/null @@ -1,147 +0,0 @@ -/** - * @file methods/ann/layer/minibatch_discrimination_impl.hpp - * @author Saksham Bansal - * - * Implementation of the MiniBatchDiscrimination layer class. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_LAYER_MINIBATCH_DISCRIMINATION_IMPL_HPP -#define MLPACK_METHODS_ANN_LAYER_MINIBATCH_DISCRIMINATION_IMPL_HPP - -// In case it hasn't yet been included. -#include "minibatch_discrimination.hpp" - -namespace mlpack { -namespace ann /** Artificial Neural Network. */ { - -template -MiniBatchDiscrimination::MiniBatchDiscrimination() : - A(0), - B(0), - C(0), - batchSize(0) -{ - // Nothing to do here. -} - -template -MiniBatchDiscrimination::MiniBatchDiscrimination( - const size_t inSize, - const size_t outSize, - const size_t features) : - A(inSize), - B(outSize - inSize), - C(features), - batchSize(0) -{ - weights.set_size(A * B * C, 1); -} - -template -void MiniBatchDiscrimination::Reset() -{ - weight = arma::mat(weights.memptr(), B * C, A, false, false); -} - -template -template -void MiniBatchDiscrimination::Forward( - const arma::Mat& input, arma::Mat& output) -{ - batchSize = input.n_cols; - tempM = weight * input; - M = arma::cube(tempM.memptr(), B, C, batchSize, false, false); - distances.set_size(B, batchSize, batchSize); - output.set_size(B, batchSize); - - for (size_t i = 0; i < M.n_slices; ++i) - { - output.col(i).ones(); - for (size_t j = 0; j < M.n_slices; ++j) - { - if (j < i) - { - output.col(i) += distances.slice(j).col(i); - } - else if (i == j) - { - continue; - } - else - { - distances.slice(i).col(j) = - arma::exp(-arma::sum(abs(M.slice(i) - M.slice(j)), 1)); - output.col(i) += distances.slice(i).col(j); - } - } - } - - output = join_cols(input, output); // (A + B) x batchSize -} - -template -template -void MiniBatchDiscrimination::Backward( - const arma::Mat& /* input */, const arma::Mat& gy, arma::Mat& g) -{ - g = gy.head_rows(A); - arma::Mat gM = gy.tail_rows(B); - deltaM.zeros(B, C, batchSize); - - for (size_t i = 0; i < M.n_slices; ++i) - { - for (size_t j = 0; j < M.n_slices; ++j) - { - if (i == j) - { - continue; - } - arma::mat t = arma::sign(M.slice(i) - M.slice(j)); - t.each_col() %= - distances.slice(std::min(i, j)).col(std::max(i, j)) % gM.col(i); - deltaM.slice(i) -= t; - deltaM.slice(j) += t; - } - } - - deltaTemp = arma::mat(deltaM.memptr(), B * C, batchSize, false, false); - g += weight.t() * deltaTemp; -} - -template -template -void MiniBatchDiscrimination::Gradient( - const arma::Mat& input, - const arma::Mat& /* error */, - arma::Mat& gradient) -{ - gradient = arma::vectorise(deltaTemp * input.t()); -} - -template -template -void MiniBatchDiscrimination::serialize( - Archive& ar, const uint32_t /* version */) -{ - ar(CEREAL_NVP(A)); - ar(CEREAL_NVP(B)); - ar(CEREAL_NVP(C)); - - // This is inefficient, but we have to allocate this memory so that - // WeightSetVisitor gets the right size. - if (cereal::is_loading()) - { - weights.set_size(A * B * C, 1); - } -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/layer/multi_layer.hpp b/src/mlpack/methods/ann/layer/multi_layer.hpp new file mode 100644 index 0000000000..3b9564949b --- /dev/null +++ b/src/mlpack/methods/ann/layer/multi_layer.hpp @@ -0,0 +1,252 @@ +/** + * @file methods/ann/layer/multi_layer.hpp + * @author Ryan Curtin + * + * Base class for neural network layers that are wrappers around other layers. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_LAYER_MULTI_LAYER_HPP +#define MLPACK_METHODS_ANN_LAYER_MULTI_LAYER_HPP + +#include "../make_alias.hpp" +#include "layer.hpp" + +namespace mlpack { +namespace ann { + +/** + * A "multi-layer" is a layer that is a wrapper around other layers. It passes + * the input through all of its child layers sequentially, returning the output + * from the last layer. + * + * It's likely not very useful to use this layer directly; instead, this layer + * is meant as a base class for use by other layers that must store and use + * multiple layers. + * + * @tparam MatType Matrix representation to accept as input and use for + * computation. + */ +template +class MultiLayer : public Layer +{ + public: + /** + * Create an empty MultiLayer that holds no layers of its own. Be sure to add + * layers with Add() before using! + */ + MultiLayer(); + + //! Copy the given MultiLayer. + MultiLayer(const MultiLayer& other); + //! Take ownership of the layers of the given MultiLayer. + MultiLayer(MultiLayer&& other); + //! Copy the given MultiLayer. + MultiLayer& operator=(const MultiLayer& other); + //! Take ownership of the given MultiLayer. + MultiLayer& operator=(MultiLayer&& other); + + //! Virtual destructor: delete all held layers. + virtual ~MultiLayer() + { + for (size_t i = 0; i < network.size(); ++i) + delete network[i]; + } + + //! Create a copy of the MultiLayer (this is safe for polymorphic use). + virtual MultiLayer* Clone() const { return new MultiLayer(*this); } + + /** + * Perform a forward pass with the given input data. `output` is expected to + * have the correct size (e.g. number of rows equal to `OutputSize()` of the + * last held layer; number of columns equal to `input.n_cols`). + * + * @param input Input data to pass through the MultiLayer. + * @param output Matrix to store output in. + */ + virtual void Forward(const MatType& input, MatType& output); + + /** + * Perform a forward pass with the given input data, but only on a subset of + * the layers in the MultiLayer. `output` is expected to have the correct + * size (e.g. number of rows equal to `OutputSize()` of the last layer to be + * computed; number of columns equal to `input.n_cols`). + * + * @param input Input data to pass through the MultiLayer. + * @param output Matrix to store output in. + * @param start Index of first layer to pass data through. + * @param end Index of last layer to pass data through. + */ + void Forward(const MatType& input, + MatType& output, + const size_t start, + const size_t end); + + /** + * Perform a backward pass with the given data. `gy` is expected to be the + * propagated error from the subsequent layer (or output), `input` is expected + * to be the output from this layer when `Forward()` was called, and `g` will + * store the propagated error from this layer (to be passed to the previous + * layer as `gy`). + * + * It is expected that `g` has the correct size already (e.g., number of rows + * equal to `OutputSize()` of the previous layer, and number of columns equal + * to `input.n_cols`). + * + * This function is expected to be called for the same input data as + * `Forward()` was just called for. + * + * @param input Output of Forward(). + * @param gy Propagated error from next layer. + * @param g Matrix to store propagated error in for previous layer. + */ + virtual void Backward(const MatType& input, + const MatType& gy, + MatType& g); + + /** + * Compute the gradients of each layer. + * + * This function is expected to be called for the same input data as + * `Forward()` and `Backward()` were just called for. That is, `input` here + * should be the same data as `Forward()` was called with. + * + * `gradient` is expected to have the correct size already (e.g., number of + * rows equal to 1, and number of columns equal to `WeightSize()`). + * + * @param input Original input data provided to Forward(). + * @param error Error as computed by `Backward()`. + * @param gradient Matrix to store the gradients in. + */ + virtual void Gradient(const MatType& input, + const MatType& error, + MatType& gradient); + + /** + * Set the weights of the layer to use the memory given as `weightsPtr`. + */ + virtual void SetWeights(typename MatType::elem_type* weightsPtr); + + /** + * Return the number of weights in the MultiLayer. This is the sum of the + * number of weights in each layer. + */ + virtual size_t WeightSize() const; + + /** + * Compute the output dimensions of the MultiLayer using `InputDimensions()`. + * This computes the dimensions of each layer held by the MultiLayer, and the + * output dimensions are set to the output dimensions of the last layer. + */ + virtual void ComputeOutputDimensions(); + + /** + * Compute the loss that should be added to the objective. + */ + virtual double Loss() const; + + /* + * Add a new module to the model. + * + * @param args The layer parameter. + */ + template + void Add(Args... args) + { + network.push_back(new LayerType(args...)); + layerOutputs.push_back(MatType()); + layerDeltas.push_back(MatType()); + layerGradients.push_back(MatType()); + } + + /* + * Add a new module to the model. + * + * @param layer The Layer to be added to the model. + */ + void Add(Layer* layer) + { + network.push_back(layer); + layerOutputs.push_back(MatType()); + layerDeltas.push_back(MatType()); + layerGradients.push_back(MatType()); + } + + //! Get the network (series of layers) held by this MultiLayer. + const std::vector*> Network() const + { + return network; + } + //! Modify the network (series of layers) held by this MultiLayer. Be + //! careful! + std::vector*>& Network() { return network; } + + //! Serialize the MultiLayer. + template + void serialize(Archive& ar, const uint32_t /* version */); + + protected: + /** + * Initialize memory that will be used by each layer for the forward pass, + * assuming that the input will have the given `batchSize`. When `Forward()` + * is called, each internally-held layer will output its results into the + * memory allocated by this function (this is the internal member + * `layerOutputMatrix` and its aliases `layerOutputs`). + */ + void InitializeForwardPassMemory(const size_t batchSize); + + /** + * Initialize memory that will be used by each layer for the backwards pass, + * assuming that the input will have the given `batchSize`. When `Backward()` + * is called, each internally-held layer will output the results of its + * backwards pass into the memory allocated by this function (this is the + * internal member `layerDeltaMatrix` and its aliases `layerDeltas`). + */ + void InitializeBackwardPassMemory(const size_t batchSize); + + /** + * Initialize memory for the gradient pass. This sets the internal aliases + * `layerGradients` appropriately using the memory from the given `gradient`, + * such that each layer will output its gradient (via its `Gradient()` method) + * into the appropriate member of `layerGradients`. + */ + void InitializeGradientPassMemory(MatType& gradient); + + //! The internally-held network. + std::vector*> network; + + // Total number of elements in the input, cached for convenience. + size_t inSize; + // Total number of input elements for *every* layer. + size_t totalInputSize; + // Total number of output elements for *every* layer. + size_t totalOutputSize; + + //! This matrix stores all of the outputs of each layer when Forward() is + //! called. See `InitializeForwardPassMemory()`. + MatType layerOutputMatrix; + //! These are aliases of `layerOutputMatrix` for each layer. + std::vector layerOutputs; + + //! This matrix stores all of the backwards pass results of each layer when + //! Backward() is called. See `InitializeBackwardPassMemory()`. + MatType layerDeltaMatrix; + //! These are aliases of `layerDeltaMatrix` for each layer. + std::vector layerDeltas; + + //! Gradient aliases for each layer. Note that this is *only* valid in the + //! context of `Gradient()`! We have it as a class member to avoid + //! reallocating the `MatType`s each call to `Gradient()`. + std::vector layerGradients; +}; + +} // namespace ann +} // namespace mlpack + +// Include implementation. +#include "multi_layer_impl.hpp" + +#endif diff --git a/src/mlpack/methods/ann/layer/multi_layer_impl.hpp b/src/mlpack/methods/ann/layer/multi_layer_impl.hpp new file mode 100644 index 0000000000..55ab7719a1 --- /dev/null +++ b/src/mlpack/methods/ann/layer/multi_layer_impl.hpp @@ -0,0 +1,414 @@ +/** + * @file methods/ann/layer/multi_layer_impl.hpp + * @author Ryan Curtin + * + * Implementation of the base class for neural network layers that are wrappers + * around other layers. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_LAYER_MULTI_LAYER_IMPL_HPP +#define MLPACK_METHODS_ANN_LAYER_MULTI_LAYER_IMPL_HPP + +#include "multi_layer.hpp" + +namespace mlpack { +namespace ann { + +template +MultiLayer::MultiLayer() : + inSize(0), + totalInputSize(0), + totalOutputSize(0) +{ + // Nothing to do. +} + +template +MultiLayer::MultiLayer(const MultiLayer& other) : + Layer(other), + inSize(other.inSize), + totalInputSize(other.totalInputSize), + totalOutputSize(other.totalOutputSize), + layerOutputMatrix(other.layerOutputMatrix), + layerDeltaMatrix(other.layerDeltaMatrix) +{ + // Copy each layer. + for (size_t i = 0; i < other.network.size(); ++i) + network.push_back(other.network[i]->Clone()); + + // Ensure that the aliases for layers during passes have the right size. + layerOutputs.resize(network.size(), MatType()); + layerDeltas.resize(network.size(), MatType()); + layerGradients.resize(network.size(), MatType()); + + // layerOutputs, layerDeltas, and layerGradients will be reset the next time + // Forward(), Backward(), or Gradient() is called. +} + +template +MultiLayer::MultiLayer(MultiLayer&& other) : + Layer(other), + network(std::move(other.network)), + inSize(std::move(other.inSize)), + totalInputSize(std::move(other.totalInputSize)), + totalOutputSize(std::move(other.totalOutputSize)), + layerOutputMatrix(std::move(other.layerOutputMatrix)), + layerDeltaMatrix(std::move(other.layerDeltaMatrix)) +{ + // Ensure that the aliases for layers during passes have the right size. + layerOutputs.resize(network.size(), MatType()); + layerDeltas.resize(network.size(), MatType()); + layerGradients.resize(network.size(), MatType()); + + // layerOutputs, layerDeltas, and layerGradients will be reset the next time + // Forward(), Backward(), or Gradient() is called. + + other.layerOutputs.clear(); + other.layerDeltas.clear(); + other.layerGradients.clear(); +} + +template +MultiLayer& MultiLayer::operator=(const MultiLayer& other) +{ + if (this != &other) + { + Layer::operator=(other); + + network.clear(); + layerOutputs.clear(); + layerDeltas.clear(); + layerGradients.clear(); + + inSize = other.inSize; + totalInputSize = other.totalInputSize; + totalOutputSize = other.totalOutputSize; + + layerOutputMatrix = other.layerOutputMatrix; + layerDeltaMatrix = other.layerDeltaMatrix; + + for (size_t i = 0; i < other.network.size(); ++i) + network.push_back(other.network[i]->Clone()); + + // Ensure that the aliases for layers during passes have the right size. + layerOutputs.resize(network.size(), MatType()); + layerDeltas.resize(network.size(), MatType()); + layerGradients.resize(network.size(), MatType()); + } + + return *this; +} + +template +MultiLayer& MultiLayer::operator=(MultiLayer&& other) +{ + if (this != &other) + { + Layer::operator=(other); + + layerOutputs.clear(); + layerDeltas.clear(); + layerGradients.clear(); + + inSize = std::move(other.inSize); + totalInputSize = std::move(other.totalInputSize); + totalOutputSize = std::move(other.totalOutputSize); + + network = std::move(other.network); + + layerOutputs.resize(network.size(), MatType()); + layerDeltas.resize(network.size(), MatType()); + layerGradients.resize(network.size(), MatType()); + + other.layerOutputs.clear(); + other.layerDeltas.clear(); + other.layerGradients.clear(); + } + + return *this; +} + +template +void MultiLayer::Forward( + const MatType& input, MatType& output) +{ + Forward(input, output, 0, network.size() - 1); +} + +template +void MultiLayer::Forward( + const MatType& input, + MatType& output, + const size_t start, + const size_t end) +{ + // Make sure training/testing mode is set right in each layer. + for (size_t i = 0; i < network.size(); ++i) + network[i]->Training() = this->training; + + // Note that we use `output` for the last layer; layerOutputs is only used for + // intermediate values between layers. + if ((end - start) > 0) + { + // Initialize memory for the forward pass (if needed). + InitializeForwardPassMemory(input.n_cols); + + network[start]->Forward(input, layerOutputs[start]); + for (size_t i = start + 1; i < end; ++i) + network[i]->Forward(layerOutputs[i - 1], layerOutputs[i]); + network[end]->Forward(layerOutputs[end - 1], output); + } + else if ((end - start) == 0 && network.size() > 0) + { + network[start]->Forward(input, output); + } + else + { + // Empty network? + output = input; + } +} + +template +void MultiLayer::Backward( + const MatType& input, const MatType& gy, MatType& g) +{ + if (network.size() > 1) + { + // Initialize memory for the backward pass (if needed). + InitializeBackwardPassMemory(input.n_cols); + + network.back()->Backward(input, gy, layerDeltas.back()); + for (size_t i = network.size() - 2; i > 0; --i) + network[i]->Backward(layerOutputs[i], layerDeltas[i + 1], layerDeltas[i]); + network[0]->Backward(layerOutputs[0], layerDeltas[1], g); + } + else if (network.size() == 1) + { + network[0]->Backward(input, gy, g); + } + else + { + // Empty network? + g = input; + } +} + +template +void MultiLayer::Gradient( + const MatType& input, const MatType& error, MatType& gradient) +{ + // We assume gradient has the right size already. + + // Pass gradients through each layer. + if (network.size() > 1) + { + // Initialize memory for the gradient pass (if needed). + InitializeGradientPassMemory(gradient); + + network.front()->Gradient(input, layerDeltas[1], layerGradients.front()); + for (size_t i = 1; i < network.size() - 1; ++i) + { + network[i]->Gradient(layerOutputs[i - 1], layerDeltas[i + 1], + layerGradients[i]); + } + network.back()->Gradient(layerOutputs[network.size() - 2], error, + layerGradients.back()); + } + else if (network.size() == 1) + { + network[0]->Gradient(input, error, gradient); + } + else + { + // Nothing to do if the network is empty... there is no gradient. + } +} + +template +void MultiLayer::SetWeights(typename MatType::elem_type* weightsPtr) +{ + size_t start = 0; + const size_t totalWeightSize = WeightSize(); + for (size_t i = 0; i < network.size(); ++i) + { + const size_t weightSize = network[i]->WeightSize(); + + // Sanity check: ensure we aren't passing memory past the end of the + // parameters. + Log::Assert(start + weightSize <= totalWeightSize, + "FNN::SetLayerMemory(): parameter size does not match total layer " + "weight size!"); + + network[i]->SetWeights(weightsPtr + start); + start += weightSize; + } + + // Technically this check should be unnecessary, but there's nothing wrong + // with a little paranoia... + Log::Assert(start == totalWeightSize, + "FNN::SetLayerMemory(): total layer weight size does not match parameter " + "size!"); +} + +template +size_t MultiLayer::WeightSize() const +{ + // Sum the weights in each layer. + size_t total = 0; + for (size_t i = 0; i < network.size(); ++i) + total += network[i]->WeightSize(); + return total; +} + +template +void MultiLayer::ComputeOutputDimensions() +{ + inSize = 0; + totalInputSize = 0; + totalOutputSize = 0; + + // Propagate the input dimensions forward to the output. + network.front()->InputDimensions() = this->inputDimensions; + inSize = this->inputDimensions[0]; + for (size_t i = 1; i < this->inputDimensions.size(); ++i) + inSize *= this->inputDimensions[i]; + totalInputSize += inSize; + + for (size_t i = 1; i < network.size(); ++i) + { + network[i]->InputDimensions() = network[i - 1]->OutputDimensions(); + size_t layerInputSize = network[i]->InputDimensions()[0]; + for (size_t j = 1; j < network[i]->InputDimensions().size(); ++j) + layerInputSize *= network[i]->InputDimensions()[j]; + + totalInputSize += layerInputSize; + totalOutputSize += layerInputSize; + } + + size_t lastLayerSize = network.back()->OutputDimensions()[0]; + for (size_t i = 1; i < network.back()->OutputDimensions().size(); ++i) + lastLayerSize *= network.back()->OutputDimensions()[i]; + + totalOutputSize += lastLayerSize; + this->outputDimensions = network.back()->OutputDimensions(); +} + +template +double MultiLayer::Loss() const +{ + double loss = 0.0; + for (size_t i = 0; i < network.size(); ++i) + loss += network[i]->Loss(); + + return loss; +} + +template +template +void MultiLayer::serialize( + Archive& ar, const uint32_t /* version */) +{ + ar(cereal::base_class>(this)); + + ar(CEREAL_VECTOR_POINTER(network)); + ar(CEREAL_NVP(inSize)); + ar(CEREAL_NVP(totalInputSize)); + ar(CEREAL_NVP(totalOutputSize)); + + if (Archive::is_loading::value) + { + layerOutputMatrix.clear(); + layerDeltaMatrix.clear(); + layerGradients.clear(); + layerOutputs.resize(network.size(), MatType()); + layerDeltas.resize(network.size(), MatType()); + layerGradients.resize(network.size(), MatType()); + } +} + +template +void MultiLayer::InitializeForwardPassMemory(const size_t batchSize) +{ + // We need to initialize memory to store the output of each layer's Forward() + // call. We'll do this all in one matrix, but, the size of this matrix + // depends on the batch size we are using for computation. We avoid resizing + // layerOutputMatrix down, unless we only need 10% or less of it. + if (batchSize * totalOutputSize > layerOutputMatrix.n_elem || + batchSize * totalOutputSize < + std::floor(0.1 * layerOutputMatrix.n_elem)) + { + // All outputs will be represented by one big block of memory. + layerOutputMatrix = MatType(1, batchSize * totalOutputSize); + } + + // Now, create an alias to the right place for each layer. We assume that + // layerOutputs is already sized correctly (this should be done by Add()). + size_t start = 0; + for (size_t i = 0; i < layerOutputs.size(); ++i) + { + const size_t layerOutputSize = network[i]->OutputSize(); + MakeAlias(layerOutputs[i], layerOutputMatrix.colptr(start), + layerOutputSize, batchSize); + start += batchSize * layerOutputSize; + } +} + +template +void MultiLayer::InitializeBackwardPassMemory( + const size_t batchSize) +{ + // We need to initialize memory to store the output of each layer's Backward() + // call. We do this similarly to InitializeForwardPassMemory(), but we must + // store a matrix to use as the delta for each layer. + if (batchSize * totalInputSize > layerDeltaMatrix.n_elem || + batchSize * totalInputSize < std::floor(0.1 * layerDeltaMatrix.n_elem)) + { + // All deltas will be represented by one big block of memory. + layerDeltaMatrix = MatType(1, batchSize * totalInputSize); + } + + // Now, create an alias to the right place for each layer. We assume that + // layerDeltas is already sized correctly (this should be done by Add()). + size_t start = 0; + for (size_t i = 0; i < layerDeltas.size(); ++i) + { + size_t layerInputSize = 1; + if (i == 0) + { + for (size_t j = 0; j < this->inputDimensions.size(); ++j) + layerInputSize *= this->inputDimensions[j]; + } + else + { + layerInputSize = network[i - 1]->OutputSize(); + } + MakeAlias(layerDeltas[i], layerDeltaMatrix.colptr(start), layerInputSize, + batchSize); + start += batchSize * layerInputSize; + } +} + +template +void MultiLayer::InitializeGradientPassMemory(MatType& gradient) +{ + // We need to initialize memory to store the gradients of each layer. To do + // this, we need to know the weight size of each layer. + size_t gradientStart = 0; + for (size_t i = 0; i < network.size(); ++i) + { + const size_t weightSize = network[i]->WeightSize(); + MakeAlias(layerGradients[i], gradient.memptr() + gradientStart, + weightSize, 1); + gradientStart += weightSize; + } +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/layer/multiply_constant_impl.hpp b/src/mlpack/methods/ann/layer/multiply_constant_impl.hpp deleted file mode 100644 index 4c02fbd1fa..0000000000 --- a/src/mlpack/methods/ann/layer/multiply_constant_impl.hpp +++ /dev/null @@ -1,96 +0,0 @@ -/** - * @file methods/ann/layer/multiply_constant_impl.hpp - * @author Marcus Edel - * - * Implementation of the MultiplyConstantLayer class, which multiplies the - * input by a (non-learnable) constant. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_LAYER_MULTIPLY_CONSTANT_IMPL_HPP -#define MLPACK_METHODS_ANN_LAYER_MULTIPLY_CONSTANT_IMPL_HPP - -// In case it hasn't yet been included. -#include "multiply_constant.hpp" - -namespace mlpack { -namespace ann /** Artificial Neural Network. */ { - -template -MultiplyConstant::MultiplyConstant( - const double scalar) : scalar(scalar) -{ - // Nothing to do here. -} - -template -MultiplyConstant::MultiplyConstant( - const MultiplyConstant& layer) : - scalar(layer.scalar) -{ - // Nothing to do here. -} - -template -MultiplyConstant::MultiplyConstant( - MultiplyConstant&& layer) : - scalar(std::move(layer.scalar)) -{ - // Nothing to do here. -} - -template -MultiplyConstant& -MultiplyConstant::operator=( - const MultiplyConstant& layer) -{ - if (this != &layer) - { - scalar = layer.scalar; - } - return *this; -} - -template -MultiplyConstant& -MultiplyConstant::operator=( - MultiplyConstant&& layer) -{ - if (this != &layer) - { - scalar = std::move(layer.scalar); - } - return *this; -} - -template -template -void MultiplyConstant::Forward( - const InputType& input, OutputType& output) -{ - output = input * scalar; -} - -template -template -void MultiplyConstant::Backward( - const DataType& /* input */, const DataType& gy, DataType& g) -{ - g = gy * scalar; -} - -template -template -void MultiplyConstant::serialize( - Archive& ar, const uint32_t /* version */) -{ - ar(CEREAL_NVP(scalar)); -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/layer/multiply_merge_impl.hpp b/src/mlpack/methods/ann/layer/multiply_merge_impl.hpp deleted file mode 100644 index 29cd111482..0000000000 --- a/src/mlpack/methods/ann/layer/multiply_merge_impl.hpp +++ /dev/null @@ -1,190 +0,0 @@ -/** - * @file methods/ann/layer/multiply_merge_impl.hpp - * @author Haritha Nair - * - * Definition of the MultiplyMerge module which multiplies the output of the - * given modules element-wise. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_LAYER_MULTIPLY_MERGE_IMPL_HPP -#define MLPACK_METHODS_ANN_LAYER_MULTIPLY_MERGE_IMPL_HPP - -// In case it hasn't yet been included. -#include "multiply_merge.hpp" - -#include "../visitor/forward_visitor.hpp" -#include "../visitor/backward_visitor.hpp" -#include "../visitor/gradient_visitor.hpp" - -namespace mlpack { -namespace ann /** Artificial Neural Network. */ { - -template -MultiplyMerge::MultiplyMerge( - const bool model, const bool run) : - model(model), run(run), ownsLayer(!model) -{ - // Nothing to do here. -} - -template -MultiplyMerge::MultiplyMerge( - const MultiplyMerge& layer) : - model(layer.model), - run(layer.run), - ownsLayer(layer.ownsLayer), - network(layer.network), - weights(layer.weights) -{ - // Nothing to do here. -} - -template -MultiplyMerge::MultiplyMerge( - MultiplyMerge&& layer) : - model(std::move(layer.model)), - run(std::move(layer.run)), - ownsLayer(std::move(layer.ownsLayer)), - network(std::move(layer.network)), - weights(std::move(layer.weights)) -{ - // Nothing to do here. -} - -template -MultiplyMerge& -MultiplyMerge::operator=( - const MultiplyMerge& layer) -{ - if (this != &layer) - { - model = layer.model; - run = layer.run; - ownsLayer = layer.ownsLayer; - network = layer.network; - weights = layer.weights; - } - return *this; -} - -template -MultiplyMerge& -MultiplyMerge::operator=( - MultiplyMerge&& layer) -{ - if (this != &layer) - { - model = std::move(layer.model); - run = std::move(layer.run); - ownsLayer = std::move(layer.ownsLayer); - network = std::move(layer.network); - weights = std::move(layer.weights); - } - return *this; -} - -template -MultiplyMerge::~MultiplyMerge() -{ - if (ownsLayer) - { - std::for_each(network.begin(), network.end(), - boost::apply_visitor(deleteVisitor)); - } -} - -template -template -void MultiplyMerge::Forward( - const InputType& input, OutputType& output) -{ - if (run) - { - for (size_t i = 0; i < network.size(); ++i) - { - boost::apply_visitor(ForwardVisitor(input, - boost::apply_visitor(outputParameterVisitor, network[i])), - network[i]); - } - } - - output = boost::apply_visitor(outputParameterVisitor, network.front()); - for (size_t i = 1; i < network.size(); ++i) - { - output %= boost::apply_visitor(outputParameterVisitor, network[i]); - } -} - -template -template -void MultiplyMerge::Backward( - const arma::Mat& /* input */, const arma::Mat& gy, arma::Mat& g) -{ - if (run) - { - for (size_t i = 0; i < network.size(); ++i) - { - boost::apply_visitor(BackwardVisitor(boost::apply_visitor( - outputParameterVisitor, network[i]), gy, - boost::apply_visitor(deltaVisitor, network[i])), network[i]); - } - - g = boost::apply_visitor(deltaVisitor, network[0]); - for (size_t i = 1; i < network.size(); ++i) - { - g += boost::apply_visitor(deltaVisitor, network[i]); - } - } - else - g = gy; -} - -template -template -void MultiplyMerge::Gradient( - const arma::Mat& input, - const arma::Mat& error, - arma::Mat& /* gradient */ ) -{ - if (run) - { - for (size_t i = 0; i < network.size(); ++i) - { - boost::apply_visitor(GradientVisitor(input, error), network[i]); - } - } -} - -template -template -void MultiplyMerge::serialize( - Archive& ar, const uint32_t /* version */) -{ - // Be sure to clear other layers before loading. - if (cereal::is_loading()) - network.clear(); - - ar(CEREAL_VECTOR_VARIANT_POINTER(network)); - ar(CEREAL_NVP(model)); - ar(CEREAL_NVP(run)); - ar(CEREAL_NVP(ownsLayer)); -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/layer/noisylinear.hpp b/src/mlpack/methods/ann/layer/noisylinear.hpp index 993f80725f..feac41131f 100644 --- a/src/mlpack/methods/ann/layer/noisylinear.hpp +++ b/src/mlpack/methods/ann/layer/noisylinear.hpp @@ -14,6 +14,8 @@ #include +#include "layer.hpp" + namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -21,55 +23,42 @@ namespace ann /** Artificial Neural Network. */ { * Implementation of the NoisyLinear layer class. It represents a single * layer of a neural network, with parametric noise added to its weights. * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam MatType Matrix representation to accept as input and use for + * computation. */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class NoisyLinear +template +class NoisyLinearType : public Layer { public: - //! Create the NoisyLinear object. - NoisyLinear(); - /** * Create the NoisyLinear layer object using the specified number of units. * - * @param inSize The number of input units. * @param outSize The number of output units. */ - NoisyLinear(const size_t inSize, - const size_t outSize); + NoisyLinearType(const size_t outSize = 0); - //! Copy constructor. - NoisyLinear(const NoisyLinear&); + //! Clone the NoisyLinearType object. This handles polymorphism correctly. + NoisyLinearType* Clone() const { return new NoisyLinearType(*this); } - //! Move constructor. - NoisyLinear(NoisyLinear&&); + // Virtual destructor. + virtual ~NoisyLinearType() { } - //! Operator= copy constructor. - NoisyLinear& operator=(const NoisyLinear& layer); + //! Copy the given NoisyLinear layer (but not weights). + NoisyLinearType(const NoisyLinearType& other); + //! Take ownership of the given NoisyLinear layer (but not weights). + NoisyLinearType(NoisyLinearType&& other); + //! Copy the given NoisyLinear layer (but not weights). + NoisyLinearType& operator=(const NoisyLinearType& other); + //! Take ownership of the given NoisyLinear layer (but not weights). + NoisyLinearType& operator=(NoisyLinearType&& other); - //! Operator= move constructor. - NoisyLinear& operator=(NoisyLinear&& layer); + //! Reset the layer parameter. + void SetWeights(typename MatType::elem_type* weightsPtr); - /* - * Reset the layer parameter. - */ - void Reset(); - - /* - * Reset the noise parameters(epsilons). - */ + //! Reset the noise parameters (epsilons). void ResetNoise(); - /* - * Reset the values of layer parameters (factorized gaussian noise). - */ + //! Reset the values of layer parameters (factorized gaussian noise). void ResetParameters(); /** @@ -79,8 +68,7 @@ class NoisyLinear * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template - void Forward(const arma::Mat& input, arma::Mat& output); + void Forward(const MatType& input, MatType& output); /** * Ordinary feed backward pass of a neural network, calculating the function @@ -91,117 +79,80 @@ class NoisyLinear * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g); + void Backward(const MatType& /* input */, + const MatType& gy, + MatType& g); - /* + /** * Calculate the gradient using the output delta and the input activation. * * @param input The input parameter used for calculating the gradient. * @param error The calculated error. * @param gradient The calculated gradient. */ - template - void Gradient(const arma::Mat& input, - const arma::Mat& error, - arma::Mat& gradient); + void Gradient(const MatType& input, + const MatType& error, + MatType& gradient); //! Get the parameters. - OutputDataType const& Parameters() const { return weights; } + MatType const& Parameters() const { return weights; } //! Modify the parameters. - OutputDataType& Parameters() { return weights; } - - //! Get the input parameter. - InputDataType const& InputParameter() const { return inputParameter; } - //! Modify the input parameter. - InputDataType& InputParameter() { return inputParameter; } - - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } - - //! Get the input size. - size_t InputSize() const { return inSize; } - - //! Get the output size. - size_t OutputSize() const { return outSize; } - - //! Get the gradient. - OutputDataType const& Gradient() const { return gradient; } - //! Modify the gradient. - OutputDataType& Gradient() { return gradient; } + MatType& Parameters() { return weights; } //! Get the shape of the input. - size_t InputShape() const - { - return inSize; - } - //! Modify the bias weights of the layer. - arma::mat& Bias() { return bias; } + MatType& Bias() { return bias; } - //! Get size of weights. + //! Compute the number of parameters in the layer. size_t WeightSize() const { return (outSize * inSize + outSize) * 2; } - /** - * Serialize the layer - */ + + //! Compute the output dimensions of the layer given `InputDimensions()`. + void ComputeOutputDimensions(); + + //! Serialize the layer. template void serialize(Archive& ar, const uint32_t /* version */); private: - //! Locally-stored number of input units. - size_t inSize; - //! Locally-stored number of output units. size_t outSize; + //! Locally stored number of input units. + size_t inSize; + //! Locally-stored weight object. - OutputDataType weights; + MatType weights; //! Locally-stored weight parameters. - OutputDataType weight; + MatType weight; //! Locally-stored weight-mean parameters. - OutputDataType weightMu; + MatType weightMu; //! Locally-stored weight-standard-deviation parameters. - OutputDataType weightSigma; + MatType weightSigma; //! Locally-stored weight-epsilon parameters. - OutputDataType weightEpsilon; + MatType weightEpsilon; //! Locally-stored bias parameters. - OutputDataType bias; + MatType bias; //! Locally-stored bias-mean parameters. - OutputDataType biasMu; + MatType biasMu; //! Locally-stored bias-standard-deviation parameters. - OutputDataType biasSigma; + MatType biasSigma; //! Locally-stored bias-epsilon parameters. - OutputDataType biasEpsilon; + MatType biasEpsilon; - //! Locally-stored delta object. - OutputDataType delta; +}; // class NoisyLinearType - //! Locally-stored gradient object. - OutputDataType gradient; +// Convenience typedefs. - //! Locally-stored input parameter object. - InputDataType inputParameter; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; -}; // class NoisyLinear +// Standard noisy linear layer. +typedef NoisyLinearType NoisyLinear; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/noisylinear_impl.hpp b/src/mlpack/methods/ann/layer/noisylinear_impl.hpp index c7a37b5863..4df8685283 100644 --- a/src/mlpack/methods/ann/layer/noisylinear_impl.hpp +++ b/src/mlpack/methods/ann/layer/noisylinear_impl.hpp @@ -18,107 +18,92 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -NoisyLinear::NoisyLinear() : - inSize(0), - outSize(0) +template +NoisyLinearType::NoisyLinearType(const size_t outSize) : + Layer(), + outSize(outSize), + inSize(0) { // Nothing to do here. } -template -NoisyLinear::NoisyLinear( - const NoisyLinear& layer) : - inSize(layer.inSize), - outSize(layer.outSize), - weights(layer.weights) +template +NoisyLinearType::NoisyLinearType(const NoisyLinearType& other) : + Layer(other), + outSize(other.outSize), + inSize(other.inSize) { - Reset(); + // Nothing to do. } -template -NoisyLinear::NoisyLinear( - const size_t inSize, - const size_t outSize) : - inSize(inSize), - outSize(outSize) +template +NoisyLinearType::NoisyLinearType(NoisyLinearType&& other) : + Layer(std::move(other)), + outSize(std::move(other.outSize)), + inSize(std::move(other.inSize)) { - weights.set_size(WeightSize(), 1); - weightEpsilon.set_size(outSize, inSize); - biasEpsilon.set_size(outSize, 1); + // Nothing to do. } -template -NoisyLinear::NoisyLinear( - NoisyLinear&& layer) : - inSize(std::move(layer.inSize)), - outSize(std::move(layer.outSize)), - weights(std::move(layer.weights)) +template +NoisyLinearType& +NoisyLinearType::operator=(const NoisyLinearType& other) { - layer.inSize = 0; - layer.outSize = 0; - layer.weights = nullptr; - Reset(); -} - -template -NoisyLinear& -NoisyLinear::operator=(const NoisyLinear& layer) -{ - if (this != &layer) + if (&other != this) { - inSize = layer.inSize; - outSize = layer.outSize; - weights = layer.weights; - Reset(); + Layer::operator=(other); + outSize = other.outSize; + inSize = other.inSize; } + return *this; } -template -NoisyLinear& -NoisyLinear::operator=(NoisyLinear&& layer) +template +NoisyLinearType& +NoisyLinearType::operator=(NoisyLinearType&& other) { - if (this != &layer) + if (&other != this) { - inSize = std::move(layer.inSize); - layer.inSize = 0; - outSize = std::move(layer.outSize); - layer.outSize = 0; - weights = std::move(layer.weights); - layer.weights = nullptr; - Reset(); + Layer::operator=(std::move(other)); + outSize = std::move(other.outSize); + inSize = std::move(other.inSize); } + return *this; } -template -void NoisyLinear::Reset() +template +void NoisyLinearType::SetWeights( + typename MatType::elem_type* weightsPtr) { - weightMu = arma::mat(weights.memptr(), - outSize, inSize, false, false); - biasMu = arma::mat(weights.memptr() + weightMu.n_elem, - outSize, 1, false, false); - weightSigma = arma::mat(weights.memptr() + weightMu.n_elem + biasMu.n_elem, - outSize, inSize, false, false); - biasSigma = arma::mat(weights.memptr() + weightMu.n_elem * 2 + biasMu.n_elem, - outSize, 1, false, false); + MakeAlias(weights, weightsPtr, 1, (outSize * inSize + outSize) * 2); + + MakeAlias(weightMu, weightsPtr, outSize, inSize); + MakeAlias(biasMu, weightsPtr + weightMu.n_elem, outSize, 1); + MakeAlias(weightSigma, weightsPtr + weightMu.n_elem + biasMu.n_elem, outSize, + inSize); + MakeAlias(biasSigma, weightsPtr + weightMu.n_elem * 2 + biasMu.n_elem, + outSize, 1); + this->ResetNoise(); } -template -void NoisyLinear::ResetNoise() +template +void NoisyLinearType::ResetNoise() { - arma::mat epsilonIn = arma::randn(inSize, 1); + MatType epsilonIn = arma::randn(inSize, 1); epsilonIn = arma::sign(epsilonIn) % arma::sqrt(arma::abs(epsilonIn)); - arma::mat epsilonOut = arma::randn(outSize, 1); + + MatType epsilonOut = arma::randn(outSize, 1); epsilonOut = arma::sign(epsilonOut) % arma::sqrt(arma::abs(epsilonOut)); + weightEpsilon = epsilonOut * epsilonIn.t(); biasEpsilon = epsilonOut; } -template -void NoisyLinear::ResetParameters() +template +void NoisyLinearType::ResetParameters() { const double muRange = 1 / std::sqrt(inSize); weightMu.randu(); @@ -129,10 +114,8 @@ void NoisyLinear::ResetParameters() biasSigma.fill(0.5 / std::sqrt(outSize)); } -template -template -void NoisyLinear::Forward( - const arma::Mat& input, arma::Mat& output) +template +void NoisyLinearType::Forward(const MatType& input, MatType& output) { weight = weightMu + weightSigma % weightEpsilon; bias = biasMu + biasSigma % biasEpsilon; @@ -140,23 +123,19 @@ void NoisyLinear::Forward( output.each_col() += bias; } -template -template -void NoisyLinear::Backward( - const arma::Mat& /* input */, const arma::Mat& gy, arma::Mat& g) +template +void NoisyLinearType::Backward( + const MatType& /* input */, const MatType& gy, MatType& g) { g = weight.t() * gy; } -template -template -void NoisyLinear::Gradient( - const arma::Mat& input, - const arma::Mat& error, - arma::Mat& gradient) +template +void NoisyLinearType::Gradient( + const MatType& input, const MatType& error, MatType& gradient) { // Locally stored to prevent multiplication twice. - arma::mat weightGrad = error * input.t(); + MatType weightGrad = error * input.t(); // Gradients for mu values. gradient.rows(0, weight.n_elem - 1) = arma::vectorise(weightGrad); @@ -170,18 +149,34 @@ void NoisyLinear::Gradient( = arma::sum(error, 1) % biasEpsilon; } -template +template +void NoisyLinearType::ComputeOutputDimensions() +{ + inSize = this->inputDimensions[0]; + for (size_t i = 1; i < this->inputDimensions.size(); ++i) + inSize *= this->inputDimensions[i]; + + this->outputDimensions = std::vector(this->inputDimensions.size(), + 1); + + // The NoisyLinear layer flattens its output. + this->outputDimensions[0] = outSize; +} + +template template -void NoisyLinear::serialize( +void NoisyLinearType::serialize( Archive& ar, const uint32_t /* version */) { - ar(CEREAL_NVP(inSize)); - ar(CEREAL_NVP(outSize)); + ar(cereal::base_class>(this)); + + ar(CEREAL_NVP(outSize)); + ar(CEREAL_NVP(inSize)); - // This is inefficient, but we have to allocate this memory so that - // WeightSetVisitor gets the right size. if (cereal::is_loading()) - weights.set_size((outSize * inSize + outSize) * 2, 1); + { + ResetNoise(); + } } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/not_adapted/README.md b/src/mlpack/methods/ann/layer/not_adapted/README.md new file mode 100644 index 0000000000..ffe867395b --- /dev/null +++ b/src/mlpack/methods/ann/layer/not_adapted/README.md @@ -0,0 +1,9 @@ +Layers in this directory were written with the old boost::visitor interface. In +[#2777](https://github.com/mlpack/mlpack/pull/2777), we adapted each layer to +use inheritance instead. However, time did not permit the adaptation of all +layers, and so remaining layers that have not yet been adapted are in this +directory. + +The intention is that we will work our way through layers in this directory, +updating them to the new interface and re-enabling tests for them in separate, +follow-up PRs. If you'd like to help out, you are more than welcome to! diff --git a/src/mlpack/methods/ann/layer/adaptive_max_pooling.hpp b/src/mlpack/methods/ann/layer/not_adapted/adaptive_max_pooling.hpp similarity index 62% rename from src/mlpack/methods/ann/layer/adaptive_max_pooling.hpp rename to src/mlpack/methods/ann/layer/not_adapted/adaptive_max_pooling.hpp index fd080c42dd..ad9219759b 100644 --- a/src/mlpack/methods/ann/layer/adaptive_max_pooling.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/adaptive_max_pooling.hpp @@ -13,7 +13,9 @@ #define MLPACK_METHODS_ANN_LAYER_ADAPTIVE_MAX_POOLING_HPP #include -#include "layer_types.hpp" + +#include "layer.hpp" +#include "max_pooling.hpp" namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -21,20 +23,17 @@ namespace ann /** Artificial Neural Network. */ { /** * Implementation of the AdaptiveMaxPooling layer. * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam InputType The type of the layer's inputs. The layer automatically + * cast inputs to this type (Default: arma::mat). + * @tparam OutputType The type of the layer's Outputs. The layer automatically + * cast inputs to this type (Default: arma::mat). */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class AdaptiveMaxPooling +template +class AdaptiveMaxPoolingType : public Layer { public: //! Create the AdaptiveMaxPooling object. - AdaptiveMaxPooling(); + AdaptiveMaxPoolingType(); /** * Create the AdaptiveMaxPooling object. @@ -42,15 +41,16 @@ class AdaptiveMaxPooling * @param outputWidth Width of the output. * @param outputHeight Height of the output. */ - AdaptiveMaxPooling(const size_t outputWidth, - const size_t outputHeight); + AdaptiveMaxPoolingType(const size_t outputWidth, + const size_t outputHeight); /** * Create the AdaptiveMaxPooling object. * - * @param outputShape A two-value tuple indicating width and height of the output. + * @param outputShape A two-value tuple indicating width and height of the + * output. */ - AdaptiveMaxPooling(const std::tuple& outputShape); + AdaptiveMaxPoolingType(const std::tuple& outputShape); /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -59,8 +59,7 @@ class AdaptiveMaxPooling * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template - void Forward(const arma::Mat& input, arma::Mat& output); + void Forward(const InputType& input, OutputType& output); /** * Ordinary feed backward pass of a neural network, using 3rd-order tensors as @@ -71,48 +70,30 @@ class AdaptiveMaxPooling * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const arma::Mat& input, - const arma::Mat& gy, - arma::Mat& g); - - //! Get the output parameter. - const OutputDataType& OutputParameter() const - { return poolingLayer.OutputParameter(); } - - //! Modify the output parameter. - OutputDataType& OutputParameter() { return poolingLayer.OutputParameter(); } - - //! Get the delta. - const OutputDataType& Delta() const { return poolingLayer.Delta(); } - //! Modify the delta. - OutputDataType& Delta() { return poolingLayer.Delta(); } - - //! Get the input width. - size_t InputWidth() const { return poolingLayer.InputWidth(); } - //! Modify the input width. - size_t& InputWidth() { return poolingLayer.InputWidth(); } - - //! Get the input height. - size_t InputHeight() const { return poolingLayer.InputHeight(); } - //! Modify the input height. - size_t& InputHeight() { return poolingLayer.InputHeight(); } + void Backward(const InputType& input, + const OutputType& gy, + OutputType& g); //! Get the output width. - size_t OutputWidth() const { return outputWidth; } + size_t const& OutputWidth() const { return outputWidth; } //! Modify the output width. size_t& OutputWidth() { return outputWidth; } //! Get the output height. - size_t OutputHeight() const { return outputHeight; } + size_t const& OutputHeight() const { return outputHeight; } //! Modify the output height. size_t& OutputHeight() { return outputHeight; } - //! Get the input size. - size_t InputSize() const { return poolingLayer.InputSize(); } + //! Get the number of trainable weights. + size_t WeightSize() const { return 0; } - //! Get the output size. - size_t OutputSize() const { return poolingLayer.OutputSize(); } + const std::vector& OutputDimensions() const + { + std::vector result(this->inputDimensions.size(), 1); + result[0] = outputWidth; + result[1] = outputHeight; + return result; + } //! Get the size of the weights. size_t WeightSize() const { return 0; } @@ -127,7 +108,7 @@ class AdaptiveMaxPooling /** * Initialize Kernel Size and Stride for Adaptive Pooling. */ - void IntializeAdaptivePadding() + void InitializeAdaptivePadding() { poolingLayer.StrideWidth() = std::floor(poolingLayer.InputWidth() / outputWidth); @@ -150,7 +131,7 @@ class AdaptiveMaxPooling } //! Locally stored MaxPooling Object. - MaxPooling poolingLayer; + MaxPoolingType poolingLayer; //! Locally-stored output width. size_t outputWidth; @@ -160,7 +141,12 @@ class AdaptiveMaxPooling //! Locally-stored reset parameter used to initialize the layer once. bool reset; -}; // class AdaptiveMaxPooling +}; // class AdaptiveMaxPoolingType + +// Convenience typedefs. + +// Standard Adaptive max pooling layer. +typedef AdaptiveMaxPoolingType AdaptiveMaxPooling; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/adaptive_max_pooling_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/adaptive_max_pooling_impl.hpp similarity index 55% rename from src/mlpack/methods/ann/layer/adaptive_max_pooling_impl.hpp rename to src/mlpack/methods/ann/layer/not_adapted/adaptive_max_pooling_impl.hpp index 25cf195b6c..f01f62bdc9 100644 --- a/src/mlpack/methods/ann/layer/adaptive_max_pooling_impl.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/adaptive_max_pooling_impl.hpp @@ -18,61 +18,61 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -AdaptiveMaxPooling::AdaptiveMaxPooling() +template +AdaptiveMaxPoolingType::AdaptiveMaxPoolingType() { // Nothing to do here. } -template -AdaptiveMaxPooling::AdaptiveMaxPooling( +template +AdaptiveMaxPoolingType::AdaptiveMaxPoolingType( const size_t outputWidth, const size_t outputHeight) : - AdaptiveMaxPooling(std::tuple(outputWidth, outputHeight)) + AdaptiveMaxPoolingType(std::tuple(outputWidth, outputHeight)) { // Nothing to do here. } -template -AdaptiveMaxPooling::AdaptiveMaxPooling( +template +AdaptiveMaxPoolingType::AdaptiveMaxPoolingType( const std::tuple& outputShape): outputWidth(std::get<0>(outputShape)), outputHeight(std::get<1>(outputShape)), reset(false) { - poolingLayer = ann::MaxPooling<>(0, 0); + poolingLayer = ann::MaxPoolingType(0, 0); } -template -template -void AdaptiveMaxPooling::Forward( - const arma::Mat& input, arma::Mat& output) +template +void AdaptiveMaxPoolingType::Forward( + const InputType& input, OutputType& output) { if (!reset) { - IntializeAdaptivePadding(); + InitializeAdaptivePadding(); reset = true; } poolingLayer.Forward(input, output); } -template -template -void AdaptiveMaxPooling::Backward( - const arma::Mat& input, - const arma::Mat& gy, - arma::Mat& g) +template +void AdaptiveMaxPoolingType::Backward( + const InputType& input, + const OutputType& gy, + OutputType& g) { poolingLayer.Backward(input, gy, g); } -template +template template -void AdaptiveMaxPooling::serialize( +void AdaptiveMaxPoolingType::serialize( Archive& ar, const uint32_t /* version */) { + ar(cereal::base_class>(this)); + ar(CEREAL_NVP(outputWidth)); ar(CEREAL_NVP(outputHeight)); ar(CEREAL_NVP(reset)); diff --git a/src/mlpack/methods/ann/layer/adaptive_mean_pooling.hpp b/src/mlpack/methods/ann/layer/not_adapted/adaptive_mean_pooling.hpp similarity index 62% rename from src/mlpack/methods/ann/layer/adaptive_mean_pooling.hpp rename to src/mlpack/methods/ann/layer/not_adapted/adaptive_mean_pooling.hpp index 46a434ab54..93f0095572 100644 --- a/src/mlpack/methods/ann/layer/adaptive_mean_pooling.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/adaptive_mean_pooling.hpp @@ -1,3 +1,4 @@ +// Maybe /** * @file methods/ann/layer/adaptive_mean_pooling.hpp * @author Kartik Dutt @@ -14,7 +15,9 @@ #define MLPACK_METHODS_ANN_LAYER_ADAPTIVE_MEAN_POOLING_HPP #include -#include "layer_types.hpp" + +#include "layer.hpp" +#include "mean_pooling.hpp" namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -22,20 +25,17 @@ namespace ann /** Artificial Neural Network. */ { /** * Implementation of the AdaptiveMeanPooling. * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam InputType The type of the layer's inputs. The layer automatically + * cast inputs to this type (Default: arma::mat). + * @tparam OutputType The type of the layer's Outputs. The layer automatically + * cast inputs to this type (Default: arma::mat). */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class AdaptiveMeanPooling +template +class AdaptiveMeanPoolingType : public Layer { public: //! Create the AdaptiveMeanPooling object. - AdaptiveMeanPooling(); + AdaptiveMeanPoolingType(); /** * Create the AdaptiveMeanPooling object. @@ -43,15 +43,16 @@ class AdaptiveMeanPooling * @param outputWidth Width of the output. * @param outputHeight Height of the output. */ - AdaptiveMeanPooling(const size_t outputWidth, - const size_t outputHeight); + AdaptiveMeanPoolingType(const size_t outputWidth, + const size_t outputHeight); /** * Create the AdaptiveMeanPooling object. * - * @param outputShape A two-value tuple indicating width and height of the output. + * @param outputShape A two-value tuple indicating width and height of the + * output. */ - AdaptiveMeanPooling(const std::tuple& outputShape); + AdaptiveMeanPoolingType(const std::tuple& outputShape); /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -60,8 +61,7 @@ class AdaptiveMeanPooling * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template - void Forward(const arma::Mat& input, arma::Mat& output); + void Forward(const InputType& input, OutputType& output); /** * Ordinary feed backward pass of a neural network, using 3rd-order tensors as @@ -72,48 +72,30 @@ class AdaptiveMeanPooling * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const arma::Mat& input, - const arma::Mat& gy, - arma::Mat& g); - - //! Get the output parameter. - const OutputDataType& OutputParameter() const - { return poolingLayer.OutputParameter(); } - - //! Modify the output parameter. - OutputDataType& OutputParameter() { return poolingLayer.OutputParameter(); } - - //! Get the delta. - const OutputDataType& Delta() const { return poolingLayer.Delta(); } - //! Modify the delta. - OutputDataType& Delta() { return poolingLayer.Delta(); } - - //! Get the input width. - size_t InputWidth() const { return poolingLayer.InputWidth(); } - //! Modify the input width. - size_t& InputWidth() { return poolingLayer.InputWidth(); } - - //! Get the input height. - size_t InputHeight() const { return poolingLayer.InputHeight(); } - //! Modify the input height. - size_t& InputHeight() { return poolingLayer.InputHeight(); } + void Backward(const InputType& input, + const OutputType& gy, + OutputType& g); //! Get the output width. - size_t OutputWidth() const { return outputWidth; } + size_t const& OutputWidth() const { return outputWidth; } //! Modify the output width. size_t& OutputWidth() { return outputWidth; } //! Get the output height. - size_t OutputHeight() const { return outputHeight; } + size_t const& OutputHeight() const { return outputHeight; } //! Modify the output height. size_t& OutputHeight() { return outputHeight; } - //! Get the input size. - size_t InputSize() const { return poolingLayer.InputSize(); } + //! Get the number of trainable weights. + size_t WeightSize() const { return 0; } - //! Get the output size. - size_t OutputSize() const { return poolingLayer.OutputSize(); } + const std::vector& OutputDimensions() const + { + std::vector result(this->inputDimensions); + result[0] = outputWidth; + result[1] = outputHeight; + return result; + } //! Get the size of the weights. size_t WeightSize() const { return 0; } @@ -128,7 +110,7 @@ class AdaptiveMeanPooling /** * Initialize Kernel Size and Stride for Adaptive Pooling. */ - void IntializeAdaptivePadding() + void InitializeAdaptivePadding() { poolingLayer.StrideWidth() = std::floor(poolingLayer.InputWidth() / outputWidth); @@ -151,7 +133,7 @@ class AdaptiveMeanPooling } //! Locally stored MeanPooling Object. - MeanPooling poolingLayer; + MeanPoolingType poolingLayer; //! Locally-stored output width. size_t outputWidth; @@ -161,7 +143,12 @@ class AdaptiveMeanPooling //! Locally-stored reset parameter used to initialize the layer once. bool reset; -}; // class AdaptiveMeanPooling +}; // class AdaptiveMeanPoolingType + +// Convenience typedefs. + +// Standard Adaptive mean pooling layer. +typedef AdaptiveMeanPoolingType AdaptiveMeanPooling; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/adaptive_mean_pooling_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/adaptive_mean_pooling_impl.hpp similarity index 55% rename from src/mlpack/methods/ann/layer/adaptive_mean_pooling_impl.hpp rename to src/mlpack/methods/ann/layer/not_adapted/adaptive_mean_pooling_impl.hpp index c930246a7f..34a5a5b27f 100644 --- a/src/mlpack/methods/ann/layer/adaptive_mean_pooling_impl.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/adaptive_mean_pooling_impl.hpp @@ -18,61 +18,61 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -AdaptiveMeanPooling::AdaptiveMeanPooling() +template +AdaptiveMeanPoolingType::AdaptiveMeanPoolingType() { // Nothing to do here. } -template -AdaptiveMeanPooling::AdaptiveMeanPooling( +template +AdaptiveMeanPoolingType::AdaptiveMeanPoolingType( const size_t outputWidth, const size_t outputHeight) : - AdaptiveMeanPooling(std::tuple(outputWidth, outputHeight)) + AdaptiveMeanPoolingType(std::tuple(outputWidth, outputHeight)) { // Nothing to do here. } -template -AdaptiveMeanPooling::AdaptiveMeanPooling( +template +AdaptiveMeanPoolingType::AdaptiveMeanPoolingType( const std::tuple& outputShape): outputWidth(std::get<0>(outputShape)), outputHeight(std::get<1>(outputShape)), reset(false) { - poolingLayer = ann::MeanPooling<>(0, 0); + poolingLayer = ann::MeanPoolingType(0, 0); } -template -template -void AdaptiveMeanPooling::Forward( - const arma::Mat& input, arma::Mat& output) +template +void AdaptiveMeanPoolingType::Forward( + const InputType& input, OutputType& output) { if (!reset) { - IntializeAdaptivePadding(); + InitializeAdaptivePadding(); reset = true; } poolingLayer.Forward(input, output); } -template -template -void AdaptiveMeanPooling::Backward( - const arma::Mat& input, - const arma::Mat& gy, - arma::Mat& g) +template +void AdaptiveMeanPoolingType::Backward( + const InputType& input, + const OutputType& gy, + OutputType& g) { poolingLayer.Backward(input, gy, g); } -template +template template -void AdaptiveMeanPooling::serialize( +void AdaptiveMeanPoolingType::serialize( Archive& ar, const uint32_t /* version */) { + ar(cereal::base_class>(this)); + ar(CEREAL_NVP(outputWidth)); ar(CEREAL_NVP(outputHeight)); ar(CEREAL_NVP(reset)); diff --git a/src/mlpack/methods/ann/layer/add_merge.hpp b/src/mlpack/methods/ann/layer/not_adapted/add_merge.hpp similarity index 52% rename from src/mlpack/methods/ann/layer/add_merge.hpp rename to src/mlpack/methods/ann/layer/not_adapted/add_merge.hpp index 1c0e02cebf..bea6da12b5 100644 --- a/src/mlpack/methods/ann/layer/add_merge.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/add_merge.hpp @@ -1,3 +1,4 @@ +// Temporarily drop. /** * @file methods/ann/layer/add_merge.hpp * @author Marcus Edel @@ -15,10 +16,6 @@ #include -#include "../visitor/delete_visitor.hpp" -#include "../visitor/delta_visitor.hpp" -#include "../visitor/output_parameter_visitor.hpp" - #include "layer_types.hpp" namespace mlpack { @@ -28,18 +25,17 @@ namespace ann /** Artificial Neural Network. */ { * Implementation of the AddMerge module class. The AddMerge class accumulates * the output of various modules. * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * @tparam InputType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * @tparam OutputType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). * @tparam CustomLayers Additional custom layers that can be added. */ template< - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat, - typename... CustomLayers + typename InputType = arma::mat, + typename OutputType = arma::mat > -class AddMerge +class AddMerge : public MultiLayer { public: /** @@ -48,7 +44,7 @@ class AddMerge * @param model Expose all the network modules. * @param run Call the Forward/Backward method before the output is merged. */ - AddMerge(const bool model = false, const bool run = true); + AddMerge(const bool run = true); /** * Create the AddMerge object using the specified parameters. @@ -57,7 +53,7 @@ class AddMerge * @param run Call the Forward/Backward method before the output is merged. * @param ownsLayers Delete the layers when this is deallocated. */ - AddMerge(const bool model, const bool run, const bool ownsLayers); + AddMerge(const bool run, const bool ownsLayers); //! Destructor to release allocated memory. ~AddMerge(); @@ -69,8 +65,7 @@ class AddMerge * @param * (input) Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template - void Forward(const InputType& /* input */, OutputType& output); + void Forward(const InputType& input, OutputType& output); /** * Ordinary feed backward pass of a neural network, calculating the function @@ -81,10 +76,9 @@ class AddMerge * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g); + void Backward(const InputType& /* input */, + const OutputType& gy, + OutputType& g); /** * This is the overload of Backward() that runs only a specific layer with @@ -95,10 +89,9 @@ class AddMerge * @param g The calculated gradient. * @param index The index of the layer to run. */ - template - void Backward(const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g, + void Backward(const InputType& /* input */, + const OutputType& gy, + OutputType& g, const size_t index); /* @@ -108,10 +101,9 @@ class AddMerge * @param error The calculated error. * @param gradient The calculated gradient. */ - template - void Gradient(const arma::Mat& input, - const arma::Mat& error, - arma::Mat& gradient); + void Gradient(const InputType& input, + const OutputType& error, + OutputType& gradient); /* * This is the overload of Gradient() that runs a specific layer with the @@ -122,63 +114,24 @@ class AddMerge * @param gradient The calculated gradient. * @param The index of the layer to run. */ - template - void Gradient(const arma::Mat& input, - const arma::Mat& error, - arma::Mat& gradient, + void Gradient(const InputType& input, + const OutputType& error, + OutputType& gradient, const size_t index); - /* - * Add a new module to the model. - * - * @param args The layer parameter. - */ - template - void Add(Args... args) { network.push_back(new LayerType(args...)); } - - /* - * Add a new module to the model. - * - * @param layer The Layer to be added to the model. - */ - void Add(LayerTypes layer) { network.push_back(layer); } - - //! Get the input parameter. - InputDataType const& InputParameter() const { return inputParameter; } - //! Modify the input parameter. - InputDataType& InputParameter() { return inputParameter; } - - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } - - //! Return the model modules. - std::vector >& Model() - { - if (model) - { - return network; - } - - return empty; - } - - //! Get the parameters. - OutputDataType const& Parameters() const { return weights; } - //! Modify the parameters. - OutputDataType& Parameters() { return weights; } - //! Get the value of run parameter. bool Run() const { return run; } //! Modify the value of run parameter. bool& Run() { return run; } + const std::vector& OutputDimensions() const + { + // Propagate input size to child layers. + for (size_t i = 0; i < this->network.size(); ++i) + this->network[i]->InputDimensions() = this->inputDimensions; + return this->network.back()->OutputDimensions(); + } + /** * Serialize the layer. */ @@ -186,9 +139,6 @@ class AddMerge void serialize(Archive& ar, const uint32_t /* version */); private: - //! Parameter which indicates if the modules should be exposed. - bool model; - //! Parameter which indicates if the Forward/Backward method should be called //! before merging the output. bool run; @@ -196,36 +146,6 @@ class AddMerge //! We need this to know whether we should delete the internally-held layers //! in the destructor. bool ownsLayers; - - //! Locally-stored network modules. - std::vector > network; - - //! Locally-stored empty list of modules. - std::vector > empty; - - //! Locally-stored delete visitor module object. - DeleteVisitor deleteVisitor; - - //! Locally-stored output parameter visitor module object. - OutputParameterVisitor outputParameterVisitor; - - //! Locally-stored delta visitor module object. - DeltaVisitor deltaVisitor; - - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored gradient object. - OutputDataType gradient; - - //! Locally-stored input parameter object. - InputDataType inputParameter; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; - - //! Locally-stored weight object. - OutputDataType weights; }; // class AddMerge } // namespace ann diff --git a/src/mlpack/methods/ann/layer/not_adapted/add_merge_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/add_merge_impl.hpp new file mode 100644 index 0000000000..f1c71ae3ad --- /dev/null +++ b/src/mlpack/methods/ann/layer/not_adapted/add_merge_impl.hpp @@ -0,0 +1,145 @@ +/** + * @file methods/ann/layer/add_merge_impl.hpp + * @author Marcus Edel + * + * Definition of the AddMerge module which accumulates the output of the given + * modules. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_LAYER_ADD_MERGE_IMPL_HPP +#define MLPACK_METHODS_ANN_LAYER_ADD_MERGE_IMPL_HPP + +// In case it hasn't yet been included. +#include "add_merge.hpp" + +namespace mlpack { +namespace ann /** Artificial Neural Network. */ { + +template +AddMerge::AddMerge( + const bool run) : + run(run), ownsLayers(true) +{ + // Nothing to do here. +} + +template +AddMerge::AddMerge( + const bool run, const bool ownsLayers) : + run(run), ownsLayers(ownsLayers) +{ + // Nothing to do here. +} + +template +AddMerge::~AddMerge() +{ + +} + +template +void AddMerge::Forward( + const InputType& input, OutputType& output) +{ + this->InitializeForwardPassMemory(); + + if (run) + { + for (size_t i = 0; i < this->network.size(); ++i) + { + this->network[i]->Forward(input, this->layerOutputs[i]); + } + } + + output = this->layerOutputs.front(); + for (size_t i = 1; i < this->network.size(); ++i) + { + output += this->layerOutputs[i]; + } +} + +template +void AddMerge::Backward( + const InputType& /* input */, + const OutputType& gy, + OutputType& g) +{ + this->InitializeBackwardPassMemory(); + + if (run) + { + for (size_t i = 0; i < this->network.size(); ++i) + { + this->network[i]->Backward(this->layerOutputs[i], gy, + this->layerDeltas[i]); + } + + g = this->layerDeltas[0]; + for (size_t i = 1; i < this->network.size(); ++i) + { + g += this->layerDeltas[i]; + } + } + else + { + g = gy; + } +} + +template +void AddMerge::Backward( + const InputType& /* input */, + const OutputType& gy, + OutputType& g, + const size_t index) +{ + this->network[index]->Backward(this->layerOutputs[index], gy, g); +} + +template +void AddMerge::Gradient( + const InputType& input, + const OutputType& error, + OutputType& gradient) +{ + if (run) + { + size_t start = 0; + for (size_t i = 0; i < this->network.size(); ++i) + { + this->network[i]->Gradient(input, error, OutputType(gradient.colptr(start), + 1, this->network[i]->WeightSize(), false, true)); + start += this->network[i]->WeightSize(); + } + } +} + +template +void AddMerge::Gradient( + const InputType& input, + const OutputType& error, + OutputType& gradient, + const size_t index) +{ + this->network[index]->Gradient(input, error, gradient); +} + +template +template +void AddMerge::serialize( + Archive& ar, const uint32_t /* version */) +{ + ar(cereal::base_class>(this)); + + ar(CEREAL_NVP(run)); + ar(CEREAL_NVP(ownsLayers)); +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/layer/atrous_convolution.hpp b/src/mlpack/methods/ann/layer/not_adapted/atrous_convolution.hpp similarity index 82% rename from src/mlpack/methods/ann/layer/atrous_convolution.hpp rename to src/mlpack/methods/ann/layer/not_adapted/atrous_convolution.hpp index d6086de86f..cc0bbcea9d 100644 --- a/src/mlpack/methods/ann/layer/atrous_convolution.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/atrous_convolution.hpp @@ -1,3 +1,4 @@ +// Temporarily drop. /** * @file methods/ann/layer/atrous_convolution.hpp * @author Aarush Gupta @@ -46,10 +47,10 @@ template < typename ForwardConvolutionRule = NaiveConvolution, typename BackwardConvolutionRule = NaiveConvolution, typename GradientConvolutionRule = NaiveConvolution, - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat + typename InputType = arma::mat, + typename OutputType = arma::mat > -class AtrousConvolution +class AtrousConvolution : public Layer { public: //! Create the AtrousConvolution object. @@ -139,8 +140,7 @@ class AtrousConvolution * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template - void Forward(const arma::Mat& input, arma::Mat& output); + void Forward(const InputType& input, OutputType& output); /** * Ordinary feed backward pass of a neural network, calculating the function @@ -151,10 +151,9 @@ class AtrousConvolution * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g); + void Backward(const InputType& /* input */, + const OutputType& gy, + OutputType& g); /* * Calculate the gradient using the output delta and the input activation. @@ -163,101 +162,90 @@ class AtrousConvolution * @param error The calculated error. * @param gradient The calculated gradient. */ - template - void Gradient(const arma::Mat& /* input */, - const arma::Mat& error, - arma::Mat& gradient); + void Gradient(const InputType& /* input */, + const OutputType& error, + OutputType& gradient); //! Get the parameters. - OutputDataType const& Parameters() const { return weights; } + OutputType const& Parameters() const { return weights; } //! Modify the parameters. - OutputDataType& Parameters() { return weights; } + OutputType& Parameters() { return weights; } //! Get the weight of the layer. - arma::cube const& Weight() const { return weight; } + const arma::Cube& Weight() const + { + return weight; + } //! Modify the weight of the layer. - arma::cube& Weight() { return weight; } + arma::Cube& Weight() { return weight; } + + const std::vector& OutputDimensions() const + { + std::vector result(inputDimensions.size(), 0); + result[0] = outputWidth; + result[1] = outputHeight; + return result; + } //! Get the bias of the layer. - arma::mat const& Bias() const { return bias; } + const OutputType& Bias() const { return bias; } //! Modify the bias of the layer. - arma::mat& Bias() { return bias; } - - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } - - //! Get the gradient. - OutputDataType const& Gradient() const { return gradient; } - //! Modify the gradient. - OutputDataType& Gradient() { return gradient; } + OutputType& Bias() { return bias; } //! Get the input width. - size_t InputWidth() const { return inputWidth; } + const size_t& InputWidth() const { return inputWidth; } //! Modify input the width. size_t& InputWidth() { return inputWidth; } //! Get the input height. - size_t InputHeight() const { return inputHeight; } + const size_t& InputHeight() const { return inputHeight; } //! Modify the input height. size_t& InputHeight() { return inputHeight; } //! Get the output width. - size_t OutputWidth() const { return outputWidth; } + const size_t& OutputWidth() const { return outputWidth; } //! Modify the output width. size_t& OutputWidth() { return outputWidth; } //! Get the output height. - size_t OutputHeight() const { return outputHeight; } + const size_t& OutputHeight() const { return outputHeight; } //! Modify the output height. size_t& OutputHeight() { return outputHeight; } - //! Get the input size. - size_t InputSize() const { return inSize; } - - //! Get the output size. - size_t OutputSize() const { return outSize; } - //! Get the kernel width. - size_t KernelWidth() const { return kernelWidth; } + const size_t& KernelWidth() const { return kernelWidth; } //! Modify the kernel width. size_t& KernelWidth() { return kernelWidth; } //! Get the kernel height. - size_t KernelHeight() const { return kernelHeight; } + const size_t& KernelHeight() const { return kernelHeight; } //! Modify the kernel height. size_t& KernelHeight() { return kernelHeight; } //! Get the stride width. - size_t StrideWidth() const { return strideWidth; } + const size_t& StrideWidth() const { return strideWidth; } //! Modify the stride width. size_t& StrideWidth() { return strideWidth; } //! Get the stride height. - size_t StrideHeight() const { return strideHeight; } + const size_t& StrideHeight() const { return strideHeight; } //! Modify the stride height. size_t& StrideHeight() { return strideHeight; } //! Get the dilation rate on the X axis. - size_t DilationWidth() const { return dilationWidth; } + const size_t& DilationWidth() const { return dilationWidth; } //! Modify the dilation rate on the X axis. size_t& DilationWidth() { return dilationWidth; } //! Get the dilation rate on the Y axis. - size_t DilationHeight() const { return dilationHeight; } + const size_t& DilationHeight() const { return dilationHeight; } //! Modify the dilation rate on the Y axis. size_t& DilationHeight() { return dilationHeight; } //! Get the internal Padding layer. - ann::Padding<> const& Padding() const { return padding; } + PaddingType const& Padding() const { return padding; } //! Modify the internal Padding layer. - ann::Padding<>& Padding() { return padding; } + PaddingType& Padding() { return padding; } //! Get size of the weight matrix. size_t WeightSize() const @@ -358,13 +346,13 @@ class AtrousConvolution size_t strideHeight; //! Locally-stored weight object. - OutputDataType weights; + OutputType weights; //! Locally-stored weight object. - arma::cube weight; + arma::Cube weight; //! Locally-stored bias term object. - arma::mat bias; + OutputType bias; //! Locally-stored input width. size_t inputWidth; @@ -385,28 +373,19 @@ class AtrousConvolution size_t dilationHeight; //! Locally-stored transformed output parameter. - arma::cube outputTemp; + arma::Cube outputTemp; //! Locally-stored transformed padded input parameter. - arma::cube inputPaddedTemp; + arma::Cube inputPaddedTemp; //! Locally-stored transformed error parameter. - arma::cube gTemp; + arma::Cube gTemp; //! Locally-stored transformed gradient parameter. - arma::cube gradientTemp; + arma::Cube gradientTemp; //! Locally-stored padding layer. - ann::Padding<> padding; - - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored gradient object. - OutputDataType gradient; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; + PaddingType padding; }; // class AtrousConvolution } // namespace ann diff --git a/src/mlpack/methods/ann/layer/atrous_convolution_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/atrous_convolution_impl.hpp similarity index 81% rename from src/mlpack/methods/ann/layer/atrous_convolution_impl.hpp rename to src/mlpack/methods/ann/layer/not_adapted/atrous_convolution_impl.hpp index 2377ddee00..d18fd1f855 100644 --- a/src/mlpack/methods/ann/layer/atrous_convolution_impl.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/atrous_convolution_impl.hpp @@ -23,15 +23,15 @@ template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename InputDataType, - typename OutputDataType + typename InputType, + typename OutputType > AtrousConvolution< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - InputDataType, - OutputDataType + InputType, + OutputType >::AtrousConvolution() { // Nothing to do here. @@ -41,15 +41,15 @@ template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename InputDataType, - typename OutputDataType + typename InputType, + typename OutputType > AtrousConvolution< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - InputDataType, - OutputDataType + InputType, + OutputType >::AtrousConvolution( const size_t inSize, const size_t outSize, @@ -86,15 +86,15 @@ template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename InputDataType, - typename OutputDataType + typename InputType, + typename OutputType > AtrousConvolution< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - InputDataType, - OutputDataType + InputType, + OutputType >::AtrousConvolution( const size_t inSize, const size_t outSize, @@ -143,49 +143,49 @@ AtrousConvolution< InitializeSamePadding(padWLeft, padWRight, padHTop, padHBottom); } - padding = ann::Padding<>(padWLeft, padWRight, padHTop, padHBottom); + padding = PaddingType(padWLeft, padWRight, padHTop, + padHBottom); } template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename InputDataType, - typename OutputDataType + typename InputType, + typename OutputType > void AtrousConvolution< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - InputDataType, - OutputDataType ->::Reset() + InputType, + OutputType +>::ResetWeights(typename OutputType::elem_type* weightsPtr) { - weight = arma::cube(weights.memptr(), kernelWidth, kernelHeight, - outSize * inSize, false, false); - bias = arma::mat(weights.memptr() + weight.n_elem, - outSize, 1, false, false); + weight = arma::Cube(weightsPtr, kernelWidth, + kernelHeight, outSize * inSize, false, true); + bias = OutputType(weightsPtr + weight.n_elem, outSize, 1, false, true); } template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename InputDataType, - typename OutputDataType + typename InputType, + typename OutputType > -template void AtrousConvolution< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - InputDataType, - OutputDataType ->::Forward(const arma::Mat& input, arma::Mat& output) + InputType, + OutputType +>::Forward(const InputType& input, OutputType& output) { batchSize = input.n_cols; - arma::cube inputTemp(const_cast&>(input).memptr(), - inputWidth, inputHeight, inSize * batchSize, false, false); + arma::Cube inputTemp( + const_cast(input).memptr(), inputWidth, inputHeight, inSize * + batchSize, false, false); if (padding.PadWLeft() != 0 || padding.PadWRight() != 0 || padding.PadHTop() != 0 || padding.PadHBottom() != 0) @@ -207,8 +207,8 @@ void AtrousConvolution< padding.PadHTop(), padding.PadHBottom(), dilationHeight); output.set_size(wConv * hConv * outSize, batchSize); - outputTemp = arma::Cube(output.memptr(), wConv, hConv, - outSize * batchSize, false, false); + outputTemp = arma::Cube(output.memptr(), + wConv, hConv, outSize * batchSize, false, false); outputTemp.zeros(); for (size_t outMap = 0, outMapIdx = 0, batchCount = 0; outMap < @@ -222,7 +222,7 @@ void AtrousConvolution< for (size_t inMap = 0; inMap < inSize; inMap++, outMapIdx++) { - arma::Mat convOutput; + OutputType convOutput; if (padding.PadWLeft() != 0 || padding.PadWRight() != 0 || padding.PadHTop() != 0 || padding.PadHBottom() != 0) @@ -252,25 +252,24 @@ template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename InputDataType, - typename OutputDataType + typename InputType, + typename OutputType > -template void AtrousConvolution< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - InputDataType, - OutputDataType ->::Backward( - const arma::Mat& /* input */, const arma::Mat& gy, arma::Mat& g) + InputType, + OutputType +>::Backward(const InputType& /* input */, const OutputType& gy, OutputType& g) { - arma::cube mappedError(((arma::Mat&) gy).memptr(), outputWidth, - outputHeight, outSize * batchSize, false, false); + arma::Cube mappedError( + ((OutputType&) gy).memptr(), outputWidth, outputHeight, outSize * + batchSize, false, false); g.set_size(inputWidth * inputHeight * inSize, batchSize); - gTemp = arma::Cube(g.memptr(), inputWidth, inputHeight, - inSize * batchSize, false, false); + gTemp = arma::Cube(g.memptr(), inputWidth, + inputHeight, inSize * batchSize, false, false); gTemp.zeros(); for (size_t outMap = 0, outMapIdx = 0, batchCount = 0; outMap < @@ -284,7 +283,7 @@ void AtrousConvolution< for (size_t inMap = 0; inMap < inSize; inMap++, outMapIdx++) { - arma::Mat output, rotatedFilter; + OutputType output, rotatedFilter; Rotate180(weight.slice(outMapIdx), rotatedFilter); BackwardConvolutionRule::Convolution(mappedError.slice(outMap), @@ -311,29 +310,30 @@ template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename InputDataType, - typename OutputDataType + typename InputType, + typename OutputType > -template void AtrousConvolution< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - InputDataType, - OutputDataType + InputType, + OutputType >::Gradient( - const arma::Mat& input, - const arma::Mat& error, - arma::Mat& gradient) + const InputType& input, + const OutputType& error, + OutputType& gradient) { - arma::cube mappedError(((arma::Mat&) error).memptr(), outputWidth, - outputHeight, outSize * batchSize, false, false); - arma::cube inputTemp(const_cast&>(input).memptr(), - inputWidth, inputHeight, inSize * batchSize, false, false); + arma::Cube mappedError( + ((OutputType&) error).memptr(), outputWidth, outputHeight, outSize * + batchSize, false, false); + arma::Cube inputTemp( + const_cast(input).memptr(), inputWidth, inputHeight, + inSize * batchSize, false, false); gradient.set_size(weights.n_elem, 1); - gradientTemp = arma::Cube(gradient.memptr(), weight.n_rows, - weight.n_cols, weight.n_slices, false, false); + gradientTemp = arma::Cube(gradient.memptr(), + weight.n_rows, weight.n_cols, weight.n_slices, false, false); gradientTemp.zeros(); for (size_t outMap = 0, outMapIdx = 0, batchCount = 0; outMap < @@ -347,7 +347,7 @@ void AtrousConvolution< for (size_t inMap = 0; inMap < inSize; inMap++, outMapIdx++) { - arma::Mat inputSlice; + InputType inputSlice; if (padding.PadWLeft() != 0 || padding.PadWRight() != 0 || padding.PadHTop() != 0 || padding.PadHBottom() != 0) { @@ -358,9 +358,9 @@ void AtrousConvolution< inputSlice = inputTemp.slice(inMap + batchCount * inSize); } - arma::Mat deltaSlice = mappedError.slice(outMap); + OutputType deltaSlice = mappedError.slice(outMap); - arma::Mat output; + OutputType output; GradientConvolutionRule::Convolution(inputSlice, deltaSlice, output, strideWidth, strideHeight, 1, 1); @@ -404,18 +404,20 @@ template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename InputDataType, - typename OutputDataType + typename InputType, + typename OutputType > template void AtrousConvolution< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - InputDataType, - OutputDataType + InputType, + OutputType >::serialize(Archive& ar, const uint32_t /* version */) { + ar(cereal::base_class>(this)); + ar(CEREAL_NVP(inSize)); ar(CEREAL_NVP(outSize)); ar(CEREAL_NVP(batchSize)); @@ -430,27 +432,22 @@ void AtrousConvolution< ar(CEREAL_NVP(dilationWidth)); ar(CEREAL_NVP(dilationHeight)); ar(CEREAL_NVP(padding)); - - if (cereal::is_loading()) - { - weights.set_size((outSize * inSize * kernelWidth * kernelHeight) + outSize, - 1); - } + ar(CEREAL_NVP(weights)); } template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename InputDataType, - typename OutputDataType + typename InputType, + typename OutputType > void AtrousConvolution< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - InputDataType, - OutputDataType + InputType, + OutputType >::InitializeSamePadding(size_t& padWLeft, size_t& padWRight, size_t& padHTop, diff --git a/src/mlpack/methods/ann/layer/batch_norm.hpp b/src/mlpack/methods/ann/layer/not_adapted/batch_norm.hpp similarity index 69% rename from src/mlpack/methods/ann/layer/batch_norm.hpp rename to src/mlpack/methods/ann/layer/not_adapted/batch_norm.hpp index 52df427dba..634fbe2c29 100644 --- a/src/mlpack/methods/ann/layer/batch_norm.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/batch_norm.hpp @@ -50,10 +50,10 @@ namespace ann /** Artificial Neural Network. */ { * arma::sp_mat or arma::cube). */ template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat + typename InputType = arma::mat, + typename OutputType = arma::mat > -class BatchNorm +class BatchNorm : public Layer { public: //! Create the BatchNorm object. @@ -74,9 +74,9 @@ class BatchNorm const double momentum = 0.1); /** - * Reset the layer parameters + * Reset the layer parameters. */ - void Reset(); + void SetWeights(typename OutputType::elem_type* weightsPtr); /** * Forward pass of the Batch Normalization layer. Transforms the input data @@ -86,8 +86,7 @@ class BatchNorm * @param input Input data for the layer * @param output Resulting output activations. */ - template - void Forward(const arma::Mat& input, arma::Mat& output); + void Forward(const InputType& input, OutputType& output); /** * Backward pass through the layer. @@ -96,10 +95,9 @@ class BatchNorm * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const arma::Mat& input, - const arma::Mat& gy, - arma::Mat& g); + void Backward(const InputType& input, + const OutputType& gy, + OutputType& g); /** * Calculate the gradient using the output delta and the input activations. @@ -108,45 +106,24 @@ class BatchNorm * @param error The calculated error * @param gradient The calculated gradient. */ - template - void Gradient(const arma::Mat& input, - const arma::Mat& error, - arma::Mat& gradient); + void Gradient(const InputType& input, + const OutputType& error, + OutputType& gradient); //! Get the parameters. - OutputDataType const& Parameters() const { return weights; } + const OutputType& Parameters() const { return weights; } //! Modify the parameters. - OutputDataType& Parameters() { return weights; } - - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } - - //! Get the gradient. - OutputDataType const& Gradient() const { return gradient; } - //! Modify the gradient. - OutputDataType& Gradient() { return gradient; } - - //! Get the value of deterministic parameter. - bool Deterministic() const { return deterministic; } - //! Modify the value of deterministic parameter. - bool& Deterministic() { return deterministic; } + OutputType& Parameters() { return weights; } //! Get the mean over the training data. - OutputDataType const& TrainingMean() const { return runningMean; } + const OutputType& TrainingMean() const { return runningMean; } //! Modify the mean over the training data. - OutputDataType& TrainingMean() { return runningMean; } + OutputType& TrainingMean() { return runningMean; } //! Get the variance over the training data. - OutputDataType const& TrainingVariance() const { return runningVariance; } + const OutputType& TrainingVariance() const { return runningVariance; } //! Modify the variance over the training data. - OutputDataType& TrainingVariance() { return runningVariance; } + OutputType& TrainingVariance() { return runningVariance; } //! Get the number of input units / channels. size_t InputSize() const { return size; } @@ -187,25 +164,19 @@ class BatchNorm bool loading; //! Locally-stored scale parameter. - OutputDataType gamma; + OutputType gamma; //! Locally-stored shift parameter. - OutputDataType beta; + OutputType beta; //! Locally-stored mean object. - OutputDataType mean; + OutputType mean; //! Locally-stored variance object. - OutputDataType variance; + OutputType variance; //! Locally-stored parameters. - OutputDataType weights; - - /** - * If true then mean and variance over the training set will be considered - * instead of being calculated over the batch. - */ - bool deterministic; + OutputType weights; //! Locally-stored running mean/variance counter. size_t count; @@ -215,25 +186,16 @@ class BatchNorm double averageFactor; //! Locally-stored mean object. - OutputDataType runningMean; + OutputType runningMean; //! Locally-stored variance object. - OutputDataType runningVariance; - - //! Locally-stored gradient object. - OutputDataType gradient; - - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; + OutputType runningVariance; //! Locally-stored normalized input. - arma::cube normalized; + arma::Cube normalized; //! Locally-stored zero mean input. - arma::cube inputMean; + arma::Cube inputMean; }; // class BatchNorm } // namespace ann diff --git a/src/mlpack/methods/ann/layer/batch_norm_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/batch_norm_impl.hpp similarity index 65% rename from src/mlpack/methods/ann/layer/batch_norm_impl.hpp rename to src/mlpack/methods/ann/layer/not_adapted/batch_norm_impl.hpp index 1b6637928c..dadc719117 100644 --- a/src/mlpack/methods/ann/layer/batch_norm_impl.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/batch_norm_impl.hpp @@ -21,22 +21,21 @@ namespace mlpack { namespace ann { /** Artificial Neural Network. */ -template -BatchNorm::BatchNorm() : +template +BatchNorm::BatchNorm() : size(0), eps(1e-8), average(true), momentum(0.0), loading(false), - deterministic(false), count(0), averageFactor(0.0) { // Nothing to do here. } -template -BatchNorm::BatchNorm( +template +BatchNorm::BatchNorm( const size_t size, const double eps, const bool average, @@ -46,7 +45,6 @@ BatchNorm::BatchNorm( average(average), momentum(momentum), loading(false), - deterministic(false), count(0), averageFactor(0.0) { @@ -55,13 +53,14 @@ BatchNorm::BatchNorm( runningVariance.ones(size, 1); } -template -void BatchNorm::Reset() +template +void BatchNorm::SetWeights( + typename OutputType::elem_type* weightsPtr) { // Gamma acts as the scaling parameters for the normalized output. - gamma = arma::mat(weights.memptr(), size, 1, false, false); + gamma = OutputType(weightsPtr, size, 1, false, false); // Beta acts as the shifting parameters for the normalized output. - beta = arma::mat(weights.memptr() + gamma.n_elem, size, 1, false, false); + beta = OutputType(weightsPtr + gamma.n_elem, size, 1, false, false); if (!loading) { @@ -69,18 +68,16 @@ void BatchNorm::Reset() beta.fill(0.0); } - deterministic = false; loading = false; } -template -template -void BatchNorm::Forward( - const arma::Mat& input, - arma::Mat& output) +template +void BatchNorm::Forward( + const InputType& input, + OutputType& output) { - Log::Assert(input.n_rows % size == 0, "Input features must be divisible \ - by feature maps."); + Log::Assert(input.n_rows % size == 0, "Input features must be divisible " + "by feature maps."); const size_t batchSize = input.n_cols; const size_t inputSize = input.n_rows / size; @@ -89,7 +86,7 @@ void BatchNorm::Forward( output.set_size(arma::size(input)); // We will calculate minibatch norm on each channel / feature map. - if (!deterministic) + if (this->training) { // Check only during training, batch-size can be one during inference. if (batchSize == 1 && inputSize == 1) @@ -101,12 +98,14 @@ void BatchNorm::Forward( // Input corresponds to output from convolution layer. // Use a cube for simplicity. - arma::cube inputTemp(const_cast&>(input).memptr(), - inputSize, size, batchSize, false, false); + arma::Cube inputTemp( + const_cast(input).memptr(), inputSize, size, batchSize, + false, false); // Initialize output to same size and values for convenience. - arma::cube outputTemp(const_cast&>(output).memptr(), - inputSize, size, batchSize, false, false); + arma::Cube outputTemp( + const_cast(output).memptr(), inputSize, size, batchSize, + false, false); outputTemp = inputTemp; // Calculate mean and variance over all channels. @@ -152,8 +151,9 @@ void BatchNorm::Forward( { // Normalize the input and scale and shift the output. output = input; - arma::cube outputTemp(const_cast&>(output).memptr(), - input.n_rows / size, size, batchSize, false, false); + arma::Cube outputTemp( + const_cast(output).memptr(), input.n_rows / size, size, + batchSize, false, false); outputTemp.each_slice() -= arma::repmat(runningMean.t(), input.n_rows / size, 1); @@ -166,28 +166,29 @@ void BatchNorm::Forward( } } -template -template -void BatchNorm::Backward( - const arma::Mat& input, - const arma::Mat& gy, - arma::Mat& g) +template +void BatchNorm::Backward( + const InputType& input, + const OutputType& gy, + OutputType& g) { const arma::mat stdInv = 1.0 / arma::sqrt(variance + eps); g.set_size(arma::size(input)); - arma::cube gyTemp(const_cast&>(gy).memptr(), - input.n_rows / size, size, input.n_cols, false, false); - arma::cube gTemp(const_cast&>(g).memptr(), - input.n_rows / size, size, input.n_cols, false, false); + arma::Cube gyTemp( + const_cast(gy).memptr(), input.n_rows / size, size, + input.n_cols, false, false); + arma::Cube gTemp( + const_cast(g).memptr(), input.n_rows / size, size, + input.n_cols, false, false); // Step 1: dl / dxhat. - arma::cube norm = gyTemp.each_slice() % arma::repmat(gamma.t(), - input.n_rows / size, 1); + arma::Cube norm = + gyTemp.each_slice() % arma::repmat(gamma.t(), input.n_rows / size, 1); // Step 2: sum dl / dxhat * (x - mu) * -0.5 * stdInv^3. - arma::mat temp = arma::sum(norm % inputMean, 2); - arma::mat vars = temp % arma::repmat(arma::pow(stdInv, 3), + OutputType temp = arma::sum(norm % inputMean, 2); + OutputType vars = temp % arma::repmat(arma::pow(stdInv, 3), input.n_rows / size, 1) * -0.5; // Step 3: dl / dxhat * 1 / stdInv + variance * 2 * (x - mu) / m + @@ -198,25 +199,25 @@ void BatchNorm::Backward( // Step 4: sum (dl / dxhat * -1 / stdInv) + variance * // (sum -2 * (x - mu)) / m. - arma::mat normTemp = arma::sum(norm.each_slice() % + OutputType normTemp = arma::sum(norm.each_slice() % arma::repmat(-stdInv, input.n_rows / size, 1) , 2) / input.n_cols; gTemp.each_slice() += normTemp; } -template -template -void BatchNorm::Gradient( - const arma::Mat& /* input */, - const arma::Mat& error, - arma::Mat& gradient) +template +void BatchNorm::Gradient( + const InputType& /* input */, + const OutputType& error, + OutputType& gradient) { gradient.set_size(size + size, 1); - arma::cube errorTemp(const_cast&>(error).memptr(), - error.n_rows / size, size, error.n_cols, false, false); + arma::Cube errorTemp( + const_cast(error).memptr(), error.n_rows / size, size, + error.n_cols, false, false); // Step 5: dl / dy * xhat. - arma::mat temp = arma::sum(arma::sum(normalized % errorTemp, 0), 2); + OutputType temp = arma::sum(arma::sum(normalized % errorTemp, 0), 2); gradient.submat(0, 0, gamma.n_elem - 1, 0) = temp.t(); // Step 6: dl / dy. @@ -224,22 +225,27 @@ void BatchNorm::Gradient( gradient.submat(gamma.n_elem, 0, gradient.n_elem - 1, 0) = temp.t(); } -template +template template -void BatchNorm::serialize( +void BatchNorm::serialize( Archive& ar, const uint32_t /* version */) { + ar(cereal::base_class>(this)); + ar(CEREAL_NVP(size)); - - if (cereal::is_loading()) - { - weights.set_size(size + size, 1); - loading = true; - } - ar(CEREAL_NVP(eps)); ar(CEREAL_NVP(gamma)); ar(CEREAL_NVP(beta)); + ar(CEREAL_NVP(weights)); + + if (Archive::is_loading::value) + { + // Gamma acts as the scaling parameters for the normalized output. + gamma = arma::mat(weights.memptr(), size, 1, false, false); + // Beta acts as the shifting parameters for the normalized output. + beta = arma::mat(weights.memptr() + gamma.n_elem, size, 1, false, false); + } + ar(CEREAL_NVP(count)); ar(CEREAL_NVP(averageFactor)); ar(CEREAL_NVP(momentum)); diff --git a/src/mlpack/methods/ann/layer/bicubic_interpolation.hpp b/src/mlpack/methods/ann/layer/not_adapted/bicubic_interpolation.hpp similarity index 100% rename from src/mlpack/methods/ann/layer/bicubic_interpolation.hpp rename to src/mlpack/methods/ann/layer/not_adapted/bicubic_interpolation.hpp diff --git a/src/mlpack/methods/ann/layer/bicubic_interpolation_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/bicubic_interpolation_impl.hpp similarity index 100% rename from src/mlpack/methods/ann/layer/bicubic_interpolation_impl.hpp rename to src/mlpack/methods/ann/layer/not_adapted/bicubic_interpolation_impl.hpp diff --git a/src/mlpack/methods/ann/layer/bilinear_interpolation.hpp b/src/mlpack/methods/ann/layer/not_adapted/bilinear_interpolation.hpp similarity index 55% rename from src/mlpack/methods/ann/layer/bilinear_interpolation.hpp rename to src/mlpack/methods/ann/layer/not_adapted/bilinear_interpolation.hpp index 8595bc4d57..d8dfb3a286 100644 --- a/src/mlpack/methods/ann/layer/bilinear_interpolation.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/bilinear_interpolation.hpp @@ -1,3 +1,4 @@ +// Temporarily drop. /** * @file methods/ann/layer/bilinear_interpolation.hpp * @author Kris Singh @@ -27,35 +28,30 @@ namespace ann /** Artificial Neural Network. */ { * different known points in the grid. This way, we represent any arbitrary * point, present within the grid, as a function of those four points. * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * @tparam InputType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * @tparam OutputType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). */ template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat + typename InputType = arma::mat, + typename OutputType = arma::mat > -class BilinearInterpolation +class BilinearInterpolationType : public Layer { public: - //! Create the Bilinear Interpolation object. - BilinearInterpolation(); + //! Create the BilinearInterpolationType object. + BilinearInterpolationType(); /** - * The constructor for the Bilinear Interpolation. + * The constructor for the Bilinear Interpolation. The input size will be set + * by the given input when the layer is used. * - * @param inRowSize Number of input rows. - * @param inColSize Number of input columns. * @param outRowSize Number of output rows. * @param outColSize Number of output columns. - * @param depth Number of input slices. */ - BilinearInterpolation(const size_t inRowSize, - const size_t inColSize, - const size_t outRowSize, - const size_t outColSize, - const size_t depth); + BilinearInterpolationType(const size_t outRowSize, + const size_t outColSize); /** * Forward pass through the layer. The layer interpolates @@ -64,8 +60,7 @@ class BilinearInterpolation * @param input The input matrix. * @param output The resulting interpolated output matrix. */ - template - void Forward(const arma::Mat& input, arma::Mat& output); + void Forward(const InputType& input, OutputType& output); /** * Ordinary feed backward pass of a neural network, calculating the function @@ -78,30 +73,22 @@ class BilinearInterpolation * @param gradient The computed backward gradient. * @param output The resulting down-sampled output. */ - template - void Backward(const arma::Mat& /*input*/, - const arma::Mat& gradient, - arma::Mat& output); + void Backward(const InputType& /*input*/, + const OutputType& gradient, + OutputType& output); - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } - - //! Get the row size of the input. - size_t const& InRowSize() const { return inRowSize; } - //! Modify the row size of the input. - size_t& InRowSize() { return inRowSize; } - - //! Get the column size of the input. - size_t const& InColSize() const { return inColSize; } - //! Modify the column size of the input. - size_t& InColSize() { return inColSize; } + const std::vector& OutputDimensions() const + { + std::vector result(this->inputDimensions.size(), 0); + result[0] = outRowSize; + result[1] = outColSize; + if (result.size() > 2) + { + for (size_t i = 0; i < result.size(); ++i) + result[i] = this->inputDimensions[i]; + } + return result; + } //! Get the row size of the output. size_t const& OutRowSize() const { return outRowSize; } @@ -113,17 +100,6 @@ class BilinearInterpolation //! Modify the column size of the output. size_t& OutColSize() { return outColSize; } - //! Get the depth of the input. - size_t const& InDepth() const { return depth; } - //! Modify the depth of the input. - size_t& InDepth() { return depth; } - - //! Get the shape of the input. - size_t InputShape() const - { - return inRowSize; - } - /** * Serialize the layer. */ @@ -131,24 +107,16 @@ class BilinearInterpolation void serialize(Archive& ar, const uint32_t /* version */); private: - //! Locally stored row size of the input. - size_t inRowSize; - //! Locally stored column size of the input. - size_t inColSize; //! Locally stored row size of the output. size_t outRowSize; + //! Locally stored column size of the input. size_t outColSize; - //! Locally stored depth of the input. - size_t depth; - //! Locally stored number of input points. - size_t batchSize; - //! Locally-stored delta object. - OutputDataType delta; - //! Locally-stored output parameter object. - OutputDataType outputParameter; }; // class BilinearInterpolation +// Standard BilinearInterpolation layer. +typedef BilinearInterpolationType BilinearInterpolation; + } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/bilinear_interpolation_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/bilinear_interpolation_impl.hpp similarity index 51% rename from src/mlpack/methods/ann/layer/bilinear_interpolation_impl.hpp rename to src/mlpack/methods/ann/layer/not_adapted/bilinear_interpolation_impl.hpp index 3621f099b3..3fd621598c 100644 --- a/src/mlpack/methods/ann/layer/bilinear_interpolation_impl.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/bilinear_interpolation_impl.hpp @@ -19,69 +19,54 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { - -template -BilinearInterpolation:: -BilinearInterpolation(): - inRowSize(0), - inColSize(0), +template +BilinearInterpolationType:: +BilinearInterpolationType(): outRowSize(0), - outColSize(0), - depth(0), - batchSize(0) + outColSize(0) { // Nothing to do here. } -template -BilinearInterpolation:: -BilinearInterpolation( - const size_t inRowSize, - const size_t inColSize, - const size_t outRowSize, - const size_t outColSize, - const size_t depth): - inRowSize(inRowSize), - inColSize(inColSize), +template +BilinearInterpolationType:: +BilinearInterpolationType(const size_t outRowSize, + const size_t outColSize) : outRowSize(outRowSize), - outColSize(outColSize), - depth(depth), - batchSize(0) + outColSize(outColSize) { // Nothing to do here. } -template -template -void BilinearInterpolation::Forward( - const arma::Mat& input, arma::Mat& output) +template +void BilinearInterpolationType::Forward( + const InputType& input, OutputType& output) { - batchSize = input.n_cols; - if (output.is_empty()) - output.set_size(outRowSize * outColSize * depth, batchSize); - else - { - assert(output.n_rows == outRowSize * outColSize * depth); - assert(output.n_cols == batchSize); - } + const size_t batchSize = input.n_cols; + const size_t depth = this->inputDimensions.size() <= 2 ? 1 : + std::accumulate(this->inputDimensions.begin() + 2, this->inputDimensions.end(), 0); - assert(inRowSize >= 2); - assert(inColSize >= 2); + assert(output.n_rows == outRowSize * outColSize * depth); + assert(output.n_cols == batchSize); - arma::cube inputAsCube(const_cast&>(input).memptr(), - inRowSize, inColSize, depth * batchSize, false, false); - arma::cube outputAsCube(output.memptr(), outRowSize, outColSize, - depth * batchSize, false, true); + assert(this->inputDimensions[0] >= 2); + assert(this->inputDimensions[1] >= 2); - double scaleRow = (double) inRowSize / (double) outRowSize; - double scaleCol = (double) inColSize / (double) outColSize; + arma::Cube inputAsCube( + const_cast(input).memptr(), this->inputDimensions[0], + this->inputDimensions[1], depth * batchSize, false, false); + arma::Cube outputAsCube( + output.memptr(), outRowSize, outColSize, depth * batchSize, false, true); + + double scaleRow = (double) this->inputDimensions[0] / (double) outRowSize; + double scaleCol = (double) this->inputDimensions[1] / (double) outColSize; arma::mat22 coeffs; for (size_t i = 0; i < outRowSize; ++i) { size_t rOrigin = (size_t) std::floor(i * scaleRow); - if (rOrigin > inRowSize - 2) - rOrigin = inRowSize - 2; + if (rOrigin > this->inputDimensions[0] - 2) + rOrigin = this->inputDimensions[0] - 2; // Scaled distance of the interpolated point from the topmost row. double deltaR = i * scaleRow - rOrigin; @@ -91,8 +76,8 @@ void BilinearInterpolation::Forward( { // Scaled distance of the interpolated point from the leftmost column. size_t cOrigin = (size_t) std::floor(j * scaleCol); - if (cOrigin > inColSize - 2) - cOrigin = inColSize - 2; + if (cOrigin > this->inputDimensions[1] - 2) + cOrigin = this->inputDimensions[1] - 2; double deltaC = j * scaleCol - cOrigin; if (deltaC > 1) @@ -111,28 +96,27 @@ void BilinearInterpolation::Forward( } } -template -template -void BilinearInterpolation::Backward( - const arma::Mat& /*input*/, - const arma::Mat& gradient, - arma::Mat& output) +template +void BilinearInterpolationType::Backward( + const InputType& /*input*/, + const OutputType& gradient, + OutputType& output) { - if (output.is_empty()) - output.set_size(inRowSize * inColSize * depth, batchSize); - else - { - assert(output.n_rows == inRowSize * inColSize * depth); - assert(output.n_cols == batchSize); - } + const size_t batchSize = output.n_cols; + const size_t depth = this->inputDimensions.size() <= 2 ? 1 : + std::accumulate(this->inputDimensions.begin() + 2, this->inputDimensions.end(), 0); + + assert(output.n_rows == this->inputDimensions[0] * this->inputDimensions[1] * depth); assert(outRowSize >= 2); assert(outColSize >= 2); - arma::cube gradientAsCube(((arma::Mat&) gradient).memptr(), outRowSize, - outColSize, depth * batchSize, false, false); - arma::cube outputAsCube(output.memptr(), inRowSize, inColSize, - depth * batchSize, false, true); + arma::Cube gradientAsCube( + ((OutputType&) gradient).memptr(), outRowSize, outColSize, depth * + batchSize, false, false); + arma::Cube outputAsCube( + output.memptr(), this->inputDimensions[0], this->inputDimensions[1], depth * batchSize, + false, true); if (gradient.n_elem == output.n_elem) { @@ -140,17 +124,17 @@ void BilinearInterpolation::Backward( } else { - double scaleRow = (double)(outRowSize) / inRowSize; - double scaleCol = (double)(outColSize) / inColSize; + double scaleRow = (double)(outRowSize) / this->inputDimensions[0]; + double scaleCol = (double)(outColSize) / this->inputDimensions[1]; arma::mat22 coeffs; - for (size_t i = 0; i < inRowSize; ++i) + for (size_t i = 0; i < this->inputDimensions[0]; ++i) { size_t rOrigin = (size_t) std::floor(i * scaleRow); if (rOrigin > outRowSize - 2) rOrigin = outRowSize - 2; double deltaR = i * scaleRow - rOrigin; - for (size_t j = 0; j < inColSize; ++j) + for (size_t j = 0; j < this->inputDimensions[1]; ++j) { size_t cOrigin = (size_t) std::floor(j * scaleCol); @@ -173,16 +157,15 @@ void BilinearInterpolation::Backward( } } -template +template template -void BilinearInterpolation::serialize( +void BilinearInterpolationType::serialize( Archive& ar, const uint32_t /* version */) { - ar(CEREAL_NVP(inRowSize)); - ar(CEREAL_NVP(inColSize)); + ar(cereal::base_class>(this)); + ar(CEREAL_NVP(outRowSize)); ar(CEREAL_NVP(outColSize)); - ar(CEREAL_NVP(depth)); } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/c_relu.hpp b/src/mlpack/methods/ann/layer/not_adapted/c_relu.hpp similarity index 65% rename from src/mlpack/methods/ann/layer/c_relu.hpp rename to src/mlpack/methods/ann/layer/not_adapted/c_relu.hpp index 365111a7d7..e273fb43e6 100644 --- a/src/mlpack/methods/ann/layer/c_relu.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/c_relu.hpp @@ -14,10 +14,12 @@ #include +#include "layer.hpp" + namespace mlpack { namespace ann /** Artificial Neural Network. */ { + /** - * * A concatenated ReLU has two outputs, one ReLU and one negative ReLU, * concatenated together. In other words, for positive x it produces [x, 0], * and for negative x it produces [0, x]. Because it has two outputs, @@ -38,22 +40,21 @@ namespace ann /** Artificial Neural Network. */ { * } * @endcode * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam InputType The type of the layer's inputs. The layer automatically + * cast inputs to this type (Default: arma::mat). + * @tparam OutputType The type of the computation which also causes the output + * to also be in this type. The type also allows the computation and weight + * type to differ from the input type (Default: arma::mat). */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class CReLU +template +class CReLUType : public Layer { public: - /** - * Create the CReLU object. - */ - CReLU(); + //! Create the CReLU object. + CReLUType(); + + //! Clone the CReLUType object. This handles polymorphism correctly. + CReLUType* Clone() const { return new CReLUType(*this); } /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -63,7 +64,6 @@ class CReLU * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template void Forward(const InputType& input, OutputType& output); /** @@ -75,35 +75,17 @@ class CReLU * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const DataType& input, const DataType& gy, DataType& g); + void Backward(const InputType& input, const OutputType& gy, OutputType& g); - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } - - //! Get size of weights. - size_t WeightSize() const { return 0; } - - /** - * Serialize the layer. - */ + //! Serialize the layer. template void serialize(Archive& /* ar */, const uint32_t /* version */); +}; // class CReLUType - private: - //! Locally-stored delta object. - OutputDataType delta; +// Convenience typedefs. - //! Locally-stored output parameter object. - OutputDataType outputParameter; -}; // class CReLU +// Standard CReLU layer. +typedef CReLUType CReLU; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/c_relu_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/c_relu_impl.hpp similarity index 63% rename from src/mlpack/methods/ann/layer/c_relu_impl.hpp rename to src/mlpack/methods/ann/layer/not_adapted/c_relu_impl.hpp index dc2dbce4eb..9cf536b9ef 100644 --- a/src/mlpack/methods/ann/layer/c_relu_impl.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/c_relu_impl.hpp @@ -18,39 +18,36 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -CReLU::CReLU() +template +CReLUType::CReLUType() { // Nothing to do here. } -template template -void CReLU::Forward( +void CReLUType::Forward( const InputType& input, OutputType& output) { output = arma::join_cols(arma::max(input, 0.0 * input), arma::max( (-1 * input), 0.0 * input)); } -template -template -void CReLU::Backward( - const DataType& input, const DataType& gy, DataType& g) +template +void CReLUType::Backward( + const InputType& input, const OutputType& gy, OutputType& g) { - DataType temp; - temp = gy % (input >= 0.0); + OutputType temp = gy % (input >= 0.0); g = temp.rows(0, (input.n_rows / 2 - 1)) - temp.rows(input.n_rows / 2, (input.n_rows - 1)); } -template +template template -void CReLU::serialize( - Archive& /* ar */, +void CReLUType::serialize( + Archive& ar, const uint32_t /* version */) { - // Nothing to do here. + ar(cereal::base_class>(this)); } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/celu.hpp b/src/mlpack/methods/ann/layer/not_adapted/celu.hpp similarity index 64% rename from src/mlpack/methods/ann/layer/celu.hpp rename to src/mlpack/methods/ann/layer/not_adapted/celu.hpp index ae508703ad..7bc357ec75 100644 --- a/src/mlpack/methods/ann/layer/celu.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/celu.hpp @@ -25,6 +25,8 @@ #include +#include "layer.hpp" + namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -46,18 +48,16 @@ namespace ann /** Artificial Neural Network. */ { * \right. * @f} * - * In the deterministic mode, there is no computation of the derivative. + * When not in training mode, there is no computation of the derivative. * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam InputType The type of the layer's inputs. The layer automatically + * cast inputs to this type (Default: arma::mat). + * @tparam OutputType The type of the computation which also causes the output + * to also be in this type. The type also allows the computation and weight + * type to differ from the input type (Default: arma::mat). */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class CELU +template +class CELUType : public Layer { public: /** @@ -67,7 +67,10 @@ class CELU * * @param alpha Scale parameter for the negative factor (default = 1.0). */ - CELU(const double alpha = 1.0); + CELUType(const double alpha = 1.0); + + //! Clone the CELUType object. This handles polymorphism correctly. + CELUType* Clone() const { return new CELUType(*this); } /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -76,7 +79,6 @@ class CELU * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template void Forward(const InputType& input, OutputType& output); /** @@ -88,54 +90,29 @@ class CELU * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const DataType& input, const DataType& gy, DataType& g); - - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } + void Backward(const InputType& input, const OutputType& gy, OutputType& g); //! Get the non zero gradient. double const& Alpha() const { return alpha; } //! Modify the non zero gradient. double& Alpha() { return alpha; } - //! Get the value of deterministic parameter. - bool Deterministic() const { return deterministic; } - //! Modify the value of deterministic parameter. - bool& Deterministic() { return deterministic; } - - //! Get size of weights. - size_t WeightSize() { return 0; } - - /** - * Serialize the layer. - */ + //! Serialize the layer. template void serialize(Archive& ar, const uint32_t /* version */); private: - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; - //! Locally stored first derivative of the activation function. - arma::mat derivative; + OutputType derivative; //! CELU Hyperparameter (alpha > 0). double alpha; +}; // class CELUType - //! If true the derivative computation is disabled, see notes above. - bool deterministic; -}; // class CELU +// Convenience typedefs. + +// Standard CELU layer. +typedef CELUType CELU; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/celu_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/celu_impl.hpp similarity index 66% rename from src/mlpack/methods/ann/layer/celu_impl.hpp rename to src/mlpack/methods/ann/layer/not_adapted/celu_impl.hpp index 3cfa9b6977..76a001b091 100644 --- a/src/mlpack/methods/ann/layer/celu_impl.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/celu_impl.hpp @@ -18,10 +18,9 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -CELU::CELU(const double alpha) : - alpha(alpha), - deterministic(false) +template +CELUType::CELUType(const double alpha) : + alpha(alpha) { if (alpha == 0) { @@ -30,19 +29,18 @@ CELU::CELU(const double alpha) : } } -template template -void CELU::Forward( +void CELUType::Forward( const InputType& input, OutputType& output) { - output = arma::ones(arma::size(input)); + output = arma::ones(arma::size(input)); for (size_t i = 0; i < input.n_elem; ++i) { output(i) = (input(i) >= 0) ? input(i) : alpha * - (std::exp(input(i) / alpha) - 1); + (std::exp(input(i) / alpha) - 1); } - if (!deterministic) + if (this->training) { derivative.set_size(arma::size(input)); for (size_t i = 0; i < input.n_elem; ++i) @@ -53,21 +51,24 @@ void CELU::Forward( } } -template -template -void CELU::Backward( - const DataType& /* input */, const DataType& gy, DataType& g) +template +void CELUType::Backward( + const InputType& /* input */, const OutputType& gy, OutputType& g) { g = gy % derivative; } -template +template template -void CELU::serialize( +void CELUType::serialize( Archive& ar, const uint32_t /* version */) { + ar(cereal::base_class>(this)); + ar(CEREAL_NVP(alpha)); + if (Archive::is_loading::value) + derivative.clear(); } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/channel_shuffle.hpp b/src/mlpack/methods/ann/layer/not_adapted/channel_shuffle.hpp similarity index 100% rename from src/mlpack/methods/ann/layer/channel_shuffle.hpp rename to src/mlpack/methods/ann/layer/not_adapted/channel_shuffle.hpp diff --git a/src/mlpack/methods/ann/layer/channel_shuffle_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/channel_shuffle_impl.hpp similarity index 100% rename from src/mlpack/methods/ann/layer/channel_shuffle_impl.hpp rename to src/mlpack/methods/ann/layer/not_adapted/channel_shuffle_impl.hpp diff --git a/src/mlpack/methods/ann/layer/not_adapted/concat.hpp b/src/mlpack/methods/ann/layer/not_adapted/concat.hpp new file mode 100644 index 0000000000..1d99ff60c9 --- /dev/null +++ b/src/mlpack/methods/ann/layer/not_adapted/concat.hpp @@ -0,0 +1,222 @@ +/** + * @file methods/ann/layer/concat.hpp + * @author Marcus Edel + * @author Mehul Kumar Nirala + * + * Definition of the Concat class, which acts as a concatenation container. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_LAYER_CONCAT_HPP +#define MLPACK_METHODS_ANN_LAYER_CONCAT_HPP + +#include + +#include "layer.hpp" + +namespace mlpack { +namespace ann /** Artificial Neural Network. */ { + +/** + * Implementation of the Concat class. The Concat class works as a + * feed-forward fully connected network container which plugs various layers + * together. + * + * @tparam InputType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + */ +template < + typename InputType = arma::mat, + typename OutputType = arma::mat +> +class ConcatType : public MultiLayer +{ + public: + /** + * Create the Concat object using the specified parameters. + * + * @param run Call the Forward/Backward method before the output is merged. + */ + ConcatType(const bool run = true); + + /** + * Create the Concat object, specifying a particular axis on which the layer + * outputs should be concatenated. + * + * @param axis Concat axis. + * @param run Call the Forward/Backward method before the output is merged. + */ + ConcatType(const size_t axis, const bool run = true); + + /** + * Destroy the layers held by the model. + */ + ~ConcatType(); + + //! Clone the ConcatType object. This handles polymorphism correctly. + ConcatType* Clone() const { return new ConcatType(*this); } + + /** + * Ordinary feed forward pass of a neural network, evaluating the function + * f(x) by propagating the activity forward through f. + * + * @param input Input data used for evaluating the specified function. + * @param output Resulting output activation. + */ + void Forward(const InputType& input, OutputType& output); + + /** + * Ordinary feed backward pass of a neural network, using 3rd-order tensors as + * input, calculating the function f(x) by propagating x backwards through f. + * Using the results from the feed forward pass. + * + * @param * (input) The propagated input activation. + * @param gy The backpropagated error. + * @param g The calculated gradient. + */ + void Backward(const InputType& /* input */, + const OutputType& gy, + OutputType& g); + + /** + * This is the overload of Backward() that runs only a specific layer with + * the given input. + * + * @param * (input) The propagated input activation. + * @param gy The backpropagated error. + * @param g The calculated gradient. + * @param index The index of the layer to run. + */ + void Backward(const InputType& /* input */, + const OutputType& gy, + OutputType& g, + const size_t index); + + /** + * Calculate the gradient using the output delta and the input activation. + * + * @param input The input parameter used for calculating the gradient. + * @param error The calculated error. + * @param gradient The calculated gradient. + */ + void Gradient(const InputType& /* input */, + const OutputType& error, + OutputType& /* gradient */); + + /** + * This is the overload of Gradient() that runs a specific layer with the + * given input. + * + * @param input The input parameter used for calculating the gradient. + * @param error The calculated error. + * @param gradient The calculated gradient. + * @param The index of the layer to run. + */ + void Gradient(const InputType& input, + const OutputType& error, + OutputType& gradient, + const size_t index); + + //! Get the value of run parameter. + bool Run() const { return run; } + //! Modify the value of run parameter. + bool& Run() { return run; } + + //! Get the axis of concatenation. + const size_t& ConcatAxis() const { return axis; } + + //! Get the size of the weight matrix. + size_t WeightSize() const { return 0; } + + void ComputeOutputDimensions() + { + // The input is sent to every layer. + for (size_t i = 0; i < network.size(); ++i) + { + network[i]->InputDimensions() = this->inputDimensions; + network[i]->ComputeOutputDimensions(); + } + + // If the user did not specify an axis, we will use the last one. + // Otherwise, we must sanity check to ensure that the axis we are + // concatenating along is valid. + if (!useAxis) + { + axis = this->inputDimensions.size() - 1; + } + else if (axis >= this->inputDimensions.size()) + { + std::ostringstream oss; + oss << "Concat::ComputeOutputDimensions(): cannot concatenate outputs " + << "along axis " << axis << " when input only has " + << this->inputDimensions.size() << " axes!"; + throw std::invalid_argument(oss.str()); + } + + // Now, we concatenate the output along a specific axis. + this->outputDimensions = std::vector(this->inputDimensions.size(), + 0); + for (size_t i = 0; i < this->inputDimensions.size(); ++i) + { + if (i == axis) + { + // Accumulate output size along this axis for each layer output. + for (size_t n = 0; n < this->network.size(); ++n) + { + this->outputDimensions[i] += this->network[n]->OutputDimensions()[i]; + } + } + else + { + // Ensure that the output size is the same along this axis. + const size_t axisDim = this->network[0]->OutputDimensions()[i]; + for (size_t n = 1; n < this->network.size(); ++n) + { + const size_t axisDim2 = this->network[n]->OutputDimensions()[i]; + if (axisDim != axisDim2) + { + std::ostringstream oss; + oss << "Concat::ComputeOutputDimensions(): cannot concatenate " + << "outputs along axis " << axis << "; held layer " << n + << " has output size " << axisDim2 << " along axis " << i + << ", but the first held layer has output size " << axisDim + << "! All layers must have identical output size in any " + << "axis other than the concatenated axis."; + throw std::invalid_argument(oss.str()); + } + } + + this->outputDimensions[i] = axisDim; + } + } + } + + /** + * Serialize the layer + */ + template + void serialize(Archive& ar, const uint32_t /* version */); + + private: + //! Parameter which indicates the axis of concatenation. + size_t axis; + + //! Parameter which indicates whether to use the axis of concatenation. + bool useAxis; +}; // class ConcatType. + +// Standard Concat layer. +typedef ConcatType Concat; + +} // namespace ann +} // namespace mlpack + +// Include implementation. +#include "concat_impl.hpp" + +#endif diff --git a/src/mlpack/methods/ann/layer/not_adapted/concat_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/concat_impl.hpp new file mode 100644 index 0000000000..7e231b5f81 --- /dev/null +++ b/src/mlpack/methods/ann/layer/not_adapted/concat_impl.hpp @@ -0,0 +1,267 @@ +/** + * @file methods/ann/layer/concat_impl.hpp + * @author Marcus Edel + * @author Mehul Kumar Nirala + * + * Implementation of the Concat class, which acts as a concatenation contain. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_LAYER_CONCAT_IMPL_HPP +#define MLPACK_METHODS_ANN_LAYER_CONCAT_IMPL_HPP + +// In case it hasn't yet been included. +#include "concat.hpp" + +namespace mlpack { +namespace ann /** Artificial Neural Network. */ { + +template +ConcatType::ConcatType( + const bool run) : + axis(0), + useAxis(false) +{ + // Nothing to do. +} + +template +ConcatType::ConcatType( + const size_t axis, + const bool run) : + axis(axis), + useAxis(true) +{ + // Nothing to do. +} + +template +ConcatType::~ConcatType() +{ + // Clear memory. + for (size_t i = 0; i < this->network.size(); ++i) + delete this->network[i]; +} + +template +void ConcatType::Forward( + const InputType& input, OutputType& output) +{ + this->InitializeForwardPassMemory(); + + // Pass the input through all the layers in the network. + for (size_t i = 0; i < this->network.size(); ++i) + { + this->network[i]->Forward(input, this->layerOutputs[i]); + } + + // Now concatenate the outputs along the correct axis. + // We can actually use Armadillo to do this for us---we will treat the axis of + // interest as "columns", any axes that come before the axis of interest as + // 'flattened slices', and any axes that come after the axis of interest as + // 'flattened rows'. As a result, we will only have to do join_cols() to + // produce the right result. + // + // Note that we will have one "extra" axis in addition to + // this->outputDimensions.size(); that is the batch size (represented as the + // number of columns in `input`). + + size_t slices = (axis == 0) ? input.n_cols : + std::accumulate(this->outputDimensions.begin(), + this->outputDimensions.begin() + axis, 0) + input.n_cols; + size_t rows = (axis == this->outputDimensions.size() - 1) ? 1 : + std::accumulate(this->outputDimensions.begin() + axis + 1, + this->outputDimensions.end(), 0); + + std::vector> layerOutputAliases; + for (size_t i = 0; i < this->layerOutputs.size(); ++i) + { + layerOutputAliases.emplace_back(arma::Cube( + this->layerOutputs[i].memptr(), rows, + this->network[i]->OutputDimensions()[axis], slices, false, true); + } + + arma::Cube output(output.memptr(), rows, + this->outputDimensions[axis], slices, false, true); + + // Now get the columns from each output. + size_t startCol = 0; + for (size_t i = 0; i < layerOutputAliases.size(); ++i) + { + const size_t cols = layerOutputAliases[i].n_cols; + output.cols(startCol, startCol + cols - 1) = layerOutputAliases[i]; + startCol += cols; + } +} + +template +void ConcatType::Backward( + const InputType& /* input */, const OutputType& gy, OutputType& g) +{ + this->InitializeBackwardPassMemory(); + + // Just like the forward pass, we can treat our inputs as a cube, but here we + // have to distribute the correct parts of `gy` to the layers. + + size_t slices = (axis == 0) ? gy.n_cols : + std::accumulate(this->outputDimensions.begin(), + this->outputDimensions.begin() + axis, 0) + gy.n_cols; + size_t rows = (axis == this->outputDimensions.size() - 1) ? 1 : + std::accumulate(this->outputDimensions.begin() + axis + 1, + this->outputDimensions.end(), 0); + + arma::Cube gyTmp(gy.memptr(), rows, + this->outputDimensions[axis], slices, false, true); + + size_t startCol = 0; + for (size_t i = 0; i < this->network.size(); ++i) + { + const size_t cols = this->network[i]->OutputDimensions()[axis]; + // TODO: is delta size correct? + // TODO: no copy! + OutputType delta = gyTmp.cols(startCol, startCol + cols - 1); + // TODO: consider batch size correctly + delta.reshape( ... ); + this->network[i]->Backward(this->layerOutputs[i], delta, + this->layerDeltas[i]); + + startCol += cols; + } + + g = this->layerDeltas[0]; + for (size_t i = 1; i < this->network.size(); ++i) + { + g += this->layerDeltas[i]; + } +} + +template +void ConcatType::Backward( + const InputType& /* input */, + const OutputType& gy, + OutputType& g, + const size_t index) +{ + // We only intend to perform a backward pass on one layer. + // Thus, we need to extract the parts of gy that correspond to the desired + // layer (specified by `index`). + + size_t slices = (axis == 0) ? gy.n_cols : + std::accumulate(this->outputDimensions.begin(), + this->outputDimensions.begin() + axis, 0) + gy.n_cols; + size_t rows = (axis == this->outputDimensions.size() - 1) ? 1 : + std::accumulate(this->outputDimensions.begin() + axis + 1, + this->outputDimensions.end(), 0); + + arma::Cube gyTmp(gy.memptr(), rows, + this->outputDimensions[axis], slices, false, true); + + size_t startCol = 0; + for (size_t i = 0; i < index; ++i) + { + startCol += this->network[i]->OutputDimensions()[axis]; + } + + // TODO: no copy! + const size_t cols = this->network[index]->OutputDimensions()[axis]; + OutputType delta = gyTmp.cols(startCol, startCol + cols - 1); + delta.reshape( ... ); + + this->network[index]->Backward(this->layerOutputs[index], delta, g); +} + +template +void ConcatType::Gradient( + const InputType& input, + const OutputType& error, + OutputType& gradient) +{ + // Just like the forward pass, we can treat our inputs as a cube, but here we + // have to distribute the correct parts of `gy` to the layers. + + size_t slices = (axis == 0) ? input.n_cols : + std::accumulate(this->outputDimensions.begin(), + this->outputDimensions.begin() + axis, 0) + input.n_cols; + size_t rows = (axis == this->outputDimensions.size() - 1) ? 1 : + std::accumulate(this->outputDimensions.begin() + axis + 1, + this->outputDimensions.end(), 0); + + arma::Cube errorTmp(error.memptr(), rows, + this->outputDimensions[axis], slices, false, true); + + size_t startCol = 0; + size_t startParam = 0; + for (size_t i = 0; i < this->network.size(); ++i) + { + const size_t cols = this->network[i]->OutputDimensions()[axis]; + const size_t params = this->network[i]->WeightSize(); + + OutputType err = errorTmp.cols(startCol, startCol + cols - 1); + err.reshape(input.n_cols, err.n_elem / input.n_cols); + // TODO: what about layerGradients? + OutputType gradientAlias(gradient.colptr(startParam, 1, params, false, + true); + this->network[i]->Gradient(input, err, gradientAlias); + + startCol += cols; + startParam += params; + } +} + +// TODO: adapt +template +void ConcatType::Gradient( + const InputType& input, + const OutputType& error, + OutputType& gradient, + const size_t index) +{ + // Just like the forward pass, we can treat our inputs as a cube, but here we + // have to distribute the correct parts of `gy` to the layers. + + size_t slices = (axis == 0) ? input.n_cols : + std::accumulate(this->outputDimensions.begin(), + this->outputDimensions.begin() + axis, 0) + input.n_cols; + size_t rows = (axis == this->outputDimensions.size() - 1) ? 1 : + std::accumulate(this->outputDimensions.begin() + axis + 1, + this->outputDimensions.end(), 0); + + arma::Cube errorTmp(error.memptr(), rows, + this->outputDimensions[axis], slices, false, true); + + size_t startCol = 0; + size_t startParam = 0; + for (size_t i = 0; i < index; ++i) + { + startCol += this->network[i]->OutputDimensions()[axis]; + startParam += this->network[i]->WeightSize(); + } + + const size_t cols = this->network[index]->OutputDimensions()[axis]; + const size_t params = this->network[index]->WeightSize(); + + // TODO: no copy! + OutputType err = errorTmp.cols(startCol, startCol + cols - 1); + err.reshape(input.n_cols, err.n_elem / input.n_cols); + OutputType gradientAlias(gradient.memptr(), 1, params, false, true); + this->network[index]->Gradient(input, err, gradientAlias); +} + +template +template +void ConcatType::serialize( + Archive& ar, const uint32_t /* version */) +{ + ar(cereal::base_class>(this)); + + ar(CEREAL_NVP(axis)); + ar(CEREAL_NVP(useAxis)); +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/layer/concat_performance.hpp b/src/mlpack/methods/ann/layer/not_adapted/concat_performance.hpp similarity index 67% rename from src/mlpack/methods/ann/layer/concat_performance.hpp rename to src/mlpack/methods/ann/layer/not_adapted/concat_performance.hpp index b7ddbe1625..c565fa3eb0 100644 --- a/src/mlpack/methods/ann/layer/concat_performance.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/concat_performance.hpp @@ -24,17 +24,17 @@ namespace ann /** Artificial Neural Network. */ { * feed-forward fully connected network container which plugs performance layers * together. * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * @tparam InputType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * @tparam OutputType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). */ template < typename OutputLayerType = NegativeLogLikelihood<>, - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat + typename InputType = arma::mat, + typename OutputType = arma::mat > -class ConcatPerformance +class ConcatPerformance : public Layer { public: /** @@ -43,8 +43,7 @@ class ConcatPerformance * @param inSize The number of inputs. * @param outputLayer Output layer used to evaluate the network. */ - ConcatPerformance(const size_t inSize = 0, - OutputLayerType&& outputLayer = OutputLayerType()); + ConcatPerformance(OutputLayerType&& outputLayer = OutputLayerType()); /* * Computes the Negative log likelihood. @@ -52,8 +51,7 @@ class ConcatPerformance * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template - double Forward(const arma::Mat& input, arma::Mat& target); + void Forward(const InputType& input, OutputType& target); /** * Ordinary feed backward pass of a neural network. The negative log @@ -66,42 +64,29 @@ class ConcatPerformance * between 1 and the number of classes. * @param output The calculated error. */ - template - void Backward(const arma::Mat& input, - const arma::Mat& target, - arma::Mat& output); + void Backward(const InputType& input, + const OutputType& target, + OutputType& output); //! Get the output parameter. - OutputDataType& OutputParameter() const { return outputParameter; } + OutputType& OutputParameter() const { return outputParameter; } //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } + OutputType& OutputParameter() { return outputParameter; } //! Get the delta. - OutputDataType& Delta() const { return delta; } + OutputType& Delta() const { return delta; } //! Modify the delta. - OutputDataType& Delta() { return delta; } - - //! Get the number of inputs. - size_t InSize() const { return inSize; } + OutputType& Delta() { return delta; } /** - * Serialize the layer + * Serialize the layer. */ template void serialize(Archive& /* ar */, const uint32_t /* version */); private: - //! Locally-stored number of inputs. - size_t inSize; - //! Instantiated outputlayer used to evaluate the network. OutputLayerType outputLayer; - - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; }; // class ConcatPerformance } // namespace ann diff --git a/src/mlpack/methods/ann/layer/concat_performance_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/concat_performance_impl.hpp similarity index 61% rename from src/mlpack/methods/ann/layer/concat_performance_impl.hpp rename to src/mlpack/methods/ann/layer/not_adapted/concat_performance_impl.hpp index ee4d9b8f4b..60def206a9 100644 --- a/src/mlpack/methods/ann/layer/concat_performance_impl.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/concat_performance_impl.hpp @@ -20,15 +20,14 @@ namespace ann /** Artificial Neural Network. */ { template< typename OutputLayerType, - typename InputDataType, - typename OutputDataType + typename InputType, + typename OutputType > ConcatPerformance< OutputLayerType, - InputDataType, - OutputDataType ->::ConcatPerformance(const size_t inSize, OutputLayerType&& outputLayer) : - inSize(inSize), + InputType, + OutputType +>::ConcatPerformance(OutputLayerType&& outputLayer) : outputLayer(std::move(outputLayer)) { // Nothing to do here. @@ -36,51 +35,51 @@ ConcatPerformance< template< typename OutputLayerType, - typename InputDataType, - typename OutputDataType + typename InputType, + typename OutputType > -template -double ConcatPerformance< +void ConcatPerformance< OutputLayerType, - InputDataType, - OutputDataType ->::Forward(const arma::Mat& input, arma::Mat& target) + InputType, + OutputType +>::Forward(const InputType& input, OutputType& target) { - const size_t elements = input.n_elem / inSize; + const size_t elements = input.n_elem / inputDimensions[0]; double output = 0; - for (size_t i = 0; i < input.n_elem; i+= elements) + for (size_t i = 0; i < input.n_elem; i += elements) { - arma::mat subInput = input.submat(i, 0, i + elements - 1, 0); + InputType subInput = input.submat(i, 0, i + elements - 1, 0); output += outputLayer.Forward(subInput, target); } - return output; + // TODO: what to do with output? + //return output; + return; } template< typename OutputLayerType, - typename InputDataType, - typename OutputDataType + typename InputType, + typename OutputType > -template void ConcatPerformance< OutputLayerType, - InputDataType, - OutputDataType + InputType, + OutputType >::Backward( - const arma::Mat& input, - const arma::Mat& target, - arma::Mat& output) + const InputType& input, + const OutputType& target, + OutputType& output) { - const size_t elements = input.n_elem / inSize; + const size_t elements = input.n_elem / inputDimensions[0]; - arma::mat subInput = input.submat(0, 0, elements - 1, 0); - arma::mat subOutput; + InputType subInput = input.submat(0, 0, elements - 1, 0); + OutputType subOutput; outputLayer.Backward(subInput, target, subOutput); - output = arma::zeros(subOutput.n_elem, inSize); + output = arma::zeros(subOutput.n_elem, inputDimensions[0]); output.col(0) = subOutput; for (size_t i = elements, j = 0; i < input.n_elem; i+= elements, ++j) @@ -94,17 +93,19 @@ void ConcatPerformance< template< typename OutputLayerType, - typename InputDataType, - typename OutputDataType + typename InputType, + typename OutputType > template void ConcatPerformance< OutputLayerType, - InputDataType, - OutputDataType + InputType, + OutputType >::serialize(Archive& ar, const uint32_t /* version */) { - ar(CEREAL_NVP(inSize)); + ar(cereal::base_class>(this)); + + ar(CEREAL_NVP(outputLayer)); } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/constant.hpp b/src/mlpack/methods/ann/layer/not_adapted/constant.hpp similarity index 56% rename from src/mlpack/methods/ann/layer/constant.hpp rename to src/mlpack/methods/ann/layer/not_adapted/constant.hpp index 9a0956ddf8..bcfe997d16 100644 --- a/src/mlpack/methods/ann/layer/constant.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/constant.hpp @@ -15,6 +15,8 @@ #include +#include "layer.hpp" + namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -22,18 +24,21 @@ namespace ann /** Artificial Neural Network. */ { * Implementation of the constant layer. The constant layer outputs a given * constant value given any input value. * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam InputType The type of the layer's inputs. The layer automatically + * cast inputs to this type (Default: arma::mat). + * @tparam OutputType The type of the computation which also causes the output + * to also be in this type. The type also allows the computation and weight + * type to differ from the input type (Default: arma::mat). */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class Constant +template +class ConstantType : public Layer { public: + /** + * Create an empty Constant layer. + */ + ConstantType(); + /** * Create the Constant object that outputs a given constant scalar value * given any input value. @@ -41,7 +46,19 @@ class Constant * @param outSize The number of output units. * @param scalar The constant value used to create the constant output. */ - Constant(const size_t outSize = 0, const double scalar = 0.0); + ConstantType(const size_t outSize, const double scalar = 0); + + //! Copy another ConstantType. + ConstantType(const ConstantType& layer); + //! Take ownership of another ConstantType. + ConstantType(ConstantType&& layer); + //! Copy another ConstantType. + ConstantType& operator=(const ConstantType& layer); + //! Take ownership of another ConstantType. + ConstantType& operator=(ConstantType&& layer); + + //! Clone the ConstantType object. This handles polymorphism correctly. + ConstantType* Clone() const { return new ConstantType(*this); } /** * Ordinary feed forward pass of a neural network. The forward pass fills the @@ -50,7 +67,6 @@ class Constant * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template void Forward(const InputType& input, OutputType& output); /** @@ -61,52 +77,34 @@ class Constant * @param * (gy) The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const DataType& /* input */, - const DataType& /* gy */, - DataType& g); - - //! Get the output parameter. - OutputDataType& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } + void Backward(const InputType& /* input */, + const OutputType& /* gy */, + OutputType& g); //! Get the output size. - size_t OutSize() const { return outSize; } - - //! Get the size of the weights. - size_t WeightSize() const + const std::vector& OutputDimensions() const { - return 0; + std::vector result(this->inputDimensions.size(), 0); + result[0] = outSize; + return result; } - /** - * Serialize the layer. - */ + //! Serialize the layer. template void serialize(Archive& ar, const uint32_t /* version */); private: - //! Locally-stored number of input units. - size_t inSize; - //! Locally-stored number of output units. size_t outSize; //! Locally-stored constant output matrix. - OutputDataType constantOutput; + OutputType constantOutput; +}; // class ConstantType - //! Locally-stored delta object. - OutputDataType delta; +// Convenience typedefs. - //! Locally-stored output parameter object. - OutputDataType outputParameter; -}; // class ConstantLayer +// Standard HardShrink layer. +typedef ConstantType Constant; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/not_adapted/constant_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/constant_impl.hpp new file mode 100644 index 0000000000..601b5c2e76 --- /dev/null +++ b/src/mlpack/methods/ann/layer/not_adapted/constant_impl.hpp @@ -0,0 +1,118 @@ +/** + * @file methods/ann/layer/constant_impl.hpp + * @author Marcus Edel + * + * Implementation of the Constant class, which outputs a constant value given + * any input. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_LAYER_CONSTANT_IMPL_HPP +#define MLPACK_METHODS_ANN_LAYER_CONSTANT_IMPL_HPP + +// In case it hasn't yet been included. +#include "constant.hpp" + +namespace mlpack { +namespace ann /** Artificial Neural Network. */ { + +template +ConstantType::ConstantType() : + outSize(0) +{ + // Nothing to do. +} + +template +ConstantType::ConstantType( + const size_t outSize, + const double scalar) : + outSize(outSize) +{ + constantOutput = OutputType(outSize, 1); + constantOutput.fill(scalar); +} + +template +ConstantType::ConstantType( + const ConstantType& other) : + outSize(other.outSize), + constantOutput(other.constantOutput) +{ + // Nothing else to do. +} + +template +ConstantType::ConstantType( + ConstantType&& other) : + outSize(other.outSize), + constantOutput(std::move(other.constantOutput)) +{ + other.outSize = 1; + other.constantOutput = OutputType(other.outSize, 1); +} + +template +ConstantType& +ConstantType::operator=( + const ConstantType& other) +{ + if (this != &other) + { + outSize = other.outSize; + constantOutput = other.constantOutput; + } + + return *this; +} + +template +ConstantType& +ConstantType::operator=( + ConstantType&& other) +{ + if (this != *other) + { + outSize = other.outSize; + constantOutput = std::move(other.constantOutput); + + other.outSize = 1; + other.constantOutput = OutputType(other.outSize, 1); + } + + return *this; +} + +template +void ConstantType::Forward( + const InputType& input, OutputType& output) +{ + output = constantOutput; +} + +template +void ConstantType::Backward( + const InputType& /* input */, const OutputType& /* gy */, OutputType& g) +{ + g.zeros(); +} + +template +template +void ConstantType::serialize( + Archive& ar, const uint32_t /* version */) +{ + ar(cereal::base_class>(this)); + + ar(CEREAL_NVP(constantOutput)); + if (Archive::is_loading::value) + outSize = constantOutput.n_elem; +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/layer/elu.hpp b/src/mlpack/methods/ann/layer/not_adapted/elu.hpp similarity index 70% rename from src/mlpack/methods/ann/layer/elu.hpp rename to src/mlpack/methods/ann/layer/not_adapted/elu.hpp index 1b51455a93..292f61cc68 100644 --- a/src/mlpack/methods/ann/layer/elu.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/elu.hpp @@ -6,9 +6,9 @@ * Definition of the ELU activation function as described by Djork-Arne Clevert, * Thomas Unterthiner and Sepp Hochreiter. * - * Definition of the SELU function as introduced by - * Klambauer et. al. in Self Neural Networks. The SELU activation - * function keeps the mean and variance of the input invariant. + * Definition of the SELU function as introduced by Klambauer et. al. in Self + * Neural Networks. The SELU activation function keeps the mean and variance of + * the input invariant. * * In short, SELU = lambda * ELU, with 'alpha' and 'lambda' fixed for * normalized inputs. @@ -26,6 +26,8 @@ #include +#include "layer.hpp" + namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -61,7 +63,6 @@ namespace ann /** Artificial Neural Network. */ { * } * @endcode * - * * The SELU activation function is defined by * * @f{eqnarray*}{ @@ -92,23 +93,19 @@ namespace ann /** Artificial Neural Network. */ { * } * @endcode * - * In the deterministic mode, there is no computation of the derivative. + * In testing mode, there is no computation of the derivative. * - * @note During training deterministic should be set to false and during - * testing/inference deterministic should be set to true. * @note Make sure to use SELU activation function with normalized inputs and * weights initialized with Lecun Normal Initialization. * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam InputType The type of the layer's inputs. The layer automatically + * cast inputs to this type (Default: arma::mat). + * @tparam OutputType The type of the computation which also causes the output + * to also be in this type. The type also allows the computation and weight + * type to differ from the input type (Default: arma::mat). */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class ELU +template +class ELUType : public Layer { public: /** @@ -116,7 +113,7 @@ class ELU * * NOTE: Use this constructor for SELU activation function. */ - ELU(); + ELUType(); /** * Create the ELU object using the specified parameter. The non zero @@ -126,8 +123,10 @@ class ELU * @note Use this constructor for ELU activation function. * @param alpha Scale parameter for the negative factor. */ - ELU(const double alpha); + ELUType(const double alpha); + //! Clone the ELUType object. This handles polymorphism correctly. + ELUType* Clone() const { return new ELUType(*this); } /** * Ordinary feed forward pass of a neural network, evaluating the function * f(x) by propagating the activity forward through f. @@ -135,7 +134,6 @@ class ELU * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template void Forward(const InputType& input, OutputType& output); /** @@ -147,29 +145,13 @@ class ELU * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const DataType& input, const DataType& gy, DataType& g); - - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } + void Backward(const InputType& input, const OutputType& gy, OutputType& g); //! Get the non zero gradient. double const& Alpha() const { return alpha; } //! Modify the non zero gradient. double& Alpha() { return alpha; } - //! Get the value of deterministic parameter. - bool Deterministic() const { return deterministic; } - //! Modify the value of deterministic parameter. - bool& Deterministic() { return deterministic; } - //! Get the lambda parameter. double const& Lambda() const { return lambda; } @@ -180,31 +162,27 @@ class ELU void serialize(Archive& ar, const uint32_t /* version */); private: - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; - //! Locally stored first derivative of the activation function. - arma::mat derivative; + OutputType derivative; //! ELU Hyperparameter (0 < alpha) //! SELU parameter fixed to 1.6732632423543774 for normalized inputs. double alpha; - //! Lambda Parameter used for multiplication of ELU function. + //! Lambda parameter used for multiplication of ELU function. //! For ELU activation function, lambda = 1. //! For SELU activation function, lambda = 1.0507009873554802 for normalized //! inputs. double lambda; +}; // class ELUType - //! If true the derivative computation is disabled, see notes above. - bool deterministic; -}; // class ELU +// Convenience typedefs. -// Template alias for SELU using ELU class. -using SELU = ELU; +// Standard flexible ReLU layer. +typedef ELUType ELU; + +// Standard ELU layer. +typedef ELUType SELU; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/elu_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/elu_impl.hpp similarity index 53% rename from src/mlpack/methods/ann/layer/elu_impl.hpp rename to src/mlpack/methods/ann/layer/not_adapted/elu_impl.hpp index 9615604c86..b81d2caf89 100644 --- a/src/mlpack/methods/ann/layer/elu_impl.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/elu_impl.hpp @@ -26,66 +26,59 @@ namespace ann /** Artificial Neural Network. */ { // This constructor is called for SELU activation function. The values of // alpha and lambda are constant for normalized inputs. -template -ELU::ELU() : - alpha(1.6732632423543774), - lambda(1.0507009873554802), - deterministic(false) -{ - // Nothing to do here. -} - -// This constructor is called for ELU activation function. The value of lambda -// is fixed and equal to 1. 'alpha' is a hyperparameter. -template -ELU::ELU(const double alpha) : - alpha(alpha), - lambda(1), - deterministic(false) -{ - // Nothing to do here. -} - -template template -void ELU::Forward( +ELUType::ELUType() : + alpha(1.6732632423543774), + lambda(1.0507009873554802) +{ + // Nothing to do here. +} + +// This constructor is called for ELU activation function. The value of lambda +// is fixed and equal to 1. 'alpha' is a hyperparameter. +template +ELUType::ELUType(const double alpha) : + alpha(alpha), + lambda(1) +{ + // Nothing to do here. +} + +template +void ELUType::Forward( const InputType& input, OutputType& output) { - output = arma::ones(arma::size(input)); + output.ones(); for (size_t i = 0; i < input.n_elem; ++i) { if (input(i) < DBL_MAX) { - output(i) = (input(i) > 0) ? lambda * input(i) : lambda * - alpha * (std::exp(input(i)) - 1); + output(i) = (input(i) > 0) ? lambda * input(i) : lambda * alpha * + (std::exp(input(i)) - 1); } } - if (!deterministic) - { - derivative.set_size(arma::size(input)); - for (size_t i = 0; i < input.n_elem; ++i) - { - derivative(i) = (input(i) > 0) ? lambda : output(i) + - lambda * alpha; - } - } + if (!deterministic) + { + for (size_t i = 0; i < input.n_elem; ++i) + derivative(i) = (input(i) > 0) ? lambda : output(i) + lambda * alpha; + } } -template -template -void ELU::Backward( - const DataType& /* input */, const DataType& gy, DataType& g) +template +void ELUType::Backward( + const InputType& /* input */, const OutputType& gy, OutputType& g) { g = gy % derivative; } -template +template template -void ELU::serialize( - Archive& ar, - const uint32_t /* version */) +void ELUType::serialize( + Archive& ar, const uint32_t /* version */) { + ar(cereal::base_class>(this)); + ar(CEREAL_NVP(alpha)); ar(CEREAL_NVP(lambda)); } diff --git a/src/mlpack/methods/ann/layer/fast_lstm.hpp b/src/mlpack/methods/ann/layer/not_adapted/fast_lstm.hpp similarity index 72% rename from src/mlpack/methods/ann/layer/fast_lstm.hpp rename to src/mlpack/methods/ann/layer/not_adapted/fast_lstm.hpp index 80ddcabca6..120442b145 100644 --- a/src/mlpack/methods/ann/layer/fast_lstm.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/fast_lstm.hpp @@ -1,3 +1,4 @@ +// Temporarily drop. /** * @file methods/ann/layer/fast_lstm.hpp * @author Marcus Edel @@ -15,6 +16,7 @@ #include #include +#include "layer.hpp" namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -54,36 +56,36 @@ namespace ann /** Artificial Neural Network. */ { * * \see LSTM for a standard implementation of the LSTM layer. * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * @tparam InputType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * @tparam OutputType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). */ template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat + typename InputType = arma::mat, + typename OutputType = arma::mat > -class FastLSTM +class FastLSTMType : public Layer { public: // Convenience typedefs. - typedef typename InputDataType::elem_type InputElemType; - typedef typename OutputDataType::elem_type ElemType; + typedef typename InputType::elem_type InputET; + typedef typename OutputType::elem_type OutputET; - //! Create the Fast LSTM object. - FastLSTM(); + //! Create the FastLSTMType object. + FastLSTMType(); //! Copy Constructor - FastLSTM(const FastLSTM& layer); + FastLSTMType(const FastLSTMType& layer); //! Move Constructor - FastLSTM(FastLSTM&& layer); + FastLSTMType(FastLSTMType&& layer); //! Copy assignment operator - FastLSTM& operator=(const FastLSTM& layer); + FastLSTMType& operator=(const FastLSTMType& layer); //! Move assignment operator - FastLSTM& operator=(FastLSTM&& layer); + FastLSTMType& operator=(FastLSTMType&& layer); /** * Create the Fast LSTM layer object using the specified parameters. @@ -92,9 +94,12 @@ class FastLSTM * @param outSize The number of output units. * @param rho Maximum number of steps to backpropagate through time (BPTT). */ - FastLSTM(const size_t inSize, - const size_t outSize, - const size_t rho = std::numeric_limits::max()); + FastLSTMType(const size_t inSize, + const size_t outSize, + const size_t rho = std::numeric_limits::max()); + + //! Clone the FastLSTMType object. This handles polymorphism correctly. + FastLSTMType* Clone() const { return new FastLSTMType(*this); } /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -103,7 +108,6 @@ class FastLSTM * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template void Forward(const InputType& input, OutputType& output); /** @@ -115,17 +119,16 @@ class FastLSTM * @param gy The backpropagated error. * @param g The calculated gradient. */ - template void Backward(const InputType& input, - const ErrorType& gy, - GradientType& g); + const OutputType& gy, + OutputType& g); - /* + /** * Reset the layer parameter. */ void Reset(); - /* + /** * Resets the cell to accept a new input. This breaks the BPTT chain starts a * new one. * @@ -133,17 +136,16 @@ class FastLSTM */ void ResetCell(const size_t size); - /* + /** * Calculate the gradient using the output delta and the input activation. * * @param input The input parameter used for calculating the gradient. * @param error The calculated error. * @param gradient The calculated gradient. */ - template void Gradient(const InputType& input, - const ErrorType& error, - GradientType& gradient); + const OutputType& error, + OutputType& gradient); //! Get the maximum number of steps to backpropagate through time (BPTT). size_t Rho() const { return rho; } @@ -151,24 +153,9 @@ class FastLSTM size_t& Rho() { return rho; } //! Get the parameters. - OutputDataType const& Parameters() const { return weights; } + OutputType const& Parameters() const { return weights; } //! Modify the parameters. - OutputDataType& Parameters() { return weights; } - - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } - - //! Get the gradient. - OutputDataType const& Gradient() const { return grad; } - //! Modify the gradient. - OutputDataType& Gradient() { return grad; } + OutputType& Parameters() { return weights; } //! Get the number of input units. size_t InSize() const { return inSize; } @@ -182,14 +169,15 @@ class FastLSTM return 4 * outSize * inSize + 4 * outSize + 4 * outSize * outSize; } - //! Get the shape of the input. - size_t InputShape() const + const std::vector OutputDimensions() const { - return inSize; + std::vector result(inputDimensions.size(), 0); + result[0] = outSize; + return result; } /** - * Serialize the layer + * Serialize the layer. */ template void serialize(Archive& ar, const uint32_t /* version */); @@ -201,7 +189,6 @@ class FastLSTM * @param input The input data. * @param sigmoid The matrix to store the sigmoid approximation into. */ - template void FastSigmoid(const InputType& input, OutputType& sigmoids) { for (size_t i = 0; i < input.n_elem; ++i) @@ -214,10 +201,10 @@ class FastLSTM * @param data The given data sample for the sigmoid approximation. * @tparam The sigmoid approximation. */ - ElemType FastSigmoid(const InputElemType data) + OutputET FastSigmoid(const InputET data) { - ElemType x = 0.5 * data; - ElemType z; + OutputET x = 0.5 * data; + OutputET z; if (x >= 0) { if (x < 1.7) @@ -229,7 +216,7 @@ class FastLSTM } else { - ElemType xx = -x; + OutputET xx = -x; if (xx < 1.7) z = -(1.5 * xx / (1 + xx)); else if (xx < 3) @@ -260,10 +247,10 @@ class FastLSTM size_t gradientStep; //! Locally-stored weight object. - OutputDataType weights; + OutputType weights; //! Locally-stored previous output. - OutputDataType prevOutput; + OutputType prevOutput; //! Locally-stored batch size. size_t batchSize; @@ -276,56 +263,50 @@ class FastLSTM size_t gradientStepIdx; //! Locally-stored cell activation error. - OutputDataType cellActivationError; - - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored gradient object. - OutputDataType grad; + OutputType cellActivationError; //! Locally-stored output parameter object. - OutputDataType outputParameter; + OutputType outputParameter; //! Weights between the output and gate. - OutputDataType output2GateWeight; + OutputType output2GateWeight; //! Weights between the input and gate. - OutputDataType input2GateWeight; + OutputType input2GateWeight; //! Bias between the input and gate. - OutputDataType input2GateBias; + OutputType input2GateBias; //! Locally-stored gate parameter. - OutputDataType gate; + OutputType gate; //! Locally-stored gate activation. - OutputDataType gateActivation; + OutputType gateActivation; //! Locally-stored state activation. - OutputDataType stateActivation; + OutputType stateActivation; //! Locally-stored cell parameter. - OutputDataType cell; + OutputType cell; //! Locally-stored cell activation error. - OutputDataType cellActivation; + OutputType cellActivation; //! Locally-stored foget gate error. - OutputDataType forgetGateError; + OutputType forgetGateError; //! Locally-stored previous error. - OutputDataType prevError; - - //! Locally-stored output parameters. - OutputDataType outParameter; + OutputType prevError; //! Locally-stored current rho size. size_t rhoSize; //! Current backpropagate through time steps. size_t bpttSteps; -}; // class FastLSTM +}; // class FastLSTMType. + +// Standard FastLSTM layer. +typedef FastLSTMType FastLSTM; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/fast_lstm_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/fast_lstm_impl.hpp similarity index 81% rename from src/mlpack/methods/ann/layer/fast_lstm_impl.hpp rename to src/mlpack/methods/ann/layer/not_adapted/fast_lstm_impl.hpp index c72416bdeb..46b6824c0d 100644 --- a/src/mlpack/methods/ann/layer/fast_lstm_impl.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/fast_lstm_impl.hpp @@ -19,14 +19,14 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -FastLSTM::FastLSTM() +template +FastLSTMType::FastLSTMType() { // Nothing to do here. } -template -FastLSTM::FastLSTM( +template +FastLSTMType::FastLSTMType( const size_t inSize, const size_t outSize, const size_t rho) : inSize(inSize), outSize(outSize), @@ -45,8 +45,8 @@ FastLSTM::FastLSTM( weights.set_size(WeightSize(), 1); } -template -FastLSTM::FastLSTM(const FastLSTM& layer) : +template +FastLSTMType::FastLSTMType(const FastLSTMType& layer) : inSize(layer.inSize), outSize(layer.outSize), rho(layer.rho), @@ -57,15 +57,14 @@ FastLSTM::FastLSTM(const FastLSTM& layer) : batchSize(layer.batchSize), batchStep(layer.batchStep), gradientStepIdx(layer.gradientStepIdx), - grad(layer.grad), rhoSize(layer.rho), bpttSteps(layer.bpttSteps) { // Nothing to do here. } -template -FastLSTM::FastLSTM(FastLSTM&& layer) : +template +FastLSTMType::FastLSTMType(FastLSTMType&& layer) : inSize(std::move(layer.inSize)), outSize(std::move(layer.outSize)), rho(std::move(layer.rho)), @@ -76,16 +75,15 @@ FastLSTM::FastLSTM(FastLSTM&& layer) : batchSize(std::move(layer.batchSize)), batchStep(std::move(layer.batchStep)), gradientStepIdx(std::move(layer.gradientStepIdx)), - grad(std::move(layer.grad)), rhoSize(std::move(layer.rho)), bpttSteps(std::move(layer.bpttSteps)) { // Nothing to do here. } -template -FastLSTM& -FastLSTM::operator=(const FastLSTM& layer) +template +FastLSTMType& +FastLSTMType::operator=(const FastLSTMType& layer) { if (this != &layer) { @@ -99,16 +97,15 @@ FastLSTM::operator=(const FastLSTM& layer) batchSize = layer.batchSize; batchStep = layer.batchStep; gradientStepIdx = layer.gradientStepIdx; - grad = layer.grad; rhoSize = layer.rho; bpttSteps = layer.bpttSteps; } return *this; } -template -FastLSTM& -FastLSTM::operator=(FastLSTM&& layer) +template +FastLSTMType& +FastLSTMType::operator=(FastLSTMType&& layer) { if (this != &layer) { @@ -122,31 +119,30 @@ FastLSTM::operator=(FastLSTM&& layer) batchSize = std::move(layer.batchSize); batchStep = std::move(layer.batchStep); gradientStepIdx = std::move(layer.gradientStepIdx); - grad = std::move(layer.grad); rhoSize = std::move(layer.rho); bpttSteps = std::move(layer.bpttSteps); } return *this; } -template -void FastLSTM::Reset() +template +void FastLSTMType::Reset() { // Set the weight parameter for the input to gate layer (linear layer) using // the overall layer parameter matrix. - input2GateWeight = OutputDataType(weights.memptr(), + input2GateWeight = OutputType(weights.memptr(), 4 * outSize, inSize, false, false); - input2GateBias = OutputDataType(weights.memptr() + input2GateWeight.n_elem, + input2GateBias = OutputType(weights.memptr() + input2GateWeight.n_elem, 4 * outSize, 1, false, false); // Set the weight parameter for the output to gate layer // (linear no bias layer) using the overall layer parameter matrix. - output2GateWeight = OutputDataType(weights.memptr() + input2GateWeight.n_elem + output2GateWeight = OutputType(weights.memptr() + input2GateWeight.n_elem + input2GateBias.n_elem, 4 * outSize, outSize, false, false); } -template -void FastLSTM::ResetCell(const size_t size) +template +void FastLSTMType::ResetCell(const size_t size) { if (size == std::numeric_limits::max()) return; @@ -179,9 +175,8 @@ void FastLSTM::ResetCell(const size_t size) outParameter.zeros(outSize, (size + 1) * batchSize); } -template template -void FastLSTM::Forward( +void FastLSTMType::Forward( const InputType& input, OutputType& output) { // Check if the batch size changed, the number of cols is defines the input @@ -198,8 +193,8 @@ void FastLSTM::Forward( forwardStep, forwardStep + batchStep); gate.cols(forwardStep, forwardStep + batchStep).each_col() += input2GateBias; - arma::subview sigmoidOut = gateActivation.cols(forwardStep, - forwardStep + batchStep); + InputType sigmoidOut(gateActivation.colptr(forwardStep), + gateActivation.n_rows, batchStep, false, false); FastSigmoid( gate.submat(0, forwardStep, 3 * outSize - 1, forwardStep + batchStep), sigmoidOut); @@ -247,20 +242,19 @@ void FastLSTM::Forward( } } -template -template -void FastLSTM::Backward( - const InputType& /* input */, const ErrorType& gy, GradientType& g) +template +void FastLSTMType::Backward( + const InputType& /* input */, const OutputType& gy, OutputType& g) { - ErrorType gyLocal; + OutputType gyLocal; if (gradientStepIdx > 0) { gyLocal = gy + output2GateWeight.t() * prevError; } else { - gyLocal = ErrorType(((ErrorType&) gy).memptr(), gy.n_rows, gy.n_cols, false, - false); + gyLocal = OutputType(((OutputType&) gy).memptr(), gy.n_rows, gy.n_cols, + false, false); } cellActivationError = gyLocal % gateActivation.submat(outSize, @@ -319,12 +313,11 @@ void FastLSTM::Backward( } } -template -template -void FastLSTM::Gradient( +template +void FastLSTMType::Gradient( const InputType& input, - const ErrorType& /* error */, - GradientType& gradient) + const OutputType& /* error */, + OutputType& gradient) { // Gradient of the input to gate layer. gradient.submat(0, 0, input2GateWeight.n_elem - 1, 0) = @@ -348,11 +341,13 @@ void FastLSTM::Gradient( } } -template +template template -void FastLSTM::serialize( +void FastLSTMType::serialize( Archive& ar, const uint32_t /* version */) { + ar(cereal::base_class>(this)); + ar(CEREAL_NVP(weights)); ar(CEREAL_NVP(inSize)); ar(CEREAL_NVP(outSize)); @@ -371,7 +366,10 @@ void FastLSTM::serialize( ar(CEREAL_NVP(cellActivation)); ar(CEREAL_NVP(forgetGateError)); ar(CEREAL_NVP(prevError)); - ar(CEREAL_NVP(outParameter)); + + // Restore aliases. + if (Archive::is_loading::value) + Reset(); } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/flatten_t_swish.hpp b/src/mlpack/methods/ann/layer/not_adapted/flatten_t_swish.hpp similarity index 100% rename from src/mlpack/methods/ann/layer/flatten_t_swish.hpp rename to src/mlpack/methods/ann/layer/not_adapted/flatten_t_swish.hpp diff --git a/src/mlpack/methods/ann/layer/flatten_t_swish_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/flatten_t_swish_impl.hpp similarity index 100% rename from src/mlpack/methods/ann/layer/flatten_t_swish_impl.hpp rename to src/mlpack/methods/ann/layer/not_adapted/flatten_t_swish_impl.hpp diff --git a/src/mlpack/methods/ann/layer/flexible_relu.hpp b/src/mlpack/methods/ann/layer/not_adapted/flexible_relu.hpp similarity index 50% rename from src/mlpack/methods/ann/layer/flexible_relu.hpp rename to src/mlpack/methods/ann/layer/not_adapted/flexible_relu.hpp index 7c2ea9e4dc..279d00a5f7 100644 --- a/src/mlpack/methods/ann/layer/flexible_relu.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/flexible_relu.hpp @@ -3,10 +3,9 @@ * @author Aarush Gupta * @author Manthan-R-Sheth * - * Definition of FlexibleReLU layer as described by - * Suo Qiu, Xiangmin Xu and Bolun Cai in - * "FReLU: Flexible Rectified Linear Units for Improving Convolutional - * Neural Networks", 2018 + * Definition of the FlexibleReLU layer as described by Suo Qiu, Xiangmin Xu and + * Bolun Cai in "FReLU: Flexible Rectified Linear Units for Improving + * Convolutional Neural Networks". * * mlpack is free software; you may redistribute it and/or modify it under the * terms of the 3-clause BSD license. You should have received a copy of the @@ -18,6 +17,8 @@ #include +#include "layer.hpp" + namespace mlpack { namespace ann /**Artificial Neural Network*/ { @@ -27,10 +28,10 @@ namespace ann /**Artificial Neural Network*/ { * @f{eqnarray*}{ * f(x) &=& \max(0,x)+alpha \\ * f'(x) &=& \left\{ - * \begin{array}{lr} - * 1 & : x > 0 \\ - * 0 & : x \le 0 - * \end{array} + * \begin{array}{lr} + * 1 & : x > 0 \\ + * 0 & : x \le 0 + * \end{array} * \right. * @f} * @@ -47,34 +48,33 @@ namespace ann /**Artificial Neural Network*/ { * } * @endcode * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mar, - * arma::sp_mat or arma::cube) - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube) + * @tparam InputType The type of the layer's inputs. The layer automatically + * cast inputs to this type (Default: arma::mat). + * @tparam OutputType The type of the computation which also causes the output + * to also be in this type. The type also allows the computation and weight + * type to differ from the input type (Default: arma::mat). */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class FlexibleReLU +template +class FlexibleReLUType : public Layer { public: /** + * Create the FlexibleReLU object using the specified alpha parameter. + * The trainable alpha parameter controls the range of the ReLU function. + * (Default alpha = 0). * - * Create the FlexibleReLU object using the specified parameters. - * The non zero parameter can be adjusted by specifying the parameter - * alpha which controls the range of the relu function. (Default alpha = 0) - * This parameter is trainable. - * - * @param alpha Parameter for adjusting the range of the relu function. - * + * @param alpha Parameter to adjust the range of the ReLU function. */ - FlexibleReLU(const double alpha = 0); + FlexibleReLUType(const double alpha = 0); + + //! Clone the FlexibleReLUType object. This handles polymorphism correctly. + FlexibleReLUType* Clone() const { return new FlexibleReLUType(*this); } /** - * Reset the layer parameter. + * Reset the layer parameter (alpha). The method is called to + * assign the allocated memory to the learnable layer parameter. */ - void Reset(); + void SetWeights(typename OutputType::elem_type* weightsPtr); /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -83,7 +83,6 @@ class FlexibleReLU * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template void Forward(const InputType& input, OutputType& output); /** @@ -95,8 +94,7 @@ class FlexibleReLU * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const DataType& input, const DataType& gy, DataType& g); + void Backward(const InputType& input, const OutputType& gy, OutputType& g); /** * Calculate the gradient using the output delta and the input activation. @@ -105,36 +103,22 @@ class FlexibleReLU * @param error The calculated error. * @param gradient The calculated gradient. */ - template - void Gradient(const arma::Mat& input, - const arma::Mat& error, - arma::Mat& gradient); + void Gradient(const InputType& input, + const OutputType& error, + OutputType& gradient); //! Get the parameters. - OutputDataType const& Parameters() const { return alpha; } + OutputType const& Parameters() const { return alpha; } //! Modify the parameters. - OutputDataType& Parameters() { return alpha; } + OutputType& Parameters() { return alpha; } - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta;} - - //! Get the gradient. - OutputDataType const& Gradient() const { return gradient; } - //! Modify the gradient. - OutputDataType& Gradient() { return gradient; } - - //! Get the parameter controlling the range of the relu function. - double const& Alpha() const { return alpha; } - //! Modify the parameter controlling the range of the relu function. + //! Get the parameter controlling the range of the ReLU function. + const double& Alpha() const { return alpha; } + //! Modify the parameter controlling the range of the ReLU function. double& Alpha() { return alpha; } + const size_t WeightSize() const { return 1; } + /** * Serialize the layer. */ @@ -142,21 +126,20 @@ class FlexibleReLU void serialize(Archive& ar, const uint32_t /* version*/); private: - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; - //! Parameter object. - OutputDataType alpha; + OutputType alpha; - //! Locally-stored gradient object. - OutputDataType gradient; - - //! Parameter controlling the range of the rectifier function + //! Parameter controlling the range of the ReLU function. double userAlpha; -}; // class FlexibleReLU + + //! Whether or not a forward pass has ever been performed. + bool initialized; +}; // class FlexibleReLUType + +// Convenience typedefs. + +// Standard flexible ReLU layer. +typedef FlexibleReLUType FlexibleReLU; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/flexible_relu_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/flexible_relu_impl.hpp similarity index 51% rename from src/mlpack/methods/ann/layer/flexible_relu_impl.hpp rename to src/mlpack/methods/ann/layer/not_adapted/flexible_relu_impl.hpp index ffd695b3e5..74af9b611e 100644 --- a/src/mlpack/methods/ann/layer/flexible_relu_impl.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/flexible_relu_impl.hpp @@ -18,66 +18,67 @@ #define MLPACK_METHODS_ANN_LAYER_FLEXIBLERELU_IMPL_HPP #include "flexible_relu.hpp" -#include namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -FlexibleReLU::FlexibleReLU( - const double alpha) : userAlpha(alpha) +template +FlexibleReLUType::FlexibleReLUType(const double alpha) : + userAlpha(alpha), + initialized(false) { this->alpha.set_size(1, 1); - this->alpha(0) = userAlpha; + this->alpha(0) = alpha; } -template -void FlexibleReLU::Reset() -{ - //! Set value of alpha to the one given by user. - alpha(0) = userAlpha; -} - -template template -void FlexibleReLU::Forward( +void FlexibleReLUType::SetWeights( + typename OutputType::elem_type* weightsPtr) +{ + alpha = OutputType(weightsPtr, 1, 1, false, false); +} + +template +void FlexibleReLUType::Forward( const InputType& input, OutputType& output) { + if (!initialized) + { + alpha[0] = userAlpha; + initialized = true; + } + output = arma::clamp(input, 0.0, DBL_MAX) + alpha(0); } -template -template -void FlexibleReLU::Backward( - const DataType& input, const DataType& gy, DataType& g) +template +void FlexibleReLUType::Backward( + const InputType& input, const OutputType& gy, OutputType& g) { - //! Compute the first derivative of FlexibleReLU function. + // Compute the first derivative of FlexibleReLU function. g = gy % arma::clamp(arma::sign(input), 0.0, 1.0); } -template -template -void FlexibleReLU::Gradient( - const arma::Mat& input, - const arma::Mat& error, - arma::Mat& gradient) +template +void FlexibleReLUType::Gradient( + const InputType& input, + const OutputType& error, + OutputType& gradient) { - if (gradient.n_elem == 0) - { - gradient.set_size(1, 1); - } - gradient(0) = arma::accu(error) / input.n_cols; } - -template +template template -void FlexibleReLU::serialize( +void FlexibleReLUType::serialize( Archive& ar, const uint32_t /* version*/) { + ar(cereal::base_class>(this)); + ar(CEREAL_NVP(alpha)); + ar(CEREAL_NVP(userAlpha)); + ar(CEREAL_NVP(initialized)); } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/glimpse.hpp b/src/mlpack/methods/ann/layer/not_adapted/glimpse.hpp similarity index 80% rename from src/mlpack/methods/ann/layer/glimpse.hpp rename to src/mlpack/methods/ann/layer/not_adapted/glimpse.hpp index 99a268b6a8..ee25a0cbb6 100644 --- a/src/mlpack/methods/ann/layer/glimpse.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/glimpse.hpp @@ -1,3 +1,4 @@ +// Temporarily drop. /** * @file methods/ann/layer/glimpse.hpp * @author Marcus Edel @@ -76,16 +77,16 @@ class MeanPoolingRule * (down-scaled cropped images) of increasing scale around a given location in a * given image. * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * @tparam InputType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * @tparam OutputType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). */ template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat + typename InputType = arma::mat, + typename OutputType = arma::mat > -class Glimpse +class GlimpseType : public Layer { public: /** @@ -100,12 +101,12 @@ class Glimpse * @param inputWidth The input width of the given input data. * @param inputHeight The input height of the given input data. */ - Glimpse(const size_t inSize = 0, - const size_t size = 0, - const size_t depth = 3, - const size_t scale = 2, - const size_t inputWidth = 0, - const size_t inputHeight = 0); + GlimpseType(const size_t inSize = 0, + const size_t size = 0, + const size_t depth = 3, + const size_t scale = 2, + const size_t inputWidth = 0, + const size_t inputHeight = 0); /** * Ordinary feed forward pass of the glimpse layer. @@ -113,8 +114,7 @@ class Glimpse * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template - void Forward(const arma::Mat& input, arma::Mat& output); + void Forward(const InputType& input, OutputType& output); /** * Ordinary feed backward pass of the glimpse layer. @@ -123,27 +123,13 @@ class Glimpse * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g); - - //! Get the output parameter. - OutputDataType& OutputParameter() const {return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the detla. - OutputDataType& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } + void Backward(const InputType& /* input */, + const OutputType& gy, + OutputType& g); //! Set the locationthe x and y coordinate of the center of the output //! glimpse. - void Location(const arma::mat& location) - { - this->location = location; - } + void Location(const arma::mat& location) { this->location = location; } //! Get the input width. size_t const& InputWidth() const { return inputWidth; } @@ -165,11 +151,6 @@ class Glimpse //! Modify the output height. size_t& OutputHeight() { return outputHeight; } - //! Get the value of the deterministic parameter. - bool Deterministic() const { return deterministic; } - //! Modify the value of the deterministic parameter. - bool& Deterministic() { return deterministic; } - //! Get the number of patches to crop per glimpse. size_t const& Depth() const { return depth; } @@ -182,10 +163,14 @@ class Glimpse //! Get the used glimpse size (height = width). size_t GlimpseSize() const { return size;} - //! Get the shape of the input. - size_t InputShape() const + const std::vector OutputDimensions() const { - return inSize; + std::vector result(inputDimensions.size(), 0); + result[0] = outputWidth; + result[1] = outputHeight; + for (size_t i = 2; i < inputDimensions.size(); ++i) + result[i] = inputDimensions[i]; + return result; } /** @@ -195,7 +180,7 @@ class Glimpse void serialize(Archive& ar, const uint32_t /* version */); private: - /* + /** * Transform the given input by changing rows to columns. * * @param w The input matrix used to perform the transformation. @@ -235,10 +220,9 @@ class Glimpse * @param input The input to be apply the pooling rule. * @param output The pooled result. */ - template void Pooling(const size_t kSize, - const arma::Mat& input, - arma::Mat& output) + const InputType& input, + OutputType& output) { const size_t rStep = kSize; const size_t cStep = kSize; @@ -260,21 +244,20 @@ class Glimpse * @param error The error used to perform the unpooling operation. * @param output The pooled result. */ - template - void Unpooling(const arma::Mat& input, - const arma::Mat& error, - arma::Mat& output) + void Unpooling(const InputType& input, + const OutputType& error, + OutputType& output) { const size_t rStep = input.n_rows / error.n_rows; const size_t cStep = input.n_cols / error.n_cols; - arma::Mat unpooledError; + OutputType unpooledError; for (size_t j = 0; j < input.n_cols; j += cStep) { for (size_t i = 0; i < input.n_rows; i += rStep) { - const arma::Mat& inputArea = input(arma::span(i, i + rStep - 1), - arma::span(j, j + cStep - 1)); + const InputType& inputArea = input(arma::span(i, i + rStep - 1), + arma::span(j, j + cStep - 1)); pooling.Unpooling(inputArea, error(i / rStep, j / cStep), unpooledError); @@ -292,8 +275,7 @@ class Glimpse * @param input The input to be apply the ReSampling rule. * @param output The pooled result. */ - template - void ReSampling(const arma::Mat& input, arma::Mat& output) + void ReSampling(const InputType& input, OutputType& output) { double wRatio = (double) (input.n_rows - 1) / (size - 1); double hRatio = (double) (input.n_cols - 1) / (size - 1); @@ -337,10 +319,9 @@ class Glimpse * @param error The error used to perform the DownwardReSampling operation. * @param output The DownwardReSampled result. */ - template - void DownwardReSampling(const arma::Mat& input, - const arma::Mat& error, - arma::Mat& output) + void DownwardReSampling(const InputType& input, + const OutputType& error, + OutputType& output) { double iWidth = input.n_rows - 1; double iHeight = input.n_cols - 1; @@ -404,36 +385,30 @@ class Glimpse //! Locally-stored output height. size_t outputHeight; - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; - //! Locally-stored depth of the input. size_t inputDepth; //! Locally-stored transformed input parameter. - arma::cube inputTemp; + arma::Cube inputTemp; //! Locally-stored transformed output parameter. - arma::cube outputTemp; + arma::Cube outputTemp; //! The x and y coordinate of the center of the output glimpse. - arma::mat location; + OutputType location; //! Locally-stored object to perform the mean pooling operation. MeanPoolingRule pooling; //! Location-stored module location parameter. - std::vector locationParameter; + std::vector locationParameter; //! Location-stored transformed gradient paramter. - arma::cube gTemp; + arma::Cube gTemp; +}; // class GlimpseType - //! If true use maximum a posteriori during the forward pass. - bool deterministic; -}; // class GlimpseLayer +// Standard Glimpse layer. +typedef GlimpseType Glimpse; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/glimpse_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/glimpse_impl.hpp similarity index 76% rename from src/mlpack/methods/ann/layer/glimpse_impl.hpp rename to src/mlpack/methods/ann/layer/not_adapted/glimpse_impl.hpp index b66379bfd1..a76b77f70c 100644 --- a/src/mlpack/methods/ann/layer/glimpse_impl.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/glimpse_impl.hpp @@ -20,8 +20,8 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -Glimpse::Glimpse( +template +GlimpseType::GlimpseType( const size_t inSize, const size_t size, const size_t depth, @@ -42,13 +42,14 @@ Glimpse::Glimpse( // Nothing to do here. } -template -template -void Glimpse::Forward( - const arma::Mat& input, arma::Mat& output) +template +void GlimpseType::Forward( + const InputType& input, OutputType& output) { - inputTemp = arma::cube(input.colptr(0), inputWidth, inputHeight, inSize); - outputTemp = arma::Cube(size, size, depth * inputTemp.n_slices); + inputTemp = arma::Cube(input.colptr(0), + inputWidth, inputHeight, inSize); + outputTemp = arma::Cube(size, size, depth * + inputTemp.n_slices); location = input.submat(0, 1, 1, 1); @@ -66,7 +67,8 @@ void Glimpse::Forward( { size_t padSize = std::floor((glimpseSize - 1) / 2); - arma::Cube inputPadded = arma::zeros >( + arma::Cube inputPadded = + arma::zeros>( inputTemp.n_rows + padSize * 2, inputTemp.n_cols + padSize * 2, inputTemp.n_slices / inSize); @@ -98,9 +100,8 @@ void Glimpse::Forward( for (size_t j = (inputIdx + depthIdx * (depth - 1)), paddedSlice = 0; j < outputTemp.n_slices; j += (inSize * depth), paddedSlice++) { - arma::Mat poolingInput = inputPadded.subcube(x, y, - paddedSlice, x + glimpseSize - 1, y + glimpseSize - 1, - paddedSlice); + InputType poolingInput = inputPadded.subcube(x, y, paddedSlice, + x + glimpseSize - 1, y + glimpseSize - 1, paddedSlice); if (scale == 2) { @@ -120,19 +121,19 @@ void Glimpse::Forward( outputTemp.slice(i) = arma::trans(outputTemp.slice(i)); } - output = arma::Mat(outputTemp.memptr(), outputTemp.n_elem, 1); + output = OutputType(outputTemp.memptr(), outputTemp.n_elem, 1); outputWidth = outputTemp.n_rows; outputHeight = outputTemp.n_cols; } -template -template -void Glimpse::Backward( - const arma::Mat& /* input */, const arma::Mat& gy, arma::Mat& g) +template +void GlimpseType::Backward( + const InputType& /* input */, const OutputType& gy, OutputType& g) { // Generate a cube using the backpropagated error matrix. - arma::Cube mappedError = arma::zeros(outputWidth, + arma::Cube mappedError = + arma::zeros>(outputWidth, outputHeight, 1); location = locationParameter.back(); @@ -142,13 +143,13 @@ void Glimpse::Backward( { for (size_t i = 0; i < gy.n_cols; ++i) { - mappedError.slice(s + i) = arma::Mat(gy.memptr(), + mappedError.slice(s + i) = OutputType(gy.memptr(), outputWidth, outputHeight); } } - gTemp = arma::zeros(inputTemp.n_rows, inputTemp.n_cols, - inputTemp.n_slices); + gTemp = arma::zeros>( + inputTemp.n_rows, inputTemp.n_cols, inputTemp.n_slices); for (size_t inputIdx = 0; inputIdx < inSize; inputIdx++) { @@ -157,7 +158,8 @@ void Glimpse::Backward( { size_t padSize = std::floor((glimpseSize - 1) / 2); - arma::Cube inputPadded = arma::zeros >( + arma::Cube inputPadded = + arma::zeros>( inputTemp.n_rows + padSize * 2, inputTemp.n_cols + padSize * 2, inputTemp.n_slices / inSize); @@ -184,9 +186,8 @@ void Glimpse::Backward( for (size_t j = (inputIdx + depthIdx * (depth - 1)), paddedSlice = 0; j < mappedError.n_slices; j += (inSize * depth), paddedSlice++) { - arma::Mat poolingOutput = inputPadded.subcube(x, y, - paddedSlice, x + glimpseSize - 1, y + glimpseSize - 1, - paddedSlice); + OutputType poolingOutput = inputPadded.subcube(x, y, paddedSlice, + x + glimpseSize - 1, y + glimpseSize - 1, paddedSlice); if (scale == 2) { @@ -211,14 +212,16 @@ void Glimpse::Backward( } Transform(gTemp); - g = arma::mat(gTemp.memptr(), gTemp.n_elem, 1); + g = OutputType(gTemp.memptr(), gTemp.n_elem, 1); } -template +template template -void Glimpse::serialize( +void GlimpseType::serialize( Archive& ar, const uint32_t /* version */) { + ar(cereal::base_class>(this)); + ar(CEREAL_NVP(inSize)); ar(CEREAL_NVP(size)); ar(CEREAL_NVP(depth)); diff --git a/src/mlpack/methods/ann/layer/group_norm.hpp b/src/mlpack/methods/ann/layer/not_adapted/group_norm.hpp similarity index 100% rename from src/mlpack/methods/ann/layer/group_norm.hpp rename to src/mlpack/methods/ann/layer/not_adapted/group_norm.hpp diff --git a/src/mlpack/methods/ann/layer/group_norm_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/group_norm_impl.hpp similarity index 100% rename from src/mlpack/methods/ann/layer/group_norm_impl.hpp rename to src/mlpack/methods/ann/layer/not_adapted/group_norm_impl.hpp diff --git a/src/mlpack/methods/ann/layer/gru.hpp b/src/mlpack/methods/ann/layer/not_adapted/gru.hpp similarity index 67% rename from src/mlpack/methods/ann/layer/gru.hpp rename to src/mlpack/methods/ann/layer/not_adapted/gru.hpp index c895ee7f81..290adb0276 100644 --- a/src/mlpack/methods/ann/layer/gru.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/gru.hpp @@ -1,3 +1,4 @@ +// Temporarily drop. /** * @file methods/ann/layer/gru.hpp * @author Sumedh Ghaisas @@ -31,9 +32,6 @@ #include -#include "../visitor/delta_visitor.hpp" -#include "../visitor/output_parameter_visitor.hpp" - #include "layer_types.hpp" #include "add_merge.hpp" #include "sequential.hpp" @@ -46,16 +44,16 @@ namespace ann /** Artificial Neural Network. */ { * * This cell can be used in RNN networks. * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * @tparam InputType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * @tparam OutputType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). */ template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat + typename InputType = arma::mat, + typename OutputType = arma::mat > -class GRU +class GRU : public Layer { public: //! Create the GRU object. @@ -79,8 +77,7 @@ class GRU * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template - void Forward(const arma::Mat& input, arma::Mat& output); + void Forward(const InputType& input, OutputType& output); /** * Ordinary feed backward pass of a neural network, calculating the function @@ -91,10 +88,9 @@ class GRU * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g); + void Backward(const InputType& /* input */, + const OutputType& gy, + OutputType& g); /* * Calculate the gradient using the output delta and the input activation. @@ -103,10 +99,9 @@ class GRU * @param error The calculated error. * @param gradient The calculated gradient. */ - template - void Gradient(const arma::Mat& input, - const arma::Mat& /* error */, - arma::Mat& /* gradient */); + void Gradient(const InputType& input, + const OutputType& /* error */, + OutputType& /* gradient */); /* * Resets the cell to accept a new input. This breaks the BPTT chain starts a @@ -116,38 +111,18 @@ class GRU */ void ResetCell(const size_t size); - //! The value of the deterministic parameter. - bool Deterministic() const { return deterministic; } - //! Modify the value of the deterministic parameter. - bool& Deterministic() { return deterministic; } - //! Get the maximum number of steps to backpropagate through time (BPTT). size_t Rho() const { return rho; } //! Modify the maximum number of steps to backpropagate through time (BPTT). size_t& Rho() { return rho; } //! Get the parameters. - OutputDataType const& Parameters() const { return weights; } + OutputType const& Parameters() const { return weights; } //! Modify the parameters. - OutputDataType& Parameters() { return weights; } - - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } - - //! Get the gradient. - OutputDataType const& Gradient() const { return gradient; } - //! Modify the gradient. - OutputDataType& Gradient() { return gradient; } + OutputType& Parameters() { return weights; } //! Get the model modules. - std::vector >& Model() { return network; } + std::vector*>& Model() { return network; } //! Get the number of input units. size_t InSize() const { return inSize; } @@ -181,37 +156,28 @@ class GRU size_t batchSize; //! Locally-stored weight object. - OutputDataType weights; + OutputType weights; //! Locally-stored input 2 gate module. - LayerTypes<> input2GateModule; + Layer* input2GateModule; //! Locally-stored output 2 gate module. - LayerTypes<> output2GateModule; + Layer* output2GateModule; //! Locally-stored output hidden state 2 gate module. - LayerTypes<> outputHidden2GateModule; + Layer* outputHidden2GateModule; //! Locally-stored input gate module. - LayerTypes<> inputGateModule; + Layer* inputGateModule; //! Locally-stored hidden state module. - LayerTypes<> hiddenStateModule; + Layer* hiddenStateModule; //! Locally-stored forget gate module. - LayerTypes<> forgetGateModule; - - //! Locally-stored output parameter visitor. - OutputParameterVisitor outputParameterVisitor; - - //! Locally-stored delta visitor. - DeltaVisitor deltaVisitor; - - //! Locally-stored delete visitor. - DeleteVisitor deleteVisitor; + Layer* forgetGateModule; //! Locally-stored list of network modules. - std::vector > network; + std::vector*> network; //! Locally-stored number of forward steps. size_t forwardStep; @@ -223,34 +189,34 @@ class GRU size_t gradientStep; //! Locally-stored output parameters. - std::list outParameter; + std::list outParameter; //! Matrix of all zeroes to initialize the output - arma::mat allZeros; + OutputType allZeros; //! Iterator pointed to the last output produced by the cell - std::list::iterator prevOutput; + typename std::list::iterator prevOutput; //! Iterator pointed to the last output processed by backward - std::list::iterator backIterator; + typename std::list::iterator backIterator; //! Iterator pointed to the last output processed by gradient - std::list::iterator gradIterator; + typename std::list::iterator gradIterator; //! Locally-stored previous error. - arma::mat prevError; + OutputType prevError; //! If true dropout and scaling is disabled, see notes above. bool deterministic; //! Locally-stored delta object. - OutputDataType delta; + OutputType delta; //! Locally-stored gradient object. - OutputDataType gradient; + OutputType gradient; //! Locally-stored output parameter object. - OutputDataType outputParameter; + OutputType outputParameter; }; // class GRU } // namespace ann diff --git a/src/mlpack/methods/ann/layer/not_adapted/gru_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/gru_impl.hpp new file mode 100644 index 0000000000..2e7fb208c2 --- /dev/null +++ b/src/mlpack/methods/ann/layer/not_adapted/gru_impl.hpp @@ -0,0 +1,369 @@ +/** + * @file methods/ann/layer/gru_impl.hpp + * @author Sumedh Ghaisas + * + * Implementation of the GRU class, which implements a gru network + * layer. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_LAYER_GRU_IMPL_HPP +#define MLPACK_METHODS_ANN_LAYER_GRU_IMPL_HPP + +// In case it hasn't yet been included. +#include "gru.hpp" + +namespace mlpack { +namespace ann /** Artificial Neural Network. */ { + +template +GRU::GRU() +{ + // Nothing to do here. +} + +template +GRU::GRU( + const size_t inSize, + const size_t outSize, + const size_t rho) : + inSize(inSize), + outSize(outSize), + rho(rho), + batchSize(1), + forwardStep(0), + backwardStep(0), + gradientStep(0) +{ + // Input specific linear layers(for zt, rt, ot). + input2GateModule = new LinearType(inSize, 3 * outSize); + + // Previous output gates (for zt and rt). + output2GateModule = new LinearNoBiasType(outSize, + 2 * outSize); + + // Previous output gate for ot. + outputHidden2GateModule = new LinearNoBiasType(outSize, + outSize); + + network.push_back(input2GateModule); + network.push_back(output2GateModule); + network.push_back(outputHidden2GateModule); + + inputGateModule = new SigmoidLayer(); + forgetGateModule = new SigmoidLayer(); + hiddenStateModule = new TanHLayer(); + + network.push_back(inputGateModule); + network.push_back(hiddenStateModule); + network.push_back(forgetGateModule); + + prevError = arma::zeros(3 * outSize, batchSize); + + allZeros = arma::zeros(outSize, batchSize); + + outParameter.emplace_back(allZeros.memptr(), + allZeros.n_rows, allZeros.n_cols, false, true); + + prevOutput = outParameter.begin(); + backIterator = outParameter.end(); + gradIterator = outParameter.end(); +} + +template +void GRU::Forward( + const InputType& input, OutputType& output) +{ + if (input.n_cols != batchSize) + { + batchSize = input.n_cols; + prevError.resize(3 * outSize, batchSize); + allZeros.zeros(outSize, batchSize); + // Batch size better not change during an iteration... + if (outParameter.size() > 1) + { + Log::Fatal << "GRU<>::Forward(): batch size cannot change during a " + << "forward pass!" << std::endl; + } + + outParameter.clear(); + outParameter.emplace_back(allZeros.memptr(), + allZeros.n_rows, allZeros.n_cols, false, true); + + prevOutput = outParameter.begin(); + backIterator = outParameter.end(); + gradIterator = outParameter.end(); + } + + // Process the input linearly(zt, rt, ot). + input2GateModule->Forward(input, input2GateModule->OutputParameter()); + + // Process the output(zt, rt) linearly. + output2GateModule->Forward(*prevOutput, output2GateModule->OutputParameter()); + + // Merge the outputs(zt and rt). + output = input2GateModule->OutputParameter().submat(0, 0, 2 * outSize - 1, + batchSize - 1) + output2GateModule->OutputParameter(); + + // Pass the first outSize through inputGate(it). + inputGateModule->Forward(output.submat( 0, 0, 1 * outSize - 1, batchSize - 1), + inputGateModule->OutputParameter()); + + // Pass the second through forgetGate. + forgetGateModule->Forward(output.submat( 1 * outSize, 0, 2 * outSize - 1, + batchSize - 1), forgetGateModule->OutputParameter()); + + OutputType modInput = forgetGateModule->OutputParameter() % *prevOutput; + + // Pass that through the outputHidden2GateModule. + outputHidden2GateModule->Forward(modInput, + outputHidden2GateModule->OutputParameter()); + + // Merge for ot. + OutputType outputH = input2GateModule->OutputParameter().submat(2 * outSize, + 0, 3 * outSize - 1, batchSize - 1) + + outputHidden2GateModule->OutputParameter(); + + // Pass it through hiddenGate. + hiddenStateModule->ForwardVisitor(outputH, + hiddenStateModule->OutputParameter()); + + // Update the output (nextOutput): cmul1 + cmul2 + // Where cmul1 is input gate * prevOutput and + // cmul2 is (1 - input gate) * hidden gate. + output = (inputGateModule->OutputParameter() + % (*prevOutput - hiddenStateModule->OutputParameter())) + + hiddenStateModule->OutputParameter(); + + forwardStep++; + if (forwardStep == rho) + { + forwardStep = 0; + if (this->training) + { + outParameter.emplace_back(allZeros.memptr(), + allZeros.n_rows, allZeros.n_cols, false, true); + prevOutput = --outParameter.end(); + } + else + { + *prevOutput = arma::mat(allZeros.memptr(), + allZeros.n_rows, allZeros.n_cols, false, true); + } + } + else if (this->training) + { + outParameter.push_back(output); + prevOutput = --outParameter.end(); + } + else + { + if (forwardStep == 1) + { + outParameter.clear(); + outParameter.push_back(output); + + prevOutput = outParameter.begin(); + } + else + { + *prevOutput = output; + } + } +} + +template +void GRU::Backward( + const InputType& input, const OutputType& gy, OutputType& g) +{ + if (input.n_cols != batchSize) + { + batchSize = input.n_cols; + prevError.resize(3 * outSize, batchSize); + allZeros.zeros(outSize, batchSize); + // Batch size better not change during an iteration... + if (outParameter.size() > 1) + { + Log::Fatal << "GRU<>::Forward(): batch size cannot change during a " + << "forward pass!" << std::endl; + } + + outParameter.clear(); + outParameter.emplace_back(allZeros.memptr(), + allZeros.n_rows, allZeros.n_cols, false, true); + + prevOutput = outParameter.begin(); + backIterator = outParameter.end(); + gradIterator = outParameter.end(); + } + + OutputType gyLocal; + if ((outParameter.size() - backwardStep - 1) % rho != 0 && backwardStep != 0) + { + gyLocal = gy + output2GateModule->Delta(); + } + else + { + gyLocal = OutputType(((OutputType&) gy).memptr(), gy.n_rows, gy.n_cols, + false, false); + } + + if (backIterator == outParameter.end()) + { + backIterator = --(--outParameter.end()); + } + + // Delta zt. + OutputType dZt = gyLocal % (*backIterator - + hiddenStateModule->OutputParameter()); + + // Delta ot. + OutputType dOt = gyLocal % (arma::ones(outSize, batchSize) - + inputGateModule->OutputParameter()); + + // Delta of input gate. + inputGateModule->Backward(inputGateModule->OutputParameter(), dZt, + inputGateModule->Delta()); + + // Delta of hidden gate. + hiddenStateModule->Backward(hiddenStateModule->OutputParameter(), dOt, + hiddenStateModule->Delta()); + + // Delta of outputHidden2GateModule. + outputHidden2GateModule->Backward(outputHidden2GateModule->OutputParameter(), + hiddenStateModule->Delta(), outputHidden2GateModule->Delta()); + + // Delta rt. + OutputType dRt = outputHidden2GateModule->Delta() % *backIterator; + + // Delta of forget gate. + forgetGateModule->Backward(forgetGateModule->OutputParameter(), dRt, + forgetGateModule->Delta()); + + // Put delta zt. + prevError.submat(0, 0, 1 * outSize - 1, batchSize - 1) = + inputGateModule->Delta(); + + // Put delta rt. + prevError.submat(1 * outSize, 0, 2 * outSize - 1, batchSize - 1) = + forgetGateModule->Delta(); + + // Put delta ot. + prevError.submat(2 * outSize, 0, 3 * outSize - 1, batchSize - 1) = + hiddenStateModule->Delta(); + + // Get delta ht - 1 for input gate and forget gate. + OutputType prevErrorSubview = prevError.submat(0, 0, 2 * outSize - 1, + batchSize - 1); + output2GateModule->Backward(input2GateModule->OutputParameter(), + prevErrorSubview, output2GateModule->Delta()); + + // Add delta ht - 1 from hidden state. + output2GateModule->Delta() += outputHidden2GateModule->Delta() % + forgetGateModule->OutputParameter(); + + // Add delta ht - 1 from ht. + output2GateModule->Delta() += gyLocal % inputGateModule->OutputParameter(); + + // Get delta input. + input2GateModule->Backward(input2GateModule->OutputParameter(), prevError, + input2GateModule->Delta()); + + backwardStep++; + backIterator--; + + g = input2GateModule->Delta(); +} + +template +void GRU::Gradient( + const InputType& input, + const OutputType& /* error */, + OutputType& /* gradient */) +{ + if (input.n_cols != batchSize) + { + batchSize = input.n_cols; + prevError.resize(3 * outSize, batchSize); + allZeros.zeros(outSize, batchSize); + // Batch size better not change during an iteration... + if (outParameter.size() > 1) + { + Log::Fatal << "GRU<>::Forward(): batch size cannot change during a " + << "forward pass!" << std::endl; + } + + outParameter.clear(); + outParameter.emplace_back(allZeros.memptr(), + allZeros.n_rows, allZeros.n_cols, false, true); + + prevOutput = outParameter.begin(); + backIterator = outParameter.end(); + gradIterator = outParameter.end(); + } + + if (gradIterator == outParameter.end()) + { + gradIterator = --(--outParameter.end()); + } + + input2GateModule->Gradient(input, prevError, input2GateModule->Gradient()); + + output2GateModule->Gradient(*gradIterator, + prevError.submat(0, 0, 2 * outSize - 1, batchSize - 1), + output2GateModule->Gradient()); + + outputHidden2GateModule->Gradient( + *gradIterator % forgetGateModule->OutputParameter(), + prevError.submat(2 * outSize, 0, 3 * outSize - 1, batchSize - 1), + outputHidden2GateModule->Gradient()); + + gradIterator--; +} + +template +void GRU::ResetCell(const size_t /* size */) +{ + outParameter.clear(); + outParameter.emplace_back(allZeros.memptr(), + allZeros.n_rows, allZeros.n_cols, false, true); + + prevOutput = outParameter.begin(); + backIterator = outParameter.end(); + gradIterator = outParameter.end(); + + forwardStep = 0; + backwardStep = 0; +} + +template +template +void GRU::serialize( + Archive& ar, const uint32_t /* version */) +{ + ar(cereal::base_class>(this)); + + // If necessary, clean memory from the old model. + // TODO: CEREAL_POINTER() should clean memory automatically... + + ar(CEREAL_NVP(inSize)); + ar(CEREAL_NVP(outSize)); + ar(CEREAL_NVP(rho)); + + ar(CEREAL_NVP(weights)); + + ar(CEREAL_POINTER(input2GateModule)); + ar(CEREAL_POINTER(output2GateModule)); + ar(CEREAL_POINTER(outputHidden2GateModule)); + ar(CEREAL_POINTER(inputGateModule)); + ar(CEREAL_POINTER(forgetGateModule)); + ar(CEREAL_POINTER(hiddenStateModule)); +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/layer/hard_tanh.hpp b/src/mlpack/methods/ann/layer/not_adapted/hard_tanh.hpp similarity index 71% rename from src/mlpack/methods/ann/layer/hard_tanh.hpp rename to src/mlpack/methods/ann/layer/not_adapted/hard_tanh.hpp index 3c49add924..b214ff095b 100644 --- a/src/mlpack/methods/ann/layer/hard_tanh.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/hard_tanh.hpp @@ -14,6 +14,8 @@ #include +#include "layer.hpp" + namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -37,16 +39,14 @@ namespace ann /** Artificial Neural Network. */ { * \right. * @f} * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam InputType The type of the layer's inputs. The layer automatically + * cast inputs to this type (Default: arma::mat). + * @tparam OutputType The type of the computation which also causes the output + * to also be in this type. The type also allows the computation and weight + * type to differ from the input type (Default: arma::mat). */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class HardTanH +template +class HardTanHType : public Layer { public: /** @@ -57,7 +57,10 @@ class HardTanH * @param maxValue Range of the linear region maximum value. * @param minValue Range of the linear region minimum value. */ - HardTanH(const double maxValue = 1, const double minValue = -1); + HardTanHType(const double maxValue = 1, const double minValue = -1); + + //! Clone the HardTanHType object. This handles polymorphism correctly. + HardTanHType* Clone() const { return new HardTanHType(*this); } /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -66,7 +69,6 @@ class HardTanH * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template void Forward(const InputType& input, OutputType& output); /** @@ -78,20 +80,7 @@ class HardTanH * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const DataType& input, - const DataType& gy, - DataType& g); - - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } + void Backward(const InputType& input, const OutputType& gy, OutputType& g); //! Get the maximum value. double const& MaxValue() const { return maxValue; } @@ -110,18 +99,17 @@ class HardTanH void serialize(Archive& ar, const uint32_t /* version */); private: - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; - //! Maximum value for the HardTanH function. double maxValue; //! Minimum value for the HardTanH function. double minValue; -}; // class HardTanH +}; // class HardTanHType + +// Convenience typedefs. + +// Standard HardTanH layer. +typedef HardTanHType HardTanH; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/hard_tanh_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/hard_tanh_impl.hpp similarity index 71% rename from src/mlpack/methods/ann/layer/hard_tanh_impl.hpp rename to src/mlpack/methods/ann/layer/not_adapted/hard_tanh_impl.hpp index 30eb8c2a1f..8778412d77 100644 --- a/src/mlpack/methods/ann/layer/hard_tanh_impl.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/hard_tanh_impl.hpp @@ -18,8 +18,8 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -HardTanH::HardTanH( +template +HardTanHType::HardTanHType( const double maxValue, const double minValue) : maxValue(maxValue), @@ -28,12 +28,10 @@ HardTanH::HardTanH( // Nothing to do here. } -template template -void HardTanH::Forward( +void HardTanHType::Forward( const InputType& input, OutputType& output) { - output = input; for (size_t i = 0; i < input.n_elem; ++i) { output(i) = (output(i) > maxValue ? maxValue : @@ -41,10 +39,9 @@ void HardTanH::Forward( } } -template -template -void HardTanH::Backward( - const DataType& input, const DataType& gy, DataType& g) +template +void HardTanHType::Backward( + const InputType& input, const OutputType& gy, OutputType& g) { g = gy; for (size_t i = 0; i < input.n_elem; ++i) @@ -56,12 +53,14 @@ void HardTanH::Backward( } } -template +template template -void HardTanH::serialize( +void HardTanHType::serialize( Archive& ar, const uint32_t /* version */) { + ar(cereal::base_class>(this)); + ar(CEREAL_NVP(maxValue)); ar(CEREAL_NVP(minValue)); } diff --git a/src/mlpack/methods/ann/layer/hardshrink.hpp b/src/mlpack/methods/ann/layer/not_adapted/hardshrink.hpp similarity index 67% rename from src/mlpack/methods/ann/layer/hardshrink.hpp rename to src/mlpack/methods/ann/layer/not_adapted/hardshrink.hpp index 5817acb713..df6c61756c 100644 --- a/src/mlpack/methods/ann/layer/hardshrink.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/hardshrink.hpp @@ -17,6 +17,8 @@ #include +#include "layer.hpp" + namespace mlpack { namespace ann /** Artifical Neural Network. */ { @@ -37,23 +39,28 @@ namespace ann /** Artifical Neural Network. */ { * \f} * * \f$\lambda\f$ is set to 0.5 by default. + * + * @tparam InputType The type of the layer's inputs. The layer automatically + * cast inputs to this type (Default: arma::mat). + * @tparam OutputType The type of the computation which also causes the output + * to also be in this type. The type also allows the computation and weight + * type to differ from the input type (Default: arma::mat). */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class HardShrink +template +class HardShrinkType : public Layer { public: /** * Create HardShrink object using specified hyperparameter lambda. * - * @param lambda Is calculated by multiplying the - * noise level sigma of the input(noisy image) and a - * coefficient 'a' which is one of the training parameters. - * Default value of lambda is 0.5. + * @param lambda Is calculated by multiplying the noise level sigma of the + * input(noisy image) and a coefficient 'a' which is one of the training + * parameters. Default value of lambda is 0.5. */ - HardShrink(const double lambda = 0.5); + HardShrinkType(const double lambda = 0.5); + + //! Clone the HardShrinkType object. This handles polymorphism correctly. + HardShrinkType* Clone() const { return new HardShrinkType(*this); } /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -62,7 +69,6 @@ class HardShrink * @param input Input data used for evaluating the Hard Shrink function. * @param output Resulting output activation. */ - template void Forward(const InputType& input, OutputType& output); /** @@ -74,42 +80,26 @@ class HardShrink * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const DataType& input, - DataType& gy, - DataType& g); - - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } + void Backward(const InputType& input, const OutputType& gy, OutputType& g); //! Get the hyperparameter lambda. double const& Lambda() const { return lambda; } //! Modify the hyperparameter lambda. double& Lambda() { return lambda; } - /** - * Serialize the layer. - */ + //! Serialize the layer. template void serialize(Archive& ar, const uint32_t /* version */); private: - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; - //! Locally-stored hyperparameter lambda. double lambda; -}; // class HardShrink +}; // class HardShrinkType + +// Convenience typedefs. + +// Standard HardShrink layer. +typedef HardShrinkType HardShrink; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/hardshrink_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/hardshrink_impl.hpp similarity index 63% rename from src/mlpack/methods/ann/layer/hardshrink_impl.hpp rename to src/mlpack/methods/ann/layer/not_adapted/hardshrink_impl.hpp index 5969a281dd..b1098cbb32 100644 --- a/src/mlpack/methods/ann/layer/hardshrink_impl.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/hardshrink_impl.hpp @@ -20,37 +20,35 @@ namespace ann /** Artificial Neural Network. */ { // This constructor is called for Hard Shrink activation function. // 'lambda' is a hyperparameter. -template -HardShrink::HardShrink(const double lambda) : +template +HardShrinkType::HardShrinkType(const double lambda) : lambda(lambda) { // Nothing to do here. } -template template -void HardShrink::Forward( +void HardShrinkType::Forward( const InputType& input, OutputType& output) { output = ((input > lambda) + (input < -lambda)) % input; } -template -template -void HardShrink::Backward( - const DataType& input, DataType& gy, DataType& g) +template +void HardShrinkType::Backward( + const InputType& input, const OutputType& gy, OutputType& g) { - DataType derivative; - derivative = (arma::ones(arma::size(input)) - (input == 0)); - g = gy % derivative; + g = gy % (arma::ones(arma::size(input)) - (input == 0)); } -template +template template -void HardShrink::serialize( +void HardShrinkType::serialize( Archive& ar, const uint32_t /* version */) { + ar(cereal::base_class>(this)); + ar(CEREAL_NVP(lambda)); } diff --git a/src/mlpack/methods/ann/layer/not_adapted/highway.hpp b/src/mlpack/methods/ann/layer/not_adapted/highway.hpp new file mode 100644 index 0000000000..749149e1c3 --- /dev/null +++ b/src/mlpack/methods/ann/layer/not_adapted/highway.hpp @@ -0,0 +1,157 @@ +// Temporarily drop. +/** + * @file methods/ann/layer/highway.hpp + * @author Konstantin Sidorov + * @author Saksham Bansal + * + * Definition of the Highway layer. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_LAYER_HIGHWAY_HPP +#define MLPACK_METHODS_ANN_LAYER_HIGHWAY_HPP + +#include + +#include "layer.hpp" + +namespace mlpack { +namespace ann /** Artificial Neural Network. */ { + +/** + * Implementation of the Highway layer. The Highway class can vary its behavior + * between that of feed-forward fully connected network container and that + * of a layer which simply passes its inputs through depending on the transform + * gate. Note that the size of the input and output matrices of this class + * should be equal. + * + * For more information, refer the following paper. + * + * @code + * @article{Srivastava2015, + * author = {Rupesh Kumar Srivastava, Klaus Greff, Jurgen Schmidhuber}, + * title = {Training Very Deep Networks}, + * journal = {Advances in Neural Information Processing Systems}, + * year = {2015}, + * url = {https://arxiv.org/abs/1507.06228}, + * } + * @endcode + * + * @tparam InputType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + */ +template < + typename InputType = arma::mat, + typename OutputType = arma::mat +> +class HighwayType : public MultiLayer +{ + public: + //! Create the HighwayType object. + HighwayType(); + + //! Destroy the HighwayType object. + virtual ~HighwayType(); + + //! Clone the HighwayType object. This handles polymorphism correctly. + HighwayType* Clone() const { return new HighwayType(*this); } + + //! Copy the given HighwayType (but not weights). + HighwayType(const HighwayType& other); + //! Take ownership of the given HighwayType (but not weights). + HighwayType(HighwayType&& other); + //! Copy the given HighwayType (but not weights). + HighwayType& operator=(const HighwayType& other); + //! Take ownership of the given HighwayType (but not weights). + HighwayType& operator=(HighwayType&& other); + + void SetWeights(typename OutputType::elem_type* weightsPtr); + + /** + * Ordinary feed-forward pass of a neural network, evaluating the function + * f(x) by propagating the activity forward through f. + * + * @param input Input data used for evaluating the specified function. + * @param output Resulting output activation. + */ + void Forward(const InputType& input, OutputType& output); + + /** + * Ordinary feed-backward pass of a neural network, calculating the function + * f(x) by propagating x backwards through f. Using the results from the + * feed-forward pass. + * + * @param * (input) The propagated input activation. + * @param gy The backpropagated error. + * @param g The calculated gradient. + */ + void Backward(const InputType& /* input */, + const OutputType& gy, + OutputType& g); + + /** + * Calculate the gradient using the output delta and the input activation. + * + * @param input The input parameter used for calculating the gradient. + * @param error The calculated error. + * @param gradient The calculated gradient. + */ + void Gradient(const InputType& input, + const OutputType& error, + OutputType& gradient); + + //! Get the parameters. + OutputType const& Parameters() const { return weights; } + //! Modify the parameters. + OutputType& Parameters() { return weights; } + + //! Get the number of trainable weights. + size_t WeightSize() const + { + size_t result = this->totalInputSize * (this->totalInputSize + 1); + for (size_t i = 0; i < this->network.size(); ++i) + result += this->network[i]->WeightSize(); + return result; + } + + /** + * Serialize the layer. + */ + template + void serialize(Archive& ar, const uint32_t /* version */); + + private: + //! Locally-stored weight object. + OutputType weights; + + //! Weights for transformation of output. + OutputType transformWeight; + + //! Bias for transformation of output. + OutputType transformBias; + + //! Locally-stored transform gate parameters. + OutputType transformGate; + + //! Locally-stored transform gate activation. + OutputType transformGateActivation; + + //! Locally-stored transform gate error. + OutputType transformGateError; +}; // class HighwayType + +// Standard Highway layer. +typedef HighwayType Highway; + +} // namespace ann +} // namespace mlpack + +// Include implementation. +#include "highway_impl.hpp" + +#endif diff --git a/src/mlpack/methods/ann/layer/not_adapted/highway_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/highway_impl.hpp new file mode 100644 index 0000000000..0b019a8486 --- /dev/null +++ b/src/mlpack/methods/ann/layer/not_adapted/highway_impl.hpp @@ -0,0 +1,194 @@ +/** + * @file methods/ann/layer/highway_impl.hpp + * @author Konstantin Sidorov + * @author Saksham Bansal + * + * Implementation of Highway layer. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_LAYER_HIGHWAY_IMPL_HPP +#define MLPACK_METHODS_ANN_LAYER_HIGHWAY_IMPL_HPP + +// In case it hasn't yet been included. +#include "highway.hpp" + +namespace mlpack { +namespace ann /** Artificial Neural Network. */ { + +template +HighwayType::HighwayType() +{ + // Nothing to do here. + // TODO: how do we add the child layers ?? (read paper ...) +} + +template +HighwayType::~HighwayType() +{ + // Nothing to do. +} + +template +HighwayType::HighwayType(const HighwayType& other) : + MultiLayer(other) +{ + // Nothing to do. +} + +template +HighwayType::HighwayType(HighwayType&& other) : + MultiLayer(std::move(other)) +{ + // Nothing to do. +} + +template +HighwayType& +HighwayType::operator=(const HighwayType& other) +{ + if (&other == this) + { + MultiLayer::operator=(other); + } + + return *this; +} + +template +HighwayType& +HighwayType::operator=(HighwayType&& other) +{ + if (&other == this) + { + MultiLayer::operator=(std::move(other)); + } + + return *this; +} + +template +void HighwayType::SetWeights( + typename OutputType::elem_type* weightsPtr) +{ + transformWeight = OutputType(weightsPtr, this->inSize, + this->inSize, false, false); + transformBias = OutputType(weightsPtr + transformWeight.n_elem, + this->inSize, 1, false, false); + + size_t start = transformWeight.n_elem + transformBias.n_elem; + for (size_t i = 0; i < this->network.size(); ++i) + { + this->network[i]->SetWeights(weightsPtr + start); + start += this->network[i]->WeightSize(); + } +} + +template +void HighwayType::Forward( + const InputType& input, OutputType& output) +{ + this->InitializeForwardPassMemory(input.n_cols); + + this->network.front()->Forward(input, this->layerOutputs.front()); + + for (size_t i = 1; i < this->network.size(); ++i) + { + this->network[i]->Forward(this->layerOutputs[i - 1], this->layerOutputs[i]); + } + + output = this->layerOutputs.back(); // TODO: can this be cleaned up? + + // TODO: move to ComputeOutputDimensions() + if (arma::size(output) != arma::size(input)) + { + Log::Fatal << "The sizes of the output and input matrices of the Highway" + << " network should be equal. Please examine the network layers."; + } + + transformGate = transformWeight * input; + transformGate.each_col() += transformBias; + transformGateActivation = 1.0 /(1 + arma::exp(-transformGate)); + output = (this->layerOutputs.back() % transformGateActivation) + + (input % (1 - transformGateActivation)); +} + +template +void HighwayType::Backward( + const InputType& input, + const OutputType& gy, + OutputType& g) +{ + this->InitializeBackwardPassMemory(input.n_cols); + + OutputType gyTransform = gy % transformGateActivation; + this->network.back()->Backward(this->layerOutputs.back(), gyTransform, + this->layerDeltas.back()); + + for (size_t i = 2; i < this->network.size() + 1; ++i) + { + this->network[this->network.size() - i]->Backward( + this->layerOutputs[this->network.size() - i], + this->layerDeltas[this->network.size() - i + 1], + this->layerDeltas[this->network.size() - i]); + } + + transformGateError = gy % (gy - input) % + transformGateActivation % (1.0 - transformGateActivation); + g = this->layerDeltas.front() + (transformWeight.t() * transformGateError) + + (gy % (1 - transformGateActivation)); +} + +template +void HighwayType::Gradient( + const InputType& input, + const OutputType& error, + OutputType& gradient) +{ + // Create an alias for the gradient that only refers to the elements in the + // network itself. + OutputType layerGradient(gradient.memptr() + (this->inSize * + (this->inSize + 1)), 1, gradient.n_elem - (this->inSize * + (this->inSize + 1)), false, true); + this->InitializeGradientPassMemory(layerGradient); + + OutputType errorTransform = error % transformGateActivation; + this->network.back()->Gradient( + this->layerOutputs[this->network.size() - 2], + errorTransform, + this->layerGradients[this->network.size() - 1]); + + for (size_t i = 2; i < this->network.size(); ++i) + { + this->network[this->network.size() - i]->Gradient( + this->layerOutputs[this->network.size() - i - 1], + this->layerDeltas[this->network.size() - i], + this->layerGradients[this->network.size() - i]); + } + + this->network.front()->Gradient( + input, + this->layerDeltas[1], + this->layerGradients.front()); + + gradient.submat(0, 0, transformWeight.n_elem - 1, 0) = arma::vectorise( + transformGateError * input.t()); + gradient.submat(transformWeight.n_elem, 0, transformWeight.n_elem + + transformBias.n_elem - 1, 0) = arma::sum(transformGateError, 1); +} + +template +template +void HighwayType::serialize( + Archive& ar, const uint32_t /* version */) +{ + ar(cereal::base_class>(this)); +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/layer/instance_norm.hpp b/src/mlpack/methods/ann/layer/not_adapted/instance_norm.hpp similarity index 100% rename from src/mlpack/methods/ann/layer/instance_norm.hpp rename to src/mlpack/methods/ann/layer/not_adapted/instance_norm.hpp diff --git a/src/mlpack/methods/ann/layer/instance_norm_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/instance_norm_impl.hpp similarity index 100% rename from src/mlpack/methods/ann/layer/instance_norm_impl.hpp rename to src/mlpack/methods/ann/layer/not_adapted/instance_norm_impl.hpp diff --git a/src/mlpack/methods/ann/layer/isrlu.hpp b/src/mlpack/methods/ann/layer/not_adapted/isrlu.hpp similarity index 100% rename from src/mlpack/methods/ann/layer/isrlu.hpp rename to src/mlpack/methods/ann/layer/not_adapted/isrlu.hpp diff --git a/src/mlpack/methods/ann/layer/isrlu_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/isrlu_impl.hpp similarity index 100% rename from src/mlpack/methods/ann/layer/isrlu_impl.hpp rename to src/mlpack/methods/ann/layer/not_adapted/isrlu_impl.hpp diff --git a/src/mlpack/methods/ann/layer/join.hpp b/src/mlpack/methods/ann/layer/not_adapted/join.hpp similarity index 61% rename from src/mlpack/methods/ann/layer/join.hpp rename to src/mlpack/methods/ann/layer/not_adapted/join.hpp index 38286a9508..ee5a7acd00 100644 --- a/src/mlpack/methods/ann/layer/join.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/join.hpp @@ -17,24 +17,30 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { +// TODO: should we clarify the comments? This seems to join together points of +// a different batch +// TODO: I don't understand this layer well enough to update it... /** * Implementation of the Join module class. The Join class accumulates * the output of various modules. * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * @tparam InputType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * @tparam OutputType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). */ template< - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat + typename InputType = arma::mat, + typename OutputType = arma::mat > -class Join +class JoinType : public Layer { public: - //! Create the Join object. - Join(); + //! Create the JoinType object. + JoinType(); + + //! Clone the JoinType object. This handles polymorphism correctly. + JoinType* Clone() const { return new JoinType(*this); } /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -43,7 +49,6 @@ class Join * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template void Forward(const InputType& input, OutputType& output); /** @@ -55,20 +60,19 @@ class Join * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g); + void Backward(const InputType& /* input */, + const OutputType& gy, + OutputType& g); - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } + // This layer simply flattens its input into a vector. + const std::vector OutputDimensions() const + { + // TODO: it's not clear what to do here + std::vector result(inputDimensions.size(), 0); + result[0] = std::accumulate(inputDimensions.begin(), inputDimensions.end(), + 0); + return result; + } /** * Serialize the layer. @@ -82,13 +86,10 @@ class Join //! Locally-stored number of input cols. size_t inSizeCols; +}; // class JoinType - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; -}; // class Join +//Standard Join layer. +typedef JoinType Join; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/join_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/join_impl.hpp similarity index 63% rename from src/mlpack/methods/ann/layer/join_impl.hpp rename to src/mlpack/methods/ann/layer/not_adapted/join_impl.hpp index a886d7ac44..aef5a9cc5a 100644 --- a/src/mlpack/methods/ann/layer/join_impl.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/join_impl.hpp @@ -18,17 +18,16 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -Join::Join() : +template +JoinType::JoinType() : inSizeRows(0), inSizeCols(0) { // Nothing to do here. } -template template -void Join::Forward( +void JoinType::Forward( const InputType& input, OutputType& output) { inSizeRows = input.n_rows; @@ -36,23 +35,24 @@ void Join::Forward( output = arma::vectorise(input); } -template -template -void Join::Backward( - const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g) +template +void JoinType::Backward( + const InputType& /* input */, + const OutputType& gy, + OutputType& g) { - g = arma::mat(((arma::Mat&) gy).memptr(), inSizeRows, inSizeCols, false, + g = OutputType(((OutputType&) gy).memptr(), inSizeRows, inSizeCols, false, false); } -template +template template -void Join::serialize( +void JoinType::serialize( Archive& ar, const uint32_t /* version */) { + ar(cereal::base_class>(this)); + ar(CEREAL_NVP(inSizeRows)); ar(CEREAL_NVP(inSizeCols)); } diff --git a/src/mlpack/methods/ann/layer/layer_norm.hpp b/src/mlpack/methods/ann/layer/not_adapted/layer_norm.hpp similarity index 67% rename from src/mlpack/methods/ann/layer/layer_norm.hpp rename to src/mlpack/methods/ann/layer/not_adapted/layer_norm.hpp index c22408c221..822e09fc7d 100644 --- a/src/mlpack/methods/ann/layer/layer_norm.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/layer_norm.hpp @@ -53,20 +53,20 @@ namespace ann /** Artificial Neural Network. */ { * } * @endcode * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * @tparam InputType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * @tparam OutputType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). */ template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat + typename InputType = arma::mat, + typename OutputType = arma::mat > -class LayerNorm +class LayerNormType : public Layer { public: - //! Create the LayerNorm object. - LayerNorm(); + //! Create the LayerNormType object. + LayerNormType(); /** * Create the LayerNorm object for a specified number of input units. @@ -74,7 +74,10 @@ class LayerNorm * @param size The number of input units. * @param eps The epsilon added to variance to ensure numerical stability. */ - LayerNorm(const size_t size, const double eps = 1e-8); + LayerNormType(const size_t size, const double eps = 1e-8); + + //! Clone the LayerNormType object. This handles polymorphism correctly. + LayerNormType* Clone() const { return new LayerNormType(*this); } /** * Reset the layer parameters. @@ -89,8 +92,7 @@ class LayerNorm * @param input Input data for the layer. * @param output Resulting output activations. */ - template - void Forward(const arma::Mat& input, arma::Mat& output); + void Forward(const InputType& input, OutputType& output); /** * Backward pass through the layer. @@ -99,10 +101,9 @@ class LayerNorm * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const arma::Mat& input, - const arma::Mat& gy, - arma::Mat& g); + void Backward(const InputType& input, + const OutputType& gy, + OutputType& g); /** * Calculate the gradient using the output delta and the input activations. @@ -111,36 +112,20 @@ class LayerNorm * @param error The calculated error. * @param gradient The calculated gradient. */ - template - void Gradient(const arma::Mat& input, - const arma::Mat& error, - arma::Mat& gradient); + void Gradient(const InputType& input, + const OutputType& error, + OutputType& gradient); //! Get the parameters. - OutputDataType const& Parameters() const { return weights; } + OutputType const& Parameters() const { return weights; } //! Modify the parameters. - OutputDataType& Parameters() { return weights; } - - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } - - //! Get the gradient. - OutputDataType const& Gradient() const { return gradient; } - //! Modify the gradient. - OutputDataType& Gradient() { return gradient; } + OutputType& Parameters() { return weights; } //! Get the mean across single training data. - OutputDataType Mean() { return mean; } + OutputType Mean() { return mean; } //! Get the variance across single training data. - OutputDataType Variance() { return variance; } + OutputType Variance() { return variance; } //! Get the number of input units. size_t InSize() const { return size; } @@ -148,11 +133,7 @@ class LayerNorm //! Get the value of epsilon. double Epsilon() const { return eps; } - //! Get the shape of the input. - size_t InputShape() const - { - return size; - } + const size_t WeightSize() const { return 2 * size; } /** * Serialize the layer. @@ -171,35 +152,29 @@ class LayerNorm bool loading; //! Locally-stored scale parameter. - OutputDataType gamma; + OutputType gamma; //! Locally-stored shift parameter. - OutputDataType beta; + OutputType beta; //! Locally-stored parameters. - OutputDataType weights; + OutputType weights; //! Locally-stored mean object. - OutputDataType mean; + OutputType mean; //! Locally-stored variance object. - OutputDataType variance; - - //! Locally-stored gradient object. - OutputDataType gradient; - - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; + OutputType variance; //! Locally-stored normalized input. - OutputDataType normalized; + OutputType normalized; //! Locally-stored zero mean input. - OutputDataType inputMean; -}; // class LayerNorm + OutputType inputMean; +}; // class LayerNormType + +// Standard LayerNorm type +typedef LayerNormType LayerNorm; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/layer_norm_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/layer_norm_impl.hpp similarity index 60% rename from src/mlpack/methods/ann/layer/layer_norm_impl.hpp rename to src/mlpack/methods/ann/layer/not_adapted/layer_norm_impl.hpp index ae4ced5cf8..3381bc93fe 100644 --- a/src/mlpack/methods/ann/layer/layer_norm_impl.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/layer_norm_impl.hpp @@ -20,8 +20,8 @@ namespace mlpack { namespace ann { /** Artificial Neural Network. */ -template -LayerNorm::LayerNorm() : +template +LayerNormType::LayerNormType() : size(0), eps(1e-8), loading(false) @@ -29,8 +29,8 @@ LayerNorm::LayerNorm() : // Nothing to do here. } -template -LayerNorm::LayerNorm( +template +LayerNormType::LayerNormType( const size_t size, const double eps) : size(size), eps(eps), @@ -39,11 +39,12 @@ LayerNorm::LayerNorm( weights.set_size(size + size, 1); } -template -void LayerNorm::Reset() +template +void LayerNormType::SetWeights( + typename OutputType::elem_type* weightsPtr) { - gamma = arma::mat(weights.memptr(), size, 1, false, false); - beta = arma::mat(weights.memptr() + gamma.n_elem, size, 1, false, false); + gamma = OutputType(weightsPtr, size, 1, false, false); + beta = OutputType(weightsPtr + gamma.n_elem, size, 1, false, false); if (!loading) { @@ -54,10 +55,9 @@ void LayerNorm::Reset() loading = false; } -template -template -void LayerNorm::Forward( - const arma::Mat& input, arma::Mat& output) +template +void LayerNormType::Forward( + const InputType& input, OutputType& output) { mean = arma::mean(input, 0); variance = arma::var(input, 1, 0); @@ -75,18 +75,17 @@ void LayerNorm::Forward( output.each_col() += beta; } -template -template -void LayerNorm::Backward( - const arma::Mat& input, const arma::Mat& gy, arma::Mat& g) +template +void LayerNormType::Backward( + const InputType& input, const OutputType& gy, OutputType& g) { - const arma::mat stdInv = 1.0 / arma::sqrt(variance + eps); + const OutputType stdInv = 1.0 / arma::sqrt(variance + eps); // dl / dxhat. - const arma::mat norm = gy.each_col() % gamma; + const OutputType norm = gy.each_col() % gamma; // sum dl / dxhat * (x - mu) * -0.5 * stdInv^3. - const arma::mat var = arma::sum(norm % inputMean, 0) % + const OutputType var = arma::sum(norm % inputMean, 0) % arma::pow(stdInv, 3.0) * -0.5; // dl / dxhat * 1 / stdInv + variance * 2 * (x - mu) / m + @@ -99,12 +98,11 @@ void LayerNorm::Backward( g.each_row() += arma::sum(norm.each_row() % -stdInv, 0) / input.n_rows; } -template -template -void LayerNorm::Gradient( - const arma::Mat& /* input */, - const arma::Mat& error, - arma::Mat& gradient) +template +void LayerNormType::Gradient( + const InputType& /* input */, + const OutputType& error, + OutputType& gradient) { gradient.set_size(size + size, 1); @@ -116,22 +114,22 @@ void LayerNorm::Gradient( arma::sum(error, 1); } -template +template template -void LayerNorm::serialize( +void LayerNormType::serialize( Archive& ar, const uint32_t /* version */) { - ar(CEREAL_NVP(size)); + ar(cereal::base_class>(this)); + ar(CEREAL_NVP(size)); + ar(CEREAL_NVP(eps)); + + // Ensure that we don't set the values of the weights if we have already + // learned them. if (cereal::is_loading()) { - weights.set_size(size + size, 1); loading = true; } - - ar(CEREAL_NVP(eps)); - ar(CEREAL_NVP(gamma)); - ar(CEREAL_NVP(beta)); } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/lookup.hpp b/src/mlpack/methods/ann/layer/not_adapted/lookup.hpp similarity index 64% rename from src/mlpack/methods/ann/layer/lookup.hpp rename to src/mlpack/methods/ann/layer/not_adapted/lookup.hpp index 6f6796f193..714924949d 100644 --- a/src/mlpack/methods/ann/layer/lookup.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/lookup.hpp @@ -14,7 +14,7 @@ #define MLPACK_METHODS_ANN_LAYER_LOOKUP_HPP #include -#include +#include "layer.hpp" namespace mlpack { namespace ann /* Artificial Neural Network. */ { @@ -29,16 +29,16 @@ namespace ann /* Artificial Neural Network. */ { * The input shape : (sequenceLength, batchSize). * The output shape : (embeddingSize, sequenceLength, batchSize). * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * @tparam InputType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * @tparam OutputType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). */ template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat + typename InputType = arma::mat, + typename OutputType = arma::mat > -class Lookup +class LookupType : public Layer { public: /** @@ -47,7 +47,10 @@ class Lookup * @param vocabSize The size of the vocabulary. * @param embeddingSize The length of each embedding vector. */ - Lookup(const size_t vocabSize = 0, const size_t embeddingSize = 0); + LookupType(const size_t vocabSize = 0, const size_t embeddingSize = 0); + + //! Clone the LookupType object. This handles polymorphism correctly. + LookupType* Clone() const { return new LookupType(*this); } /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -56,8 +59,7 @@ class Lookup * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template - void Forward(const arma::Mat& input, arma::Mat& output); + void Forward(const InputType& input, OutputType& output); /** * Ordinary feed backward pass of a neural network, calculating the function @@ -68,10 +70,9 @@ class Lookup * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g); + void Backward(const InputType& /* input */, + const OutputType& gy, + OutputType& g); /** * Calculate the gradient using the output delta and the input activation. @@ -80,30 +81,14 @@ class Lookup * @param error The calculated error. * @param gradient The calculated gradient. */ - template - void Gradient(const arma::Mat& input, - const arma::Mat& error, - arma::Mat& gradient); + void Gradient(const InputType& input, + const OutputType& error, + OutputType& gradient); //! Get the parameters. - OutputDataType const& Parameters() const { return weights; } + OutputType const& Parameters() const { return weights; } //! Modify the parameters. - OutputDataType& Parameters() { return weights; } - - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } - - //! Get the gradient. - OutputDataType const& Gradient() const { return gradient; } - //! Modify the gradient. - OutputDataType& Gradient() { return gradient; } + OutputType& Parameters() { return weights; } //! Get the size of the vocabulary. size_t VocabSize() const { return vocabSize; } @@ -111,6 +96,19 @@ class Lookup //! Get the length of each embedding vector. size_t EmbeddingSize() const { return embeddingSize; } + //! Get the number of trainable parameters. + const size_t WeightSize() const { return embeddingSize * vocabSize; } + + //! Get the dimensions of the output. This layer adds an extra dimension for + //! the embedding. + const std::vector& OutputDimensions() const + { + std::vector result(inputDimensions.size() + 1, embeddingSize); + for (size_t i = 0; i < inputDimensions.size(); ++i) + result[i + 1] = inputDimensions[i]; + return result; + } + /** * Serialize the layer */ @@ -125,21 +123,14 @@ class Lookup size_t embeddingSize; //! Locally-stored weight object. - OutputDataType weights; - - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored gradient object. - OutputDataType gradient; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; + OutputType weights; }; // class Lookup // Alias for using as embedding layer. -template -using Embedding = Lookup; +// template +// using Embedding = Lookup; +typedef LookupType Lookup; +typedef LookupType Embedding; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/lookup_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/lookup_impl.hpp similarity index 58% rename from src/mlpack/methods/ann/layer/lookup_impl.hpp rename to src/mlpack/methods/ann/layer/not_adapted/lookup_impl.hpp index 7755f83342..d1e36d2f29 100644 --- a/src/mlpack/methods/ann/layer/lookup_impl.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/lookup_impl.hpp @@ -19,26 +19,30 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -Lookup::Lookup( +template +LookupType::LookupType( const size_t vocabSize, const size_t embeddingSize) : vocabSize(vocabSize), embeddingSize(embeddingSize) { - weights.set_size(embeddingSize, vocabSize); + // Nothing to do. } -template -template -void Lookup::Forward( - const arma::Mat& input, arma::Mat& output) +template +void LookupType::SetWeights( + typename OutputType::elem_type* weightsPtr) +{ + weights = OutputType(weightsPtr, embeddingSize, vocabSize, false, true); +} + +template +void LookupType::Forward( + const InputType& input, OutputType& output) { const size_t seqLength = input.n_rows; const size_t batchSize = input.n_cols; - output.set_size(embeddingSize * seqLength, batchSize); - for (size_t i = 0; i < batchSize; ++i) { // ith column of output is a vectorized form of a matrix of shape @@ -49,32 +53,29 @@ void Lookup::Forward( } } -template -template -void Lookup::Backward( - const arma::Mat& /* input */, - const arma::Mat& /* gy */, - arma::Mat& /* g */) +template +void LookupType::Backward( + const InputType& /* input */, + const OutputType& /* gy */, + OutputType& /* g */) { Log::Fatal << "Lookup cannot be used as an intermediate layer." << std::endl; } -template -template -void Lookup::Gradient( - const arma::Mat& input, - const arma::Mat& error, - arma::Mat& gradient) +template +void LookupType::Gradient( + const InputType& input, + const OutputType& error, + OutputType& gradient) { + typedef typename arma::Cube CubeType; const size_t seqLength = input.n_rows; const size_t batchSize = input.n_cols; - arma::Cube errorTemp(const_cast&>(error).memptr(), + const CubeType errorTemp(const_cast(error).memptr(), embeddingSize, seqLength, batchSize, false, false); - gradient.set_size(arma::size(weights)); gradient.zeros(); - for (size_t i = 0; i < batchSize; ++i) { gradient.cols(arma::conv_to::from(input.col(i)) - 1) @@ -82,18 +83,15 @@ void Lookup::Gradient( } } -template +template template -void Lookup::serialize( +void LookupType::serialize( Archive& ar, const uint32_t /* version */) { + ar(cereal::base_class>(this)); + ar(CEREAL_NVP(vocabSize)); ar(CEREAL_NVP(embeddingSize)); - - // This is inefficient, but we have to allocate this memory so that - // WeightSetVisitor gets the right size. - if (cereal::is_loading()) - weights.set_size(embeddingSize, vocabSize); } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/lp_pooling.hpp b/src/mlpack/methods/ann/layer/not_adapted/lp_pooling.hpp similarity index 100% rename from src/mlpack/methods/ann/layer/lp_pooling.hpp rename to src/mlpack/methods/ann/layer/not_adapted/lp_pooling.hpp diff --git a/src/mlpack/methods/ann/layer/lp_pooling_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/lp_pooling_impl.hpp similarity index 100% rename from src/mlpack/methods/ann/layer/lp_pooling_impl.hpp rename to src/mlpack/methods/ann/layer/not_adapted/lp_pooling_impl.hpp diff --git a/src/mlpack/methods/ann/layer/mean_pooling.hpp b/src/mlpack/methods/ann/layer/not_adapted/mean_pooling.hpp similarity index 73% rename from src/mlpack/methods/ann/layer/mean_pooling.hpp rename to src/mlpack/methods/ann/layer/not_adapted/mean_pooling.hpp index 4156667beb..7973fc1aa5 100644 --- a/src/mlpack/methods/ann/layer/mean_pooling.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/mean_pooling.hpp @@ -21,20 +21,20 @@ namespace ann /** Artificial Neural Network. */ { /** * Implementation of the MeanPooling. * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * @tparam InputType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * @tparam OutputType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). */ template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat + typename InputType = arma::mat, + typename OutputType = arma::mat > -class MeanPooling +class MeanPoolingType : public Layer { public: - //! Create the MeanPooling object. - MeanPooling(); + //! Create the MeanPoolingType object. + MeanPoolingType(); /** * Create the MeanPooling object using the specified number of units. @@ -45,11 +45,14 @@ class MeanPooling * @param strideHeight Width of the stride operation. * @param floor Set to true to use floor method. */ - MeanPooling(const size_t kernelWidth, - const size_t kernelHeight, - const size_t strideWidth = 1, - const size_t strideHeight = 1, - const bool floor = true); + MeanPoolingType(const size_t kernelWidth, + const size_t kernelHeight, + const size_t strideWidth = 1, + const size_t strideHeight = 1, + const bool floor = true); + + // TODO: copy constructor / move constructor + MeanPoolingType* Clone() const { return new MeanPoolingType(*this); } /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -58,8 +61,7 @@ class MeanPooling * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template - void Forward(const arma::Mat& input, arma::Mat& output); + void Forward(const InputType& input, OutputType& output); /** * Ordinary feed backward pass of a neural network, using 3rd-order tensors as @@ -70,64 +72,27 @@ class MeanPooling * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g); - - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } - - //! Get the intput width. - size_t const& InputWidth() const { return inputWidth; } - //! Modify the input width. - size_t& InputWidth() { return inputWidth; } - - //! Get the input height. - size_t const& InputHeight() const { return inputHeight; } - //! Modify the input height. - size_t& InputHeight() { return inputHeight; } - - //! Get the output width. - size_t const& OutputWidth() const { return outputWidth; } - //! Modify the output width. - size_t& OutputWidth() { return outputWidth; } - - //! Get the output height. - size_t const& OutputHeight() const { return outputHeight; } - //! Modify the output height. - size_t& OutputHeight() { return outputHeight; } - - //! Get the input size. - size_t InputSize() const { return inSize; } - - //! Get the output size. - size_t OutputSize() const { return outSize; } + void Backward(const InputType& /* input */, + const OutputType& gy, + OutputType& g); //! Get the kernel width. - size_t KernelWidth() const { return kernelWidth; } + size_t const& KernelWidth() const { return kernelWidth; } //! Modify the kernel width. size_t& KernelWidth() { return kernelWidth; } //! Get the kernel height. - size_t KernelHeight() const { return kernelHeight; } + size_t const& KernelHeight() const { return kernelHeight; } //! Modify the kernel height. size_t& KernelHeight() { return kernelHeight; } //! Get the stride width. - size_t StrideWidth() const { return strideWidth; } + size_t const& StrideWidth() const { return strideWidth; } //! Modify the stride width. size_t& StrideWidth() { return strideWidth; } //! Get the stride height. - size_t StrideHeight() const { return strideHeight; } + size_t const& StrideHeight() const { return strideHeight; } //! Modify the stride height. size_t& StrideHeight() { return strideHeight; } @@ -136,13 +101,40 @@ class MeanPooling //! Modify the value of the rounding operation bool& Floor() { return floor; } - //! Get the value of the deterministic parameter. - bool Deterministic() const { return deterministic; } - //! Modify the value of the deterministic parameter. - bool& Deterministic() { return deterministic; } + //! Get the size of the output. + const std::vector OutputDimensions() const + { + outputDimensions = this->inputDimensions; - //! Get the size of the weights. - size_t WeightSize() const { return 0; } + // Compute the size of the output. + if (floor) + { + outputDimensions[0] = std::floor((this->inputDimensions[0] - + (double) kernelWidth) / (double) strideWidth + 1); + outputDimensions[1] = std::floor((this->inputDimensions[1] - + (double) kernelHeight) / (double) strideHeight + 1); + offset = 0; + } + else + { + outputDimensions[0] = std::ceil((this->inputDimensions[0] - + (double) kernelWidth) / (double) strideWidth + 1); + outputDimensions[1] = std::ceil((this->inputDimensions[1] - + (double) kernelHeight) / (double) strideHeight + 1); + offset = 1; + } + + // Higher dimensions are not modified. + for (size_t i = 2; i < this->inputDimensions.size(); ++i) + outputDimensions[i] = this->inputDimensions[i]; + + // Cache input size and output size. + channels = 1; + for (size_t i = 2; i < this->inputDimensions.size(); ++i) + channels *= this->inputDimensions[i]; + + return outputDimensions; + } /** * Serialize the layer. @@ -157,8 +149,7 @@ class MeanPooling * @param input The input to be apply the pooling rule. * @param output The pooled result. */ - template - void Pooling(const arma::Mat& input, arma::Mat& output) + void Pooling(const InputType& input, OutputType& output) { arma::Mat inputPre = input; @@ -174,9 +165,9 @@ class MeanPooling for (size_t i = 0, rowidx = 0; i < output.n_rows; ++i, rowidx += strideWidth) { - double val = 0.0; - size_t rowEnd = rowidx + kernelWidth - 1; - size_t colEnd = colidx + kernelHeight - 1; + InputType subInput = input( + arma::span(rowidx, rowidx + kernelWidth - 1 - offset), + arma::span(colidx, colidx + kernelHeight - 1 - offset)); if (rowEnd > input.n_rows - 1) rowEnd = input.n_rows - 1; @@ -205,10 +196,9 @@ class MeanPooling * @param input The input to be apply the unpooling rule. * @param output The pooled result. */ - template - void Unpooling(const arma::Mat& input, - const arma::Mat& error, - arma::Mat& output) + void Unpooling(const InputType& input, + const OutputType& error, + OutputType& output) { // This condition comes by comparing the number of operations involved in the brute // force method and the prefix method. Let the area of error be errorArea and area @@ -221,7 +211,8 @@ class MeanPooling const bool condition = (error.n_elem * kernelHeight * kernelWidth) > (4 * error.n_elem + 2 * input.n_elem); - if (condition) + OutputType unpooledError; + for (size_t j = 0; j < input.n_cols - cStep; j += cStep) { // If this condition is true then theoritically the prefix sum method of // unpooling is faster. The aim of unpooling is to add @@ -260,7 +251,8 @@ class MeanPooling { for (size_t i = 0, rowidx = 0; i < input.n_rows; i += strideWidth, ++rowidx) { - // We have to add error(i, j) to output(span(rowidx, rowEnd), span(colidx, colEnd)). + // We have to add error(i, j) to output(span(rowidx, rowEnd), + // span(colidx, colEnd)). // The steps of prefix sum method: // // 1. For each (i, j) perform: @@ -362,52 +354,21 @@ class MeanPooling //! Rounding operation used. bool floor; - //! Locally-stored number of input channels. - size_t inSize; + //! Locally-stored number channels. + size_t channels; - //! Locally-stored number of output channels. - size_t outSize; - - //! Locally-stored input width. - size_t inputWidth; - - //! Locally-stored input height. - size_t inputHeight; - - //! Locally-stored output width. - size_t outputWidth; - - //! Locally-stored output height. - size_t outputHeight; - - //! Locally-stored reset parameter used to initialize the module once. - bool reset; - - //! If true use maximum a posteriori during the forward pass. - bool deterministic; + //! Locally-stored cached output dimensions. + std::vector outputDimensions; //! Locally-stored number of input units. size_t batchSize; - //! Locally-stored output parameter. - arma::cube outputTemp; - - //! Locally-stored transformed input parameter. - arma::cube inputTemp; - - //! Locally-stored transformed output parameter. - arma::cube gTemp; - - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored gradient object. - OutputDataType gradient; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; -}; // class MeanPooling + //! Cached last-seen input. + arma::Cube inputTemp; +}; // class MeanPoolingType +// Standard MeanPooling layer. +typedef MeanPoolingType MeanPooling; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/not_adapted/mean_pooling_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/mean_pooling_impl.hpp new file mode 100644 index 0000000000..845f264c72 --- /dev/null +++ b/src/mlpack/methods/ann/layer/not_adapted/mean_pooling_impl.hpp @@ -0,0 +1,108 @@ +/** + * @file methods/ann/layer/mean_pooling_impl.hpp + * @author Marcus Edel + * @author Nilay Jain + * + * Implementation of the MeanPooling layer class. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_LAYER_MEAN_POOLING_IMPL_HPP +#define MLPACK_METHODS_ANN_LAYER_MEAN_POOLING_IMPL_HPP + +// In case it hasn't yet been included. +#include "mean_pooling.hpp" + +namespace mlpack { +namespace ann /** Artificial Neural Network. */ { + +template +MeanPoolingType::MeanPoolingType() +{ + // Nothing to do here. +} + +template +MeanPoolingType::MeanPoolingType( + const size_t kernelWidth, + const size_t kernelHeight, + const size_t strideWidth, + const size_t strideHeight, + const bool floor) : + kernelWidth(kernelWidth), + kernelHeight(kernelHeight), + strideWidth(strideWidth), + strideHeight(strideHeight), + floor(floor), + channels(0), + offset(0), + batchSize(0) +{ + // Nothing to do here. +} + +template +void MeanPoolingType::Forward( + const InputType& input, OutputType& output) +{ + batchSize = input.n_cols; + inputTemp = arma::Cube( + const_cast(input).memptr(), this->inputDimensions[0], + this->inputDimensions[1], batchSize * channels, false, false); + + arma::Cube outputTemp(output.memptr(), + outputDimensions[0], outputDimensions[1], batchSize * channels, false, + true); + + for (size_t s = 0; s < inputTemp.n_slices; s++) + Pooling(inputTemp.slice(s), outputTemp.slice(s)); +} + +template +void MeanPoolingType::Backward( + const InputType& /* input */, + const OutputType& gy, + OutputType& g) +{ + arma::Cube mappedError( + ((OutputType&) gy).memptr(), outputDimensions[0], outputDimensions[1], + batchSize * channels, false, true); + + arma::Cube gTemp(g.memptr(), + this->inputDimensions[0], this->inputDimensions[1], channels * batchSize, + false, true); + + for (size_t s = 0; s < mappedError.n_slices; s++) + { + Unpooling(inputTemp.slice(s), mappedError.slice(s), gTemp.slice(s)); + } +} + +template +template +void MeanPoolingType::serialize( + Archive& ar, + const uint32_t /* version */) +{ + ar(cereal::base_class>(this)); + + ar(CEREAL_NVP(kernelWidth)); + ar(CEREAL_NVP(kernelHeight)); + ar(CEREAL_NVP(strideWidth)); + ar(CEREAL_NVP(strideHeight)); + ar(CEREAL_NVP(batchSize)); + ar(CEREAL_NVP(floor)); + ar(CEREAL_NVP(outputDimensions)); + ar(CEREAL_NVP(offset)); + + if (Archive::is_loading::value) + inputTemp.clear(); +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/layer/minibatch_discrimination.hpp b/src/mlpack/methods/ann/layer/not_adapted/minibatch_discrimination.hpp similarity index 61% rename from src/mlpack/methods/ann/layer/minibatch_discrimination.hpp rename to src/mlpack/methods/ann/layer/not_adapted/minibatch_discrimination.hpp index 3448f36b5d..513021095f 100644 --- a/src/mlpack/methods/ann/layer/minibatch_discrimination.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/minibatch_discrimination.hpp @@ -41,16 +41,16 @@ namespace ann /** Artificial Neural Network. */ { * } * @endcode * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * @tparam InputType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * @tparam OutputType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). */ template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat + typename InputType = arma::mat, + typename OutputType = arma::mat > -class MiniBatchDiscrimination +class MiniBatchDiscrimination : public Layer { public: //! Create the MiniBatchDiscrimination object. @@ -60,18 +60,16 @@ class MiniBatchDiscrimination * Create the MiniBatchDiscrimination layer object using the specified * number of units. * - * @param inSize The number of input units. * @param outSize The number of output units. * @param features The number of features to compute for each dimension. */ - MiniBatchDiscrimination(const size_t inSize, - const size_t outSize, + MiniBatchDiscrimination(const size_t outSize, const size_t features); /** * Reset the layer parameter. */ - void Reset(); + void SetWeights(typename OutputType::elem_type* weightsPtr); /** * Ordinary feed-forward pass of a neural network, evaluating the function @@ -80,8 +78,7 @@ class MiniBatchDiscrimination * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template - void Forward(const arma::Mat& input, arma::Mat& output); + void Forward(const InputType& input, OutputType& output); /** * Ordinary feed-backward pass of a neural network, calculating the function @@ -92,10 +89,9 @@ class MiniBatchDiscrimination * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g); + void Backward(const InputType& /* input */, + const OutputType& gy, + OutputType& g); /** * Calculate the gradient using the output delta and the input activation. @@ -104,35 +100,27 @@ class MiniBatchDiscrimination * @param * (error) The calculated error. * @param gradient The calculated gradient. */ - template - void Gradient(const arma::Mat& input, - const arma::Mat& /* error */, - arma::Mat& gradient); + void Gradient(const InputType& input, + const OutputType& /* error */, + OutputType& gradient); //! Get the parameters. - OutputDataType const& Parameters() const { return weights; } + OutputType const& Parameters() const { return weights; } //! Modify the parameters. - OutputDataType& Parameters() { return weights; } + OutputType& Parameters() { return weights; } - //! Get the input parameter. - InputDataType const& InputParameter() const { return inputParameter; } - //! Modify the input parameter. - InputDataType& InputParameter() { return inputParameter; } + const size_t WeightSize() const { return a * b * c; } - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } + const std::vector OutputDimensions() const + { + a = std::accumulate(inputDimensions.begin(), inputDimensions.end(), 0); + std::vector outputDimensions(inputDimensions.size(), 1); + // TODO: not sure if this is right... we just interpret it all as + // one-dimensional. + outputDimensions[0] = a + b; - //! Get the delta. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } - - //! Get the gradient. - OutputDataType const& Gradient() const { return gradient; } - //! Modify the gradient. - OutputDataType& Gradient() { return gradient; } + return outputDimensions; + } //! Get the shape of the input. size_t InputShape() const @@ -148,43 +136,22 @@ class MiniBatchDiscrimination private: //! Locally-stored dimensions of weight. - size_t A, B, C; + size_t a, b, c; //! Locally-stored input batch size. size_t batchSize; - //! Locally-stored temporary features object. - arma::mat tempM; - //! Locally-stored weight object. - OutputDataType weights; + OutputType weights; - //! Locally-stored weight parameters. - OutputDataType weight; - - //! Locally-stored features of input. - arma::cube M; + //! Locally-stored features of input. Cached to avoid recomputation. + InputType M; //! Locally-stored delta for features object. - arma::cube deltaM; + arma::Cube deltaM; //! Locally-stored L1 distances between features. - arma::cube distances; - - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored temporary delta object. - OutputDataType deltaTemp; - - //! Locally-stored gradient object. - OutputDataType gradient; - - //! Locally-stored input parameter object. - InputDataType inputParameter; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; + arma::Cube distances; }; // class MiniBatchDiscrimination } // namespace ann diff --git a/src/mlpack/methods/ann/layer/not_adapted/minibatch_discrimination_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/minibatch_discrimination_impl.hpp new file mode 100644 index 0000000000..ea098a4c25 --- /dev/null +++ b/src/mlpack/methods/ann/layer/not_adapted/minibatch_discrimination_impl.hpp @@ -0,0 +1,138 @@ +/** + * @file methods/ann/layer/minibatch_discrimination_impl.hpp + * @author Saksham Bansal + * + * Implementation of the MiniBatchDiscrimination layer class. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_LAYER_MINIBATCH_DISCRIMINATION_IMPL_HPP +#define MLPACK_METHODS_ANN_LAYER_MINIBATCH_DISCRIMINATION_IMPL_HPP + +// In case it hasn't yet been included. +#include "minibatch_discrimination.hpp" + +namespace mlpack { +namespace ann /** Artificial Neural Network. */ { + +template +MiniBatchDiscrimination::MiniBatchDiscrimination() : + A(0), + B(0), + C(0), + batchSize(0) +{ + // Nothing to do here. +} + +template +MiniBatchDiscrimination::MiniBatchDiscrimination( + const size_t outSize, + const size_t features) : + a(0), // This will be set when OutputDimensions() is called. + b(outSize - inSize), + c(features), + batchSize(0) +{ + // Nothing to do. +} + +template +void MiniBatchDiscrimination::SetWeights( + typename OutputType::elem_type* weightsPtr) +{ + weights = OutputType(weightsPtr, b * c, a, false, false); +} + +template +void MiniBatchDiscrimination::Forward( + const InputType& input, OutputType& output) +{ + batchSize = input.n_cols; + M = weight * input; + arma::Cube cubeM(M.memptr(), b, c, batchSize, + false, false); + distances.set_size(b, batchSize, batchSize); + + for (size_t i = 0; i < cubeM.n_slices; ++i) + { + output.col(i).subvec(0, a - 1) = input.col(i); + output.col(i).subvec(a, output.n_rows - 1).ones(); + for (size_t j = 0; j < cubeM.n_slices; ++j) + { + if (j < i) + { + output.col(i).subvec(a, output.n_rows - 1) += distances.slice(j).col(i); + } + else if (i == j) + { + continue; + } + else + { + distances.slice(i).col(j) = + arma::exp(-arma::sum(abs(cubeM.slice(i) - cubeM.slice(j)), 1)); + output.col(i) += distances.slice(i).col(j); + } + } + } +} + +template +void MiniBatchDiscrimination::Backward( + const InputType& /* input */, const OutputType& gy, OutputType& g) +{ + g = gy.head_rows(a); + OutputType gM = gy.tail_rows(B); + deltaM.zeros(b, c, batchSize); + + for (size_t i = 0; i < M.n_slices; ++i) + { + for (size_t j = 0; j < M.n_slices; ++j) + { + if (i == j) + { + continue; + } + InputType t = arma::sign(M.slice(i) - M.slice(j)); + t.each_col() %= + distances.slice(std::min(i, j)).col(std::max(i, j)) % gM.col(i); + deltaM.slice(i) -= t; + deltaM.slice(j) += t; + } + } + + OutputType deltaTemp(deltaM.memptr(), b * c, batchSize, false, true); + g += weight.t() * deltaTemp; +} + +template +void MiniBatchDiscrimination::Gradient( + const InputType& input, + const OutputType& /* error */, + OutputType& gradient) +{ + gradient = arma::vectorise(deltaTemp * input.t()); +} + +template +template +void MiniBatchDiscrimination::serialize( + Archive& ar, const uint32_t /* version */) +{ + ar(cereal::base_class>(this)); + + ar(CEREAL_NVP(a)); + ar(CEREAL_NVP(b)); + ar(CEREAL_NVP(c)); +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/layer/multihead_attention.hpp b/src/mlpack/methods/ann/layer/not_adapted/multihead_attention.hpp similarity index 70% rename from src/mlpack/methods/ann/layer/multihead_attention.hpp rename to src/mlpack/methods/ann/layer/not_adapted/multihead_attention.hpp index 0d7506ea51..e17cb67263 100644 --- a/src/mlpack/methods/ann/layer/multihead_attention.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/multihead_attention.hpp @@ -1,3 +1,4 @@ +// Temporarily drop. /** * @file methods/ann/layer/multihead_attention.hpp * @author Mrityunjay Tripathi @@ -48,25 +49,26 @@ namespace ann /** Artificial Neural Network. */ { * of shape `(embedDim * tgtSeqLen, batchSize)`. The embeddings are stored * consequently. * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * @tparam InputType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * @tparam OutputType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). * @tparam RegularizerType Type of the regularizer to be used. */ template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat, + typename InputType = arma::mat, + typename OutputType = arma::mat, typename RegularizerType = NoRegularizer > -class MultiheadAttention +class MultiheadAttentionType : public Layer { public: /** * Default constructor. */ - MultiheadAttention(); + MultiheadAttentionType(); + // TODO: does srcSeqLen need to be given? /** * Create the MultiheadAttention object using the specified modules. * @@ -74,16 +76,27 @@ class MultiheadAttention * @param srcSeqLen Source sequence length. * @param embedDim Total dimension of the model. * @param numHeads Number of parallel attention heads. + * @param attnMask Two dimensional Attention Mask. + * @param keyPaddingMask Key Padding Mask. */ - MultiheadAttention(const size_t tgtSeqLen, - const size_t srcSeqLen, - const size_t embedDim, - const size_t numHeads); + MultiheadAttentionType(const size_t tgtSeqLen, + const size_t srcSeqLen, + const size_t embedDim, + const size_t numHeads, + const InputType& attnmask = InputType(), + const InputType& keyPaddingMask = InputType()); + + //! Clone the MultiheadAttentionType object. This handles polymorphism + //! correctly. + MultiheadAttentionType* Clone() const + { + return new MultiheadAttentionType(*this); + } /** * Reset the layer parameters. */ - void Reset(); + void SetWeights(typename OutputType::elem_type* weightsPtr); /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -92,8 +105,7 @@ class MultiheadAttention * @param input The query matrix. * @param output Resulting output activation. */ - template - void Forward(const arma::Mat& input, arma::Mat& output); + void Forward(const InputType& input, OutputType& output); /** * Ordinary feed backward pass of a neural network, calculating the function @@ -103,10 +115,9 @@ class MultiheadAttention * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g); + void Backward(const InputType& /* input */, + const OutputType& gy, + OutputType& g); /** * Calculate the gradient using the output delta and the input activation. @@ -115,10 +126,9 @@ class MultiheadAttention * @param error The calculated error. * @param gradient The calculated gradient. */ - template - void Gradient(const arma::Mat& input, - const arma::Mat& error, - arma::Mat& gradient); + void Gradient(const InputType& input, + const OutputType& error, + OutputType& gradient); //! Get the size of the weights. size_t WeightSize() const { return 4 * (embedDim + 1) * embedDim; } @@ -150,34 +160,27 @@ class MultiheadAttention size_t& NumHeads() { return numHeads; } //! Get the two dimensional Attention Mask. - OutputDataType const& AttentionMask() const { return attnMask; } + OutputType const& AttentionMask() const { return attnMask; } //! Modify the two dimensional Attention Mask. - OutputDataType& AttentionMask() { return attnMask; } + OutputType& AttentionMask() { return attnMask; } //! Get Key Padding Mask. - OutputDataType const& KeyPaddingMask() const { return keyPaddingMask; } + OutputType const& KeyPaddingMask() const { return keyPaddingMask; } //! Modify the Key Padding Mask. - OutputDataType& KeyPaddingMask() { return keyPaddingMask; } + OutputType& KeyPaddingMask() { return keyPaddingMask; } - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } + const size_t WeightSize() const { return (4 * embedDim + 4) * embedDim; } - //! Get the delta. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } + const std::vector OutputDimensions() const + { + // This returns the output as a 2-dimensional (embedDim * tgtSeqLen) + // matrix. + std::vector outputDimensions(inputDimensions.size(), 1); + outputDimensions[0] = embedDim; + outputDimensions[1] = tgtSeqLen; - //! Get the gradient. - OutputDataType const& Gradient() const { return grad; } - //! Modify the gradient. - OutputDataType& Gradient() { return grad; } - - //! Get the parameters. - OutputDataType const& Parameters() const { return weights; } - //! Modify the parameters. - OutputDataType& Parameters() { return weights; } + return outputDimensions; + } size_t InputShape() const { @@ -185,16 +188,16 @@ class MultiheadAttention } private: - //! Element Type of the input. - typedef typename OutputDataType::elem_type ElemType; + //! Element Type of the output. + typedef typename OutputType::elem_type ElemType; //! Target sequence length. size_t tgtSeqLen; - //! Source sequence lenght. + //! Source sequence length. size_t srcSeqLen; - //! Locally-stored module output size. + //! Locally-stored dimensionality of each embedding vector. size_t embedDim; //! Locally-stored number of parallel attention heads. @@ -204,37 +207,37 @@ class MultiheadAttention size_t headDim; //! Two dimensional Attention Mask of shape (tgtSeqLen, srcSeqLen). - OutputDataType attnMask; + OutputType attnMask; //! Key Padding Mask. - OutputDataType keyPaddingMask; + OutputType keyPaddingMask; //! Locally-stored weight matrix associated with query. - OutputDataType queryWt; + OutputType queryWt; //! Locally-stored weight matrix associated with key. - OutputDataType keyWt; + OutputType keyWt; //! Locally-stored weight matrix associated with value. - OutputDataType valueWt; + OutputType valueWt; //! Locally-stored weight matrix associated with attnWt. - OutputDataType outWt; + OutputType outWt; //! Locally-stored bias associated with query. - OutputDataType qBias; + OutputType qBias; //! Locally-stored bias associated with key. - OutputDataType kBias; + OutputType kBias; //! Locall-stored bias associated with value. - OutputDataType vBias; + OutputType vBias; //! Locally-stored bias associated with attnWt. - OutputDataType outBias; + OutputType outBias; //! Locally-stored weights parameter. - OutputDataType weights; + OutputType weights; //! Locally-stored projected query matrix over linear layer. arma::Cube qProj; @@ -252,20 +255,16 @@ class MultiheadAttention arma::Cube attnOut; //! Softmax layer to represent the probabilities of next sequence. - Softmax softmax; - - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored gradient. - OutputDataType grad; - - //! Locally-stored output parameter. - OutputDataType outputParameter; + Softmax softmax; //! Locally-stored regularizer object. RegularizerType regularizer; }; // class MultiheadAttention + +// Standard MultiheadAttention layer using no regularization. +typedef MultiheadAttentionType + MultiheadAttention; + } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/multihead_attention_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/multihead_attention_impl.hpp similarity index 79% rename from src/mlpack/methods/ann/layer/multihead_attention_impl.hpp rename to src/mlpack/methods/ann/layer/not_adapted/multihead_attention_impl.hpp index 3d687d93af..f61d485ab1 100644 --- a/src/mlpack/methods/ann/layer/multihead_attention_impl.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/multihead_attention_impl.hpp @@ -21,31 +21,35 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -MultiheadAttention:: -MultiheadAttention() : +template +MultiheadAttentionType:: +MultiheadAttentionType() : tgtSeqLen(0), srcSeqLen(0), embedDim(0), numHeads(0), - headDim(0) + headDim(0), + attnMask(InputType()), + keyPaddingMask(InputType()) { // Nothing to do here. } -template -MultiheadAttention:: -MultiheadAttention( +template +MultiheadAttentionType:: +MultiheadAttentionType( const size_t tgtSeqLen, const size_t srcSeqLen, const size_t embedDim, - const size_t numHeads) : + const size_t numHeads, + const InputType& attnMask, + const InputType& keyPaddingMask) : tgtSeqLen(tgtSeqLen), srcSeqLen(srcSeqLen), embedDim(embedDim), - numHeads(numHeads) + numHeads(numHeads), + attnMask(attnMask), + keyPaddingMask(keyPaddingMask) { if (embedDim % numHeads != 0) { @@ -54,41 +58,38 @@ MultiheadAttention( } headDim = embedDim / numHeads; - weights.set_size(WeightSize(), 1); } -template -void MultiheadAttention:: -Reset() +template +void MultiheadAttentionType::SetWeights( + typename OutputType::elem_type* weightsPtr) { - typedef typename arma::Mat MatType; + weights = OutputType(weightsPtr, 1, (4 * embedDim + 4) * embedDim, false, + true); - queryWt = MatType(weights.memptr(), embedDim, embedDim, false, false); - keyWt = MatType(weights.memptr() + embedDim * embedDim, - embedDim, embedDim, false, false); - valueWt = MatType(weights.memptr() + 2 * embedDim * embedDim, - embedDim, embedDim, false, false); - outWt = MatType(weights.memptr() + 3 * embedDim * embedDim, - embedDim, embedDim, false, false); + queryWt = OutputType(weightsPtr, embedDim, embedDim, false, true); + keyWt = OutputType(weightsPtr + embedDim * embedDim, embedDim, embedDim, + false, true); + valueWt = OutputType(weightsPtr + 2 * embedDim * embedDim, embedDim, embedDim, + false, true); + outWt = OutputType(weightsPtr + 3 * embedDim * embedDim, embedDim, embedDim, + false, true); - qBias = MatType(weights.memptr() - + 4 * embedDim * embedDim, embedDim, 1, false, false); - kBias = MatType(weights.memptr() - + (4 * embedDim + 1) * embedDim, embedDim, 1, false, false); - vBias = MatType(weights.memptr() - + (4 * embedDim + 2) * embedDim, embedDim, 1, false, false); - outBias = MatType(weights.memptr() - + (4 * embedDim + 3) * embedDim, 1, embedDim, false, false); + qBias = OutputType(weightsPtr + 4 * embedDim * embedDim, embedDim, 1, false, + true); + kBias = OutputType(weightsPtr + (4 * embedDim + 1) * embedDim, embedDim, 1, + false, true); + vBias = OutputType(weightsPtr + (4 * embedDim + 2) * embedDim, embedDim, 1, + false, true); + outBias = OutputType(weightsPtr + (4 * embedDim + 3) * embedDim, 1, embedDim, + false, true); } -template -template -void MultiheadAttention:: -Forward(const arma::Mat& input, arma::Mat& output) +template +void MultiheadAttentionType:: +Forward(const InputType& input, OutputType& output) { - typedef typename arma::Cube CubeType; + typedef typename arma::Cube CubeType; if (input.n_rows != embedDim * (tgtSeqLen + 2 * srcSeqLen)) { @@ -104,12 +105,12 @@ Forward(const arma::Mat& input, arma::Mat& output) // The shape of q : (embedDim, tgtSeqLen, batchSize). // The shape of k : (embedDim, srcSeqLen, batchSize). // The shape of v : (embedDim, srcSeqLen, batchSize). - const CubeType q(const_cast&>(input).memptr(), + const CubeType q(const_cast(input).memptr(), embedDim, tgtSeqLen, batchSize, false, false); - const CubeType k(const_cast&>(input).memptr() + + const CubeType k(const_cast(input).memptr() + embedDim * tgtSeqLen * batchSize, embedDim, srcSeqLen, batchSize, false, false); - const CubeType v(const_cast&>(input).memptr() + + const CubeType v(const_cast(input).memptr() + embedDim * (tgtSeqLen + srcSeqLen) * batchSize, embedDim, srcSeqLen, batchSize, false, false); @@ -188,15 +189,13 @@ Forward(const arma::Mat& input, arma::Mat& output) } } -template -template -void MultiheadAttention:: -Backward(const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g) +template +void MultiheadAttentionType:: +Backward(const InputType& /* input */, + const OutputType& gy, + OutputType& g) { - typedef typename arma::Cube CubeType; + typedef typename arma::Cube CubeType; if (gy.n_rows != tgtSeqLen * embedDim) { @@ -210,7 +209,7 @@ Backward(const arma::Mat& /* input */, // The shape of gyTemp : (tgtSeqLen, embedDim, batchSize). // We need not split it into n heads now because this is the part when // output were concatenated from n heads. - CubeType gyTemp(const_cast&>(gy).memptr(), embedDim, + CubeType gyTemp(const_cast(gy).memptr(), embedDim, tgtSeqLen, batchSize, true, false); // The shape of gyTemp : (embedDim, tgtSeqLen, batchSize). @@ -280,16 +279,13 @@ Backward(const arma::Mat& /* input */, } } -template -template -void MultiheadAttention:: -Gradient(const arma::Mat& input, - const arma::Mat& error, - arma::Mat& gradient) +template +void MultiheadAttentionType:: +Gradient(const InputType& input, + const OutputType& error, + OutputType& gradient) { - typedef typename arma::Cube CubeType; - typedef typename arma::Mat MatType; + typedef typename arma::Cube CubeType; if (input.n_rows != embedDim * (tgtSeqLen + 2 * srcSeqLen)) { @@ -307,16 +303,16 @@ Gradient(const arma::Mat& input, // The shape of gradient : (4 * embedDim * embedDim + 4 * embedDim, 1). gradient.set_size(arma::size(weights)); - const CubeType q(const_cast(input).memptr(), + const CubeType q(const_cast(input).memptr(), embedDim, tgtSeqLen, batchSize, false, false); - const CubeType k(const_cast(input).memptr() + q.n_elem, + const CubeType k(const_cast(input).memptr() + q.n_elem, embedDim, srcSeqLen, batchSize, false, false); - const CubeType v(const_cast(input).memptr() + q.n_elem + k.n_elem, + const CubeType v(const_cast(input).memptr() + q.n_elem + k.n_elem, embedDim, srcSeqLen, batchSize, false, false); // Reshape the propagated error into a cube. // The shape of errorTemp : (embedDim, tgtSeqLen, batchSize). - CubeType errorTemp(const_cast&>(error).memptr(), embedDim, + CubeType errorTemp(const_cast(error).memptr(), embedDim, tgtSeqLen, batchSize, true, false); // Gradient wrt. outBias, i.e. dL/d(outBias). @@ -430,22 +426,40 @@ Gradient(const arma::Mat& input, regularizer.Evaluate(weights, gradient); } -template +template template -void MultiheadAttention:: +void MultiheadAttentionType:: serialize(Archive& ar, const uint32_t /* version */) { + ar(cereal::base_class>(this)); + ar(CEREAL_NVP(tgtSeqLen)); ar(CEREAL_NVP(srcSeqLen)); ar(CEREAL_NVP(embedDim)); ar(CEREAL_NVP(numHeads)); ar(CEREAL_NVP(headDim)); + ar(CEREAL_NVP(softmax)); + ar(CEREAL_NVP(regularizer)); - // This is inefficient, but we have to allocate this memory so that - // WeightSetVisitor gets the right size. - if (cereal::is_loading()) - weights.set_size(4 * embedDim * (embedDim + 1), 1); + if (Archive::is_loading::value) + { + attnMask.clear(); + keyPaddingMask.clear(); + queryWt.clear(); + keyWt.clear(); + valueWt.clear(); + outWt.clear(); + qBias.clear(); + kBias.clear(); + vBias.clear(); + outBias.clear(); + weights.clear(); + qProj.clear(); + kProj.clear(); + vProj.clear(); + scores.clear(); + attnOut.clear(); + } } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/multiply_constant.hpp b/src/mlpack/methods/ann/layer/not_adapted/multiply_constant.hpp similarity index 64% rename from src/mlpack/methods/ann/layer/multiply_constant.hpp rename to src/mlpack/methods/ann/layer/not_adapted/multiply_constant.hpp index baa4744c23..60e0c72d1a 100644 --- a/src/mlpack/methods/ann/layer/multiply_constant.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/multiply_constant.hpp @@ -15,6 +15,8 @@ #include +#include "layer.hpp" + namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -22,22 +24,25 @@ namespace ann /** Artificial Neural Network. */ { * Implementation of the multiply constant layer. The multiply constant layer * multiplies the input by a (non-learnable) constant. * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam InputType The type of the layer's inputs. The layer automatically + * cast inputs to this type (Default: arma::mat). + * @tparam OutputType The type of the computation which also causes the output + * to also be in this type. The type also allows the computation and weight + * type to differ from the input type (Default: arma::mat). */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class MultiplyConstant +template +class MultiplyConstantType : public Layer { public: - /** - * Create the MultiplyConstant object. - */ - MultiplyConstant(const double scalar = 1.0); + //! Create the MultiplyConstant object. + MultiplyConstantType(const double scalar = 1.0); + + //! Clone the MultiplyConstantType object. This handles polymorphism + //! correctly. + MultiplyConstantType* Clone() const + { + return new MultiplyConstantType(*this); + } //! Copy Constructor. MultiplyConstant(const MultiplyConstant& layer); @@ -58,7 +63,6 @@ class MultiplyConstant * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template void Forward(const InputType& input, OutputType& output); /** @@ -69,43 +73,28 @@ class MultiplyConstant * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const DataType& /* input */, const DataType& gy, DataType& g); - - //! Get the output parameter. - OutputDataType& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } + void Backward(const InputType& /* input */, + const OutputType& gy, + OutputType& g); //! Get the scalar multiplier. double Scalar() const { return scalar; } //! Modify the scalar multiplier. double& Scalar() { return scalar; } - //! Get the size of the weights. - size_t WeightSize() const { return 0; } - - /** - * Serialize the layer. - */ + //! Serialize the layer. template void serialize(Archive& ar, const uint32_t /* version */); private: //! Locally-stored constant scalar value. double scalar; +}; // class MultiplyConstantType - //! Locally-stored delta object. - OutputDataType delta; +// Convenience typedefs. - //! Locally-stored output parameter object. - OutputDataType outputParameter; -}; // class MultiplyConstant +// Standard MultiplyConstant layer. +typedef MultiplyConstantType MultiplyConstant; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/not_adapted/multiply_constant_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/multiply_constant_impl.hpp new file mode 100644 index 0000000000..61ae392dea --- /dev/null +++ b/src/mlpack/methods/ann/layer/not_adapted/multiply_constant_impl.hpp @@ -0,0 +1,56 @@ +/** + * @file methods/ann/layer/multiply_constant_impl.hpp + * @author Marcus Edel + * + * Implementation of the MultiplyConstantLayer class, which multiplies the + * input by a (non-learnable) constant. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_LAYER_MULTIPLY_CONSTANT_IMPL_HPP +#define MLPACK_METHODS_ANN_LAYER_MULTIPLY_CONSTANT_IMPL_HPP + +// In case it hasn't yet been included. +#include "multiply_constant.hpp" + +namespace mlpack { +namespace ann /** Artificial Neural Network. */ { + +template +MultiplyConstantType::MultiplyConstantType( + const double scalar) : scalar(scalar) +{ + // Nothing to do here. +} + +template +void MultiplyConstantType::Forward( + const InputType& input, OutputType& output) +{ + output = input * scalar; +} + +template +void MultiplyConstantType::Backward( + const InputType& /* input */, const OutputType& gy, OutputType& g) +{ + g = gy * scalar; +} + +template +template +void MultiplyConstantType::serialize( + Archive& ar, const uint32_t /* version */) +{ + ar(cereal::base_class>(this)); + + ar(CEREAL_NVP(scalar)); +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/layer/multiply_merge.hpp b/src/mlpack/methods/ann/layer/not_adapted/multiply_merge.hpp similarity index 51% rename from src/mlpack/methods/ann/layer/multiply_merge.hpp rename to src/mlpack/methods/ann/layer/not_adapted/multiply_merge.hpp index 5c3d9ba6c0..b690871063 100644 --- a/src/mlpack/methods/ann/layer/multiply_merge.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/multiply_merge.hpp @@ -15,9 +15,9 @@ #include -#include "../visitor/delete_visitor.hpp" -#include "../visitor/delta_visitor.hpp" -#include "../visitor/output_parameter_visitor.hpp" +// #include "../visitor/delete_visitor.hpp" +// #include "../visitor/delta_visitor.hpp" +// #include "../visitor/output_parameter_visitor.hpp" #include "layer_types.hpp" @@ -28,18 +28,16 @@ namespace ann /** Artificial Neural Network. */ { * Implementation of the MultiplyMerge module class. The MultiplyMerge class * multiplies the output of various modules element-wise. * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * @tparam InputType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * @tparam OutputType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). - * @tparam CustomLayers Additional custom layers that can be added. */ template< - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat, - typename... CustomLayers + typename InputType = arma::mat, + typename OutputType = arma::mat > -class MultiplyMerge +class MultiplyMergeType : public MultiLayer { public: /** @@ -48,7 +46,7 @@ class MultiplyMerge * @param model Expose all the network modules. * @param run Call the Forward/Backward method before the output is merged. */ - MultiplyMerge(const bool model = false, const bool run = true); + MultiplyMergeType(const bool model = false, const bool run = true); //! Copy Constructor. MultiplyMerge(const MultiplyMerge& layer); @@ -63,7 +61,10 @@ class MultiplyMerge MultiplyMerge& operator=(MultiplyMerge&& layer); //! Destructor to release allocated memory. - ~MultiplyMerge(); + ~MultiplyMergeType(); + + //! Clone the MultiplyMergeType object. This handles polymorphism correctly. + MultiplyMergeType* Clone() const { return new MultiplyMergeType(*this); } /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -72,7 +73,6 @@ class MultiplyMerge * @param * (input) Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template void Forward(const InputType& /* input */, OutputType& output); /** @@ -84,10 +84,9 @@ class MultiplyMerge * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g); + void Backward(const InputType& /* input */, + const OutputType& gy, + OutputType& g); /* * Calculate the gradient using the output delta and the input activation. @@ -96,56 +95,14 @@ class MultiplyMerge * @param error The calculated error. * @param gradient The calculated gradient. */ - template - void Gradient(const arma::Mat& input, - const arma::Mat& error, - arma::Mat& gradient); - - /* - * Add a new module to the model. - * - * @param args The layer parameter. - */ - template - void Add(Args... args) { network.push_back(new LayerType(args...)); } - - /* - * Add a new module to the model. - * - * @param layer The Layer to be added to the model. - */ - void Add(LayerTypes layer) { network.push_back(layer); } - - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } - - //! Get the gradient. - OutputDataType const& Gradient() const { return gradient; } - //! Modify the gradient. - OutputDataType& Gradient() { return gradient; } - - //! Return the model modules. - std::vector >& Model() - { - if (model) - { - return network; - } - - return empty; - } + void Gradient(const InputType& input, + const OutputType& error, + OutputType& gradient); //! Get the parameters. - OutputDataType const& Parameters() const { return weights; } + OutputType const& Parameters() const { return weights; } //! Modify the parameters. - OutputDataType& Parameters() { return weights; } + OutputType& Parameters() { return weights; } //! Get the size of the weights. size_t WeightSize() const { return 0; } @@ -157,9 +114,6 @@ class MultiplyMerge void serialize(Archive& ar, const uint32_t /* version */); private: - //! Parameter which indicates if the modules should be exposed. - bool model; - //! Parameter which indicates if the Forward/Backward method should be called //! before merging the output. bool run; @@ -167,33 +121,12 @@ class MultiplyMerge //! We need this to know whether we should delete the layer in the destructor. bool ownsLayer; - //! Locally-stored network modules. - std::vector > network; - - //! Locally-stored empty list of modules. - std::vector > empty; - - //! Locally-stored delete visitor module object. - DeleteVisitor deleteVisitor; - - //! Locally-stored output parameter visitor module object. - OutputParameterVisitor outputParameterVisitor; - - //! Locally-stored delta visitor module object. - DeltaVisitor deltaVisitor; - - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored gradient object. - OutputDataType gradient; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; - //! Locally-stored weight object. - OutputDataType weights; -}; // class MultiplyMerge + OutputType weights; +}; // class MultiplyMergeType + +// Standard MultiplyMerge layer. +typedef MultiplyMergeType MultiplyMerge; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/not_adapted/multiply_merge_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/multiply_merge_impl.hpp new file mode 100644 index 0000000000..596f61b445 --- /dev/null +++ b/src/mlpack/methods/ann/layer/not_adapted/multiply_merge_impl.hpp @@ -0,0 +1,116 @@ +/** + * @file methods/ann/layer/multiply_merge_impl.hpp + * @author Haritha Nair + * + * Definition of the MultiplyMerge module which multiplies the output of the + * given modules element-wise. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_LAYER_MULTIPLY_MERGE_IMPL_HPP +#define MLPACK_METHODS_ANN_LAYER_MULTIPLY_MERGE_IMPL_HPP + +// In case it hasn't yet been included. +#include "multiply_merge.hpp" + +// #include "../visitor/forward_visitor.hpp" +// #include "../visitor/backward_visitor.hpp" +// #include "../visitor/gradient_visitor.hpp" + +namespace mlpack { +namespace ann /** Artificial Neural Network. */ { + +template +MultiplyMergeType::MultiplyMergeType( + const bool run) : + run(run) +{ + // Nothing to do here. +} + +// TODO: is this destructor needed? +template +MultiplyMergeType::~MultiplyMergeType() +{ + for (size_t i = 0; i < network.size(); ++i) + delete network[i]; +} + +template +void MultiplyMergeType::Forward( + const InputType& input, OutputType& output) +{ + InitializeForwardPassMemory(); + + if (run) + { + for (size_t i = 0; i < network.size(); ++i) + { + network[i]->Forward(input, layerOutputs[i]); + } + } + + output = layerOutputs.front(); + for (size_t i = 1; i < network.size(); ++i) + { + output %= layerOutputs[i]; + } +} + +template +void MultiplyMergeType::Backward( + const InputType& /* input */, const OutputType& gy, OutputType& g) +{ + InitializeBackwardPassMemory(); + + if (run) + { + for (size_t i = 0; i < network.size(); ++i) + { + network[i]->Backward(layerOutputs[i], gy, layerDeltas[i]); + } + + g = layerDeltas.front(); + for (size_t i = 1; i < network.size(); ++i) + { + g += layerDeltas[i]; + } + } + else + { + g = gy; + } +} + +template +void MultiplyMergeType::Gradient( + const InputType& input, + const OutputType& error, + OutputType& /* gradient */ ) +{ + if (run) + { + for (size_t i = 0; i < network.size(); ++i) + { + network[i]->Gradient(input, error, layerGradients[i]); + } + } +} + +template +template +void MultiplyMergeType::serialize( + Archive& ar, const uint32_t /* version */) +{ + ar(cereal::base_class>(this)); + + ar(CEREAL_NVP(run)); +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/layer/nearest_interpolation.hpp b/src/mlpack/methods/ann/layer/not_adapted/nearest_interpolation.hpp similarity index 100% rename from src/mlpack/methods/ann/layer/nearest_interpolation.hpp rename to src/mlpack/methods/ann/layer/not_adapted/nearest_interpolation.hpp diff --git a/src/mlpack/methods/ann/layer/nearest_interpolation_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/nearest_interpolation_impl.hpp similarity index 100% rename from src/mlpack/methods/ann/layer/nearest_interpolation_impl.hpp rename to src/mlpack/methods/ann/layer/not_adapted/nearest_interpolation_impl.hpp diff --git a/src/mlpack/methods/ann/layer/parametric_relu.hpp b/src/mlpack/methods/ann/layer/not_adapted/parametric_relu.hpp similarity index 64% rename from src/mlpack/methods/ann/layer/parametric_relu.hpp rename to src/mlpack/methods/ann/layer/not_adapted/parametric_relu.hpp index f40be33b2a..5fc3ff83a3 100644 --- a/src/mlpack/methods/ann/layer/parametric_relu.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/parametric_relu.hpp @@ -17,6 +17,8 @@ #include +#include "layer.hpp" + namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -33,16 +35,14 @@ namespace ann /** Artificial Neural Network. */ { * \right. * @f} * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam InputType The type of the layer's inputs. The layer automatically + * cast inputs to this type (Default: arma::mat). + * @tparam OutputType The type of the computation which also causes the output + * to also be in this type. The type also allows the computation and weight + * type to differ from the input type (Default: arma::mat). */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class PReLU +template +class PReLUType : public Layer { public: /** @@ -53,12 +53,13 @@ class PReLU * * @param userAlpha Non zero gradient */ - PReLU(const double userAlpha = 0.03); + PReLUType(const double userAlpha = 0.03); - /* - * Reset the layer parameter. - */ - void Reset(); + //! Clone the PReLUType object. This handles polymorphism correctly. + PReLUType* Clone() const { return new PReLUType(*this); } + + //! Reset the layer parameter. + void SetWeights(typename OutputType::elem_type* weightsPtr); /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -67,7 +68,6 @@ class PReLU * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template void Forward(const InputType& input, OutputType& output); /** @@ -79,8 +79,7 @@ class PReLU * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const DataType& input, const DataType& gy, DataType& g); + void Backward(const InputType& input, const OutputType& gy, OutputType& g); /** * Calculate the gradient using the output delta and the input activation. @@ -89,30 +88,14 @@ class PReLU * @param error The calculated error. * @param gradient The calculated gradient. */ - template - void Gradient(const arma::Mat& input, - const arma::Mat& error, - arma::Mat& gradient); + void Gradient(const InputType& input, + const OutputType& error, + OutputType& gradient); //! Get the parameters. - OutputDataType const& Parameters() const { return alpha; } + OutputType const& Parameters() const { return alpha; } //! Modify the parameters. - OutputDataType& Parameters() { return alpha; } - - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } - - //! Get the gradient. - OutputDataType const& Gradient() const { return gradient; } - //! Modify the gradient. - OutputDataType& Gradient() { return gradient; } + OutputType& Parameters() { return alpha; } //! Get the non zero gradient. double const& Alpha() const { return alpha(0); } @@ -129,22 +112,18 @@ class PReLU void serialize(Archive& ar, const uint32_t /* version */); private: - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; - //! Leakyness Parameter object. - OutputDataType alpha; - - //! Locally-stored gradient object. - OutputDataType gradient; + OutputType alpha; //! Leakyness Parameter given by user in the range 0 < alpha < 1. double userAlpha; }; // class PReLU +// Convenience typedefs. + +// Standard PReLU layer. +typedef PReLUType PReLU; + } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/parametric_relu_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/parametric_relu_impl.hpp similarity index 51% rename from src/mlpack/methods/ann/layer/parametric_relu_impl.hpp rename to src/mlpack/methods/ann/layer/not_adapted/parametric_relu_impl.hpp index a584603f5e..97ce441c09 100644 --- a/src/mlpack/methods/ann/layer/parametric_relu_impl.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/parametric_relu_impl.hpp @@ -12,8 +12,8 @@ * 3-clause BSD license along with mlpack. If not, see * http://www.opensource.org/licenses/BSD-3-Clause for more information. */ -#ifndef MLPACK_METHODS_ANN_LAYER_PReLU_IMPL_HPP -#define MLPACK_METHODS_ANN_LAYER_PReLU_IMPL_HPP +#ifndef MLPACK_METHODS_ANN_LAYER_PRELU_IMPL_HPP +#define MLPACK_METHODS_ANN_LAYER_PRELU_IMPL_HPP // In case it hasn't yet been included. #include "parametric_relu.hpp" @@ -21,66 +21,66 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -PReLU::PReLU( +template +PReLUType::PReLUType( const double userAlpha) : userAlpha(userAlpha) { alpha.set_size(WeightSize(), 1); alpha(0) = userAlpha; } -template -void PReLU::Reset() +template +void PReLUType::SetWeights( + typename OutputType::elem_type* weightsPtr) { + alpha = arma::mat(weightsPtr, 1, 1, false, false); + //! Set value of alpha to the one given by user. - alpha = arma::mat(alpha.memptr(), 1, 1, false, false); + // TODO: this doesn't even make any sense. is it trainable or not? + // why is there userAlpha? is that for initialization only? + alpha(0) = userAlpha; } -template template -void PReLU::Forward( +void PReLUType::Forward( const InputType& input, OutputType& output) { - output = arma::max(input, alpha(0) * input); + // TODO: use transform()? + output = input; + arma::uvec negative = arma::find(input < 0); + output(negative) = input(negative) * alpha(0); } -template -template -void PReLU::Backward( - const DataType& input, const DataType& gy, DataType& g) +template +void PReLUType::Backward( + const InputType& input, const OutputType& gy, OutputType& g) { - DataType derivative; + OutputType derivative; derivative.set_size(arma::size(input)); for (size_t i = 0; i < input.n_elem; ++i) - { derivative(i) = (input(i) >= 0) ? 1 : alpha(0); - } g = gy % derivative; } -template -template -void PReLU::Gradient( - const arma::Mat& input, - const arma::Mat& error, - arma::Mat& gradient) +template +void PReLUType::Gradient( + const InputType& input, + const OutputType& error, + OutputType& gradient) { - if (gradient.n_elem == 0) - { - gradient = arma::zeros(1, 1); - } - - arma::mat zeros = arma::zeros(input.n_rows, input.n_cols); + OutputType zeros = arma::zeros(input.n_rows, input.n_cols); gradient(0) = arma::accu(error % arma::min(zeros, input)) / input.n_cols; } -template +template template -void PReLU::serialize( +void PReLUType::serialize( Archive& ar, const uint32_t /* version */) { + ar(cereal::base_class>(this)); + ar(CEREAL_NVP(alpha)); } diff --git a/src/mlpack/methods/ann/layer/pixel_shuffle.hpp b/src/mlpack/methods/ann/layer/not_adapted/pixel_shuffle.hpp similarity index 100% rename from src/mlpack/methods/ann/layer/pixel_shuffle.hpp rename to src/mlpack/methods/ann/layer/not_adapted/pixel_shuffle.hpp diff --git a/src/mlpack/methods/ann/layer/pixel_shuffle_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/pixel_shuffle_impl.hpp similarity index 100% rename from src/mlpack/methods/ann/layer/pixel_shuffle_impl.hpp rename to src/mlpack/methods/ann/layer/not_adapted/pixel_shuffle_impl.hpp diff --git a/src/mlpack/methods/ann/layer/positional_encoding.hpp b/src/mlpack/methods/ann/layer/not_adapted/positional_encoding.hpp similarity index 63% rename from src/mlpack/methods/ann/layer/positional_encoding.hpp rename to src/mlpack/methods/ann/layer/not_adapted/positional_encoding.hpp index 8678426414..a1317b3e52 100644 --- a/src/mlpack/methods/ann/layer/positional_encoding.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/positional_encoding.hpp @@ -1,3 +1,4 @@ +// Temporarily drop. /** * @file methods/ann/layer/positional_encoding.hpp * @author Mrityunjay Tripathi @@ -25,22 +26,22 @@ namespace ann /** Artificial Neural Network. */ { * `(embedDim * maxSequenceLength, batchSize)`. The embeddings are stored * consequently. * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * @tparam InputType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). */ template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat + typename InputType = arma::mat, + typename OutputType = arma::mat > -class PositionalEncoding +class PositionalEncodingType : public Layer { public: /** - * Create PositionalEncoding object. + * Create PositionalEncodingType object. */ - PositionalEncoding(); + PositionalEncodingType(); /** * Create the PositionalEncoding layer object using the specified parameters. @@ -48,8 +49,11 @@ class PositionalEncoding * @param embedDim The length of the embedding vector. * @param maxSequenceLength Number of tokens in each sequence. */ - PositionalEncoding(const size_t embedDim, - const size_t maxSequenceLength); + PositionalEncodingType(const size_t embedDim, + const size_t maxSequenceLength); + + //! Clone the PositionalEncodingType object. This handles polymorphism correctly. + PositionalEncodingType* Clone() const { return new PositionalEncodingType(*this); } /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -58,8 +62,7 @@ class PositionalEncoding * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template - void Forward(const arma::Mat& input, arma::Mat& output); + void Forward(const InputType& input, OutputType& output); /** * Ordinary feed backward pass of a neural network, calculating the function @@ -70,28 +73,12 @@ class PositionalEncoding * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g); - - //! Get the input parameter. - InputDataType const& InputParameter() const { return inputParameter; } - //! Modify the input parameter. - InputDataType& InputParameter() { return inputParameter; } - - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } + void Backward(const InputType& /* input */, + const OutputType& gy, + OutputType& g); //! Get the positional encoding vector. - InputDataType const& Encoding() const { return positionalEncoding; } + InputType const& Encoding() const { return positionalEncoding; } size_t InputShape() const { @@ -117,17 +104,11 @@ class PositionalEncoding size_t maxSequenceLength; //! Locally-stored positional encodings. - InputDataType positionalEncoding; + InputType positionalEncoding; +}; // class PositionalEncodingTest - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored input parameter object. - InputDataType inputParameter; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; -}; // class PositionalEncoding +// Standard PositionalEncoding layer. +typedef PositionalEncodingType PositionalEncoding; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/positional_encoding_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/positional_encoding_impl.hpp similarity index 59% rename from src/mlpack/methods/ann/layer/positional_encoding_impl.hpp rename to src/mlpack/methods/ann/layer/not_adapted/positional_encoding_impl.hpp index 8dc63a5894..30ad88ae2f 100644 --- a/src/mlpack/methods/ann/layer/positional_encoding_impl.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/positional_encoding_impl.hpp @@ -19,16 +19,16 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -PositionalEncoding::PositionalEncoding() : +template +PositionalEncodingType::PositionalEncodingType() : embedDim(0), maxSequenceLength(0) { // Nothing to do here. } -template -PositionalEncoding::PositionalEncoding( +template +PositionalEncodingType::PositionalEncodingType( const size_t embedDim, const size_t maxSequenceLength) : embedDim(embedDim), @@ -37,14 +37,14 @@ PositionalEncoding::PositionalEncoding( InitPositionalEncoding(); } -template -void PositionalEncoding::InitPositionalEncoding() +template +void PositionalEncodingType::InitPositionalEncoding() { positionalEncoding.set_size(maxSequenceLength, embedDim); - const InputDataType position = arma::regspace(0, 1, maxSequenceLength - 1); - const InputDataType divTerm = arma::exp(arma::regspace(0, 2, embedDim - 1) + const InputType position = arma::regspace(0, 1, maxSequenceLength - 1); + const InputType divTerm = arma::exp(arma::regspace(0, 2, embedDim - 1) * (- std::log(10000.0) / embedDim)); - const InputDataType theta = position * divTerm.t(); + const InputType theta = position * divTerm.t(); for (size_t i = 0; i < theta.n_cols; ++i) { positionalEncoding.col(2 * i) = arma::sin(theta.col(i)); @@ -53,10 +53,9 @@ void PositionalEncoding::InitPositionalEncoding() positionalEncoding = arma::vectorise(positionalEncoding.t()); } -template -template -void PositionalEncoding::Forward( - const arma::Mat& input, arma::Mat& output) +template +void PositionalEncodingType::Forward( + const InputType& input, OutputType& output) { if (input.n_rows != embedDim * maxSequenceLength) Log::Fatal << "Incorrect input dimensions!" << std::endl; @@ -64,19 +63,20 @@ void PositionalEncoding::Forward( output = input.each_col() + positionalEncoding; } -template -template -void PositionalEncoding::Backward( - const arma::Mat& /* input */, const arma::Mat& gy, arma::Mat& g) +template +void PositionalEncodingType::Backward( + const InputType& /* input */, const OutputType& gy, OutputType& g) { g = gy; } -template +template template -void PositionalEncoding::serialize( +void PositionalEncodingType::serialize( Archive& ar, const uint32_t /* version */) { + ar(cereal::base_class>(this)); + ar(CEREAL_NVP(embedDim)); ar(CEREAL_NVP(maxSequenceLength)); diff --git a/src/mlpack/methods/ann/layer/recurrent.hpp b/src/mlpack/methods/ann/layer/not_adapted/recurrent.hpp similarity index 54% rename from src/mlpack/methods/ann/layer/recurrent.hpp rename to src/mlpack/methods/ann/layer/not_adapted/recurrent.hpp index b82fcb175b..836d1aa4b8 100644 --- a/src/mlpack/methods/ann/layer/recurrent.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/recurrent.hpp @@ -14,12 +14,6 @@ #include -#include "../visitor/delete_visitor.hpp" -#include "../visitor/delta_visitor.hpp" -#include "../visitor/copy_visitor.hpp" -#include "../visitor/output_parameter_visitor.hpp" -#include "../visitor/input_shape_visitor.hpp" - #include "layer_types.hpp" #include "add_merge.hpp" #include "sequential.hpp" @@ -31,17 +25,16 @@ namespace ann /** Artificial Neural Network. */ { * Implementation of the RecurrentLayer class. Recurrent layers can be used * similarly to feed-forward layers. * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * @tparam InputType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * @tparam OutputType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). */ template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat, - typename... CustomLayers + typename InputType = arma::mat, + typename OutputType = arma::mat > -class Recurrent +class Recurrent : public MultiLayer { public: /** @@ -79,8 +72,7 @@ class Recurrent * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template - void Forward(const arma::Mat& input, arma::Mat& output); + void Forward(const InputType& input, OutputType& output); /** * Ordinary feed backward pass of a neural network, calculating the function @@ -91,10 +83,9 @@ class Recurrent * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g); + void Backward(const InputType& /* input */, + const OutputType& gy, + OutputType& g); /* * Calculate the gradient using the output delta and the input activation. @@ -103,38 +94,9 @@ class Recurrent * @param error The calculated error. * @param gradient The calculated gradient. */ - template - void Gradient(const arma::Mat& input, - const arma::Mat& error, - arma::Mat& /* gradient */); - - //! Get the model modules. - std::vector >& Model() { return network; } - - //! The value of the deterministic parameter. - bool Deterministic() const { return deterministic; } - //! Modify the value of the deterministic parameter. - bool& Deterministic() { return deterministic; } - - //! Get the parameters. - OutputDataType const& Parameters() const { return parameters; } - //! Modify the parameters. - OutputDataType& Parameters() { return parameters; } - - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } - - //! Get the gradient. - OutputDataType const& Gradient() const { return gradient; } - //! Modify the gradient. - OutputDataType& Gradient() { return gradient; } + void Gradient(const InputType& input, + const OutputType& error, + OutputType& /* gradient */); //! Get the number of steps to backpropagate through time. size_t const& Rho() const { return rho; } @@ -149,23 +111,17 @@ class Recurrent void serialize(Archive& ar, const uint32_t /* version */); private: - //! Locally-stored delete visitor module object. - DeleteVisitor deleteVisitor; - - //! Locally-stored copy visitor - CopyVisitor copyVisitor; - //! Locally-stored start module. - LayerTypes startModule; + Layer* startModule; //! Locally-stored input module. - LayerTypes inputModule; + Layer* inputModule; //! Locally-stored feedback module. - LayerTypes feedbackModule; + Layer* feedbackModule; //! Locally-stored transfer module. - LayerTypes transferModule; + Layer* transferModule; //! Number of steps to backpropagate through time (BPTT). size_t rho; @@ -179,48 +135,26 @@ class Recurrent //! Locally-stored number of gradient steps. size_t gradientStep; - //! If true dropout and scaling is disabled, see notes above. - bool deterministic; - - //! To know whether this object allocated memory. We need this to know - //! whether we should delete the metric member variable in the destructor. - bool ownsLayer; - //! Locally-stored weight object. - OutputDataType parameters; + OutputType parameters; //! Locally-stored initial module. - LayerTypes initialModule; + SequentialType* initialModule; //! Locally-stored recurrent module. - LayerTypes recurrentModule; + SequentialType* recurrentModule; //! Locally-stored model modules. - std::vector > network; + std::vector*> network; //! Locally-stored merge module. - LayerTypes mergeModule; - - //! Locally-stored delta visitor. - DeltaVisitor deltaVisitor; - - //! Locally-stored output parameter visitor. - OutputParameterVisitor outputParameterVisitor; + AddMerge* mergeModule; //! Locally-stored feedback output parameters. - std::vector feedbackOutputParameter; - - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored gradient object. - OutputDataType gradient; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; + std::vector feedbackOutputParameter; //! Locally-stored recurrent error parameter. - arma::mat recurrentError; + OutputType recurrentError; }; // class Recurrent } // namespace ann diff --git a/src/mlpack/methods/ann/layer/recurrent_attention.hpp b/src/mlpack/methods/ann/layer/not_adapted/recurrent_attention.hpp similarity index 57% rename from src/mlpack/methods/ann/layer/recurrent_attention.hpp rename to src/mlpack/methods/ann/layer/not_adapted/recurrent_attention.hpp index 63838dc479..7783a3f4c9 100644 --- a/src/mlpack/methods/ann/layer/recurrent_attention.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/recurrent_attention.hpp @@ -14,11 +14,6 @@ #include -#include "../visitor/delta_visitor.hpp" -#include "../visitor/output_parameter_visitor.hpp" -#include "../visitor/reset_visitor.hpp" -#include "../visitor/weight_size_visitor.hpp" - #include "layer_types.hpp" #include "add_merge.hpp" #include "sequential.hpp" @@ -26,6 +21,7 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { +// TODO: refactor recurrent layer /** * This class implements the Recurrent Model for Visual Attention, using a * variety of possible layer implementations. @@ -43,16 +39,16 @@ namespace ann /** Artificial Neural Network. */ { * } * @endcode * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * @tparam InputType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * @tparam OutputType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). */ template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat + typename InputType = arma::mat, + typename OutputType = arma::mat > -class RecurrentAttention +class RecurrentAttention : public MultiLayer { public: /** @@ -82,8 +78,7 @@ class RecurrentAttention * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template - void Forward(const arma::Mat& input, arma::Mat& output); + void Forward(const InputType& input, OutputType& output); /** * Ordinary feed backward pass of a neural network, calculating the function @@ -94,10 +89,9 @@ class RecurrentAttention * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g); + void Backward(const InputType& /* input */, + const OutputType& gy, + OutputType& g); /* * Calculate the gradient using the output delta and the input activation. @@ -106,41 +100,9 @@ class RecurrentAttention * @param * (error) The calculated error. * @param * (gradient) The calculated gradient. */ - template - void Gradient(const arma::Mat& /* input */, - const arma::Mat& /* error */, - arma::Mat& /* gradient */); - - //! Get the model modules. - std::vector>& Model() { return network; } - - //! The value of the deterministic parameter. - bool Deterministic() const { return deterministic; } - //! Modify the value of the deterministic parameter. - bool& Deterministic() { return deterministic; } - - //! Get the parameters. - OutputDataType const& Parameters() const { return parameters; } - //! Modify the parameters. - OutputDataType& Parameters() { return parameters; } - - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } - - //! Get the gradient. - OutputDataType const& Gradient() const { return gradient; } - //! Modify the gradient. - OutputDataType& Gradient() { return gradient; } - - //! Get the module output size. - size_t OutSize() const { return outSize; } + void Gradient(const InputType& /* input */, + const OutputType& /* error */, + OutputType& /* gradient */); //! Get the number of steps to backpropagate through time. size_t const& Rho() const { return rho; } @@ -160,20 +122,18 @@ class RecurrentAttention // Gradient of the action module. if (backwardStep == (rho - 1)) { - boost::apply_visitor(GradientVisitor(initialInput, actionError), - actionModule); + actionModule->Gradient(initialInput, actionError, + actionModule->Gradient()); } else { - boost::apply_visitor(GradientVisitor(boost::apply_visitor( - outputParameterVisitor, actionModule), actionError), - actionModule); + actionModule->Gradient(actionModule->OutputParameter(), actionError, + actionModule->Gradient()); } // Gradient of the recurrent module. - boost::apply_visitor(GradientVisitor(boost::apply_visitor( - outputParameterVisitor, rnnModule), recurrentError), - rnnModule); + rnnModule->Gradient(rnnModule->OutputParameter(), recurrentError, + rnnModule->Gradient()); attentionGradient += intermediateGradient; } @@ -182,10 +142,10 @@ class RecurrentAttention size_t outSize; //! Locally-stored start module. - LayerTypes<> rnnModule; + Layer* rnnModule; //! Locally-stored input module. - LayerTypes<> actionModule; + Layer* actionModule; //! Number of steps to backpropagate through time (BPTT). size_t rho; @@ -196,62 +156,38 @@ class RecurrentAttention //! Locally-stored number of backward steps. size_t backwardStep; - //! If true dropout and scaling is disabled, see notes above. - bool deterministic; - //! Locally-stored weight object. - OutputDataType parameters; + OutputType parameters; //! Locally-stored model modules. - std::vector> network; - - //! Locally-stored weight size visitor. - WeightSizeVisitor weightSizeVisitor; - - //! Locally-stored delta visitor. - DeltaVisitor deltaVisitor; - - //! Locally-stored output parameter visitor. - OutputParameterVisitor outputParameterVisitor; + std::vector*> network; //! Locally-stored feedback output parameters. - std::vector feedbackOutputParameter; + std::vector feedbackOutputParameter; //! List of all module parameters for the backward pass (BBTT). - std::vector moduleOutputParameter; - - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored gradient object. - OutputDataType gradient; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; + std::vector moduleOutputParameter; //! Locally-stored recurrent error parameter. - arma::mat recurrentError; + OutputType recurrentError; //! Locally-stored action error parameter. - arma::mat actionError; + OutputType actionError; //! Locally-stored action delta. - arma::mat actionDelta; + OutputType actionDelta; //! Locally-stored recurrent delta. - arma::mat rnnDelta; + OutputType rnnDelta; //! Locally-stored initial action input. - arma::mat initialInput; - - //! Locally-stored reset visitor. - ResetVisitor resetVisitor; + InputType initialInput; //! Locally-stored attention gradient. - arma::mat attentionGradient; + OutputType attentionGradient; //! Locally-stored intermediate gradient for the attention module. - arma::mat intermediateGradient; + OutputType intermediateGradient; }; // class RecurrentAttention } // namespace ann diff --git a/src/mlpack/methods/ann/layer/not_adapted/recurrent_attention_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/recurrent_attention_impl.hpp new file mode 100644 index 0000000000..62d19bec87 --- /dev/null +++ b/src/mlpack/methods/ann/layer/not_adapted/recurrent_attention_impl.hpp @@ -0,0 +1,237 @@ +/** + * @file methods/ann/layer/recurrent_attention_impl.hpp + * @author Marcus Edel + * + * Implementation of the RecurrentAttention class. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_LAYER_RECURRENT_ATTENTION_IMPL_HPP +#define MLPACK_METHODS_ANN_LAYER_RECURRENT_ATTENTION_IMPL_HPP + +// In case it hasn't yet been included. +#include "recurrent_attention.hpp" + +namespace mlpack { +namespace ann /** Artificial Neural Network. */ { + +template +RecurrentAttention::RecurrentAttention() : + outSize(0), + rho(0), + forwardStep(0), + backwardStep(0), + deterministic(false) +{ + // Nothing to do. +} + +template +template +RecurrentAttention::RecurrentAttention( + const size_t outSize, + const RNNModuleType& rnn, + const ActionModuleType& action, + const size_t rho) : + outSize(outSize), + rnnModule(new RNNModuleType(rnn)), + actionModule(new ActionModuleType(action)), + rho(rho), + forwardStep(0), + backwardStep(0), + deterministic(false) +{ + network.push_back(rnnModule); + network.push_back(actionModule); +} + +template +void RecurrentAttention::Forward( + const InputType& input, OutputType& output) +{ + InitializeForwardPassMemory(); + + // Convenience naming. + OutputType& rnnOutput = layerOutputs.front(); + OutputType& actionOutput = layerOutputs.back(); + + // Initialize the action input. + if (initialInput.is_empty()) + { + initialInput = arma::zeros(outSize, input.n_cols); + } + + // Propagate through the action and recurrent module. + for (forwardStep = 0; forwardStep < rho; ++forwardStep) + { + if (forwardStep == 0) + { + actionModule->Forward(initialInput, actionOutput); + } + else + { + actionModule->Forward(rnnOutput, actionOutput); + } + + // Initialize the glimpse input. + InputType glimpseInput = arma::zeros(input.n_elem, 2); + glimpseInput.col(0) = input; + glimpseInput.submat(0, 1, actionOutput.n_elem - 1, 1) = + actionOutput; + + rnnModule->Forward(glimpseInput, rnnOutput); + + // Save the output parameter when training the module. + if (!deterministic) + { + for (size_t l = 0; l < network.size(); ++l) + { + // TODO: what if network[i] has a Model()? + // TODO: what does this actually do? do we need it? + moduleOutputParameter.push_back(network[l]->OutputParameter()); + } + } + } + + output = rnnOutput; + + forwardStep = 0; + backwardStep = 0; +} + +template +void RecurrentAttention::Backward( + const InputType& /* input */, + const OutputType& gy, + OutputType& g) +{ + InitializeBackwardPassMemory(); + + // Convenience names. + OutputType& rnnOutput = layerOutputs.front(); + OutputType& actionOutput = layerOutputs.back(); + OutputType& rnnGradient = layerGradients.front(); + OutputType& actionGradient = layerGradients.back(); + + if (intermediateGradient.is_empty() && backwardStep == 0) + { + // Initialize the attention gradients. + // TODO: do rnnModule or actionModule have a Model()? We may need to + // account for those weights too. + size_t weights = rnnModule->Parameters().n_elem + + actionModule->Parameters().n_elem; + + intermediateGradient = arma::zeros(weights, 1); + attentionGradient = arma::zeros(weights, 1); + + // Initialize the action error. + actionError = arma::zeros(actionOutput.n_rows, actionOutput.n_cols); + } + + // Propagate the attention gradients. + if (backwardStep == 0) + { + size_t offset = 0; + // TODO: what if rnnModule has a Model()? + rnnGradient = OutputType(intermediateGradient.memptr() + offset, + rnnModule->Parameters().n_rows, rnnModule->Parameters().n_cols, false, + false); + offset += rnnModule->Parameters().n_elem; + actionGradient = OutputType(intermediateGradient.memptr() + offset, + actionModule->Parameters().n_rows, actionModule->Parameters().n_cols, + false, false); + + attentionGradient.zeros(); + } + + // Back-propagate through time. + for (; backwardStep < rho; backwardStep++) + { + if (backwardStep == 0) + { + recurrentError = gy; + } + else + { + recurrentError = actionDelta; + } + + for (size_t l = 0; l < network.size(); ++l) + { + // TODO: handle case where HasModelCheck is true + network[network.size() - 1 - l] = moduleOutputParameter.back(); + moduleOutputParameter.pop_back(); + } + + if (backwardStep == (rho - 1)) + { + actionModule->Backward(actionOutput, actionError, actionDelta); + } + else + { + actionModule->Backward(initialInput, actionError, actionDelta); + } + + rnnModule->Backward(rnnOutput, recurrentError, rnnDelta); + + if (backwardStep == 0) + { + g = rnnDelta.col(1); + } + else + { + g += rnnDelta.col(1); + } + + IntermediateGradient(); + } +} + +template +void RecurrentAttention::Gradient( + const InputType& /* input */, + const OutputType& /* error */, + OutputType& /* gradient */) +{ + // Convenience naming. + OutputType& rnnGradient = layerGradients.front(); + OutputType& actionGradient = layerGradients.back(); + + size_t offset = 0; + // TODO: handle case where rnnModule or actionModule have a model + if (rnnModule->Parameters().n_elem != 0) + { + rnnGradient = attentionGradient.submat(offset, 0, offset + + rnnModule->Parameters().n_elem - 1, 0); + offset += rnnModule->Parameters().n_elem; + } + + if (actionModule->Parameters().n_elem != 0) + { + actionGradient = attentionGradient.submat(offset, 0, offset + + actionModule->Parameters().n_elem - 1, 0); + } +} + +template +template +void RecurrentAttention::serialize( + Archive& ar, const uint32_t /* version */) +{ + ar(cereal::base_class>(this)); + + ar(CEREAL_NVP(rho)); + ar(CEREAL_NVP(outSize)); + ar(CEREAL_NVP(forwardStep)); + ar(CEREAL_NVP(backwardStep)); + + // TODO: lots of clearing? +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/layer/not_adapted/recurrent_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/recurrent_impl.hpp new file mode 100644 index 0000000000..b99972c9b8 --- /dev/null +++ b/src/mlpack/methods/ann/layer/not_adapted/recurrent_impl.hpp @@ -0,0 +1,284 @@ +/** + * @file methods/ann/layer/recurrent_impl.hpp + * @author Marcus Edel + * + * Implementation of the Recurrent class. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_LAYER_RECURRENT_IMPL_HPP +#define MLPACK_METHODS_ANN_LAYER_RECURRENT_IMPL_HPP + +// In case it hasn't yet been included. +#include "recurrent.hpp" + +namespace mlpack { +namespace ann /** Artificial Neural Network. */ { + +template +Recurrent::Recurrent() : + rho(0), + forwardStep(0), + backwardStep(0), + gradientStep(0) +{ + // Nothing to do. +} + +template +template< + typename StartModuleType, + typename InputModuleType, + typename FeedbackModuleType, + typename TransferModuleType +> +Recurrent::Recurrent( + const StartModuleType& start, + const InputModuleType& input, + const FeedbackModuleType& feedback, + const TransferModuleType& transfer, + const size_t rho) : + startModule(new StartModuleType(start)), + inputModule(new InputModuleType(input)), + feedbackModule(new FeedbackModuleType(feedback)), + transferModule(new TransferModuleType(transfer)), + rho(rho), + forwardStep(0), + backwardStep(0), + gradientStep(0) +{ + initialModule = new SequentialType(); + mergeModule = new AddMerge(false, false, false); + recurrentModule = new SequentialType(false, false); + + initialModule->Add(inputModule); + initialModule->Add(startModule); + initialModule->Add(transferModule); + + mergeModule->Add(inputModule); + mergeModule->Add(feedbackModule); + + recurrentModule->Add(mergeModule); + recurrentModule->Add(transferModule); + + network.push_back(initialModule); + network.push_back(mergeModule); + network.push_back(feedbackModule); + network.push_back(recurrentModule); +} + +template +Recurrent::Recurrent( + const Recurrent& network) : + rho(network.rho), + forwardStep(network.forwardStep), + backwardStep(network.backwardStep), + gradientStep(network.gradientStep) +{ + startModule = network.startModule->Clone(); + inputModule = network.inputModule->Clone(); + feedbackModule = network.feedbackModule->Clone(); + transferModule = network.transferModule->Clone(); + + initialModule = new SequentialType(); + mergeModule = new AddMerge(false, false, false); + recurrentModule = new SequentialType(false, false); + + initialModule->Add(inputModule); + initialModule->Add(startModule); + initialModule->Add(transferModule); + + mergeModule->Add(inputModule); + mergeModule->Add(feedbackModule); + + recurrentModule->Add(mergeModule); + recurrentModule->Add(transferModule); + + this->network.push_back(initialModule); + this->network.push_back(mergeModule); + this->network.push_back(feedbackModule); + this->network.push_back(recurrentModule); +} + +template +void Recurrent::Forward( + const InputType& input, OutputType& output) +{ + InitializeForwardPassMemory(); + + // Convenience names. + OutputType& inputOutput = layerOutputs[0]; + OutputType& mergeOutput = layerOutputs[1]; + OutputType& feedbackOutput = layerOutputs[2]; + OutputType& recurrentOutput = layerOutputs[3]; + + if (forwardStep == 0) + { + initialModule->Forward(input, output); + } + else + { + inputModule->Forward(input, inputOutput); + // TODO: how to get transferModule output? + feedbackModule->Forward(transferModule->OutputParameter(), + feedbackOutput); + recurrentModule->Forward(input, output); + } + + // TODO: how to get transferModule output? + output = transferModule->OutputParameter(); + + // Save the feedback output parameter when training the module. + if (this->training) + { + feedbackOutputParameter.push_back(output); + } + + forwardStep++; + if (forwardStep == rho) + { + forwardStep = 0; + backwardStep = 0; + + if (!recurrentError.is_empty()) + { + recurrentError.zeros(); + } + } +} + +template +void Recurrent::Backward( + const InputType& /* input */, const OutputType& gy, OutputType& g) +{ + InitializeBackwardPassMemory(); + + // Convenience names. + OutputType& inputOutput = layerOutputs[0]; + OutputType& mergeOutput = layerOutputs[1]; + OutputType& feedbackOutput = layerOutputs[2]; + OutputType& recurrentOutput = layerOutputs[3]; + OutputType& inputDelta = layerDeltas[0]; + OutputType& mergeDelta = layerDeltas[1]; + OutputType& feedbackDelta = layerDeltas[2]; + OutputType& recurrentDelta = layerDeltas[3]; + + if (!recurrentError.is_empty()) + { + recurrentError += gy; + } + else + { + recurrentError = gy; + } + + if (backwardStep < (rho - 1)) + { + recurrentModule->Backward(recurrentOutput, recurrentError, recurrentDelta); + inputModule->Backward(inputOutput, recurrentDelta, g); + feedbackModule->Backward(feedbackOutput, recurrentDelta, feedbackDelta); + } + else + { + // TODO: how to get these parameters? + initialModule->Backward(initialModule->OutputParameter(), recurrentError, + g); + } + + recurrentError = feedbackDelta; + backwardStep++; +} + +template +void Recurrent::Gradient( + const InputType& input, + const OutputType& error, + OutputType& /* gradient */) +{ + // Convenience names. + OutputType& inputOutput = layerOutputs[0]; + OutputType& mergeOutput = layerOutputs[1]; + OutputType& feedbackOutput = layerOutputs[2]; + OutputType& recurrentOutput = layerOutputs[3]; + OutputType& inputDelta = layerDeltas[0]; + OutputType& mergeDelta = layerDeltas[1]; + OutputType& feedbackDelta = layerDeltas[2]; + OutputType& recurrentDelta = layerDeltas[3]; + OutputType& inputGradient = layerGradients[0]; + OutputType& mergeGradient = layerGradients[1]; + OutputType& feedbackGradient = layerGradients[2]; + OutputType& recurrentGradient = layerGradients[3]; + + if (gradientStep < (rho - 1)) + { + recurrentModule->Gradient(input, error, recurrentGradient); + inputModule->Gradient(input, mergeDelta, inputGradient); + feedbackModule->Gradient( + feedbackOutputParameter[feedbackOutputParameter.size() - 2 - + gradientStep], mergeDelta, feedbackGradient); + } + else + { + recurrentGradient.zeros(); + inputGradient.zeros(); + feedbackGradient.zeros(); + + // TODO: how to do this? + initialModule->Gradient(input, startModule->Delta(), + initialModule->Gradient()); + } + + gradientStep++; + if (gradientStep == rho) + { + gradientStep = 0; + feedbackOutputParameter.clear(); + } +} + +template +template +void Recurrent::serialize( + Archive& ar, const uint32_t /* version */) +{ + ar(cereal::base_class>(this)); + + // TODO: overhaul this + + ar(CEREAL_POINTER(startModule)); + ar(CEREAL_POINTER(inputModule)); + ar(CEREAL_POINTER(feedbackModule)); + ar(CEREAL_POINTER(transferModule)); + ar(CEREAL_NVP(rho)); + + // Set up the network. + if (cereal::is_loading()) + { + initialModule = new SequentialType(); + mergeModule = new AddMerge(false, false, false); + recurrentModule = new SequentialType(false, false); + + initialModule->Add(inputModule); + initialModule->Add(startModule); + initialModule->Add(transferModule); + + mergeModule->Add(inputModule); + mergeModule->Add(feedbackModule); + + recurrentModule->Add(mergeModule); + recurrentModule->Add(transferModule); + + network.push_back(initialModule); + network.push_back(mergeModule); + network.push_back(feedbackModule); + network.push_back(recurrentModule); + } +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/layer/reinforce_normal.hpp b/src/mlpack/methods/ann/layer/not_adapted/reinforce_normal.hpp similarity index 64% rename from src/mlpack/methods/ann/layer/reinforce_normal.hpp rename to src/mlpack/methods/ann/layer/not_adapted/reinforce_normal.hpp index 5f5c8a00e8..894c2b1c9f 100644 --- a/src/mlpack/methods/ann/layer/reinforce_normal.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/reinforce_normal.hpp @@ -14,6 +14,7 @@ #define MLPACK_METHODS_ANN_LAYER_REINFORCE_NORMAL_HPP #include +#include "layer.hpp" namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -22,16 +23,16 @@ namespace ann /** Artificial Neural Network. */ { * Implementation of the reinforce normal layer. The reinforce normal layer * implements the REINFORCE algorithm for the normal distribution. * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * @tparam InputType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * @tparam OutputType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). */ template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat + typename InputType = arma::mat, + typename OutputType = arma::mat > -class ReinforceNormal +class ReinforceNormalType : public Layer { public: /** @@ -39,7 +40,10 @@ class ReinforceNormal * * @param stdev Standard deviation used during the forward and backward pass. */ - ReinforceNormal(const double stdev = 1.0); + ReinforceNormalType(const double stdev = 1.0); + + //! Clone the ReinforceNormalType object. This handles polymorphism correctly. + ReinforceNormalType* Clone() const { return new ReinforceNormalType(*this); } /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -48,8 +52,7 @@ class ReinforceNormal * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template - void Forward(const arma::Mat& input, arma::Mat& output); + void Forward(const InputType& input, OutputType& output); /** * Ordinary feed backward pass of a neural network, calculating the function @@ -60,31 +63,17 @@ class ReinforceNormal * @param * (gy) The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const DataType& input, const DataType& /* gy */, DataType& g); - - //! Get the output parameter. - OutputDataType& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } - - //! Get the value of the deterministic parameter. - bool Deterministic() const { return deterministic; } - //! Modify the value of the deterministic parameter. - bool& Deterministic() { return deterministic; } + void Backward(const InputType& input, + const OutputType& /* gy */, + OutputType& g); //! Get the value of the reward parameter. - double Reward() const { return reward; } + double const& Reward() const { return reward; } //! Modify the value of the deterministic parameter. double& Reward() { return reward; } //! Get the standard deviation used during forward and backward pass. - double StandardDeviation() const { return stdev; } + double const& StandardDeviation() const { return stdev; } /** * Serialize the layer @@ -99,18 +88,15 @@ class ReinforceNormal //! Locally-stored reward parameter. double reward; - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; - //! Locally-stored output module parameter parameters. - std::vector moduleInputParameter; + std::vector moduleInputParameter; //! If true use maximum a posteriori during the forward pass. bool deterministic; -}; // class ReinforceNormal +}; // class ReinforceNormalType. + +// Standard ReinforceNormal layer. +typedef ReinforceNormalType ReinforceNormal; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/reinforce_normal_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/reinforce_normal_impl.hpp similarity index 68% rename from src/mlpack/methods/ann/layer/reinforce_normal_impl.hpp rename to src/mlpack/methods/ann/layer/not_adapted/reinforce_normal_impl.hpp index c2f92df476..d98820e5db 100644 --- a/src/mlpack/methods/ann/layer/reinforce_normal_impl.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/reinforce_normal_impl.hpp @@ -20,21 +20,21 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { template -ReinforceNormal::ReinforceNormal( +ReinforceNormalType::ReinforceNormalType( const double stdev) : stdev(stdev), reward(0.0), deterministic(false) { // Nothing to do here. } -template -template -void ReinforceNormal::Forward( - const arma::Mat& input, arma::Mat& output) +template +void ReinforceNormalType::Forward( + const InputType& input, OutputType& output) { if (!deterministic) { // Multiply by standard deviations and re-center the means to the mean. - output = output.randn(input.n_rows, input.n_cols) * stdev + input; + output = arma::randn(input.n_rows, input.n_cols) * + stdev + input; moduleInputParameter.push_back(input); } @@ -45,10 +45,9 @@ void ReinforceNormal::Forward( } } -template -template -void ReinforceNormal::Backward( - const DataType& input, const DataType& /* gy */, DataType& g) +template +void ReinforceNormalType::Backward( + const InputType& input, const OutputType& /* gy */, OutputType& g) { g = (input - moduleInputParameter.back()) / std::pow(stdev, 2.0); @@ -59,11 +58,13 @@ void ReinforceNormal::Backward( moduleInputParameter.pop_back(); } -template +template template -void ReinforceNormal::serialize( +void ReinforceNormalType::serialize( Archive& ar, const uint32_t /* version */) { + ar(cereal::base_class>(this)); + ar(CEREAL_NVP(stdev)); } diff --git a/src/mlpack/methods/ann/layer/relu6.hpp b/src/mlpack/methods/ann/layer/not_adapted/relu6.hpp similarity index 100% rename from src/mlpack/methods/ann/layer/relu6.hpp rename to src/mlpack/methods/ann/layer/not_adapted/relu6.hpp diff --git a/src/mlpack/methods/ann/layer/relu6_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/relu6_impl.hpp similarity index 100% rename from src/mlpack/methods/ann/layer/relu6_impl.hpp rename to src/mlpack/methods/ann/layer/not_adapted/relu6_impl.hpp diff --git a/src/mlpack/methods/ann/layer/reparametrization.hpp b/src/mlpack/methods/ann/layer/not_adapted/reparametrization.hpp similarity index 54% rename from src/mlpack/methods/ann/layer/reparametrization.hpp rename to src/mlpack/methods/ann/layer/not_adapted/reparametrization.hpp index d3a183b9fd..833fa4c1a0 100644 --- a/src/mlpack/methods/ann/layer/reparametrization.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/reparametrization.hpp @@ -15,15 +15,15 @@ #include -#include "layer_types.hpp" -#include "../activation_functions/softplus_function.hpp" +#include "layer.hpp" +// #include "../activation_functions/softplus_function.hpp" namespace mlpack { namespace ann /** Artificial Neural Network. */ { /** - * Implementation of the Reparametrization layer class. This layer samples from the - * given parameters of a normal distribution. + * Implementation of the Reparametrization layer class. This layer samples from + * the given parameters of a normal distribution. * * This class also supports beta-VAE, a state-of-the-art framework for * automated discovery of interpretable factorised latent representations from @@ -38,61 +38,77 @@ namespace ann /** Artificial Neural Network. */ { * author = {Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, * Xavier Glorot, Matthew Botvinick, Shakir Mohamed and * Alexander Lerchner | Google DeepMind}, - * journal = {2017 International Conference on Learning Representations(ICLR)}, + * journal = {2017 International Conference on Learning Representations + * (ICLR)}, * year = {2017}, * url = {https://deepmind.com/research/publications/beta-VAE-Learning-Basic-Visual-Concepts-with-a-Constrained-Variational-Framework} * } * @endcode * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * @tparam InputType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * @tparam OutputType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). */ template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat + typename InputType = arma::mat, + typename OutputType = arma::mat > -class Reparametrization +class ReparametrizationType : public Layer { public: - //! Create the Reparametrization object. - Reparametrization(); - /** - * Create the Reparametrization layer object using the specified sample vector size. + * Create the Reparametrization layer object. Note that the inputs are + * expected to be the parameters of the normal distribution; see the + * documentation for Forward(). * - * @param latentSize The number of output latent units. * @param stochastic Whether we want random sample or constant. * @param includeKl Whether we want to include KL loss in backward function. * @param beta The beta (hyper)parameter for beta-VAE mentioned above. */ - Reparametrization(const size_t latentSize, - const bool stochastic = true, - const bool includeKl = true, - const double beta = 1); + ReparametrizationType(const bool stochastic = true, + const bool includeKl = true, + const double beta = 1); + + /** + * Clone the ReparametrizationType object. This handles polymorphism + * correctly. + */ + ReparametrizationType* Clone() const + { + return new ReparametrizationType(*this); + } + + // Virtual destructor. + virtual ~ReparametrizationType() { } //! Copy Constructor. - Reparametrization(const Reparametrization& layer); + ReparametrizationType(const ReparametrizationType& layer); //! Move Constructor. - Reparametrization(Reparametrization&& layer); + ReparametrizationType(ReparametrizationType&& layer); //! Copy assignment operator. - Reparametrization& operator=(const Reparametrization& layer); + ReparametrizationType& operator=(const ReparametrizationType& layer); //! Move assignment operator. - Reparametrization& operator=(Reparametrization&& layer); + ReparametrizationType& operator=(ReparametrizationType&& layer); /** * Ordinary feed forward pass of a neural network, evaluating the function * f(x) by propagating the activity forward through f. * + * Note that `input` is expected to be the parameters of the distribution. + * The first `input.n_rows / 2` elements correspond to the + * pre-standard-deviation values for each output element, and the second + * `input.n_rows / 2` elements correspond to the means for each element. + * Thus, the output size of the layer is the number of input elements divided + * by 2. + * * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template - void Forward(const arma::Mat& input, arma::Mat& output); + void Forward(const InputType& input, OutputType& output); /** * Ordinary feed backward pass of a neural network, calculating the function @@ -103,60 +119,54 @@ class Reparametrization * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const arma::Mat& input, - const arma::Mat& gy, - arma::Mat& g); - - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } - - //! Get the output size. - size_t const& OutputSize() const { return latentSize; } - //! Modify the output size. - size_t& OutputSize() { return latentSize; } + void Backward(const InputType& input, + const OutputType& gy, + OutputType& g); //! Get the KL divergence with standard normal. - double Loss() - { - if (!includeKl) - return 0; - - return -0.5 * beta * arma::accu(2 * arma::log(stdDev) - arma::pow(stdDev, 2) - - arma::pow(mean, 2) + 1) / mean.n_cols; - } + double Loss(); //! Get the value of the stochastic parameter. bool Stochastic() const { return stochastic; } + //! Modify the value of the stochastic parameter. + bool& Stochastic() { return stochastic; } //! Get the value of the includeKl parameter. bool IncludeKL() const { return includeKl; } + //! Modify the value of the includeKl parameter. + bool& IncludeKL() { return includeKl; } //! Get the value of the beta hyperparameter. double Beta() const { return beta; } + //! Modify the value of the beta hyperparameter. + double& Beta() { return beta; } - size_t InputShape() const + void ComputeOutputDimensions() { - return 2 * latentSize; + const size_t inputElem = std::accumulate(this->inputDimensions.begin(), + this->inputDimensions.end(), 0); + if (inputElem % 2 != 0) + { + std::ostringstream oss; + oss << "Reparametrization layer requires that the total number of input " + << "elements is divisible by 2! (Received input with " << inputElem + << " total elements.)"; + throw std::invalid_argument(oss.str()); + } + + this->outputDimensions = std::vector( + this->inputDimensions.size(), 1); + // This flattens the input, and removes half the elements. + this->outputDimensions[0] = inputElem / 2; } /** - * Serialize the layer + * Serialize the layer. */ template void serialize(Archive& ar, const uint32_t /* version */); private: - //! Locally-stored number of output units. - size_t latentSize; - //! If false, sample will be constant. bool stochastic; @@ -166,25 +176,22 @@ class Reparametrization //! The beta hyperparameter for constrained variational frameworks. double beta; - //! Locally-stored delta object. - OutputDataType delta; - //! Locally-stored current gaussian sample. - OutputDataType gaussianSample; + OutputType gaussianSample; //! Locally-stored current mean. - OutputDataType mean; + OutputType mean; //! Locally-stored pre standard deviation. //! After softplus activation gives standard deviation. - OutputDataType preStdDev; + OutputType preStdDev; //! Locally-stored current standard deviation. - OutputDataType stdDev; + OutputType stdDev; +}; // class ReparametrizationType - //! Locally-stored output parameter object. - OutputDataType outputParameter; -}; // class Reparametrization +// Standard Reparametrization layer. +typedef ReparametrizationType Reparametrization; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/not_adapted/reparametrization_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/reparametrization_impl.hpp new file mode 100644 index 0000000000..72e9093242 --- /dev/null +++ b/src/mlpack/methods/ann/layer/not_adapted/reparametrization_impl.hpp @@ -0,0 +1,152 @@ +/** + * @file methods/ann/layer/reparametrization_impl.hpp + * @author Atharva Khandait + * + * Implementation of the Reparametrization layer class which samples from a + * gaussian distribution. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_LAYER_REPARAMETRIZATION_IMPL_HPP +#define MLPACK_METHODS_ANN_LAYER_REPARAMETRIZATION_IMPL_HPP + +// In case it hasn't yet been included. +#include "reparametrization.hpp" + +namespace mlpack { +namespace ann /** Artificial Neural Network. */ { + +template +ReparametrizationType::ReparametrizationType( + const bool stochastic, + const bool includeKl, + const double beta) : + stochastic(stochastic), + includeKl(includeKl), + beta(beta) +{ + if (includeKl == false && beta != 1) + { + Log::Info << "The beta parameter will be ignored as KL divergence is not " + << "included." << std::endl; + } +} + +template +ReparametrizationType::ReparametrizationType( + const ReparametrizationType& layer) : + Layer(layer), + stochastic(layer.stochastic), + includeKl(layer.includeKl), + beta(layer.beta) +{ + // Nothing to do here. +} + +template +ReparametrizationType::ReparametrizationType( + ReparametrizationType&& layer) : + Layer(std::move(layer)), + stochastic(std::move(layer.stochastic)), + includeKl(std::move(layer.includeKl)), + beta(std::move(layer.beta)) +{ + // Nothing to do here. +} + +template +ReparametrizationType& +ReparametrizationType:: +operator=(const ReparametrizationType& layer) +{ + if (this != &layer) + { + Layer::operator=(layer); + stochastic = layer.stochastic; + includeKl = layer.includeKl; + beta = layer.beta; + } + + return *this; +} + +template +ReparametrizationType& +ReparametrizationType:: +operator=(ReparametrizationType&& layer) +{ + if (this != &layer) + { + Layer::operator=(std::move(layer)); + stochastic = std::move(layer.stochastic); + includeKl = std::move(layer.includeKl); + beta = std::move(layer.beta); + } + + return *this; +} + +template +void ReparametrizationType::Forward( + const InputType& input, OutputType& output) +{ + const size_t latentSize = this->outputDimensions[0]; + mean = input.submat(latentSize, 0, 2 * latentSize - 1, input.n_cols - 1); + preStdDev = input.submat(0, 0, latentSize - 1, input.n_cols - 1); + + if (stochastic) + gaussianSample = arma::randn(latentSize, input.n_cols); + else + gaussianSample = arma::ones(latentSize, input.n_cols) * 0.7; + + SoftplusFunction::Fn(preStdDev, stdDev); + output = mean + stdDev % gaussianSample; +} + +template +void ReparametrizationType::Backward( + const InputType& /* input */, const OutputType& gy, OutputType& g) +{ + OutputType tmp; + SoftplusFunction::Deriv(preStdDev, tmp); + + if (includeKl) + { + g = join_cols(gy % std::move(gaussianSample) % tmp + (-1 / stdDev + stdDev) + % tmp * beta, gy + mean * beta / mean.n_cols); + } + else + { + g = join_cols(gy % std::move(gaussianSample) % tmp, gy); + } +} + +template +double ReparametrizationType::Loss() +{ + if (!includeKl) + return 0; + + return -0.5 * beta * arma::accu(2 * arma::log(stdDev) - arma::pow(stdDev, 2) + - arma::pow(mean, 2) + 1) / mean.n_cols; +} + +template +template +void ReparametrizationType::serialize( + Archive& ar, const uint32_t /* version */) +{ + ar(cereal::base_class>(this)); + + ar(CEREAL_NVP(stochastic)); + ar(CEREAL_NVP(includeKl)); + ar(CEREAL_NVP(beta)); +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/layer/select.hpp b/src/mlpack/methods/ann/layer/not_adapted/select.hpp similarity index 53% rename from src/mlpack/methods/ann/layer/select.hpp rename to src/mlpack/methods/ann/layer/not_adapted/select.hpp index f67d57e37f..001991658f 100644 --- a/src/mlpack/methods/ann/layer/select.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/select.hpp @@ -14,31 +14,38 @@ #include +#include "layer.hpp" + namespace mlpack { namespace ann /** Artificial Neural Network. */ { /** - * The select module selects the specified column from a given input matrix. + * The select module selects the specified dimensions from a given input point. * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * @tparam InputType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * @tparam OutputType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). */ template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat + typename InputType = arma::mat, + typename OutputType = arma::mat > -class Select +class SelectType : public Layer { public: /** * Create the Select object. * - * @param index The column which should be extracted from the given input. - * @param elements The number of elements that should be used. + * @param index The first dimension to extract from the input. + * @param elements The number of elements that should be used. If 0 is given, + * then all dimensions starting with index up to the number of dimensions + * are used. */ - Select(const size_t index = 0, const size_t elements = 0); + SelectType(const size_t index = 0, const size_t elements = 0); + + //! Clone the SelectType object. This handles polymorphism correctly. + SelectType* Clone() const { return new SelectType(*this); } /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -47,8 +54,7 @@ class Select * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template - void Forward(const arma::Mat& input, arma::Mat& output); + void Forward(const InputType& input, OutputType& output); /** * Ordinary feed backward pass of a neural network, calculating the function @@ -59,26 +65,15 @@ class Select * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g); - - //! Get the output parameter. - OutputDataType& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } + void Backward(const InputType& /* input */, + const OutputType& gy, + OutputType& g); //! Get the column index. - size_t const& Index() const { return index; } + const size_t& Index() const { return index; } //! Get the number of elements selected. - size_t const& NumElements() const { return elements; } + const size_t& NumElements() const { return elements; } /** * Serialize the layer @@ -86,19 +81,34 @@ class Select template void serialize(Archive& ar, const uint32_t /* version */); + const std::vector OutputDimensions() const + { + std::vector outputDimensions(inputDimensions.size(), 1); + if (elements > 0) + { + outputDimensions[0] = elements; + } + else + { + // Compute the total number of dimensions. + const size_t totalDims = std::accumulate(inputDimensions.begin(), + inputDimensions.end(), 0); + outputDimensions[0] = (totalDims - index); + } + + return outputDimensions; + } + private: //! Locally-stored column index. size_t index; //! Locally-stored number of elements selected. size_t elements; +}; // class SelectType - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; -}; // class Select +// Standard Select layer. +typedef SelectType Select; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/select_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/select_impl.hpp similarity index 58% rename from src/mlpack/methods/ann/layer/select_impl.hpp rename to src/mlpack/methods/ann/layer/not_adapted/select_impl.hpp index 55baa59e8c..1d36b4d517 100644 --- a/src/mlpack/methods/ann/layer/select_impl.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/select_impl.hpp @@ -18,8 +18,8 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -Select::Select( +template +SelectType::SelectType( const size_t index, const size_t elements) : index(index), @@ -28,43 +28,44 @@ Select::Select( // Nothing to do here. } -template -template -void Select::Forward( - const arma::Mat& input, arma::Mat& output) +template +void SelectType::Forward( + const InputType& input, OutputType& output) { if (elements == 0) { - output = input.col(index); + output = input.rows(index, input.n_rows - 1); } else { - output = input.submat(0, index, elements - 1, index); + output = input.rows(index, index + elements - 1); } } -template -template -void Select::Backward( - const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g) +template +void SelectType::Backward( + const InputType& /* input */, + const OutputType& gy, + OutputType& g) { + // TODO: not sure if this is right if (elements == 0) { g = gy; } else { - g = gy.submat(0, 0, elements - 1, 0); + g = gy.rows(0, elements - 1); } } -template +template template -void Select::serialize( +void SelectType::serialize( Archive& ar, const uint32_t /* version */) { + ar(cereal::base_class>(this)); + ar(CEREAL_NVP(index)); ar(CEREAL_NVP(elements)); } diff --git a/src/mlpack/methods/ann/layer/not_adapted/sequential.hpp b/src/mlpack/methods/ann/layer/not_adapted/sequential.hpp new file mode 100644 index 0000000000..471d5a1f35 --- /dev/null +++ b/src/mlpack/methods/ann/layer/not_adapted/sequential.hpp @@ -0,0 +1,150 @@ +/** + * @file methods/ann/layer/sequential.hpp + * @author Marcus Edel + * + * Definition of the Sequential class, which acts as a feed-forward fully + * connected network container. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_LAYER_SEQUENTIAL_HPP +#define MLPACK_METHODS_ANN_LAYER_SEQUENTIAL_HPP + +#include + +#include "layer.hpp" + +namespace mlpack { +namespace ann /** Artificial Neural Network. */ { + +/** + * Implementation of the Sequential class. The sequential class works as a + * feed-forward fully connected network container which plugs various layers + * together. + * + * This class can also be used as a container for a residual block. In that + * case, the sizes of the input and output matrices of this class should be + * equal. A typedef has been added for use as a Residual<> class. + * + * For more information, refer the following paper. + * + * @code + * @article{He15, + * author = {Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun}, + * title = {Deep Residual Learning for Image Recognition}, + * year = {2015}, + * url = {https://arxiv.org/abs/1512.03385}, + * eprint = {1512.03385}, + * } + * @endcode + * + * Note: If this class is used as the first layer of a network, it should be + * preceded by IdentityLayer<>. + * + * Note: This class should at least have two layers for a call to its Gradient() + * function. + * + * @tparam InputType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam Residual If true, use the object as a Residual block. + */ +template < + typename InputType = arma::mat, + typename OutputType = arma::mat, + bool Residual = false +> +class SequentialType : public MultiLayer +{ + public: + /** + * Create the Sequential object. + */ + SequentialType(); + + /** + * Create the Sequential object using the specified parameters. + * + * @param ownsLayers If true, then this module will delete its layers when + * deallocated. + */ + SequentialType(const bool ownsLayers); + + //! Copy constructor. + SequentialType(const SequentialType& layer); + + //! Copy assignment operator. + SequentialType& operator=(const SequentialType& layer); + + //! Destroy the Sequential object. + ~SequentialType(); + + //! Clone the SequentialType object. This handles polymorphism correctly. + SequentialType* Clone() const { return new SequentialType(*this); } + + /** + * Ordinary feed forward pass of a neural network, evaluating the function + * f(x) by propagating the activity forward through f. + * + * @param input Input data used for evaluating the specified function. + * @param output Resulting output activation. + */ + void Forward(const InputType& input, OutputType& output); + + /** + * Ordinary feed backward pass of a neural network, using 3rd-order tensors as + * input, calculating the function f(x) by propagating x backwards through f. + * Using the results from the feed forward pass. + * + * @param * (input) The propagated input activation. + * @param gy The backpropagated error. + * @param g The calculated gradient. + */ + void Backward(const InputType& /* input */, + const OutputType& gy, + OutputType& g); + + /** + * Calculate the gradient using the output delta and the input activation. + * + * @param input The input parameter used for calculating the gradient. + * @param error The calculated error. + * @param gradient The calculated gradient. + */ + void Gradient(const InputType& input, + const OutputType& error, + OutputType& /* gradient */); + + size_t InputShape() const; + + /** + * Serialize the layer + */ + template + void serialize(Archive& ar, const uint32_t /* version */); + + private: + //! Indicator if we already initialized the model. + bool reset; + + //! Whether we are responsible for deleting the layers held in this module. + bool ownsLayers; +}; // class SequentialType + +// Standard Sequential layer. +typedef SequentialType Sequential; + +// Standard Residual layer. +typedef SequentialType Residual; + +} // namespace ann +} // namespace mlpack + +// Include implementation. +#include "sequential_impl.hpp" + +#endif diff --git a/src/mlpack/methods/ann/layer/not_adapted/sequential_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/sequential_impl.hpp new file mode 100644 index 0000000000..6cac9d869f --- /dev/null +++ b/src/mlpack/methods/ann/layer/not_adapted/sequential_impl.hpp @@ -0,0 +1,163 @@ +/** + * @file methods/ann/layer/sequential_impl.hpp + * @author Marcus Edel + * + * Implementation of the Sequential class, which acts as a feed-forward fully + * connected network container. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_LAYER_SEQUENTIAL_IMPL_HPP +#define MLPACK_METHODS_ANN_LAYER_SEQUENTIAL_IMPL_HPP + +// In case it hasn't yet been included. +#include "sequential.hpp" + +// TODO: can this be merged with MultiLayer more closely? + +namespace mlpack { +namespace ann /** Artificial Neural Network. */ { + +template +SequentialType:: +SequentialType() : + reset(false), ownsLayers(true) +{ + // Nothing to do here. +} + +template +SequentialType:: +SequentialType(const bool ownsLayers) : + reset(false), ownsLayers(ownsLayers) +{ + // Nothing to do here. +} + +template +SequentialType:: +SequentialType(const SequentialType& layer) : + reset(layer.reset), + ownsLayers(layer.ownsLayers) +{ + // Nothing to do here. +} + +template +SequentialType& +SequentialType:: +operator = (const SequentialType& layer) +{ + if (this != &layer) + { + reset = layer.reset; + ownsLayers = layer.ownsLayers; + network = layer.network; + // Call copy constructor of parent... + } + return *this; +} + + +template +SequentialType::~SequentialType() +{ + if (ownsLayers) + { + for (size_t i = 0; i < network.size(); ++i) + delete network[i]; + } +} + +template +void SequentialType:: +Forward(const InputType& input, OutputType& output) +{ + InitializeForwardPassMemory(); + + network.front()->Forward(input, layerOutputs.front()); + + for (size_t i = 1; i < network.size(); ++i) + { + network[i]->Forward(layerOutputs[i - 1], layerOutputs[i]); + } + + // TODO: optimization is possible here + output = layerOutputs.back(); + + if (Residual) + { + if (arma::size(output) != arma::size(input)) + { + Log::Fatal << "The sizes of the output and input matrices of the Residual" + << " block should be equal. Please examine the network architecture." + << std::endl; + } + output += input; + } +} + +template +void SequentialType::Backward( + const InputType& /* input */, + const OutputType& gy, + OutputType& g) +{ + InitializeBackwardPassMemory(); + + network.back()->Backward(layerOutputs.back(), gy, layerDeltas.back()); + + for (size_t i = 2; i < network.size() + 1; ++i) + { + network[network.size() - i]->Backward(layerOutputs[network.size() - i], + layerDeltas[network.size() - i + 1], layerDeltas[network.size() - i]); + } + + g = layerDeltas.front(); + + if (Residual) + { + g += gy; + } +} + +template +void SequentialType:: +Gradient(const InputType& input, + const OutputType& error, + OutputType& /* gradient */) +{ + InitializeGradientPassMemory(); + + network.back()->Gradient(layerOutputs[network.size() - 2], error, + layerGradients.back()); + + for (size_t i = 2; i < network.size(); ++i) + { + network[network.size() - i]->Gradient( + layerOutputs[network.size() - i - 1], + layerDeltas[network.size() - i + 1], + layerGradients[network.size() - i] + ); + } + + network.front()->Gradient(input, layerDeltas[1], layerGradients.front()); +} + +template +template +void SequentialType::serialize( + Archive& ar, const uint32_t /* version */) +{ + ar(cereal::base_class>(this)); + + ar(CEREAL_NVP(ownsLayers)); +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/layer/softmax.hpp b/src/mlpack/methods/ann/layer/not_adapted/softmax.hpp similarity index 58% rename from src/mlpack/methods/ann/layer/softmax.hpp rename to src/mlpack/methods/ann/layer/not_adapted/softmax.hpp index 43fd0856c9..18f2cd9db6 100644 --- a/src/mlpack/methods/ann/layer/softmax.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/softmax.hpp @@ -16,6 +16,8 @@ #include +#include "layer.hpp" + namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -26,22 +28,21 @@ namespace ann /** Artificial Neural Network. */ { * numbers. It should be used for inference only and not with NLL loss (use * LogSoftMax instead). * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam InputType The type of the layer's inputs. The layer automatically + * cast inputs to this type (Default: arma::mat). + * @tparam OutputType The type of the computation which also causes the output + * to also be in this type. The type also allows the computation and weight + * type to differ from the input type (Default: arma::mat). */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class Softmax +template +class SoftmaxType : public Layer { public: - /** - * Create the Softmax object. - */ - Softmax(); + //! Create the Softmax object. + SoftmaxType(); + + //! Clone the SoftmaxType object. This handles polymorphism correctly. + SoftmaxType* Clone() const { return new SoftmaxType(*this); } /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -50,7 +51,6 @@ class Softmax * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template void Forward(const InputType& input, OutputType& output); /** @@ -62,37 +62,19 @@ class Softmax * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const arma::Mat& input, - const arma::Mat& gy, - arma::Mat& g); + void Backward(const InputType& input, const OutputType& gy, OutputType& g); - //! Get the output parameter. - OutputDataType& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the size of the weights. - size_t WeightSize() const { return 0; } - - //! Get the delta. - InputDataType& Delta() const { return delta; } - //! Modify the delta. - InputDataType& Delta() { return delta; } - - /** - * Serialize the layer. - */ + //! Serialize the layer. template - void serialize(Archive& /* ar */, const uint32_t /* version */); + void serialize(Archive& ar, const uint32_t /* version */); private: - //! Locally-stored delta object. - OutputDataType delta; +}; // class SoftmaxType - //! Locally-stored output parameter object. - OutputDataType outputParameter; -}; // class Softmax +// Convenience typedefs. + +// Standard Linear layer using no regularization. +typedef SoftmaxType Softmax; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/softmax_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/softmax_impl.hpp similarity index 65% rename from src/mlpack/methods/ann/layer/softmax_impl.hpp rename to src/mlpack/methods/ann/layer/not_adapted/softmax_impl.hpp index 500e0e108e..34fbed04e4 100644 --- a/src/mlpack/methods/ann/layer/softmax_impl.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/softmax_impl.hpp @@ -19,15 +19,14 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -Softmax::Softmax() +template +SoftmaxType::SoftmaxType() { // Nothing to do here. } -template template -void Softmax::Forward( +void SoftmaxType::Forward( const InputType& input, OutputType& output) { @@ -36,23 +35,22 @@ void Softmax::Forward( output = softmaxInput.each_row() / sum(softmaxInput, 0); } -template -template -void Softmax::Backward( - const arma::Mat& input, - const arma::Mat& gy, - arma::Mat& g) +template +void SoftmaxType::Backward( + const InputType& input, + const OutputType& gy, + OutputType& g) { g = input % (gy - arma::repmat(arma::sum(gy % input), input.n_rows, 1)); } -template +template template -void Softmax::serialize( - Archive& /* ar */, +void SoftmaxType::serialize( + Archive& ar, const uint32_t /* version */) { - // Nothing to do here. + ar(cereal::base_class>(this)); } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/softmin.hpp b/src/mlpack/methods/ann/layer/not_adapted/softmin.hpp similarity index 57% rename from src/mlpack/methods/ann/layer/softmin.hpp rename to src/mlpack/methods/ann/layer/not_adapted/softmin.hpp index 57809992c2..ffb8c2e43d 100644 --- a/src/mlpack/methods/ann/layer/softmin.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/softmin.hpp @@ -15,6 +15,8 @@ #include +#include "layer.hpp" + namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -22,23 +24,22 @@ namespace ann /** Artificial Neural Network. */ { * Implementation of the Softmin layer. The Softmin function takes as a input * a vector of K real numbers, rescaling them so that the elements of the * K-dimensional output vector lie in the range [0, 1] and sum to 1. - * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * + * @tparam InputType The type of the layer's inputs. The layer automatically + * cast inputs to this type (Default: arma::mat). + * @tparam OutputType The type of the computation which also causes the output + * to also be in this type. The type also allows the computation and weight + * type to differ from the input type (Default: arma::mat). */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class Softmin +template +class SoftminType : public Layer { public: - /** - * Create the Softmin object. - */ - Softmin(); + //! Create the Softmin object. + SoftminType(); + + //! Clone the SoftminType object. This handles polymorphism correctly. + SoftminType* Clone() const { return new SoftminType(*this); } /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -47,7 +48,6 @@ class Softmin * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template void Forward(const InputType& input, OutputType& output); /** @@ -59,34 +59,18 @@ class Softmin * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const arma::Mat& input, - const arma::Mat& gy, - arma::Mat& g); + void Backward(const InputType& input, const OutputType& gy, OutputType& g); - //! Get the output parameter. - OutputDataType& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - InputDataType& Delta() const { return delta; } - //! Modify the delta. - InputDataType& Delta() { return delta; } - - /** - * Serialize the layer. - */ + //! Serialize the layer. template - void serialize(Archive& /* ar */, const uint32_t /* version */); + void serialize(Archive& ar, const uint32_t /* version */); +}; // class SoftminType - private: - //! Locally-stored delta object. - OutputDataType delta; +// Convenience typedefs. + +// Standard Softmin layer using no regularization. +typedef SoftminType Softmin; - //! Locally stored output parameter object. - OutputDataType outputParameter; -}; // class Softmin } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/softmin_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/softmin_impl.hpp similarity index 65% rename from src/mlpack/methods/ann/layer/softmin_impl.hpp rename to src/mlpack/methods/ann/layer/not_adapted/softmin_impl.hpp index 6945256b44..4e200f2943 100644 --- a/src/mlpack/methods/ann/layer/softmin_impl.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/softmin_impl.hpp @@ -18,15 +18,14 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -Softmin::Softmin() +template +SoftminType::SoftminType() { // Nothing to do here. } -template template -void Softmin::Forward( +void SoftminType::Forward( const InputType& input, OutputType& output) { @@ -35,23 +34,22 @@ void Softmin::Forward( output = softminInput.each_row() / sum(softminInput, 0); } -template -template -void Softmin::Backward( - const arma::Mat& input, - const arma::Mat& gy, - arma::Mat& g) +template +void SoftminType::Backward( + const InputType& input, + const OutputType& gy, + OutputType& g) { g = input % (gy - arma::repmat(arma::sum(gy % input), input.n_rows, 1)); } -template +template template -void Softmin::serialize( - Archive& /* ar */, +void SoftminType::serialize( + Archive& ar, const uint32_t /* version */) { - // Nothing to do here. + ar(cereal::base_class>(this)); } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/softshrink.hpp b/src/mlpack/methods/ann/layer/not_adapted/softshrink.hpp similarity index 66% rename from src/mlpack/methods/ann/layer/softshrink.hpp rename to src/mlpack/methods/ann/layer/not_adapted/softshrink.hpp index 93db85f55b..ac3f11319c 100644 --- a/src/mlpack/methods/ann/layer/softshrink.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/softshrink.hpp @@ -18,6 +18,8 @@ #include +#include "layer.hpp" + namespace mlpack { namespace ann /** Artifical Neural Network. */ { @@ -43,26 +45,25 @@ namespace ann /** Artifical Neural Network. */ { * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class SoftShrink +template +class SoftShrinkType : public Layer { public: /** - * Create Soft Shrink object using specified hyperparameter lambda. + * Create SoftShrink object using specified hyperparameter lambda. * * @param lambda The noise level of an image depends on settings of an - * imaging device. The settings can be used to select appropriate - * parameters for denoising methods. It is proportional to the noise - * level entered by the user. - * And it is calculated by multiplying the - * noise level sigma of the input(noisy image) and a - * coefficient 'a' which is one of the training parameters. - * Default value of lambda is 0.5. + * imaging device. The settings can be used to select appropriate + * parameters for denoising methods. It is proportional to the noise + * level entered by the user. And it is calculated by multiplying the + * noise level sigma of the input(noisy image) and a coefficient 'a' + * which is one of the training parameters. Default value of lambda + * is 0.5. */ - SoftShrink(const double lambda = 0.5); + SoftShrinkType(const double lambda = 0.5); + + //! Clone the SoftShrinkType object. This handles polymorphism correctly. + SoftShrinkType* Clone() const { return new SoftShrinkType(*this); } /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -71,7 +72,6 @@ class SoftShrink * @param input Input data used for evaluating the Soft Shrink function. * @param output Resulting output activation */ - template void Forward(const InputType& input, OutputType& output); /** @@ -83,42 +83,26 @@ class SoftShrink * @param gy The backpropagated error. * @param g The calculated gradient */ - template - void Backward(const DataType& input, - DataType& gy, - DataType& g); - - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } + void Backward(const InputType& input, const OutputType& gy, OutputType& g); //! Get the hyperparameter lambda. double const& Lambda() const { return lambda; } //! Modify the hyperparameter lambda. double& Lambda() { return lambda; } - /** - * Serialize the layer. - */ + //! Serialize the layer. template void serialize(Archive& ar, const uint32_t /* version */); private: - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; - //! Locally-stored hyperparamater lambda. double lambda; -}; // class SoftShrink +}; // class SoftShrinkType + +// Convenience typedefs. + +// Standard SoftShrink layer. +typedef SoftShrinkType SoftShrink; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/softshrink_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/softshrink_impl.hpp similarity index 59% rename from src/mlpack/methods/ann/layer/softshrink_impl.hpp rename to src/mlpack/methods/ann/layer/not_adapted/softshrink_impl.hpp index f4be4b1136..80358de911 100644 --- a/src/mlpack/methods/ann/layer/softshrink_impl.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/softshrink_impl.hpp @@ -20,38 +20,36 @@ namespace ann /** Artificial Neural Network. */ { // This constructor is called for Soft Shrink activation function. // lambda is a hyperparameter. -template -SoftShrink::SoftShrink(const double lambda) : +template +SoftShrinkType::SoftShrinkType(const double lambda) : lambda(lambda) { // Nothing to do here. } -template template -void SoftShrink::Forward( +void SoftShrinkType::Forward( const InputType& input, OutputType& output) { - output = (input > lambda) % (input - lambda) + ( - input < -lambda) % (input + lambda); + output = (input > lambda) % (input - lambda) + + (input < -lambda) % (input + lambda); } -template -template -void SoftShrink::Backward( - const DataType& input, DataType& gy, DataType& g) +template +void SoftShrinkType::Backward( + const InputType& input, const OutputType& gy, OutputType& g) { - DataType derivative; - derivative = (arma::ones(arma::size(input)) - (input == 0)); - g = gy % derivative; + g = gy % (arma::ones(arma::size(input)) - (input == 0)); } -template +template template -void SoftShrink::serialize( +void SoftShrinkType::serialize( Archive& ar, const uint32_t /* version */) { + ar(cereal::base_class>(this)); + ar(CEREAL_NVP(lambda)); } diff --git a/src/mlpack/methods/ann/layer/spatial_dropout.hpp b/src/mlpack/methods/ann/layer/not_adapted/spatial_dropout.hpp similarity index 66% rename from src/mlpack/methods/ann/layer/spatial_dropout.hpp rename to src/mlpack/methods/ann/layer/not_adapted/spatial_dropout.hpp index e44388b97d..0fe1c08ef3 100644 --- a/src/mlpack/methods/ann/layer/spatial_dropout.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/spatial_dropout.hpp @@ -1,3 +1,4 @@ +// Temporarily drop. /** * @file methods/ann/layer/spatial_dropout.hpp * @author Anjishnu Mukherjee @@ -15,9 +16,12 @@ #include #include +#include "layer.hpp" + namespace mlpack { namespace ann /** Artificial Neural Network. */ { +// TODO: this could likely use inputDimensions to remove the `size` parameter! /** * Implementation of the SpatialDropout layer. * @@ -36,27 +40,29 @@ namespace ann /** Artificial Neural Network. */ { * } * @endcode * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam InputType The type of the layer's inputs. The layer automatically + * cast inputs to this type (Default: arma::mat). + * @tparam OutputType The type of the computation which also causes the output + * to also be in this type. The type also allows the computation and weight + * type to differ from the input type (Default: arma::mat). */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class SpatialDropout +template +class SpatialDropoutType : public Layer { public: //! Create the SpatialDropout object. - SpatialDropout(); + SpatialDropoutType(); + /** * Create the SpatialDropout object using the specified parameters. * * @param size The number of channels of each input image. * @param ratio The probability of each channel getting dropped. */ - SpatialDropout(const size_t size, const double ratio = 0.5); + SpatialDropoutType(const size_t size, const double ratio = 0.5); + + //! Clone the SpatialDropoutType object. This handles polymorphism correctly. + SpatialDropoutType* Clone() const { return new SpatialDropoutType(*this); } /** * Ordinary feed forward pass of the SpatialDropout layer. @@ -64,8 +70,7 @@ class SpatialDropout * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template - void Forward(const arma::Mat& input, arma::Mat& output); + void Forward(const InputType& input, OutputType& output); /** * Ordinary feed backward pass of the SpatialDropout layer. @@ -74,20 +79,7 @@ class SpatialDropout * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const arma::Mat& input, - const arma::Mat& gy, - arma::Mat& g); - - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } + void Backward(const InputType& input, const OutputType& gy, OutputType& g); //! Get the number of channels. size_t Size() const { return size; } @@ -95,11 +87,6 @@ class SpatialDropout //! Modify the number of channels. size_t& Size() { return size; } - //! Get the value of the deterministic parameter. - bool Deterministic() const { return deterministic; } - //! Modify the value of the deterministic parameter. - bool& Deterministic() { return deterministic; } - //! Get the probability value. double Ratio() const { return ratio; } @@ -110,21 +97,13 @@ class SpatialDropout scale = 1.0 / (1.0 - ratio); } - /** - * Serialize the layer. - */ + //! Serialize the layer. template void serialize(Archive& ar, const uint32_t /* version */); private: - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; - //! Locally-stored mast object. - OutputDataType mask; + OutputType mask; //! The number of channels of each input image. size_t size; @@ -143,11 +122,13 @@ class SpatialDropout //! The number of pixels in each feature map. size_t inputSize; - - //! If true dropout and scaling are disabled. - bool deterministic; }; // class SpatialDropout +// Convenience typedefs. + +// Standard SpatialDropout layer. +typedef SpatialDropoutType SpatialDropout; + } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/spatial_dropout_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/spatial_dropout_impl.hpp similarity index 57% rename from src/mlpack/methods/ann/layer/spatial_dropout_impl.hpp rename to src/mlpack/methods/ann/layer/not_adapted/spatial_dropout_impl.hpp index 4bd2cb767b..d8ae24010b 100644 --- a/src/mlpack/methods/ann/layer/spatial_dropout_impl.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/spatial_dropout_impl.hpp @@ -18,21 +18,20 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -SpatialDropout::SpatialDropout() : +template +SpatialDropoutType::SpatialDropoutType() : size(0), ratio(0.5), scale(1.0 / (1.0 - ratio)), reset(false), batchSize(0), - inputSize(0), - deterministic(false) + inputSize(0) { // Nothing to do here. } -template -SpatialDropout::SpatialDropout( +template +SpatialDropoutType::SpatialDropoutType( const size_t size, const double ratio) : size(size), @@ -40,16 +39,14 @@ SpatialDropout::SpatialDropout( scale(1.0 / (1.0 - ratio)), reset(false), batchSize(0), - inputSize(0), - deterministic(false) + inputSize(0) { // Nothing to do here. } -template -template -void SpatialDropout::Forward( - const arma::Mat& input, arma::Mat& output) +template +void SpatialDropoutType::Forward( + const InputType& input, OutputType& output) { Log::Assert(input.n_rows % size == 0, "Input features must be divisible \ by feature maps."); @@ -62,16 +59,20 @@ void SpatialDropout::Forward( } if (deterministic) + { output = input; + } else { output.zeros(arma::size(input)); - arma::cube inputTemp(const_cast(input).memptr(), inputSize, - size, batchSize, false, false); - arma::cube outputTemp(const_cast(output).memptr(), inputSize, - size, batchSize, false, false); - arma::mat probabilities(1, size); - arma::mat maskRow(1, size); + arma::Cube inputTemp( + const_cast(input).memptr(), inputSize, size, batchSize, + false, true); + arma::Cube outputTemp( + const_cast(output).memptr(), inputSize, size, batchSize, + false, true); + OutputType probabilities(1, size); + OutputType maskRow(1, size); probabilities.fill(ratio); ann::BernoulliDistribution<> bernoulli_dist(probabilities, false); maskRow = bernoulli_dist.Sample(); @@ -82,36 +83,39 @@ void SpatialDropout::Forward( } } -template -template -void SpatialDropout::Backward( - const arma::Mat& input, const arma::Mat& gy, arma::Mat& g) +template +void SpatialDropoutType::Backward( + const InputType& input, const OutputType& gy, OutputType& g) { g.zeros(arma::size(input)); - arma::cube gyTemp(const_cast(gy).memptr(), inputSize, size, - batchSize, false, false); - arma::cube gTemp(const_cast(g).memptr(), inputSize, size, - batchSize, false, false); + arma::Cube gyTemp( + const_cast(gy).memptr(), inputSize, size, batchSize, false, + true); + arma::Cube gTemp( + const_cast(g).memptr(), inputSize, size, batchSize, false, + true); for (size_t n = 0; n < batchSize; n++) gTemp.slice(n) = gyTemp.slice(n) % mask * scale; } -template +template template -void SpatialDropout::serialize( +void SpatialDropoutType::serialize( Archive& ar, const uint32_t /* version */) { + ar(cereal::base_class>(this)); + ar(CEREAL_NVP(size)); ar(CEREAL_NVP(ratio)); ar(CEREAL_NVP(batchSize)); ar(CEREAL_NVP(inputSize)); ar(CEREAL_NVP(reset)); - ar(CEREAL_NVP(deterministic)); // Reset scale. - scale = 1.0 / (1.0 - ratio); + if (Archive::is_loading::value) + scale = 1.0 / (1.0 - ratio); } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/subview.hpp b/src/mlpack/methods/ann/layer/not_adapted/subview.hpp similarity index 69% rename from src/mlpack/methods/ann/layer/subview.hpp rename to src/mlpack/methods/ann/layer/not_adapted/subview.hpp index bc99b0cc44..77a8a85b3a 100644 --- a/src/mlpack/methods/ann/layer/subview.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/subview.hpp @@ -13,7 +13,8 @@ #define MLPACK_METHODS_ANN_LAYER_SUBVIEW_HPP #include -#include + +#include "layer.hpp" namespace mlpack { namespace ann { @@ -22,34 +23,31 @@ namespace ann { * Implementation of the subview layer. The subview layer modifies the input to * a submatrix of required size. * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * @tparam InputType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * @tparam OutputType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). */ template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat + typename InputType = arma::mat, + typename OutputType = arma::mat > -class Subview +class SubviewType : public Layer { public: /** * Create the Subview layer object using the specified range of input to * accept. * - * @param inSize Width of sample. * @param beginRow Starting row index. * @param endRow Ending row index. * @param beginCol Starting column index. * @param endCol Ending column index. */ - Subview(const size_t inSize = 1, - const size_t beginRow = 0, - const size_t endRow = 0, - const size_t beginCol = 0, - const size_t endCol = 0) : - inSize(inSize), + SubviewType(const size_t beginRow = 0, + const size_t endRow = 0, + const size_t beginCol = 0, + const size_t endCol = 0) : beginRow(beginRow), endRow(endRow), beginCol(beginCol), @@ -58,6 +56,9 @@ class Subview /* Nothing to do here */ } + //! Clone the SubviewType object. This handles polymorphism correctly. + SubviewType* Clone() const { return new SubviewType(*this); } + /** * Ordinary feed forward pass of a neural network, evaluating the function * f(x) by propagating the activity forward through f. @@ -65,17 +66,18 @@ class Subview * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template void Forward(const InputType& input, OutputType& output) { - size_t batchSize = input.n_cols / inSize; + size_t batchSize = input.n_cols; // Check if subview parameters are within the indices of input sample. - endRow = ((endRow < input.n_rows) && (endRow >= beginRow))? - endRow : (input.n_rows - 1); - endCol = ((endCol < inSize) && (endCol >= beginCol)) ? - endCol : (inSize - 1); + // TODO: this seems incorrect + endRow = ((endRow < inputDimensions[0]) && (endRow >= beginRow))? + endRow : (inputDimensions[0] - 1); + endCol = ((endCol < inputDimensions[1]) && (endCol >= beginCol)) ? + endCol : (inputDimensions[1] - 1); + // TODO: this is maybe not right? output.set_size( (endRow - beginRow + 1) * (endCol - beginCol + 1), batchSize); @@ -111,27 +113,13 @@ class Subview * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g) + void Backward(const InputType& /* input */, + const OutputType& gy, + OutputType& g) { g = gy; } - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } - - //! Get the width of each sample. - size_t InSize() const { return inSize; } - //! Get the starting row index of subview vector or matrix. size_t const& BeginRow() const { return beginRow; } //! Modify the width of each sample. @@ -152,13 +140,33 @@ class Subview //! Modify the width of each sample. size_t& EndCol() { return endCol; } + const std::vector OutputDimensions() const + { + // TODO: relax this restriction + for (size_t i = 2; i < inputDimensions.size(); ++i) + { + if (inputDimensions[i] > 1) + { + throw std::invalid_argument("Subview(): layer input must be two-" + "dimensional!"); + } + } + + std::vector outputDimensions(inputDimensions); + outputDimensions[0] = (endRow - beginRow + 1); + outputDimensions[1] = (endCol - beginCol + 1); + + return outputDimensions; + } + /** * Serialize the layer. */ template void serialize(Archive& ar, const uint32_t /* version */) { - ar(CEREAL_NVP(inSize)); + ar(cereal::base_class>(this)); + ar(CEREAL_NVP(beginRow)); ar(CEREAL_NVP(endRow)); ar(CEREAL_NVP(beginCol)); @@ -166,9 +174,6 @@ class Subview } private: - //! Width of each sample. - size_t inSize; - //! Starting row index of subview vector or matrix. size_t beginRow; @@ -180,13 +185,10 @@ class Subview //! Ending column index of subview vector or matrix. size_t endCol; +}; // class SubviewType - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; -}; // class Subview +// Standard Subview layer. +typedef SubviewType Subview; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/transposed_convolution.hpp b/src/mlpack/methods/ann/layer/not_adapted/transposed_convolution.hpp similarity index 72% rename from src/mlpack/methods/ann/layer/transposed_convolution.hpp rename to src/mlpack/methods/ann/layer/not_adapted/transposed_convolution.hpp index f637ca3355..f3103d492b 100644 --- a/src/mlpack/methods/ann/layer/transposed_convolution.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/transposed_convolution.hpp @@ -1,3 +1,4 @@ +// Temporarily drop. /** * @file methods/ann/layer/transposed_convolution.hpp * @author Shikhar Jaiswal @@ -21,7 +22,7 @@ #include #include -#include "layer_types.hpp" +#include "layer.hpp" #include "padding.hpp" namespace mlpack { @@ -34,23 +35,23 @@ namespace ann /** Artificial Neural Network. */ { * @tparam ForwardConvolutionRule Convolution to perform forward process. * @tparam BackwardConvolutionRule Convolution to perform backward process. * @tparam GradientConvolutionRule Convolution to calculate gradient. - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * @tparam InputType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * @tparam OutputType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). */ template < typename ForwardConvolutionRule = NaiveConvolution, typename BackwardConvolutionRule = NaiveConvolution, typename GradientConvolutionRule = NaiveConvolution, - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat + typename InputType = arma::mat, + typename OutputType = arma::mat > -class TransposedConvolution +class TransposedConvolutionType : public Layer { public: //! Create the Transposed Convolution object. - TransposedConvolution(); + TransposedConvolutionType(); /** * Create the Transposed Convolution object using the specified number of @@ -76,19 +77,20 @@ class TransposedConvolution * @param outputHeight The height of the output data. * @param paddingType The type of padding (Valid or Same). Defaults to None. */ - TransposedConvolution(const size_t inSize, - const size_t outSize, - const size_t kernelWidth, - const size_t kernelHeight, - const size_t strideWidth = 1, - const size_t strideHeight = 1, - const size_t padW = 0, - const size_t padH = 0, - const size_t inputWidth = 0, - const size_t inputHeight = 0, - const size_t outputWidth = 0, - const size_t outputHeight = 0, - const std::string& paddingType = "None"); + // TODO: remove inputWidth and inputHeight? + TransposedConvolutionType(const size_t inSize, + const size_t outSize, + const size_t kernelWidth, + const size_t kernelHeight, + const size_t strideWidth = 1, + const size_t strideHeight = 1, + const size_t padW = 0, + const size_t padH = 0, + const size_t inputWidth = 0, + const size_t inputHeight = 0, + const size_t outputWidth = 0, + const size_t outputHeight = 0, + const std::string& paddingType = "None"); /** * Create the Transposed Convolution object using the specified number of @@ -118,24 +120,24 @@ class TransposedConvolution * @param outputHeight The height of the output data. * @param paddingType The type of padding (Valid or Same). Defaults to None. */ - TransposedConvolution(const size_t inSize, - const size_t outSize, - const size_t kernelWidth, - const size_t kernelHeight, - const size_t strideWidth, - const size_t strideHeight, - const std::tuple& padW, - const std::tuple& padH, - const size_t inputWidth = 0, - const size_t inputHeight = 0, - const size_t outputWidth = 0, - const size_t outputHeight = 0, - const std::string& paddingType = "None"); + TransposedConvolutionType(const size_t inSize, + const size_t outSize, + const size_t kernelWidth, + const size_t kernelHeight, + const size_t strideWidth, + const size_t strideHeight, + const std::tuple& padW, + const std::tuple& padH, + const size_t inputWidth = 0, + const size_t inputHeight = 0, + const size_t outputWidth = 0, + const size_t outputHeight = 0, + const std::string& paddingType = "None"); /* * Set the weight and bias term. */ - void Reset(); + void SetWeights(const typename OutputType::elem_type* weightsPtr); /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -144,8 +146,7 @@ class TransposedConvolution * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template - void Forward(const arma::Mat& input, arma::Mat& output); + void Forward(const InputType& input, OutputType& output); /** * Ordinary feed backward pass of a neural network, calculating the function @@ -156,135 +157,122 @@ class TransposedConvolution * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g); + void Backward(const InputType& /* input */, + const OutputType& gy, + OutputType& g); - /* + /** * Calculate the gradient using the output delta and the input activation. * * @param * (input) The input parameter used for calculating the gradient. * @param error The calculated error. * @param gradient The calculated gradient. */ - template - void Gradient(const arma::Mat& /* input */, - const arma::Mat& error, - arma::Mat& gradient); + void Gradient(const InputType& /* input */, + const OutputType& error, + OutputType& gradient); //! Get the parameters. - OutputDataType const& Parameters() const { return weights; } + OutputType const& Parameters() const { return weights; } //! Modify the parameters. - OutputDataType& Parameters() { return weights; } + OutputType& Parameters() { return weights; } //! Get the weight of the layer. - arma::cube const& Weight() const { return weight; } + arma::Cube const& Weight() const + { + return weight; + } //! Modify the weight of the layer. - arma::cube& Weight() { return weight; } + arma::Cube& Weight() { return weight; } //! Get the bias of the layer. - arma::mat const& Bias() const { return bias; } + OutputType const& Bias() const { return bias; } //! Modify the bias of the layer. - arma::mat& Bias() { return bias; } - - //! Get the input parameter. - InputDataType const& InputParameter() const { return inputParameter; } - //! Modify the input parameter. - InputDataType& InputParameter() { return inputParameter; } - - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } - - //! Get the gradient. - OutputDataType const& Gradient() const { return gradient; } - //! Modify the gradient. - OutputDataType& Gradient() { return gradient; } + OutputType& Bias() { return bias; } //! Get the input width. - size_t InputWidth() const { return inputWidth; } + size_t const& InputWidth() const { return inputWidth; } //! Modify input the width. size_t& InputWidth() { return inputWidth; } //! Get the input height. - size_t InputHeight() const { return inputHeight; } + size_t const& InputHeight() const { return inputHeight; } //! Modify the input height. size_t& InputHeight() { return inputHeight; } //! Get the output width. - size_t OutputWidth() const { return outputWidth; } + size_t const& OutputWidth() const { return outputWidth; } //! Modify the output width. size_t& OutputWidth() { return outputWidth; } //! Get the output height. - size_t OutputHeight() const { return outputHeight; } + size_t const& OutputHeight() const { return outputHeight; } //! Modify the output height. size_t& OutputHeight() { return outputHeight; } //! Get the input size. - size_t InputSize() const { return inSize; } + size_t const& InputSize() const { return inSize; } //! Get the output size. - size_t OutputSize() const { return outSize; } + size_t const& OutputSize() const { return outSize; } //! Get the kernel width. - size_t KernelWidth() const { return kernelWidth; } + size_t const& KernelWidth() const { return kernelWidth; } //! Modify the kernel width. size_t& KernelWidth() { return kernelWidth; } //! Get the kernel height. - size_t KernelHeight() const { return kernelHeight; } + size_t const& KernelHeight() const { return kernelHeight; } //! Modify the kernel height. size_t& KernelHeight() { return kernelHeight; } //! Get the stride width. - size_t StrideWidth() const { return strideWidth; } + size_t const& StrideWidth() const { return strideWidth; } //! Modify the stride width. size_t& StrideWidth() { return strideWidth; } //! Get the stride height. - size_t StrideHeight() const { return strideHeight; } + size_t const& StrideHeight() const { return strideHeight; } //! Modify the stride height. size_t& StrideHeight() { return strideHeight; } //! Get the top padding height. - size_t PadHTop() const { return padHTop; } + size_t const& PadHTop() const { return padHTop; } //! Modify the top padding height. size_t& PadHTop() { return padHTop; } //! Get the bottom padding height. - size_t PadHBottom() const { return padHBottom; } + size_t const& PadHBottom() const { return padHBottom; } //! Modify the bottom padding height. size_t& PadHBottom() { return padHBottom; } //! Get the left padding width. - size_t PadWLeft() const { return padWLeft; } + size_t const& PadWLeft() const { return padWLeft; } //! Modify the left padding width. size_t& PadWLeft() { return padWLeft; } //! Get the right padding width. - size_t PadWRight() const { return padWRight; } + size_t const& PadWRight() const { return padWRight; } //! Modify the right padding width. size_t& PadWRight() { return padWRight; } - //! Get the shape of the input. - size_t InputShape() const - { - return inputHeight * inputWidth * inSize; - } - //! Get the size of the weight matrix. size_t WeightSize() const { return (outSize * inSize * kernelWidth * kernelHeight) + outSize; } + + const std::vector& OutputDimensions() const + { + std::vector result(inputDimensions.size(), 0); + result[0] = outputWidth; + result[1] = outputHeight; + // Higher dimensions are unmodified. + for (size_t i = 2; i < inputDimensions.size(); ++i) + result[i] = inputDimensions[i]; + return result; + } + /** * Serialize the layer. */ @@ -425,13 +413,13 @@ class TransposedConvolution size_t aH; //! Locally-stored weight object. - OutputDataType weights; + OutputType weights; //! Locally-stored weight object. - arma::cube weight; + arma::Cube weight; //! Locally-stored bias term object. - arma::mat bias; + OutputType bias; //! Locally-stored input width. size_t inputWidth; @@ -446,38 +434,35 @@ class TransposedConvolution size_t outputHeight; //! Locally-stored transformed output parameter. - arma::cube outputTemp; + arma::Cube outputTemp; //! Locally-stored transformed padded input parameter. - arma::cube inputPaddedTemp; + arma::Cube inputPaddedTemp; //! Locally-stored transformed expanded input parameter. - arma::cube inputExpandedTemp; + arma::Cube inputExpandedTemp; //! Locally-stored transformed error parameter. - arma::cube gTemp; + arma::Cube gTemp; //! Locally-stored transformed gradient parameter. - arma::cube gradientTemp; + arma::Cube gradientTemp; //! Locally-stored padding layer for forward propagation. - ann::Padding<> paddingForward; + ann::Padding paddingForward; //! Locally-stored padding layer for back propagation. - ann::Padding<> paddingBackward; + ann::Padding paddingBackward; +}; // class TransposedConvolutionType - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored gradient object. - OutputDataType gradient; - - //! Locally-stored input parameter object. - InputDataType inputParameter; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; -}; // class TransposedConvolution +// Standard TransposedConvolution +typedef TransposedConvolutionType< + NaiveConvolution, + NaiveConvolution, + NaiveConvolution, + arma::mat, + arma::mat +> TransposedConvolution; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/transposed_convolution_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/transposed_convolution_impl.hpp similarity index 79% rename from src/mlpack/methods/ann/layer/transposed_convolution_impl.hpp rename to src/mlpack/methods/ann/layer/not_adapted/transposed_convolution_impl.hpp index d932acb6a7..a660133fe9 100644 --- a/src/mlpack/methods/ann/layer/transposed_convolution_impl.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/transposed_convolution_impl.hpp @@ -23,16 +23,16 @@ template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename InputDataType, - typename OutputDataType + typename InputType, + typename OutputType > -TransposedConvolution< +TransposedConvolutionType< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - InputDataType, - OutputDataType ->::TransposedConvolution() + InputType, + OutputType +>::TransposedConvolutionType() { // Nothing to do here. } @@ -41,16 +41,16 @@ template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename InputDataType, - typename OutputDataType + typename InputType, + typename OutputType > -TransposedConvolution< +TransposedConvolutionType< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - InputDataType, - OutputDataType ->::TransposedConvolution( + InputType, + OutputType +>::TransposedConvolutionType( const size_t inSize, const size_t outSize, const size_t kernelWidth, @@ -64,7 +64,7 @@ TransposedConvolution< const size_t outputWidth, const size_t outputHeight, const std::string& paddingType) : - TransposedConvolution( + TransposedConvolutionType( inSize, outSize, kernelWidth, @@ -86,16 +86,16 @@ template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename InputDataType, - typename OutputDataType + typename InputType, + typename OutputType > -TransposedConvolution< +TransposedConvolutionType< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - InputDataType, - OutputDataType ->::TransposedConvolution( + InputType, + OutputType +>::TransposedConvolutionType( const size_t inSize, const size_t outSize, const size_t kernelWidth, @@ -152,10 +152,10 @@ TransposedConvolution< const size_t padWidthRightForward = kernelWidth - padWRight - 1; const size_t padHeightBottomtForward = kernelHeight - padHBottom - 1; - paddingForward = ann::Padding<>(padWidthLeftForward, + paddingForward = ann::Padding(padWidthLeftForward, padWidthRightForward + aW, padHeightTopForward, padHeightBottomtForward + aH); - paddingBackward = ann::Padding<>(padWLeft, padWRight, padHTop, padHBottom); + paddingBackward = ann::Padding(padWLeft, padWRight, padHTop, padHBottom); // Check if the output height and width are possible given the other // parameters of the layer. @@ -174,42 +174,42 @@ template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename InputDataType, - typename OutputDataType + typename InputType, + typename OutputType > -void TransposedConvolution< +void TransposedConvolutionType< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - InputDataType, - OutputDataType ->::Reset() + InputType, + OutputType +>::SetWeights(typename OutputType::elem_type* weightPtr) { - weight = arma::cube(weights.memptr(), kernelWidth, kernelHeight, - outSize * inSize, false, false); - bias = arma::mat(weights.memptr() + weight.n_elem, - outSize, 1, false, false); + weight = arma::Cube(weightPtr, + kernelWidth, kernelHeight, outSize * inSize, false, false); + bias = arma::Mat(weightsPtr + + weight.n_elem, outSize, 1, false, false); } template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename InputDataType, - typename OutputDataType + typename InputType, + typename OutputType > -template -void TransposedConvolution< +void TransposedConvolutionType< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - InputDataType, - OutputDataType ->::Forward(const arma::Mat& input, arma::Mat& output) + InputType, + OutputType +>::Forward(const InputType& input, OutputType& output) { batchSize = input.n_cols; - arma::cube inputTemp(const_cast&>(input).memptr(), - inputWidth, inputHeight, inSize * batchSize, false, false); + arma::Cube inputTemp( + const_cast(input).memptr(), inputWidth, inputHeight, + inSize * batchSize, false, false); if (strideWidth > 1 || strideHeight > 1) { @@ -231,9 +231,9 @@ void TransposedConvolution< } else { - inputPaddedTemp = arma::Cube(inputExpandedTemp.memptr(), - inputExpandedTemp.n_rows, inputExpandedTemp.n_cols, - inputExpandedTemp.n_slices, false, false);; + inputPaddedTemp = arma::Cube( + inputExpandedTemp.memptr(), inputExpandedTemp.n_rows, + inputExpandedTemp.n_cols, inputExpandedTemp.n_slices, false, false);; } } else if (paddingForward.PadWLeft() != 0 || @@ -253,8 +253,8 @@ void TransposedConvolution< } output.set_size(outputWidth * outputHeight * outSize, batchSize); - outputTemp = arma::Cube(output.memptr(), outputWidth, outputHeight, - outSize * batchSize, false, false); + outputTemp = arma::Cube(output.memptr(), + outputWidth, outputHeight, outSize * batchSize, false, false); outputTemp.zeros(); for (size_t outMap = 0, outMapIdx = 0, batchCount = 0; outMap < @@ -268,7 +268,7 @@ void TransposedConvolution< for (size_t inMap = 0; inMap < inSize; inMap++, outMapIdx++) { - arma::Mat convOutput, rotatedFilter; + OutputType convOutput, rotatedFilter; Rotate180(weight.slice(outMapIdx), rotatedFilter); if (strideWidth > 1 || @@ -298,22 +298,22 @@ template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename InputDataType, - typename OutputDataType + typename InputType, + typename OutputType > -template -void TransposedConvolution< +void TransposedConvolutionType< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - InputDataType, - OutputDataType + InputType, + OutputType >::Backward( - const arma::Mat& /* input */, const arma::Mat& gy, arma::Mat& g) + const InputType& /* input */, const OutputType& gy, OutputType& g) { - arma::Cube mappedError(((arma::Mat&) gy).memptr(), outputWidth, + arma::Cube mappedError( + ((OutputType&) gy).memptr(), outputWidth, outputHeight, outSize * batchSize, false, false); - arma::Cube mappedErrorPadded; + arma::Cube mappedErrorPadded; if (paddingBackward.PadWLeft() != 0 || paddingBackward.PadWRight() != 0 || paddingBackward.PadHTop() != 0 || paddingBackward.PadHBottom() != 0) { @@ -329,8 +329,8 @@ void TransposedConvolution< } } g.set_size(inputWidth * inputHeight * inSize, batchSize); - gTemp = arma::Cube(g.memptr(), inputWidth, inputHeight, inSize * - batchSize, false, false); + gTemp = arma::Cube(g.memptr(), inputWidth, + inputHeight, inSize * batchSize, false, false); gTemp.zeros(); @@ -345,7 +345,7 @@ void TransposedConvolution< for (size_t inMap = 0; inMap < inSize; inMap++, outMapIdx++) { - arma::Mat output; + OutputType output; if (paddingBackward.PadWLeft() != 0 || paddingBackward.PadWRight() != 0 || paddingBackward.PadHTop() != 0 || paddingBackward.PadHBottom() != 0) @@ -368,32 +368,33 @@ template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename InputDataType, - typename OutputDataType + typename InputType, + typename OutputType > -template -void TransposedConvolution< +void TransposedConvolutionType< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - InputDataType, - OutputDataType + InputType, + OutputType >::Gradient( - const arma::Mat& input, - const arma::Mat& error, - arma::Mat& gradient) + const InputType& input, + const OutputType& error, + OutputType& gradient) { - arma::Cube mappedError(((arma::Mat&) error).memptr(), outputWidth, - outputHeight, outSize * batchSize, false, false); - arma::cube inputTemp(const_cast&>(input).memptr(), - inputWidth, inputHeight, inSize * batchSize, false, false); + arma::Cube mappedError( + ((OutputType&) error).memptr(), outputWidth, outputHeight, + outSize * batchSize, false, false); + arma::Cube inputTemp( + const_cast(input).memptr(), inputWidth, inputHeight, + inSize * batchSize, false, false); gradient.set_size(weights.n_elem, 1); - gradientTemp = arma::Cube(gradient.memptr(), weight.n_rows, - weight.n_cols, weight.n_slices, false, false); + gradientTemp = arma::Cube(gradient.memptr(), + weight.n_rows, weight.n_cols, weight.n_slices, false, false); gradientTemp.zeros(); - arma::Mat inputSlice, output, deltaSlice, rotatedOutput; + OutputType inputSlice, output, deltaSlice, rotatedOutput; for (size_t outMap = 0, outMapIdx = 0, batchCount = 0; outMap < outSize * batchSize; outMap++) @@ -437,18 +438,20 @@ template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename InputDataType, - typename OutputDataType + typename InputType, + typename OutputType > template -void TransposedConvolution< +void TransposedConvolutionType< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - InputDataType, - OutputDataType + InputType, + OutputType >::serialize(Archive& ar, const uint32_t /* version */) { + ar(cereal::base_class>(this)); + ar(CEREAL_NVP(inSize)); ar(CEREAL_NVP(outSize)); ar(CEREAL_NVP(batchSize)); @@ -469,8 +472,6 @@ void TransposedConvolution< if (cereal::is_loading()) { - weights.set_size((outSize * inSize * kernelWidth * kernelHeight) + outSize, - 1); size_t totalPadWidth = padWLeft + padWRight; size_t totalPadHeight = padHTop + padHBottom; aW = (outputWidth + kernelWidth - totalPadWidth - 2) % strideWidth; @@ -482,20 +483,20 @@ template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename InputDataType, - typename OutputDataType + typename InputType, + typename OutputType > -void TransposedConvolution< +void TransposedConvolutionType< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - InputDataType, - OutputDataType + InputType, + OutputType >::InitializeSamePadding(){ /** * Using O=s*(I-1) + K -2P + A * where - * s=stride + * s=stride * I=Input Shape * K=Kernel Size * P=Padding diff --git a/src/mlpack/methods/ann/layer/virtual_batch_norm.hpp b/src/mlpack/methods/ann/layer/not_adapted/virtual_batch_norm.hpp similarity index 60% rename from src/mlpack/methods/ann/layer/virtual_batch_norm.hpp rename to src/mlpack/methods/ann/layer/not_adapted/virtual_batch_norm.hpp index 43b9a1d3a3..1d47a44e54 100644 --- a/src/mlpack/methods/ann/layer/virtual_batch_norm.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/virtual_batch_norm.hpp @@ -13,14 +13,16 @@ #define MLPACK_METHODS_ANN_LAYER_VIRTUALBATCHNORM_HPP #include +#include "layer.hpp" namespace mlpack { namespace ann /** Artificial Neural Network. */ { +// TODO: what about sizes for this layer? /** * Declaration of the VirtualBatchNorm layer class. Instead of using the - * batch statistics for normalizing on a mini-batch, it uses a reference subset of - * the data for calculating the normalization statistics. + * batch statistics for normalizing on a mini-batch, it uses a reference subset + * of the data for calculating the normalization statistics. * * For more information, refer to the following paper, * @@ -34,49 +36,55 @@ namespace ann /** Artificial Neural Network. */ { * } * @endcode * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * @tparam InputType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * @tparam OutputType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). */ template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat + typename InputType = arma::mat, + typename OutputType = arma::mat > -class VirtualBatchNorm +class VirtualBatchNormType : public Layer { public: //! Create the VirtualBatchNorm object. - VirtualBatchNorm(); + VirtualBatchNormType(); /** - * Create the VirtualBatchNorm layer object for a specified number of input units. + * Create the VirtualBatchNorm layer object for a specified number of input + * units. * * @param referenceBatch The data from which the normalization * statistics are computed. * @param size The number of input units / channels. * @param eps The epsilon added to variance to ensure numerical stability. */ - template - VirtualBatchNorm(const arma::Mat& referenceBatch, - const size_t size, - const double eps = 1e-8); + VirtualBatchNormType(const InputType& referenceBatch, + const size_t size, + const double eps = 1e-8); + + //! Clone the VirtualBatchNormType object. This handles polymorphism + //! correctly. + VirtualBatchNormType* Clone() const + { + return new VirtualBatchNormType(*this); + } /** * Reset the layer parameters. */ - void Reset(); + void SetWeights(typename OutputType::elem_type* weightsPtr); /** - * Forward pass of the Virtual Batch Normalization layer. Transforms the input data - * into zero mean and unit variance, scales the data by a factor gamma and - * shifts it by beta. + * Forward pass of the Virtual Batch Normalization layer. Transforms the input + * data into zero mean and unit variance, scales the data by a factor gamma + * and shifts it by beta. * * @param input Input data for the layer. * @param output Resulting output activations. */ - template - void Forward(const arma::Mat& input, arma::Mat& output); + void Forward(const InputType& input, OutputType& output); /** * Backward pass through the layer. @@ -85,10 +93,9 @@ class VirtualBatchNorm * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g); + void Backward(const InputType& /* input */, + const OutputType& gy, + OutputType& g); /** * Calculate the gradient using the output delta and the input activations. @@ -97,30 +104,14 @@ class VirtualBatchNorm * @param error The calculated error. * @param gradient The calculated gradient. */ - template - void Gradient(const arma::Mat& /* input */, - const arma::Mat& error, - arma::Mat& gradient); + void Gradient(const InputType& /* input */, + const OutputType& error, + OutputType& gradient); //! Get the parameters. - OutputDataType const& Parameters() const { return weights; } + OutputType const& Parameters() const { return weights; } //! Modify the parameters. - OutputDataType& Parameters() { return weights; } - - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } - - //! Get the gradient. - OutputDataType const& Gradient() const { return gradient; } - //! Modify the gradient. - OutputDataType& Gradient() { return gradient; } + OutputType& Parameters() { return weights; } //! Get the number of input units. size_t InSize() const { return size; } @@ -128,6 +119,11 @@ class VirtualBatchNorm //! Get the epsilon value. double Epsilon() const { return eps; } + const size_t WeightSize() const + { + return 2 * size; + } + /** * Serialize the layer. */ @@ -145,19 +141,19 @@ class VirtualBatchNorm bool loading; //! Locally-stored scale parameter. - OutputDataType gamma; + OutputType gamma; //! Locally-stored shift parameter. - OutputDataType beta; + OutputType beta; //! Locally-stored parameters. - OutputDataType weights; + OutputType weights; //! Mean of features in the reference batch. - OutputDataType referenceBatchMean; + OutputType referenceBatchMean; //! Variance of features in the reference batch. - OutputDataType referenceBatchMeanSquared; + OutputType referenceBatchMeanSquared; //! The coefficient for reference batch statistics. double oldCoefficient; @@ -166,29 +162,20 @@ class VirtualBatchNorm double newCoefficient; //! Locally-stored mean object. - OutputDataType mean; + OutputType mean; //! Locally-stored variance object. - OutputDataType variance; - - //! Locally-stored gradient object. - OutputDataType gradient; - - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; - - //! Locally-stored input parameter object. - OutputDataType inputParameter; + OutputType variance; //! Locally-stored normalized input. - OutputDataType normalized; + OutputType normalized; //! Locally-stored zero mean input. - OutputDataType inputSubMean; -}; // class VirtualBatchNorm + OutputType inputSubMean; +}; // class VirtualBatchNormType + +// Standard VirtualBatchNorm layer. +typedef VirtualBatchNormType VirtualBatchNorm; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/virtual_batch_norm_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/virtual_batch_norm_impl.hpp similarity index 61% rename from src/mlpack/methods/ann/layer/virtual_batch_norm_impl.hpp rename to src/mlpack/methods/ann/layer/not_adapted/virtual_batch_norm_impl.hpp index 112c625b9b..c87d5e6579 100644 --- a/src/mlpack/methods/ann/layer/virtual_batch_norm_impl.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/virtual_batch_norm_impl.hpp @@ -19,8 +19,8 @@ namespace mlpack { namespace ann { /** Artificial Neural Network. */ -template -VirtualBatchNorm::VirtualBatchNorm() : +template +VirtualBatchNormType::VirtualBatchNormType() : size(0), eps(1e-8), loading(false), @@ -29,29 +29,27 @@ VirtualBatchNorm::VirtualBatchNorm() : { // Nothing to do here. } -template -template -VirtualBatchNorm::VirtualBatchNorm( - const arma::Mat& referenceBatch, +template +VirtualBatchNormType::VirtualBatchNormType( + const InputType& referenceBatch, const size_t size, const double eps) : size(size), eps(eps), loading(false) { - weights.set_size(size + size, 1); - referenceBatchMean = arma::mean(referenceBatch, 1); referenceBatchMeanSquared = arma::mean(arma::square(referenceBatch), 1); newCoefficient = 1.0 / (referenceBatch.n_cols + 1); oldCoefficient = 1 - newCoefficient; } -template -void VirtualBatchNorm::Reset() +template +void VirtualBatchNormType::SetWeights( + typename OutputType::elem_type* weightsPtr) { - gamma = arma::mat(weights.memptr(), size, 1, false, false); - beta = arma::mat(weights.memptr() + gamma.n_elem, size, 1, false, false); + gamma = OutputType(weightsPtr, size, 1, false, false); + beta = OutputType(weightsPtr + gamma.n_elem, size, 1, false, false); if (!loading) { @@ -62,20 +60,19 @@ void VirtualBatchNorm::Reset() loading = false; } -template -template -void VirtualBatchNorm::Forward( - const arma::Mat& input, arma::Mat& output) +template +void VirtualBatchNormType::Forward( + const InputType& input, OutputType& output) { Log::Assert(input.n_rows % size == 0, "Input features must be divisible \ by feature maps."); inputParameter = input; - arma::mat inputMean = arma::mean(input, 1); - arma::mat inputMeanSquared = arma::mean(arma::square(input), 1); + InputType inputMean = arma::mean(input, 1); + InputType inputMeanSquared = arma::mean(arma::square(input), 1); mean = oldCoefficient * referenceBatchMean + newCoefficient * inputMean; - arma::mat meanSquared = oldCoefficient * referenceBatchMeanSquared + + OutputType meanSquared = oldCoefficient * referenceBatchMeanSquared + newCoefficient * inputMeanSquared; variance = meanSquared - arma::square(mean); // Normalize the input. @@ -90,18 +87,19 @@ void VirtualBatchNorm::Forward( output.each_col() += beta; } -template -template -void VirtualBatchNorm::Backward( - const arma::Mat& /* input */, const arma::Mat& gy, arma::Mat& g) +template +void VirtualBatchNormType::Backward( + const InputType& /* input */, + const OutputType& gy, + OutputType& g) { - const arma::mat stdInv = 1.0 / arma::sqrt(variance + eps); + const OutputType stdInv = 1.0 / arma::sqrt(variance + eps); // dl / dxhat. - const arma::mat norm = gy.each_col() % gamma; + const OutputType norm = gy.each_col() % gamma; // sum dl / dxhat * (x - mu) * -0.5 * stdInv^3. - const arma::mat var = arma::sum(norm % inputSubMean, 1) % + const OutputType var = arma::sum(norm % inputSubMean, 1) % arma::pow(stdInv, 3.0) * -0.5; // dl / dxhat * 1 / stdInv + variance * 2 * (x - mu) / m + @@ -115,12 +113,11 @@ void VirtualBatchNorm::Backward( mean * -2)) * newCoefficient / inputParameter.n_cols; } -template -template -void VirtualBatchNorm::Gradient( - const arma::Mat& /* input */, - const arma::Mat& error, - arma::Mat& gradient) +template +void VirtualBatchNormType::Gradient( + const InputType& /* input */, + const OutputType& error, + OutputType& gradient) { gradient.set_size(size + size, 1); @@ -132,22 +129,21 @@ void VirtualBatchNorm::Gradient( arma::sum(error, 1); } -template +template template -void VirtualBatchNorm::serialize( +void VirtualBatchNormType::serialize( Archive& ar, const uint32_t /* version */) { + ar(cereal::base_class>(this)); + ar(CEREAL_NVP(size)); + ar(CEREAL_NVP(eps)); if (cereal::is_loading()) { weights.set_size(size + size, 1); loading = true; } - - ar(CEREAL_NVP(eps)); - ar(CEREAL_NVP(gamma)); - ar(CEREAL_NVP(beta)); } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/weight_norm.hpp b/src/mlpack/methods/ann/layer/not_adapted/weight_norm.hpp similarity index 58% rename from src/mlpack/methods/ann/layer/weight_norm.hpp rename to src/mlpack/methods/ann/layer/not_adapted/weight_norm.hpp index 2bdcd7e955..b8e39326b0 100644 --- a/src/mlpack/methods/ann/layer/weight_norm.hpp +++ b/src/mlpack/methods/ann/layer/not_adapted/weight_norm.hpp @@ -13,14 +13,7 @@ #define MLPACK_METHODS_ANN_LAYER_WEIGHTNORM_HPP #include -#include "layer_types.hpp" - -#include "../visitor/delete_visitor.hpp" -#include "../visitor/delta_visitor.hpp" -#include "../visitor/output_parameter_visitor.hpp" -#include "../visitor/reset_visitor.hpp" -#include "../visitor/weight_size_visitor.hpp" -#include "../visitor/weight_set_visitor.hpp" +#include "layer.hpp" namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -51,30 +44,45 @@ namespace ann /** Artificial Neural Network. */ { * arma::sp_mat or arma::cube). * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). - * @tparam CustomLayers Additional custom layers that can be added. */ template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat, - typename... CustomLayers + typename InputType = arma::mat, + typename OutputType = arma::mat > -class WeightNorm +class WeightNormType : public Layer { public: + /** + * Create an empty WeightNorm layer. + */ + WeightNormType(); + /** * Create the WeightNorm layer object. * * @param layer The layer whose weights are needed to be normalized. */ - WeightNorm(LayerTypes layer = LayerTypes()); + WeightNormType(Layer* layer); //! Destructor to release allocated memory. - ~WeightNorm(); + ~WeightNormType(); + + //! Create a WeightNorm layer by copying the given layer. + WeightNormType(const WeightNormType& other); + //! Create a WeightNorm layer by taking ownership of the other layer. + WeightNormType(WeightNormType&& other); + //! Copy the given layer. + WeightNormType& operator=(const WeightNormType& other); + //! Take ownership of the data in the given layer. + WeightNormType& operator=(WeightNormType&& other); + + //! Clone the WeightNormType object. This handles polymorphism correctly. + WeightNormType* Clone() const { return new WeightNormType(*this); } /** * Reset the layer parameters. */ - void Reset(); + void SetWeights(typename OutputType::elem_type* weightsPtr); /** * Forward pass of the WeightNorm layer. Calculates the weights of the @@ -85,8 +93,7 @@ class WeightNorm * @param input Input data for the layer. * @param output Resulting output activations. */ - template - void Forward(const arma::Mat& input, arma::Mat& output); + void Forward(const InputType& input, OutputType& output); /** * Backward pass through the layer. This function calls the Backward() @@ -96,10 +103,9 @@ class WeightNorm * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const arma::Mat& input, - const arma::Mat& gy, - arma::Mat& g); + void Backward(const InputType& input, + const OutputType& gy, + OutputType& g); /** * Calculate the gradient using the output delta, input activations and the @@ -109,33 +115,25 @@ class WeightNorm * @param error The calculated error. * @param gradient The calculated gradient. */ - template - void Gradient(const arma::Mat& input, - const arma::Mat& error, - arma::Mat& gradient); - - //! Get the delta. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } - - //! Get the gradient. - OutputDataType const& Gradient() const { return gradient; } - //! Modify the gradient. - OutputDataType& Gradient() { return gradient; } - - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } + void Gradient(const InputType& input, + const OutputType& error, + OutputType& gradient); //! Get the parameters. - OutputDataType const& Parameters() const { return weights; } + OutputType const& Parameters() const { return weights; } //! Modify the parameters. - OutputDataType& Parameters() { return weights; } + OutputType& Parameters() { return weights; } //! Get the wrapped layer. - LayerTypes const& Layer() { return wrappedLayer; } + Layer* const& WrappedLayer() { return wrappedLayer; } + + const size_t WeightSize() const { return wrappedLayer->WeightSize(); } + + const std::vector OutputDimensions() const + { + wrappedLayer->InputDimensions() = inputDimensions; + return wrappedLayer->OutputDimensions(); + } /** * Serialize the layer. @@ -147,54 +145,33 @@ class WeightNorm //! Locally-stored number of bias elements in the weights of wrapped layer. size_t biasWeightSize; - //! Locally-stored delete visitor module object. - DeleteVisitor deleteVisitor; - - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored delta visitor module object. - DeltaVisitor deltaVisitor; - - //! Locally-stored gradient object. - OutputDataType gradient; - //! Locally-stored wrapped layer. - LayerTypes wrappedLayer; + Layer* wrappedLayer; //! Locally stored number of elements in the weights of wrapped layer. size_t layerWeightSize; - //! Locally-stored output parameter object. - OutputDataType outputParameter; - - //! Locally-stored output parameter visitor module object. - OutputParameterVisitor outputParameterVisitor; - //! Reset the gradient for all modules that implement the Gradient function. - void ResetGradients(arma::mat& gradient); - - //! Locally-stored reset visitor. - ResetVisitor resetVisitor; + void ResetGradients(OutputType& gradient); //! Locally-stored scalar parameter. - OutputDataType scalarParameter; + OutputType scalarParameter; //! Locally-stored parameter vector. - OutputDataType vectorParameter; + OutputType vectorParameter; //! Locally-stored parameters. - OutputDataType weights; - - //! Locally-stored weight size visitor. - WeightSizeVisitor weightSizeVisitor; + OutputType weights; //! Locally-stored gradients of wrappedLayer. - OutputDataType layerGradients; + OutputType layerGradients; //! Locally-stored weights of wrappedLayer. - OutputDataType layerWeights; -}; // class WeightNorm + OutputType layerWeights; +}; // class WeightNormType. + +// Standard WeightNorm layer. +typedef WeightNormType WeightNorm; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/not_adapted/weight_norm_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/weight_norm_impl.hpp new file mode 100644 index 0000000000..16bfbd8ce5 --- /dev/null +++ b/src/mlpack/methods/ann/layer/not_adapted/weight_norm_impl.hpp @@ -0,0 +1,202 @@ +/** + * @file methods/ann/layer/weight_norm_impl.hpp + * @author Toshal Agrawal + * + * Implementation of the WeightNorm Layer. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ + +#ifndef MLPACK_METHODS_ANN_LAYER_WEIGHTNORM_IMPL_HPP +#define MLPACK_METHODS_ANN_LAYER_WEIGHTNORM_IMPL_HPP + +// In case it is not included. +#include "weight_norm.hpp" + +namespace mlpack { +namespace ann { /** Artificial Neural Network. */ + +template +WeightNormType:: +WeightNormType() : wrappedLayer(new LinearType()) +{ + layerWeightSize = wrappedLayer->WeightSize(); + weights.set_size(layerWeightSize + 1, 1); + + layerWeights.set_size(layerWeightSize, 1); + layerGradients.set_size(layerWeightSize, 1); +} + +template +WeightNormType:: +WeightNormType(Layer* layer) : wrappedLayer(layer) +{ + layerWeightSize = wrappedLayer->WeightSize(); + weights.set_size(layerWeightSize + 1, 1); + + layerWeights.set_size(layerWeightSize, 1); + layerGradients.set_size(layerWeightSize, 1); +} + +template +WeightNormType::WeightNormType( + const WeightNormType& other) : + wrappedLayer(other.wrappedLayer->Clone()), + layerWeightSize(other.layerWeightSize), + weights(other.weights), + layerGradients(other.layerGradients), + layerWeights(other.layerWeights) +{ + // Nothing else to do. +} + +template +WeightNormType::WeightNormType( + WeightNormType&& other) : + wrappedLayer(std::move(other.wrappedLayer)), + layerWeightSize(other.layerWeightSize), + weights(std::move(other.weights)), + layerGradients(std::move(other.layerGradients)), + layerWeights(std::move(other.layerWeights)) +{ + // Reset the other layer. + other = WeightNormType(); +} + +template +WeightNormType& +WeightNormType::operator=( + const WeightNormType& other) +{ + if (this != &other) + { + wrappedLayer = other.wrappedLayer->Clone(); + layerWeightSize = other.layerWeightSize; + weights = other.weights; + layerWeights = other.layerWeights; + layerGradients = other.layerGradients; + } + + return *this; +} + +template +WeightNormType& +WeightNormType::operator=( + WeightNormType&& other) +{ + if (this != &other) + { + wrappedLayer = std::move(other.wrappedLayer); + layerWeightSize = other.layerWeightSize; + weights = std::move(other.weights); + layerWeights = std::move(other.layerWeights); + layerGradients = std::move(other.layerGradients); + + // Reset the other layer. + other = WeightNormType(); + } + + return *this; +} + +template +WeightNormType::~WeightNormType() +{ + delete wrappedLayer; +} + +template +void WeightNormType::SetWeights( + typename OutputType::elem_type* weightsPtr) +{ + // Set the weights of the inside layer to layerWeights. + // This is done to set the non-bias terms correctly. + /* boost::apply_visitor(WeightSetVisitor(layerWeights, 0), wrappedLayer); */ + wrappedLayer->SetWeights(weightsPtr); + wrappedLayer->Parameters() = OutputType(layerWeights.memptr(), + wrappedLayer->Parameters().n_rows, wrappedLayer->Parameters().n_cols, + false, false); + + /* biasWeightSize = boost::apply_visitor(BiasSetVisitor(weights, 0), */ + /* wrappedLayer); */ + biasWeightSize = 0; + + vectorParameter = OutputType(weights.memptr() + biasWeightSize, + layerWeightSize - biasWeightSize, 1, false, false); + + scalarParameter = OutputType(weights.memptr() + layerWeightSize, 1, 1, false, + false); +} + +template +void WeightNormType::Forward( + const InputType& input, OutputType& output) +{ + // Initialize the non-bias weights of wrapped layer. + const double normVectorParameter = arma::norm(vectorParameter, 2); + layerWeights.rows(0, layerWeightSize - biasWeightSize - 1) = + scalarParameter(0) * vectorParameter / normVectorParameter; + + wrappedLayer->Forward(input, output); +} + +template +void WeightNormType::Backward( + const InputType& input, const OutputType& gy, OutputType& g) +{ + wrappedLayer->Backward(input, gy, g); +} + +// TODO: this part is not trivial... +template +void WeightNormType::Gradient( + const InputType& input, + const OutputType& error, + OutputType& gradient) +{ + ResetGradients(layerGradients); + + // Calculate the gradients of the wrapped layer. + wrappedLayer->Gradient(input, error, gradient); + + // Store the norm of vector parameter temporarily. + const double normVectorParameter = arma::norm(vectorParameter, 2); + + // Set the gradients of the bias terms. + if (biasWeightSize != 0) + { + gradient.rows(0, biasWeightSize - 1) = OutputType(layerGradients.memptr() + + layerWeightSize - biasWeightSize, biasWeightSize, 1, false, false); + } + + // Calculate the gradients of the scalar parameter. + gradient[gradient.n_rows - 1] = arma::accu(layerGradients.rows(0, + layerWeightSize - biasWeightSize - 1) % vectorParameter) / + normVectorParameter; + + // Calculate the gradients of the vector parameter. + gradient.rows(biasWeightSize, layerWeightSize - 1) = + scalarParameter(0) / normVectorParameter * (layerGradients.rows(0, + layerWeightSize - biasWeightSize - 1) - gradient[gradient.n_rows - 1] / + normVectorParameter * vectorParameter); +} + +template +template +void WeightNormType::serialize( + Archive& ar, const uint32_t /* version */) +{ + ar(cereal::base_class*>(this)); + + ar(CEREAL_POINTER(wrappedLayer)); + ar(CEREAL_NVP(layerWeightSize)); +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/layer/padding.hpp b/src/mlpack/methods/ann/layer/padding.hpp index b7bfcab976..018a28479b 100644 --- a/src/mlpack/methods/ann/layer/padding.hpp +++ b/src/mlpack/methods/ann/layer/padding.hpp @@ -13,25 +13,20 @@ #define MLPACK_METHODS_ANN_LAYER_PADDING_HPP #include -#include +#include "layer.hpp" namespace mlpack { namespace ann /** Artificial Neural Network. */ { /** - * Implementation of the Padding module class. The Padding module applies a bias term - * to the incoming data. + * Implementation of the Padding module class. The Padding module applies + * (zero-valued) padding on the input data. * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam MatType Matrix representation to accept as input and use for + * computation. */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class Padding +template +class PaddingType : public Layer { public: /** @@ -44,12 +39,13 @@ class Padding * @param inputWidth Width of the input. * @param inputHeight Height of the input. */ - Padding(const size_t padWLeft = 0, - const size_t padWRight = 0, - const size_t padHTop = 0, - const size_t padHBottom = 0, - const size_t inputWidth = 0, - const size_t inputHeight = 0); + PaddingType(const size_t padWLeft = 0, + const size_t padWRight = 0, + const size_t padHTop = 0, + const size_t padHBottom = 0); + + //! Clone the PaddingType object. This handles polymorphism correctly. + PaddingType* Clone() const { return new PaddingType(*this); } /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -58,8 +54,7 @@ class Padding * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template - void Forward(const arma::Mat& input, arma::Mat& output); + void Forward(const MatType& input, MatType& output); /** * Ordinary feed backward pass of a neural network, calculating the function @@ -70,20 +65,9 @@ class Padding * @param gy The backpropagated error. * @param g The calculated gradient. */ - template - void Backward(const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g); - - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } + void Backward(const MatType& /* input */, + const MatType& gy, + MatType& g); //! Get the left padding width. size_t PadWLeft() const { return padWLeft; } @@ -105,25 +89,8 @@ class Padding //! Modify the bottom padding width. size_t& PadHBottom() { return padHBottom; } - //! Get the input width. - size_t InputWidth() const { return inputWidth; } - //! Modify the input width. - size_t& InputWidth() { return inputWidth; } - - //! Get the input height. - size_t InputHeight() const { return inputHeight; } - //! Modify the input height. - size_t& InputHeight() { return inputHeight; } - - //! Get the output width. - size_t OutputWidth() const { return outputWidth; } - //! Modify the output width. - size_t& OutputWidth() { return outputWidth; } - - //! Get the output height. - size_t OutputHeight() const { return outputHeight; } - //! Modify the output height. - size_t& OutputHeight() { return outputHeight; } + //! Compute the output dimensions of the layer using `InputDimensions()`. + void ComputeOutputDimensions(); /** * Serialize the layer. @@ -144,36 +111,12 @@ class Padding //! Locally-stored bottom padding height. size_t padHBottom; - //! Locally-stored number of rows and columns of input. - size_t nRows, nCols; + //! Cached number of input maps. + size_t totalInMaps; +}; // class PaddingType - //! Locally-stored input height. - size_t inputHeight; - - //! Locally-stored input width. - size_t inputWidth; - - //! Locally-stored output height. - size_t outputHeight; - - //! Locally-stored output width. - size_t outputWidth; - - //! Locally-stored number of input channels. - size_t inSize; - - //! Locally-stored cube input parameter. - arma::cube inputTemp; - - //! Locally-stored output parameter. - arma::cube outputTemp; - - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; -}; // class Padding +// Standard Padding layer. +typedef PaddingType Padding; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/padding_impl.hpp b/src/mlpack/methods/ann/layer/padding_impl.hpp index 9a57f69458..2c2811209b 100644 --- a/src/mlpack/methods/ann/layer/padding_impl.hpp +++ b/src/mlpack/methods/ann/layer/padding_impl.hpp @@ -19,85 +19,121 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -Padding::Padding( +template +PaddingType::PaddingType( const size_t padWLeft, const size_t padWRight, const size_t padHTop, - const size_t padHBottom, - const size_t inputWidth, - const size_t inputHeight) : + const size_t padHBottom) : padWLeft(padWLeft), padWRight(padWRight), padHTop(padHTop), padHBottom(padHBottom), - nRows(0), - nCols(0), - inputHeight(inputWidth), - inputWidth(inputHeight), - inSize(0) + totalInMaps(0) { // Nothing to do here. } -template -template -void Padding::Forward( - const arma::Mat& input, arma::Mat& output) +template +void PaddingType::Forward(const MatType& input, MatType& output) { - nRows = input.n_rows; - nCols = input.n_cols; + // Make an alias of the input and output so that we can deal with the first + // two dimensions directly. + arma::Cube reshapedInput( + (typename MatType::elem_type*) input.memptr(), + this->inputDimensions[0], this->inputDimensions[1], totalInMaps * + input.n_cols, false, true); + arma::Cube reshapedOutput(output.memptr(), + this->outputDimensions[0], this->outputDimensions[1], totalInMaps * + output.n_cols, false, true); - if (inputWidth == 0 || inputHeight == 0) + // Set the padding parts to 0. + if (padWLeft > 0) { - output = arma::zeros(nRows + padWLeft + padWRight, - nCols + padHTop + padHBottom); - output.submat(padWLeft, padHTop, padWLeft + nRows - 1, - padHTop + nCols - 1) = input; - } - else - { - inSize = input.n_elem / (inputWidth * inputHeight * nCols); - inputTemp = arma::Cube(const_cast&>(input).memptr(), - inputWidth, inputHeight, inSize * nCols, false, false); - outputTemp = arma::zeros>(inputWidth + padWLeft + padWRight, - inputHeight + padHTop + padHBottom, inSize * nCols); - for (size_t i = 0; i < inputTemp.n_slices; ++i) - { - outputTemp.slice(i).submat(padWLeft, padHTop, padWLeft + inputWidth - 1, - padHTop + inputHeight - 1) = inputTemp.slice(i); - } - - output = arma::Mat(outputTemp.memptr(), outputTemp.n_elem / nCols, - nCols); + reshapedOutput.tube(0, + 0, + reshapedOutput.n_rows - 1, + padWLeft - 1).zeros(); } - outputWidth = inputWidth + padWLeft + padWRight; - outputHeight = inputHeight + padHTop + padHBottom; + if (padHTop > 0) + { + reshapedOutput.tube(0, + padWLeft, + padHTop - 1, + padWLeft + this->inputDimensions[1] - 1).zeros(); + } + + if (padWRight > 0) + { + reshapedOutput.tube(0, + padWLeft + this->inputDimensions[1], + reshapedOutput.n_rows - 1, + reshapedOutput.n_cols - 1).zeros(); + } + + if (padHBottom > 0) + { + reshapedOutput.tube(padHTop + this->inputDimensions[0], + padWLeft, + reshapedOutput.n_rows - 1, + padWLeft + this->inputDimensions[1] - 1).zeros(); + } + + // Copy the input matrix. + reshapedOutput.tube(padHTop, + padWLeft, + padHTop + this->inputDimensions[0] - 1, + padWLeft + this->inputDimensions[1] - 1) = reshapedInput; } -template -template -void Padding::Backward( - const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g) +template +void PaddingType::Backward( + const MatType& /* input */, + const MatType& gy, + MatType& g) { - g = gy.submat(padWLeft, padHTop, padWLeft + nRows - 1, - padHTop + nCols - 1); + // Reshape g and gy so that extracting the un-padded input is easier to + // understand. + arma::Cube reshapedGy( + (typename MatType::elem_type*) gy.memptr(), this->outputDimensions[0], + this->outputDimensions[1], totalInMaps * gy.n_cols, false, true); + arma::Cube reshapedG(g.memptr(), + this->inputDimensions[0], this->inputDimensions[1], totalInMaps * + g.n_cols, false, true); + + reshapedG = reshapedGy.tube(padHTop, + padWLeft, + padHTop + this->inputDimensions[0] - 1, + padWLeft + this->inputDimensions[1] - 1); } -template +template +void PaddingType::ComputeOutputDimensions() +{ + this->outputDimensions = this->inputDimensions; + + this->outputDimensions[0] += padHTop + padHBottom; + this->outputDimensions[1] += padWLeft + padWRight; + + // Higher dimensions remain unchanged. But, we will cache the product of + // these higher dimensions. + totalInMaps = 1; + for (size_t i = 2; i < this->inputDimensions.size(); ++i) + totalInMaps *= this->inputDimensions[i]; +} + +template template -void Padding::serialize( - Archive& ar, const uint32_t /* version */) +void PaddingType::serialize(Archive& ar, const uint32_t /* version */) { + ar(cereal::base_class>(this)); + ar(CEREAL_NVP(padWLeft)); ar(CEREAL_NVP(padWRight)); ar(CEREAL_NVP(padHTop)); ar(CEREAL_NVP(padHBottom)); - ar(CEREAL_NVP(inputWidth)); - ar(CEREAL_NVP(inputHeight)); + ar(CEREAL_NVP(totalInMaps)); } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/radial_basis_function.hpp b/src/mlpack/methods/ann/layer/radial_basis_function.hpp index 6f84303360..9865ede4d4 100644 --- a/src/mlpack/methods/ann/layer/radial_basis_function.hpp +++ b/src/mlpack/methods/ann/layer/radial_basis_function.hpp @@ -10,22 +10,21 @@ * 3-clause BSD license along with mlpack. If not, see * http://www.opensource.org/licenses/BSD-3-Clause for more information. */ -#ifndef MLPACK_METHODS_ANN_LAYER_RBF_HPP -#define MLPACK_METHODS_ANN_LAYER_RBF_HPP +#ifndef MLPACK_METHODS_ANN_LAYER_RADIAL_BASIS_FUNCTION_HPP +#define MLPACK_METHODS_ANN_LAYER_RADIAL_BASIS_FUNCTION_HPP #include #include -#include "layer_types.hpp" +#include "layer.hpp" namespace mlpack { namespace ann /** Artificial Neural Network. */ { - /** - * Implementation of the Radial Basis Function layer. The RBF class when use with a - * non-linear activation function acts as a Radial Basis Function which can be used - * with Feed-Forward neural network. + * Implementation of the Radial Basis Function layer. The RBFType class, when + * used with a non-linear activation function, acts as a Radial Basis Function + * which can be used with a feed-forward neural network. * * For more information, refer to the following paper, * @@ -38,37 +37,47 @@ namespace ann /** Artificial Neural Network. */ { * } * @endcode * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam MatType Matrix representation to accept as input and use for + * computation. * @tparam Activation Type of the activation function (mlpack::ann::Gaussian). */ template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat, + typename MatType = arma::mat, typename Activation = GaussianFunction > -class RBF +class RBFType : public Layer { public: - //! Create the RBF object. - RBF(); + //! Create the RBFType object. + RBFType(); /** * Create the Radial Basis Function layer object using the specified * parameters. * - * @param inSize The number of input units. * @param outSize The number of output units. * @param centres The centres calculated using k-means of data. * @param betas The beta value to be used with centres. */ - RBF(const size_t inSize, - const size_t outSize, - arma::mat& centres, - double betas = 0); + RBFType(const size_t outSize, + MatType& centres, + double betas = 0); + + //! Clone the LinearType object. This handles polymorphism correctly. + RBFType* Clone() const { return new RBFType(*this); } + + // Virtual destructor. + virtual ~RBFType() { } + + //! Copy the given RBFType layer. + RBFType(const RBFType& other); + //! Take ownership of the given RBFType layer. + RBFType(RBFType&& other); + //! Copy the given RBFType layer. + RBFType& operator=(const RBFType& other); + //! Take ownership of the given RBFType layer. + RBFType& operator=(RBFType&& other); /** * Ordinary feed forward pass of the radial basis function. @@ -76,51 +85,21 @@ class RBF * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - template - void Forward(const arma::Mat& input, arma::Mat& output); + void Forward(const MatType& input, MatType& output); /** * Ordinary feed backward pass of the radial basis function. - * */ - template - void Backward(const arma::Mat& /* input */, - const arma::Mat& /* gy */, - arma::Mat& /* g */); + void Backward(const MatType& /* input */, + const MatType& /* gy */, + MatType& /* g */); - //! Get the output parameter. - OutputDataType const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - //! Get the parameters. - - //! Get the input parameter. - InputDataType const& InputParameter() const { return inputParameter; } - //! Modify the input parameter. - InputDataType& InputParameter() { return inputParameter; } - - //! Get the input size. - size_t InputSize() const { return inSize; } - - //! Get the output size. - size_t OutputSize() const { return outSize; } - - //! Get the detla. - OutputDataType const& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } + //! Compute the output dimensions of the layer given `InputDimensions()`. The + //! RBFType layer flattens the input. + void ComputeOutputDimensions(); //! Get the size of the weights. - size_t WeightSize() const - { - return 0; - } - - //! Get the shape of the input. - size_t InputShape() const - { - return inSize; - } + size_t WeightSize() const { return 0; } /** * Serialize the layer. @@ -129,33 +108,20 @@ class RBF void serialize(Archive& ar, const uint32_t /* version */); private: - //! Locally-stored number of input units. - size_t inSize; - //! Locally-stored number of output units. size_t outSize; - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; - - //! Locally-stored the sigmas values. - double sigmas; - //! Locally-stored the betas values. double betas; //! Locally-stored the learnable centre of the shape. - InputDataType centres; - - //! Locally-stored input parameter object. - InputDataType inputParameter; + MatType centres; //! Locally-stored the output distances of the shape. - OutputDataType distances; -}; // class RBF + MatType distances; +}; // class RBFType + +typedef RBFType RBF; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/radial_basis_function_impl.hpp b/src/mlpack/methods/ann/layer/radial_basis_function_impl.hpp index 8ba09b0ab6..55887792cd 100644 --- a/src/mlpack/methods/ann/layer/radial_basis_function_impl.hpp +++ b/src/mlpack/methods/ann/layer/radial_basis_function_impl.hpp @@ -2,14 +2,13 @@ * @file radial_basis_function_impl.hpp * @author Himanshu Pathak * - * * mlpack is free software; you may redistribute it and/or modify it under the * terms of the 3-clause BSD license. You should have received a copy of the * 3-clause BSD license along with mlpack. If not, see * http://www.opensource.org/licenses/BSD-3-Clause for more information. */ -#ifndef MLPACK_METHODS_ANN_LAYER_RBF_IMPL_HPP -#define MLPACK_METHODS_ANN_LAYER_RBF_IMPL_HPP +#ifndef MLPACK_METHODS_ANN_LAYER_RADIAL_BASIS_FUNCTION_IMPL_HPP +#define MLPACK_METHODS_ANN_LAYER_RADIAL_BASIS_FUNCTION_IMPL_HPP // In case it hasn't yet been included. #include "radial_basis_function.hpp" @@ -17,85 +16,146 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -RBF::RBF() : - inSize(0), +template +RBFType::RBFType() : + Layer(), outSize(0), - sigmas(0), betas(0) { // Nothing to do here. } -template -RBF::RBF( - const size_t inSize, +template +RBFType::RBFType( const size_t outSize, - arma::mat& centres, + MatType& centres, double betas) : - inSize(inSize), outSize(outSize), betas(betas), centres(centres) { - sigmas = 0; + double sigmas = 0; if (betas == 0) { for (size_t i = 0; i < centres.n_cols; i++) { - double max_dis = 0; - arma::mat temp = centres.each_col() - centres.col(i); - max_dis = arma::accu(arma::max(arma::pow(arma::sum( + double maxDis = 0; + MatType temp = centres.each_col() - centres.col(i); + maxDis = arma::accu(arma::max(arma::pow(arma::sum( arma::pow((temp), 2), 0), 0.5).t())); - if (max_dis > sigmas) - sigmas = max_dis; + if (maxDis > sigmas) + sigmas = maxDis; } this->betas = std::pow(2 * outSize, 0.5) / sigmas; } } -template -template -void RBF::Forward( - const arma::Mat& input, - arma::Mat& output) +RBFType::RBFType(const RBFType& other) : + Layer(other), + outSize(other.outSize), + betas(other.betas), + centres(other.centres) { - distances = arma::mat(outSize, input.n_cols); + // Nothing to do. +} + +template +RBFType::RBFType(RBFType&& other) : + Layer(other), + outSize(other.outSize), + betas(other.betas), + centres(std::move(other.centres)) +{ + // Nothing to do. +} + +template +RBFType& +RBFType::operator=(const RBFType& other) +{ + if (&other != this) + { + Layer::operator=(other); + outSize = other.outSize; + betas = other.betas; + centres = other.centres; + } + + return *this; +} + +template +RBFType& +RBFType::operator=(RBFType&& other) +{ + if (&other != this) + { + Layer::operator=(std::move(other)); + outSize = std::move(other.outSize); + betas = std::move(other.betas); + centres = std::move(other.centres); + } + + return *this; +} + +template +void RBFType::Forward( + const MatType& input, + MatType& output) +{ + // Sanity check: make sure the dimensions are right. + if (input.n_rows != centres.n_rows) + { + Log::Fatal << "RBFType::Forward(): input size (" << input.n_rows << ") does" + << " not match given center size (" << centres.n_rows << ")!" + << std::endl; + } + + distances = MatType(outSize, input.n_cols); for (size_t i = 0; i < input.n_cols; i++) { - arma::mat temp = centres.each_col() - input.col(i); + MatType temp = centres.each_col() - input.col(i); distances.col(i) = arma::pow(arma::sum( - arma::pow((temp), 2), 0), 0.5).t(); + arma::pow((temp), 2), 0), 0.5).t(); } - Activation::Fn(distances * std::pow(betas, 0.5), - output); + Activation::Fn(distances * std::pow(betas, 0.5), output); } -template -template -void RBF::Backward( - const arma::Mat& /* input */, - const arma::Mat& /* gy */, - arma::Mat& /* g */) +template +void RBFType::Backward( + const MatType& /* input */, + const MatType& /* gy */, + MatType& /* g */) { // Nothing to do here. } -template +template +void RBFType::ComputeOutputDimensions() +{ + this->outputDimensions = std::vector(this->inputDimensions.size(), 1); + + // This flattens the input. + this->outputDimensions[0] = outSize; +} + +template template -void RBF::serialize( +void RBFType::serialize( Archive& ar, const uint32_t /* version */) { + ar(cereal::base_class>(this)); + ar(CEREAL_NVP(distances)); ar(CEREAL_NVP(centres)); + ar(CEREAL_NVP(betas)); } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/recurrent_attention_impl.hpp b/src/mlpack/methods/ann/layer/recurrent_attention_impl.hpp deleted file mode 100644 index abc3da7727..0000000000 --- a/src/mlpack/methods/ann/layer/recurrent_attention_impl.hpp +++ /dev/null @@ -1,226 +0,0 @@ -/** - * @file methods/ann/layer/recurrent_attention_impl.hpp - * @author Marcus Edel - * - * Implementation of the RecurrentAttention class. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_LAYER_RECURRENT_ATTENTION_IMPL_HPP -#define MLPACK_METHODS_ANN_LAYER_RECURRENT_ATTENTION_IMPL_HPP - -// In case it hasn't yet been included. -#include "recurrent_attention.hpp" - -#include "../visitor/load_output_parameter_visitor.hpp" -#include "../visitor/save_output_parameter_visitor.hpp" -#include "../visitor/backward_visitor.hpp" -#include "../visitor/forward_visitor.hpp" -#include "../visitor/gradient_set_visitor.hpp" -#include "../visitor/gradient_update_visitor.hpp" -#include "../visitor/gradient_visitor.hpp" - -namespace mlpack { -namespace ann /** Artificial Neural Network. */ { - -template -RecurrentAttention::RecurrentAttention() : - outSize(0), - rho(0), - forwardStep(0), - backwardStep(0), - deterministic(false) -{ - // Nothing to do. -} - -template -template -RecurrentAttention::RecurrentAttention( - const size_t outSize, - const RNNModuleType& rnn, - const ActionModuleType& action, - const size_t rho) : - outSize(outSize), - rnnModule(new RNNModuleType(rnn)), - actionModule(new ActionModuleType(action)), - rho(rho), - forwardStep(0), - backwardStep(0), - deterministic(false) -{ - network.push_back(rnnModule); - network.push_back(actionModule); -} - -template -template -void RecurrentAttention::Forward( - const arma::Mat& input, arma::Mat& output) -{ - // Initialize the action input. - if (initialInput.is_empty()) - { - initialInput = arma::zeros(outSize, input.n_cols); - } - - // Propagate through the action and recurrent module. - for (forwardStep = 0; forwardStep < rho; ++forwardStep) - { - if (forwardStep == 0) - { - boost::apply_visitor(ForwardVisitor(initialInput, - boost::apply_visitor(outputParameterVisitor, actionModule)), - actionModule); - } - else - { - boost::apply_visitor(ForwardVisitor(boost::apply_visitor( - outputParameterVisitor, rnnModule), boost::apply_visitor( - outputParameterVisitor, actionModule)), actionModule); - } - - // Initialize the glimpse input. - arma::mat glimpseInput = arma::zeros(input.n_elem, 2); - glimpseInput.col(0) = input; - glimpseInput.submat(0, 1, boost::apply_visitor(outputParameterVisitor, - actionModule).n_elem - 1, 1) = boost::apply_visitor( - outputParameterVisitor, actionModule); - - boost::apply_visitor(ForwardVisitor(glimpseInput, - boost::apply_visitor(outputParameterVisitor, rnnModule)), - rnnModule); - - // Save the output parameter when training the module. - if (!deterministic) - { - for (size_t l = 0; l < network.size(); ++l) - { - boost::apply_visitor(SaveOutputParameterVisitor( - moduleOutputParameter), network[l]); - } - } - } - - output = boost::apply_visitor(outputParameterVisitor, rnnModule); - - forwardStep = 0; - backwardStep = 0; -} - -template -template -void RecurrentAttention::Backward( - const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g) -{ - if (intermediateGradient.is_empty() && backwardStep == 0) - { - // Initialize the attention gradients. - size_t weights = boost::apply_visitor(weightSizeVisitor, rnnModule) + - boost::apply_visitor(weightSizeVisitor, actionModule); - - intermediateGradient = arma::zeros(weights, 1); - attentionGradient = arma::zeros(weights, 1); - - // Initialize the action error. - actionError = arma::zeros( - boost::apply_visitor(outputParameterVisitor, actionModule).n_rows, - boost::apply_visitor(outputParameterVisitor, actionModule).n_cols); - } - - // Propagate the attention gradients. - if (backwardStep == 0) - { - size_t offset = 0; - offset += boost::apply_visitor(GradientSetVisitor( - intermediateGradient, offset), rnnModule); - boost::apply_visitor(GradientSetVisitor( - intermediateGradient, offset), actionModule); - - attentionGradient.zeros(); - } - - // Back-propagate through time. - for (; backwardStep < rho; backwardStep++) - { - if (backwardStep == 0) - { - recurrentError = gy; - } - else - { - recurrentError = actionDelta; - } - - for (size_t l = 0; l < network.size(); ++l) - { - boost::apply_visitor(LoadOutputParameterVisitor( - moduleOutputParameter), network[network.size() - 1 - l]); - } - - if (backwardStep == (rho - 1)) - { - boost::apply_visitor(BackwardVisitor(boost::apply_visitor( - outputParameterVisitor, actionModule), actionError, - actionDelta), actionModule); - } - else - { - boost::apply_visitor(BackwardVisitor(initialInput, actionError, - actionDelta), actionModule); - } - - boost::apply_visitor(BackwardVisitor(boost::apply_visitor( - outputParameterVisitor, rnnModule), recurrentError, rnnDelta), - rnnModule); - - if (backwardStep == 0) - { - g = rnnDelta.col(1); - } - else - { - g += rnnDelta.col(1); - } - - IntermediateGradient(); - } -} - -template -template -void RecurrentAttention::Gradient( - const arma::Mat& /* input */, - const arma::Mat& /* error */, - arma::Mat& /* gradient */) -{ - size_t offset = 0; - offset += boost::apply_visitor(GradientUpdateVisitor( - attentionGradient, offset), rnnModule); - boost::apply_visitor(GradientUpdateVisitor( - attentionGradient, offset), actionModule); -} - -template -template -void RecurrentAttention::serialize( - Archive& ar, const uint32_t /* version */) -{ - ar(CEREAL_NVP(rho)); - ar(CEREAL_NVP(outSize)); - ar(CEREAL_NVP(forwardStep)); - ar(CEREAL_NVP(backwardStep)); - - ar(CEREAL_VARIANT_POINTER(rnnModule)); - ar(CEREAL_VARIANT_POINTER(actionModule)); -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/layer/recurrent_impl.hpp b/src/mlpack/methods/ann/layer/recurrent_impl.hpp deleted file mode 100644 index 046c48fa0f..0000000000 --- a/src/mlpack/methods/ann/layer/recurrent_impl.hpp +++ /dev/null @@ -1,361 +0,0 @@ -/** - * @file methods/ann/layer/recurrent_impl.hpp - * @author Marcus Edel - * - * Implementation of the Recurrent class. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_LAYER_RECURRENT_IMPL_HPP -#define MLPACK_METHODS_ANN_LAYER_RECURRENT_IMPL_HPP - -// In case it hasn't yet been included. -#include "recurrent.hpp" - -#include "../visitor/add_visitor.hpp" -#include "../visitor/backward_visitor.hpp" -#include "../visitor/gradient_visitor.hpp" -#include "../visitor/gradient_zero_visitor.hpp" -#include "../visitor/input_shape_visitor.hpp" - -namespace mlpack { -namespace ann /** Artificial Neural Network. */ { - -template -Recurrent::Recurrent() : - rho(0), - forwardStep(0), - backwardStep(0), - gradientStep(0), - deterministic(false), - ownsLayer(false) -{ - // Nothing to do. -} - -template -template< - typename StartModuleType, - typename InputModuleType, - typename FeedbackModuleType, - typename TransferModuleType -> -Recurrent::Recurrent( - const StartModuleType& start, - const InputModuleType& input, - const FeedbackModuleType& feedback, - const TransferModuleType& transfer, - const size_t rho) : - startModule(new StartModuleType(start)), - inputModule(new InputModuleType(input)), - feedbackModule(new FeedbackModuleType(feedback)), - transferModule(new TransferModuleType(transfer)), - rho(rho), - forwardStep(0), - backwardStep(0), - gradientStep(0), - deterministic(false), - ownsLayer(true) -{ - initialModule = new Sequential<>(); - mergeModule = new AddMerge<>(false, false, false); - recurrentModule = new Sequential<>(false, false); - - boost::apply_visitor(AddVisitor(inputModule), - initialModule); - boost::apply_visitor(AddVisitor(startModule), - initialModule); - boost::apply_visitor(AddVisitor(transferModule), - initialModule); - - boost::apply_visitor(AddVisitor(inputModule), mergeModule); - boost::apply_visitor(AddVisitor(feedbackModule), - mergeModule); - boost::apply_visitor(AddVisitor(mergeModule), - recurrentModule); - boost::apply_visitor(AddVisitor(transferModule), - recurrentModule); - - network.push_back(initialModule); - network.push_back(mergeModule); - network.push_back(feedbackModule); - network.push_back(recurrentModule); -} - -template -Recurrent::Recurrent( - const Recurrent& network) : - rho(network.rho), - forwardStep(network.forwardStep), - backwardStep(network.backwardStep), - gradientStep(network.gradientStep), - deterministic(network.deterministic), - ownsLayer(network.ownsLayer) -{ - startModule = boost::apply_visitor(copyVisitor, network.startModule); - inputModule = boost::apply_visitor(copyVisitor, network.inputModule); - feedbackModule = boost::apply_visitor(copyVisitor, network.feedbackModule); - transferModule = boost::apply_visitor(copyVisitor, network.transferModule); - initialModule = new Sequential<>(); - mergeModule = new AddMerge<>(false, false, false); - recurrentModule = new Sequential<>(false, false); - - boost::apply_visitor(AddVisitor(inputModule), - initialModule); - boost::apply_visitor(AddVisitor(startModule), - initialModule); - boost::apply_visitor(AddVisitor(transferModule), - initialModule); - - boost::apply_visitor(AddVisitor(inputModule), mergeModule); - boost::apply_visitor(AddVisitor(feedbackModule), - mergeModule); - boost::apply_visitor(AddVisitor(mergeModule), - recurrentModule); - boost::apply_visitor(AddVisitor(transferModule), - recurrentModule); - this->network.push_back(initialModule); - this->network.push_back(mergeModule); - this->network.push_back(feedbackModule); - this->network.push_back(recurrentModule); -} - -template -size_t -Recurrent::InputShape() const -{ - const size_t inputShapeStartModule = boost::apply_visitor(InShapeVisitor(), - startModule); - - // Return the input shape of the first module that we have. - if (inputShapeStartModule != 0) - { - return inputShapeStartModule; - } - // If input shape of first module is 0. - else - { - // Return input shape of the second module that we have. - const size_t inputShapeInputModule = boost::apply_visitor(InShapeVisitor(), - inputModule); - if (inputShapeInputModule != 0) - { - return inputShapeInputModule; - } - else // If the input shape of second module is 0. - { - // Return input shape of the third module that we have. - const size_t inputShapeFeedbackModule = boost::apply_visitor( - InShapeVisitor(), feedbackModule); - if (inputShapeFeedbackModule != 0) - { - return inputShapeFeedbackModule; - } - else // If the input shape of the third module is 0. - { - // Return the shape of the fourth module that we have. - const size_t inputShapeTransferModule = boost::apply_visitor( - InShapeVisitor(), transferModule); - if (inputShapeTransferModule != 0) - { - return inputShapeTransferModule; - } - else // If the input shape of the fourth module is 0. - { - return 0; - } - } - } - } -} - -template -template -void Recurrent::Forward( - const arma::Mat& input, arma::Mat& output) -{ - if (forwardStep == 0) - { - boost::apply_visitor(ForwardVisitor(input, output), initialModule); - } - else - { - boost::apply_visitor(ForwardVisitor(input, - boost::apply_visitor(outputParameterVisitor, inputModule)), - inputModule); - - boost::apply_visitor(ForwardVisitor(boost::apply_visitor( - outputParameterVisitor, transferModule), - boost::apply_visitor(outputParameterVisitor, feedbackModule)), - feedbackModule); - - boost::apply_visitor(ForwardVisitor(input, output), recurrentModule); - } - - output = boost::apply_visitor(outputParameterVisitor, transferModule); - - // Save the feedback output parameter when training the module. - if (!deterministic) - { - feedbackOutputParameter.push_back(output); - } - - forwardStep++; - if (forwardStep == rho) - { - forwardStep = 0; - backwardStep = 0; - - if (!recurrentError.is_empty()) - { - recurrentError.zeros(); - } - } -} - -template -template -void Recurrent::Backward( - const arma::Mat& /* input */, const arma::Mat& gy, arma::Mat& g) -{ - if (!recurrentError.is_empty()) - { - recurrentError += gy; - } - else - { - recurrentError = gy; - } - - if (backwardStep < (rho - 1)) - { - boost::apply_visitor(BackwardVisitor(boost::apply_visitor( - outputParameterVisitor, recurrentModule), recurrentError, - boost::apply_visitor(deltaVisitor, recurrentModule)), - recurrentModule); - - boost::apply_visitor(BackwardVisitor(boost::apply_visitor( - outputParameterVisitor, inputModule), - boost::apply_visitor(deltaVisitor, recurrentModule), g), - inputModule); - - boost::apply_visitor(BackwardVisitor(boost::apply_visitor( - outputParameterVisitor, feedbackModule), - boost::apply_visitor(deltaVisitor, recurrentModule), - boost::apply_visitor(deltaVisitor, feedbackModule)), feedbackModule); - } - else - { - boost::apply_visitor(BackwardVisitor(boost::apply_visitor( - outputParameterVisitor, initialModule), recurrentError, g), - initialModule); - } - - recurrentError = boost::apply_visitor(deltaVisitor, feedbackModule); - backwardStep++; -} - -template -template -void Recurrent::Gradient( - const arma::Mat& input, - const arma::Mat& error, - arma::Mat& /* gradient */) -{ - if (gradientStep < (rho - 1)) - { - boost::apply_visitor(GradientVisitor(input, error), recurrentModule); - - boost::apply_visitor(GradientVisitor(input, - boost::apply_visitor(deltaVisitor, mergeModule)), inputModule); - - boost::apply_visitor(GradientVisitor( - feedbackOutputParameter[feedbackOutputParameter.size() - 2 - - gradientStep], boost::apply_visitor(deltaVisitor, - mergeModule)), feedbackModule); - } - else - { - boost::apply_visitor(GradientZeroVisitor(), recurrentModule); - boost::apply_visitor(GradientZeroVisitor(), inputModule); - boost::apply_visitor(GradientZeroVisitor(), feedbackModule); - - boost::apply_visitor(GradientVisitor(input, - boost::apply_visitor(deltaVisitor, startModule)), initialModule); - } - - gradientStep++; - if (gradientStep == rho) - { - gradientStep = 0; - feedbackOutputParameter.clear(); - } -} - -template -template -void Recurrent::serialize( - Archive& ar, const uint32_t /* version */) -{ - // Clean up memory, if we are loading. - if (cereal::is_loading()) - { - // Clear old things, if needed. - boost::apply_visitor(DeleteVisitor(), recurrentModule); - boost::apply_visitor(DeleteVisitor(), initialModule); - boost::apply_visitor(DeleteVisitor(), startModule); - network.clear(); - } - - ar(CEREAL_VARIANT_POINTER(startModule)); - ar(CEREAL_VARIANT_POINTER(inputModule)); - ar(CEREAL_VARIANT_POINTER(feedbackModule)); - ar(CEREAL_VARIANT_POINTER(transferModule)); - ar(CEREAL_NVP(rho)); - ar(CEREAL_NVP(ownsLayer)); - - // Set up the network. - if (cereal::is_loading()) - { - initialModule = new Sequential<>(); - mergeModule = new AddMerge<>(false, false, false); - recurrentModule = new Sequential<>(false, false); - - boost::apply_visitor(AddVisitor(inputModule), - initialModule); - boost::apply_visitor(AddVisitor(startModule), - initialModule); - boost::apply_visitor(AddVisitor(transferModule), - initialModule); - - boost::apply_visitor(AddVisitor(inputModule), - mergeModule); - boost::apply_visitor(AddVisitor(feedbackModule), - mergeModule); - boost::apply_visitor(AddVisitor(mergeModule), - recurrentModule); - boost::apply_visitor(AddVisitor(transferModule), - recurrentModule); - - network.push_back(initialModule); - network.push_back(mergeModule); - network.push_back(feedbackModule); - network.push_back(recurrentModule); - } -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/layer/recurrent_layer.hpp b/src/mlpack/methods/ann/layer/recurrent_layer.hpp new file mode 100644 index 0000000000..118bb2385e --- /dev/null +++ b/src/mlpack/methods/ann/layer/recurrent_layer.hpp @@ -0,0 +1,107 @@ +/** + * @file methods/ann/layer/recurrent_layer.hpp + * @author Ryan Curtin + * + * Base layer for recurrent neural network layers. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with the mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_LAYER_RECURRENT_LAYER_HPP +#define MLPACK_METHODS_ANN_LAYER_RECURRENT_LAYER_HPP + +#include +#include "layer.hpp" + +namespace mlpack { +namespace ann { + +/** + * The `RecurrentLayer` provides a base layer for all layers that have recurrent + * functionality and store state between steps in a recurrent network. Any + * RecurrentLayer should only be used with a network type such as `RNN` that + * supports recurrent layers. + * + * Any recurrent layer that inherits from `RecurrentLayer` must implement the + * `ClearRecurrentState(bpttSteps, batchSize)` function; this function should + * allocate space to store previous states with the given batch size. See the + * documentation for that function for more details. + * + * @tparam MatType Matrix representation to accept as input and use for + * computation. + */ +template +class RecurrentLayer : public Layer +{ + public: + /** + * Create the RecurrentLayer. + */ + RecurrentLayer(); + + // Virtual destructor is required for classes using inheritance. + virtual ~RecurrentLayer() { } + + //! Copy the given RecurrentLayer. + RecurrentLayer(const RecurrentLayer& other); + //! Take ownership of the given RecurrentLayer. + RecurrentLayer(RecurrentLayer&& other); + //! Copy the given RecurrentLayer. + RecurrentLayer& operator=(const RecurrentLayer& other); + //! Take ownership of the given RecurrentLayer. + RecurrentLayer& operator=(RecurrentLayer&& other); + + /** + * ClearRecurrentState() is called before any forward pass of a recurrent + * network. This function is responsible for allocating any memory necessary + * to store `bpttSteps` steps of previous forward and backward passes, with a + * batch size of `batchSize`. + * + * Any internal state of the recurrent layer should be set to 0. + */ + virtual void ClearRecurrentState( + const size_t bpttSteps, + const size_t batchSize) = 0; + + //! Get the current step index to use in a forward or backward pass. + size_t CurrentStep() const { return currentStep; } + //! Modify the current step index to use in a forward or backward pass. + //! (Don't do this inside of your recurrent layer's implementation! This is + //! meant to be done by the enclosing network.) + size_t& CurrentStep() { return currentStep; } + + //! Get the previous step index, representing the value of CurrentStep() in + //! the previous call to Forward() or Backward(). + size_t PreviousStep() const { return previousStep; } + //! Modify the previous step index, representing the value of CurrentStep() in + //! the previous call to Forward() or Backward(). (Don't modify this inside + //! of your recurrent layer's implementation! This is meant to be done by the + //! enclosing network.) + size_t& PreviousStep() { return previousStep; } + + //! If Forward() or Backward() has been called since ClearRecurrentState(), + //! this will return true. This should be used to determine if recurrent + //! state should be considered in computations. + bool HasPreviousStep() const { return previousStep != size_t(-1); } + + //! Serialize the recurrent layer. + template + void serialize(Archive& ar, const uint32_t /* version */); + + private: + //! The current index of the step. This is set by the enclosing network + //! during forward and backward passes. + size_t currentStep; + //! The previous index of the step. This is set by the enclosing network + //! during forward and backward passes. + size_t previousStep; +}; + +} // namespace ann +} // namespace mlpack + +#include "recurrent_layer_impl.hpp" + +#endif diff --git a/src/mlpack/methods/ann/layer/recurrent_layer_impl.hpp b/src/mlpack/methods/ann/layer/recurrent_layer_impl.hpp new file mode 100644 index 0000000000..92eeb3da11 --- /dev/null +++ b/src/mlpack/methods/ann/layer/recurrent_layer_impl.hpp @@ -0,0 +1,84 @@ +/** + * @file methods/ann/layer/recurrent_layer_impl.hpp + * @author Ryan Curtin + * + * Base layer for recurrent neural network layers. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with the mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_LAYER_RECURRENT_LAYER_IMPL_HPP +#define MLPACK_METHODS_ANN_LAYER_RECURRENT_LAYER_IMPL_HPP + +// In case it hasn't been included yet. +#include "recurrent_layer.hpp" + +namespace mlpack { +namespace ann { + +template +RecurrentLayer::RecurrentLayer() : + Layer(), + currentStep(0), + previousStep(0) +{ /* Nothing to do. */ } + +template +RecurrentLayer::RecurrentLayer(const RecurrentLayer& other) : + Layer(other), + currentStep(other.currentStep), + previousStep(other.previousStep) +{ /* Nothing else to do. */ } + +template +RecurrentLayer::RecurrentLayer(RecurrentLayer&& other) : + Layer(std::move(other)), + currentStep(std::move(other.currentStep)), + previousStep(std::move(other.previousStep)) +{ /* Nothing else to do. */ } + +template +RecurrentLayer& +RecurrentLayer::operator=(const RecurrentLayer& other) +{ + if (this != &other) + { + Layer::operator=(other); + currentStep = other.currentStep; + previousStep = other.previousStep; + } + + return *this; +} + +template +RecurrentLayer& +RecurrentLayer::operator=(RecurrentLayer&& other) +{ + if (this != &other) + { + Layer::operator=(std::move(other)); + currentStep = std::move(other.currentStep); + previousStep = std::move(other.previousStep); + } + + return *this; +} + +template +template +void RecurrentLayer::serialize( + Archive& ar, const uint32_t /* version */) +{ + ar(cereal::base_class>(this)); + + ar(CEREAL_NVP(currentStep)); + ar(CEREAL_NVP(previousStep)); +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/layer/reparametrization_impl.hpp b/src/mlpack/methods/ann/layer/reparametrization_impl.hpp deleted file mode 100644 index 117e67a620..0000000000 --- a/src/mlpack/methods/ann/layer/reparametrization_impl.hpp +++ /dev/null @@ -1,156 +0,0 @@ -/** - * @file methods/ann/layer/reparametrization_impl.hpp - * @author Atharva Khandait - * - * Implementation of the Reparametrization layer class which samples from a - * gaussian distribution. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_LAYER_REPARAMETRIZATION_IMPL_HPP -#define MLPACK_METHODS_ANN_LAYER_REPARAMETRIZATION_IMPL_HPP - -// In case it hasn't yet been included. -#include "reparametrization.hpp" - -namespace mlpack { -namespace ann /** Artificial Neural Network. */ { - -template -Reparametrization::Reparametrization() : - latentSize(0), - stochastic(true), - includeKl(true), - beta(1) -{ - // Nothing to do here. -} - -template -Reparametrization::Reparametrization( - const size_t latentSize, - const bool stochastic, - const bool includeKl, - const double beta) : - latentSize(latentSize), - stochastic(stochastic), - includeKl(includeKl), - beta(beta) -{ - if (includeKl == false && beta != 1) - { - Log::Info << "The beta parameter will be ignored as KL divergence is not " - << "included." << std::endl; - } -} - -template -Reparametrization::Reparametrization( - const Reparametrization& layer) : - latentSize(layer.latentSize), - stochastic(layer.stochastic), - includeKl(layer.includeKl), - beta(layer.beta) -{ - // Nothing to do here. -} - -template -Reparametrization::Reparametrization( - Reparametrization&& layer) : - latentSize(std::move(layer.latentSize)), - stochastic(std::move(layer.stochastic)), - includeKl(std::move(layer.includeKl)), - beta(std::move(layer.beta)) -{ - // Nothing to do here. -} - -template -Reparametrization& -Reparametrization:: -operator=(const Reparametrization& layer) -{ - if (this != &layer) - { - latentSize = layer.latentSize; - stochastic = layer.stochastic; - includeKl = layer.includeKl; - beta = layer.beta; - } - return *this; -} - -template -Reparametrization& -Reparametrization:: -operator=(Reparametrization&& layer) -{ - if (this != &layer) - { - latentSize = std::move(layer.latentSize); - stochastic = std::move(layer.stochastic); - includeKl = std::move(layer.includeKl); - beta = std::move(layer.beta); - } - return *this; -} - - -template -template -void Reparametrization::Forward( - const arma::Mat& input, arma::Mat& output) -{ - if (input.n_rows != 2 * latentSize) - { - Log::Fatal << "The output size of layer before the Reparametrization " - << "layer should be 2 * latent size of the Reparametrization layer!" - << std::endl; - } - - mean = input.submat(latentSize, 0, 2 * latentSize - 1, input.n_cols - 1); - preStdDev = input.submat(0, 0, latentSize - 1, input.n_cols - 1); - - if (stochastic) - gaussianSample = arma::randn >(latentSize, input.n_cols); - else - gaussianSample = arma::ones >(latentSize, input.n_cols) * 0.7; - - SoftplusFunction::Fn(preStdDev, stdDev); - output = mean + stdDev % gaussianSample; -} - -template -template -void Reparametrization::Backward( - const arma::Mat& /* input */, const arma::Mat& gy, arma::Mat& g) -{ - SoftplusFunction::Deriv(preStdDev, g); - - if (includeKl) - { - g = join_cols(gy % std::move(gaussianSample) % g + (-1 / stdDev + stdDev) - % g * beta, gy + mean * beta / mean.n_cols); - } - else - g = join_cols(gy % std::move(gaussianSample) % g, gy); -} - -template -template -void Reparametrization::serialize( - Archive& ar, const uint32_t /* version */) -{ - ar(CEREAL_NVP(latentSize)); - ar(CEREAL_NVP(stochastic)); - ar(CEREAL_NVP(includeKl)); -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/layer/sequential.hpp b/src/mlpack/methods/ann/layer/sequential.hpp deleted file mode 100644 index bd1857b1ba..0000000000 --- a/src/mlpack/methods/ann/layer/sequential.hpp +++ /dev/null @@ -1,267 +0,0 @@ -/** - * @file methods/ann/layer/sequential.hpp - * @author Marcus Edel - * - * Definition of the Sequential class, which acts as a feed-forward fully - * connected network container. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_LAYER_SEQUENTIAL_HPP -#define MLPACK_METHODS_ANN_LAYER_SEQUENTIAL_HPP - -#include - -#include "../visitor/delete_visitor.hpp" -#include "../visitor/copy_visitor.hpp" -#include "../visitor/delta_visitor.hpp" -#include "../visitor/output_height_visitor.hpp" -#include "../visitor/output_parameter_visitor.hpp" -#include "../visitor/output_width_visitor.hpp" -#include "../visitor/input_shape_visitor.hpp" - -#include "layer_types.hpp" -#include "add_merge.hpp" - -namespace mlpack { -namespace ann /** Artificial Neural Network. */ { - -/** - * Implementation of the Sequential class. The sequential class works as a - * feed-forward fully connected network container which plugs various layers - * together. - * - * This class can also be used as a container for a residual block. In that - * case, the sizes of the input and output matrices of this class should be - * equal. A typedef has been added for use as a Residual<> class. - * - * For more information, refer the following paper. - * - * @code - * @article{He15, - * author = {Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun}, - * title = {Deep Residual Learning for Image Recognition}, - * year = {2015}, - * url = {https://arxiv.org/abs/1512.03385}, - * eprint = {1512.03385}, - * } - * @endcode - * - * Note: If this class is used as the first layer of a network, it should be - * preceded by IdentityLayer<>. - * - * Note: This class should at least have two layers for a call to its Gradient() - * function. - * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam Residual If true, use the object as a Residual block. - */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat, - bool Residual = false, - typename... CustomLayers -> -class Sequential -{ - public: - /** - * Create the Sequential object using the specified parameters. - * - * @param model Expose the all network modules. - */ - Sequential(const bool model = true); - - /** - * Create the Sequential object using the specified parameters. - * - * @param model Expose all the network modules. - * @param ownsLayers If true, then this module will delete its layers when - * deallocated. - */ - Sequential(const bool model, const bool ownsLayers); - - //! Copy constructor. - Sequential(const Sequential& layer); - - //! Copy assignment operator. - Sequential& operator = (const Sequential& layer); - - //! Destroy the Sequential object. - ~Sequential(); - - /** - * Ordinary feed forward pass of a neural network, evaluating the function - * f(x) by propagating the activity forward through f. - * - * @param input Input data used for evaluating the specified function. - * @param output Resulting output activation. - */ - template - void Forward(const arma::Mat& input, arma::Mat& output); - - /** - * Ordinary feed backward pass of a neural network, using 3rd-order tensors as - * input, calculating the function f(x) by propagating x backwards through f. - * Using the results from the feed forward pass. - * - * @param * (input) The propagated input activation. - * @param gy The backpropagated error. - * @param g The calculated gradient. - */ - template - void Backward(const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g); - - /* - * Calculate the gradient using the output delta and the input activation. - * - * @param input The input parameter used for calculating the gradient. - * @param error The calculated error. - * @param gradient The calculated gradient. - */ - template - void Gradient(const arma::Mat& input, - const arma::Mat& error, - arma::Mat& /* gradient */); - - /* - * Add a new module to the model. - * - * @param args The layer parameter. - */ - template - void Add(Args... args) { network.push_back(new LayerType(args...)); } - - /* - * Add a new module to the model. - * - * @param layer The Layer to be added to the model. - */ - void Add(LayerTypes layer) { network.push_back(layer); } - - //! Return the model modules. - std::vector >& Model() - { - if (model) - { - return network; - } - - return empty; - } - - //! Return the initial point for the optimization. - const arma::mat& Parameters() const { return parameters; } - //! Modify the initial point for the optimization. - arma::mat& Parameters() { return parameters; } - - //! Get the input parameter. - arma::mat const& InputParameter() const { return inputParameter; } - //! Modify the input parameter. - arma::mat& InputParameter() { return inputParameter; } - - //! Get the output parameter. - arma::mat const& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - arma::mat& OutputParameter() { return outputParameter; } - - //! Get the delta. - arma::mat const& Delta() const { return delta; } - //! Modify the delta. - arma::mat& Delta() { return delta; } - - //! Get the gradient. - arma::mat const& Gradient() const { return gradient; } - //! Modify the gradient. - arma::mat& Gradient() { return gradient; } - - size_t InputShape() const; - - /** - * Serialize the layer - */ - template - void serialize(Archive& ar, const uint32_t /* version */); - - private: - //! Parameter which indicates if the modules should be exposed. - bool model; - - //! Indicator if we already initialized the model. - bool reset; - - //! Locally-stored network modules. - std::vector > network; - - //! Locally-stored model parameters. - arma::mat parameters; - - //! Locally-stored delta visitor. - DeltaVisitor deltaVisitor; - - //! Locally-stored output parameter visitor. - OutputParameterVisitor outputParameterVisitor; - - //! Locally-stored delete visitor. - DeleteVisitor deleteVisitor; - - //! Locally-stored empty list of modules. - std::vector > empty; - - //! Locally-stored delta object. - arma::mat delta; - - //! Locally-stored input parameter object. - arma::mat inputParameter; - - //! Locally-stored output parameter object. - arma::mat outputParameter; - - //! Locally-stored gradient object. - arma::mat gradient; - - //! Locally-stored output width visitor. - OutputWidthVisitor outputWidthVisitor; - - //! Locally-stored output height visitor. - OutputHeightVisitor outputHeightVisitor; - - //! Locally-stored copy visitor - CopyVisitor copyVisitor; - - //! The input width. - size_t width; - - //! The input height. - size_t height; - - //! Whether we are responsible for deleting the layers held in this module. - bool ownsLayers; -}; // class Sequential - -/* - * Convenience typedef for use as Residual<> layer. - */ -template< - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat, - typename... CustomLayers -> -using Residual = Sequential< - InputDataType, OutputDataType, true, CustomLayers...>; - -} // namespace ann -} // namespace mlpack - -// Include implementation. -#include "sequential_impl.hpp" - -#endif diff --git a/src/mlpack/methods/ann/layer/sequential_impl.hpp b/src/mlpack/methods/ann/layer/sequential_impl.hpp deleted file mode 100644 index 1290a15ebb..0000000000 --- a/src/mlpack/methods/ann/layer/sequential_impl.hpp +++ /dev/null @@ -1,270 +0,0 @@ -/** - * @file methods/ann/layer/sequential_impl.hpp - * @author Marcus Edel - * - * Implementation of the Sequential class, which acts as a feed-forward fully - * connected network container. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_LAYER_SEQUENTIAL_IMPL_HPP -#define MLPACK_METHODS_ANN_LAYER_SEQUENTIAL_IMPL_HPP - -// In case it hasn't yet been included. -#include "sequential.hpp" - -#include "../visitor/forward_visitor.hpp" -#include "../visitor/backward_visitor.hpp" -#include "../visitor/gradient_visitor.hpp" -#include "../visitor/set_input_height_visitor.hpp" -#include "../visitor/set_input_width_visitor.hpp" -#include "../visitor/input_shape_visitor.hpp" - -namespace mlpack { -namespace ann /** Artificial Neural Network. */ { - -template -Sequential:: -Sequential(const bool model) : - model(model), reset(false), width(0), height(0), ownsLayers(!model) -{ - // Nothing to do here. -} - -template -Sequential:: -Sequential(const bool model, const bool ownsLayers) : - model(model), reset(false), width(0), height(0), ownsLayers(ownsLayers) -{ - // Nothing to do here. -} - -template -Sequential:: -Sequential(const Sequential& layer) : - model(layer.model), - reset(layer.reset), - width(layer.width), - height(layer.height), - ownsLayers(layer.ownsLayers) -{ - // Nothing to do here. -} - -template -Sequential& -Sequential:: -operator = (const Sequential& layer) -{ - if (this != &layer) - { - model = layer.model; - reset = layer.reset; - width = layer.width; - height = layer.height; - ownsLayers = layer.ownsLayers; - parameters = layer.parameters; - network.clear(); - // Build new layers according to source network. - for (size_t i = 0; i < layer.network.size(); ++i) - { - this->network.push_back(boost::apply_visitor(copyVisitor, - layer.network[i])); - } - } - return *this; -} - - -template -Sequential< - InputDataType, OutputDataType, Residual, CustomLayers...>::~Sequential() -{ - if (!model && ownsLayers) - { - for (LayerTypes& layer : network) - boost::apply_visitor(deleteVisitor, layer); - } -} - -template -size_t Sequential:: -InputShape() const -{ - size_t inputShape = 0; - - for (size_t l = 0; l < network.size(); ++l) - { - if (inputShape == 0) - inputShape = boost::apply_visitor(InShapeVisitor(), network[l]); - else - break; - } - - return inputShape; -} - -template -template -void Sequential:: -Forward(const arma::Mat& input, arma::Mat& output) -{ - boost::apply_visitor(ForwardVisitor(input, - boost::apply_visitor(outputParameterVisitor, network.front())), - network.front()); - - if (!reset) - { - if (boost::apply_visitor(outputWidthVisitor, network.front()) != 0) - { - width = boost::apply_visitor(outputWidthVisitor, network.front()); - } - - if (boost::apply_visitor(outputHeightVisitor, network.front()) != 0) - { - height = boost::apply_visitor(outputHeightVisitor, network.front()); - } - } - - for (size_t i = 1; i < network.size(); ++i) - { - if (!reset) - { - // Set the input width. - boost::apply_visitor(SetInputWidthVisitor(width), network[i]); - - // Set the input height. - boost::apply_visitor(SetInputHeightVisitor(height), network[i]); - } - - boost::apply_visitor(ForwardVisitor(boost::apply_visitor( - outputParameterVisitor, network[i - 1]), - boost::apply_visitor(outputParameterVisitor, network[i])), - network[i]); - - if (!reset) - { - // Get the output width. - if (boost::apply_visitor(outputWidthVisitor, network[i]) != 0) - { - width = boost::apply_visitor(outputWidthVisitor, network[i]); - } - - // Get the output height. - if (boost::apply_visitor(outputHeightVisitor, network[i]) != 0) - { - height = boost::apply_visitor(outputHeightVisitor, network[i]); - } - } - } - - if (!reset) - { - reset = true; - } - - output = boost::apply_visitor(outputParameterVisitor, network.back()); - - if (Residual) - { - if (arma::size(output) != arma::size(input)) - { - Log::Fatal << "The sizes of the output and input matrices of the Residual" - << " block should be equal. Please examine the network architecture." - << std::endl; - } - output += input; - } -} - -template -template -void Sequential< - InputDataType, OutputDataType, Residual, CustomLayers...>::Backward( - const arma::Mat& /* input */, - const arma::Mat& gy, - arma::Mat& g) -{ - boost::apply_visitor(BackwardVisitor(boost::apply_visitor( - outputParameterVisitor, network.back()), gy, - boost::apply_visitor(deltaVisitor, network.back())), - network.back()); - - for (size_t i = 2; i < network.size() + 1; ++i) - { - boost::apply_visitor(BackwardVisitor(boost::apply_visitor( - outputParameterVisitor, network[network.size() - i]), - boost::apply_visitor(deltaVisitor, network[network.size() - i + 1]), - boost::apply_visitor(deltaVisitor, network[network.size() - i])), - network[network.size() - i]); - } - - g = boost::apply_visitor(deltaVisitor, network.front()); - - if (Residual) - { - g += gy; - } -} - -template -template -void Sequential:: -Gradient(const arma::Mat& input, - const arma::Mat& error, - arma::Mat& /* gradient */) -{ - boost::apply_visitor(GradientVisitor(boost::apply_visitor( - outputParameterVisitor, network[network.size() - 2]), error), - network.back()); - - for (size_t i = 2; i < network.size(); ++i) - { - boost::apply_visitor(GradientVisitor(boost::apply_visitor( - outputParameterVisitor, network[network.size() - i - 1]), - boost::apply_visitor(deltaVisitor, network[network.size() - i + 1])), - network[network.size() - i]); - } - - boost::apply_visitor(GradientVisitor(input, - boost::apply_visitor(deltaVisitor, network[1])), network.front()); -} - -template -template -void Sequential< - InputDataType, OutputDataType, Residual, CustomLayers...>::serialize( - Archive& ar, const uint32_t /* version */) -{ - // If loading, delete the old layers. - if (cereal::is_loading()) - { - for (LayerTypes& layer : network) - { - boost::apply_visitor(deleteVisitor, layer); - } - } - - ar(CEREAL_NVP(model)); - ar(CEREAL_VECTOR_VARIANT_POINTER(network)); - - ar(CEREAL_NVP(ownsLayers)); -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/layer/serialization.hpp b/src/mlpack/methods/ann/layer/serialization.hpp new file mode 100644 index 0000000000..5e13d2a9b4 --- /dev/null +++ b/src/mlpack/methods/ann/layer/serialization.hpp @@ -0,0 +1,53 @@ +/** + * @file serialization.hpp + * @author Ryan Curtin + * + * Set up polymorphic serialization correctly for layer types. If you need + * custom serialization for a non-standard type, you will have to use the macros + * in this file. + */ +#ifndef MLPACK_METHODS_ANN_LAYER_SERIALIZATION_HPP +#define MLPACK_METHODS_ANN_LAYER_SERIALIZATION_HPP + +#define CEREAL_REGISTER_MLPACK_LAYERS(...) \ + CEREAL_REGISTER_TYPE(mlpack::ann::Layer<__VA_ARGS__>); \ + CEREAL_REGISTER_TYPE(mlpack::ann::MultiLayer<__VA_ARGS__>); \ + CEREAL_REGISTER_TYPE(mlpack::ann::RecurrentLayer<__VA_ARGS__>); \ + CEREAL_REGISTER_TYPE(mlpack::ann::AddType<__VA_ARGS__>); \ + CEREAL_REGISTER_TYPE(mlpack::ann::AlphaDropoutType<__VA_ARGS__>); \ + /* Base layers from base_layer.hpp. */ \ + CEREAL_REGISTER_TYPE(mlpack::ann::SigmoidType<__VA_ARGS__>); \ + CEREAL_REGISTER_TYPE(mlpack::ann::ReLUType<__VA_ARGS__>); \ + CEREAL_REGISTER_TYPE(mlpack::ann::TanHType<__VA_ARGS__>); \ + CEREAL_REGISTER_TYPE(mlpack::ann::SoftPlusType<__VA_ARGS__>); \ + CEREAL_REGISTER_TYPE(mlpack::ann::HardSigmoidType<__VA_ARGS__>); \ + CEREAL_REGISTER_TYPE(mlpack::ann::SwishType<__VA_ARGS__>); \ + CEREAL_REGISTER_TYPE(mlpack::ann::MishType<__VA_ARGS__>); \ + CEREAL_REGISTER_TYPE(mlpack::ann::LiSHTType<__VA_ARGS__>); \ + CEREAL_REGISTER_TYPE(mlpack::ann::GELUType<__VA_ARGS__>); \ + CEREAL_REGISTER_TYPE(mlpack::ann::ElliotType<__VA_ARGS__>); \ + CEREAL_REGISTER_TYPE(mlpack::ann::ElishType<__VA_ARGS__>); \ + CEREAL_REGISTER_TYPE(mlpack::ann::GaussianType<__VA_ARGS__>); \ + /* (end of base_layer.hpp) */ \ + CEREAL_REGISTER_TYPE(mlpack::ann::ConcatenateType<__VA_ARGS__>); \ + CEREAL_REGISTER_TYPE(mlpack::ann::ConvolutionType< \ + mlpack::ann::NaiveConvolution, \ + mlpack::ann::NaiveConvolution, \ + mlpack::ann::NaiveConvolution, \ + __VA_ARGS__>); \ + CEREAL_REGISTER_TYPE(mlpack::ann::DropConnectType<__VA_ARGS__>); \ + CEREAL_REGISTER_TYPE(mlpack::ann::DropoutType<__VA_ARGS__>); \ + CEREAL_REGISTER_TYPE(mlpack::ann::LeakyReLUType<__VA_ARGS__>); \ + CEREAL_REGISTER_TYPE(mlpack::ann::Linear3DType<__VA_ARGS__>); \ + CEREAL_REGISTER_TYPE(mlpack::ann::LinearType<__VA_ARGS__>); \ + CEREAL_REGISTER_TYPE(mlpack::ann::LinearNoBiasType<__VA_ARGS__>); \ + CEREAL_REGISTER_TYPE(mlpack::ann::LogSoftMaxType<__VA_ARGS__>); \ + CEREAL_REGISTER_TYPE(mlpack::ann::LSTMType<__VA_ARGS__>); \ + CEREAL_REGISTER_TYPE(mlpack::ann::MaxPoolingType<__VA_ARGS__>); \ + CEREAL_REGISTER_TYPE(mlpack::ann::NoisyLinearType<__VA_ARGS__>); \ + CEREAL_REGISTER_TYPE(mlpack::ann::PaddingType<__VA_ARGS__>); \ + CEREAL_REGISTER_TYPE(mlpack::ann::RBFType<__VA_ARGS__>); \ + +CEREAL_REGISTER_MLPACK_LAYERS(arma::mat); + +#endif diff --git a/src/mlpack/methods/ann/layer/vr_class_reward_impl.hpp b/src/mlpack/methods/ann/layer/vr_class_reward_impl.hpp deleted file mode 100644 index c04da10594..0000000000 --- a/src/mlpack/methods/ann/layer/vr_class_reward_impl.hpp +++ /dev/null @@ -1,107 +0,0 @@ -/** - * @file methods/ann/layer/vr_class_reward_impl.hpp - * @author Marcus Edel - * - * Implementation of the VRClassReward class, which implements the variance - * reduced classification reinforcement layer. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_LAYER_VR_CLASS_REWARD_IMPL_HPP -#define MLPACK_METHODS_ANN_LAYER_VR_CLASS_REWARD_IMPL_HPP - -// In case it hasn't yet been included. -#include "vr_class_reward.hpp" - -#include "../visitor/reward_set_visitor.hpp" - -namespace mlpack { -namespace ann /** Artificial Neural Network. */ { - -template -VRClassReward::VRClassReward( - const double scale, - const bool sizeAverage) : - scale(scale), - sizeAverage(sizeAverage), - reward(0) -{ - // Nothing to do here. -} - -template -template -double VRClassReward::Forward( - const InputType& input, const TargetType& target) -{ - double output = 0; - for (size_t i = 0; i < input.n_cols - 1; ++i) - { - size_t currentTarget = target(i) - 1; - Log::Assert(currentTarget < input.n_rows, - "Target class out of range."); - - output -= input(currentTarget, i); - } - - reward = 0; - arma::uword index = 0; - - for (size_t i = 0; i < input.n_cols - 1; ++i) - { - input.unsafe_col(i).max(index); - reward = ((index + 1) == target(i)) * scale; - } - - if (sizeAverage) - { - return output - reward / (input.n_cols - 1); - } - - return output - reward; -} - -template -template -void VRClassReward::Backward( - const InputType& input, - const TargetType& target, - OutputType& output) -{ - output = arma::zeros(input.n_rows, input.n_cols); - for (size_t i = 0; i < (input.n_cols - 1); ++i) - { - size_t currentTarget = target(i) - 1; - Log::Assert(currentTarget < input.n_rows, - "Target class out of range."); - - output(currentTarget, i) = -1; - } - - double vrReward = reward - input(0, 1); - if (sizeAverage) - { - vrReward /= input.n_cols - 1; - } - - const double norm = sizeAverage ? 2.0 / (input.n_cols - 1) : 2.0; - - output(0, 1) = norm * (input(0, 1) - reward); - boost::apply_visitor(RewardSetVisitor(vrReward), network.back()); -} - -template -template -void VRClassReward::serialize( - Archive& /* ar */, const uint32_t /* version */) -{ - // Nothing to do here. -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/layer/weight_norm_impl.hpp b/src/mlpack/methods/ann/layer/weight_norm_impl.hpp deleted file mode 100644 index c534cc36df..0000000000 --- a/src/mlpack/methods/ann/layer/weight_norm_impl.hpp +++ /dev/null @@ -1,165 +0,0 @@ -/** - * @file methods/ann/layer/weight_norm_impl.hpp - * @author Toshal Agrawal - * - * Implementation of the WeightNorm Layer. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ - -#ifndef MLPACK_METHODS_ANN_LAYER_WEIGHTNORM_IMPL_HPP -#define MLPACK_METHODS_ANN_LAYER_WEIGHTNORM_IMPL_HPP - -// In case it is not included. -#include "weight_norm.hpp" - -#include "../visitor/forward_visitor.hpp" -#include "../visitor/backward_visitor.hpp" -#include "../visitor/gradient_visitor.hpp" -#include "../visitor/bias_set_visitor.hpp" - -namespace mlpack { -namespace ann { /** Artificial Neural Network. */ - -template -WeightNorm::WeightNorm( - LayerTypes layer) : - wrappedLayer(layer) -{ - layerWeightSize = boost::apply_visitor(weightSizeVisitor, wrappedLayer); - weights.set_size(layerWeightSize + 1, 1); - - layerWeights.set_size(layerWeightSize, 1); - layerGradients.set_size(layerWeightSize, 1); -} - -template -WeightNorm::~WeightNorm() -{ - boost::apply_visitor(deleteVisitor, wrappedLayer); -} - -template -void WeightNorm::Reset() -{ - // Set the weights of the inside layer to layerWeights. - // This is done to set the non-bias terms correctly. - boost::apply_visitor(WeightSetVisitor(layerWeights, 0), wrappedLayer); - - boost::apply_visitor(resetVisitor, wrappedLayer); - - biasWeightSize = boost::apply_visitor(BiasSetVisitor(weights, 0), - wrappedLayer); - - vectorParameter = arma::mat(weights.memptr() + biasWeightSize, - layerWeightSize - biasWeightSize, 1, false, false); - - scalarParameter = arma::mat(weights.memptr() + layerWeightSize, 1, 1, false, - false); -} - -template -template -void WeightNorm::Forward( - const arma::Mat& input, arma::Mat& output) -{ - // Initialize the non-bias weights of wrapped layer. - const double normVectorParameter = arma::norm(vectorParameter, 2); - layerWeights.rows(0, layerWeightSize - biasWeightSize - 1) = - scalarParameter(0) * vectorParameter / normVectorParameter; - - boost::apply_visitor(ForwardVisitor(input, - boost::apply_visitor(outputParameterVisitor, wrappedLayer)), - wrappedLayer); - - output = boost::apply_visitor(outputParameterVisitor, wrappedLayer); -} - -template -template -void WeightNorm::Backward( - const arma::Mat& /* input */, const arma::Mat& gy, arma::Mat& g) -{ - boost::apply_visitor(BackwardVisitor(boost::apply_visitor( - outputParameterVisitor, wrappedLayer), gy, - boost::apply_visitor(deltaVisitor, wrappedLayer)), wrappedLayer); - - g = boost::apply_visitor(deltaVisitor, wrappedLayer); -} - -template -template -void WeightNorm::Gradient( - const arma::Mat& input, - const arma::Mat& error, - arma::Mat& gradient) -{ - ResetGradients(layerGradients); - - // Calculate the gradients of the wrapped layer. - boost::apply_visitor(GradientVisitor(input, error), wrappedLayer); - - // Store the norm of vector parameter temporarily. - const double normVectorParameter = arma::norm(vectorParameter, 2); - - // Set the gradients of the bias terms. - if (biasWeightSize != 0) - { - gradient.rows(0, biasWeightSize - 1) = arma::mat(layerGradients.memptr() + - layerWeightSize - biasWeightSize, biasWeightSize, 1, false, false); - } - - // Calculate the gradients of the scalar parameter. - gradient[gradient.n_rows - 1] = arma::accu(layerGradients.rows(0, - layerWeightSize - biasWeightSize - 1) % vectorParameter) / - normVectorParameter; - - // Calculate the gradients of the vector parameter. - gradient.rows(biasWeightSize, layerWeightSize - 1) = - scalarParameter(0) / normVectorParameter * (layerGradients.rows(0, - layerWeightSize - biasWeightSize - 1) - gradient[gradient.n_rows - 1] / - normVectorParameter * vectorParameter); -} - -template -void WeightNorm::ResetGradients( - arma::mat& gradient) -{ - boost::apply_visitor(GradientSetVisitor(gradient, 0), wrappedLayer); -} - -template -template -void WeightNorm::serialize( - Archive& ar, const uint32_t /* version */) -{ - if (cereal::is_loading()) - { - boost::apply_visitor(deleteVisitor, wrappedLayer); - } - - ar(CEREAL_VARIANT_POINTER(wrappedLayer)); - ar(CEREAL_NVP(layerWeightSize)); - - // If we are loading, we need to initialize the weights. - if (cereal::is_loading()) - { - weights.set_size(layerWeightSize + 1, 1); - } -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/loss_functions/CMakeLists.txt b/src/mlpack/methods/ann/loss_functions/CMakeLists.txt index a3c97ff4f6..35116a8831 100644 --- a/src/mlpack/methods/ann/loss_functions/CMakeLists.txt +++ b/src/mlpack/methods/ann/loss_functions/CMakeLists.txt @@ -47,6 +47,8 @@ set(SOURCES soft_margin_loss_impl.hpp triplet_margin_loss.hpp triplet_margin_loss_impl.hpp + vr_class_reward.hpp + vr_class_reward_impl.hpp ) # Add directory name to sources. diff --git a/src/mlpack/methods/ann/loss_functions/binary_cross_entropy_loss.hpp b/src/mlpack/methods/ann/loss_functions/binary_cross_entropy_loss.hpp index 8893227bc7..eae005c1d6 100644 --- a/src/mlpack/methods/ann/loss_functions/binary_cross_entropy_loss.hpp +++ b/src/mlpack/methods/ann/loss_functions/binary_cross_entropy_loss.hpp @@ -18,19 +18,14 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { /** - * The binary-cross-entropy performance function measures the - * Binary Cross Entropy between the target and the output. + * The binary-cross-entropy performance function measures the Binary Cross + * Entropy between the target and the output. * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam MatType Matrix representation to accept as input and use for + * computation. */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class BCELoss +template +class BCELossType { public: /** @@ -45,7 +40,7 @@ class BCELoss * 'sum' reduction is used and the output will be summed. It * is set to true by default. */ - BCELoss(const double eps = 1e-10, const bool reduction = true); + BCELossType(const double eps = 1e-10, const bool reduction = true); /** * Computes the cross-entropy function. @@ -54,9 +49,8 @@ class BCELoss * function. * @param target The target vector. */ - template - typename PredictionType::elem_type Forward(const PredictionType& prediction, - const TargetType& target); + typename MatType::elem_type Forward(const MatType& prediction, + const MatType& target); /** * Ordinary feed backward pass of a neural network. @@ -66,15 +60,9 @@ class BCELoss * @param target The target vector. * @param loss The calculated error. */ - template - void Backward(const PredictionType& prediction, - const TargetType& target, - LossType& loss); - - //! Get the output parameter. - OutputDataType& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } + void Backward(const MatType& prediction, + const MatType& target, + MatType& loss); //! Get the epsilon. double Eps() const { return eps; } @@ -94,25 +82,23 @@ class BCELoss void serialize(Archive& ar, const uint32_t /* version */); private: - //! Locally-stored output parameter object. - OutputDataType outputParameter; - //! The minimum value used for computing logarithms and denominators double eps; //! Boolean value that tells if reduction is 'sum' or 'mean'. bool reduction; -}; // class BCELoss +}; // class BCELossType + +// Default typedef for typical `arma::mat` usage. +typedef BCELossType BCELoss; /** - * Adding alias of BCELoss. + * Alias of BCELossType. */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -using CrossEntropyError = BCELoss< - InputDataType, OutputDataType>; +typedef BCELossType CrossEntropyError; + +template +using CrossEntropyErrorType = BCELossType; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/binary_cross_entropy_loss_impl.hpp b/src/mlpack/methods/ann/loss_functions/binary_cross_entropy_loss_impl.hpp index a217141564..0855cc580c 100644 --- a/src/mlpack/methods/ann/loss_functions/binary_cross_entropy_loss_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/binary_cross_entropy_loss_impl.hpp @@ -18,21 +18,19 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -BCELoss::BCELoss( +template +BCELossType::BCELossType( const double eps, const bool reduction) : eps(eps), reduction(reduction) { // Nothing to do here. } -template -template -typename PredictionType::elem_type -BCELoss::Forward( - const PredictionType& prediction, - const TargetType& target) +template +typename MatType::elem_type BCELossType::Forward( + const MatType& prediction, + const MatType& target) { - typedef typename PredictionType::elem_type ElemType; + typedef typename MatType::elem_type ElemType; ElemType lossSum = -arma::accu(target % arma::log(prediction + eps) + (1. - target) % arma::log(1. - prediction + eps)); @@ -43,12 +41,11 @@ BCELoss::Forward( return lossSum / target.n_elem; } -template -template -void BCELoss::Backward( - const PredictionType& prediction, - const TargetType& target, - LossType& loss) +template +void BCELossType::Backward( + const MatType& prediction, + const MatType& target, + MatType& loss) { loss = (1. - target) / (1. - prediction + eps) - target / (prediction + eps); @@ -56,9 +53,9 @@ void BCELoss::Backward( loss /= target.n_elem; } -template +template template -void BCELoss::serialize( +void BCELossType::serialize( Archive& ar, const uint32_t /* version */) { diff --git a/src/mlpack/methods/ann/loss_functions/cosine_embedding_loss.hpp b/src/mlpack/methods/ann/loss_functions/cosine_embedding_loss.hpp index 362d0d4e3b..9fc2ee8d35 100644 --- a/src/mlpack/methods/ann/loss_functions/cosine_embedding_loss.hpp +++ b/src/mlpack/methods/ann/loss_functions/cosine_embedding_loss.hpp @@ -26,21 +26,16 @@ namespace ann /** Artificial Neural Network. */ { * f(x) = 1 - cos(x1, x2) , for y = 1 * f(x) = max(0, cos(x1, x2) - margin) , for y = -1 * @f} - * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * + * @tparam MatType Matrix representation to accept as input and use for + * computation. */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class CosineEmbeddingLoss +template +class CosineEmbeddingLossType { public: /** - * Create the CosineEmbeddingLoss object. + * Create the CosineEmbeddingLossType object. * * @param margin Increases cosine distance in case of dissimilarity. * Refer definition of cosine-embedding-loss above. @@ -52,7 +47,7 @@ class CosineEmbeddingLoss * 'sum' reduction is used and the output will be summed. It * is set to true by default. */ - CosineEmbeddingLoss(const double margin = 0.0, + CosineEmbeddingLossType(const double margin = 0.0, const bool similarity = true, const bool reduction = true); @@ -63,9 +58,8 @@ class CosineEmbeddingLoss * function. * @param target The target vector. */ - template - typename PredictionType::elem_type Forward(const PredictionType& prediction, - const TargetType& target); + typename MatType::elem_type Forward(const MatType& prediction, + const MatType& target); /** * Ordinary feed backward pass of a neural network. @@ -75,25 +69,9 @@ class CosineEmbeddingLoss * @param target The target vector. * @param loss The calculated error. */ - template - void Backward(const PredictionType& prediction, - const TargetType& target, - LossType& loss); - - //! Get the input parameter. - InputDataType& InputParameter() const { return inputParameter; } - //! Modify the input parameter. - InputDataType& InputParameter() { return inputParameter; } - - //! Get the output parameter. - OutputDataType& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } + void Backward(const MatType& prediction, + const MatType& target, + MatType& loss); //! Get the reduction type, represented as boolean //! (false 'mean' reduction, true 'sum' reduction). @@ -118,15 +96,6 @@ class CosineEmbeddingLoss void serialize(Archive& ar, const uint32_t /* version */); private: - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored input parameter object. - InputDataType inputParameter; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; - //! Locally-stored value of margin hyper-parameter. double margin; @@ -135,7 +104,10 @@ class CosineEmbeddingLoss //! Boolean value that tells if reduction is 'sum' or 'mean'. bool reduction; -}; // class CosineEmbeddingLoss +}; // class CosineEmbeddingLossType + +// Default typedef for typical `arma::mat` usage. +typedef CosineEmbeddingLossType CosineEmbeddingLoss; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/cosine_embedding_loss_impl.hpp b/src/mlpack/methods/ann/loss_functions/cosine_embedding_loss_impl.hpp index bee9906032..b21c743653 100644 --- a/src/mlpack/methods/ann/loss_functions/cosine_embedding_loss_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/cosine_embedding_loss_impl.hpp @@ -18,30 +18,28 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -CosineEmbeddingLoss::CosineEmbeddingLoss( +template +CosineEmbeddingLossType::CosineEmbeddingLossType( const double margin, const bool similarity, const bool reduction): margin(margin), similarity(similarity), reduction(reduction) { // Nothing to do here. } -template -template -typename PredictionType::elem_type -CosineEmbeddingLoss::Forward( - const PredictionType& prediction, - const TargetType& target) +template +typename MatType::elem_type CosineEmbeddingLossType::Forward( + const MatType& prediction, + const MatType& target) { - typedef typename PredictionType::elem_type ElemType; + typedef typename MatType::elem_type ElemType; const size_t cols = prediction.n_cols; const size_t batchSize = prediction.n_elem / cols; if (arma::size(prediction) != arma::size(target)) Log::Fatal << "Input Tensors must have same dimensions." << std::endl; - arma::colvec inputTemp1 = arma::vectorise(prediction); - arma::colvec inputTemp2 = arma::vectorise(target); + arma::Col inputTemp1 = arma::vectorise(prediction); + arma::Col inputTemp2 = arma::vectorise(target); ElemType lossSum = 0.0; for (size_t i = 0; i < inputTemp1.n_elem; i += cols) @@ -64,25 +62,24 @@ CosineEmbeddingLoss::Forward( return (ElemType) lossSum / batchSize; } -template -template -void CosineEmbeddingLoss::Backward( - const PredictionType& prediction, - const TargetType& target, - LossType& loss) +template +void CosineEmbeddingLossType::Backward( + const MatType& prediction, + const MatType& target, + MatType& loss) { - typedef typename PredictionType::elem_type ElemType; + typedef typename MatType::elem_type ElemType; const size_t cols = prediction.n_cols; const size_t batchSize = prediction.n_elem / cols; if (arma::size(prediction) != arma::size(target)) Log::Fatal << "Input Tensors must have same dimensions." << std::endl; - arma::colvec inputTemp1 = arma::vectorise(prediction); - arma::colvec inputTemp2 = arma::vectorise(target); + arma::Col inputTemp1 = arma::vectorise(prediction); + arma::Col inputTemp2 = arma::vectorise(target); loss.set_size(arma::size(inputTemp1)); - arma::colvec outputTemp(loss.memptr(), inputTemp1.n_elem, + arma::Col outputTemp(loss.memptr(), inputTemp1.n_elem, false, false); for (size_t i = 0; i < inputTemp1.n_elem; i += cols) { @@ -106,9 +103,9 @@ void CosineEmbeddingLoss::Backward( } } -template +template template -void CosineEmbeddingLoss::serialize( +void CosineEmbeddingLossType::serialize( Archive& ar, const uint32_t /* version */) { ar(CEREAL_NVP(margin)); diff --git a/src/mlpack/methods/ann/loss_functions/dice_loss.hpp b/src/mlpack/methods/ann/loss_functions/dice_loss.hpp index ce4a80f54d..2e7b89b4be 100644 --- a/src/mlpack/methods/ann/loss_functions/dice_loss.hpp +++ b/src/mlpack/methods/ann/loss_functions/dice_loss.hpp @@ -38,24 +38,19 @@ namespace ann /** Artificial Neural Network. */ { * } * @endcode * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam MatType Matrix representation to accept as input and use for + * computation. */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class DiceLoss +template +class DiceLossType { public: /** - * Create the DiceLoss object. + * Create the DiceLossType object. * * @param smooth The Laplace smoothing parameter. */ - DiceLoss(const double smooth = 1); + DiceLossType(const double smooth = 1); /** * Computes the dice loss function. @@ -64,9 +59,8 @@ class DiceLoss * function. * @param target The target vector. */ - template - typename PredictionType::elem_type Forward(const PredictionType& prediction, - const TargetType& target); + typename MatType::elem_type Forward(const MatType& prediction, + const MatType& target); /** * Ordinary feed backward pass of a neural network. @@ -76,15 +70,9 @@ class DiceLoss * @param target The target vector. * @param loss The calculated error. */ - template - void Backward(const PredictionType& prediction, - const TargetType& target, - LossType& loss); - - //! Get the output parameter. - OutputDataType& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } + void Backward(const MatType& prediction, + const MatType& target, + MatType& loss); //! Get the smooth. double Smooth() const { return smooth; } @@ -98,12 +86,12 @@ class DiceLoss void serialize(Archive& ar, const uint32_t /* version */); private: - //! Locally-stored output parameter object. - OutputDataType outputParameter; - //! The parameter to avoid overfitting. double smooth; -}; // class DiceLoss +}; // class DiceLossType + +// Default typedef for typical `arma::mat` usage. +typedef DiceLossType DiceLoss; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/dice_loss_impl.hpp b/src/mlpack/methods/ann/loss_functions/dice_loss_impl.hpp index 2a1835dc70..a98534d086 100644 --- a/src/mlpack/methods/ann/loss_functions/dice_loss_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/dice_loss_impl.hpp @@ -18,41 +18,38 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -DiceLoss::DiceLoss( - const double smooth) : smooth(smooth) +template +DiceLossType::DiceLossType(const double smooth) : smooth(smooth) { // Nothing to do here. } -template -template -typename PredictionType::elem_type DiceLoss - ::Forward(const PredictionType& prediction, - const TargetType& target) +template +typename MatType::elem_type DiceLossType::Forward( + const MatType& prediction, + const MatType& target) { return 1 - ((2 * arma::accu(target % prediction) + smooth) / - (arma::accu(target % target) + arma::accu( - prediction % prediction) + smooth)); + (arma::accu(target % target) + arma::accu( + prediction % prediction) + smooth)); } -template -template -void DiceLoss::Backward( - const PredictionType& prediction, - const TargetType& target, - LossType& loss) +template +void DiceLossType::Backward( + const MatType& prediction, + const MatType& target, + MatType& loss) { loss = -2 * (target * (arma::accu(prediction % prediction) + - arma::accu(target % target) + smooth) - prediction * - (2 * arma::accu(target % prediction) + smooth)) / std::pow( - arma::accu(target % target) + arma::accu(prediction % prediction) - + smooth, 2.0); + arma::accu(target % target) + smooth) - prediction * + (2 * arma::accu(target % prediction) + smooth)) / std::pow( + arma::accu(target % target) + arma::accu(prediction % prediction) + + smooth, 2.0); } -template +template template -void DiceLoss::serialize( +void DiceLossType::serialize( Archive& ar, const uint32_t /* version */) { diff --git a/src/mlpack/methods/ann/loss_functions/earth_mover_distance.hpp b/src/mlpack/methods/ann/loss_functions/earth_mover_distance.hpp index e8013aac57..4f6da12ea1 100644 --- a/src/mlpack/methods/ann/loss_functions/earth_mover_distance.hpp +++ b/src/mlpack/methods/ann/loss_functions/earth_mover_distance.hpp @@ -21,20 +21,15 @@ namespace ann /** Artificial Neural Network. */ { * The earth mover distance function measures the network's performance * according to the Kantorovich-Rubinstein duality approximation. * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam MatType Matrix representation to accept as input and use for + * computation. */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class EarthMoverDistance +template +class EarthMoverDistanceType { public: /** - * Create the EarthMoverDistance object. + * Create the EarthMoverDistanceType object. * * @param reduction Specifies the reduction to apply to the output. If false, * 'mean' reduction is used, where sum of the output will be @@ -42,7 +37,7 @@ class EarthMoverDistance * 'sum' reduction is used and the output will be summed. It * is set to true by default. */ - EarthMoverDistance(const bool reduction = true); + EarthMoverDistanceType(const bool reduction = true); /** * Ordinary feed forward pass of a neural network. @@ -51,9 +46,8 @@ class EarthMoverDistance * function. * @param target The target vector. */ - template - typename PredictionType::elem_type Forward(const PredictionType& prediction, - const TargetType& target); + typename MatType::elem_type Forward(const MatType& prediction, + const MatType& target); /** * Ordinary feed backward pass of a neural network. @@ -63,15 +57,9 @@ class EarthMoverDistance * @param target The target vector. * @param loss The calculated error. */ - template - void Backward(const PredictionType& prediction, - const TargetType& target, - LossType& loss); - - //! Get the output parameter. - OutputDataType& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } + void Backward(const MatType& prediction, + const MatType& target, + MatType& loss); //! Get the reduction type, represented as boolean //! (false 'mean' reduction, true 'sum' reduction). @@ -86,12 +74,12 @@ class EarthMoverDistance void serialize(Archive& ar, const uint32_t /* version */); private: - //! Locally-stored output parameter object. - OutputDataType outputParameter; - //! Boolean value that tells if reduction is 'sum' or 'mean'. bool reduction; -}; // class EarthMoverDistance +}; // class EarthMoverDistanceType + +// Default typedef for typical `arma::mat` usage. +typedef EarthMoverDistanceType EarthMoverDistance; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/earth_mover_distance_impl.hpp b/src/mlpack/methods/ann/loss_functions/earth_mover_distance_impl.hpp index a049e1dc5a..a0e0ecaeb1 100644 --- a/src/mlpack/methods/ann/loss_functions/earth_mover_distance_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/earth_mover_distance_impl.hpp @@ -18,22 +18,19 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -EarthMoverDistance - ::EarthMoverDistance(const bool reduction) : reduction(reduction) +template +EarthMoverDistanceType::EarthMoverDistanceType(const bool reduction) : + reduction(reduction) { // Nothing to do here. } -template -template -typename PredictionType::elem_type -EarthMoverDistance::Forward( - const PredictionType& prediction, - const TargetType& target) +template +typename MatType::elem_type EarthMoverDistanceType::Forward( + const MatType& prediction, + const MatType& target) { - typename PredictionType::elem_type lossSum = - -arma::accu(target % prediction); + typename MatType::elem_type lossSum = -arma::accu(target % prediction); if (reduction) return lossSum; @@ -41,12 +38,11 @@ EarthMoverDistance::Forward( return lossSum / target.n_elem; } -template -template -void EarthMoverDistance::Backward( - const PredictionType& /* prediction */, - const TargetType& target, - LossType& loss) +template +void EarthMoverDistanceType::Backward( + const MatType& /* prediction */, + const MatType& target, + MatType& loss) { loss = -target; @@ -54,9 +50,9 @@ void EarthMoverDistance::Backward( loss = loss / target.n_elem; } -template +template template -void EarthMoverDistance::serialize( +void EarthMoverDistanceType::serialize( Archive& ar, const uint32_t /* version */) { diff --git a/src/mlpack/methods/ann/loss_functions/empty_loss.hpp b/src/mlpack/methods/ann/loss_functions/empty_loss.hpp index 8cc8caae9d..525823a37f 100644 --- a/src/mlpack/methods/ann/loss_functions/empty_loss.hpp +++ b/src/mlpack/methods/ann/loss_functions/empty_loss.hpp @@ -23,22 +23,17 @@ namespace ann /** Artificial Neural Network. */ { * The empty loss does nothing, letting the user calculate the loss outside * the model. * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam MatType Matrix representation to accept as input and use for + * computation. */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class EmptyLoss +template +class EmptyLossType { public: /** - * Create the EmptyLoss object. + * Create the EmptyLossType object. */ - EmptyLoss(); + EmptyLossType(); /** * Computes the Empty loss function. @@ -47,8 +42,7 @@ class EmptyLoss * function. * @param target The target vector. */ - template - double Forward(const PredictionType& input, const TargetType& target); + double Forward(const MatType& input, const MatType& target); /** * Ordinary feed backward pass of a neural network. @@ -58,11 +52,17 @@ class EmptyLoss * @param target The target vector. * @param loss The calculated error. */ - template - void Backward(const PredictionType& prediction, - const TargetType& target, - LossType& loss); -}; // class EmptyLoss + void Backward(const MatType& prediction, + const MatType& target, + MatType& loss); + + //! Serialize the EmptyLossType. + template + void serialize(Archive& ar, const uint32_t /* version */) { } +}; // class EmptyLossType + +// Default typedef for typical `arma::mat` usage. +typedef EmptyLossType EmptyLoss; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/empty_loss_impl.hpp b/src/mlpack/methods/ann/loss_functions/empty_loss_impl.hpp index 190792030e..1083fbcfea 100644 --- a/src/mlpack/methods/ann/loss_functions/empty_loss_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/empty_loss_impl.hpp @@ -20,26 +20,24 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -EmptyLoss::EmptyLoss() +template +EmptyLossType::EmptyLossType() { // Nothing to do here. } -template -template -double EmptyLoss::Forward( - const PredictionType& /* prediction */, const TargetType& /* target */) +template +double EmptyLossType::Forward( + const MatType& /* prediction */, const MatType& /* target */) { return 0; } -template -template -void EmptyLoss::Backward( - const PredictionType& /* prediction */, - const TargetType& target, - LossType& loss) +template +void EmptyLossType::Backward( + const MatType& /* prediction */, + const MatType& target, + MatType& loss) { loss = target; } diff --git a/src/mlpack/methods/ann/loss_functions/hinge_embedding_loss.hpp b/src/mlpack/methods/ann/loss_functions/hinge_embedding_loss.hpp index 74def487b8..40fe951650 100644 --- a/src/mlpack/methods/ann/loss_functions/hinge_embedding_loss.hpp +++ b/src/mlpack/methods/ann/loss_functions/hinge_embedding_loss.hpp @@ -24,16 +24,11 @@ namespace ann /** Artificial Neural Network. */ { * The Hinge Embedding loss function is often used to compute the loss * between y_true and y_pred. * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam MatType Matrix representation to accept as input and use for + * computation. */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class HingeEmbeddingLoss +template +class HingeEmbeddingLossType { public: /** @@ -45,7 +40,7 @@ class HingeEmbeddingLoss * 'sum' reduction is used and the output will be summed. It * is set to true by default. */ - HingeEmbeddingLoss(const bool reduction = true); + HingeEmbeddingLossType(const bool reduction = true); /** * Computes the Hinge Embedding loss function. @@ -54,9 +49,8 @@ class HingeEmbeddingLoss * function. * @param target Target data to compare with. */ - template - typename PredictionType::elem_type Forward(const PredictionType& prediction, - const TargetType& target); + typename MatType::elem_type Forward(const MatType& prediction, + const MatType& target); /** * Ordinary feed backward pass of a neural network. @@ -66,15 +60,9 @@ class HingeEmbeddingLoss * @param target The target vector. * @param loss The calculated error. */ - template - void Backward(const PredictionType& prediction, - const TargetType& target, - LossType& loss); - - //! Get the output parameter. - OutputDataType& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } + void Backward(const MatType& prediction, + const MatType& target, + MatType& loss); //! Get the reduction type, represented as boolean //! (false 'mean' reduction, true 'sum' reduction). @@ -89,13 +77,12 @@ class HingeEmbeddingLoss void serialize(Archive& ar, const uint32_t /* version */); private: - //! Locally-stored output parameter object. - OutputDataType outputParameter; - - //! Boolean values that tells if reduction - // is 'sum' or 'mean'. + //! Boolean values that tells if reduction is 'sum' or 'mean'. bool reduction; -}; // class HingeEmbeddingLoss +}; // class HingeEmbeddingLossType + +// Default typedef for typical `arma::mat` usage. +typedef HingeEmbeddingLossType HingeEmbeddingLoss; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/hinge_embedding_loss_impl.hpp b/src/mlpack/methods/ann/loss_functions/hinge_embedding_loss_impl.hpp index 701891d089..26773b6523 100644 --- a/src/mlpack/methods/ann/loss_functions/hinge_embedding_loss_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/hinge_embedding_loss_impl.hpp @@ -19,22 +19,20 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -HingeEmbeddingLoss - ::HingeEmbeddingLoss(const bool reduction) : reduction(reduction) +template +HingeEmbeddingLossType::HingeEmbeddingLossType(const bool reduction) : + reduction(reduction) { // Nothing to do here. } -template -template -typename PredictionType::elem_type -HingeEmbeddingLoss::Forward( - const PredictionType& prediction, - const TargetType& target) +template +typename MatType::elem_type HingeEmbeddingLossType::Forward( + const MatType& prediction, + const MatType& target) { - PredictionType loss = (1 - target) / 2 + prediction % (target); - typename PredictionType::elem_type lossSum = arma::accu(loss); + MatType loss = (1 - target) / 2 + prediction % (target); + typename MatType::elem_type lossSum = arma::accu(loss); if (reduction) return lossSum; @@ -42,12 +40,11 @@ HingeEmbeddingLoss::Forward( return lossSum / target.n_elem; } -template -template -void HingeEmbeddingLoss::Backward( - const PredictionType& prediction, - const TargetType& target, - LossType& loss) +template +void HingeEmbeddingLossType::Backward( + const MatType& /* prediction */, + const MatType& target, + MatType& loss) { loss = target; @@ -55,9 +52,9 @@ void HingeEmbeddingLoss::Backward( loss = loss / target.n_elem; } -template +template template -void HingeEmbeddingLoss::serialize( +void HingeEmbeddingLossType::serialize( Archive& ar, const uint32_t /* version */) { diff --git a/src/mlpack/methods/ann/loss_functions/hinge_loss.hpp b/src/mlpack/methods/ann/loss_functions/hinge_loss.hpp index 7829b20348..7b1023078a 100644 --- a/src/mlpack/methods/ann/loss_functions/hinge_loss.hpp +++ b/src/mlpack/methods/ann/loss_functions/hinge_loss.hpp @@ -25,20 +25,15 @@ namespace ann /** Artificial Neural Network. */ { * The hinge loss \f$l(y_true, y_pred)\f$ is defined as * \f$l(y_true, y_pred) = max(0, 1 - y_true*y_pred)\f$. * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam MatType Matrix representation to accept as input and use for + * computation. */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class HingeLoss +template +class HingeLossType { public: /** - * Create HingeLoss object. + * Create HingeLossType object. * * @param reduction Specifies the reduction to apply to the output. If false, * 'mean' reduction is used, where sum of the output will be @@ -46,7 +41,7 @@ class HingeLoss * true, 'sum' reduction is used and the output will be * summed. It is set to true by default. */ - HingeLoss(const bool reduction = true); + HingeLossType(const bool reduction = true); /** * Computes the Hinge loss function. @@ -55,9 +50,8 @@ class HingeLoss * function. * @param target Target data to compare with. */ - template - typename PredictionType::elem_type Forward(const PredictionType& prediction, - const TargetType& target); + typename MatType::elem_type Forward(const MatType& prediction, + const MatType& target); /** * Ordinary feed backward pass of a neural network. @@ -67,15 +61,9 @@ class HingeLoss * @param target The target vector. * @param loss The calculated error. */ - template - void Backward(const PredictionType& prediction, - const TargetType& target, - LossType& loss); - - //! Get the output parameter. - OutputDataType& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } + void Backward(const MatType& prediction, + const MatType& target, + MatType& loss); //! Get the reduction type, represented as boolean //! (false 'mean' reduction, true 'sum' reduction). @@ -90,12 +78,12 @@ class HingeLoss void serialize(Archive& ar, const uint32_t /* version */); private: - //! Locally-stored output parameter object. - OutputDataType outputParameter; - - //! Boolean value that tells if reduction is 'sum' or 'mean'. + //! The boolean value that tells if reduction is sum or mean. bool reduction; -}; // class HingeLoss +}; // class HingeLossType + +// Default typedef for typical `arma::mat` usage. +typedef HingeLossType HingeLoss; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/hinge_loss_impl.hpp b/src/mlpack/methods/ann/loss_functions/hinge_loss_impl.hpp index 6de5a553fa..05403e32e8 100644 --- a/src/mlpack/methods/ann/loss_functions/hinge_loss_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/hinge_loss_impl.hpp @@ -19,26 +19,24 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -HingeLoss::HingeLoss(const bool reduction): +template +HingeLossType::HingeLossType(const bool reduction): reduction(reduction) { // Nothing to do here. } -template -template -typename PredictionType::elem_type -HingeLoss::Forward( - const PredictionType& prediction, - const TargetType& target) +template +typename MatType::elem_type HingeLossType::Forward( + const MatType& prediction, + const MatType& target) { - TargetType temp = target - (target == 0); - TargetType temp_zeros(size(target), arma::fill::zeros); + MatType temp = target - (target == 0); + MatType temp_zeros(size(target), arma::fill::zeros); - PredictionType loss = arma::max(temp_zeros, 1 - prediction % temp); + MatType loss = arma::max(temp_zeros, 1 - prediction % temp); - typename PredictionType::elem_type lossSum = arma::accu(loss); + typename MatType::elem_type lossSum = arma::accu(loss); if (reduction) return lossSum; @@ -46,23 +44,22 @@ HingeLoss::Forward( return lossSum / loss.n_elem; } -template -template -void HingeLoss::Backward( - const PredictionType& prediction, - const TargetType& target, - LossType& loss) +template +void HingeLossType::Backward( + const MatType& prediction, + const MatType& target, + MatType& loss) { - TargetType temp = target - (target == 0); + MatType temp = target - (target == 0); loss = (prediction < (1 / temp)) % -temp; if (!reduction) loss /= target.n_elem; } -template +template template -void HingeLoss::serialize( +void HingeLossType::serialize( Archive& ar, const uint32_t /* version */) { diff --git a/src/mlpack/methods/ann/loss_functions/huber_loss.hpp b/src/mlpack/methods/ann/loss_functions/huber_loss.hpp index c1533c84f6..8596a260fc 100644 --- a/src/mlpack/methods/ann/loss_functions/huber_loss.hpp +++ b/src/mlpack/methods/ann/loss_functions/huber_loss.hpp @@ -24,20 +24,15 @@ namespace ann /** Artificial Neural Network. */ { * and linear for large values, with equal values and slopes of the different * sections at the two points where \f$ |y - f(x)| = delta \f$. * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam MatType Matrix representation to accept as input and use for + * computation. */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class HuberLoss +template +class HuberLossType { public: /** - * Create the HuberLoss object. + * Create the HuberLossType object. * * @param delta The threshold value upto which squared error is followed and * after which absolute error is considered. @@ -47,7 +42,7 @@ class HuberLoss * 'sum' reduction is used and the output will be summed. It * is set to true by default. */ - HuberLoss(const double delta = 1.0, const bool reduction = true); + HuberLossType(const double delta = 1.0, const bool reduction = true); /** * Computes the Huber Loss function. @@ -56,9 +51,8 @@ class HuberLoss * function. * @param target The target vector. */ - template - typename PredictionType::elem_type Forward(const PredictionType& prediction, - const TargetType& target); + typename MatType::elem_type Forward(const MatType& prediction, + const MatType& target); /** * Ordinary feed backward pass of a neural network. @@ -68,15 +62,9 @@ class HuberLoss * @param target The target vector. * @param loss The calculated error. */ - template - void Backward(const PredictionType& prediction, - const TargetType& target, - LossType& loss); - - //! Get the output parameter. - OutputDataType& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } + void Backward(const MatType& prediction, + const MatType& target, + MatType& loss); //! Get the value of delta. double Delta() const { return delta; } @@ -96,15 +84,15 @@ class HuberLoss void serialize(Archive& ar, const uint32_t /* version */); private: - //! Locally-stored output parameter object. - OutputDataType outputParameter; - //! Hyperparameter `delta` defines the point upto which MSE is considered. double delta; //! Boolean value that tells if reduction is 'sum' or 'mean'. bool reduction; -}; // class HuberLoss +}; // class HuberLossType + +// Default typedef for typical `arma::mat` usage. +typedef HuberLossType HuberLoss; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/huber_loss_impl.hpp b/src/mlpack/methods/ann/loss_functions/huber_loss_impl.hpp index f2e496aaed..907a51e552 100644 --- a/src/mlpack/methods/ann/loss_functions/huber_loss_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/huber_loss_impl.hpp @@ -18,30 +18,28 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -HuberLoss::HuberLoss( - const double delta, - const bool reduction): - delta(delta), - reduction(reduction) +template +HuberLossType::HuberLossType( + const double delta, + const bool reduction): + delta(delta), + reduction(reduction) { // Nothing to do here. } -template -template -typename PredictionType::elem_type -HuberLoss::Forward( - const PredictionType& prediction, - const TargetType& target) +template +typename MatType::elem_type HuberLossType::Forward( + const MatType& prediction, + const MatType& target) { - typedef typename PredictionType::elem_type ElemType; + typedef typename MatType::elem_type ElemType; ElemType lossSum = 0; for (size_t i = 0; i < prediction.n_elem; ++i) { - const ElemType absError = std::abs(target[i] - prediction[i]); - lossSum += absError > delta ? - delta * (absError - 0.5 * delta) : 0.5 * std::pow(absError, 2); + const ElemType absError = std::abs(target[i] - prediction[i]); + lossSum += absError > delta ? + delta * (absError - 0.5 * delta) : 0.5 * std::pow(absError, 2); } if (reduction) @@ -50,14 +48,13 @@ HuberLoss::Forward( return lossSum / target.n_elem; } -template -template -void HuberLoss::Backward( - const PredictionType& prediction, - const TargetType& target, - LossType& loss) +template +void HuberLossType::Backward( + const MatType& prediction, + const MatType& target, + MatType& loss) { - typedef typename PredictionType::elem_type ElemType; + typedef typename MatType::elem_type ElemType; loss.set_size(size(prediction)); for (size_t i = 0; i < loss.n_elem; ++i) @@ -72,9 +69,9 @@ void HuberLoss::Backward( loss = loss / target.n_elem; } -template +template template -void HuberLoss::serialize( +void HuberLossType::serialize( Archive& ar, const uint32_t /* version */) { diff --git a/src/mlpack/methods/ann/loss_functions/kl_divergence.hpp b/src/mlpack/methods/ann/loss_functions/kl_divergence.hpp index 0962c3c4e4..16e6471da5 100644 --- a/src/mlpack/methods/ann/loss_functions/kl_divergence.hpp +++ b/src/mlpack/methods/ann/loss_functions/kl_divergence.hpp @@ -33,16 +33,11 @@ namespace ann /** Artificial Neural Network. */ { * } * @endcode * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam MatType Matrix representation to accept as input and use for + * computation. */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class KLDivergence +template +class KLDivergenceType { public: /** @@ -55,7 +50,7 @@ class KLDivergence * 'sum' reduction is used and the output will be summed. It * is set to true by default. */ - KLDivergence(const bool reduction = true); + KLDivergenceType(const bool reduction = true); /** * Computes the Kullback–Leibler divergence error function. @@ -64,9 +59,8 @@ class KLDivergence * function. * @param target Target data to compare with. */ - template - typename PredictionType::elem_type Forward(const PredictionType& prediction, - const TargetType& target); + typename MatType::elem_type Forward(const MatType& prediction, + const MatType& target); /** * Ordinary feed backward pass of a neural network. @@ -76,34 +70,29 @@ class KLDivergence * @param target The target vector. * @param loss The calculated error. */ - template - void Backward(const PredictionType& prediction, - const TargetType& target, - LossType& loss); - - //! Get the output parameter. - OutputDataType& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } + void Backward(const MatType& prediction, + const MatType& target, + MatType& loss); //! Get the reduction type, represented as boolean //! (false 'mean' reduction, true 'sum' reduction). bool Reduction() const { return reduction; } //! Modify the type of reduction used. bool& Reduction() { return reduction; } + /** - * Serialize the loss function + * Serialize the loss function. */ template void serialize(Archive& ar, const uint32_t /* version */); private: - //! Locally-stored output parameter object. - OutputDataType outputParameter; - //! Boolean value that tells if reduction is 'sum' or 'mean'. bool reduction; -}; // class KLDivergence +}; // class KLDivergenceType + +// Default typedef for typical `arma::mat` usage. +typedef KLDivergenceType KLDivergence; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/kl_divergence_impl.hpp b/src/mlpack/methods/ann/loss_functions/kl_divergence_impl.hpp index 6ff54ac21b..8088972b06 100644 --- a/src/mlpack/methods/ann/loss_functions/kl_divergence_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/kl_divergence_impl.hpp @@ -19,22 +19,20 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -KLDivergence::KLDivergence(const bool reduction): +template +KLDivergenceType::KLDivergenceType(const bool reduction) : reduction(reduction) { // Nothing to do here. } -template -template -typename PredictionType::elem_type -KLDivergence::Forward( - const PredictionType& prediction, - const TargetType& target) +template +typename MatType::elem_type KLDivergenceType::Forward( + const MatType& prediction, + const MatType& target) { - PredictionType loss = target % (arma::log(target) - prediction); - typename PredictionType::elem_type lossSum = arma::accu(loss); + MatType loss = target % (arma::log(target) - prediction); + typename MatType::elem_type lossSum = arma::accu(loss); if (reduction) return lossSum; @@ -42,22 +40,21 @@ KLDivergence::Forward( return lossSum / target.n_elem; } -template -template -void KLDivergence::Backward( - const PredictionType& prediction, - const TargetType& target, - LossType& loss) +template +void KLDivergenceType::Backward( + const MatType& /* prediction */, + const MatType& target, + MatType& loss) { - loss = - target; + loss = -target; if (!reduction) loss = loss / target.n_elem; } -template +template template -void KLDivergence::serialize( +void KLDivergenceType::serialize( Archive& ar, const uint32_t /* version */) { diff --git a/src/mlpack/methods/ann/loss_functions/l1_loss.hpp b/src/mlpack/methods/ann/loss_functions/l1_loss.hpp index e8494ca915..cd02255fa9 100644 --- a/src/mlpack/methods/ann/loss_functions/l1_loss.hpp +++ b/src/mlpack/methods/ann/loss_functions/l1_loss.hpp @@ -21,20 +21,15 @@ namespace ann /** Artificial Neural Network. */ { * The L1 loss is a loss function that measures the mean absolute error (MAE) * between each element in the input x and target y. * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam MatType Matrix representation to accept as input and use for + * computation. */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class L1Loss +template +class L1LossType { public: /** - * Create the L1Loss object. + * Create the L1LossType object. * * @param reduction Specifies the reduction to apply to the output. If false, * 'mean' reduction is used, where sum of the output will be @@ -43,7 +38,7 @@ class L1Loss * is set to true by default. * */ - L1Loss(const bool reduction = true); + L1LossType(const bool reduction = true); /** * Computes the L1 Loss function. @@ -52,9 +47,8 @@ class L1Loss * function. * @param target The target vector. */ - template - typename PredictionType::elem_type Forward(const PredictionType& prediction, - const TargetType& target); + typename MatType::elem_type Forward(const MatType& prediction, + const MatType& target); /** * Ordinary feed backward pass of a neural network. @@ -64,15 +58,9 @@ class L1Loss * @param target The target vector. * @param loss The calculated error. */ - template - void Backward(const PredictionType& prediction, - const TargetType& target, - LossType& loss); - - //! Get the output parameter. - OutputDataType& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } + void Backward(const MatType& prediction, + const MatType& target, + MatType& loss); //! Get the reduction type, represented as boolean //! (false 'mean' reduction, true 'sum' reduction). @@ -87,12 +75,12 @@ class L1Loss void serialize(Archive& ar, const uint32_t /* version */); private: - //! Locally-stored output parameter object. - OutputDataType outputParameter; - //! Boolean value that tells if reduction is 'sum' or 'mean'. bool reduction; -}; // class L1Loss +}; // class L1LossType + +// Default typedef for typical `arma::mat` usage. +typedef L1LossType L1Loss; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/l1_loss_impl.hpp b/src/mlpack/methods/ann/loss_functions/l1_loss_impl.hpp index dfdfbc573d..4694f40ba0 100644 --- a/src/mlpack/methods/ann/loss_functions/l1_loss_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/l1_loss_impl.hpp @@ -18,22 +18,20 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -L1Loss::L1Loss(const bool reduction): - reduction(reduction) +template +L1LossType::L1LossType(const bool reduction): + reduction(reduction) { // Nothing to do here. } -template -template -typename PredictionType::elem_type -L1Loss::Forward( - const PredictionType& prediction, - const TargetType& target) +template +typename MatType::elem_type L1LossType::Forward( + const MatType& prediction, + const MatType& target) { - PredictionType loss = arma::abs(prediction - target); - typename PredictionType::elem_type lossSum = arma::accu(loss); + MatType loss = arma::abs(prediction - target); + typename MatType::elem_type lossSum = arma::accu(loss); if (reduction) return lossSum; @@ -41,22 +39,21 @@ L1Loss::Forward( return lossSum / target.n_elem; } -template -template -void L1Loss::Backward( - const PredictionType& prediction, - const TargetType& target, - LossType& loss) +template +void L1LossType::Backward( + const MatType& prediction, + const MatType& target, + MatType& loss) { loss = arma::sign(prediction - target); - + if (!reduction) loss = loss / target.n_elem; } -template +template template -void L1Loss::serialize( +void L1LossType::serialize( Archive& ar, const uint32_t /* version */) { diff --git a/src/mlpack/methods/ann/loss_functions/log_cosh_loss.hpp b/src/mlpack/methods/ann/loss_functions/log_cosh_loss.hpp index 23aa6baa4e..550f40279a 100644 --- a/src/mlpack/methods/ann/loss_functions/log_cosh_loss.hpp +++ b/src/mlpack/methods/ann/loss_functions/log_cosh_loss.hpp @@ -23,16 +23,11 @@ namespace ann /** Artificial Neural Network. */ { * variational auto encoder. This function is the log of hyperbolic * cosine of difference between true values and predicted values. * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam MatType Matrix representation to accept as input and use for + * computation. */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class LogCoshLoss +template +class LogCoshLossType { public: /** @@ -50,7 +45,7 @@ class LogCoshLoss * 'sum' reduction is used and the output will be summed. It * is set to true by default. */ - LogCoshLoss(const double a = 1.0, const bool reduction = true); + LogCoshLossType(const double a = 1.0, const bool reduction = true); /** * Computes the Log-Hyperbolic-Cosine loss function. @@ -59,9 +54,8 @@ class LogCoshLoss * function. * @param target Target data to compare with. */ - template - typename PredictionType::elem_type Forward(const PredictionType& prediction, - const TargetType& target); + typename MatType::elem_type Forward(const MatType& prediction, + const MatType& target); /** * Ordinary feed backward pass of a neural network. @@ -71,15 +65,9 @@ class LogCoshLoss * @param target The target vector. * @param loss The calculated error. */ - template - void Backward(const PredictionType& prediction, - const TargetType& target, - LossType& loss); - - //! Get the output parameter. - OutputDataType& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } + void Backward(const MatType& prediction, + const MatType& target, + MatType& loss); //! Get the value of hyperparameter a. double A() const { return a; } @@ -99,15 +87,15 @@ class LogCoshLoss void serialize(Archive& ar, const uint32_t /* version */); private: - //! Locally-stored output parameter object. - OutputDataType outputParameter; - //! Hyperparameter a for smoothening function curve. double a; //! Boolean value that tells if reduction is 'sum' or 'mean'. bool reduction; -}; // class LogCoshLoss +}; // class LogCoshLossType + +// Default typedef for typical `arma::mat` usage. +typedef LogCoshLossType LogCoshLoss; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/log_cosh_loss_impl.hpp b/src/mlpack/methods/ann/loss_functions/log_cosh_loss_impl.hpp index 7c4bba7b9b..3ecfe59253 100644 --- a/src/mlpack/methods/ann/loss_functions/log_cosh_loss_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/log_cosh_loss_impl.hpp @@ -19,22 +19,22 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -LogCoshLoss::LogCoshLoss( - const double a, const bool reduction) : - a(a) , reduction(reduction) +template +LogCoshLossType::LogCoshLossType( + const double a, + const bool reduction) : + a(a), + reduction(reduction) { Log::Assert(a > 0, "Hyper-Parameter \'a\' must be positive"); } -template -template -typename PredictionType::elem_type -LogCoshLoss::Forward( - const PredictionType& prediction, - const TargetType& target) +template +typename MatType::elem_type LogCoshLossType::Forward( + const MatType& prediction, + const MatType& target) { - typename PredictionType::elem_type lossSum = + typename MatType::elem_type lossSum = arma::accu(arma::log(arma::cosh(a * (target - prediction)))) / a; if (reduction) @@ -43,12 +43,11 @@ LogCoshLoss::Forward( return lossSum / target.n_elem; } -template -template -void LogCoshLoss::Backward( - const PredictionType& prediction, - const TargetType& target, - LossType& loss) +template +void LogCoshLossType::Backward( + const MatType& prediction, + const MatType& target, + MatType& loss) { loss = arma::tanh(a * (target - prediction)); @@ -56,9 +55,9 @@ void LogCoshLoss::Backward( loss = loss / target.n_elem; } -template +template template -void LogCoshLoss::serialize( +void LogCoshLossType::serialize( Archive& ar, const uint32_t /* version */) { diff --git a/src/mlpack/methods/ann/loss_functions/margin_ranking_loss.hpp b/src/mlpack/methods/ann/loss_functions/margin_ranking_loss.hpp index fabb65d78a..b158c444bc 100644 --- a/src/mlpack/methods/ann/loss_functions/margin_ranking_loss.hpp +++ b/src/mlpack/methods/ann/loss_functions/margin_ranking_loss.hpp @@ -22,21 +22,19 @@ namespace ann /** Artificial Neural Network. */ { * values of 1 or -1. If the label is 1 then the first input should be ranked * higher than the second input at a distance larger than a margin, and vice- * versa if the label is -1. - * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * + * @tparam MatType Matrix representation to accept as input and use for + * computation. */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class MarginRankingLoss +template +class MarginRankingLossType { public: /** - * Create the MarginRankingLoss object with Hyperparameter margin. + * Create the MarginRankingLossType object with Hyperparameter margin. + * Hyperparameter margin defines a minimum distance between correctly ranked + * samples. + * * @param margin defines a minimum distance between correctly ranked samples. * @param reduction Specifies the reduction to apply to the output. If false, * 'mean' reduction is used, where sum of the output will be @@ -44,7 +42,7 @@ class MarginRankingLoss * 'sum' reduction is used and the output will be summed. It * is set to true by default. */ - MarginRankingLoss(const double margin = 1.0, const bool reduction = true); + MarginRankingLossType(const double margin = 1.0, const bool reduction = true); /** * Computes the Margin Ranking Loss function. @@ -53,9 +51,8 @@ class MarginRankingLoss * function. * @param target The label vector which contains values of -1 or 1. */ - template - typename PredictionType::elem_type Forward(const PredictionType& prediction, - const TargetType& target); + typename MatType::elem_type Forward(const MatType& prediction, + const MatType& target); /** * Ordinary feed backward pass of a neural network. @@ -65,19 +62,9 @@ class MarginRankingLoss * @param target The label vector which contains -1 or 1 values. * @param loss The calculated error. */ - template < - typename PredictionType, - typename TargetType, - typename LossType - > - void Backward(const PredictionType& prediction, - const TargetType& target, - LossType& loss); - - //! Get the output parameter. - OutputDataType& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } + void Backward(const MatType& prediction, + const MatType& target, + MatType& loss); //! Get the margin parameter. double Margin() const { return margin; } @@ -97,15 +84,15 @@ class MarginRankingLoss void serialize(Archive& ar, const uint32_t /* version */); private: - //! Locally-stored output parameter object. - OutputDataType outputParameter; - //! The margin value used in calculating Margin Ranking Loss. double margin; //! Boolean value that tells if reduction is 'sum' or 'mean'. bool reduction; -}; // class MarginRankingLoss +}; // class MarginRankingLossType + +// Default typedef for typical `arma::mat` usage. +typedef MarginRankingLossType MarginRankingLoss; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/margin_ranking_loss_impl.hpp b/src/mlpack/methods/ann/loss_functions/margin_ranking_loss_impl.hpp index 033357d9e8..6551ac4df0 100644 --- a/src/mlpack/methods/ann/loss_functions/margin_ranking_loss_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/margin_ranking_loss_impl.hpp @@ -18,25 +18,24 @@ namespace mlpack { namespace ann /** Artifical Neural Network. */ { -template -MarginRankingLoss::MarginRankingLoss( - const double margin, const bool reduction): - margin(margin), reduction(reduction) +template +MarginRankingLossType::MarginRankingLossType( + const double margin, const bool reduction) : + margin(margin), + reduction(reduction) { // Nothing to do here. } -template -template -typename PredictionType::elem_type -MarginRankingLoss::Forward( - const PredictionType& prediction, - const TargetType& target) +template +typename MatType::elem_type MarginRankingLossType::Forward( + const MatType& prediction, + const MatType& target) { const int predictionRows = prediction.n_rows; - const PredictionType& prediction1 = prediction.rows(0, + const MatType& prediction1 = prediction.rows(0, predictionRows / 2 - 1); - const PredictionType& prediction2 = prediction.rows(predictionRows / 2, + const MatType& prediction2 = prediction.rows(predictionRows / 2, predictionRows - 1); double lossSum = arma::accu(arma::max(arma::zeros(size(target)), @@ -48,26 +47,21 @@ MarginRankingLoss::Forward( return lossSum / target.n_elem; } -template -template < - typename PredictionType, - typename TargetType, - typename LossType -> -void MarginRankingLoss::Backward( - const PredictionType& prediction, - const TargetType& target, - LossType& loss) +template +void MarginRankingLossType::Backward( + const MatType& prediction, + const MatType& target, + MatType& loss) { const int predictionRows = prediction.n_rows; - const PredictionType& prediction1 = prediction.rows(0, + const MatType& prediction1 = prediction.rows(0, predictionRows / 2 - 1); - const PredictionType& prediction2 = prediction.rows(predictionRows / 2, + const MatType& prediction2 = prediction.rows(predictionRows / 2, predictionRows - 1); - LossType lossPrediction1 = -target % (prediction1 - prediction2) + margin; + MatType lossPrediction1 = -target % (prediction1 - prediction2) + margin; lossPrediction1.elem(arma::find(lossPrediction1 >= 0)).ones(); lossPrediction1.elem(arma::find(lossPrediction1 < 0)).zeros(); - LossType lossPrediction2 = lossPrediction1; + MatType lossPrediction2 = lossPrediction1; lossPrediction1 = -target % lossPrediction1; lossPrediction2 = target % lossPrediction2; loss = arma::join_cols(lossPrediction1, lossPrediction2); @@ -76,9 +70,9 @@ void MarginRankingLoss::Backward( loss = loss / target.n_elem; } -template +template template -void MarginRankingLoss::serialize( +void MarginRankingLossType::serialize( Archive& ar, const uint32_t /* version */) { diff --git a/src/mlpack/methods/ann/loss_functions/mean_absolute_percentage_error.hpp b/src/mlpack/methods/ann/loss_functions/mean_absolute_percentage_error.hpp index d6eb6e5e89..ed3abef2c9 100644 --- a/src/mlpack/methods/ann/loss_functions/mean_absolute_percentage_error.hpp +++ b/src/mlpack/methods/ann/loss_functions/mean_absolute_percentage_error.hpp @@ -37,22 +37,17 @@ namespace ann /** Artificial Neural Network. */ { * } * @endcode * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam MatType Matrix representation to accept as input and use for + * computation. */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class MeanAbsolutePercentageError +template +class MeanAbsolutePercentageErrorType { public: /** - * Create the MeanAbsolutePercentageError object. + * Create the MeanAbsolutePercentageErrorType object. */ - MeanAbsolutePercentageError(); + MeanAbsolutePercentageErrorType(); /** * Computes the mean absolute percentage error function. @@ -61,9 +56,8 @@ class MeanAbsolutePercentageError * function. * @param target The target vector. */ - template - typename PredictionType::elem_type Forward(const PredictionType& prediction, - const TargetType& target); + typename MatType::elem_type Forward(const MatType& prediction, + const MatType& target); /** * Ordinary feed backward pass of a neural network. @@ -73,26 +67,19 @@ class MeanAbsolutePercentageError * @param target The target vector. * @param loss The calculated error. */ - template - void Backward(const PredictionType& prediction, - const TargetType& target, - LossType& loss); - - //! Get the output parameter. - OutputDataType& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } + void Backward(const MatType& prediction, + const MatType& target, + MatType& loss); /** - * Serialize the layer. - */ + * Serialize the layer. + */ template - void serialize(Archive& ar, const unsigned int /* version */); + void serialize(Archive& ar, const unsigned int /* version */) { } +}; // class MeanAbsolutePercentageErrorType - private: - //! Locally-stored output parameter object. - OutputDataType outputParameter; -}; // class MeanAbsolutePercentageError +// Default typedef for typical `arma::mat` usage. +typedef MeanAbsolutePercentageErrorType MeanAbsolutePercentageError; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/mean_absolute_percentage_error_impl.hpp b/src/mlpack/methods/ann/loss_functions/mean_absolute_percentage_error_impl.hpp index b573654e7f..8185de852d 100644 --- a/src/mlpack/methods/ann/loss_functions/mean_absolute_percentage_error_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/mean_absolute_percentage_error_impl.hpp @@ -18,45 +18,32 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -MeanAbsolutePercentageError:: -MeanAbsolutePercentageError() +template +MeanAbsolutePercentageErrorType::MeanAbsolutePercentageErrorType() { // Nothing to do here. } -template -template -typename PredictionType::elem_type -MeanAbsolutePercentageError::Forward( - const PredictionType& prediction, - const TargetType& target) +template +typename MatType::elem_type MeanAbsolutePercentageErrorType::Forward( + const MatType& prediction, + const MatType& target) { - PredictionType loss = arma::abs((prediction - target) / target); + MatType loss = arma::abs((prediction - target) / target); return arma::accu(loss) * (100 / target.n_cols); } -template -template -void MeanAbsolutePercentageError::Backward( - const PredictionType& prediction, - const TargetType& target, - LossType& loss) +template +void MeanAbsolutePercentageErrorType::Backward( + const MatType& prediction, + const MatType& target, + MatType& loss) { loss = (((arma::conv_to::from(prediction < target) * -2) + 1) / target) * (100 / target.n_cols); } -template -template -void MeanAbsolutePercentageError::serialize( - Archive& /* ar */, - const unsigned int /* version */) -{ - // Nothing to do here. -} - } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/mean_bias_error.hpp b/src/mlpack/methods/ann/loss_functions/mean_bias_error.hpp index fb84b2ba4c..6c8324f8b5 100644 --- a/src/mlpack/methods/ann/loss_functions/mean_bias_error.hpp +++ b/src/mlpack/methods/ann/loss_functions/mean_bias_error.hpp @@ -18,23 +18,18 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { /** - * The mean bias error performance function measures the network's - * performance according to the mean of errors. + * The mean bias error performance function measures the network's performance + * according to the mean of errors. * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam MatType Matrix representation to accept as input and use for + * computation. */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class MeanBiasError +template +class MeanBiasErrorType { public: /** - * Create the MeanBiasError object. + * Create the MeanBiasErrorType object. * * @param reduction Specifies the reduction to apply to * the output. If false, 'mean' reduction @@ -44,7 +39,7 @@ class MeanBiasError * is used and the output will be summed. * It is set to true by default. */ - MeanBiasError(const bool reduction = true); + MeanBiasErrorType(const bool reduction = true); /** * Computes the mean bias error function. @@ -53,9 +48,8 @@ class MeanBiasError * function. * @param target The target vector. */ - template - typename PredictionType::elem_type Forward(const PredictionType& prediction, - const TargetType& target); + typename MatType::elem_type Forward(const MatType& prediction, + const MatType& target); /** * Ordinary feed backward pass of a neural network. @@ -65,15 +59,9 @@ class MeanBiasError * @param target The target vector. * @param loss The calculated error. */ - template - void Backward(const PredictionType& prediction, - const TargetType& target, - LossType& loss); - - //! Get the output parameter. - OutputDataType& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } + void Backward(const MatType& prediction, + const MatType& target, + MatType& loss); //! Get the reduction type, represented as boolean //! (false 'mean' reduction, true 'sum' reduction). @@ -88,12 +76,12 @@ class MeanBiasError void serialize(Archive& ar, const uint32_t /* version */); private: - //! Locally-stored output parameter object. - OutputDataType outputParameter; - //! Boolean value that tells if reduction is 'sum' or 'mean'. bool reduction; -}; // class MeanBiasError +}; // class MeanBiasErrorType + +// Default typedef for typical `arma::mat` usage. +typedef MeanBiasErrorType MeanBiasError; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/mean_bias_error_impl.hpp b/src/mlpack/methods/ann/loss_functions/mean_bias_error_impl.hpp index fa801ff275..6473eda8e0 100644 --- a/src/mlpack/methods/ann/loss_functions/mean_bias_error_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/mean_bias_error_impl.hpp @@ -19,22 +19,20 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -MeanBiasError:: - MeanBiasError(const bool reduction) : reduction(reduction) +template +MeanBiasErrorType::MeanBiasErrorType(const bool reduction) : + reduction(reduction) { // Nothing to do here } -template -template -typename PredictionType::elem_type -MeanBiasError::Forward( - const PredictionType& prediction, - const TargetType& target) +template +typename MatType::elem_type MeanBiasErrorType::Forward( + const MatType& prediction, + const MatType& target) { - PredictionType loss = target - prediction; - typename PredictionType::elem_type lossSum = arma::accu(loss); + MatType loss = target - prediction; + typename MatType::elem_type lossSum = arma::accu(loss); if (reduction) return lossSum; @@ -42,12 +40,11 @@ MeanBiasError::Forward( return lossSum / target.n_elem; } -template -template -void MeanBiasError::Backward( - const PredictionType& prediction, - const TargetType& /* target */, - LossType& loss) +template +void MeanBiasErrorType::Backward( + const MatType& prediction, + const MatType& /* target */, + MatType& loss) { loss.set_size(arma::size(prediction)); loss.fill(-1.0); @@ -56,9 +53,9 @@ void MeanBiasError::Backward( loss = loss / loss.n_elem; } -template +template template -void MeanBiasError::serialize( +void MeanBiasErrorType::serialize( Archive& ar, const uint32_t /* version */) { diff --git a/src/mlpack/methods/ann/loss_functions/mean_squared_error.hpp b/src/mlpack/methods/ann/loss_functions/mean_squared_error.hpp index 6006d76d12..6065951f38 100644 --- a/src/mlpack/methods/ann/loss_functions/mean_squared_error.hpp +++ b/src/mlpack/methods/ann/loss_functions/mean_squared_error.hpp @@ -21,21 +21,15 @@ namespace ann /** Artificial Neural Network. */ { * The mean squared error performance function measures the network's * performance according to the mean of squared errors. * - * @tparam ActivationFunction Activation function used for the embedding layer. - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam MatType Matrix representation to accept as input and use for + * computation. */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class MeanSquaredError +template +class MeanSquaredErrorType { public: /** - * Create the MeanSquaredError object. + * Create the MeanSquaredErrorType object. * * @param reduction Specifies the reduction to apply to the output. If false, * 'mean' reduction is used, where sum of the output will be @@ -43,7 +37,7 @@ class MeanSquaredError * 'sum' reduction is used and the output will be summed. It * is set to true by default. */ - MeanSquaredError(const bool reduction = true); + MeanSquaredErrorType(const bool reduction = true); /** * Computes the mean squared error function. @@ -52,9 +46,8 @@ class MeanSquaredError * function. * @param target The target vector. */ - template - typename PredictionType::elem_type Forward(const PredictionType& prediction, - const TargetType& target); + typename MatType::elem_type Forward(const MatType& prediction, + const MatType& target); /** * Ordinary feed backward pass of a neural network. @@ -64,16 +57,10 @@ class MeanSquaredError * @param target The target vector. * @param loss The calculated error. */ - template - void Backward(const PredictionType& prediction, - const TargetType& target, - LossType& loss); + void Backward(const MatType& prediction, + const MatType& target, + MatType& loss); - //! Get the output parameter. - OutputDataType& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - //! Get the reduction type, represented as boolean //! (false 'mean' reduction, true 'sum' reduction). bool Reduction() const { return reduction; } @@ -87,12 +74,12 @@ class MeanSquaredError void serialize(Archive& ar, const uint32_t /* version */); private: - //! Locally-stored output parameter object. - OutputDataType outputParameter; - //! Boolean value that tells if reduction is 'sum' or 'mean'. bool reduction; -}; // class MeanSquaredError +}; // class MeanSquaredErrorType + +// Default typedef for typical `arma::mat` usage. +typedef MeanSquaredErrorType MeanSquaredError; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/mean_squared_error_impl.hpp b/src/mlpack/methods/ann/loss_functions/mean_squared_error_impl.hpp index 3bd64bc638..c2938a6e3a 100644 --- a/src/mlpack/methods/ann/loss_functions/mean_squared_error_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/mean_squared_error_impl.hpp @@ -18,21 +18,19 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -MeanSquaredError - ::MeanSquaredError(const bool reduction) : reduction(reduction) +template +MeanSquaredErrorType::MeanSquaredErrorType(const bool reduction) : + reduction(reduction) { // Nothing to do here. } -template -template -typename PredictionType::elem_type -MeanSquaredError::Forward( - const PredictionType& prediction, - const TargetType& target) +template +typename MatType::elem_type MeanSquaredErrorType::Forward( + const MatType& prediction, + const MatType& target) { - typename PredictionType::elem_type lossSum = + typename MatType::elem_type lossSum = arma::accu(arma::square(prediction - target)); if (reduction) @@ -41,12 +39,11 @@ MeanSquaredError::Forward( return lossSum / target.n_elem; } -template -template -void MeanSquaredError::Backward( - const PredictionType& prediction, - const TargetType& target, - LossType& loss) +template +void MeanSquaredErrorType::Backward( + const MatType& prediction, + const MatType& target, + MatType& loss) { loss = 2 * (prediction - target); @@ -54,9 +51,9 @@ void MeanSquaredError::Backward( loss = loss / target.n_elem; } -template +template template -void MeanSquaredError::serialize( +void MeanSquaredErrorType::serialize( Archive& ar, const uint32_t /* version */) { diff --git a/src/mlpack/methods/ann/loss_functions/mean_squared_logarithmic_error.hpp b/src/mlpack/methods/ann/loss_functions/mean_squared_logarithmic_error.hpp index bdd702ef54..833066f876 100644 --- a/src/mlpack/methods/ann/loss_functions/mean_squared_logarithmic_error.hpp +++ b/src/mlpack/methods/ann/loss_functions/mean_squared_logarithmic_error.hpp @@ -18,23 +18,18 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { /** - * The mean squared logarithmic error performance function measures the network's - * performance according to the mean of squared logarithmic errors. + * The mean squared logarithmic error performance function measures the + * network's performance according to the mean of squared logarithmic errors. * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam MatType Matrix representation to accept as input and use for + * computation. */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class MeanSquaredLogarithmicError +template +class MeanSquaredLogarithmicErrorType { public: /** - * Create the MeanSquaredLogarithmicError object. + * Create the MeanSquaredLogarithmicErrorType object. * * @param reduction Specifies the reduction to apply to the output. If false, * 'mean' reduction is used, where sum of the output will be @@ -42,7 +37,7 @@ class MeanSquaredLogarithmicError * 'sum' reduction is used and the output will be summed. It * is set to true by default. */ - MeanSquaredLogarithmicError(const bool reduction = true); + MeanSquaredLogarithmicErrorType(const bool reduction = true); /** * Computes the mean squared logarithmic error function. @@ -51,9 +46,8 @@ class MeanSquaredLogarithmicError * function. * @param target The target vector. */ - template - typename PredictionType::elem_type Forward(const PredictionType& prediction, - const TargetType& target); + typename MatType::elem_type Forward(const MatType& prediction, + const MatType& target); /** * Ordinary feed backward pass of a neural network. @@ -63,15 +57,9 @@ class MeanSquaredLogarithmicError * @param target The target vector. * @param loss The calculated error. */ - template - void Backward(const PredictionType& prediction, - const TargetType& target, - LossType& loss); - - //! Get the output parameter. - OutputDataType& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } + void Backward(const MatType& prediction, + const MatType& target, + MatType& loss); //! Get the reduction type, represented as boolean //! (false 'mean' reduction, true 'sum' reduction). @@ -86,12 +74,12 @@ class MeanSquaredLogarithmicError void serialize(Archive& ar, const uint32_t /* version */); private: - //! Locally-stored output parameter object. - OutputDataType outputParameter; - //! Boolean value that tells if reduction is 'sum' or 'mean'. bool reduction; -}; // class MeanSquaredLogarithmicError +}; // class MeanSquaredLogarithmicErrorType + +// Default typedef for typical `arma::mat` usage. +typedef MeanSquaredLogarithmicErrorType MeanSquaredLogarithmicError; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/mean_squared_logarithmic_error_impl.hpp b/src/mlpack/methods/ann/loss_functions/mean_squared_logarithmic_error_impl.hpp index 4b1d3740c7..a59d71a7bc 100644 --- a/src/mlpack/methods/ann/loss_functions/mean_squared_logarithmic_error_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/mean_squared_logarithmic_error_impl.hpp @@ -18,21 +18,20 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -MeanSquaredLogarithmicError -::MeanSquaredLogarithmicError(const bool reduction) : reduction(reduction) +template +MeanSquaredLogarithmicErrorType::MeanSquaredLogarithmicErrorType( + const bool reduction) : + reduction(reduction) { // Nothing to do here. } -template -template -typename PredictionType::elem_type -MeanSquaredLogarithmicError::Forward( - const PredictionType& prediction, - const TargetType& target) +template +typename MatType::elem_type MeanSquaredLogarithmicErrorType::Forward( + const MatType& prediction, + const MatType& target) { - typename PredictionType::elem_type lossSum = + typename MatType::elem_type lossSum = arma::accu(arma::square(arma::log(1.0 + target) - arma::log(1.0 + prediction))); @@ -42,12 +41,11 @@ MeanSquaredLogarithmicError::Forward( return lossSum / target.n_elem; } -template -template -void MeanSquaredLogarithmicError::Backward( - const PredictionType& prediction, - const TargetType& target, - LossType& loss) +template +void MeanSquaredLogarithmicErrorType::Backward( + const MatType& prediction, + const MatType& target, + MatType& loss) { loss = 2 * (arma::log(1. + prediction) - arma::log(1. + target)) / (1. + prediction); @@ -56,9 +54,9 @@ void MeanSquaredLogarithmicError::Backward( loss = loss / target.n_elem; } -template +template template -void MeanSquaredLogarithmicError::serialize( +void MeanSquaredLogarithmicErrorType::serialize( Archive& ar, const uint32_t /* version */) { diff --git a/src/mlpack/methods/ann/loss_functions/multilabel_softmargin_loss.hpp b/src/mlpack/methods/ann/loss_functions/multilabel_softmargin_loss.hpp index e707410155..008cf09a9c 100644 --- a/src/mlpack/methods/ann/loss_functions/multilabel_softmargin_loss.hpp +++ b/src/mlpack/methods/ann/loss_functions/multilabel_softmargin_loss.hpp @@ -22,20 +22,21 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { /** - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * The Multi-label Soft Margin Loss function. + * + * It is a criterion that optimizes a multi-label one-versus-all loss based on + * max-entropy, between input x and target y of size (N, C) where N is the + * batch size and C is the number of classes. + * + * @tparam MatType Matrix representation to accept as input and use for + * computation. */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class MultiLabelSoftMarginLoss +template +class MultiLabelSoftMarginLossType { public: /** - * Create the MultiLabelSoftMarginLoss object. + * Create the MultiLabelSoftMarginLossType object. * * @param reduction Specifies the reduction to apply to the output. If false, * 'mean' reduction is used, where sum of the output will be @@ -45,8 +46,10 @@ class MultiLabelSoftMarginLoss * @param weights A manual rescaling weight given to each class. It is a * (1, numClasses) row vector. */ - MultiLabelSoftMarginLoss(const bool reduction = true, - const arma::rowvec& weights = arma::rowvec()); + MultiLabelSoftMarginLossType( + const bool reduction = true, + const arma::Row& weights = + arma::Row()); /** * Computes the Multi Label Soft Margin Loss function. @@ -54,9 +57,8 @@ class MultiLabelSoftMarginLoss * @param input Input data used for evaluating the specified function. * @param target The target vector with same shape as input. */ - template - typename InputType::elem_type Forward(const InputType& input, - const TargetType& target); + typename MatType::elem_type Forward(const MatType& input, + const MatType& target); /** * Ordinary feed backward pass of a neural network. @@ -65,20 +67,20 @@ class MultiLabelSoftMarginLoss * @param target The target vector. * @param output The calculated error. */ - template - void Backward(const InputType& input, - const TargetType& target, - OutputType& output); - - //! Get the output parameter. - OutputDataType& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } + void Backward(const MatType& input, + const MatType& target, + MatType& output); //! Get the weights assigned to each class. - const arma::rowvec& ClassWeights() const { return classWeights; } + const arma::Row& ClassWeights() const + { + return classWeights; + } //! Modify the weights assigned to each class. - arma::rowvec& ClassWeights() { return classWeights; } + arma::Row& ClassWeights() + { + return classWeights; + } //! Get the reduction type, represented as boolean //! (false 'mean' reduction, true 'sum' reduction). @@ -93,18 +95,18 @@ class MultiLabelSoftMarginLoss void serialize(Archive& ar, const unsigned int /* version */); private: - //! Locally-stored output parameter object. - OutputDataType outputParameter; - - //! Boolean value that tells if reduction is 'sum' or 'mean'. + //! The boolean value that tells if reduction is sum or mean. bool reduction; //! A (1, numClasses) shaped vector with weights for each class. - arma::rowvec classWeights; + arma::Row classWeights; // An internal parameter used during initialisation of class weights. bool weighted; -}; // class MultiLabelSoftMarginLoss +}; // class MultiLabelSoftMarginLossType + +// Default typedef for typical `arma::mat` usage. +typedef MultiLabelSoftMarginLossType MultiLabelSoftMarginLoss; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/multilabel_softmargin_loss_impl.hpp b/src/mlpack/methods/ann/loss_functions/multilabel_softmargin_loss_impl.hpp index a497a591d3..453254f100 100644 --- a/src/mlpack/methods/ann/loss_functions/multilabel_softmargin_loss_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/multilabel_softmargin_loss_impl.hpp @@ -18,11 +18,10 @@ namespace mlpack { namespace ann /** Artifical Neural Network. */ { -template -MultiLabelSoftMarginLoss:: -MultiLabelSoftMarginLoss( +template +MultiLabelSoftMarginLossType::MultiLabelSoftMarginLossType( const bool reduction, - const arma::rowvec& weights) : + const arma::Row& weights) : reduction(reduction), weighted(false) { @@ -33,11 +32,9 @@ MultiLabelSoftMarginLoss( } } -template -template -typename InputType::elem_type -MultiLabelSoftMarginLoss::Forward( - const InputType& input, const TargetType& target) +template +typename MatType::elem_type MultiLabelSoftMarginLossType::Forward( + const MatType& input, const MatType& target) { if (!weighted) { @@ -45,9 +42,9 @@ MultiLabelSoftMarginLoss::Forward( weighted = true; } - InputType logSigmoid = arma::log((1 / (1 + arma::exp(-input)))); - InputType logSigmoidNeg = arma::log(1 / (1 + arma::exp(input))); - InputType loss = arma::mean(arma::sum(-(target % logSigmoid + + MatType logSigmoid = arma::log((1 / (1 + arma::exp(-input)))); + MatType logSigmoidNeg = arma::log(1 / (1 + arma::exp(input))); + MatType loss = arma::mean(arma::sum(-(target % logSigmoid + (1 - target) % logSigmoidNeg)) % classWeights, 1); if (reduction) @@ -56,15 +53,14 @@ MultiLabelSoftMarginLoss::Forward( return arma::as_scalar(loss / input.n_rows); } -template -template -void MultiLabelSoftMarginLoss::Backward( - const InputType& input, - const TargetType& target, - OutputType& output) +template +void MultiLabelSoftMarginLossType::Backward( + const MatType& input, + const MatType& target, + MatType& output) { output.set_size(size(input)); - InputType sigmoid = (1 / (1 + arma::exp(-input))); + MatType sigmoid = (1 / (1 + arma::exp(-input))); output = -(target % (1 - sigmoid) - (1 - target) % sigmoid) % arma::repmat(classWeights, target.n_rows, 1) / output.n_elem; @@ -72,9 +68,9 @@ void MultiLabelSoftMarginLoss::Backward( output = output * input.n_rows; } -template +template template -void MultiLabelSoftMarginLoss::serialize( +void MultiLabelSoftMarginLossType::serialize( Archive& ar, const unsigned int /* version */) { diff --git a/src/mlpack/methods/ann/loss_functions/negative_log_likelihood.hpp b/src/mlpack/methods/ann/loss_functions/negative_log_likelihood.hpp index 49e8d06957..f78a875268 100644 --- a/src/mlpack/methods/ann/loss_functions/negative_log_likelihood.hpp +++ b/src/mlpack/methods/ann/loss_functions/negative_log_likelihood.hpp @@ -2,7 +2,7 @@ * @file methods/ann/loss_functions/negative_log_likelihood.hpp * @author Marcus Edel * - * Definition of the NegativeLogLikelihood class. + * Definition of the NegativeLogLikelihoodType class. * * mlpack is free software; you may redistribute it and/or modify it under the * terms of the 3-clause BSD license. You should have received a copy of the @@ -20,23 +20,18 @@ namespace ann /** Artificial Neural Network. */ { /** * Implementation of the negative log likelihood layer. The negative log * likelihood layer expects that the input contains log-probabilities for each - * class. The layer also expects a class index, in the range between 0 and - * number of classes -1, as target when calling the Forward function. + * class. The layer also expects a class index in the range [0, numClasses - 1] + * number of classes, as target when calling the Forward function. * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam MatType Matrix representation to accept as input and use for + * computation. */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class NegativeLogLikelihood +template +class NegativeLogLikelihoodType { public: /** - * Create the NegativeLogLikelihoodLayer object. + * Create the NegativeLogLikelihoodTypeLayer object. * * @param reduction Specifies the reduction to apply to the output. If false, * 'mean' reduction is used, where sum of the output will be @@ -44,7 +39,7 @@ class NegativeLogLikelihood * 'sum' reduction is used and the output will be summed. It * is set to true by default. */ - NegativeLogLikelihood(const bool reduction = true); + NegativeLogLikelihoodType(const bool reduction = true); /** * Computes the Negative log likelihood. @@ -54,9 +49,8 @@ class NegativeLogLikelihood * @param target The target vector, that contains the class index in the range * between 1 and the number of classes. */ - template - typename PredictionType::elem_type Forward(const PredictionType& prediction, - const TargetType& target); + double Forward(const MatType& prediction, + const MatType& target); /** * Ordinary feed backward pass of a neural network. The negative log @@ -70,25 +64,9 @@ class NegativeLogLikelihood * between 1 and the number of classes. * @param loss The calculated error. */ - template - void Backward(const PredictionType& prediction, - const TargetType& target, - LossType& loss); - - //! Get the input parameter. - InputDataType& InputParameter() const { return inputParameter; } - //! Modify the input parameter. - InputDataType& InputParameter() { return inputParameter; } - - //! Get the output parameter. - OutputDataType& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType& Delta() const { return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } + void Backward(const MatType& prediction, + const MatType& target, + MatType& loss); //! Get the reduction type, represented as boolean //! (false 'mean' reduction, true 'sum' reduction). @@ -103,18 +81,12 @@ class NegativeLogLikelihood void serialize(Archive& /* ar */, const uint32_t /* version */); private: - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored input parameter object. - InputDataType inputParameter; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; - //! Boolean value that tells if reduction is 'sum' or 'mean'. bool reduction; -}; // class NegativeLogLikelihood +}; // class NegativeLogLikelihoodType + +// Default typedef for typical `arma::mat` usage. +typedef NegativeLogLikelihoodType NegativeLogLikelihood; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/negative_log_likelihood_impl.hpp b/src/mlpack/methods/ann/loss_functions/negative_log_likelihood_impl.hpp index b33979d130..82c258f838 100644 --- a/src/mlpack/methods/ann/loss_functions/negative_log_likelihood_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/negative_log_likelihood_impl.hpp @@ -2,7 +2,7 @@ * @file methods/ann/loss_functions/negative_log_likelihood_impl.hpp * @author Marcus Edel * - * Implementation of the NegativeLogLikelihood class. + * Implementation of the NegativeLogLikelihoodType class. * * mlpack is free software; you may redistribute it and/or modify it under the * terms of the 3-clause BSD license. You should have received a copy of the @@ -18,21 +18,19 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -NegativeLogLikelihood - ::NegativeLogLikelihood(const bool reduction) : reduction(reduction) +template +NegativeLogLikelihoodType::NegativeLogLikelihoodType( + const bool reduction) : reduction(reduction) { // Nothing to do here. } -template -template -typename PredictionType::elem_type -NegativeLogLikelihood::Forward( - const PredictionType& prediction, - const TargetType& target) +template +double NegativeLogLikelihoodType::Forward( + const MatType& prediction, + const MatType& target) { - typedef typename PredictionType::elem_type ElemType; + typedef typename MatType::elem_type ElemType; ElemType lossSum = 0; for (size_t i = 0; i < prediction.n_cols; ++i) { @@ -48,14 +46,13 @@ NegativeLogLikelihood::Forward( return lossSum / target.n_elem; } -template -template -void NegativeLogLikelihood::Backward( - const PredictionType& prediction, - const TargetType& target, - LossType& loss) +template +void NegativeLogLikelihoodType::Backward( + const MatType& prediction, + const MatType& target, + MatType& loss) { - loss = arma::zeros(prediction.n_rows, prediction.n_cols); + loss = arma::zeros(prediction.n_rows, prediction.n_cols); for (size_t i = 0; i < prediction.n_cols; ++i) { Log::Assert(target(i) >= 0 && target(i) < prediction.n_rows, @@ -68,9 +65,9 @@ void NegativeLogLikelihood::Backward( loss = loss / target.n_elem; } -template +template template -void NegativeLogLikelihood::serialize( +void NegativeLogLikelihoodType::serialize( Archive& ar, const uint32_t /* version */) { ar(CEREAL_NVP(reduction)); diff --git a/src/mlpack/methods/ann/loss_functions/poisson_nll_loss.hpp b/src/mlpack/methods/ann/loss_functions/poisson_nll_loss.hpp index e3ba107423..b06f366958 100644 --- a/src/mlpack/methods/ann/loss_functions/poisson_nll_loss.hpp +++ b/src/mlpack/methods/ann/loss_functions/poisson_nll_loss.hpp @@ -2,7 +2,7 @@ * @file methods/ann/loss_functions/poisson_nll_loss.hpp * @author Mrityunjay Tripathi * - * Definition of the PoissonNLLLoss class. It is the negative log likelihood of + * Definition of the PoissonNLLLossType class. It is the negative log likelihood of * the Poisson distribution. * * mlpack is free software; you may redistribute it and/or modify it under the @@ -20,24 +20,18 @@ namespace ann /** Artificial Neural Network. */ { /** * Implementation of the Poisson negative log likelihood loss. This loss - * function expects input for each class. It also expects a class index, - * in the range between 1 and the number of classes, as target when calling - * the Forward function. + * function expects input for each class. It also expects a class index, in the + * range [0, numClasses - 1], as target when calling the Forward function. * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam MatType Matrix representation to accept as input and use for + * computation. */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class PoissonNLLLoss +template +class PoissonNLLLossType { public: /** - * Create the PoissonNLLLoss object. + * Create the PoissonNLLLossType object. * * @param logInput If true the loss is computed as * \f$ \exp(input) - target \cdot input \f$, if false then the loss is @@ -51,9 +45,9 @@ class PoissonNLLLoss * 'sum' reduction is used and the output will be summed. It * is set to true by default. */ - PoissonNLLLoss(const bool logInput = true, + PoissonNLLLossType(const bool logInput = true, const bool full = false, - const typename InputDataType::elem_type eps = 1e-08, + const typename MatType::elem_type eps = 1e-08, const bool reduction = true); /** @@ -64,9 +58,8 @@ class PoissonNLLLoss * @param target The target vector, that contains the class index in the range * between 1 and the number of classes. */ - template - typename InputDataType::elem_type Forward(const PredictionType& prediction, - const TargetType& target); + typename MatType::elem_type Forward(const MatType& prediction, + const MatType& target); /** * Ordinary feed backward pass of a neural network. The Poisson Negative Log @@ -80,20 +73,9 @@ class PoissonNLLLoss * between 1 and the number of classes. * @param loss The calculated error. */ - template - void Backward(const PredictionType& prediction, - const TargetType& target, - LossType& loss); - - //! Get the input parameter. - InputDataType& InputParameter() const { return inputParameter; } - //! Modify the input parameter. - InputDataType& InputParameter() { return inputParameter; } - - //! Get the output parameter. - OutputDataType& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } + void Backward(const MatType& prediction, + const MatType& target, + MatType& loss); //! Get the value of logInput. logInput is a boolean value that tells if //! logits are given as input. @@ -111,16 +93,17 @@ class PoissonNLLLoss //! Get the value of eps. eps is a small value required to prevent 0 in //! logarithms and denominators. - typename InputDataType::elem_type Eps() const { return eps; } + typename MatType::elem_type Eps() const { return eps; } //! Modify the value of eps. eps is a small value required to prevent 0 in //! logarithms and denominators. - typename InputDataType::elem_type& Eps() { return eps; } + typename MatType::elem_type& Eps() { return eps; } //! Get the reduction type, represented as boolean //! (false 'mean' reduction, true 'sum' reduction). bool Reduction() const { return reduction; } //! Modify the type of reduction used. bool& Reduction() { return reduction; } + /** * Serialize the layer. */ @@ -140,12 +123,6 @@ class PoissonNLLLoss } } - //! Locally-stored input parameter object. - InputDataType inputParameter; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; - //! Boolean value that tells if logits are given as input. bool logInput; @@ -154,12 +131,14 @@ class PoissonNLLLoss bool full; //! eps is a small value required to prevent 0 in logarithms and denominators. - typename InputDataType::elem_type eps; + typename MatType::elem_type eps; //! Boolean value that tells if reduction is 'sum' or 'mean'. bool reduction; +}; // class PoissonNLLLossType -}; // class PoissonNLLLoss +// Default typedef for typical `arma::mat` usage. +typedef PoissonNLLLossType PoissonNLLLoss; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/poisson_nll_loss_impl.hpp b/src/mlpack/methods/ann/loss_functions/poisson_nll_loss_impl.hpp index 3152d73f56..c23d0d0ad8 100644 --- a/src/mlpack/methods/ann/loss_functions/poisson_nll_loss_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/poisson_nll_loss_impl.hpp @@ -2,7 +2,7 @@ * @file methods/ann/loss_functions/poisson_nll_loss_impl.hpp * @author Mrityunjay Tripathi * - * Implementation of the PoissonNLLLoss class. + * Implementation of the PoissonNLLLossType class. * * mlpack is free software; you may redistribute it and/or modify it under the * terms of the 3-clause BSD license. You should have received a copy of the @@ -19,12 +19,12 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -PoissonNLLLoss::PoissonNLLLoss( +template +PoissonNLLLossType::PoissonNLLLossType( const bool logInput, const bool full, - const typename InputDataType::elem_type eps, - const bool reduction): + const typename MatType::elem_type eps, + const bool reduction) : logInput(logInput), full(full), eps(eps), @@ -33,14 +33,12 @@ PoissonNLLLoss::PoissonNLLLoss( Log::Assert(eps >= 0, "Epsilon (eps) must be greater than or equal to zero."); } -template -template -typename InputDataType::elem_type -PoissonNLLLoss::Forward( - const PredictionType& prediction, - const TargetType& target) +template +typename MatType::elem_type PoissonNLLLossType::Forward( + const MatType& prediction, + const MatType& target) { - PredictionType loss(arma::size(prediction)); + MatType loss(arma::size(prediction)); if (logInput) loss = arma::exp(prediction) - target % prediction; @@ -53,11 +51,11 @@ PoissonNLLLoss::Forward( if (full) { const auto mask = target > 1.0; - const PredictionType approx = target % arma::log(target) - target + const MatType approx = target % arma::log(target) - target + 0.5 * arma::log(2 * M_PI * target); loss.elem(arma::find(mask)) += approx.elem(arma::find(mask)); } - typename PredictionType::elem_type lossSum = arma::accu(loss); + typename MatType::elem_type lossSum = arma::accu(loss); if (reduction) return lossSum; @@ -65,12 +63,11 @@ PoissonNLLLoss::Forward( return lossSum / loss.n_elem; } -template -template -void PoissonNLLLoss::Backward( - const PredictionType& prediction, - const TargetType& target, - LossType& loss) +template +void PoissonNLLLossType::Backward( + const MatType& prediction, + const MatType& target, + MatType& loss) { loss.set_size(size(prediction)); @@ -83,9 +80,9 @@ void PoissonNLLLoss::Backward( loss = loss / loss.n_elem; } -template +template template -void PoissonNLLLoss::serialize( +void PoissonNLLLossType::serialize( Archive& ar, const uint32_t /* version */) { diff --git a/src/mlpack/methods/ann/loss_functions/reconstruction_loss.hpp b/src/mlpack/methods/ann/loss_functions/reconstruction_loss.hpp index 454c22cb31..15a290fb8c 100644 --- a/src/mlpack/methods/ann/loss_functions/reconstruction_loss.hpp +++ b/src/mlpack/methods/ann/loss_functions/reconstruction_loss.hpp @@ -23,22 +23,19 @@ namespace ann /** Artificial Neural Network. */ { * performance equal to the negative log probability of the target with * the input distribution. * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam MatType Matrix representation to accept as input and use for + * computation. * @tparam DistType The type of distribution parametrized by the input. */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat, - typename DistType = BernoulliDistribution +template< + typename MatType = arma::mat, + typename DistType = BernoulliDistribution > -class ReconstructionLoss +class ReconstructionLossType { public: /** - * Create the ReconstructionLoss object. + * Create the ReconstructionLossType object. * * @param reduction Specifies the reduction to apply to the output. If false, * 'mean' reduction is used, where sum of the output will be @@ -46,7 +43,7 @@ class ReconstructionLoss * 'sum' reduction is used and the output will be summed. It * is set to true by default. */ - ReconstructionLoss(const bool reduction = true); + ReconstructionLossType(const bool reduction = true); /** * Computes the reconstruction loss. @@ -55,9 +52,8 @@ class ReconstructionLoss * function. * @param target The target matrix. */ - template - typename PredictionType::elem_type Forward(const PredictionType& prediction, - const TargetType& target); + typename MatType::elem_type Forward(const MatType& prediction, + const MatType& target); /** * Ordinary feed backward pass of a neural network. @@ -67,15 +63,9 @@ class ReconstructionLoss * @param target The target matrix. * @param loss The calculated error. */ - template - void Backward(const PredictionType& prediction, - const TargetType& target, - LossType& loss); - - //! Get the output parameter. - OutputDataType& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } + void Backward(const MatType& prediction, + const MatType& target, + MatType& loss); //! Get the reduction type, represented as boolean //! (false 'mean' reduction, true 'sum' reduction). @@ -93,12 +83,12 @@ class ReconstructionLoss //! Locally-stored distribution object. DistType dist; - //! Locally-stored output parameter object. - OutputDataType outputParameter; - //! Boolean value that tells if reduction is 'sum' or 'mean'. bool reduction; -}; // class ReconstructionLoss +}; // class ReconstructionLossType + +// Default typedef for typical `arma::mat` usage. +typedef ReconstructionLossType ReconstructionLoss; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/reconstruction_loss_impl.hpp b/src/mlpack/methods/ann/loss_functions/reconstruction_loss_impl.hpp index ccb70b90f0..f1927c1d0c 100644 --- a/src/mlpack/methods/ann/loss_functions/reconstruction_loss_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/reconstruction_loss_impl.hpp @@ -18,24 +18,20 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -ReconstructionLoss< - InputDataType, - OutputDataType, - DistType ->::ReconstructionLoss(const bool reduction) : reduction(reduction) +template +ReconstructionLossType::ReconstructionLossType( + const bool reduction) : + reduction(reduction) { // Nothing to do here. } -template -template -typename PredictionType::elem_type -ReconstructionLoss::Forward( - const PredictionType& prediction, const TargetType& target) +template +typename MatType::elem_type ReconstructionLossType::Forward( + const MatType& prediction, const MatType& target) { dist = DistType(prediction); - typename PredictionType::elem_type lossSum = -dist.LogProbability(target); + typename MatType::elem_type lossSum = -dist.LogProbability(target); if (reduction) return lossSum; @@ -43,12 +39,11 @@ ReconstructionLoss::Forward( return lossSum / target.n_elem; } -template -template -void ReconstructionLoss::Backward( - const PredictionType& /* prediction */, - const TargetType& target, - LossType& loss) +template +void ReconstructionLossType::Backward( + const MatType& /* prediction */, + const MatType& target, + MatType& loss) { dist.LogProbBackward(target, loss); loss *= -1; @@ -57,12 +52,13 @@ void ReconstructionLoss::Backward( loss = loss / target.n_elem; } -template +template template -void ReconstructionLoss::serialize( - Archive& ar, +void ReconstructionLossType::serialize( + Archive& ar, const uint32_t /* version */) { + ar(CEREAL_NVP(dist)); ar(CEREAL_NVP(reduction)); } diff --git a/src/mlpack/methods/ann/loss_functions/sigmoid_cross_entropy_error.hpp b/src/mlpack/methods/ann/loss_functions/sigmoid_cross_entropy_error.hpp index 04f9419521..6c66efc947 100644 --- a/src/mlpack/methods/ann/loss_functions/sigmoid_cross_entropy_error.hpp +++ b/src/mlpack/methods/ann/loss_functions/sigmoid_cross_entropy_error.hpp @@ -19,7 +19,7 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { /** - * The SigmoidCrossEntropyError performance function measures the network's + * The SigmoidCrossEntropyErrorType performance function measures the network's * performance according to the cross-entropy function between the input and * target distributions. This function calculates the cross entropy * given the real values instead of providing the sigmoid activations. @@ -40,28 +40,23 @@ namespace ann /** Artificial Neural Network. */ { * } * @endcode * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam MatType Matrix representation to accept as input and use for + * computation. */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class SigmoidCrossEntropyError +template +class SigmoidCrossEntropyErrorType { public: /** - * Create the SigmoidCrossEntropyError object. + * Create the SigmoidCrossEntropyErrorType object. * * @param reduction Specifies the reduction to apply to the output. If false, * 'mean' reduction is used, where sum of the output will be * divided by the number of elements in the output. If true, * 'sum' reduction is used and the output will be summed. It * is set to true by default. - */ - SigmoidCrossEntropyError(const bool reduction = true); + */ + SigmoidCrossEntropyErrorType(const bool reduction = true); /** * Computes the Sigmoid CrossEntropy Error functions. @@ -70,10 +65,9 @@ class SigmoidCrossEntropyError * function. * @param target The target vector. */ - template - inline typename PredictionType::elem_type Forward( - const PredictionType& prediction, - const TargetType& target); + inline typename MatType::elem_type Forward( + const MatType& prediction, + const MatType& target); /** * Ordinary feed backward pass of a neural network. @@ -83,15 +77,9 @@ class SigmoidCrossEntropyError * @param target The target vector. * @param loss The calculated error. */ - template - inline void Backward(const PredictionType& prediction, - const TargetType& target, - LossType& loss); - - //! Get the output parameter. - OutputDataType& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } + inline void Backward(const MatType& prediction, + const MatType& target, + MatType& loss); //! Get the reduction type, represented as boolean //! (false 'mean' reduction, true 'sum' reduction). @@ -106,12 +94,12 @@ class SigmoidCrossEntropyError void serialize(Archive& ar, const uint32_t /* version */); private: - //! Locally-stored output parameter object. - OutputDataType outputParameter; - //! Boolean value that tells if reduction is 'sum' or 'mean'. bool reduction; -}; // class SigmoidCrossEntropy +}; // class SigmoidCrossEntropyErrorType + +// Default typedef for typical `arma::mat` usage. +typedef SigmoidCrossEntropyErrorType SigmoidCrossEntropyError; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/sigmoid_cross_entropy_error_impl.hpp b/src/mlpack/methods/ann/loss_functions/sigmoid_cross_entropy_error_impl.hpp index e29e4a7478..7526495579 100644 --- a/src/mlpack/methods/ann/loss_functions/sigmoid_cross_entropy_error_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/sigmoid_cross_entropy_error_impl.hpp @@ -20,21 +20,21 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -SigmoidCrossEntropyError -::SigmoidCrossEntropyError(const bool reduction): reduction(reduction) +template +SigmoidCrossEntropyErrorType::SigmoidCrossEntropyErrorType( + const bool reduction) : + reduction(reduction) { // Nothing to do here. } -template -template -inline typename PredictionType::elem_type -SigmoidCrossEntropyError::Forward( - const PredictionType& prediction, - const TargetType& target) +template +inline typename MatType::elem_type +SigmoidCrossEntropyErrorType::Forward( + const MatType& prediction, + const MatType& target) { - typedef typename PredictionType::elem_type ElemType; + typedef typename MatType::elem_type ElemType; ElemType maximum = 0; for (size_t i = 0; i < prediction.n_elem; ++i) { @@ -50,12 +50,11 @@ SigmoidCrossEntropyError::Forward( return lossSum / target.n_elem; } -template -template -inline void SigmoidCrossEntropyError::Backward( - const PredictionType& prediction, - const TargetType& target, - LossType& loss) +template +inline void SigmoidCrossEntropyErrorType::Backward( + const MatType& prediction, + const MatType& target, + MatType& loss) { loss = 1.0 / (1.0 + arma::exp(-prediction)) - target; @@ -63,10 +62,10 @@ inline void SigmoidCrossEntropyError::Backward( loss = loss / target.n_elem; } -template +template template -void SigmoidCrossEntropyError::serialize( - Archive& ar , +void SigmoidCrossEntropyErrorType::serialize( + Archive& ar, const uint32_t /* version */) { ar(CEREAL_NVP(reduction)); diff --git a/src/mlpack/methods/ann/loss_functions/soft_margin_loss.hpp b/src/mlpack/methods/ann/loss_functions/soft_margin_loss.hpp index a44e321fcd..41d6c4d62f 100644 --- a/src/mlpack/methods/ann/loss_functions/soft_margin_loss.hpp +++ b/src/mlpack/methods/ann/loss_functions/soft_margin_loss.hpp @@ -22,20 +22,21 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { /** - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * The Soft Margin Loss function. + * + * It is a criterion that optimizes a two-class classification logistic loss, + * between input x and target y, both having the same shape, with the target + * containing only the values 1 or -1. + * + * @tparam MatType Matrix representation to accept as input and use for + * computation. */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class SoftMarginLoss +template +class SoftMarginLossType { public: /** - * Create the SoftMarginLoss object. + * Create the SoftMarginLossType object. * * @param reduction Specifies the reduction to apply to the output. If false, * 'mean' reduction is used, where sum of the output will be @@ -43,7 +44,7 @@ class SoftMarginLoss * true, 'sum' reduction is used and the output will be * summed. It is set to true by default. */ - SoftMarginLoss(const bool reduction = true); + SoftMarginLossType(const bool reduction = true); /** * Computes the Soft Margin Loss function. @@ -52,9 +53,8 @@ class SoftMarginLoss * function. * @param target The target vector with same shape as input. */ - template - typename PredictionType::elem_type Forward(const PredictionType& prediction, - const TargetType& target); + typename MatType::elem_type Forward(const MatType& prediction, + const MatType& target); /** * Ordinary feed backward pass of a neural network. @@ -64,15 +64,9 @@ class SoftMarginLoss * @param target The target vector. * @param loss The calculated error. */ - template - void Backward(const PredictionType& prediction, - const TargetType& target, - LossType& loss); - - //! Get the output parameter. - OutputDataType& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } + void Backward(const MatType& prediction, + const MatType& target, + MatType& loss); //! Get the reduction type, represented as boolean //! (false 'mean' reduction, true 'sum' reduction). @@ -87,12 +81,12 @@ class SoftMarginLoss void serialize(Archive& ar, const uint32_t version); private: - //! Locally-stored output parameter object. - OutputDataType outputParameter; - //! Boolean value that tells if reduction is 'sum' or 'mean'. bool reduction; -}; // class SoftMarginLoss +}; // class SoftMarginLossType + +// Default typedef for typical `arma::mat` usage. +typedef SoftMarginLossType SoftMarginLoss; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/soft_margin_loss_impl.hpp b/src/mlpack/methods/ann/loss_functions/soft_margin_loss_impl.hpp index 40453564a3..36908ac99e 100644 --- a/src/mlpack/methods/ann/loss_functions/soft_margin_loss_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/soft_margin_loss_impl.hpp @@ -18,21 +18,19 @@ namespace mlpack { namespace ann /** Artifical Neural Network. */ { -template -SoftMarginLoss:: -SoftMarginLoss(const bool reduction) : reduction(reduction) +template +SoftMarginLossType:: +SoftMarginLossType(const bool reduction) : reduction(reduction) { // Nothing to do here. } -template -template -typename PredictionType::elem_type -SoftMarginLoss::Forward( - const PredictionType& prediction, const TargetType& target) +template +typename MatType::elem_type SoftMarginLossType::Forward( + const MatType& prediction, const MatType& target) { - PredictionType loss = arma::log(1 + arma::exp(-target % prediction)); - typename PredictionType::elem_type lossSum = arma::accu(loss); + MatType loss = arma::log(1 + arma::exp(-target % prediction)); + typename MatType::elem_type lossSum = arma::accu(loss); if (reduction) return lossSum; @@ -40,26 +38,25 @@ SoftMarginLoss::Forward( return lossSum / prediction.n_elem; } -template -template -void SoftMarginLoss::Backward( - const PredictionType& prediction, - const TargetType& target, - LossType& loss) +template +void SoftMarginLossType::Backward( + const MatType& prediction, + const MatType& target, + MatType& loss) { loss.set_size(size(prediction)); - PredictionType temp = arma::exp(-target % prediction); - PredictionType numerator = -target % temp; - PredictionType denominator = 1 + temp; + MatType temp = arma::exp(-target % prediction); + MatType numerator = -target % temp; + MatType denominator = 1 + temp; loss = numerator / denominator; if (!reduction) loss = loss / prediction.n_elem; } -template +template template -void SoftMarginLoss::serialize( +void SoftMarginLossType::serialize( Archive& ar, const uint32_t /* version */) { ar(CEREAL_NVP(reduction)); diff --git a/src/mlpack/methods/ann/loss_functions/triplet_margin_loss.hpp b/src/mlpack/methods/ann/loss_functions/triplet_margin_loss.hpp index 980d863d17..3f3a6dffb9 100644 --- a/src/mlpack/methods/ann/loss_functions/triplet_margin_loss.hpp +++ b/src/mlpack/methods/ann/loss_functions/triplet_margin_loss.hpp @@ -24,38 +24,34 @@ namespace ann /** Artificial Neural Network. */ { * of the positive (truthy) and negative (falsy) inputs. * The distance between two samples A and B is defined as square of L2 norm * of A-B. - * + * * For more information, refer the following paper. * * @code * @article{Schroff2015, * author = {Florian Schroff, Dmitry Kalenichenko, James Philbin}, - * title = {FaceNet: A Unified Embedding for Face Recognition and Clustering}, + * title = {FaceNet: A Unified Embedding for Face Recognition and + * Clustering}, * year = {2015}, * url = {https://arxiv.org/abs/1503.03832}, * } * @endcode * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam MatType Matrix representation to accept as input and use for + * computation. */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class TripletMarginLoss +template +class TripletMarginLossType { public: /** - * Create the TripletMarginLoss object. + * Create the TripletMarginLossType object. * * @param margin The minimum value by which the distance between * Anchor and Negative sample exceeds the distance * between Anchor and Positive sample. */ - TripletMarginLoss(const double margin = 1.0); + TripletMarginLossType(const double margin = 1.0); /** * Computes the Triplet Margin Loss function. @@ -63,9 +59,9 @@ class TripletMarginLoss * @param prediction Concatenated anchor and positive sample. * @param target The negative sample. */ - template - typename PredictionType::elem_type Forward(const PredictionType& prediction, - const TargetType& target); + typename MatType::elem_type Forward(const MatType& prediction, + const MatType& target); + /** * Ordinary feed backward pass of a neural network. * @@ -73,15 +69,9 @@ class TripletMarginLoss * @param target The negative sample. * @param loss The calculated error. */ - template - void Backward(const PredictionType& prediction, - const TargetType& target, - LossType& loss); - - //! Get the output parameter. - OutputDataType& OutputParameter() const { return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } + void Backward(const MatType& prediction, + const MatType& target, + MatType& loss); //! Get the value of margin. double Margin() const { return margin; } @@ -95,12 +85,12 @@ class TripletMarginLoss void serialize(Archive& ar, const unsigned int /* version */); private: - //! Locally-stored output parameter object. - OutputDataType outputParameter; - //! The margin value used in calculating Triplet Margin Loss. double margin; -}; // class TripletLossMargin +}; // class TripletMarginLoss + +// Default typedef for typical `arma::mat` usage. +typedef TripletMarginLossType TripletMarginLoss; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/triplet_margin_loss_impl.hpp b/src/mlpack/methods/ann/loss_functions/triplet_margin_loss_impl.hpp index 2a43bc4ac4..3ad7083726 100644 --- a/src/mlpack/methods/ann/loss_functions/triplet_margin_loss_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/triplet_margin_loss_impl.hpp @@ -19,49 +19,42 @@ namespace mlpack { namespace ann /** Artifical Neural Network. */ { -template -TripletMarginLoss::TripletMarginLoss( - const double margin) : margin(margin) +template +TripletMarginLossType::TripletMarginLossType(const double margin) : + margin(margin) { // Nothing to do here. } -template -template -typename PredictionType::elem_type -TripletMarginLoss::Forward( - const PredictionType& prediction, - const TargetType& target) +template +typename MatType::elem_type TripletMarginLossType::Forward( + const MatType& prediction, + const MatType& target) { - PredictionType anchor = + MatType anchor = prediction.submat(0, 0, prediction.n_rows / 2 - 1, prediction.n_cols - 1); - PredictionType positive = + MatType positive = prediction.submat(prediction.n_rows / 2, 0, prediction.n_rows - 1, prediction.n_cols - 1); return std::max(0.0, arma::accu(arma::pow(anchor - positive, 2)) - arma::accu(arma::pow(anchor - target, 2)) + margin) / anchor.n_cols; } -template -template < - typename PredictionType, - typename TargetType, - typename LossType -> -void TripletMarginLoss::Backward( - const PredictionType& prediction, - const TargetType& target, - LossType& loss) +template +void TripletMarginLossType::Backward( + const MatType& prediction, + const MatType& target, + MatType& loss) { - PredictionType positive = + MatType positive = prediction.submat(prediction.n_rows / 2, 0, prediction.n_rows - 1, prediction.n_cols - 1); loss = 2 * (target - positive) / target.n_cols; } -template +template template -void TripletMarginLoss::serialize( +void TripletMarginLossType::serialize( Archive& ar, const unsigned int /* version */) { diff --git a/src/mlpack/methods/ann/layer/vr_class_reward.hpp b/src/mlpack/methods/ann/loss_functions/vr_class_reward.hpp similarity index 58% rename from src/mlpack/methods/ann/layer/vr_class_reward.hpp rename to src/mlpack/methods/ann/loss_functions/vr_class_reward.hpp index 2d6f626c75..8e0fe13a37 100644 --- a/src/mlpack/methods/ann/layer/vr_class_reward.hpp +++ b/src/mlpack/methods/ann/loss_functions/vr_class_reward.hpp @@ -1,8 +1,8 @@ /** - * @file methods/ann/layer/vr_class_reward.hpp + * @file methods/ann/loss_functions/vr_class_reward.hpp * @author Marcus Edel * - * Definition of the VRClassReward class, which implements the variance + * Definition of the VRClassRewardType class, which implements the variance * reduced classification reinforcement layer. * * mlpack is free software; you may redistribute it and/or modify it under the @@ -10,41 +10,34 @@ * 3-clause BSD license along with mlpack. If not, see * http://www.opensource.org/licenses/BSD-3-Clause for more information. */ -#ifndef MLPACK_METHODS_ANN_LAYER_VR_CLASS_REWARD_HPP -#define MLPACK_METHODS_ANN_LAYER_VR_CLASS_REWARD_HPP +#ifndef MLPACK_METHODS_ANN_LOSS_FUNCTIONS_VR_CLASS_REWARD_HPP +#define MLPACK_METHODS_ANN_LOSS_FUNCTIONS_VR_CLASS_REWARD_HPP #include -#include "layer_types.hpp" - namespace mlpack { namespace ann /** Artificial Neural Network. */ { /** * Implementation of the variance reduced classification reinforcement layer. * This layer is meant to be used in combination with the reinforce normal layer - * (ReinforceNormalLayer), which expects that an reward: - * (1 for success, 0 otherwise). + * (ReinforceNormalLayer), which expects that the reward is 1 for success, and 0 + * otherwise. * - * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). + * @tparam MatType Matrix representation to accept as input and use for + * computation. */ -template < - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat -> -class VRClassReward +template +class VRClassRewardType { public: /** - * Create the VRClassReward object. + * Create the VRClassRewardType object. * * @param scale Parameter used to scale the reward. * @param sizeAverage Take the average over all batches. */ - VRClassReward(const double scale = 1, const bool sizeAverage = true); + VRClassRewardType(const double scale = 1, const bool sizeAverage = true); /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -54,8 +47,8 @@ class VRClassReward * @param target The target vector, that contains the class index in the range * between 1 and the number of classes. */ - template - double Forward(const InputType& input, const TargetType& target); + typename MatType::elem_type Forward(const MatType& input, + const MatType& target); /** * Ordinary feed backward pass of a neural network. The negative log @@ -68,38 +61,30 @@ class VRClassReward * between 1 and the number of classes. * @param output The calculated error. */ - template - void Backward(const InputType& input, - const TargetType& target, - OutputType& output); + void Backward(const MatType& input, const MatType& target, MatType& output); - //! Get the output parameter. - OutputDataType& OutputParameter() const {return outputParameter; } - //! Modify the output parameter. - OutputDataType& OutputParameter() { return outputParameter; } - - //! Get the delta. - OutputDataType& Delta() const {return delta; } - //! Modify the delta. - OutputDataType& Delta() { return delta; } - - /* + /** * Add a new module to the model. * * @param args The layer parameter. */ - template + template void Add(Args... args) { network.push_back(new LayerType(args...)); } - /* + /** * Add a new module to the model. * * @param layer The Layer to be added to the model. */ - void Add(LayerTypes<> layer) { network.push_back(layer); } + void Add(Layer* layer) + { + network.push_back(layer); + } - //! Get the network modules. - std::vector >& Model() { return network; } + //! Get the network. + const std::vector*>& Network() const { return network; } + //! Modify the network. + std::vector*>& Network() { return network; } //! Get the value of parameter sizeAverage. bool SizeAverage() const { return sizeAverage; } @@ -123,15 +108,12 @@ class VRClassReward //! Locally stored reward parameter. double reward; - //! Locally-stored delta object. - OutputDataType delta; - - //! Locally-stored output parameter object. - OutputDataType outputParameter; - //! Locally-stored network modules. - std::vector > network; -}; // class VRClassReward + std::vector*> network; +}; // class VRClassRewardType + +// Default typedef for typical `arma::mat` usage. +typedef VRClassRewardType VRClassReward; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/vr_class_reward_impl.hpp b/src/mlpack/methods/ann/loss_functions/vr_class_reward_impl.hpp new file mode 100644 index 0000000000..761f8529a3 --- /dev/null +++ b/src/mlpack/methods/ann/loss_functions/vr_class_reward_impl.hpp @@ -0,0 +1,104 @@ +/** + * @file methods/ann/loss_functions/vr_class_reward_impl.hpp + * @author Marcus Edel + * + * Implementation of the VRClassRewardType class, which implements the variance + * reduced classification reinforcement layer. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_LOSS_FUNCTIONS_VR_CLASS_REWARD_IMPL_HPP +#define MLPACK_METHODS_ANN_LOSS_FUNCTIONS_VR_CLASS_REWARD_IMPL_HPP + +// In case it hasn't yet been included. +#include "vr_class_reward.hpp" + +namespace mlpack { +namespace ann /** Artificial Neural Network. */ { + +template +VRClassRewardType::VRClassRewardType( + const double scale, + const bool sizeAverage) : + scale(scale), + sizeAverage(sizeAverage), + reward(0) +{ + // Nothing to do here. +} + +template +typename MatType::elem_type VRClassRewardType::Forward( + const MatType& input, const MatType& target) +{ + double output = 0; + for (size_t i = 0; i < input.n_cols - 1; ++i) + { + const size_t currentTarget = target(i); + Log::Assert(currentTarget < input.n_rows, "Target class out of range."); + + output -= input(currentTarget, i); + } + + reward = 0; + arma::uword index = 0; + + for (size_t i = 0; i < input.n_cols - 1; ++i) + { + input.unsafe_col(i).max(index); + reward = (index == target(i)) * scale; + } + + if (sizeAverage) + { + return output - reward / (input.n_cols - 1); + } + + return output - reward; +} + +template +void VRClassRewardType::Backward( + const MatType& input, + const MatType& target, + MatType& output) +{ + output = arma::zeros(input.n_rows, input.n_cols); + for (size_t i = 0; i < (input.n_cols - 1); ++i) + { + const size_t currentTarget = target(i); + Log::Assert(currentTarget < input.n_rows, "Target class out of range."); + + output(currentTarget, i) = -1; + } + + double vrReward = reward - input(0, 1); + if (sizeAverage) + { + vrReward /= input.n_cols - 1; + } + + const double norm = sizeAverage ? 2.0 / (input.n_cols - 1) : 2.0; + + output(0, 1) = norm * (input(0, 1) - reward); + network.back()->Reward() = vrReward; +} + +template +template +void VRClassRewardType::serialize( + Archive& ar, const uint32_t /* version */) +{ + ar(CEREAL_NVP(scale)); + ar(CEREAL_NVP(sizeAverage)); + ar(CEREAL_NVP(reward)); + ar(CEREAL_NVP(network)); +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/make_alias.hpp b/src/mlpack/methods/ann/make_alias.hpp new file mode 100644 index 0000000000..65b68565d3 --- /dev/null +++ b/src/mlpack/methods/ann/make_alias.hpp @@ -0,0 +1,55 @@ +/** + * @file make_alias.hpp + * @author Ryan Curtin + * + * Implementation of `MakeAlias()`, a utility function. This is meant to be + * used in `SetWeights()` calls in various layers, to wrap internal weight + * objects as aliases around the given memory pointers. + */ +#ifndef MLPACK_METHODS_ANN_MAKE_ALIAS_HPP +#define MLPACK_METHODS_ANN_MAKE_ALIAS_HPP + +#include + +namespace mlpack { +namespace ann { + +/** + * Reconstruct `m` as an alias around the memory `newMem`, with size `numRows` x + * `numCols`. + */ +template +void MakeAlias(MatType& m, + typename MatType::elem_type* newMem, + const size_t numRows, + const size_t numCols) +{ + // We use placement new to reinitialize the object, since the copy and move + // assignment operators in Armadillo will end up copying memory instead of + // making an alias. + m.~MatType(); + new (&m) MatType(newMem, numRows, numCols, false, true); +} + +/** + * Reconstruct `c` as an alias around the memory` newMem`, with size `numRows` x + * `numCols` x `numSlices`. + */ +template +void MakeAlias(CubeType& c, + typename CubeType::elem_type* newMem, + const size_t numRows, + const size_t numCols, + const size_t numSlices) +{ + // We use placement new to reinitialize the object, since the copy and move + // assignment operators in Armadillo will end up copying memory instead of + // making an alias. + c.~CubeType(); + new (&c) CubeType(newMem, numRows, numCols, numSlices, false, true); +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/brnn.hpp b/src/mlpack/methods/ann/not_adapted/brnn.hpp similarity index 99% rename from src/mlpack/methods/ann/brnn.hpp rename to src/mlpack/methods/ann/not_adapted/brnn.hpp index e3170474b4..ac1e77f3dc 100644 --- a/src/mlpack/methods/ann/brnn.hpp +++ b/src/mlpack/methods/ann/not_adapted/brnn.hpp @@ -24,7 +24,6 @@ #include "init_rules/network_init.hpp" #include #include -#include #include #include diff --git a/src/mlpack/methods/ann/brnn_impl.hpp b/src/mlpack/methods/ann/not_adapted/brnn_impl.hpp similarity index 100% rename from src/mlpack/methods/ann/brnn_impl.hpp rename to src/mlpack/methods/ann/not_adapted/brnn_impl.hpp diff --git a/src/mlpack/methods/ann/gan/CMakeLists.txt b/src/mlpack/methods/ann/not_adapted/gan/CMakeLists.txt similarity index 100% rename from src/mlpack/methods/ann/gan/CMakeLists.txt rename to src/mlpack/methods/ann/not_adapted/gan/CMakeLists.txt diff --git a/src/mlpack/methods/ann/gan/gan.hpp b/src/mlpack/methods/ann/not_adapted/gan/gan.hpp similarity index 100% rename from src/mlpack/methods/ann/gan/gan.hpp rename to src/mlpack/methods/ann/not_adapted/gan/gan.hpp diff --git a/src/mlpack/methods/ann/gan/gan_impl.hpp b/src/mlpack/methods/ann/not_adapted/gan/gan_impl.hpp similarity index 100% rename from src/mlpack/methods/ann/gan/gan_impl.hpp rename to src/mlpack/methods/ann/not_adapted/gan/gan_impl.hpp diff --git a/src/mlpack/methods/ann/gan/gan_policies.hpp b/src/mlpack/methods/ann/not_adapted/gan/gan_policies.hpp similarity index 100% rename from src/mlpack/methods/ann/gan/gan_policies.hpp rename to src/mlpack/methods/ann/not_adapted/gan/gan_policies.hpp diff --git a/src/mlpack/methods/ann/gan/metrics/CMakeLists.txt b/src/mlpack/methods/ann/not_adapted/gan/metrics/CMakeLists.txt similarity index 100% rename from src/mlpack/methods/ann/gan/metrics/CMakeLists.txt rename to src/mlpack/methods/ann/not_adapted/gan/metrics/CMakeLists.txt diff --git a/src/mlpack/methods/ann/gan/metrics/inception_score.hpp b/src/mlpack/methods/ann/not_adapted/gan/metrics/inception_score.hpp similarity index 100% rename from src/mlpack/methods/ann/gan/metrics/inception_score.hpp rename to src/mlpack/methods/ann/not_adapted/gan/metrics/inception_score.hpp diff --git a/src/mlpack/methods/ann/gan/metrics/inception_score_impl.hpp b/src/mlpack/methods/ann/not_adapted/gan/metrics/inception_score_impl.hpp similarity index 100% rename from src/mlpack/methods/ann/gan/metrics/inception_score_impl.hpp rename to src/mlpack/methods/ann/not_adapted/gan/metrics/inception_score_impl.hpp diff --git a/src/mlpack/methods/ann/gan/wgan_impl.hpp b/src/mlpack/methods/ann/not_adapted/gan/wgan_impl.hpp similarity index 100% rename from src/mlpack/methods/ann/gan/wgan_impl.hpp rename to src/mlpack/methods/ann/not_adapted/gan/wgan_impl.hpp diff --git a/src/mlpack/methods/ann/gan/wgangp_impl.hpp b/src/mlpack/methods/ann/not_adapted/gan/wgangp_impl.hpp similarity index 100% rename from src/mlpack/methods/ann/gan/wgangp_impl.hpp rename to src/mlpack/methods/ann/not_adapted/gan/wgangp_impl.hpp diff --git a/src/mlpack/methods/ann/rbm/CMakeLists.txt b/src/mlpack/methods/ann/not_adapted/rbm/CMakeLists.txt similarity index 100% rename from src/mlpack/methods/ann/rbm/CMakeLists.txt rename to src/mlpack/methods/ann/not_adapted/rbm/CMakeLists.txt diff --git a/src/mlpack/methods/ann/rbm/rbm.hpp b/src/mlpack/methods/ann/not_adapted/rbm/rbm.hpp similarity index 100% rename from src/mlpack/methods/ann/rbm/rbm.hpp rename to src/mlpack/methods/ann/not_adapted/rbm/rbm.hpp diff --git a/src/mlpack/methods/ann/rbm/rbm_impl.hpp b/src/mlpack/methods/ann/not_adapted/rbm/rbm_impl.hpp similarity index 100% rename from src/mlpack/methods/ann/rbm/rbm_impl.hpp rename to src/mlpack/methods/ann/not_adapted/rbm/rbm_impl.hpp diff --git a/src/mlpack/methods/ann/rbm/rbm_policies.hpp b/src/mlpack/methods/ann/not_adapted/rbm/rbm_policies.hpp similarity index 100% rename from src/mlpack/methods/ann/rbm/rbm_policies.hpp rename to src/mlpack/methods/ann/not_adapted/rbm/rbm_policies.hpp diff --git a/src/mlpack/methods/ann/rbm/spike_slab_rbm_impl.hpp b/src/mlpack/methods/ann/not_adapted/rbm/spike_slab_rbm_impl.hpp similarity index 100% rename from src/mlpack/methods/ann/rbm/spike_slab_rbm_impl.hpp rename to src/mlpack/methods/ann/not_adapted/rbm/spike_slab_rbm_impl.hpp diff --git a/src/mlpack/methods/ann/regularizer/lregularizer.hpp b/src/mlpack/methods/ann/regularizer/lregularizer.hpp index 96a88e2959..41538ae752 100644 --- a/src/mlpack/methods/ann/regularizer/lregularizer.hpp +++ b/src/mlpack/methods/ann/regularizer/lregularizer.hpp @@ -44,7 +44,7 @@ class LRegularizer template void Evaluate(const MatType& weight, MatType& gradient); - //! Serialize the regularizer (nothing to do). + //! Serialize the regularizer. template void serialize(Archive& ar, const uint32_t /* version */); diff --git a/src/mlpack/methods/ann/regularizer/no_regularizer.hpp b/src/mlpack/methods/ann/regularizer/no_regularizer.hpp index 920c8d3057..3a4f321c1f 100644 --- a/src/mlpack/methods/ann/regularizer/no_regularizer.hpp +++ b/src/mlpack/methods/ann/regularizer/no_regularizer.hpp @@ -44,6 +44,12 @@ class NoRegularizer { // Nothing to do here. } + + template + void serialize(Archive& /* ar */, const uint32_t /* version */) + { + // Nothing to do. + } }; } // namespace ann diff --git a/src/mlpack/methods/ann/regularizer/orthogonal_regularizer.hpp b/src/mlpack/methods/ann/regularizer/orthogonal_regularizer.hpp index 3e56521165..eacc07e20a 100644 --- a/src/mlpack/methods/ann/regularizer/orthogonal_regularizer.hpp +++ b/src/mlpack/methods/ann/regularizer/orthogonal_regularizer.hpp @@ -55,7 +55,7 @@ class OrthogonalRegularizer template void Evaluate(const MatType& weight, MatType& gradient); - //! Serialize the regularizer (nothing to do). + //! Serialize the regularizer. template void serialize(Archive& ar, const uint32_t /* version */); diff --git a/src/mlpack/methods/ann/rnn.hpp b/src/mlpack/methods/ann/rnn.hpp index 949aecee9b..9fb3f14203 100644 --- a/src/mlpack/methods/ann/rnn.hpp +++ b/src/mlpack/methods/ann/rnn.hpp @@ -13,43 +13,32 @@ #define MLPACK_METHODS_ANN_RNN_HPP #include - -#include "visitor/delete_visitor.hpp" -#include "visitor/delta_visitor.hpp" -#include "visitor/output_parameter_visitor.hpp" -#include "visitor/reset_visitor.hpp" - -#include "init_rules/network_init.hpp" - -#include -#include -#include -#include - #include +#include "ffn.hpp" + namespace mlpack { namespace ann /** Artificial Neural Network. */ { /** - * Implementation of a standard recurrent neural network container. + * Definition of a standard recurrent neural network container. A recurrent + * neural network can handle recurrent layers (i.e. `RecurrentLayer`s), which + * hold internal state and are passed sequences of data as inputs. + * + * As opposed to the standard `FFN`, which takes data in a matrix format where + * each column is a data point, the `RNN` takes a cube format where each column + * is a data point and each slice is a time step. * * @tparam OutputLayerType The output layer type used to evaluate the network. * @tparam InitializationRuleType Rule used to initialize the weight matrix. */ template< - typename OutputLayerType = NegativeLogLikelihood<>, - typename InitializationRuleType = RandomInitialization, - typename... CustomLayers -> + typename OutputLayerType = NegativeLogLikelihood, + typename InitializationRuleType = RandomInitialization, + typename MatType = arma::mat> class RNN { public: - //! Convenience typedef for the internal model construction. - using NetworkType = RNN; - /** * Create the RNN object. * @@ -59,63 +48,67 @@ class RNN * If you want to pass in a parameter and discard the original parameter * object, be sure to use std::move to avoid unnecessary copy. * - * @param rho Maximum number of steps to backpropagate through time (BPTT). - * @param single Predict only the last element of the input sequence. + * @param bpttSteps Number of time steps to use for BPTT (backpropagation + * through time) when training. + * @param single If true, then the network will expect only a single timestep + * for responses. (That is, every input sequence only has one single + * output; so, `responses.n_slices` should be 1 when calling `Train()`.) * @param outputLayer Output layer used to evaluate the network. * @param initializeRule Optional instantiated InitializationRule object * for initializing the network parameter. */ - RNN(const size_t rho, + RNN(const size_t bpttSteps = 0, const bool single = false, OutputLayerType outputLayer = OutputLayerType(), InitializationRuleType initializeRule = InitializationRuleType()); //! Copy constructor. RNN(const RNN&); - //! Move constructor. RNN(RNN&&); - - //! Copy assignment operator. + //! Copy operator. RNN& operator=(const RNN&); - - //! Move assignment operator + //! Move assignment operator. RNN& operator=(RNN&&); - //! Destructor to release allocated memory. + //! Destroy the RNN and release any memory it is holding. ~RNN(); /** - * Check if the optimizer has MaxIterations() parameter, if it does - * then check if it's value is less than the number of datapoints - * in the dataset. + * Add a new module to the model. * - * @tparam OptimizerType Type of optimizer to use to train the model. - * @param optimizer optimizer used in the training process. - * @param samples Number of datapoints in the dataset. + * @param args The layer parameter. */ - template - typename std::enable_if< - HasMaxIterations - ::value, void>::type - WarnMessageMaxIterations(OptimizerType& optimizer, size_t samples) const; + template + void Add(Args... args) { network.template Add(args...); } /** - * Check if the optimizer has MaxIterations() parameter, if it - * doesn't then simply return from the function. + * Add a new module to the model. * - * @tparam OptimizerType Type of optimizer to use to train the model. - * @param optimizer optimizer used in the training process. - * @param samples Number of datapoints in the dataset. + * @param layer The Layer to be added to the model. */ - template - typename std::enable_if< - !HasMaxIterations - ::value, void>::type - WarnMessageMaxIterations(OptimizerType& optimizer, size_t samples) const; + void Add(Layer* layer) { network.Add(layer); } + + //! Get the network model. + const std::vector*>& Network() const + { + return network.Network().Network(); + } /** - * Train the recurrent neural network on the given input data using the given + * Modify the network model. Be careful! If you change the structure of the + * network or parameters for layers, its state may become invalid, and the + * next time it is used for any operation the parameters will be reset. + * + * Don't add any layers like this; use `Add()` instead. + */ + std::vector*>& Network() + { + return network.Network().Network(); + } + + /** + * Train the recurrent network on the given input data using the given * optimizer. * * This will use the existing model parameters as a starting point for the @@ -125,13 +118,6 @@ class RNN * If you want to pass in a parameter and discard the original parameter * object, be sure to use std::move to avoid unnecessary copy. * - * The format of the data should be as follows: - * - each slice should correspond to a time step - * - each column should correspond to a data point - * - each row should correspond to a dimension - * So, e.g., predictors(i, j, k) is the i'th dimension of the j'th data point - * at time slice k. - * * @tparam OptimizerType Type of optimizer to use to train the model. * @tparam CallbackTypes Types of Callback Functions. * @param predictors Input training variables. @@ -142,15 +128,16 @@ class RNN * @return The final objective of the trained model (NaN or Inf on error). */ template - double Train(arma::cube predictors, - arma::cube responses, - OptimizerType& optimizer, - CallbackTypes&&... callbacks); + typename MatType::elem_type Train( + arma::Cube predictors, + arma::Cube responses, + OptimizerType& optimizer, + CallbackTypes&&... callbacks); /** - * Train the recurrent neural network on the given input data. By default, the - * SGD optimization algorithm is used, but others can be specified - * (such as ens::RMSprop). + * Train the recurrent network on the given input data. By default, the + * RMSProp optimization algorithm is used, but others can be specified + * (such as ens::SGD). * * This will use the existing model parameters as a starting point for the * optimization. If this is not what you want, then you should access the @@ -159,25 +146,19 @@ class RNN * If you want to pass in a parameter and discard the original parameter * object, be sure to use std::move to avoid unnecessary copy. * - * The format of the data should be as follows: - * - each slice should correspond to a time step - * - each column should correspond to a data point - * - each row should correspond to a dimension - * So, e.g., predictors(i, j, k) is the i'th dimension of the j'th data point - * at time slice k. - * * @tparam OptimizerType Type of optimizer to use to train the model. - * @tparam CallbackTypes Types of Callback Functions. * @param predictors Input training variables. + * @tparam CallbackTypes Types of Callback Functions. * @param responses Outputs results from input training variables. * @param callbacks Callback function for ensmallen optimizer `OptimizerType`. * See https://www.ensmallen.org/docs.html#callback-documentation. * @return The final objective of the trained model (NaN or Inf on error). */ - template - double Train(arma::cube predictors, - arma::cube responses, - CallbackTypes&&... callbacks); + template + typename MatType::elem_type Train( + arma::Cube predictors, + arma::Cube responses, + CallbackTypes&&... callbacks); /** * Predict the responses to a given set of predictors. The responses will @@ -187,42 +168,101 @@ class RNN * If you want to pass in a parameter and discard the original parameter * object, be sure to use std::move to avoid unnecessary copy. * - * The format of the data should be as follows: - * - each slice should correspond to a time step - * - each column should correspond to a data point - * - each row should correspond to a dimension - * So, e.g., predictors(i, j, k) is the i'th dimension of the j'th data point - * at time slice k. The responses will be in the same format. - * * @param predictors Input predictors. * @param results Matrix to put output predictions of responses into. - * @param batchSize Number of points to predict at once. + * @param batchSize Batch size to use for prediction. */ - void Predict(arma::cube predictors, - arma::cube& results, - const size_t batchSize = 256); + void Predict(arma::Cube predictors, + arma::Cube& results, + const size_t batchSize = 128); + + // Return the nujmber of weights in the model. + size_t WeightSize() { return network.WeightSize(); } /** - * Evaluate the recurrent neural network with the given parameters. This - * function is usually called by the optimizer to train the model. + * Set the logical dimensions of the input. `Train()` and `Predict()` expect + * data to be passed such that one point corresponds to one column, but this + * data is allowed to be an arbitrary higher-order tensor. + * + * So, if the input is meant to be 28x28x3 images, then the input data to + * `Train()` or `Predict()` should have 28*28*3 = 2352 rows, and + * `InputDImensions()` should be set to `{ 28, 28, 3}`. Then, the layers of + * the network will interpret each input point as a 3-dimensional image + * instead of a 1-dimensional vector. + * + * If `InputDimensions()` is left unset before training, the data will be + * assumed to be a 1-dimensional vector. + */ + std::vector& InputDimensions() { return network.InputDimensions(); } + //! Get the logical dimensions of the input. + const std::vector& InputDimensions() const + { + return network.InputDimensions(); + } + + //! Return the initial point for the optimization. + const MatType& Parameters() const { return network.Parameters(); } + //! Modify the initial point for the optimization. + MatType& Parameters() { return network.Parameters(); } + + //! Return the number of steps allowed for BPTT. + size_t BPTTSteps() const { return bpttSteps; } + //! Modify the number of steps allowed for BPTT. + size_t& BPTTSteps() { return bpttSteps; } + + /** + * Reset the stored data of the network entirely. This reset all weights of + * each layer using `InitializationRuleType`, and prepares the network to + * accept a (flat 1-d) input size of `inputDimensionality` (if passed), or + * whatever input size has been set with `InputDimensions()`. + * + * This also resets the mode of the network to prediction mode (not training + * mode). See `SetNetworkMode()` for more information. + */ + void Reset(const size_t inputDimensionality = 0); + + /** + * Set all the layers in the network to training mode, if `training` is + * `true`, or set all the layers in the network to testing mode, if `training` + * is `false`. + */ + void SetNetworkMode(const bool training) { network.SetNetworkMode(training); } + + /** + * Evaluate the recurrent network with the given predictors and responses. + * This functions is usually used to monitor progress while training. + * + * @param predictors Input variables. + * @param responses Target outputs for input variables. + */ + typename MatType::elem_type Evaluate( + const arma::Cube& predictors, + const arma::Cube& responses); + + //! Serialize the model. + template + void serialize(Archive& ar, const uint32_t /* version */); + + // + // Only ensmallen utility functions for training are found below here. + // They generally aren't useful otherwise. + // + + /** + * Evaluate the recurrent network with the given parameters. This function + * is usually called by the optimizer to train the model. * * @param parameters Matrix model parameters. - * @param begin Index of the starting point to use for objective function - * evaluation. - * @param batchSize Number of points to be passed at a time to use for - * objective function evaluation. - * @param deterministic Whether or not to train or test the model. Note some - * layer act differently in training or testing mode. */ - double Evaluate(const arma::mat& parameters, - const size_t begin, - const size_t batchSize, - const bool deterministic); + typename MatType::elem_type Evaluate(const MatType& parameters); - /** - * Evaluate the recurrent neural network with the given parameters. This - * function is usually called by the optimizer to train the model. This just - * calls the other overload of Evaluate() with deterministic = true. + /** + * Evaluate the recurrent network with the given parameters, but using only + * a number of data points. This is useful for optimizers such as SGD, which + * require a separable objective function. + * + * Note that the network may return different results depending on the mode it + * is in (see `SetNetworkMode()`). * * @param parameters Matrix model parameters. * @param begin Index of the starting point to use for objective function @@ -230,13 +270,26 @@ class RNN * @param batchSize Number of points to be passed at a time to use for * objective function evaluation. */ - double Evaluate(const arma::mat& parameters, - const size_t begin, - const size_t batchSize); + typename MatType::elem_type Evaluate(const MatType& parameters, + const size_t begin, + const size_t batchSize); /** - * Evaluate the recurrent neural network with the given parameters. This - * function is usually called by the optimizer to train the model. + * Evaluate the recurrent network with the given parameters. + * This function is usually called by the optimizer to train the model. + * This just calls the overload of EvaluateWithGradient() with batchSize = 1. + * + * @param parameters Matrix model parameters. + * @param gradient Matrix to output gradient into. + */ + template + typename MatType::elem_type EvaluateWithGradient(const MatType& parameters, + GradType& gradient); + + /** + * Evaluate the recurrent network with the given parameters, but using only + * a number of data points. This is useful for optimizers such as SGD, which + * require a separable objective function. * * @param parameters Matrix model parameters. * @param begin Index of the starting point to use for objective function @@ -246,16 +299,15 @@ class RNN * objective function evaluation. */ template - double EvaluateWithGradient(const arma::mat& parameters, - const size_t begin, - GradType& gradient, - const size_t batchSize); + typename MatType::elem_type EvaluateWithGradient(const MatType& parameters, + const size_t begin, + GradType& gradient, + const size_t batchSize); /** - * Evaluate the gradient of the recurrent neural network with the given - * parameters, and with respect to only one point in the dataset. This is - * useful for optimizers such as SGD, which require a separable objective - * function. + * Evaluate the gradient of the recurrent network with the given parameters, + * and with respect to only a number of points in the dataset. This is useful + * for optimizers such as SGD, which require a separable objective function. * * @param parameters Matrix of the model parameters to be optimized. * @param begin Index of the starting point to use for objective function @@ -264,191 +316,69 @@ class RNN * @param batchSize Number of points to be processed as a batch for objective * function gradient evaluation. */ - void Gradient(const arma::mat& parameters, + template + void Gradient(const MatType& parameters, const size_t begin, - arma::mat& gradient, + GradType& gradient, const size_t batchSize); + //! Return the number of separable functions (the number of predictor points). + size_t NumFunctions() const { return predictors.n_cols; } + /** - * Shuffle the order of function visitation. This may be called by the - * optimizer. + * Note: this function is implement so that it can be used by ensmallen's + * optimizers. It's not generally meant to be used otherwise. + * + * Shuffle the order of function visitation. (This is equivalent to shuffling + * the dataset during training.) */ void Shuffle(); - /* - * Add a new module to the model. - * - * @param args The layer parameter. - */ - template - void Add(Args... args) { network.push_back(new LayerType(args...)); } - - /* - * Add a new module to the model. - * - * @param layer The Layer to be added to the model. - */ - void Add(LayerTypes layer) { network.push_back(layer); } - - //! Return the number of separable functions (the number of predictor points). - size_t NumFunctions() const { return numFunctions; } - - //! Return the initial point for the optimization. - const arma::mat& Parameters() const { return parameter; } - //! Modify the initial point for the optimization. - arma::mat& Parameters() { return parameter; } - - //! Return the maximum length of backpropagation through time. - const size_t& Rho() const { return rho; } - //! Modify the maximum length of backpropagation through time. - size_t& Rho() { return rho; } - - //! Get the matrix of responses to the input data points. - const arma::cube& Responses() const { return responses; } - //! Modify the matrix of responses to the input data points. - arma::cube& Responses() { return responses; } - - //! Get the matrix of data points (predictors). - const arma::cube& Predictors() const { return predictors; } - //! Modify the matrix of data points (predictors). - arma::cube& Predictors() { return predictors; } - /** - * Reset the state of the network. This ensures that all internally-held - * gradients are set to 0, all memory cells are reset, and the parameters - * matrix is the right size. + * Prepare the network for the given data. + * This function won't actually trigger training process. + * + * @param predictors Input data variables. + * @param responses Outputs results from input data variables. */ - void Reset(); - - /** - * Reset the module information (weights/parameters). - */ - void ResetParameters(); - - //! Serialize the model. - template - void serialize(Archive& ar, const uint32_t /* version */); + void ResetData(arma::Cube predictors, + arma::Cube responses); private: // Helper functions. - /** - * The Forward algorithm (part of the Forward-Backward algorithm). Computes - * forward probabilities for each module. - * - * @param input Data sequence to compute probabilities for. - */ - template - void Forward(const InputType& input); /** - * Reset the state of RNN cells in the network for new input sequence. + * Iterate over all layers and reset the recurrent layers' states. Prepare + * each recurrent layer to store up to `memorySize` previous states, operating + * with a batch size of `batchSize`. */ - void ResetCells(); + void ResetMemoryState(const size_t memorySize, const size_t batchSize); - /** - * The Backward algorithm (part of the Forward-Backward algorithm). Computes - * backward pass for module. - */ - void Backward(); + //! Set the previous step index of all recurrent layers to `step`. + void SetPreviousStep(const size_t step); + //! Set the current step index of all recurrent layers to `step`. + void SetCurrentStep(const size_t step); - /** - * Iterate through all layer modules and update the the gradient using the - * layer defined optimizer. - */ - template - void Gradient(const InputType& input); - - /** - * Reset the module status by setting the current deterministic parameter - * for all modules that implement the Deterministic function. - */ - void ResetDeterministic(); - - /** - * Reset the gradient for all modules that implement the Gradient function. - */ - void ResetGradients(arma::mat& gradient); - - //! Number of steps to backpropagate through time (BPTT). - size_t rho; - - //! Instantiated outputlayer used to evaluate the network. - OutputLayerType outputLayer; - - //! Instantiated InitializationRule object for initializing the network - //! parameter. - InitializationRuleType initializeRule; - - //! The input size. - size_t inputSize; - - //! The output size. - size_t outputSize; - - //! The target size. - size_t targetSize; - - //! Indicator if we already trained the model. - bool reset; - - //! Only predict the last element of the input sequence. + //! Number of timesteps to consider for backpropagation through time (BPTT). + size_t bpttSteps; + //! Whether the network expects only one single response per sequence, or one + //! response per time step. bool single; - //! Locally-stored model modules. - std::vector > network; + //! The network itself is stored in this FFN object. Note that this network + //! may contain recursive layers, and thus we will be responsible for + //! occasionally resetting any memory cells. + FFN network; - //! The matrix of data points (predictors). - arma::cube predictors; + //! The matrix of data points (predictors). This member is empty, except + //! during training---we must store a local copy of the training data since + //! the ensmallen optimizer will not provide training data. + arma::Cube predictors; - //! The matrix of responses to the input data points. - arma::cube responses; - - //! Matrix of (trained) parameters. - arma::mat parameter; - - //! The number of separable functions (the number of predictor points). - size_t numFunctions; - - //! The current error for the backward pass. - arma::mat error; - - //! Locally-stored delta visitor. - DeltaVisitor deltaVisitor; - - //! Locally-stored output parameter visitor. - OutputParameterVisitor outputParameterVisitor; - - //! List of all module parameters for the backward pass (BBTT). - std::vector moduleOutputParameter; - - //! Locally-stored weight size visitor. - WeightSizeVisitor weightSizeVisitor; - - //! Locally-stored copy visitor - CopyVisitor copyVisitor; - - //! Locally-stored reset visitor. - ResetVisitor resetVisitor; - - //! Locally-stored delete visitor. - DeleteVisitor deleteVisitor; - - //! The current evaluation mode (training or testing). - bool deterministic; - - //! The current gradient for the gradient pass. - arma::mat currentGradient; - - // The BRN class should have access to internal members. - template< - typename OutputLayerType1, - typename MergeLayerType1, - typename MergeOutputType1, - typename InitializationRuleType1, - typename... CustomLayers1 - > - friend class BRNN; -}; // class RNN + //! The matrix of responses to the input data points. This member is empty, + //! except during training. + arma::Cube responses; +}; // class RNNType } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/rnn_impl.hpp b/src/mlpack/methods/ann/rnn_impl.hpp index a7e7c65f3e..a276af3e91 100644 --- a/src/mlpack/methods/ann/rnn_impl.hpp +++ b/src/mlpack/methods/ann/rnn_impl.hpp @@ -14,621 +14,603 @@ // In case it hasn't been included yet. #include "rnn.hpp" - -#include "visitor/load_output_parameter_visitor.hpp" -#include "visitor/save_output_parameter_visitor.hpp" -#include "visitor/forward_visitor.hpp" -#include "visitor/backward_visitor.hpp" -#include "visitor/reset_cell_visitor.hpp" -#include "visitor/deterministic_set_visitor.hpp" -#include "visitor/gradient_set_visitor.hpp" -#include "visitor/gradient_visitor.hpp" -#include "visitor/weight_set_visitor.hpp" - -#include "util/check_input_shape.hpp" +#include "layer/recurrent_layer.hpp" namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -RNN::RNN( - const size_t rho, +template< + typename OutputLayerType, + typename InitializationRuleType, + typename MatType +> +RNN< + OutputLayerType, + InitializationRuleType, + MatType +>::RNN( + const size_t bpttSteps, const bool single, OutputLayerType outputLayer, InitializationRuleType initializeRule) : - rho(rho), - outputLayer(std::move(outputLayer)), - initializeRule(std::move(initializeRule)), - inputSize(0), - outputSize(0), - targetSize(0), - reset(false), + bpttSteps(bpttSteps), single(single), - numFunctions(0), - deterministic(true) + network(std::move(outputLayer), std::move(initializeRule)) { /* Nothing to do here */ } -template -RNN::RNN( +template< + typename OutputLayerType, + typename InitializationRuleType, + typename MatType +> +RNN< + OutputLayerType, + InitializationRuleType, + MatType +>::RNN( const RNN& network) : - rho(network.rho), - outputLayer(network.outputLayer), - initializeRule(network.initializeRule), - inputSize(network.inputSize), - outputSize(network.outputSize), - targetSize(network.targetSize), - reset(network.reset), + bpttSteps(network.bpttSteps), single(network.single), - parameter(network.parameter), - numFunctions(network.numFunctions), - deterministic(network.deterministic) + network(network.network) { - for (size_t i = 0; i < network.network.size(); ++i) - { - this->network.push_back(boost::apply_visitor(copyVisitor, - network.network[i])); - boost::apply_visitor(resetVisitor, this->network.back()); - } + // Nothing else to do. } -template -RNN::RNN( +template< + typename OutputLayerType, + typename InitializationRuleType, + typename MatType +> +RNN< + OutputLayerType, + InitializationRuleType, + MatType +>::RNN( RNN&& network) : - rho(std::move(network.rho)), - outputLayer(std::move(network.outputLayer)), - initializeRule(std::move(network.initializeRule)), - inputSize(std::move(network.inputSize)), - outputSize(std::move(network.outputSize)), - targetSize(std::move(network.targetSize)), - reset(std::move(network.reset)), + bpttSteps(std::move(network.bpttSteps)), single(std::move(network.single)), - network(std::move(network.network)), - parameter(std::move(network.parameter)), - numFunctions(std::move(network.numFunctions)), - deterministic(std::move(network.deterministic)) + network(std::move(network.network)) { // Nothing to do here. } -template -RNN::~RNN() +template< + typename OutputLayerType, + typename InitializationRuleType, + typename MatType +> +RNN< + OutputLayerType, + InitializationRuleType, + MatType +>& +RNN< + OutputLayerType, + InitializationRuleType, + MatType +>::operator=(const RNN& other) { - for (LayerTypes& layer : network) + if (this != &other) { - boost::apply_visitor(deleteVisitor, layer); + bpttSteps = other.bpttSteps; + single = other.single; + network = other.network; + predictors.clear(); + responses.clear(); } + + return *this; } -template -template -typename std::enable_if< - HasMaxIterations - ::value, void>::type -RNN:: -WarnMessageMaxIterations(OptimizerType& optimizer, size_t samples) const +template< + typename OutputLayerType, + typename InitializationRuleType, + typename MatType +> +RNN< + OutputLayerType, + InitializationRuleType, + MatType +>& +RNN< + OutputLayerType, + InitializationRuleType, + MatType +>::operator=(RNN&& other) { - if (optimizer.MaxIterations() < samples && - optimizer.MaxIterations() != 0) + if (this != &other) { - Log::Warn << "The optimizer's maximum number of iterations " - << "is less than the size of the dataset; the " - << "optimizer will not pass over the entire " - << "dataset. To fix this, modify the maximum " - << "number of iterations to be at least equal " - << "to the number of points of your dataset " - << "(" << samples << ")." << std::endl; + bpttSteps = std::move(other.bpttSteps); + single = std::move(other.single); + network = std::move(other.network); + predictors.clear(); + responses.clear(); } + + return *this; } -template -template -typename std::enable_if< - !HasMaxIterations - ::value, void>::type -RNN:: -WarnMessageMaxIterations(OptimizerType& /* optimizer */, - size_t /* samples */) const +template< + typename OutputLayerType, + typename InitializationRuleType, + typename MatType +> +RNN< + OutputLayerType, + InitializationRuleType, + MatType +>::~RNN() { - return; + // Nothing special to do. } -template +template< + typename OutputLayerType, + typename InitializationRuleType, + typename MatType +> template -double RNN::Train( - arma::cube predictors, - arma::cube responses, +typename MatType::elem_type RNN< + OutputLayerType, + InitializationRuleType, + MatType +>::Train( + arma::Cube predictors, + arma::Cube responses, OptimizerType& optimizer, CallbackTypes&&... callbacks) { - CheckInputShape > >( - network, predictors.n_rows, "RNN<>::Train()"); + ResetData(std::move(predictors), std::move(responses)); - numFunctions = responses.n_cols; + network.WarnMessageMaxIterations(optimizer, this->predictors.n_cols); - this->predictors = std::move(predictors); - this->responses = std::move(responses); - - this->deterministic = true; - ResetDeterministic(); - - if (!reset) - { - ResetParameters(); - } - - WarnMessageMaxIterations(optimizer, this->predictors.n_cols); + // Ensure that the network can be used. + network.CheckNetwork("RNN::Train()", this->predictors.n_rows, true, true); // Train the model. - const double out = optimizer.Optimize(*this, parameter, callbacks...); + Timer::Start("rnn_optimization"); + const typename MatType::elem_type out = + optimizer.Optimize(*this, network.Parameters(), callbacks...); + Timer::Stop("rnn_optimization"); - Log::Info << "RNN::RNN(): final objective of trained model is " << out + Log::Info << "RNN::Train(): final objective of trained model is " << out << "." << std::endl; return out; } -template -void RNN::ResetCells() -{ - for (size_t i = 1; i < network.size(); ++i) - { - boost::apply_visitor(ResetCellVisitor(rho), network[i]); - } -} - -template +template< + typename OutputLayerType, + typename InitializationRuleType, + typename MatType +> template -double RNN::Train( - arma::cube predictors, - arma::cube responses, +typename MatType::elem_type RNN< + OutputLayerType, + InitializationRuleType, + MatType +>::Train( + arma::Cube predictors, + arma::Cube responses, CallbackTypes&&... callbacks) { - CheckInputShape > >( - network, predictors.n_rows, "RNN<>::Train()"); - - numFunctions = responses.n_cols; - - this->predictors = std::move(predictors); - this->responses = std::move(responses); - - this->deterministic = true; - ResetDeterministic(); - - if (!reset) - { - ResetParameters(); - } - OptimizerType optimizer; - - WarnMessageMaxIterations(optimizer, this->predictors.n_cols); - - // Train the model. - const double out = optimizer.Optimize(*this, parameter, callbacks...); - - Log::Info << "RNN::RNN(): final objective of trained model is " << out - << "." << std::endl; - return out; + return Train(std::move(predictors), std::move(responses), optimizer, + callbacks...); } -template -void RNN::Predict( - arma::cube predictors, arma::cube& results, const size_t batchSize) -{ - CheckInputShape > >( - network, predictors.n_rows, "RNN<>::Predict()"); - - ResetCells(); - - if (parameter.is_empty()) - { - ResetParameters(); - } - - if (!deterministic) - { - deterministic = true; - ResetDeterministic(); - } - - const size_t effectiveBatchSize = std::min(batchSize, - size_t(predictors.n_cols)); - - Forward(arma::mat(predictors.slice(0).colptr(0), predictors.n_rows, - effectiveBatchSize, false, true)); - arma::mat resultsTemp = boost::apply_visitor(outputParameterVisitor, - network.back()); - - outputSize = resultsTemp.n_rows; - results = arma::zeros(outputSize, predictors.n_cols, rho); - results.slice(0).submat(0, 0, results.n_rows - 1, - effectiveBatchSize - 1) = resultsTemp; - - // Process in accordance with the given batch size. - for (size_t begin = 0; begin < predictors.n_cols; begin += batchSize) - { - const size_t effectiveBatchSize = std::min(batchSize, - size_t(predictors.n_cols - begin)); - for (size_t seqNum = !begin; seqNum < rho; ++seqNum) - { - Forward(arma::mat(predictors.slice(seqNum).colptr(begin), - predictors.n_rows, effectiveBatchSize, false, true)); - - results.slice(seqNum).submat(0, begin, results.n_rows - 1, begin + - effectiveBatchSize - 1) = boost::apply_visitor(outputParameterVisitor, - network.back()); - } - } -} - -template -double RNN::Evaluate( - const arma::mat& /* parameters */, - const size_t begin, - const size_t batchSize, - const bool deterministic) -{ - if (parameter.is_empty()) - { - ResetParameters(); - } - - if (deterministic != this->deterministic) - { - this->deterministic = deterministic; - ResetDeterministic(); - } - - if (!inputSize) - { - inputSize = predictors.n_rows; - targetSize = responses.n_rows; - } - else if (targetSize == 0) - { - targetSize = responses.n_rows; - } - - ResetCells(); - - double performance = 0; - size_t responseSeq = 0; - - for (size_t seqNum = 0; seqNum < rho; ++seqNum) - { - // Wrap a matrix around our data to avoid a copy. - arma::mat stepData(predictors.slice(seqNum).colptr(begin), - predictors.n_rows, batchSize, false, true); - Forward(stepData); - if (!single) - { - responseSeq = seqNum; - } - - performance += outputLayer.Forward(boost::apply_visitor( - outputParameterVisitor, network.back()), - arma::mat(responses.slice(responseSeq).colptr(begin), - responses.n_rows, batchSize, false, true)); - } - - if (outputSize == 0) - { - outputSize = boost::apply_visitor(outputParameterVisitor, - network.back()).n_elem / batchSize; - } - - return performance; -} - -template -double RNN::Evaluate( - const arma::mat& parameters, - const size_t begin, +template< + typename OutputLayerType, + typename InitializationRuleType, + typename MatType +> +void RNN< + OutputLayerType, + InitializationRuleType, + MatType +>::Predict( + arma::Cube predictors, + arma::Cube& results, const size_t batchSize) { - return Evaluate(parameters, begin, batchSize, true); + // Ensure that the network is configured correctly. + network.CheckNetwork("RNN::Predict()", predictors.n_rows, true, false); + + results.set_size(network.network.OutputSize(), predictors.n_cols, + predictors.n_slices); + + MatType inputAlias, outputAlias; + for (size_t i = 0; i < predictors.n_cols; i += batchSize) + { + const size_t effectiveBatchSize = std::min(batchSize, + size_t(predictors.n_cols) - i); + + // Since we aren't doing a backward pass, we don't actually need to store + // the state for each time step---we can fit it all in one buffer. + ResetMemoryState(1, effectiveBatchSize); + SetPreviousStep(size_t(-1)); + SetCurrentStep(size_t(0)); + + // Iterate over all time steps. + for (size_t t = 0; t < predictors.n_slices; ++t) + { + // If it is after the first step, we have a previous state. + if (t == 1) + SetPreviousStep(size_t(0)); + + // Create aliases for the input and output. + MakeAlias(inputAlias, + (typename MatType::elem_type*) predictors.slice(t).colptr(i), + predictors.n_rows, effectiveBatchSize); + MakeAlias(outputAlias, results.slice(t).colptr(i), results.n_rows, + effectiveBatchSize); + + network.Forward(inputAlias, outputAlias); + } + } } -template -template -double RNN:: -EvaluateWithGradient(const arma::mat& /* parameters */, - const size_t begin, - GradType& gradient, - const size_t batchSize) +template< + typename OutputLayerType, + typename InitializationRuleType, + typename MatType +> +void RNN< + OutputLayerType, + InitializationRuleType, + MatType +>::Reset(const size_t inputDimensionality) { - // Initialize passed gradient. - if (gradient.is_empty()) - { - if (parameter.is_empty()) - { - ResetParameters(); - } + // This is a reimplementation of FFN::Reset() that correctly prints + // "RNN::Reset()". + network.Parameters().clear(); - gradient = arma::zeros(parameter.n_rows, parameter.n_cols); + if (inputDimensionality != 0) + { + network.CheckNetwork("RNN::Reset()", inputDimensionality, true, false); } else { - gradient.zeros(); + const size_t inputDims = std::accumulate(network.InputDimensions().begin(), + network.InputDimensions().end(), 0); + network.CheckNetwork("RNN::Reset()", inputDims, true, false); + } +} + +template< + typename OutputLayerType, + typename InitializationRuleType, + typename MatType +> +template +void RNN< + OutputLayerType, + InitializationRuleType, + MatType +>::serialize( + Archive& ar, const uint32_t /* version */) +{ + ar(CEREAL_NVP(bpttSteps)); + ar(CEREAL_NVP(single)); + ar(CEREAL_NVP(network)); + + if (Archive::is_loading::value) + { + // We can clear these members, since it's not possible to serialize in the + // middle of training and resume. + predictors.clear(); + responses.clear(); + } +} + +template< + typename OutputLayerType, + typename InitializationRuleType, + typename MatType +> +typename MatType::elem_type RNN< + OutputLayerType, + InitializationRuleType, + MatType +>::Evaluate( + const MatType& /* parameters */, + const size_t begin, + const size_t batchSize) +{ + // Ensure the network is valid. + network.CheckNetwork("RNN::Evaluate()", predictors.n_rows); + + // The core of the computation here is to pass through each step. Since we + // are not computing the gradient, we can be "clever" and use only one memory + // cell---we don't need to know about the past. + ResetMemoryState(1, batchSize); + SetCurrentStep(0); + SetPreviousStep(size_t(-1)); + MatType output(network.network.OutputSize(), batchSize); + + typename MatType::elem_type loss = 0.0; + MatType stepData, responseData; + for (size_t t = 0; t < predictors.n_slices; ++t) + { + if (t == 1) + SetPreviousStep(0); + + // Manually reset the data of the network to be an alias of the current time + // step. + MakeAlias(network.predictors, predictors.slice(t).colptr(begin), + predictors.n_rows, batchSize); + const size_t responseStep = (single) ? 0 : t; + MakeAlias(network.responses, responses.slice(responseStep).colptr(begin), + responses.n_rows, batchSize); + + loss += network.Evaluate(output, begin, batchSize); } - if (this->deterministic) + return loss; +} + +template< + typename OutputLayerType, + typename InitializationRuleType, + typename MatType +> +template +typename MatType::elem_type RNN< + OutputLayerType, + InitializationRuleType, + MatType +>::EvaluateWithGradient( + const MatType& parameters, + GradType& gradient) +{ + return EvaluateWithGradient(parameters, 0, gradient, predictors.n_cols); +} + +template< + typename OutputLayerType, + typename InitializationRuleType, + typename MatType +> +template +typename MatType::elem_type RNN< + OutputLayerType, + InitializationRuleType, + MatType +>::EvaluateWithGradient( + const MatType& /* parameters */, + const size_t begin, + GradType& gradient, + const size_t batchSize) +{ + network.CheckNetwork("RNN::EvaluateWithGradient()", predictors.n_rows); + + typename MatType::elem_type loss = 0; + + // We must save anywhere between 1 and `bpttSteps` states, but we are limited + // by `predictors.n_slices`. + const size_t effectiveBPTTSteps = std::max(size_t(1), + std::min(bpttSteps, size_t(predictors.n_slices))); + + ResetMemoryState(effectiveBPTTSteps, batchSize); + SetPreviousStep(size_t(-1)); + arma::Cube outputs( + network.network.OutputSize(), batchSize, effectiveBPTTSteps); + + // If `bpttSteps` is less than the number of time steps in the data, then for + // the first few steps, we won't actually need to hold onto any historical + // information, since BPTT will never go back that far. + const size_t extraSteps = (predictors.n_slices - effectiveBPTTSteps + 1); + MatType stepData, outputData, responseData; + for (size_t t = 0; t < std::min(size_t(predictors.n_slices), extraSteps); ++t) { - this->deterministic = false; - ResetDeterministic(); + SetCurrentStep(0); + + // Make an alias of the step's data. + MakeAlias(stepData, predictors.slice(t).colptr(begin), predictors.n_rows, + batchSize); + MakeAlias(outputData, outputs.slice(t).memptr(), outputs.n_rows, + outputs.n_cols); + network.network.Forward(stepData, outputData); + + const size_t responseStep = (single) ? 0 : t; + MakeAlias(responseData, responses.slice(responseStep).colptr(begin), + responses.n_rows, batchSize); + + loss += network.outputLayer.Forward(outputData, responseData); + + SetPreviousStep(0); } - if (!inputSize) + // Next, we reach the time steps that will be used for BPTT, for which we must + // preserve step data. + for (size_t t = extraSteps; t < predictors.n_slices; ++t) { - inputSize = predictors.n_rows; - targetSize = responses.n_rows; - } - else if (targetSize == 0) - { - targetSize = responses.n_rows; - } + SetCurrentStep(t - extraSteps + 1); - ResetCells(); - - double performance = 0; - size_t responseSeq = 0; - const size_t effectiveRho = std::min(rho, size_t(responses.size())); - - for (size_t seqNum = 0; seqNum < effectiveRho; ++seqNum) - { // Wrap a matrix around our data to avoid a copy. - arma::mat stepData(predictors.slice(seqNum).colptr(begin), - predictors.n_rows, batchSize, false, true); - Forward(stepData); - if (!single) - { - responseSeq = seqNum; - } + MakeAlias(stepData, predictors.slice(t).colptr(begin), predictors.n_rows, + batchSize); + MakeAlias(outputData, outputs.slice(t).memptr(), outputs.n_rows, + outputs.n_cols); + network.network.Forward(stepData, outputData); - for (size_t l = 0; l < network.size(); ++l) - { - boost::apply_visitor(SaveOutputParameterVisitor(moduleOutputParameter), - network[l]); - } + const size_t responseStep = (single) ? 0 : t; + MakeAlias(responseData, responses.slice(responseStep).colptr(begin), + responses.n_rows, batchSize); - performance += outputLayer.Forward(boost::apply_visitor( - outputParameterVisitor, network.back()), - arma::mat(responses.slice(responseSeq).colptr(begin), - responses.n_rows, batchSize, false, true)); + loss += network.outputLayer.Forward(outputData, responseData); + + SetPreviousStep(t - extraSteps + 1); } - if (outputSize == 0) - { - outputSize = boost::apply_visitor(outputParameterVisitor, - network.back()).n_elem / batchSize; - } + // Add loss (this is not dependent on time steps, and should only be added + // once). + loss += network.network.Loss(); // Initialize current/working gradient. - if (currentGradient.is_empty()) - { - currentGradient = arma::zeros(parameter.n_rows, - parameter.n_cols); - } + gradient.zeros(network.Parameters().n_rows, network.Parameters().n_cols); + GradType currentGradient; + currentGradient.zeros(network.Parameters().n_rows, + network.Parameters().n_cols); - ResetGradients(currentGradient); - - for (size_t seqNum = 0; seqNum < effectiveRho; ++seqNum) + SetPreviousStep(size_t(-1)); + const size_t minStep = predictors.n_slices - effectiveBPTTSteps + 1; + for (size_t t = predictors.n_slices; t >= minStep; --t) { + SetCurrentStep(t - 1); + currentGradient.zeros(); - for (size_t l = 0; l < network.size(); ++l) - { - boost::apply_visitor(LoadOutputParameterVisitor(moduleOutputParameter), - network[network.size() - 1 - l]); - } + MatType error(outputs.n_rows, outputs.n_cols); - if (single && seqNum > 0) + // Set up the response by backpropagating through the output layer. Note + // that if we are in 'single' mode, we don't care what the network outputs + // until the input sequence is done, so there is no error for any timestep + // other than the first one. + if (single && (t - 1) < responses.n_slices - 1) { error.zeros(); } - else if (single && seqNum == 0) - { - outputLayer.Backward(boost::apply_visitor( - outputParameterVisitor, network.back()), - arma::mat(responses.slice(0).colptr(begin), - responses.n_rows, batchSize, false, true), error); - } else { - outputLayer.Backward(boost::apply_visitor( - outputParameterVisitor, network.back()), - arma::mat(responses.slice(effectiveRho - seqNum - 1).colptr(begin), - responses.n_rows, batchSize, false, true), error); + MakeAlias(outputData, outputs.slice(t - 1).colptr(0), outputs.n_rows, + outputs.n_cols); + const size_t respStep = (single) ? 0 : t - 1; + MakeAlias(responseData, responses.slice(respStep).colptr(begin), + responses.n_rows, batchSize); + network.outputLayer.Backward(outputData, responseData, error); } - Backward(); - Gradient( - arma::mat(predictors.slice(effectiveRho - seqNum - 1).colptr(begin), - predictors.n_rows, batchSize, false, true)); + // Now pass that error backwards through the network. + MakeAlias(outputData, outputs.slice(t - 1).colptr(0), outputs.n_rows, + outputs.n_cols); + MatType networkDelta; + network.network.Backward(outputData, error, networkDelta); + + MakeAlias(stepData, predictors.slice(t - 1).colptr(begin), + predictors.n_rows, batchSize); + network.network.Gradient(stepData, error, currentGradient); gradient += currentGradient; + + SetPreviousStep(t - 1); } - return performance; + return loss; } -template -void RNN::Gradient( - const arma::mat& parameters, +template< + typename OutputLayerType, + typename InitializationRuleType, + typename MatType +> +template +void RNN< + OutputLayerType, + InitializationRuleType, + MatType +>::Gradient( + const MatType& parameters, const size_t begin, - arma::mat& gradient, + GradType& gradient, const size_t batchSize) { this->EvaluateWithGradient(parameters, begin, gradient, batchSize); } -template -void RNN::Shuffle() +template< + typename OutputLayerType, + typename InitializationRuleType, + typename MatType +> +void RNN< + OutputLayerType, + InitializationRuleType, + MatType +>::Shuffle() { - arma::cube newPredictors, newResponses; - math::ShuffleData(predictors, responses, newPredictors, newResponses); - - predictors = std::move(newPredictors); - responses = std::move(newResponses); + math::ShuffleData(predictors, responses, predictors, responses); } -template -void RNN::ResetParameters() +template< + typename OutputLayerType, + typename InitializationRuleType, + typename MatType +> +void RNN< + OutputLayerType, + InitializationRuleType, + MatType +>::ResetData( + arma::Cube predictors, + arma::Cube responses) { - ResetDeterministic(); - - // Reset the network parameter with the given initialization rule. - NetworkInitialization networkInit(initializeRule); - networkInit.Initialize(network, parameter); - - reset = true; + this->predictors = std::move(predictors); + this->responses = std::move(responses); } -template -void RNN::Reset() +template< + typename OutputLayerType, + typename InitializationRuleType, + typename MatType +> +void RNN< + OutputLayerType, + InitializationRuleType, + MatType +>::ResetMemoryState(const size_t memorySize, const size_t batchSize) { - ResetParameters(); - ResetCells(); - currentGradient.zeros(); - ResetGradients(currentGradient); -} - -template -void RNN::ResetDeterministic() -{ - DeterministicSetVisitor deterministicSetVisitor(deterministic); - std::for_each(network.begin(), network.end(), - boost::apply_visitor(deterministicSetVisitor)); -} - -template -void RNN::ResetGradients( - arma::mat& gradient) -{ - size_t offset = 0; - for (LayerTypes& layer : network) + // Iterate over all layers and set the memory size. + for (Layer* l : network.Network()) { - offset += boost::apply_visitor(GradientSetVisitor(gradient, offset), layer); + // We can only call ClearRecurrentState() on RecurrentLayers. + RecurrentLayer* r = + dynamic_cast*>(l); + if (r != nullptr) + r->ClearRecurrentState(memorySize, batchSize); } } -template -template -void RNN::Forward(const InputType& input) +template< + typename OutputLayerType, + typename InitializationRuleType, + typename MatType +> +void RNN< + OutputLayerType, + InitializationRuleType, + MatType +>::SetPreviousStep(const size_t step) { - boost::apply_visitor(ForwardVisitor(input, - boost::apply_visitor(outputParameterVisitor, network.front())), - network.front()); - - for (size_t i = 1; i < network.size(); ++i) + // Iterate over all layers and set the memory size. + for (Layer* l : network.Network()) { - boost::apply_visitor(ForwardVisitor( - boost::apply_visitor(outputParameterVisitor, network[i - 1]), - boost::apply_visitor(outputParameterVisitor, network[i])), - network[i]); + // We can only call SetPreviousStep() on RecurrentLayers. + RecurrentLayer* r = + dynamic_cast*>(l); + if (r != nullptr) + r->PreviousStep() = step; } } -template -void RNN::Backward() +template< + typename OutputLayerType, + typename InitializationRuleType, + typename MatType +> +void RNN< + OutputLayerType, + InitializationRuleType, + MatType +>::SetCurrentStep(const size_t step) { - boost::apply_visitor(BackwardVisitor( - boost::apply_visitor(outputParameterVisitor, network.back()), - error, boost::apply_visitor(deltaVisitor, - network.back())), network.back()); - - for (size_t i = 2; i < network.size(); ++i) + // Iterate over all layers and set the memory size. + for (Layer* l : network.Network()) { - boost::apply_visitor(BackwardVisitor( - boost::apply_visitor(outputParameterVisitor, - network[network.size() - i]), boost::apply_visitor( - deltaVisitor, network[network.size() - i + 1]), - boost::apply_visitor(deltaVisitor, network[network.size() - i])), - network[network.size() - i]); - } -} - -template -template -void RNN::Gradient(const InputType& input) -{ - boost::apply_visitor(GradientVisitor(input, - boost::apply_visitor(deltaVisitor, network[1])), network.front()); - - for (size_t i = 1; i < network.size() - 1; ++i) - { - boost::apply_visitor(GradientVisitor( - boost::apply_visitor(outputParameterVisitor, network[i - 1]), - boost::apply_visitor(deltaVisitor, network[i + 1])), - network[i]); - } -} - -template -template -void RNN::serialize( - Archive& ar, const uint32_t /* version */) -{ - ar(CEREAL_NVP(parameter)); - ar(CEREAL_NVP(rho)); - ar(CEREAL_NVP(single)); - ar(CEREAL_NVP(inputSize)); - ar(CEREAL_NVP(outputSize)); - ar(CEREAL_NVP(targetSize)); - ar(CEREAL_NVP(reset)); - - if (cereal::is_loading()) - { - std::for_each(network.begin(), network.end(), - boost::apply_visitor(deleteVisitor)); - network.clear(); - } - - ar(CEREAL_VECTOR_VARIANT_POINTER(network)); - - // If we are loading, we need to initialize the weights. - if (cereal::is_loading()) - { - size_t offset = 0; - for (LayerTypes& layer : network) - { - offset += boost::apply_visitor(WeightSetVisitor(parameter, offset), - layer); - - boost::apply_visitor(resetVisitor, layer); - } - - deterministic = true; - ResetDeterministic(); + // We can only call SetPreviousStep() on RecurrentLayers. + RecurrentLayer* r = + dynamic_cast*>(l); + if (r != nullptr) + r->CurrentStep() = step; } } diff --git a/src/mlpack/methods/ann/util/CMakeLists.txt b/src/mlpack/methods/ann/util/CMakeLists.txt deleted file mode 100644 index dffec0c265..0000000000 --- a/src/mlpack/methods/ann/util/CMakeLists.txt +++ /dev/null @@ -1,14 +0,0 @@ -# Define the files we need to compile -# Anything not in this list will not be compiled into mlpack. -set(SOURCES - check_input_shape.hpp -) - -# Add directory name to sources. -set(DIR_SRCS) -foreach(file ${SOURCES}) - set(DIR_SRCS ${DIR_SRCS} ${CMAKE_CURRENT_SOURCE_DIR}/${file}) -endforeach() -# Append sources (with directory name) to list of all mlpack sources (used at -# the parent scope). -set(MLPACK_SRCS ${MLPACK_SRCS} ${DIR_SRCS} PARENT_SCOPE) \ No newline at end of file diff --git a/src/mlpack/methods/ann/util/check_input_shape.hpp b/src/mlpack/methods/ann/util/check_input_shape.hpp deleted file mode 100644 index 59f2c72da2..0000000000 --- a/src/mlpack/methods/ann/util/check_input_shape.hpp +++ /dev/null @@ -1,53 +0,0 @@ -/** - * @file methods/ann/util/check_input_shape.hpp - * @author Khizir Siddiqui - * @author Nippun Sharma - * - * Definition of the CheckInputShape() function that checks - * whether the shape of input is consistent with the first layer - * of the neural network. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ - -#ifndef MLPACK_METHODS_ANN_UTIL_CHECK_INPUT_SHAPE_HPP -#define MLPACK_METHODS_ANN_UTIL_CHECK_INPUT_SHAPE_HPP - -#include - -namespace mlpack { -namespace ann /** Artificial Neural Network. */{ - -template -void CheckInputShape(const T& network, - const size_t inputShape, - const std::string& functionName) -{ - for (size_t l = 0; l < network.size(); ++l) - { - size_t layerInShape = boost::apply_visitor(InShapeVisitor(), network[l]); - if (layerInShape == 0) - { - continue; - } - else if (layerInShape == inputShape) - { - break; - } - else - { - std::string estr = functionName + ": the first layer of the network " + - "expects " + std::to_string(layerInShape) + " elements, but the " + - "input has " + std::to_string(inputShape) + " dimensions!"; - throw std::logic_error(estr); - } - } -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/visitor/CMakeLists.txt b/src/mlpack/methods/ann/visitor/CMakeLists.txt deleted file mode 100644 index fa207d6092..0000000000 --- a/src/mlpack/methods/ann/visitor/CMakeLists.txt +++ /dev/null @@ -1,71 +0,0 @@ -# Define the files we need to compile -# Anything not in this list will not be compiled into mlpack. -set(SOURCES - add_visitor.hpp - add_visitor_impl.hpp - backward_visitor.hpp - backward_visitor_impl.hpp - bias_set_visitor.hpp - bias_set_visitor_impl.hpp - copy_visitor.hpp - copy_visitor_impl.hpp - delete_visitor.hpp - delete_visitor_impl.hpp - delta_visitor.hpp - delta_visitor_impl.hpp - deterministic_set_visitor.hpp - deterministic_set_visitor_impl.hpp - forward_visitor.hpp - forward_visitor_impl.hpp - gradient_set_visitor.hpp - gradient_set_visitor_impl.hpp - gradient_update_visitor.hpp - gradient_update_visitor_impl.hpp - gradient_visitor.hpp - gradient_visitor_impl.hpp - gradient_zero_visitor.hpp - gradient_zero_visitor_impl.hpp - load_output_parameter_visitor.hpp - load_output_parameter_visitor_impl.hpp - loss_visitor.hpp - loss_visitor_impl.hpp - output_height_visitor.hpp - output_height_visitor_impl.hpp - output_parameter_visitor.hpp - output_parameter_visitor_impl.hpp - output_width_visitor.hpp - output_width_visitor_impl.hpp - parameters_set_visitor.hpp - parameters_set_visitor_impl.hpp - parameters_visitor.hpp - parameters_visitor_impl.hpp - reset_cell_visitor.hpp - reset_cell_visitor_impl.hpp - reset_visitor.hpp - reset_visitor_impl.hpp - reward_set_visitor.hpp - reward_set_visitor_impl.hpp - run_set_visitor.hpp - run_set_visitor_impl.hpp - save_output_parameter_visitor.hpp - save_output_parameter_visitor_impl.hpp - set_input_height_visitor.hpp - set_input_height_visitor_impl.hpp - set_input_width_visitor.hpp - set_input_width_visitor_impl.hpp - weight_set_visitor.hpp - weight_set_visitor_impl.hpp - weight_size_visitor.hpp - weight_size_visitor_impl.hpp - input_shape_visitor.hpp - input_shape_visitor_impl.hpp -) - -# Add directory name to sources. -set(DIR_SRCS) -foreach(file ${SOURCES}) - set(DIR_SRCS ${DIR_SRCS} ${CMAKE_CURRENT_SOURCE_DIR}/${file}) -endforeach() -# Append sources (with directory name) to list of all mlpack sources (used at -# the parent scope). -set(MLPACK_SRCS ${MLPACK_SRCS} ${DIR_SRCS} PARENT_SCOPE) diff --git a/src/mlpack/methods/ann/visitor/add_visitor.hpp b/src/mlpack/methods/ann/visitor/add_visitor.hpp deleted file mode 100644 index fe8096a0ba..0000000000 --- a/src/mlpack/methods/ann/visitor/add_visitor.hpp +++ /dev/null @@ -1,64 +0,0 @@ -/** - * @file methods/ann/visitor/add_visitor.hpp - * @author Marcus Edel - * - * This file provides an abstraction for the Add() function for different - * layers and automatically directs any parameter to the right layer type. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_ADD_VISITOR_HPP -#define MLPACK_METHODS_ANN_VISITOR_ADD_VISITOR_HPP - -#include -#include - -#include - -namespace mlpack { -namespace ann { - -/** - * AddVisitor exposes the Add() method of the given module. - */ -template -class AddVisitor : public boost::static_visitor -{ - public: - //! Exposes the Add() method of the given module. - template - AddVisitor(T newLayer); - - //! Exposes the Add() method. - template - void operator()(LayerType* layer) const; - - void operator()(MoreTypes layer) const; - - private: - //! The layer that should be added. - LayerTypes newLayer; - - //! Only add the layer if the module implements the Add() function. - template - typename std::enable_if< - HasAddCheck)>::value, void>::type - LayerAdd(T* layer) const; - - //! Do not add the layer if the module doesn't implement the Add() function. - template - typename std::enable_if< - !HasAddCheck)>::value, void>::type - LayerAdd(T* layer) const; -}; - -} // namespace ann -} // namespace mlpack - -// Include implementation. -#include "add_visitor_impl.hpp" - -#endif diff --git a/src/mlpack/methods/ann/visitor/add_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/add_visitor_impl.hpp deleted file mode 100644 index 62725e95ed..0000000000 --- a/src/mlpack/methods/ann/visitor/add_visitor_impl.hpp +++ /dev/null @@ -1,64 +0,0 @@ -/** - * @file methods/ann/visitor/add_visitor_impl.hpp - * @author Marcus Edel - * - * Implementation of the Add() function layer abstraction. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_ADD_VISITOR_IMPL_HPP -#define MLPACK_METHODS_ANN_VISITOR_ADD_VISITOR_IMPL_HPP - -// In case it hasn't been included yet. -#include "add_visitor.hpp" - -namespace mlpack { -namespace ann { - -//! AddVisitor visitor class. -template -template -inline AddVisitor::AddVisitor(T newLayer) : - newLayer(std::move(newLayer)) -{ - /* Nothing to do here. */ -} - -template -template -inline void AddVisitor::operator()(LayerType* layer) const -{ - LayerAdd(layer); -} - -template -inline void AddVisitor::operator()(MoreTypes layer) const -{ - layer.apply_visitor(*this); -} - -template -template -inline typename std::enable_if< - HasAddCheck)>::value, void>::type -AddVisitor::LayerAdd(T* layer) const -{ - layer->Add(newLayer); -} - -template -template -inline typename std::enable_if< - !HasAddCheck)>::value, void>::type -AddVisitor::LayerAdd(T* /* layer */) const -{ - /* Nothing to do here. */ -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/visitor/backward_visitor.hpp b/src/mlpack/methods/ann/visitor/backward_visitor.hpp deleted file mode 100644 index e59ff53eef..0000000000 --- a/src/mlpack/methods/ann/visitor/backward_visitor.hpp +++ /dev/null @@ -1,85 +0,0 @@ -/** - * @file methods/ann/visitor/backward_visitor.hpp - * @author Marcus Edel - * - * This file provides an abstraction for the Backward() function for different - * layers and automatically directs any parameter to the right layer type. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_BACKWARD_VISITOR_HPP -#define MLPACK_METHODS_ANN_VISITOR_BACKWARD_VISITOR_HPP - -#include -#include - -#include - -namespace mlpack { -namespace ann { - -/** - * BackwardVisitor executes the Backward() function given the input, error and - * delta parameter. - */ -class BackwardVisitor : public boost::static_visitor -{ - public: - //! Execute the Backward() function given the input, error and delta - //! parameter. - BackwardVisitor(const arma::mat& input, - const arma::mat& error, - arma::mat& delta); - - //! Execute the Backward() function for the layer with the specified index. - BackwardVisitor(const arma::mat& input, - const arma::mat& error, - arma::mat& delta, - const size_t index); - - //! Execute the Backward() function. - template - void operator()(LayerType* layer) const; - - void operator()(MoreTypes layer) const; - - private: - //! The input parameter set. - const arma::mat& input; - - //! The error parameter. - const arma::mat& error; - - //! The delta parameter. - arma::mat& delta; - - //! The index of the layer to run. - size_t index; - - //! Indicates whether to use index or not - bool hasIndex; - - //! Execute the Backward() function if the module does not have Run() - //! check. - template - typename std::enable_if< - !HasRunCheck::value, void>::type - LayerBackward(T* layer, arma::mat& input) const; - - //! Execute the Backward() function if the module is has Run() function. - template - typename std::enable_if< - HasRunCheck::value, void>::type - LayerBackward(T* layer, arma::mat& input) const; -}; - -} // namespace ann -} // namespace mlpack - -// Include implementation. -#include "backward_visitor_impl.hpp" - -#endif diff --git a/src/mlpack/methods/ann/visitor/backward_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/backward_visitor_impl.hpp deleted file mode 100644 index 24f6c180d9..0000000000 --- a/src/mlpack/methods/ann/visitor/backward_visitor_impl.hpp +++ /dev/null @@ -1,84 +0,0 @@ -/** - * @file methods/ann/visitor/backward_visitor_impl.hpp - * @author Marcus Edel - * - * Implementation of the Backward() function layer abstraction. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_BACKWARD_VISITOR_IMPL_HPP -#define MLPACK_METHODS_ANN_VISITOR_BACKWARD_VISITOR_IMPL_HPP - -// In case it hasn't been included yet. -#include "backward_visitor.hpp" - -namespace mlpack { -namespace ann { - -//! BackwardVisitor visitor class. -inline BackwardVisitor::BackwardVisitor(const arma::mat& input, - const arma::mat& error, - arma::mat& delta) : - input(input), - error(error), - delta(delta), - index(0), - hasIndex(false) -{ - /* Nothing to do here. */ -} - -inline BackwardVisitor::BackwardVisitor(const arma::mat& input, - const arma::mat& error, - arma::mat& delta, - const size_t index) : - input(input), - error(error), - delta(delta), - index(index), - hasIndex(true) -{ - /* Nothing to do here. */ -} - -template -inline void BackwardVisitor::operator()(LayerType* layer) const -{ - LayerBackward(layer, layer->OutputParameter()); -} - -inline void BackwardVisitor::operator()(MoreTypes layer) const -{ - layer.apply_visitor(*this); -} - -template -inline typename std::enable_if< - !HasRunCheck::value, void>::type -BackwardVisitor::LayerBackward(T* layer, arma::mat& /* input */) const -{ - layer->Backward(input, error, delta); -} - -template -inline typename std::enable_if< - HasRunCheck::value, void>::type -BackwardVisitor::LayerBackward(T* layer, arma::mat& /* input */) const -{ - if (!hasIndex) - { - layer->Backward(input, error, delta); - } - else - { - layer->Backward(input, error, delta, index); - } -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/visitor/bias_set_visitor.hpp b/src/mlpack/methods/ann/visitor/bias_set_visitor.hpp deleted file mode 100644 index c081c73a2a..0000000000 --- a/src/mlpack/methods/ann/visitor/bias_set_visitor.hpp +++ /dev/null @@ -1,82 +0,0 @@ -/** - * @file methods/ann/visitor/bias_set_visitor.hpp - * @author Toshal Agrawal - * - * This file provides an abstraction for the Bias() function for different - * layers and automatically directs any parameter to the right layer type. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_BIAS_SET_VISITOR_HPP -#define MLPACK_METHODS_ANN_VISITOR_BIAS_SET_VISITOR_HPP - -#include - -#include - -namespace mlpack { -namespace ann { - -/** - * BiasSetVisitor updates the module bias parameters given the parameters set. - */ -class BiasSetVisitor : public boost::static_visitor -{ - public: - //! Update the bias parameters given the parameters' set and offset. - BiasSetVisitor(arma::mat& weight, const size_t offset = 0); - - //! Update the parameters' set. - template - size_t operator()(LayerType* layer) const; - - size_t operator()(MoreTypes layer) const; - - private: - //! The parameters' set. - arma::mat& weight; - - //! The parameters' offset. - const size_t offset; - - //! Do not update the bias parameters if the module doesn't implement the - //! Bias() or Model() function. - template - typename std::enable_if< - !HasBiasCheck::value && - !HasModelCheck::value, size_t>::type - LayerSize(T* layer) const; - - //! Update the bias parameters if the module implements the Model() function. - template - typename std::enable_if< - !HasBiasCheck::value && - HasModelCheck::value, size_t>::type - LayerSize(T* layer) const; - - //! Update the bias parameters if the module implements the Bias() function. - template - typename std::enable_if< - HasBiasCheck::value && - !HasModelCheck::value, size_t>::type - LayerSize(T* layer) const; - - //! Update the bias parameters if the module implements the Model() and - //! Bias() function. - template - typename std::enable_if< - HasBiasCheck::value && - HasModelCheck::value, size_t>::type - LayerSize(T* layer) const; -}; - -} // namespace ann -} // namespace mlpack - -// Include implementation. -#include "bias_set_visitor_impl.hpp" - -#endif diff --git a/src/mlpack/methods/ann/visitor/bias_set_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/bias_set_visitor_impl.hpp deleted file mode 100644 index 2eacbb61d6..0000000000 --- a/src/mlpack/methods/ann/visitor/bias_set_visitor_impl.hpp +++ /dev/null @@ -1,101 +0,0 @@ -/** - * @file methods/ann/visitor/bias_set_visitor_impl.hpp - * @author Toshal Agrawal - * - * Implementation of the Bias() function layer abstraction. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_BIAS_SET_VISITOR_IMPL_HPP -#define MLPACK_METHODS_ANN_VISITOR_BIAS_SET_VISITOR_IMPL_HPP - -// In case it hasn't been included yet. -#include "bias_set_visitor.hpp" - -namespace mlpack { -namespace ann { - -//! BiasSetVisitor visitor class. -inline BiasSetVisitor::BiasSetVisitor(arma::mat& weight, const size_t offset) : - weight(weight), - offset(offset) -{ - /* Nothing to do here. */ -} - -template -inline size_t BiasSetVisitor::operator()(LayerType* layer) const -{ - return LayerSize(layer); -} - -inline size_t BiasSetVisitor::operator()(MoreTypes layer) const -{ - return layer.apply_visitor(*this); -} - -template -inline typename std::enable_if< - !HasBiasCheck::value && - !HasModelCheck::value, size_t>::type -BiasSetVisitor::LayerSize(T* /* layer */) const -{ - return 0; -} - -template -inline typename std::enable_if< - !HasBiasCheck::value && - HasModelCheck::value, size_t>::type -BiasSetVisitor::LayerSize(T* layer) const -{ - size_t modelOffset = 0; - - for (size_t i = 0; i < layer->Model().size(); ++i) - { - modelOffset += boost::apply_visitor(BiasSetVisitor( - weight, modelOffset + offset), layer->Model()[i]); - } - - return modelOffset; -} - -template -inline typename std::enable_if< - HasBiasCheck::value && - !HasModelCheck::value, size_t>::type -BiasSetVisitor::LayerSize(T* layer) const -{ - layer->Bias() = arma::mat(weight.memptr() + offset, - layer->Bias().n_rows, layer->Bias().n_cols, false, false); - - return layer->Bias().n_elem; -} - -template -inline typename std::enable_if< - HasBiasCheck::value && - HasModelCheck::value, size_t>::type -BiasSetVisitor::LayerSize(T* layer) const -{ - layer->Bias() = arma::mat(weight.memptr() + offset, - layer->Bias().n_rows, layer->Bias().n_cols, false, false); - - size_t modelOffset = layer->Bias().n_elem; - - for (size_t i = 0; i < layer->Model().size(); ++i) - { - modelOffset += boost::apply_visitor(BiasSetVisitor( - weight, modelOffset + offset), layer->Model()[i]); - } - - return modelOffset; -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/visitor/copy_visitor.hpp b/src/mlpack/methods/ann/visitor/copy_visitor.hpp deleted file mode 100644 index 14bf5f91ad..0000000000 --- a/src/mlpack/methods/ann/visitor/copy_visitor.hpp +++ /dev/null @@ -1,41 +0,0 @@ -/** - * @file methods/ann/visitor/copy_visitor.hpp - * @author Shangtong Zhang - * - * This file provides an abstraction for copy between layers. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_COPY_VISITOR_HPP -#define MLPACK_METHODS_ANN_VISITOR_COPY_VISITOR_HPP - -#include -#include - -namespace mlpack { -namespace ann { - -/** - * This visitor is to support copy constructor for neural network module. - * We want a layer-wise copy rather than simple duplicate the pointer. - */ -template -class CopyVisitor : public boost::static_visitor > -{ - public: - template - LayerTypes operator()(LayerType*) const; - - LayerTypes operator()(MoreTypes) const; -}; - -} // namespace ann -} // namespace mlpack - -// Include implementation -#include "copy_visitor_impl.hpp" -#endif - diff --git a/src/mlpack/methods/ann/visitor/copy_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/copy_visitor_impl.hpp deleted file mode 100644 index d143e18799..0000000000 --- a/src/mlpack/methods/ann/visitor/copy_visitor_impl.hpp +++ /dev/null @@ -1,39 +0,0 @@ -/** - * @file methods/ann/visitor/copy_visitor_impl.hpp - * @author Shangtong Zhang - * - * This file provides an implementation for copy between layers - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_COPY_VISITOR_IMPL_HPP -#define MLPACK_METHODS_ANN_VISITOR_COPY_VISITOR_IMPL_HPP - -#include -#include - -namespace mlpack { -namespace ann { - -template -template -inline LayerTypes -CopyVisitor::operator()(LayerType* layer) const -{ - return new LayerType(*layer); -} - -template -inline LayerTypes -CopyVisitor::operator()(MoreTypes layer) const -{ - return layer.apply_visitor(*this); -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/visitor/delete_visitor.hpp b/src/mlpack/methods/ann/visitor/delete_visitor.hpp deleted file mode 100644 index d65f4e3e4d..0000000000 --- a/src/mlpack/methods/ann/visitor/delete_visitor.hpp +++ /dev/null @@ -1,51 +0,0 @@ -/** - * @file methods/ann/visitor/delete_visitor.hpp - * @author Marcus Edel - * - * This file provides an abstraction for the Delete() function for different - * layers and automatically directs any parameter to the right layer type. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_DELETE_VISITOR_HPP -#define MLPACK_METHODS_ANN_VISITOR_DELETE_VISITOR_HPP - -#include -#include - -#include - -namespace mlpack { -namespace ann { - -/** - * DeleteVisitor executes the destructor of the instantiated object. - */ -class DeleteVisitor : public boost::static_visitor -{ - public: - //! Execute the destructor if the layer does not hold layers internally. - template - typename std::enable_if< - !HasModelCheck::value, void>::type - operator()(LayerType* layer) const; - - //! Execute the destructor if the layer does hold layers internally. - template - typename std::enable_if< - HasModelCheck::value, void>::type - operator()(LayerType* layer) const; - - void operator()(MoreTypes layer) const; -}; - -} // namespace ann -} // namespace mlpack - -// Include implementation. -#include "delete_visitor_impl.hpp" - -#endif diff --git a/src/mlpack/methods/ann/visitor/delete_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/delete_visitor_impl.hpp deleted file mode 100644 index f9f43936e3..0000000000 --- a/src/mlpack/methods/ann/visitor/delete_visitor_impl.hpp +++ /dev/null @@ -1,53 +0,0 @@ -/** - * @file methods/ann/visitor/delete_visitor_impl.hpp - * @author Marcus Edel - * - * Implementation of the Delete() function layer abstraction. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_DELETE_VISITOR_IMPL_HPP -#define MLPACK_METHODS_ANN_VISITOR_DELETE_VISITOR_IMPL_HPP - -// In case it hasn't been included yet. -#include "delete_visitor.hpp" - -namespace mlpack { -namespace ann { - -//! DeleteVisitor visitor class. -template -inline typename std::enable_if< - !HasModelCheck::value, void>::type -DeleteVisitor::operator()(LayerType* layer) const -{ - if (layer) - delete layer; -} - -template -inline typename std::enable_if< - HasModelCheck::value, void>::type -DeleteVisitor::operator()(LayerType* layer) const -{ - if (layer) - { - for (size_t i = 0; i < layer->Model().size(); ++i) - boost::apply_visitor(DeleteVisitor(), layer->Model()[i]); - - delete layer; - } -} - -inline void DeleteVisitor::operator()(MoreTypes layer) const -{ - layer.apply_visitor(*this); -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/visitor/delta_visitor.hpp b/src/mlpack/methods/ann/visitor/delta_visitor.hpp deleted file mode 100644 index 12ecfd2017..0000000000 --- a/src/mlpack/methods/ann/visitor/delta_visitor.hpp +++ /dev/null @@ -1,43 +0,0 @@ -/** - * @file methods/ann/visitor/delta_visitor.hpp - * @author Marcus Edel - * - * This file provides an abstraction for the Delta() function for different - * layers and automatically directs any parameter to the right layer type. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_DELTA_VISITOR_HPP -#define MLPACK_METHODS_ANN_VISITOR_DELTA_VISITOR_HPP - -#include -#include - -#include - -namespace mlpack { -namespace ann { - -/** - * DeltaVisitor exposes the delta parameter of the given module. - */ -class DeltaVisitor : public boost::static_visitor -{ - public: - //! Return the delta parameter. - template - arma::mat& operator()(LayerType* layer) const; - - arma::mat& operator()(MoreTypes layer) const; -}; - -} // namespace ann -} // namespace mlpack - -// Include implementation. -#include "delta_visitor_impl.hpp" - -#endif diff --git a/src/mlpack/methods/ann/visitor/delta_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/delta_visitor_impl.hpp deleted file mode 100644 index 3a8e2b6b92..0000000000 --- a/src/mlpack/methods/ann/visitor/delta_visitor_impl.hpp +++ /dev/null @@ -1,36 +0,0 @@ -/** - * @file methods/ann/visitor/delta_visitor_impl.hpp - * @author Marcus Edel - * - * Implementation of the Delta() function layer abstraction. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_DELTA_VISITOR_IMPL_HPP -#define MLPACK_METHODS_ANN_VISITOR_DELTA_VISITOR_IMPL_HPP - -// In case it hasn't been included yet. -#include "delta_visitor.hpp" - -namespace mlpack { -namespace ann { - -//! DeltaVisitor visitor class. -template -inline arma::mat& DeltaVisitor::operator()(LayerType *layer) const -{ - return layer->Delta(); -} - -inline arma::mat& DeltaVisitor::operator()(MoreTypes layer) const -{ - return layer.apply_visitor(*this); -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/visitor/deterministic_set_visitor.hpp b/src/mlpack/methods/ann/visitor/deterministic_set_visitor.hpp deleted file mode 100644 index f70d740e80..0000000000 --- a/src/mlpack/methods/ann/visitor/deterministic_set_visitor.hpp +++ /dev/null @@ -1,83 +0,0 @@ -/** - * @file methods/ann/visitor/deterministic_set_visitor.hpp - * @author Marcus Edel - * - * This file provides an abstraction for the Deterministic() function for - * different layers and automatically directs any parameter to the right layer - * type. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_DETERMINISTIC_SET_VISITOR_HPP -#define MLPACK_METHODS_ANN_VISITOR_DETERMINISTIC_SET_VISITOR_HPP - -#include - -#include - -namespace mlpack { -namespace ann { - -/** - * DeterministicSetVisitor set the deterministic parameter given the - * deterministic value. - */ -class DeterministicSetVisitor : public boost::static_visitor -{ - public: - //! Set the deterministic parameter given the current deterministic value. - DeterministicSetVisitor(const bool deterministic = true); - - //! Set the deterministic parameter. - template - void operator()(LayerType* layer) const; - - void operator()(MoreTypes layer) const; - - private: - //! The deterministic parameter. - const bool deterministic; - - //! Set the deterministic parameter if the module implements the - //! Deterministic() and Model() function. - template - typename std::enable_if< - HasDeterministicCheck::value && - HasModelCheck::value, void>::type - LayerDeterministic(T* layer) const; - - //! Set the deterministic parameter if the module implements the - //! Model() function. - template - typename std::enable_if< - !HasDeterministicCheck::value && - HasModelCheck::value, void>::type - LayerDeterministic(T* layer) const; - - //! Set the deterministic parameter if the module implements the - //! Deterministic() function. - template - typename std::enable_if< - HasDeterministicCheck::value && - !HasModelCheck::value, void>::type - LayerDeterministic(T* layer) const; - - //! Do not set the deterministic parameter if the module doesn't implement the - //! Deterministic() or Model() function. - template - typename std::enable_if< - !HasDeterministicCheck::value && - !HasModelCheck::value, void>::type - LayerDeterministic(T* layer) const; -}; - -} // namespace ann -} // namespace mlpack - -// Include implementation. -#include "deterministic_set_visitor_impl.hpp" - -#endif diff --git a/src/mlpack/methods/ann/visitor/deterministic_set_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/deterministic_set_visitor_impl.hpp deleted file mode 100644 index 06d8bafb03..0000000000 --- a/src/mlpack/methods/ann/visitor/deterministic_set_visitor_impl.hpp +++ /dev/null @@ -1,88 +0,0 @@ -/** - * @file methods/ann/visitor/deterministic_set_visitor_impl.hpp - * @author Marcus Edel - * - * Implementation of the Deterministic() function layer abstraction. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_DETERMINISTIC_SET_VISITOR_IMPL_HPP -#define MLPACK_METHODS_ANN_VISITOR_DETERMINISTIC_SET_VISITOR_IMPL_HPP - -// In case it hasn't been included yet. -#include "deterministic_set_visitor.hpp" - -namespace mlpack { -namespace ann { - -//! DeterministicSetVisitor visitor class. -inline DeterministicSetVisitor::DeterministicSetVisitor( - const bool deterministic) : deterministic(deterministic) -{ - /* Nothing to do here. */ -} - -template -inline void DeterministicSetVisitor::operator()(LayerType* layer) const -{ - LayerDeterministic(layer); -} - -inline void DeterministicSetVisitor::operator()(MoreTypes layer) const -{ - layer.apply_visitor(*this); -} - -template -inline typename std::enable_if< - HasDeterministicCheck::value && - HasModelCheck::value, void>::type -DeterministicSetVisitor::LayerDeterministic(T* layer) const -{ - layer->Deterministic() = deterministic; - - for (size_t i = 0; i < layer->Model().size(); ++i) - { - boost::apply_visitor(DeterministicSetVisitor(deterministic), - layer->Model()[i]); - } -} - -template -inline typename std::enable_if< - !HasDeterministicCheck::value && - HasModelCheck::value, void>::type -DeterministicSetVisitor::LayerDeterministic(T* layer) const -{ - for (size_t i = 0; i < layer->Model().size(); ++i) - { - boost::apply_visitor(DeterministicSetVisitor(deterministic), - layer->Model()[i]); - } -} - -template -inline typename std::enable_if< - HasDeterministicCheck::value && - !HasModelCheck::value, void>::type -DeterministicSetVisitor::LayerDeterministic(T* layer) const -{ - layer->Deterministic() = deterministic; -} - -template -inline typename std::enable_if< - !HasDeterministicCheck::value && - !HasModelCheck::value, void>::type -DeterministicSetVisitor::LayerDeterministic(T* /* input */) const -{ - /* Nothing to do here. */ -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/visitor/forward_visitor.hpp b/src/mlpack/methods/ann/visitor/forward_visitor.hpp deleted file mode 100644 index ac825f11ee..0000000000 --- a/src/mlpack/methods/ann/visitor/forward_visitor.hpp +++ /dev/null @@ -1,54 +0,0 @@ -/** - * @file methods/ann/visitor/forward_visitor.hpp - * @author Marcus Edel - * - * This file provides an abstraction for the Forward() function for different - * layers and automatically directs any parameter to the right layer type. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_FORWARD_VISITOR_HPP -#define MLPACK_METHODS_ANN_VISITOR_FORWARD_VISITOR_HPP - -#include -#include - -#include - -namespace mlpack { -namespace ann { - -/** - * ForwardVisitor executes the Forward() function given the input and output - * parameter. - */ -class ForwardVisitor : public boost::static_visitor -{ - public: - //! Execute the Forward() function given the input and output parameter. - ForwardVisitor(const arma::mat& input, arma::mat& output); - - //! Execute the Forward() function. - template - void operator()(LayerType* layer) const; - - void operator()(MoreTypes layer) const; - - private: - //! The input parameter set. - const arma::mat& input; - - //! The output parameter set. - arma::mat& output; -}; - -} // namespace ann -} // namespace mlpack - -// Include implementation. -#include "forward_visitor_impl.hpp" - -#endif diff --git a/src/mlpack/methods/ann/visitor/forward_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/forward_visitor_impl.hpp deleted file mode 100644 index 248d852c53..0000000000 --- a/src/mlpack/methods/ann/visitor/forward_visitor_impl.hpp +++ /dev/null @@ -1,43 +0,0 @@ -/** - * @file methods/ann/visitor/forward_visitor_impl.hpp - * @author Marcus Edel - * - * Implementation of the Forward() function layer abstraction. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_FORWARD_VISITOR_IMPL_HPP -#define MLPACK_METHODS_ANN_VISITOR_FORWARD_VISITOR_IMPL_HPP - -// In case it hasn't been included yet. -#include "forward_visitor.hpp" - -namespace mlpack { -namespace ann { - -//! ForwardVisitor visitor class. -inline ForwardVisitor::ForwardVisitor(const arma::mat& input, arma::mat& output) : - input(input), - output(output) -{ - /* Nothing to do here. */ -} - -template -inline void ForwardVisitor::operator()(LayerType* layer) const -{ - layer->Forward(input, output); -} - -inline void ForwardVisitor::operator()(MoreTypes layer) const -{ - layer.apply_visitor(*this); -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/visitor/gradient_set_visitor.hpp b/src/mlpack/methods/ann/visitor/gradient_set_visitor.hpp deleted file mode 100644 index 4c492b3f0e..0000000000 --- a/src/mlpack/methods/ann/visitor/gradient_set_visitor.hpp +++ /dev/null @@ -1,82 +0,0 @@ -/** - * @file methods/ann/visitor/gradient_set_visitor.hpp - * @author Marcus Edel - * - * This file provides an abstraction for the Gradient() function for different - * layers and automatically directs any parameter to the right layer type. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_GRADIENT_SET_VISITOR_HPP -#define MLPACK_METHODS_ANN_VISITOR_GRADIENT_SET_VISITOR_HPP - -#include - -#include - -namespace mlpack { -namespace ann { - -/** - * GradientSetVisitor update the gradient parameter given the gradient set. - */ -class GradientSetVisitor : public boost::static_visitor -{ - public: - //! Update the gradient parameter given the gradient set. - GradientSetVisitor(arma::mat& gradient, size_t offset = 0); - - //! Update the gradient parameter. - template - size_t operator()(LayerType* layer) const; - - size_t operator()(MoreTypes layer) const; - - private: - //! The gradient set. - arma::mat& gradient; - - //! The gradient offset. - size_t offset; - - //! Update the gradient if the module implements the Gradient() function. - template - typename std::enable_if< - HasGradientCheck::value && - !HasModelCheck::value, size_t>::type - LayerGradients(T* layer, arma::mat& input) const; - - //! Update the gradient if the module implements the Model() function. - template - typename std::enable_if< - !HasGradientCheck::value && - HasModelCheck::value, size_t>::type - LayerGradients(T* layer, arma::mat& input) const; - - //! Update the gradient if the module implements the Gradient() and Model() - //! function. - template - typename std::enable_if< - HasGradientCheck::value && - HasModelCheck::value, size_t>::type - LayerGradients(T* layer, arma::mat& input) const; - - //! Do not update the gradient parameter if the module doesn't implement the - //! Gradient() or Model() function. - template - typename std::enable_if< - !HasGradientCheck::value && - !HasModelCheck::value, size_t>::type - LayerGradients(T* layer, P& input) const; -}; - -} // namespace ann -} // namespace mlpack - -// Include implementation. -#include "gradient_set_visitor_impl.hpp" - -#endif diff --git a/src/mlpack/methods/ann/visitor/gradient_set_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/gradient_set_visitor_impl.hpp deleted file mode 100644 index 85e148da25..0000000000 --- a/src/mlpack/methods/ann/visitor/gradient_set_visitor_impl.hpp +++ /dev/null @@ -1,100 +0,0 @@ -/** - * @file methods/ann/visitor/gradient_set_visitor_impl.hpp - * @author Marcus Edel - * - * Implementation of the Gradient() function layer abstraction. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_GRADIENT_SET_VISITOR_IMPL_HPP -#define MLPACK_METHODS_ANN_VISITOR_GRADIENT_SET_VISITOR_IMPL_HPP - -// In case it hasn't been included yet. -#include "gradient_set_visitor.hpp" - -namespace mlpack { -namespace ann { - -//! GradientSetVisitor visitor class. -inline GradientSetVisitor::GradientSetVisitor(arma::mat& gradient, - size_t offset) : - gradient(gradient), - offset(offset) -{ - /* Nothing to do here. */ -} - -template -inline size_t GradientSetVisitor::operator()(LayerType* layer) const -{ - return LayerGradients(layer, layer->OutputParameter()); -} - -inline size_t GradientSetVisitor::operator()(MoreTypes layer) const -{ - return layer.apply_visitor(*this); -} - -template -inline typename std::enable_if< - HasGradientCheck::value && - !HasModelCheck::value, size_t>::type -GradientSetVisitor::LayerGradients(T* layer, arma::mat& /* input */) const -{ - layer->Gradient() = arma::mat(gradient.memptr() + offset, - layer->Parameters().n_rows, layer->Parameters().n_cols, false, false); - - return layer->Parameters().n_elem; -} - -template -inline typename std::enable_if< - !HasGradientCheck::value && - HasModelCheck::value, size_t>::type -GradientSetVisitor::LayerGradients(T* layer, arma::mat& /* input */) const -{ - size_t modelOffset = 0; - for (size_t i = 0; i < layer->Model().size(); ++i) - { - modelOffset += boost::apply_visitor(GradientSetVisitor( - gradient, modelOffset + offset), layer->Model()[i]); - } - - return modelOffset; -} - -template -inline typename std::enable_if< - HasGradientCheck::value && - HasModelCheck::value, size_t>::type -GradientSetVisitor::LayerGradients(T* layer, arma::mat& /* input */) const -{ - layer->Gradient() = arma::mat(gradient.memptr() + offset, - layer->Parameters().n_rows, layer->Parameters().n_cols, false, false); - - size_t modelOffset = layer->Parameters().n_elem; - for (size_t i = 0; i < layer->Model().size(); ++i) - { - modelOffset += boost::apply_visitor(GradientSetVisitor( - gradient, modelOffset + offset), layer->Model()[i]); - } - - return modelOffset; -} - -template -inline typename std::enable_if< - !HasGradientCheck::value && - !HasModelCheck::value, size_t>::type -GradientSetVisitor::LayerGradients(T* /* layer */, P& /* input */) const -{ - return 0; -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/visitor/gradient_update_visitor.hpp b/src/mlpack/methods/ann/visitor/gradient_update_visitor.hpp deleted file mode 100644 index feedf0d299..0000000000 --- a/src/mlpack/methods/ann/visitor/gradient_update_visitor.hpp +++ /dev/null @@ -1,82 +0,0 @@ -/** - * @file methods/ann/visitor/gradient_update_visitor.hpp - * @author Marcus Edel - * - * This file provides an abstraction for the Gradient() function for different - * layers and automatically directs any parameter to the right layer type. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_GRADIENT_UPDATE_VISITOR_HPP -#define MLPACK_METHODS_ANN_VISITOR_GRADIENT_UPDATE_VISITOR_HPP - -#include - -#include - -namespace mlpack { -namespace ann { - -/** - * GradientUpdateVisitor update the gradient parameter given the gradient set. - */ -class GradientUpdateVisitor : public boost::static_visitor -{ - public: - //! Update the gradient parameter given the gradient set. - GradientUpdateVisitor(arma::mat& gradient, size_t offset = 0); - - //! Update the gradient parameter. - template - size_t operator()(LayerType* layer) const; - - size_t operator()(MoreTypes layer) const; - - private: - //! The gradient set. - arma::mat& gradient; - - //! The gradient offset. - size_t offset; - - //! Update the gradient if the module implements the Gradient() function. - template - typename std::enable_if< - HasGradientCheck::value && - !HasModelCheck::value, size_t>::type - LayerGradients(T* layer, arma::mat& input) const; - - //! Update the gradient if the module implements the Model() function. - template - typename std::enable_if< - !HasGradientCheck::value && - HasModelCheck::value, size_t>::type - LayerGradients(T* layer, arma::mat& input) const; - - //! Update the gradient if the module implements the Gradient() and Model() - //! function. - template - typename std::enable_if< - HasGradientCheck::value && - HasModelCheck::value, size_t>::type - LayerGradients(T* layer, arma::mat& input) const; - - //! Do not update the gradient parameter if the module doesn't implement the - //! Gradient() or Model() function. - template - typename std::enable_if< - !HasGradientCheck::value && - !HasModelCheck::value, size_t>::type - LayerGradients(T* layer, P& input) const; -}; - -} // namespace ann -} // namespace mlpack - -// Include implementation. -#include "gradient_update_visitor_impl.hpp" - -#endif diff --git a/src/mlpack/methods/ann/visitor/gradient_update_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/gradient_update_visitor_impl.hpp deleted file mode 100644 index f233eaf65d..0000000000 --- a/src/mlpack/methods/ann/visitor/gradient_update_visitor_impl.hpp +++ /dev/null @@ -1,106 +0,0 @@ -/** - * @file methods/ann/visitor/gradient_update_visitor_impl.hpp - * @author Marcus Edel - * - * Implementation of the Gradient() function layer abstraction. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_GRADIENT_UPDATE_VISITOR_IMPL_HPP -#define MLPACK_METHODS_ANN_VISITOR_GRADIENT_UPDATE_VISITOR_IMPL_HPP - -// In case it hasn't been included yet. -#include "gradient_update_visitor.hpp" - -namespace mlpack { -namespace ann { - -//! GradientUpdateVisitor visitor class. -inline GradientUpdateVisitor::GradientUpdateVisitor(arma::mat& gradient, - size_t offset) : - gradient(gradient), - offset(offset) -{ - /* Nothing to do here. */ -} - -template -inline size_t GradientUpdateVisitor::operator()(LayerType* layer) const -{ - return LayerGradients(layer, layer->OutputParameter()); -} - -inline size_t GradientUpdateVisitor::operator()(MoreTypes layer) const -{ - return layer.apply_visitor(*this); -} - -template -inline typename std::enable_if< - HasGradientCheck::value && - !HasModelCheck::value, size_t>::type -GradientUpdateVisitor::LayerGradients(T* layer, arma::mat& /* input */) const -{ - if (layer->Parameters().n_elem != 0) - { - layer->Gradient() = gradient.submat(offset, 0, - offset + layer->Parameters().n_elem - 1, 0);; - } - - return layer->Parameters().n_elem; -} - -template -inline typename std::enable_if< - !HasGradientCheck::value && - HasModelCheck::value, size_t>::type -GradientUpdateVisitor::LayerGradients(T* layer, arma::mat& /* input */) const -{ - size_t modelOffset = 0; - for (size_t i = 0; i < layer->Model().size(); ++i) - { - modelOffset += boost::apply_visitor(GradientUpdateVisitor( - gradient, modelOffset + offset), layer->Model()[i]); - } - - return modelOffset; -} - -template -inline typename std::enable_if< - HasGradientCheck::value && - HasModelCheck::value, size_t>::type -GradientUpdateVisitor::LayerGradients(T* layer, arma::mat& /* input */) const -{ - if (layer->Parameters().n_elem != 0) - { - layer->Gradient() = gradient.submat(offset, 0, - offset + layer->Parameters().n_elem - 1, 0);; - } - - size_t modelOffset = layer->Parameters().n_elem; - for (size_t i = 0; i < layer->Model().size(); ++i) - { - modelOffset += boost::apply_visitor(GradientUpdateVisitor( - gradient, modelOffset + offset), layer->Model()[i]); - } - - return modelOffset; -} - -template -inline typename std::enable_if< - !HasGradientCheck::value && - !HasModelCheck::value, size_t>::type -GradientUpdateVisitor::LayerGradients(T* /* layer */, P& /* input */) const -{ - return 0; -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/visitor/gradient_visitor.hpp b/src/mlpack/methods/ann/visitor/gradient_visitor.hpp deleted file mode 100644 index fc04c96161..0000000000 --- a/src/mlpack/methods/ann/visitor/gradient_visitor.hpp +++ /dev/null @@ -1,89 +0,0 @@ -/** - * @file methods/ann/visitor/gradient_visitor.hpp - * @author Marcus Edel - * - * This file provides an abstraction for the Gradient() function for different - * layers and automatically directs any parameter to the right layer type. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_GRADIENT_VISITOR_HPP -#define MLPACK_METHODS_ANN_VISITOR_GRADIENT_VISITOR_HPP - -#include -#include - -#include - -namespace mlpack { -namespace ann { - -/** - * SearchModeVisitor executes the Gradient() method of the given module using - * the input and delta parameter. - */ -class GradientVisitor : public boost::static_visitor -{ - public: - //! Executes the Gradient() method of the given module using the input and - //! delta parameter. - GradientVisitor(const arma::mat& input, const arma::mat& delta); - - //! Executes the Gradient() method for the layer with the specified index. - GradientVisitor(const arma::mat& input, - const arma::mat& delta, - const size_t index); - - //! Executes the Gradient() method. - template - void operator()(LayerType* layer) const; - - void operator()(MoreTypes layer) const; - - private: - //! The input set. - const arma::mat& input; - - //! The delta parameter. - const arma::mat& delta; - - //! Index of the layer to run. - size_t index; - - //! Indicates whether to use index or not - bool hasIndex; - - //! Execute the Gradient() function if the module implements the Gradient() - //! function. - template - typename std::enable_if< - HasGradientCheck::value && - !HasRunCheck::value, void>::type - LayerGradients(T* layer, arma::mat& input) const; - - //! Execute the Gradient() function if the module implements the Gradient() - //! and has a Run() function. - template - typename std::enable_if< - HasGradientCheck::value && - HasRunCheck::value, void>::type - LayerGradients(T* layer, arma::mat& input) const; - - //! Do not execute the Gradient() function if the module doesn't implement - //! the Gradient() function. - template - typename std::enable_if< - !HasGradientCheck::value, void>::type - LayerGradients(T* layer, P& input) const; -}; - -} // namespace ann -} // namespace mlpack - -// Include implementation. -#include "gradient_visitor_impl.hpp" - -#endif diff --git a/src/mlpack/methods/ann/visitor/gradient_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/gradient_visitor_impl.hpp deleted file mode 100644 index 3537aa9959..0000000000 --- a/src/mlpack/methods/ann/visitor/gradient_visitor_impl.hpp +++ /dev/null @@ -1,90 +0,0 @@ -/** - * @file methods/ann/visitor/gradient_visitor_impl.hpp - * @author Marcus Edel - * - * Implementation of the Gradient() function layer abstraction. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_GRADIENT_VISITOR_IMPL_HPP -#define MLPACK_METHODS_ANN_VISITOR_GRADIENT_VISITOR_IMPL_HPP - -// In case it hasn't been included yet. -#include "gradient_visitor.hpp" - -namespace mlpack { -namespace ann { - -//! GradientVisitor visitor class. -inline GradientVisitor::GradientVisitor(const arma::mat& input, - const arma::mat& delta) : - input(input), - delta(delta), - index(0), - hasIndex(false) -{ - /* Nothing to do here. */ -} - -inline GradientVisitor::GradientVisitor(const arma::mat& input, - const arma::mat& delta, - const size_t index) : - input(input), - delta(delta), - index(index), - hasIndex(true) -{ - /* Nothing to do here. */ -} - -template -inline void GradientVisitor::operator()(LayerType* layer) const -{ - LayerGradients(layer, layer->OutputParameter()); -} - -inline void GradientVisitor::operator()(MoreTypes layer) const -{ - layer.apply_visitor(*this); -} - -template -inline typename std::enable_if< - HasGradientCheck::value && - !HasRunCheck::value, void>::type -GradientVisitor::LayerGradients(T* layer, arma::mat& /* input */) const -{ - layer->Gradient(input, delta, layer->Gradient()); -} - -template -inline typename std::enable_if< - HasGradientCheck::value && - HasRunCheck::value, void>::type -GradientVisitor::LayerGradients(T* layer, arma::mat& /* input */) const -{ - if (!hasIndex) - { - layer->Gradient(input, delta, layer->Gradient()); - } - else - { - layer->Gradient(input, delta, layer->Gradient(), index); - } -} - -template -inline typename std::enable_if< - !HasGradientCheck::value, void>::type -GradientVisitor::LayerGradients(T* /* layer */, P& /* input */) const -{ - /* Nothing to do here. */ -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/visitor/gradient_zero_visitor.hpp b/src/mlpack/methods/ann/visitor/gradient_zero_visitor.hpp deleted file mode 100644 index 3480796089..0000000000 --- a/src/mlpack/methods/ann/visitor/gradient_zero_visitor.hpp +++ /dev/null @@ -1,60 +0,0 @@ -/** - * @file methods/ann/visitor/gradient_zero_visitor.hpp - * @author Marcus Edel - * - * This file provides an abstraction for the Gradient() function for different - * layers and automatically directs any parameter to the right layer type. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_GRADIENT_ZERO_VISITOR_HPP -#define MLPACK_METHODS_ANN_VISITOR_GRADIENT_ZERO_VISITOR_HPP - -#include -#include - -#include - -namespace mlpack { -namespace ann { - -/* - * GradientZeroVisitor set the gradient to zero for the given module. - */ -class GradientZeroVisitor : public boost::static_visitor -{ - public: - //! Set the gradient to zero for the given module. - GradientZeroVisitor(); - - //! Set the gradient to zero. - template - void operator()(LayerType* layer) const; - - void operator()(MoreTypes layer) const; - - private: - //! Set the gradient to zero if the module implements the Gradient() function. - template - typename std::enable_if< - HasGradientCheck::value, void>::type - LayerGradients(T* layer, arma::mat& input) const; - - //! Do not set the gradient to zero if the module doesn't implement the - //! Gradient() function. - template - typename std::enable_if< - !HasGradientCheck::value, void>::type - LayerGradients(T* layer, P& input) const; -}; - -} // namespace ann -} // namespace mlpack - -// Include implementation. -#include "gradient_zero_visitor_impl.hpp" - -#endif diff --git a/src/mlpack/methods/ann/visitor/gradient_zero_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/gradient_zero_visitor_impl.hpp deleted file mode 100644 index de39a692a6..0000000000 --- a/src/mlpack/methods/ann/visitor/gradient_zero_visitor_impl.hpp +++ /dev/null @@ -1,57 +0,0 @@ -/** - * @file methods/ann/visitor/gradient_zero_visitor_impl.hpp - * @author Marcus Edel - * - * Implementation of the Gradient() function layer abstraction. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_GRADIENT_ZERO_VISITOR_IMPL_HPP -#define MLPACK_METHODS_ANN_VISITOR_GRADIENT_ZERO_VISITOR_IMPL_HPP - -// In case it hasn't been included yet. -#include "gradient_zero_visitor.hpp" - -namespace mlpack { -namespace ann { - -//! GradientZeroVisitor visitor class. -inline GradientZeroVisitor::GradientZeroVisitor() -{ - /* Nothing to do here. */ -} - -template -inline void GradientZeroVisitor::operator()(LayerType* layer) const -{ - LayerGradients(layer, layer->OutputParameter()); -} - -inline void GradientZeroVisitor::operator()(MoreTypes layer) const -{ - layer.apply_visitor(*this); -} - -template -inline typename std::enable_if< - HasGradientCheck::value, void>::type -GradientZeroVisitor::LayerGradients(T* layer, arma::mat& /* input */) const -{ - layer->Gradient().zeros(); -} - -template -inline typename std::enable_if< - !HasGradientCheck::value, void>::type -GradientZeroVisitor::LayerGradients(T* /* layer */, P& /* input */) const -{ - /* Nothing to do here. */ -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/visitor/input_shape_visitor.hpp b/src/mlpack/methods/ann/visitor/input_shape_visitor.hpp deleted file mode 100644 index c27135aae3..0000000000 --- a/src/mlpack/methods/ann/visitor/input_shape_visitor.hpp +++ /dev/null @@ -1,58 +0,0 @@ -/** - * @file methods/ann/visitor/input_shape_visitor.hpp - * @author Khizir Siddiqui - * @author Nippun Sharma - * - * This file provides an abstraction for the InputShape() function for - * different layers and automatically directs any parameter to the right layer - * type. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_INPUT_SHAPE_VISITOR_HPP -#define MLPACK_METHODS_ANN_VISITOR_INPUT_SHAPE_VISITOR_HPP - -#include -#include - -#include - -namespace mlpack { -namespace ann { - -/** - * InShapeVisitor returns the input shape a Layer expects. - */ -class InShapeVisitor : public boost::static_visitor -{ - public: - //! Return the input shape of layer. - template - size_t operator()(LayerType* layer) const; - - size_t operator()(MoreTypes layer) const; - - private: - //! If the module doesn't implement the InputShape() function return 0. - template - typename std::enable_if< - !HasInputShapeCheck::value, size_t>::type - LayerInputShape(T* layer) const; - - //! If the module implements the InputShape() function returns the input shape. - template - typename std::enable_if< - HasInputShapeCheck::value, size_t>::type - LayerInputShape(T* layer) const; -}; - -} // namespace ann -} // namespace mlpack - -// Include implementation. -#include "input_shape_visitor_impl.hpp" - -#endif diff --git a/src/mlpack/methods/ann/visitor/input_shape_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/input_shape_visitor_impl.hpp deleted file mode 100644 index bda5f7b604..0000000000 --- a/src/mlpack/methods/ann/visitor/input_shape_visitor_impl.hpp +++ /dev/null @@ -1,53 +0,0 @@ -/** - * @file methods/ann/visitor/input_shape_visitor_impl.hpp - * @author Khizir Siddiqui - * @author Nippun Sharma - * - * Implementation of the InputShape() function layer abstraction. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_INPUT_SHAPE_VISITOR_IMPL_HPP -#define MLPACK_METHODS_ANN_VISITOR_INPUT_SHAPE_VISITOR_IMPL_HPP - -// In case it hasn't been included yet. -#include "input_shape_visitor.hpp" - -namespace mlpack { -namespace ann { - -//! InShapeVisitor visitor class. -template -inline std::size_t InShapeVisitor::operator()(LayerType* layer) const -{ - return LayerInputShape(layer); -} - -inline std::size_t InShapeVisitor::operator()(MoreTypes layer) const -{ - return layer.apply_visitor(*this); -} - -template -inline typename std::enable_if< - !HasInputShapeCheck::value, std::size_t>::type -InShapeVisitor::LayerInputShape(T* /* layer */) const -{ - return 0; -} - -template -inline typename std::enable_if< - HasInputShapeCheck::value, std::size_t>::type -InShapeVisitor::LayerInputShape(T* layer) const -{ - return layer->InputShape(); -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/visitor/load_output_parameter_visitor.hpp b/src/mlpack/methods/ann/visitor/load_output_parameter_visitor.hpp deleted file mode 100644 index 2546d52f90..0000000000 --- a/src/mlpack/methods/ann/visitor/load_output_parameter_visitor.hpp +++ /dev/null @@ -1,65 +0,0 @@ -/** - * @file methods/ann/visitor/load_output_parameter_visitor.hpp - * @author Marcus Edel - * - * This file provides an abstraction for the OutputParameter() function for - * different layers and automatically directs any parameter to the right layer - * type. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_LOAD_OUTPUT_PARAMETER_VISITOR_HPP -#define MLPACK_METHODS_ANN_VISITOR_LOAD_OUTPUT_PARAMETER_VISITOR_HPP - -#include - -#include - -namespace mlpack { -namespace ann { - -/** - * LoadOutputParameterVisitor restores the output parameter using the given - * parameter set. - */ -class LoadOutputParameterVisitor : public boost::static_visitor -{ - public: - //! Restore the output parameter given a parameter set. - LoadOutputParameterVisitor(std::vector& parameter); - - //! Restore the output parameter. - template - void operator()(LayerType* layer) const; - - void operator()(MoreTypes layer) const; - - private: - //! The parameter set. - std::vector& parameter; - - //! Restore the output parameter for a module which doesn't implement the - //! Model() function. - template - typename std::enable_if< - !HasModelCheck::value, void>::type - OutputParameter(T* layer) const; - - //! Restore the output parameter for a module which implements the Model() - //! function. - template - typename std::enable_if< - HasModelCheck::value, void>::type - OutputParameter(T* layer) const; -}; - -} // namespace ann -} // namespace mlpack - -// Include implementation. -#include "load_output_parameter_visitor_impl.hpp" - -#endif diff --git a/src/mlpack/methods/ann/visitor/load_output_parameter_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/load_output_parameter_visitor_impl.hpp deleted file mode 100644 index 5c384643ee..0000000000 --- a/src/mlpack/methods/ann/visitor/load_output_parameter_visitor_impl.hpp +++ /dev/null @@ -1,66 +0,0 @@ -/** - * @file methods/ann/visitor/load_output_parameter_visitor_impl.hpp - * @author Marcus Edel - * - * Implementation of the OutputParameter() function layer abstraction. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_LOAD_OUTPUT_PARAMETER_VISITOR_IMPL_HPP -#define MLPACK_METHODS_ANN_VISITOR_LOAD_OUTPUT_PARAMETER_VISITOR_IMPL_HPP - -// In case it hasn't been included yet. -#include "load_output_parameter_visitor.hpp" - -namespace mlpack { -namespace ann { - -//! LoadOutputParameterVisitor visitor class. -inline LoadOutputParameterVisitor::LoadOutputParameterVisitor( - std::vector& parameter) : parameter(parameter) -{ - /* Nothing to do here. */ -} - -template -inline void LoadOutputParameterVisitor::operator()(LayerType* layer) const -{ - OutputParameter(layer); -} - -inline void LoadOutputParameterVisitor::operator()(MoreTypes layer) const -{ - layer.apply_visitor(*this); -} - -template -inline typename std::enable_if< - !HasModelCheck::value, void>::type -LoadOutputParameterVisitor::OutputParameter(T* layer) const -{ - layer->OutputParameter() = parameter.back(); - parameter.pop_back(); -} - -template -inline typename std::enable_if< - HasModelCheck::value, void>::type -LoadOutputParameterVisitor::OutputParameter(T* layer) const -{ - for (size_t i = 0; i < layer->Model().size(); ++i) - { - boost::apply_visitor(LoadOutputParameterVisitor(parameter), - layer->Model()[layer->Model().size() - i - 1]); - } - - layer->OutputParameter() = parameter.back(); - parameter.pop_back(); -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/visitor/loss_visitor.hpp b/src/mlpack/methods/ann/visitor/loss_visitor.hpp deleted file mode 100644 index d9ca99763f..0000000000 --- a/src/mlpack/methods/ann/visitor/loss_visitor.hpp +++ /dev/null @@ -1,71 +0,0 @@ -/** - * @file methods/ann/visitor/loss_visitor.hpp - * @author Atharva Khandait - * - * This file provides an abstraction for the Loss() function for different - * layers and automatically directs any parameter to the right layer type. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_LOSS_VISITOR_HPP -#define MLPACK_METHODS_ANN_VISITOR_LOSS_VISITOR_HPP - -#include - -#include - -namespace mlpack { -namespace ann { - -/** - * LossVisitor exposes the Loss() method of the given module. - */ -class LossVisitor : public boost::static_visitor -{ - public: - //! Return the Loss. - template - double operator()(LayerType* layer) const; - - double operator()(MoreTypes layer) const; - - private: - //! Return 0 if the module doesn't implement the Loss() or Model() function. - template - typename std::enable_if< - !HasLoss::value && - !HasModelCheck::value, double>::type - LayerLoss(T* layer) const; - - //! Return the output height if the module implements the Loss() function. - template - typename std::enable_if< - HasLoss::value && - !HasModelCheck::value, double>::type - LayerLoss(T* layer) const; - - //! Return the loss if the module implements the Model() function. - template - typename std::enable_if< - !HasLoss::value && - HasModelCheck::value, double>::type - LayerLoss(T* layer) const; - - //! Return the loss if the module implements the Model() or loss() function. - template - typename std::enable_if< - HasLoss::value && - HasModelCheck::value, double>::type - LayerLoss(T* layer) const; -}; - -} // namespace ann -} // namespace mlpack - -// Include implementation. -#include "loss_visitor_impl.hpp" - -#endif diff --git a/src/mlpack/methods/ann/visitor/loss_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/loss_visitor_impl.hpp deleted file mode 100644 index ee8d6cb402..0000000000 --- a/src/mlpack/methods/ann/visitor/loss_visitor_impl.hpp +++ /dev/null @@ -1,99 +0,0 @@ -/** - * @file methods/ann/visitor/loss_visitor_impl.hpp - * @author Atharva Khandait - * - * Implementation of the Loss() function layer abstraction. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_LOSS_VISITOR_IMPL_HPP -#define MLPACK_METHODS_ANN_VISITOR_LOSS_VISITOR_IMPL_HPP - -// In case it hasn't been included yet. -#include "loss_visitor.hpp" - -namespace mlpack { -namespace ann { - -//! LossVisitor visitor class. -template -inline double LossVisitor::operator()(LayerType* layer) const -{ - return LayerLoss(layer); -} - -inline double LossVisitor::operator()(MoreTypes layer) const -{ - return layer.apply_visitor(*this); -} - -template -inline typename std::enable_if< - !HasLoss::value && - !HasModelCheck::value, double>::type -LossVisitor::LayerLoss(T* /* layer */) const -{ - return 0; -} - -template -inline typename std::enable_if< - HasLoss::value && - !HasModelCheck::value, double>::type -LossVisitor::LayerLoss(T* layer) const -{ - return layer->Loss(); -} - -template -inline typename std::enable_if< - !HasLoss::value && - HasModelCheck::value, double>::type -LossVisitor::LayerLoss(T* layer) const -{ - for (size_t i = 0; i < layer->Model().size(); ++i) - { - double loss = boost::apply_visitor(LossVisitor(), - layer->Model()[layer->Model().size() - 1 - i]); - - if (loss != 0) - { - return loss; - } - } - - return 0; -} - -template -inline typename std::enable_if< - HasLoss::value && - HasModelCheck::value, double>::type -LossVisitor::LayerLoss(T* layer) const -{ - double loss = layer->Loss(); - - if (loss == 0) - { - for (size_t i = 0; i < layer->Model().size(); ++i) - { - loss = boost::apply_visitor(LossVisitor(), - layer->Model()[layer->Model().size() - 1 - i]); - - if (loss != 0) - { - return loss; - } - } - } - - return loss; -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/visitor/output_height_visitor.hpp b/src/mlpack/methods/ann/visitor/output_height_visitor.hpp deleted file mode 100644 index f14350b9ef..0000000000 --- a/src/mlpack/methods/ann/visitor/output_height_visitor.hpp +++ /dev/null @@ -1,75 +0,0 @@ -/** - * @file methods/ann/visitor/output_height_visitor.hpp - * @author Marcus Edel - * - * This file provides an abstraction for the OutputHeight() function for - * different layers and automatically directs any parameter to the right layer - * type. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_OUTPUT_HEIGHT_VISITOR_HPP -#define MLPACK_METHODS_ANN_VISITOR_OUTPUT_HEIGHT_VISITOR_HPP - -#include - -#include - -namespace mlpack { -namespace ann { - -/** - * OutputHeightVisitor exposes the OutputHeight() method of the given module. - */ -class OutputHeightVisitor : public boost::static_visitor -{ - public: - //! Return the output height. - template - size_t operator()(LayerType* layer) const; - - size_t operator()(MoreTypes layer) const; - - private: - //! Return 0 if the module doesn't implement the InputHeight() or Model() - //! function. - template - typename std::enable_if< - !HasInputHeight::value && - !HasModelCheck::value, size_t>::type - LayerOutputHeight(T* layer) const; - - //! Return the output height if the module implements the InputHeight() - //! function. - template - typename std::enable_if< - HasInputHeight::value && - !HasModelCheck::value, size_t>::type - LayerOutputHeight(T* layer) const; - - //! Return the output height if the module implements the Model() function. - template - typename std::enable_if< - !HasInputHeight::value && - HasModelCheck::value, size_t>::type - LayerOutputHeight(T* layer) const; - - //! Return the output height if the module implements the Model() or - //! InputHeight() function. - template - typename std::enable_if< - HasInputHeight::value && - HasModelCheck::value, size_t>::type - LayerOutputHeight(T* layer) const; -}; - -} // namespace ann -} // namespace mlpack - -// Include implementation. -#include "output_height_visitor_impl.hpp" - -#endif diff --git a/src/mlpack/methods/ann/visitor/output_height_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/output_height_visitor_impl.hpp deleted file mode 100644 index ae219da220..0000000000 --- a/src/mlpack/methods/ann/visitor/output_height_visitor_impl.hpp +++ /dev/null @@ -1,99 +0,0 @@ -/** - * @file methods/ann/visitor/output_height_visitor_impl.hpp - * @author Marcus Edel - * - * Implementation of the OutputHeight() function layer abstraction. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_OUTPUT_HEIGHT_VISITOR_IMPL_HPP -#define MLPACK_METHODS_ANN_VISITOR_OUTPUT_HEIGHT_VISITOR_IMPL_HPP - -// In case it hasn't been included yet. -#include "output_height_visitor.hpp" - -namespace mlpack { -namespace ann { - -//! OutputHeightVisitor visitor class. -template -inline size_t OutputHeightVisitor::operator()(LayerType* layer) const -{ - return LayerOutputHeight(layer); -} - -inline size_t OutputHeightVisitor::operator()(MoreTypes layer) const -{ - return layer.apply_visitor(*this); -} - -template -inline typename std::enable_if< - !HasInputHeight::value && - !HasModelCheck::value, size_t>::type -OutputHeightVisitor::LayerOutputHeight(T* /* layer */) const -{ - return 0; -} - -template -inline typename std::enable_if< - HasInputHeight::value && - !HasModelCheck::value, size_t>::type -OutputHeightVisitor::LayerOutputHeight(T* layer) const -{ - return layer->OutputHeight(); -} - -template -inline typename std::enable_if< - !HasInputHeight::value && - HasModelCheck::value, size_t>::type -OutputHeightVisitor::LayerOutputHeight(T* layer) const -{ - for (size_t i = 0; i < layer->Model().size(); ++i) - { - size_t outputHeight = boost::apply_visitor(OutputHeightVisitor(), - layer->Model()[layer->Model().size() - 1 - i]); - - if (outputHeight != 0) - { - return outputHeight; - } - } - - return 0; -} - -template -inline typename std::enable_if< - HasInputHeight::value && - HasModelCheck::value, size_t>::type -OutputHeightVisitor::LayerOutputHeight(T* layer) const -{ - size_t outputHeight = layer->OutputHeight(); - - if (outputHeight == 0) - { - for (size_t i = 0; i < layer->Model().size(); ++i) - { - outputHeight = boost::apply_visitor(OutputHeightVisitor(), - layer->Model()[layer->Model().size() - 1 - i]); - - if (outputHeight != 0) - { - return outputHeight; - } - } - } - - return outputHeight; -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/visitor/output_parameter_visitor.hpp b/src/mlpack/methods/ann/visitor/output_parameter_visitor.hpp deleted file mode 100644 index 6a12226464..0000000000 --- a/src/mlpack/methods/ann/visitor/output_parameter_visitor.hpp +++ /dev/null @@ -1,43 +0,0 @@ -/** - * @file methods/ann/visitor/output_parameter_visitor.hpp - * @author Marcus Edel - * - * This file provides an abstraction for the OutputParameter() function for - * different layers and automatically directs any parameter to the right layer type. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_OUTPUT_PARAMETER_VISITOR_HPP -#define MLPACK_METHODS_ANN_VISITOR_OUTPUT_PARAMETER_VISITOR_HPP - -#include -#include - -#include - -namespace mlpack { -namespace ann { - -/** - * OutputParameterVisitor exposes the output parameter of the given module. - */ -class OutputParameterVisitor : public boost::static_visitor -{ - public: - //! Return the output parameter set. - template - arma::mat& operator()(LayerType* layer) const; - - arma::mat& operator()(MoreTypes layer) const; -}; - -} // namespace ann -} // namespace mlpack - -// Include implementation. -#include "output_parameter_visitor_impl.hpp" - -#endif diff --git a/src/mlpack/methods/ann/visitor/output_parameter_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/output_parameter_visitor_impl.hpp deleted file mode 100644 index 5086669548..0000000000 --- a/src/mlpack/methods/ann/visitor/output_parameter_visitor_impl.hpp +++ /dev/null @@ -1,36 +0,0 @@ -/** - * @file methods/ann/visitor/output_parameter_visitor_impl.hpp - * @author Marcus Edel - * - * Implementation of the OutputParameter() function layer abstraction. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_OUTPUT_PARAMETER_VISITOR_IMPL_HPP -#define MLPACK_METHODS_ANN_VISITOR_OUTPUT_PARAMETER_VISITOR_IMPL_HPP - -// In case it hasn't been included yet. -#include "output_parameter_visitor.hpp" - -namespace mlpack { -namespace ann { - -//! OutputParameterVisitor visitor class. -template -inline arma::mat& OutputParameterVisitor::operator()(LayerType *layer) const -{ - return layer->OutputParameter(); -} - -inline arma::mat& OutputParameterVisitor::operator()(MoreTypes layer) const -{ - return layer.apply_visitor(*this); -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/visitor/output_width_visitor.hpp b/src/mlpack/methods/ann/visitor/output_width_visitor.hpp deleted file mode 100644 index d6a0fa83ed..0000000000 --- a/src/mlpack/methods/ann/visitor/output_width_visitor.hpp +++ /dev/null @@ -1,75 +0,0 @@ -/** - * @file methods/ann/visitor/output_width_visitor.hpp - * @author Marcus Edel - * - * This file provides an abstraction for the OutputWidth() function for - * different layers and automatically directs any parameter to the right layer - * type. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_OUTPUT_WIDTH_VISITOR_HPP -#define MLPACK_METHODS_ANN_VISITOR_OUTPUT_WIDTH_VISITOR_HPP - -#include - -#include - -namespace mlpack { -namespace ann { - -/** - * OutputWidthVisitor exposes the OutputWidth() method of the given module. - */ -class OutputWidthVisitor : public boost::static_visitor -{ - public: - //! Return the output width. - template - size_t operator()(LayerType* layer) const; - - size_t operator()(MoreTypes layer) const; - - private: - //! Return 0 if the module doesn't implement the InputWidth() or Model() - //! function. - template - typename std::enable_if< - !HasInputWidth::value && - !HasModelCheck::value, size_t>::type - LayerOutputWidth(T* layer) const; - - //! Return the output width if the module implements the InputWidth() - //! function. - template - typename std::enable_if< - HasInputWidth::value && - !HasModelCheck::value, size_t>::type - LayerOutputWidth(T* layer) const; - - //! Return the output width if the module implements the Model() function. - template - typename std::enable_if< - !HasInputWidth::value && - HasModelCheck::value, size_t>::type - LayerOutputWidth(T* layer) const; - - //! Return the output width if the module implements the Model() or - //! InputWidth() function. - template - typename std::enable_if< - HasInputWidth::value && - HasModelCheck::value, size_t>::type - LayerOutputWidth(T* layer) const; -}; - -} // namespace ann -} // namespace mlpack - -// Include implementation. -#include "output_width_visitor_impl.hpp" - -#endif diff --git a/src/mlpack/methods/ann/visitor/output_width_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/output_width_visitor_impl.hpp deleted file mode 100644 index 3d68087d83..0000000000 --- a/src/mlpack/methods/ann/visitor/output_width_visitor_impl.hpp +++ /dev/null @@ -1,99 +0,0 @@ -/** - * @file methods/ann/visitor/output_width_visitor_impl.hpp - * @author Marcus Edel - * - * Implementation of the OutputWidth() function layer abstraction. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_OUTPUT_WIDTH_VISITOR_IMPL_HPP -#define MLPACK_METHODS_ANN_VISITOR_OUTPUT_WIDTH_VISITOR_IMPL_HPP - -// In case it hasn't been included yet. -#include "output_width_visitor.hpp" - -namespace mlpack { -namespace ann { - -//! OutputWidthVisitor visitor class. -template -inline size_t OutputWidthVisitor::operator()(LayerType* layer) const -{ - return LayerOutputWidth(layer); -} - -inline size_t OutputWidthVisitor::operator()(MoreTypes layer) const -{ - return layer.apply_visitor(*this); -} - -template -inline typename std::enable_if< - !HasInputWidth::value && - !HasModelCheck::value, size_t>::type -OutputWidthVisitor::LayerOutputWidth(T* /* layer */) const -{ - return 0; -} - -template -inline typename std::enable_if< - HasInputWidth::value && - !HasModelCheck::value, size_t>::type -OutputWidthVisitor::LayerOutputWidth(T* layer) const -{ - return layer->OutputWidth(); -} - -template -inline typename std::enable_if< - !HasInputWidth::value && - HasModelCheck::value, size_t>::type -OutputWidthVisitor::LayerOutputWidth(T* layer) const -{ - for (size_t i = 0; i < layer->Model().size(); ++i) - { - size_t outputWidth = boost::apply_visitor(OutputWidthVisitor(), - layer->Model()[layer->Model().size() - 1 - i]); - - if (outputWidth != 0) - { - return outputWidth; - } - } - - return 0; -} - -template -inline typename std::enable_if< - HasInputWidth::value && - HasModelCheck::value, size_t>::type -OutputWidthVisitor::LayerOutputWidth(T* layer) const -{ - size_t outputWidth = layer->OutputWidth(); - - if (outputWidth == 0) - { - for (size_t i = 0; i < layer->Model().size(); ++i) - { - outputWidth = boost::apply_visitor(OutputWidthVisitor(), - layer->Model()[layer->Model().size() - 1 - i]); - - if (outputWidth != 0) - { - return outputWidth; - } - } - } - - return outputWidth; -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/visitor/parameters_set_visitor.hpp b/src/mlpack/methods/ann/visitor/parameters_set_visitor.hpp deleted file mode 100644 index c8cba59bb2..0000000000 --- a/src/mlpack/methods/ann/visitor/parameters_set_visitor.hpp +++ /dev/null @@ -1,64 +0,0 @@ -/** - * @file methods/ann/visitor/parameters_set_visitor.hpp - * @author Marcus Edel - * - * This file provides an abstraction for the Parameters() function for different - * layers and automatically directs any parameter to the right layer type. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_PARAMETERS_SET_VISITOR_HPP -#define MLPACK_METHODS_ANN_VISITOR_PARAMETERS_SET_VISITOR_HPP - -#include -#include - -#include - -namespace mlpack { -namespace ann { - -/** - * ParametersSetVisitor update the parameters set using the given matrix. - */ -class ParametersSetVisitor : public boost::static_visitor -{ - public: - //! Update the parameters set given the parameters matrix. - ParametersSetVisitor(arma::mat& parameters); - - //! Update the parameters set. - template - void operator()(LayerType *layer) const; - - void operator()(MoreTypes layer) const; - - private: - //! The parameters set. - arma::mat& parameters; - - //! Do not update the parameters set if the module doesn't implement the - //! Parameters() function. - template - typename std::enable_if< - !HasParametersCheck::value, void>::type - LayerParameters(T* layer, P& output) const; - - //! Update the parameters set if the module implements the Parameters() - //! function. - template - typename std::enable_if< - HasParametersCheck::value, void>::type - LayerParameters(T* layer, P& output) const; -}; - -} // namespace ann -} // namespace mlpack - -// Include implementation. -#include "parameters_set_visitor_impl.hpp" - -#endif diff --git a/src/mlpack/methods/ann/visitor/parameters_set_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/parameters_set_visitor_impl.hpp deleted file mode 100644 index 820a5ab464..0000000000 --- a/src/mlpack/methods/ann/visitor/parameters_set_visitor_impl.hpp +++ /dev/null @@ -1,58 +0,0 @@ -/** - * @file methods/ann/visitor/parameters_set_visitor_impl.hpp - * @author Marcus Edel - * - * Implementation of the Parameters() function layer abstraction. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_PARAMETERS_SET_VISITOR_IMPL_HPP -#define MLPACK_METHODS_ANN_VISITOR_PARAMETERS_SET_VISITOR_IMPL_HPP - -// In case it hasn't been included yet. -#include "parameters_set_visitor.hpp" - -namespace mlpack { -namespace ann { - -//! ParametersSetVisitor visitor class. -inline ParametersSetVisitor::ParametersSetVisitor(arma::mat& parameters) : - parameters(parameters) -{ - /* Nothing to do here. */ -} - -template -inline void ParametersSetVisitor::operator()(LayerType *layer) const -{ - LayerParameters(layer, layer->OutputParameter()); -} - -inline void ParametersSetVisitor::operator()(MoreTypes layer) const -{ - layer.apply_visitor(*this); -} - -template -inline typename std::enable_if< - !HasParametersCheck::value, void>::type -ParametersSetVisitor::LayerParameters(T* /* layer */, P& /* output */) const -{ - /* Nothing to do here. */ -} - -template -inline typename std::enable_if< - HasParametersCheck::value, void>::type -ParametersSetVisitor::LayerParameters(T* layer, P& /* output */) const -{ - layer->Parameters() = parameters; -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/visitor/parameters_visitor.hpp b/src/mlpack/methods/ann/visitor/parameters_visitor.hpp deleted file mode 100644 index 36a0b30bf5..0000000000 --- a/src/mlpack/methods/ann/visitor/parameters_visitor.hpp +++ /dev/null @@ -1,64 +0,0 @@ -/** - * @file methods/ann/visitor/parameters_visitor.hpp - * @author Marcus Edel - * - * This file provides an abstraction for the Parameters() function for different - * layers and automatically directs any parameter to the right layer type. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_PARAMETERS_VISITOR_HPP -#define MLPACK_METHODS_ANN_VISITOR_PARAMETERS_VISITOR_HPP - -#include -#include - -#include - -namespace mlpack { -namespace ann { - -/** - * ParametersVisitor exposes the parameters set of the given module and stores - * the parameters set into the given matrix. - */ -class ParametersVisitor : public boost::static_visitor -{ - public: - //! Store the parameters set into the given parameters matrix. - ParametersVisitor(arma::mat& parameters); - - //! Set the parameters set. - template - void operator()(LayerType* layer) const; - - void operator()(MoreTypes layer) const; - - private: - //! The parameters set. - arma::mat& parameters; - - //! Do not set the parameters set if the module doesn't implement the - //! Parameters() function. - template - typename std::enable_if< - !HasParametersCheck::value, void>::type - LayerParameters(T* layer, P& output) const; - - //! Set the parameters set if the module implements the Parameters() function. - template - typename std::enable_if< - HasParametersCheck::value, void>::type - LayerParameters(T* layer, P& output) const; -}; - -} // namespace ann -} // namespace mlpack - -// Include implementation. -#include "parameters_visitor_impl.hpp" - -#endif diff --git a/src/mlpack/methods/ann/visitor/parameters_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/parameters_visitor_impl.hpp deleted file mode 100644 index c58604c995..0000000000 --- a/src/mlpack/methods/ann/visitor/parameters_visitor_impl.hpp +++ /dev/null @@ -1,58 +0,0 @@ -/** - * @file methods/ann/visitor/parameters_visitor_impl.hpp - * @author Marcus Edel - * - * Implementation of the Parameters() function layer abstraction. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_PARAMETERS_VISITOR_IMPL_HPP -#define MLPACK_METHODS_ANN_VISITOR_PARAMETERS_VISITOR_IMPL_HPP - -// In case it hasn't been included yet. -#include "parameters_visitor.hpp" - -namespace mlpack { -namespace ann { - -//! ParametersVisitor visitor class. -inline ParametersVisitor::ParametersVisitor(arma::mat& parameters) : - parameters(parameters) -{ - /* Nothing to do here. */ -} - -template -inline void ParametersVisitor::operator()(LayerType *layer) const -{ - LayerParameters(layer, layer->OutputParameter()); -} - -inline void ParametersVisitor::operator()(MoreTypes layer) const -{ - layer.apply_visitor(*this); -} - -template -inline typename std::enable_if< - !HasParametersCheck::value, void>::type -ParametersVisitor::LayerParameters(T* /* layer */, P& /* output */) const -{ - /* Nothing to do here. */ -} - -template -inline typename std::enable_if< - HasParametersCheck::value, void>::type -ParametersVisitor::LayerParameters(T* layer, P& /* output */) const -{ - parameters = layer->Parameters(); -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/visitor/reset_cell_visitor.hpp b/src/mlpack/methods/ann/visitor/reset_cell_visitor.hpp deleted file mode 100644 index ba88ce161b..0000000000 --- a/src/mlpack/methods/ann/visitor/reset_cell_visitor.hpp +++ /dev/null @@ -1,62 +0,0 @@ -/** - * @file methods/ann/visitor/reset_cell_visitor.hpp - * @author Sumedh Ghaisas - * - * Boost static visitor abstraction for calling ResetCell function on RNN cells. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_RESET_CELL_VISITOR_HPP -#define MLPACK_METHODS_ANN_VISITOR_RESET_CELL_VISITOR_HPP - -#include -#include - -#include - -namespace mlpack { -namespace ann { - -/** - * ResetCellVisitor executes the ResetCell() function. - */ -class ResetCellVisitor : public boost::static_visitor -{ - public: - //! Reset the cell using the given size. - ResetCellVisitor(const size_t size); - - //! Execute the ResetCell() function. - template - void operator()(LayerType* layer) const; - - void operator()(MoreTypes layer) const; - - private: - size_t size; - - //! Execute the ResetCell() function for a module which implements - //! the ResetCell() function. - template - typename std::enable_if< - HasResetCellCheck::value, void>::type - ResetCell(T* layer) const; - - //! Do not execute the Reset() function for a module which doesn't implement - // the Reset() or Model() function. - template - typename std::enable_if< - !HasResetCellCheck::value, void>::type - ResetCell(T* layer) const; -}; - -} // namespace ann -} // namespace mlpack - -// Include implementation. -#include "reset_cell_visitor_impl.hpp" - -#endif diff --git a/src/mlpack/methods/ann/visitor/reset_cell_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/reset_cell_visitor_impl.hpp deleted file mode 100644 index c687a553c0..0000000000 --- a/src/mlpack/methods/ann/visitor/reset_cell_visitor_impl.hpp +++ /dev/null @@ -1,58 +0,0 @@ -/** - * @file methods/ann/visitor/reset_cell_visitor_impl.hpp - * @author Sumedh Ghaisas - * - * Implementation of the ResetCell() function layer abstraction. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_RESET_CELL_VISITOR_IMPL_HPP -#define MLPACK_METHODS_ANN_VISITOR_RESET_CELL_VISITOR_IMPL_HPP - -// In case it hasn't been included yet. -#include "reset_cell_visitor.hpp" - -namespace mlpack { -namespace ann { - -//! ResetVisitor visitor class. -inline ResetCellVisitor::ResetCellVisitor(const size_t size) : size(size) -{ - /* Nothing to do here. */ -} - -//! ResetVisitor visitor class. -template -inline void ResetCellVisitor::operator()(LayerType* layer) const -{ - ResetCell(layer); -} - -inline void ResetCellVisitor::operator()(MoreTypes layer) const -{ - layer.apply_visitor(*this); -} - -template -inline typename std::enable_if< - HasResetCellCheck::value, void>::type -ResetCellVisitor::ResetCell(T* layer) const -{ - layer->ResetCell(size); -} - -template -inline typename std::enable_if< - !HasResetCellCheck::value, void>::type -ResetCellVisitor::ResetCell(T* /* layer */) const -{ - /* Nothing to do here. */ -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/visitor/reset_visitor.hpp b/src/mlpack/methods/ann/visitor/reset_visitor.hpp deleted file mode 100644 index 72545cf53d..0000000000 --- a/src/mlpack/methods/ann/visitor/reset_visitor.hpp +++ /dev/null @@ -1,75 +0,0 @@ -/** - * @file methods/ann/visitor/reset_visitor.hpp - * @author Marcus Edel - * - * This file provides an abstraction for the Reset() function for different - * layers and automatically directs any parameter to the right layer type. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_RESET_VISITOR_HPP -#define MLPACK_METHODS_ANN_VISITOR_RESET_VISITOR_HPP - -#include - -#include - -namespace mlpack { -namespace ann { - -/** - * ResetVisitor executes the Reset() function. - */ -class ResetVisitor : public boost::static_visitor -{ - public: - //! Execute the Reset() function. - template - void operator()(LayerType* layer) const; - - void operator()(MoreTypes layer) const; - - private: - //! Execute the Reset() function for a module which implements the Reset() - //! function. - template - typename std::enable_if< - HasResetCheck::value && - !HasModelCheck::value, void>::type - ResetParameter(T* layer) const; - - //! Execute the Reset() function for a module which implements the Model() - //! function. - template - typename std::enable_if< - !HasResetCheck::value && - HasModelCheck::value, void>::type - ResetParameter(T* layer) const; - - //! Execute the Reset() function for a module which implements the Reset() - //! and Model() function. - template - typename std::enable_if< - HasResetCheck::value && - HasModelCheck::value, void>::type - ResetParameter(T* layer) const; - - //! Do not execute the Reset() function for a module which doesn't implement - // the Reset() or Model() function. - template - typename std::enable_if< - !HasResetCheck::value && - !HasModelCheck::value, void>::type - ResetParameter(T* layer) const; -}; - -} // namespace ann -} // namespace mlpack - -// Include implementation. -#include "reset_visitor_impl.hpp" - -#endif diff --git a/src/mlpack/methods/ann/visitor/reset_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/reset_visitor_impl.hpp deleted file mode 100644 index 9754c9baa5..0000000000 --- a/src/mlpack/methods/ann/visitor/reset_visitor_impl.hpp +++ /dev/null @@ -1,80 +0,0 @@ -/** - * @file methods/ann/visitor/reset_visitor_impl.hpp - * @author Marcus Edel - * - * Implementation of the Reset() function layer abstraction. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_RESET_VISITOR_IMPL_HPP -#define MLPACK_METHODS_ANN_VISITOR_RESET_VISITOR_IMPL_HPP - -// In case it hasn't been included yet. -#include "reset_visitor.hpp" - -namespace mlpack { -namespace ann { - -//! ResetVisitor visitor class. -template -inline void ResetVisitor::operator()(LayerType* layer) const -{ - ResetParameter(layer); -} - -inline void ResetVisitor::operator()(MoreTypes layer) const -{ - layer.apply_visitor(*this); -} - -template -inline typename std::enable_if< - HasResetCheck::value && - !HasModelCheck::value, void>::type -ResetVisitor::ResetParameter(T* layer) const -{ - layer->Reset(); -} - -template -inline typename std::enable_if< - !HasResetCheck::value && - HasModelCheck::value, void>::type -ResetVisitor::ResetParameter(T* layer) const -{ - for (size_t i = 0; i < layer->Model().size(); ++i) - { - boost::apply_visitor(ResetVisitor(), layer->Model()[i]); - } -} - -template -inline typename std::enable_if< - HasResetCheck::value && - HasModelCheck::value, void>::type -ResetVisitor::ResetParameter(T* layer) const -{ - for (size_t i = 0; i < layer->Model().size(); ++i) - { - boost::apply_visitor(ResetVisitor(), layer->Model()[i]); - } - - layer->Reset(); -} - -template -inline typename std::enable_if< - !HasResetCheck::value && - !HasModelCheck::value, void>::type -ResetVisitor::ResetParameter(T* /* layer */) const -{ - /* Nothing to do here. */ -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/visitor/reward_set_visitor.hpp b/src/mlpack/methods/ann/visitor/reward_set_visitor.hpp deleted file mode 100644 index a4c6301d00..0000000000 --- a/src/mlpack/methods/ann/visitor/reward_set_visitor.hpp +++ /dev/null @@ -1,81 +0,0 @@ -/** - * @file methods/ann/visitor/reward_set_visitor.hpp - * @author Marcus Edel - * - * This file provides an abstraction for the Reward() function for different - * layers and automatically directs any parameter to the right layer type. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_REWARD_SET_VISITOR_HPP -#define MLPACK_METHODS_ANN_VISITOR_REWARD_SET_VISITOR_HPP - -#include - -#include - -namespace mlpack { -namespace ann { - -/** - * RewardSetVisitor set the reward parameter given the reward value. - */ -class RewardSetVisitor : public boost::static_visitor -{ - public: - //! Set the reward parameter given the reward value. - RewardSetVisitor(const double reward); - - //! Set the reward parameter. - template - void operator()(LayerType* layer) const; - - void operator()(MoreTypes layer) const; - - private: - //! The reward value. - const double reward; - - //! Set the deterministic parameter if the module implements the - //! Deterministic() and Model() function. - template - typename std::enable_if< - HasRewardCheck::value && - HasModelCheck::value, void>::type - LayerReward(T* layer) const; - - //! Set the deterministic parameter if the module implements the - //! Model() function. - template - typename std::enable_if< - !HasRewardCheck::value && - HasModelCheck::value, void>::type - LayerReward(T* layer) const; - - //! Set the deterministic parameter if the module implements the - //! Deterministic() function. - template - typename std::enable_if< - HasRewardCheck::value && - !HasModelCheck::value, void>::type - LayerReward(T* layer) const; - - //! Do not set the deterministic parameter if the module doesn't implement the - //! Deterministic() or Model() function. - template - typename std::enable_if< - !HasRewardCheck::value && - !HasModelCheck::value, void>::type - LayerReward(T* layer) const; -}; - -} // namespace ann -} // namespace mlpack - -// Include implementation. -#include "reward_set_visitor_impl.hpp" - -#endif diff --git a/src/mlpack/methods/ann/visitor/reward_set_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/reward_set_visitor_impl.hpp deleted file mode 100644 index 8bc0eb5a21..0000000000 --- a/src/mlpack/methods/ann/visitor/reward_set_visitor_impl.hpp +++ /dev/null @@ -1,87 +0,0 @@ -/** - * @file methods/ann/visitor/reward_set_visitor_impl.hpp - * @author Marcus Edel - * - * Implementation of the Reward() function layer abstraction. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_REWARD_SET_VISITOR_IMPL_HPP -#define MLPACK_METHODS_ANN_VISITOR_REWARD_SET_VISITOR_IMPL_HPP - -// In case it hasn't been included yet. -#include "reward_set_visitor.hpp" - -namespace mlpack { -namespace ann { - -//! RewardSetVisitor visitor class. -inline RewardSetVisitor::RewardSetVisitor(const double reward) : reward(reward) -{ - /* Nothing to do here. */ -} - -template -inline void RewardSetVisitor::operator()(LayerType* layer) const -{ - LayerReward(layer); -} - -inline void RewardSetVisitor::operator()(MoreTypes layer) const -{ - layer.apply_visitor(*this); -} - -template -inline typename std::enable_if< - HasRewardCheck::value && - HasModelCheck::value, void>::type -RewardSetVisitor::LayerReward(T* layer) const -{ - layer->Reward() = reward; - - for (size_t i = 0; i < layer->Model().size(); ++i) - { - boost::apply_visitor(RewardSetVisitor(reward), - layer->Model()[i]); - } -} - -template -inline typename std::enable_if< - !HasRewardCheck::value && - HasModelCheck::value, void>::type -RewardSetVisitor::LayerReward(T* layer) const -{ - for (size_t i = 0; i < layer->Model().size(); ++i) - { - boost::apply_visitor(RewardSetVisitor(reward), - layer->Model()[i]); - } -} - -template -inline typename std::enable_if< - HasRewardCheck::value && - !HasModelCheck::value, void>::type -RewardSetVisitor::LayerReward(T* layer) const -{ - layer->Reward() = reward; -} - -template -inline typename std::enable_if< - !HasRewardCheck::value && - !HasModelCheck::value, void>::type -RewardSetVisitor::LayerReward(T* /* input */) const -{ - /* Nothing to do here. */ -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/visitor/run_set_visitor.hpp b/src/mlpack/methods/ann/visitor/run_set_visitor.hpp deleted file mode 100644 index b993ea07f2..0000000000 --- a/src/mlpack/methods/ann/visitor/run_set_visitor.hpp +++ /dev/null @@ -1,83 +0,0 @@ -/** - * @file methods/ann/visitor/run_set_visitor.hpp - * @author Saksham Bansal - * - * This file provides an abstraction for the Run() function for - * different layers and automatically directs any parameter to the right layer - * type. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_RUN_SET_VISITOR_HPP -#define MLPACK_METHODS_ANN_VISITOR_RUN_SET_VISITOR_HPP - -#include - -#include - -namespace mlpack { -namespace ann { - -/** - * RunSetVisitor set the run parameter given the - * run value. - */ -class RunSetVisitor : public boost::static_visitor -{ - public: - //! Set the run parameter given the current run value. - RunSetVisitor(const bool run = true); - - //! Set the run parameter. - template - void operator()(LayerType* layer) const; - - void operator()(MoreTypes layer) const; - - private: - //! The run parameter. - const bool run; - - //! Set the run parameter if the module implements the - //! Run() and Model() function. - template - typename std::enable_if< - HasRunCheck::value && - HasModelCheck::value, void>::type - LayerRun(T* layer) const; - - //! Set the run parameter if the module implements the - //! Model() function. - template - typename std::enable_if< - !HasRunCheck::value && - HasModelCheck::value, void>::type - LayerRun(T* layer) const; - - //! Set the run parameter if the module implements the - //! Run() function. - template - typename std::enable_if< - HasRunCheck::value && - !HasModelCheck::value, void>::type - LayerRun(T* layer) const; - - //! Do not set the run parameter if the module doesn't implement the - //! Run() or Model() function. - template - typename std::enable_if< - !HasRunCheck::value && - !HasModelCheck::value, void>::type - LayerRun(T* layer) const; -}; - -} // namespace ann -} // namespace mlpack - -// Include implementation. -#include "run_set_visitor_impl.hpp" - -#endif diff --git a/src/mlpack/methods/ann/visitor/run_set_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/run_set_visitor_impl.hpp deleted file mode 100644 index 5e0ece0217..0000000000 --- a/src/mlpack/methods/ann/visitor/run_set_visitor_impl.hpp +++ /dev/null @@ -1,88 +0,0 @@ -/** - * @file methods/ann/visitor/run_set_visitor_impl.hpp - * @author Saksham Bansal - * - * Implementation of the Run() function layer abstraction. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_RUN_SET_VISITOR_IMPL_HPP -#define MLPACK_METHODS_ANN_VISITOR_RUN_SET_VISITOR_IMPL_HPP - -// In case it hasn't been included yet. -#include "run_set_visitor.hpp" - -namespace mlpack { -namespace ann { - -//! RunSetVisitor visitor class. -inline RunSetVisitor::RunSetVisitor( - const bool run) : run(run) -{ - /* Nothing to do here. */ -} - -template -inline void RunSetVisitor::operator()(LayerType* layer) const -{ - LayerRun(layer); -} - -inline void RunSetVisitor::operator()(MoreTypes layer) const -{ - layer.apply_visitor(*this); -} - -template -inline typename std::enable_if< - HasRunCheck::value && - HasModelCheck::value, void>::type -RunSetVisitor::LayerRun(T* layer) const -{ - layer->Run() = run; - - for (size_t i = 0; i < layer->Model().size(); ++i) - { - boost::apply_visitor(RunSetVisitor(run), - layer->Model()[i]); - } -} - -template -inline typename std::enable_if< - !HasRunCheck::value && - HasModelCheck::value, void>::type -RunSetVisitor::LayerRun(T* layer) const -{ - for (size_t i = 0; i < layer->Model().size(); ++i) - { - boost::apply_visitor(RunSetVisitor(run), - layer->Model()[i]); - } -} - -template -inline typename std::enable_if< - HasRunCheck::value && - !HasModelCheck::value, void>::type -RunSetVisitor::LayerRun(T* layer) const -{ - layer->Run() = run; -} - -template -inline typename std::enable_if< - !HasRunCheck::value && - !HasModelCheck::value, void>::type -RunSetVisitor::LayerRun(T* /* input */) const -{ - /* Nothing to do here. */ -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/visitor/save_output_parameter_visitor.hpp b/src/mlpack/methods/ann/visitor/save_output_parameter_visitor.hpp deleted file mode 100644 index 2ff0c9ea4e..0000000000 --- a/src/mlpack/methods/ann/visitor/save_output_parameter_visitor.hpp +++ /dev/null @@ -1,64 +0,0 @@ -/** - * @file methods/ann/visitor/save_output_parameter_visitor.hpp - * @author Marcus Edel - * - * This file provides an abstraction for the OutputParameter() function for - * different layers and automatically directs any parameter to the right layer type. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_SAVE_OUTPUT_PARAMETER_VISITOR_HPP -#define MLPACK_METHODS_ANN_VISITOR_SAVE_OUTPUT_PARAMETER_VISITOR_HPP - -#include - -#include - -namespace mlpack { -namespace ann { - -/** - * SaveOutputParameterVisitor saves the output parameter into the given - * parameter set. - */ -class SaveOutputParameterVisitor : public boost::static_visitor -{ - public: - //! Save the output parameter into the given parameter set. - SaveOutputParameterVisitor(std::vector& parameter); - - //! Save the output parameter. - template - void operator()(LayerType* layer) const; - - void operator()(MoreTypes layer) const; - - private: - //! The parameter set. - std::vector& parameter; - - //! Save the output parameter for a module which doesn't implement the - //! Model() function. - template - typename std::enable_if< - !HasModelCheck::value, void>::type - OutputParameter(T* layer) const; - - //! Save the output parameter for a module which implements the Model() - //! function. - template - typename std::enable_if< - HasModelCheck::value, void>::type - OutputParameter(T* layer) const; -}; - -} // namespace ann -} // namespace mlpack - -// Include implementation. -#include "save_output_parameter_visitor_impl.hpp" - -#endif diff --git a/src/mlpack/methods/ann/visitor/save_output_parameter_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/save_output_parameter_visitor_impl.hpp deleted file mode 100644 index cc3559c165..0000000000 --- a/src/mlpack/methods/ann/visitor/save_output_parameter_visitor_impl.hpp +++ /dev/null @@ -1,64 +0,0 @@ -/** - * @file methods/ann/visitor/save_output_parameter_visitor_impl.hpp - * @author Marcus Edel - * - * Implementation of the OutputParameter() function layer abstraction. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_SAVE_OUTPUT_PARAMETER_VISITOR_IMPL_HPP -#define MLPACK_METHODS_ANN_VISITOR_SAVE_OUTPUT_PARAMETER_VISITOR_IMPL_HPP - -// In case it hasn't been included yet. -#include "load_output_parameter_visitor.hpp" - -namespace mlpack { -namespace ann { - -//! SaveOutputParameterVisitor visitor class. -inline SaveOutputParameterVisitor::SaveOutputParameterVisitor( - std::vector& parameter) : parameter(parameter) -{ - /* Nothing to do here. */ -} - -template -inline void SaveOutputParameterVisitor::operator()(LayerType* layer) const -{ - OutputParameter(layer); -} - -inline void SaveOutputParameterVisitor::operator()(MoreTypes layer) const -{ - layer.apply_visitor(*this); -} - -template -inline typename std::enable_if< - !HasModelCheck::value, void>::type -SaveOutputParameterVisitor::OutputParameter(T* layer) const -{ - parameter.push_back(layer->OutputParameter()); -} - -template -inline typename std::enable_if< - HasModelCheck::value, void>::type -SaveOutputParameterVisitor::OutputParameter(T* layer) const -{ - parameter.push_back(layer->OutputParameter()); - - for (size_t i = 0; i < layer->Model().size(); ++i) - { - boost::apply_visitor(SaveOutputParameterVisitor(parameter), - layer->Model()[i]); - } -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/visitor/set_input_height_visitor.hpp b/src/mlpack/methods/ann/visitor/set_input_height_visitor.hpp deleted file mode 100644 index 7e47e4ca13..0000000000 --- a/src/mlpack/methods/ann/visitor/set_input_height_visitor.hpp +++ /dev/null @@ -1,84 +0,0 @@ -/** - * @file methods/ann/visitor/set_input_height_visitor.hpp - * @author Marcus Edel - * - * This file provides an abstraction for the InputHeight() function for different - * layers and automatically directs any parameter to the right layer type. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_SET_INPUT_HEIGHT_VISITOR_HPP -#define MLPACK_METHODS_ANN_VISITOR_SET_INPUT_HEIGHT_VISITOR_HPP - -#include - -#include - -namespace mlpack { -namespace ann { - -/** - * SetInputHeightVisitor updates the input height parameter with the given input - * height. - */ -class SetInputHeightVisitor : public boost::static_visitor -{ - public: - //! Update the input height parameter with the given input height. - SetInputHeightVisitor(const size_t inputHeight = 0, const bool reset = false); - - //! Update the input height parameter. - template - bool operator()(LayerType* layer) const; - - bool operator()(MoreTypes layer) const; - - private: - //! The input height parameter. - size_t inputHeight; - - //! If set reset the height parameter if already set. - bool reset; - - //! Do nothing if the module doesn't implement the InputHeight() or Model() - //! function. - template - typename std::enable_if< - !HasInputHeight::value && - !HasModelCheck::value, bool>::type - LayerInputHeight(T* layer) const; - - //! Update the input height if the module implements the InputHeight() - //! function. - template - typename std::enable_if< - HasInputHeight::value && - !HasModelCheck::value, bool>::type - LayerInputHeight(T* layer) const; - - //! Update the input height if the module implements the Model() function. - template - typename std::enable_if< - !HasInputHeight::value && - HasModelCheck::value, bool>::type - LayerInputHeight(T* layer) const; - - //! Update the input height if the module implements the InputHeight() or - //! Model() function. - template - typename std::enable_if< - HasInputHeight::value && - HasModelCheck::value, bool>::type - LayerInputHeight(T* layer) const; -}; - -} // namespace ann -} // namespace mlpack - -// Include implementation. -#include "set_input_height_visitor_impl.hpp" - -#endif diff --git a/src/mlpack/methods/ann/visitor/set_input_height_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/set_input_height_visitor_impl.hpp deleted file mode 100644 index a5a3f8c0f7..0000000000 --- a/src/mlpack/methods/ann/visitor/set_input_height_visitor_impl.hpp +++ /dev/null @@ -1,102 +0,0 @@ -/** - * @file methods/ann/visitor/set_input_height_visitor_impl.hpp - * @author Marcus Edel - * - * Implementation of the InputHeight() function layer abstraction. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_SET_INPUT_HEIGHT_VISITOR_IMPL_HPP -#define MLPACK_METHODS_ANN_VISITOR_SET_INPUT_HEIGHT_VISITOR_IMPL_HPP - -// In case it hasn't been included yet. -#include "set_input_height_visitor.hpp" - -namespace mlpack { -namespace ann { - -//! SetInputHeightVisitor visitor class. -inline SetInputHeightVisitor::SetInputHeightVisitor(const size_t inputHeight, - const bool reset) : - inputHeight(inputHeight), - reset(reset) -{ - /* Nothing to do here. */ -} - -template -inline bool SetInputHeightVisitor::operator()(LayerType* layer) const -{ - return LayerInputHeight(layer); -} - -inline bool SetInputHeightVisitor::operator()(MoreTypes layer) const -{ - return layer.apply_visitor(*this); -} - -template -inline typename std::enable_if< - !HasInputHeight::value && - !HasModelCheck::value, bool>::type -SetInputHeightVisitor::LayerInputHeight(T* /* layer */) const -{ - return false; -} - -template -inline typename std::enable_if< - HasInputHeight::value && - !HasModelCheck::value, bool>::type -SetInputHeightVisitor::LayerInputHeight(T* layer) const -{ - if (layer->InputHeight() == 0 || reset) - { - layer->InputHeight() = inputHeight; - } - - return true; -} - -template -inline typename std::enable_if< - !HasInputHeight::value && - HasModelCheck::value, bool>::type -SetInputHeightVisitor::LayerInputHeight(T* layer) const -{ - for (size_t i = 0; i < layer->Model().size(); ++i) - { - boost::apply_visitor(SetInputHeightVisitor(inputHeight, reset), - layer->Model()[i]); - } - - return true; -} - -template -inline typename std::enable_if< - HasInputHeight::value && - HasModelCheck::value, bool>::type -SetInputHeightVisitor::LayerInputHeight(T* layer) const -{ - if (layer->InputHeight() == 0 || reset) - { - layer->InputHeight() = inputHeight; - } - - for (size_t i = 0; i < layer->Model().size(); ++i) - { - boost::apply_visitor(SetInputHeightVisitor(inputHeight, reset), - layer->Model()[i]); - } - - return true; -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/visitor/set_input_width_visitor.hpp b/src/mlpack/methods/ann/visitor/set_input_width_visitor.hpp deleted file mode 100644 index f7fbd6f4e2..0000000000 --- a/src/mlpack/methods/ann/visitor/set_input_width_visitor.hpp +++ /dev/null @@ -1,83 +0,0 @@ -/** - * @file methods/ann/visitor/set_input_width_visitor.hpp - * @author Marcus Edel - * - * This file provides an abstraction for the InputWidth() function for different - * layers and automatically directs any parameter to the right layer type. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_SET_INPUT_WIDTH_VISITOR_HPP -#define MLPACK_METHODS_ANN_VISITOR_SET_INPUT_WIDTH_VISITOR_HPP - -#include - -#include - -namespace mlpack { -namespace ann { - -/** - * SetInputWidthVisitor updates the input width parameter with the given input - * width. - */ -class SetInputWidthVisitor : public boost::static_visitor -{ - public: - //! Update the input width parameter with the given input width. - SetInputWidthVisitor(const size_t inputWidth = 0, const bool reset = false); - - //! Update the input width parameter. - template - bool operator()(LayerType* layer) const; - - bool operator()(MoreTypes layer) const; - - private: - //! The input width parameter. - size_t inputWidth; - - //! If set reset the height parameter if already set. - bool reset; - - //! Do nothing if the module doesn't implement the InputWidth() or Model() - //! function. - template - typename std::enable_if< - !HasInputWidth::value && - !HasModelCheck::value, bool>::type - LayerInputWidth(T* layer) const; - - //! Update the input width if the module implements the InputWidth() function. - template - typename std::enable_if< - HasInputWidth::value && - !HasModelCheck::value, bool>::type - LayerInputWidth(T* layer) const; - - //! Update the input width if the module implements the Model() function. - template - typename std::enable_if< - !HasInputWidth::value && - HasModelCheck::value, bool>::type - LayerInputWidth(T* layer) const; - - //! Update the input width if the module implements the InputWidth() or - //! Model() function. - template - typename std::enable_if< - HasInputWidth::value && - HasModelCheck::value, bool>::type - LayerInputWidth(T* layer) const; -}; - -} // namespace ann -} // namespace mlpack - -// Include implementation. -#include "set_input_width_visitor_impl.hpp" - -#endif diff --git a/src/mlpack/methods/ann/visitor/set_input_width_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/set_input_width_visitor_impl.hpp deleted file mode 100644 index 56224ed009..0000000000 --- a/src/mlpack/methods/ann/visitor/set_input_width_visitor_impl.hpp +++ /dev/null @@ -1,102 +0,0 @@ -/** - * @file methods/ann/visitor/set_input_width_visitor_impl.hpp - * @author Marcus Edel - * - * Implementation of the InputWidth() function layer abstraction. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_SET_INPUT_WIDTH_VISITOR_IMPL_HPP -#define MLPACK_METHODS_ANN_VISITOR_SET_INPUT_WIDTH_VISITOR_IMPL_HPP - -// In case it hasn't been included yet. -#include "set_input_width_visitor.hpp" - -namespace mlpack { -namespace ann { - -//! SetInputWidthVisitor visitor class. -inline SetInputWidthVisitor::SetInputWidthVisitor(const size_t inputWidth, - const bool reset) : - inputWidth(inputWidth), - reset(reset) -{ - /* Nothing to do here. */ -} - -template -inline bool SetInputWidthVisitor::operator()(LayerType* layer) const -{ - return LayerInputWidth(layer); -} - -inline bool SetInputWidthVisitor::operator()(MoreTypes layer) const -{ - return layer.apply_visitor(*this); -} - -template -inline typename std::enable_if< - !HasInputWidth::value && - !HasModelCheck::value, bool>::type -SetInputWidthVisitor::LayerInputWidth(T* /* layer */) const -{ - return false; -} - -template -inline typename std::enable_if< - HasInputWidth::value && - !HasModelCheck::value, bool>::type -SetInputWidthVisitor::LayerInputWidth(T* layer) const -{ - if (layer->InputWidth() == 0 || reset) - { - layer->InputWidth() = inputWidth; - } - - return true; -} - -template -inline typename std::enable_if< - !HasInputWidth::value && - HasModelCheck::value, bool>::type -SetInputWidthVisitor::LayerInputWidth(T* layer) const -{ - for (size_t i = 0; i < layer->Model().size(); ++i) - { - boost::apply_visitor(SetInputWidthVisitor(inputWidth, reset), - layer->Model()[i]); - } - - return true; -} - -template -inline typename std::enable_if< - HasInputWidth::value && - HasModelCheck::value, bool>::type -SetInputWidthVisitor::LayerInputWidth(T* layer) const -{ - if (layer->InputWidth() == 0 || reset) - { - layer->InputWidth() = inputWidth; - } - - for (size_t i = 0; i < layer->Model().size(); ++i) - { - boost::apply_visitor(SetInputWidthVisitor(inputWidth, reset), - layer->Model()[i]); - } - - return true; -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/visitor/weight_set_visitor.hpp b/src/mlpack/methods/ann/visitor/weight_set_visitor.hpp deleted file mode 100644 index 81c6c110df..0000000000 --- a/src/mlpack/methods/ann/visitor/weight_set_visitor.hpp +++ /dev/null @@ -1,82 +0,0 @@ -/** - * @file methods/ann/visitor/weight_set_visitor.hpp - * @author Marcus Edel - * - * This file provides an abstraction for the Weight() function for different - * layers and automatically directs any parameter to the right layer type. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_WEIGHT_SET_VISITOR_HPP -#define MLPACK_METHODS_ANN_VISITOR_WEIGHT_SET_VISITOR_HPP - -#include - -#include - -namespace mlpack { -namespace ann { - -/** - * WeightSetVisitor update the module parameters given the parameters set. - */ -class WeightSetVisitor : public boost::static_visitor -{ - public: - //! Update the parameters given the parameters set and offset. - WeightSetVisitor(arma::mat& weight, const size_t offset = 0); - - //! Update the parameters set. - template - size_t operator()(LayerType* layer) const; - - size_t operator()(MoreTypes layer) const; - - private: - //! The parameters set. - arma::mat& weight; - - //! The parameters offset. - const size_t offset; - - //! Do not update the parameters if the module doesn't implement the - //! Parameters() or Model() function. - template - typename std::enable_if< - !HasParametersCheck::value && - !HasModelCheck::value, size_t>::type - LayerSize(T* layer, P&& input) const; - - //! Update the parameters if the module implements the Model() function. - template - typename std::enable_if< - !HasParametersCheck::value && - HasModelCheck::value, size_t>::type - LayerSize(T* layer, P&& input) const; - - //! Update the parameters if the module implements the Parameters() function. - template - typename std::enable_if< - HasParametersCheck::value && - !HasModelCheck::value, size_t>::type - LayerSize(T* layer, P&& input) const; - - //! Update the parameters if the module implements the Model() and - //! Parameters() function. - template - typename std::enable_if< - HasParametersCheck::value && - HasModelCheck::value, size_t>::type - LayerSize(T* layer, P&& input) const; -}; - -} // namespace ann -} // namespace mlpack - -// Include implementation. -#include "weight_set_visitor_impl.hpp" - -#endif diff --git a/src/mlpack/methods/ann/visitor/weight_set_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/weight_set_visitor_impl.hpp deleted file mode 100644 index fa52a469c7..0000000000 --- a/src/mlpack/methods/ann/visitor/weight_set_visitor_impl.hpp +++ /dev/null @@ -1,100 +0,0 @@ -/** - * @file methods/ann/visitor/weight_set_visitor_impl.hpp - * @author Marcus Edel - * - * Implementation of the Weight() function layer abstraction. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_WEIGHT_SET_VISITOR_IMPL_HPP -#define MLPACK_METHODS_ANN_VISITOR_WEIGHT_SET_VISITOR_IMPL_HPP - -// In case it hasn't been included yet. -#include "weight_set_visitor.hpp" - -namespace mlpack { -namespace ann { - -//! WeightSetVisitor visitor class. -inline WeightSetVisitor::WeightSetVisitor(arma::mat& weight, - const size_t offset) : - weight(weight), - offset(offset) -{ - /* Nothing to do here. */ -} - -template -inline size_t WeightSetVisitor::operator()(LayerType* layer) const -{ - return LayerSize(layer, layer->OutputParameter()); -} - -inline size_t WeightSetVisitor::operator()(MoreTypes layer) const -{ - return layer.apply_visitor(*this); -} - -template -inline typename std::enable_if< - !HasParametersCheck::value && - !HasModelCheck::value, size_t>::type -WeightSetVisitor::LayerSize(T* /* layer */, P&& /*output */) const -{ - return 0; -} - -template -inline typename std::enable_if< - !HasParametersCheck::value && - HasModelCheck::value, size_t>::type -WeightSetVisitor::LayerSize(T* layer, P&& /*output */) const -{ - size_t modelOffset = 0; - for (size_t i = 0; i < layer->Model().size(); ++i) - { - modelOffset += boost::apply_visitor(WeightSetVisitor( - weight, modelOffset + offset), layer->Model()[i]); - } - - return modelOffset; -} - -template -inline typename std::enable_if< - HasParametersCheck::value && - !HasModelCheck::value, size_t>::type -WeightSetVisitor::LayerSize(T* layer, P&& /* output */) const -{ - layer->Parameters() = arma::mat(weight.memptr() + offset, - layer->Parameters().n_rows, layer->Parameters().n_cols, false, false); - - return layer->Parameters().n_elem; -} - -template -inline typename std::enable_if< - HasParametersCheck::value && - HasModelCheck::value, size_t>::type -WeightSetVisitor::LayerSize(T* layer, P&& /* output */) const -{ - layer->Parameters() = arma::mat(weight.memptr() + offset, - layer->Parameters().n_rows, layer->Parameters().n_cols, false, false); - - size_t modelOffset = layer->Parameters().n_elem; - for (size_t i = 0; i < layer->Model().size(); ++i) - { - modelOffset += boost::apply_visitor(WeightSetVisitor( - weight, modelOffset + offset), layer->Model()[i]); - } - - return modelOffset; -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/visitor/weight_size_visitor.hpp b/src/mlpack/methods/ann/visitor/weight_size_visitor.hpp deleted file mode 100644 index 074ca56614..0000000000 --- a/src/mlpack/methods/ann/visitor/weight_size_visitor.hpp +++ /dev/null @@ -1,76 +0,0 @@ -/** - * @file methods/ann/visitor/weight_size_visitor.hpp - * @author Marcus Edel - * - * This file provides an abstraction for the WeightSize() function for - * different layers and automatically directs any parameter to the right layer - * type. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_WEIGHT_SIZE_VISITOR_HPP -#define MLPACK_METHODS_ANN_VISITOR_WEIGHT_SIZE_VISITOR_HPP - -#include - -#include - -namespace mlpack { -namespace ann { - -/** - * WeightSizeVisitor returns the number of weights of the given module. - */ -class WeightSizeVisitor : public boost::static_visitor -{ - public: - //! Return the number of weights. - template - size_t operator()(LayerType* layer) const; - - size_t operator()(MoreTypes layer) const; - - private: - //! If the module doesn't implement the Parameters() or Model() function - //! return 0. - template - typename std::enable_if< - !HasParametersCheck::value && - !HasModelCheck::value, size_t>::type - LayerSize(T* layer, P& output) const; - - //! Return the number of parameters if the module implements the Model() - //! function. - template - typename std::enable_if< - !HasParametersCheck::value && - HasModelCheck::value, size_t>::type - LayerSize(T* layer, P& output) const; - - //! Return the number of parameters if the module implements the Parameters() - //! function. - template - typename std::enable_if< - HasParametersCheck::value && - !HasModelCheck::value, size_t>::type - LayerSize(T* layer, P& output) const; - - //! Return the accumulated number of parameters if the module implements the - //! Parameters() and Model() function. - template - typename std::enable_if< - HasParametersCheck::value && - HasModelCheck::value, size_t>::type - LayerSize(T* layer, P& output) const; -}; - -} // namespace ann -} // namespace mlpack - -// Include implementation. -#include "weight_size_visitor_impl.hpp" - -#endif diff --git a/src/mlpack/methods/ann/visitor/weight_size_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/weight_size_visitor_impl.hpp deleted file mode 100644 index 50ef266a63..0000000000 --- a/src/mlpack/methods/ann/visitor/weight_size_visitor_impl.hpp +++ /dev/null @@ -1,84 +0,0 @@ -/** - * @file methods/ann/visitor/weight_size_visitor_impl.hpp - * @author Marcus Edel - * - * Implementation of the WeightSize() function layer abstraction. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_VISITOR_WEIGHT_SIZE_VISITOR_IMPL_HPP -#define MLPACK_METHODS_ANN_VISITOR_WEIGHT_SIZE_VISITOR_IMPL_HPP - -// In case it hasn't been included yet. -#include "weight_size_visitor.hpp" - -namespace mlpack { -namespace ann { - -//! WeightSizeVisitor visitor class. -template -inline size_t WeightSizeVisitor::operator()(LayerType* layer) const -{ - return LayerSize(layer, layer->OutputParameter()); -} - -inline size_t WeightSizeVisitor::operator()(MoreTypes layer) const -{ - return layer.apply_visitor(*this); -} - -template -inline typename std::enable_if< - !HasParametersCheck::value && - !HasModelCheck::value, size_t>::type -WeightSizeVisitor::LayerSize(T* /* layer */, P& /* output */) const -{ - return 0; -} - -template -inline typename std::enable_if< - !HasParametersCheck::value && - HasModelCheck::value, size_t>::type -WeightSizeVisitor::LayerSize(T* layer, P& /* output */) const -{ - size_t weights = 0; - for (size_t i = 0; i < layer->Model().size(); ++i) - { - weights += boost::apply_visitor(WeightSizeVisitor(), layer->Model()[i]); - } - - return weights; -} - -template -inline typename std::enable_if< - HasParametersCheck::value && - !HasModelCheck::value, size_t>::type -WeightSizeVisitor::LayerSize(T* layer, P& /* output */) const -{ - return layer->Parameters().n_elem; -} - -template -inline typename std::enable_if< - HasParametersCheck::value && - HasModelCheck::value, size_t>::type -WeightSizeVisitor::LayerSize(T* layer, P& /* output */) const -{ - size_t weights = layer->Parameters().n_elem; - for (size_t i = 0; i < layer->Model().size(); ++i) - { - weights += boost::apply_visitor(WeightSizeVisitor(), layer->Model()[i]); - } - - return weights; -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/kmeans/kmeans_impl.hpp b/src/mlpack/methods/kmeans/kmeans_impl.hpp index 4195da81ac..e8f782e916 100644 --- a/src/mlpack/methods/kmeans/kmeans_impl.hpp +++ b/src/mlpack/methods/kmeans/kmeans_impl.hpp @@ -14,6 +14,7 @@ #include #include +#include namespace mlpack { namespace kmeans { @@ -161,15 +162,8 @@ Cluster(const MatType& data, // Check validity of initial guess. if (initialGuess) { - if (centroids.n_cols != clusters) - Log::Fatal << "KMeans::Cluster(): wrong number of initial cluster " - << "centroids (" << centroids.n_cols << ", should be " << clusters - << ")!" << std::endl; - - if (centroids.n_rows != data.n_rows) - Log::Fatal << "KMeans::Cluster(): initial cluster centroids have wrong " - << " dimensionality (" << centroids.n_rows << ", should be " - << data.n_rows << ")!" << std::endl; + util::CheckSameSizes(centroids, clusters, "KMeans::Cluster()", "clusters"); + util::CheckSameDimensionality(data, centroids, "KMeans::Cluster()"); } // Use the partitioner to come up with the partition assignments and calculate @@ -288,10 +282,7 @@ Cluster(const MatType& data, // Now, the initial assignments. First determine if they are necessary. if (initialAssignmentGuess) { - if (assignments.n_elem != data.n_cols) - Log::Fatal << "KMeans::Cluster(): initial cluster assignments (length " - << assignments.n_elem << ") not the same size as the dataset (size " - << data.n_cols << ")!" << std::endl; + util::CheckSameSizes(data, assignments, "KMeans::Cluster()", "assignments"); // Calculate initial centroids. arma::Row counts; diff --git a/src/mlpack/methods/linear_regression/linear_regression.cpp b/src/mlpack/methods/linear_regression/linear_regression.cpp index d3d4a2cd3f..774425ebc7 100644 --- a/src/mlpack/methods/linear_regression/linear_regression.cpp +++ b/src/mlpack/methods/linear_regression/linear_regression.cpp @@ -12,6 +12,7 @@ */ #include "linear_regression.hpp" #include +#include using namespace mlpack; using namespace mlpack::regression; @@ -57,6 +58,10 @@ double LinearRegression::Train(const arma::mat& predictors, // We store the number of rows and columns of the predictors. // Reminder: Armadillo stores the data transposed from how we think of it, // that is, columns are actually rows (see: column major order). + + // Sanity check on data. + util::CheckSameSizes(predictors, responses, "LinearRegression::Train()"); + const size_t nCols = predictors.n_cols; arma::mat p = predictors; @@ -95,7 +100,11 @@ void LinearRegression::Predict(const arma::mat& points, { // We want to be sure we have the correct number of dimensions in the // dataset. - Log::Assert(points.n_rows == parameters.n_rows - 1); + // Prevent underflow. + const size_t labels = (parameters.n_rows == 0) ? size_t(0) : + size_t(parameters.n_rows - 1); + util::CheckSameDimensionality(points, labels, "LinearRegression::Predict()", + "points"); // Get the predictions, but this ignores the intercept value // (parameters[0]). predictions = arma::trans(parameters.subvec(1, parameters.n_elem - 1)) @@ -107,7 +116,8 @@ void LinearRegression::Predict(const arma::mat& points, { // We want to be sure we have the correct number of dimensions in // the dataset. - Log::Assert(points.n_rows == parameters.n_rows); + util::CheckSameDimensionality(points, parameters, + "LinearRegression::Predict()", "points"); predictions = arma::trans(parameters) * points; } } @@ -115,6 +125,9 @@ void LinearRegression::Predict(const arma::mat& points, double LinearRegression::ComputeError(const arma::mat& predictors, const arma::rowvec& responses) const { + // Sanity check on data. + util::CheckSameSizes(predictors, responses, "LinearRegression::Train()"); + // Get the number of columns and rows of the dataset. const size_t nCols = predictors.n_cols; const size_t nRows = predictors.n_rows; diff --git a/src/mlpack/methods/perceptron/perceptron.hpp b/src/mlpack/methods/perceptron/perceptron.hpp index 4994588c40..3fdfce6e14 100644 --- a/src/mlpack/methods/perceptron/perceptron.hpp +++ b/src/mlpack/methods/perceptron/perceptron.hpp @@ -68,6 +68,29 @@ class Perceptron const size_t numClasses, const size_t maxIterations = 1000); + /** + * Constructor: construct the perceptron by building the weights matrix, which + * is later used in classification. The number of classes should be specified + * separately, and the labels vector should contain values in the range [0, + * numClasses - 1]. The data::NormalizeLabels() function can be used if the + * labels vector does not contain values in the required range. + * + * This constructor supports weights for each data point. + * + * @param data Input, training data. + * @param labels Labels of dataset. + * @param numClasses Number of classes in the dataset. + * @param instanceWeights Weight vector to use for each training point while + * training. + * @param maxIterations Maximum number of iterations for the perceptron + * learning algorithm. + */ + Perceptron(const MatType& data, + const arma::Row& labels, + const size_t numClasses, + const arma::rowvec& instanceWeights, + const size_t maxIterations = 1000); + /** * Alternate constructor which copies parameters from an already initiated * perceptron. diff --git a/src/mlpack/methods/perceptron/perceptron_impl.hpp b/src/mlpack/methods/perceptron/perceptron_impl.hpp index 46d1ff804a..7373a3843b 100644 --- a/src/mlpack/methods/perceptron/perceptron_impl.hpp +++ b/src/mlpack/methods/perceptron/perceptron_impl.hpp @@ -62,6 +62,32 @@ Perceptron::Perceptron( Train(data, labels, numClasses); } +/** + * Constructor: construct the perceptron by building the weights matrix, which + * is later used in classification. The number of classes should be specified + * separately, and the labels vector should contain values in the range [0, + * numClasses - 1]. The data::NormalizeLabels() function can be used if the + * labels vector does not contain values in the required range. + * + * This constructor supports weights for each data point. + */ +template< + typename LearnPolicy, + typename WeightInitializationPolicy, + typename MatType +> +Perceptron::Perceptron( + const MatType& data, + const arma::Row& labels, + const size_t numClasses, + const arma::rowvec& instanceWeights, + const size_t maxIterations) : + maxIterations(maxIterations) +{ + // Start training. + Train(data, labels, numClasses, instanceWeights); +} + /** * Alternate constructor which copies parameters from an already initiated * perceptron. @@ -108,13 +134,14 @@ void Perceptron::Classify( { arma::vec tempLabelMat; arma::uword maxIndex = 0; + predictedLabels.set_size(test.n_cols); // Could probably be faster if done in batch. for (size_t i = 0; i < test.n_cols; ++i) { tempLabelMat = weights.t() * test.col(i) + biases; tempLabelMat.max(maxIndex); - predictedLabels(0, i) = maxIndex; + predictedLabels(i) = maxIndex; } } diff --git a/src/mlpack/methods/reinforcement_learning/q_learning_impl.hpp b/src/mlpack/methods/reinforcement_learning/q_learning_impl.hpp index de28d05e74..7ff52cde88 100644 --- a/src/mlpack/methods/reinforcement_learning/q_learning_impl.hpp +++ b/src/mlpack/methods/reinforcement_learning/q_learning_impl.hpp @@ -53,9 +53,9 @@ QLearning< // Set up q-learning network. if (learningNetwork.Parameters().is_empty()) - learningNetwork.ResetParameters(); + learningNetwork.Reset(); - targetNetwork.ResetParameters(); + targetNetwork.Reset(); #if ENS_VERSION_MAJOR == 1 this->updater.Initialize(learningNetwork.Parameters().n_rows, diff --git a/src/mlpack/methods/reinforcement_learning/q_networks/dueling_dqn.hpp b/src/mlpack/methods/reinforcement_learning/q_networks/dueling_dqn.hpp index c4b26a353f..a55e6c34ba 100644 --- a/src/mlpack/methods/reinforcement_learning/q_networks/dueling_dqn.hpp +++ b/src/mlpack/methods/reinforcement_learning/q_networks/dueling_dqn.hpp @@ -15,7 +15,7 @@ #include #include #include -#include +#include #include #include diff --git a/src/mlpack/methods/reinforcement_learning/q_networks/simple_dqn.hpp b/src/mlpack/methods/reinforcement_learning/q_networks/simple_dqn.hpp index 54ad55f01e..d4ceb5c22c 100644 --- a/src/mlpack/methods/reinforcement_learning/q_networks/simple_dqn.hpp +++ b/src/mlpack/methods/reinforcement_learning/q_networks/simple_dqn.hpp @@ -15,7 +15,7 @@ #include #include #include -#include +#include #include namespace mlpack { @@ -43,7 +43,6 @@ class SimpleDQN /** * Construct an instance of SimpleDQN class. * - * @param inputDim Number of inputs. * @param h1 Number of neurons in hiddenlayer-1. * @param h2 Number of neurons in hiddenlayer-2. * @param outputDim Number of neurons in output layer. @@ -51,8 +50,7 @@ class SimpleDQN * @param init Specifies the initialization rule for the network. * @param outputLayer Specifies the output layer type for network. */ - SimpleDQN(const int inputDim, - const int h1, + SimpleDQN(const int h1, const int h2, const int outputDim, const bool isNoisy = false, @@ -61,21 +59,21 @@ class SimpleDQN network(outputLayer, init), isNoisy(isNoisy) { - network.Add(new ann::Linear<>(inputDim, h1)); - network.Add(new ann::ReLULayer<>()); + network.Add(new ann::Linear(h1)); + network.Add(new ann::ReLU()); if (isNoisy) { - noisyLayerIndex.push_back(network.Model().size()); - network.Add(new ann::NoisyLinear<>(h1, h2)); - network.Add(new ann::ReLULayer<>()); - noisyLayerIndex.push_back(network.Model().size()); - network.Add(new ann::NoisyLinear<>(h2, outputDim)); + noisyLayerIndex.push_back(network.Network().size()); + network.Add(new ann::NoisyLinear(h2)); + network.Add(new ann::ReLU()); + noisyLayerIndex.push_back(network.Network().size()); + network.Add(new ann::NoisyLinear(outputDim)); } else { - network.Add(new ann::Linear<>(h1, h2)); - network.Add(new ann::ReLULayer<>()); - network.Add(new ann::Linear<>(h2, outputDim)); + network.Add(new ann::Linear(h2)); + network.Add(new ann::ReLU()); + network.Add(new ann::Linear(outputDim)); } } @@ -120,9 +118,9 @@ class SimpleDQN /** * Resets the parameters of the network. */ - void ResetParameters() + void Reset() { - network.ResetParameters(); + network.Reset(); } /** @@ -132,8 +130,8 @@ class SimpleDQN { for (size_t i = 0; i < noisyLayerIndex.size(); i++) { - boost::get*> - (network.Model()[noisyLayerIndex[i]])->ResetNoise(); + dynamic_cast( + network.Network()[noisyLayerIndex[i]])->ResetNoise(); } } diff --git a/src/mlpack/methods/reinforcement_learning/sac.hpp b/src/mlpack/methods/reinforcement_learning/sac.hpp index 27ddb5cba0..55ce00aa9a 100644 --- a/src/mlpack/methods/reinforcement_learning/sac.hpp +++ b/src/mlpack/methods/reinforcement_learning/sac.hpp @@ -19,7 +19,6 @@ #include "replay/random_replay.hpp" #include #include -#include #include "training_config.hpp" namespace mlpack { diff --git a/src/mlpack/prereqs.hpp b/src/mlpack/prereqs.hpp index cc865820e1..6310364f55 100644 --- a/src/mlpack/prereqs.hpp +++ b/src/mlpack/prereqs.hpp @@ -33,6 +33,7 @@ #include #include #include +#include #include #include @@ -107,7 +108,6 @@ using enable_if_t = typename enable_if::type; #include #include #include -#include #include #include #include @@ -118,20 +118,10 @@ using enable_if_t = typename enable_if::type; #include #include #include -#include -#include #include #include #include -// If we have Boost 1.58 or older and are using C++14, the compilation is likely -// to fail due to boost::visitor issues. We will pre-emptively fail. -#if __cplusplus > 201103L && BOOST_VERSION < 105900 -#error Use of C++14 mode with Boost < 1.59 is known to cause compilation \ -problems. Instead specify the C++11 standard (-std=c++11 with gcc or clang), \ -or upgrade Boost to 1.59 or newer. -#endif - // On Visual Studio, disable C4519 (default arguments for function templates) // since it's by default an error, which doesn't even make any sense because // it's part of the C++11 standard. diff --git a/src/mlpack/tests/CMakeLists.txt b/src/mlpack/tests/CMakeLists.txt index b27b15619d..6e6b0840b5 100644 --- a/src/mlpack/tests/CMakeLists.txt +++ b/src/mlpack/tests/CMakeLists.txt @@ -11,10 +11,9 @@ add_executable(mlpack_test ann_layer_test.cpp ann_regularizer_test.cpp ann_test_tools.hpp - ann_visitor_test.cpp armadillo_svd_test.cpp arma_extend_test.cpp - async_learning_test.cpp +# async_learning_test.cpp augmented_rnns_tasks_test.cpp bayesian_linear_regression_test.cpp bias_svd_test.cpp @@ -28,7 +27,7 @@ add_executable(mlpack_test cosine_tree_test.cpp cv_test.cpp dbscan_test.cpp - dcgan_test.cpp +# dcgan_test.cpp decision_tree_regressor_test.cpp decision_tree_test.cpp det_test.cpp @@ -40,7 +39,7 @@ add_executable(mlpack_test fastmks_test.cpp feedforward_network_test.cpp feedforward_network_2_test.cpp - gan_test.cpp +# gan_test.cpp gmm_test.cpp hmm_test.cpp hpt_test.cpp @@ -60,7 +59,6 @@ add_executable(mlpack_test krann_search_test.cpp ksinit_test.cpp lars_test.cpp - layer_names_test.cpp lin_alg_test.cpp linear_regression_test.cpp lmnn_test.cpp @@ -90,18 +88,18 @@ add_executable(mlpack_test python_binding_test.cpp qdafn_test.cpp quic_svd_test.cpp - q_learning_test.cpp +# q_learning_test.cpp radical_test.cpp random_forest_test.cpp random_test.cpp randomized_svd_test.cpp range_search_test.cpp - rbm_network_test.cpp +# rbm_network_test.cpp rectangle_tree_test.cpp recurrent_network_test.cpp - rnn_reber_test.cpp +# rnn_reber_test.cpp regularized_svd_test.cpp - reward_clipping_test.cpp +# reward_clipping_test.cpp rl_components_test.cpp scaling_test.cpp size_checks_test.cpp @@ -130,7 +128,7 @@ add_executable(mlpack_test ub_tree_test.cpp union_find_test.cpp vantage_point_tree_test.cpp - wgan_test.cpp +# wgan_test.cpp xgboost_test.cpp main_tests/adaboost_test.cpp main_tests/adaboost_train_test.cpp diff --git a/src/mlpack/tests/activation_functions_test.cpp b/src/mlpack/tests/activation_functions_test.cpp index 1c4458c4f9..2cadc4375c 100644 --- a/src/mlpack/tests/activation_functions_test.cpp +++ b/src/mlpack/tests/activation_functions_test.cpp @@ -12,7 +12,7 @@ */ #include -#include +#include #include #include #include @@ -135,7 +135,7 @@ void CheckInverseCorrect(const arma::colvec input) * * @param input Input data used for evaluating the HardTanH activation function. * @param target Target data used to evaluate the HardTanH activation. - */ + * void CheckHardTanHActivationCorrect(const arma::colvec input, const arma::colvec target) { @@ -148,7 +148,7 @@ void CheckHardTanHActivationCorrect(const arma::colvec input, { REQUIRE(activations.at(i) == Approx(target.at(i)).epsilon(1e-5)); } -} +}*/ /** * Implementation of the HardTanH activation function derivative test. The @@ -157,7 +157,7 @@ void CheckHardTanHActivationCorrect(const arma::colvec input, * @param input Input data used for evaluating the HardTanH activation * function. * @param target Target data used to evaluate the HardTanH activation. - */ + * void CheckHardTanHDerivativeCorrect(const arma::colvec input, const arma::colvec target) { @@ -174,7 +174,7 @@ void CheckHardTanHDerivativeCorrect(const arma::colvec input, { REQUIRE(derivatives.at(i) == Approx(target.at(i)).epsilon(1e-5)); } -} +}*/ /** * Implementation of the LeakyReLU activation function test. The function is @@ -187,7 +187,7 @@ void CheckHardTanHDerivativeCorrect(const arma::colvec input, void CheckLeakyReLUActivationCorrect(const arma::colvec input, const arma::colvec target) { - LeakyReLU<> lrf; + LeakyReLU lrf; // Test the activation function using the entire vector as input. arma::colvec activations; @@ -210,7 +210,7 @@ void CheckLeakyReLUActivationCorrect(const arma::colvec input, void CheckLeakyReLUDerivativeCorrect(const arma::colvec input, const arma::colvec target) { - LeakyReLU<> lrf; + LeakyReLU lrf; // Test the calculation of the derivatives using the entire vector as input. arma::colvec derivatives; @@ -230,7 +230,7 @@ void CheckLeakyReLUDerivativeCorrect(const arma::colvec input, * * @param input Input data used for evaluating the ELU activation function. * @param target Target data used to evaluate the ELU activation. - */ + * void CheckELUActivationCorrect(const arma::colvec input, const arma::colvec target) { @@ -244,7 +244,7 @@ void CheckELUActivationCorrect(const arma::colvec input, { REQUIRE(activations.at(i) == Approx(target.at(i)).epsilon(1e-5)); } -} +}*/ /** * Implementation of the ELU activation function derivative test. The function @@ -252,7 +252,7 @@ void CheckELUActivationCorrect(const arma::colvec input, * * @param input Input data used for evaluating the ELU activation function. * @param target Target data used to evaluate the ELU activation. - */ + * void CheckELUDerivativeCorrect(const arma::colvec input, const arma::colvec target) { @@ -270,7 +270,7 @@ void CheckELUDerivativeCorrect(const arma::colvec input, { REQUIRE(derivatives.at(i) == Approx(target.at(i)).epsilon(1e-5)); } -} +}*/ /** * Implementation of the PReLU activation function test. The function @@ -279,7 +279,7 @@ void CheckELUDerivativeCorrect(const arma::colvec input, * @param input Input data used for evaluating the PReLU activation * function. * @param target Target data used to evaluate the PReLU activation. - */ + * void CheckPReLUActivationCorrect(const arma::colvec input, const arma::colvec target) { @@ -292,7 +292,7 @@ void CheckPReLUActivationCorrect(const arma::colvec input, { REQUIRE(activations.at(i) == Approx(target.at(i)).epsilon(1e-5)); } -} +}*/ /** * Implementation of the PReLU activation function derivative test. @@ -302,7 +302,7 @@ void CheckPReLUActivationCorrect(const arma::colvec input, * @param input Input data used for evaluating the PReLU activation * function. * @param target Target data used to evaluate the PReLU activation. - */ + * void CheckPReLUDerivativeCorrect(const arma::colvec input, const arma::colvec target) { @@ -318,7 +318,7 @@ void CheckPReLUDerivativeCorrect(const arma::colvec input, { REQUIRE(derivatives.at(i) == Approx(target.at(i)).epsilon(1e-5)); } -} +}*/ /** * Implementation of the PReLU activation function gradient test. @@ -328,7 +328,7 @@ void CheckPReLUDerivativeCorrect(const arma::colvec input, * @param input Input data used for evaluating the PReLU activation * function. * @param target Target data used to evaluate the PReLU gradient. - */ + * void CheckPReLUGradientCorrect(const arma::colvec input, const arma::colvec target) { @@ -343,7 +343,7 @@ void CheckPReLUGradientCorrect(const arma::colvec input, REQUIRE(gradient.n_rows == 1); REQUIRE(gradient.n_cols == 1); REQUIRE(gradient(0) == Approx(target(0)).epsilon(1e-5)); -} +}*/ /** * Implementation of the Hard Shrink activation function test. The function is @@ -351,7 +351,7 @@ void CheckPReLUGradientCorrect(const arma::colvec input, * * @param input Input data used for evaluating the Hard Shrink activation function. * @param target Target data used to evaluate the Hard Shrink activation. - */ + * void CheckHardShrinkActivationCorrect(const arma::colvec input, const arma::colvec target) { @@ -364,7 +364,7 @@ void CheckHardShrinkActivationCorrect(const arma::colvec input, { REQUIRE(activations.at(i) == Approx(target.at(i)).epsilon(1e-5)); } -} +}*/ /** * Implementation of the HardShrink activation function derivative test. @@ -374,7 +374,7 @@ void CheckHardShrinkActivationCorrect(const arma::colvec input, * @param input Input data used for evaluating the HardShrink activation * function. * @param target Target data used to evaluate the HardShrink activation. - */ + * void CheckHardShrinkDerivativeCorrect(const arma::colvec input, const arma::colvec target) { @@ -390,7 +390,7 @@ void CheckHardShrinkDerivativeCorrect(const arma::colvec input, { REQUIRE(derivatives.at(i) == Approx(target.at(i)).epsilon(1e-5)); } -} +}*/ /** * Implementation of the Soft Shrink activation function test. The function is @@ -399,7 +399,7 @@ void CheckHardShrinkDerivativeCorrect(const arma::colvec input, * @param input Input data used for evaluating the Soft Shrink activation * function. * @param target Target data used to evaluate the Soft Shrink activation. - */ + * void CheckSoftShrinkActivationCorrect(const arma::colvec input, const arma::colvec target) { @@ -412,7 +412,7 @@ void CheckSoftShrinkActivationCorrect(const arma::colvec input, { REQUIRE(activations.at(i) == Approx(target.at(i)).epsilon(1e-5)); } -} +}*/ /** * Implementation of the Soft Shrink activation function derivative test. @@ -422,7 +422,7 @@ void CheckSoftShrinkActivationCorrect(const arma::colvec input, * @param input Input data used for evaluating the Soft Shrink activation * function. * @param target Target data used to evaluate the Soft Shrink activation. - */ + * void CheckSoftShrinkDerivativeCorrect(const arma::colvec input, const arma::colvec target) { @@ -438,12 +438,12 @@ void CheckSoftShrinkDerivativeCorrect(const arma::colvec input, { REQUIRE(derivatives.at(i) == Approx(target.at(i)).epsilon(1e-5)); } -} +}*/ /** * Simple SELU activation test to check whether the mean and variance remain * invariant after passing normalized inputs through the function. - */ + * TEST_CASE("SELUFunctionNormalizedTest", "[ActivationFunctionsTest]") { arma::mat input = arma::randn(1000, 1); @@ -459,12 +459,12 @@ TEST_CASE("SELUFunctionNormalizedTest", "[ActivationFunctionsTest]") REQUIRE(arma::as_scalar(arma::abs(arma::var(input) - arma::var(output))) <= 0.1); -} +}*/ /** * Simple SELU activation test to check whether the mean and variance * vary significantly after passing unnormalized inputs through the function. - */ + * TEST_CASE("SELUFunctionUnnormalizedTest", "[ActivationFunctionsTest]") { const arma::colvec input("5.96402758 0.9966824 0.99975321 1 \ @@ -481,13 +481,13 @@ TEST_CASE("SELUFunctionUnnormalizedTest", "[ActivationFunctionsTest]") REQUIRE(arma::as_scalar(arma::abs(arma::var(input) - arma::var(output))) >= 0.1); -} +}*/ /** * Simple SELU derivative test to check whether the derivatives * produced by the activation function are correct. * - */ + * TEST_CASE("SELUFunctionDerivativeTest", "[ActivationFunctionsTest]") { arma::mat input = arma::ones(1000, 1); @@ -511,7 +511,7 @@ TEST_CASE("SELUFunctionDerivativeTest", "[ActivationFunctionsTest]") REQUIRE(arma::as_scalar(arma::abs(arma::mean(derivatives) - selu.Lambda() * selu.Alpha() - arma::mean(activations))) <= 10e-4); -} +}*/ /** * Implementation of the CELU activation function test. The function is @@ -519,7 +519,7 @@ TEST_CASE("SELUFunctionDerivativeTest", "[ActivationFunctionsTest]") * * @param input Input data used for evaluating the CELU activation function. * @param target Target data used to evaluate the CELU activation. - */ + * void CheckCELUActivationCorrect(const arma::colvec input, const arma::colvec target) { @@ -533,7 +533,7 @@ void CheckCELUActivationCorrect(const arma::colvec input, { REQUIRE(activations.at(i) == Approx(target.at(i)).epsilon(1e-5)); } -} +}*/ /** * Implementation of the CELU activation function derivative test. The function @@ -541,7 +541,7 @@ void CheckCELUActivationCorrect(const arma::colvec input, * * @param input Input data used for evaluating the CELU activation function. * @param target Target data used to evaluate the CELU activation. - */ + * void CheckCELUDerivativeCorrect(const arma::colvec input, const arma::colvec target) { @@ -559,7 +559,7 @@ void CheckCELUDerivativeCorrect(const arma::colvec input, { REQUIRE(derivatives.at(i) == Approx(target.at(i)).epsilon(1e-5)); } -} +}*/ /** * Implementation of the ISRLU activation function test. The function is @@ -567,7 +567,7 @@ void CheckCELUDerivativeCorrect(const arma::colvec input, * * @param input Input data used for evaluating the ISRLU activation function. * @param target Target data used to evaluate the ISRLU activation. - */ + * void CheckISRLUActivationCorrect(const arma::colvec input, const arma::colvec target) { @@ -581,7 +581,7 @@ void CheckISRLUActivationCorrect(const arma::colvec input, { REQUIRE(activations.at(i) == Approx(target.at(i)).epsilon(1e-5)); } -} +}*/ /** * Implementation of the ISRLU activation function derivative test. The function @@ -589,7 +589,7 @@ void CheckISRLUActivationCorrect(const arma::colvec input, * * @param input Input data used for evaluating the ISRLU activation function. * @param target Target data used to evaluate the ISRLU activation. - */ + * void CheckISRLUDerivativeCorrect(const arma::colvec input, const arma::colvec target) { @@ -607,7 +607,7 @@ void CheckISRLUDerivativeCorrect(const arma::colvec input, { REQUIRE(derivatives.at(i) == Approx(target.at(i)).epsilon(1e-5)); } -} +}*/ /** * Implementation of the Softmin activation function test. The function is @@ -615,7 +615,7 @@ void CheckISRLUDerivativeCorrect(const arma::colvec input, * * @param input Input data used for evaluating the Softmin activation function. * @param target Target data used to evaluate the Softmin activation. - */ + * void CheckSoftminActivationCorrect(const arma::colvec input, const arma::colvec target) { @@ -629,7 +629,7 @@ void CheckSoftminActivationCorrect(const arma::colvec input, { REQUIRE(activations.at(i) == Approx(target.at(i)).epsilon(1e-5)); } -} +}*/ /** * Implementation of the Softmin activation function derivative test. @@ -637,7 +637,7 @@ void CheckSoftminActivationCorrect(const arma::colvec input, * * @param input Input data used for evaluating the Softmin activation function. * @param target Target data used to evaluate the Softmin activation. - */ + * void CheckSoftminDerivativeCorrect(const arma::colvec input, const arma::colvec target) { @@ -658,7 +658,7 @@ void CheckSoftminDerivativeCorrect(const arma::colvec input, { REQUIRE(derivatives.at(i) == Approx(target.at(i)).epsilon(1e-5)); } -} +}*/ /** * Implementation of the Flatten T Swish activation function test. The function is @@ -667,7 +667,7 @@ void CheckSoftminDerivativeCorrect(const arma::colvec input, * @param input Input data used for evaluating the Flatten T Swish activation * function. * @param target Target data used to evaluate the Flatten T Swish activation. - */ + * void CheckFlattenTSwishActivationCorrect(const arma::colvec input, const arma::colvec target) { @@ -679,7 +679,7 @@ void CheckFlattenTSwishActivationCorrect(const arma::colvec input, { REQUIRE(activations.at(i) == Approx(target.at(i)).epsilon(1e-5)); } -} +}*/ /** * Implementation of the Softmin activation function derivative test. @@ -687,7 +687,7 @@ void CheckFlattenTSwishActivationCorrect(const arma::colvec input, * * @param input Input data used for evaluating the Softmin activation function. * @param target Target data used to evaluate the Softmin activation. - */ + * void CheckFlattenTSwishDerivateCorrect(const arma::colvec input, const arma::colvec target) { @@ -702,7 +702,7 @@ void CheckFlattenTSwishDerivateCorrect(const arma::colvec input, { REQUIRE(derivate.at(i) == Approx(target.at(i)).epsilon(1e-5)); } -} +}*/ /** * Implementation of the ReLU6 activation function derivative test. The function @@ -710,7 +710,7 @@ void CheckFlattenTSwishDerivateCorrect(const arma::colvec input, * * @param input Input data used for evaluating the ReLU6 activation function. * @param target Target data used to evaluate the ReLU6 activation. - */ + * void CheckReLU6Correct(const arma::colvec input, const arma::colvec ActivationTarget, const arma::colvec DerivativeTarget) @@ -733,11 +733,11 @@ void CheckReLU6Correct(const arma::colvec input, { REQUIRE(derivatives.at(i) == Approx(DerivativeTarget.at(i)).epsilon(1e-5)); } -} +}*/ /** * Basic test of the ReLU6 function. - */ + * TEST_CASE("ReLU6FunctionTest", "[ActivationFunctionsTest]") { const arma::colvec activationData("-2.0 3.0 0.0 6.0 24.0"); @@ -749,7 +749,7 @@ TEST_CASE("ReLU6FunctionTest", "[ActivationFunctionsTest]") const arma::colvec desiredDerivatives("0.0 1.0 0.0 0.0 0.0"); CheckReLU6Correct(activationData, desiredActivations, desiredDerivatives); -} +}*/ /** * Basic test of the tanh function. @@ -847,7 +847,7 @@ TEST_CASE("LeakyReLUFunctionTest", "[ActivationFunctionsTest]") /** * Basic test of the HardTanH function. - */ + * TEST_CASE("HardTanHFunctionTest", "[ActivationFunctionsTest]") { const arma::colvec desiredActivations("-1 1 1 -1 \ @@ -858,11 +858,11 @@ TEST_CASE("HardTanHFunctionTest", "[ActivationFunctionsTest]") CheckHardTanHActivationCorrect(activationData, desiredActivations); CheckHardTanHDerivativeCorrect(activationData, desiredDerivatives); -} +}*/ /** * Basic test of the ELU function. - */ + * TEST_CASE("ELUFunctionTest", "[ActivationFunctionsTest]") { const arma::colvec desiredActivations("-0.86466471 3.2 4.5 -1.0 \ @@ -873,7 +873,7 @@ TEST_CASE("ELUFunctionTest", "[ActivationFunctionsTest]") CheckELUActivationCorrect(activationData, desiredActivations); CheckELUDerivativeCorrect(activationData, desiredDerivatives); -} +}*/ /** * Basic test of the softplus function. @@ -898,7 +898,7 @@ TEST_CASE("SoftplusFunctionTest", "[ActivationFunctionsTest]") /** * Basic test of the PReLU function. - */ + * TEST_CASE("PReLUFunctionTest", "[ActivationFunctionsTest]") { const arma::colvec desiredActivations("-0.06 3.2 4.5 -3.006 \ @@ -911,11 +911,11 @@ TEST_CASE("PReLUFunctionTest", "[ActivationFunctionsTest]") CheckPReLUActivationCorrect(activationData, desiredActivations); CheckPReLUDerivativeCorrect(desiredActivations, desiredDerivatives); CheckPReLUGradientCorrect(activationData, desiredGradient); -} +}*/ /** * Basic test of the CReLU function. - */ + * TEST_CASE("CReLUFunctionTest", "[ActivationFunctionsTest]") { const arma::colvec desiredActivations("0 3.2 4.5 0 \ @@ -942,11 +942,11 @@ TEST_CASE("CReLUFunctionTest", "[ActivationFunctionsTest]") REQUIRE(derivatives.at(i) == Approx(desiredDerivatives.at(i)).epsilon(1e-5)); } -} +}*/ /** * Basic test of the swish function. - */ + * TEST_CASE("SwishFunctionTest", "[ActivationFunctionsTest]") { // Hand-calculated values using Python interpreter. @@ -961,7 +961,7 @@ TEST_CASE("SwishFunctionTest", "[ActivationFunctionsTest]") CheckActivationCorrect(activationData, desiredActivations); CheckDerivativeCorrect(desiredActivations, desiredDerivatives); -} +}*/ /** * Basic test of the hard sigmoid function. @@ -1049,7 +1049,7 @@ TEST_CASE("GELUFunctionTest", "[ActivationFunctionsTest]") /** * Basic test of the Hard Shrink function. - */ + * TEST_CASE("HardShrinkFunctionTest", "[ActivationFunctionsTest]") { const arma::colvec desiredActivations("-2 3.2 4.5 -100.2 1 -1 2 0"); @@ -1060,7 +1060,7 @@ TEST_CASE("HardShrinkFunctionTest", "[ActivationFunctionsTest]") desiredActivations); CheckHardShrinkDerivativeCorrect(desiredActivations, desiredDerivatives); -} +}*/ /** * Basic test of the Elliot function. @@ -1104,7 +1104,7 @@ TEST_CASE("ElishFunctionTest", "[ActivationFunctionsTest]") /** * Basic test of the Soft Shrink function. - */ + * TEST_CASE("SoftShrinkFunctionTest", "[ActivationFunctionsTest]") { const arma::colvec desiredActivations("-1.5 2.7 4 -99.7 0.5 -0.5 1.5 0"); @@ -1115,11 +1115,11 @@ TEST_CASE("SoftShrinkFunctionTest", "[ActivationFunctionsTest]") desiredActivations); CheckSoftShrinkDerivativeCorrect(desiredActivations, desiredDerivatives); -} +}*/ /** * Basic test of the CELU activation function. - */ + * TEST_CASE("CELUFunctionTest", "[ActivationFunctionsTest]") { const arma::colvec desiredActivations("-0.86466472 3.2 4.5 \ @@ -1131,11 +1131,11 @@ TEST_CASE("CELUFunctionTest", "[ActivationFunctionsTest]") CheckCELUActivationCorrect(activationData, desiredActivations); CheckCELUDerivativeCorrect(desiredActivations, desiredDerivatives); -} +}*/ /** * Basic test of the ISRLU activation function. - */ + * TEST_CASE("ISRLUFunctionTest", "[ActivationFunctionsTest]") { const arma::colvec desiredActivations("-0.89442719 3.2 4.5 \ @@ -1147,7 +1147,7 @@ TEST_CASE("ISRLUFunctionTest", "[ActivationFunctionsTest]") CheckISRLUActivationCorrect(activationData, desiredActivations); CheckISRLUDerivativeCorrect(activationData, desiredDerivatives); -} +}*/ /** * Basic test of the inverse quadratic function. @@ -1275,7 +1275,7 @@ TEST_CASE("GaussianFunctionTest", "[ActivationFunctionsTest]") /** * Basic test of the Softmin function. - */ + * TEST_CASE("SoftminFunctionTest", "[ActivationFunctionsTest]") { const arma::colvec activationData("4.2 2.4 7.0 6.4"); @@ -1291,7 +1291,7 @@ TEST_CASE("SoftminFunctionTest", "[ActivationFunctionsTest]") desiredActivations); CheckSoftminDerivativeCorrect(activationData, desiredDerivatives); -} +}*/ /** * Basic test of the Hard Swish function. @@ -1360,7 +1360,7 @@ TEST_CASE("SILUFunctionTest", "[ActivationFunctionsTest]") /** * Basic test of Flatten T Swish function. - */ + * TEST_CASE("FlattenTSwishFunctionTest", "[ActivationFunctionsTest]") { // Random Value. @@ -1378,4 +1378,4 @@ TEST_CASE("FlattenTSwishFunctionTest", "[ActivationFunctionsTest]") CheckFlattenTSwishActivationCorrect(input, desiredActivation); CheckFlattenTSwishDerivateCorrect(desiredActivation, desiredDerivation); -} +}*/ diff --git a/src/mlpack/tests/ann_layer_test.cpp b/src/mlpack/tests/ann_layer_test.cpp index b9d5452c41..cb1c5d766f 100644 --- a/src/mlpack/tests/ann_layer_test.cpp +++ b/src/mlpack/tests/ann_layer_test.cpp @@ -21,7 +21,6 @@ #include #include #include -#include #include "test_catch_tools.hpp" #include "catch.hpp" @@ -31,59 +30,59 @@ using namespace mlpack; using namespace mlpack::ann; -// network1 should be allocated with `new`, and trained on some data. -template -void CheckRNNCopyFunction(ModelType* network1, - MatType& trainData, - MatType& trainLabels, - const size_t maxEpochs) -{ - arma::cube predictions1; - arma::cube predictions2; - ens::StandardSGD opt(0.1, 1, maxEpochs * trainData.n_slices, -100, false); +// // network1 should be allocated with `new`, and trained on some data. +// template +// void CheckRNNCopyFunction(ModelType* network1, +// MatType& trainData, +// MatType& trainLabels, +// const size_t maxEpochs) +// { +// arma::cube predictions1; +// arma::cube predictions2; +// ens::StandardSGD opt(0.1, 1, maxEpochs * trainData.n_slices, -100, false); - network1->Train(trainData, trainLabels, opt); - network1->Predict(trainData, predictions1); +// network1->Train(trainData, trainLabels, opt); +// network1->Predict(trainData, predictions1); - RNN<> network2 = *network1; - delete network1; +// RNN<> network2 = *network1; +// delete network1; - // Deallocating all of network1's memory, so that network2 does not use any - // of that memory. - network2.Predict(trainData, predictions2); - CheckMatrices(predictions1, predictions2); -} +// // Deallocating all of network1's memory, so that network2 does not use any +// // of that memory. +// network2.Predict(trainData, predictions2); +// CheckMatrices(predictions1, predictions2); +// } -// network1 should be allocated with `new`, and trained on some data. -template -void CheckRNNMoveFunction(ModelType* network1, - MatType& trainData, - MatType& trainLabels, - const size_t maxEpochs) -{ - arma::cube predictions1; - arma::cube predictions2; - ens::StandardSGD opt(0.1, 1, maxEpochs * trainData.n_slices, -100, false); +// // network1 should be allocated with `new`, and trained on some data. +// template +// void CheckRNNMoveFunction(ModelType* network1, +// MatType& trainData, +// MatType& trainLabels, +// const size_t maxEpochs) +// { +// arma::cube predictions1; +// arma::cube predictions2; +// ens::StandardSGD opt(0.1, 1, maxEpochs * trainData.n_slices, -100, false); - network1->Train(trainData, trainLabels, opt); - network1->Predict(trainData, predictions1); +// network1->Train(trainData, trainLabels, opt); +// network1->Predict(trainData, predictions1); - RNN<> network2(std::move(*network1)); - delete network1; +// RNN<> network2(std::move(*network1)); +// delete network1; - // Deallocating all of network1's memory, so that network2 does not use any - // of that memory. - network2.Predict(trainData, predictions2); - CheckMatrices(predictions1, predictions2); -} +// // Deallocating all of network1's memory, so that network2 does not use any +// // of that memory. +// network2.Predict(trainData, predictions2); +// CheckMatrices(predictions1, predictions2); +// } /** * Simple add module test. - */ + * TEST_CASE("SimpleAddLayerTest", "[ANNLayerTest]") { arma::mat output, input, delta; - Add<> module(10); + Add module(10); module.Parameters().randu(); // Test the Forward function. @@ -105,10 +104,11 @@ TEST_CASE("SimpleAddLayerTest", "[ANNLayerTest]") module.Backward(input, output, delta); REQUIRE(arma::accu(output) == Approx(arma::accu(delta)).epsilon(1e-5)); } +*/ /** * Jacobian add module test. - */ + * TEST_CASE("JacobianAddLayerTest", "[ANNLayerTest]") { for (size_t i = 0; i < 5; ++i) @@ -117,17 +117,18 @@ TEST_CASE("JacobianAddLayerTest", "[ANNLayerTest]") arma::mat input; input.set_size(elements, 1); - Add<> module(elements); + Add module(elements); module.Parameters().randu(); double error = JacobianTest(module, input); REQUIRE(error <= 1e-5); } } +*/ /** * Add layer numerical gradient test. - */ + * TEST_CASE("GradientAddLayerTest", "[ANNLayerTest]") { // Add function gradient instantiation. @@ -137,13 +138,12 @@ TEST_CASE("GradientAddLayerTest", "[ANNLayerTest]") input(arma::randu(10, 1)), target(arma::mat("0")) { - model = new FFN, NguyenWidrowInitialization>(); - model->Predictors() = input; - model->Responses() = target; - model->Add >(); - model->Add >(10, 10); - model->Add >(10); - model->Add >(); + model = new FFN(); + model->ResetData(input, target); + model->Add(); + model->Add(10, 10); + model->Add(10); + model->Add(); } ~GradientFunction() @@ -160,33 +160,33 @@ TEST_CASE("GradientAddLayerTest", "[ANNLayerTest]") arma::mat& Parameters() { return model->Parameters(); } - FFN, NguyenWidrowInitialization>* model; + FFN* model; arma::mat input, target; } function; REQUIRE(CheckGradient(function) <= 1e-4); -} +}*/ /** * Test that the function that can access the outSize parameter of * the Add layer works. - */ + * TEST_CASE("AddLayerParametersTest", "[ANNLayerTest]") { // Parameter : outSize. - Add<> layer(7); + Add layer(7); // Make sure we can get the parameter successfully. REQUIRE(layer.OutputSize() == 7); -} +}*/ /** * Simple constant module test. - */ + * TEST_CASE("SimpleConstantLayerTest", "[ANNLayerTest]") { arma::mat output, input, delta; - Constant<> module(10, 3.0); + Constant module(10, 3.0); // Test the Forward function. input = arma::zeros(10, 1); @@ -205,11 +205,11 @@ TEST_CASE("SimpleConstantLayerTest", "[ANNLayerTest]") // Test the backward function. module.Backward(input, output, delta); REQUIRE(arma::accu(delta) == 0); -} +}*/ /** * Jacobian constant module test. - */ + * TEST_CASE("JacobianConstantLayerTest", "[ANNLayerTest]") { for (size_t i = 0; i < 5; ++i) @@ -218,25 +218,25 @@ TEST_CASE("JacobianConstantLayerTest", "[ANNLayerTest]") arma::mat input; input.set_size(elements, 1); - Constant<> module(elements, 1.0); + Constant module(elements, 1.0); double error = JacobianTest(module, input); REQUIRE(error <= 1e-5); } -} +}*/ /** * Test that the function that can access the outSize parameter of the * Constant layer works. - */ + * TEST_CASE("ConstantLayerParametersTest", "[ANNLayerTest]") { // Parameter : outSize. - Constant<> layer(7); + Constant layer(7); // Make sure we can get the parameter successfully. REQUIRE(layer.OutSize() == 7); -} +}*/ /** * Simple dropout module test. @@ -250,8 +250,8 @@ TEST_CASE("SimpleDropoutLayerTest", "[ANNLayerTest]") arma::mat input(1000, 1); input.fill(1 - p); - Dropout<> module(p); - module.Deterministic() = false; + Dropout module(p); + module.Training() = true; // Test the Forward function. arma::mat output; @@ -264,7 +264,7 @@ TEST_CASE("SimpleDropoutLayerTest", "[ANNLayerTest]") REQUIRE(arma::as_scalar(arma::abs(arma::mean(delta) - (1 - p))) <= 0.05); // Test the Forward function. - module.Deterministic() = true; + module.Training() = false; module.Forward(input, output); REQUIRE(arma::accu(input) == arma::accu(output)); } @@ -285,8 +285,8 @@ TEST_CASE("DropoutProbabilityTest", "[ANNLayerTest]") double nonzeroCount = 0; for (size_t i = 0; i < iterations; ++i) { - Dropout<> module(probability[trial]); - module.Deterministic() = false; + Dropout module(probability[trial]); + module.Training() = true; arma::mat output; module.Forward(input, output); @@ -310,8 +310,8 @@ TEST_CASE("DropoutProbabilityTest", "[ANNLayerTest]") TEST_CASE("NoDropoutTest", "[ANNLayerTest]") { arma::mat input = arma::ones(1500, 1); - Dropout<> module(0); - module.Deterministic() = false; + Dropout module(0); + module.Training() = true; arma::mat output; module.Forward(input, output); @@ -332,11 +332,11 @@ TEST_CASE("SimpleAlphaDropoutLayerTest", "[ANNLayerTest]") // and variance nearabout 1. arma::mat input = arma::randn(1000, 1); - AlphaDropout<> module(p); - module.Deterministic() = false; + AlphaDropout module(p); + module.Training() = true; // Test the Forward function when training phase. - arma::mat output; + arma::mat output(arma::size(input)); module.Forward(input, output); // Check whether mean remains nearly same. REQUIRE(arma::as_scalar(arma::abs(arma::mean(input) - arma::mean(output))) <= @@ -352,7 +352,7 @@ TEST_CASE("SimpleAlphaDropoutLayerTest", "[ANNLayerTest]") REQUIRE(arma::as_scalar(arma::abs(arma::mean(delta) - 0)) <= 0.05); // Test the Forward function when testing phase. - module.Deterministic() = true; + module.Training() = false; module.Forward(input, output); REQUIRE(arma::accu(input) == arma::accu(output)); } @@ -373,10 +373,10 @@ TEST_CASE("AlphaDropoutProbabilityTest", "[ANNLayerTest]") double nonzeroCount = 0; for (size_t i = 0; i < iterations; ++i) { - AlphaDropout<> module(probability[trial]); - module.Deterministic() = false; + AlphaDropout module(probability[trial]); + module.Training() = true; - arma::mat output; + arma::mat output(arma::size(input)); module.Forward(input, output); // Return a column vector containing the indices of elements of X @@ -401,8 +401,8 @@ TEST_CASE("AlphaDropoutProbabilityTest", "[ANNLayerTest]") TEST_CASE("NoAlphaDropoutTest", "[ANNLayerTest]") { arma::mat input = arma::ones(1500, 1); - AlphaDropout<> module(0); - module.Deterministic() = false; + AlphaDropout module(0); + module.Training() = false; arma::mat output; module.Forward(input, output); @@ -410,90 +410,89 @@ TEST_CASE("NoAlphaDropoutTest", "[ANNLayerTest]") REQUIRE(arma::accu(output) == arma::accu(input)); } -/** - * Simple linear module test. - */ -TEST_CASE("SimpleLinearLayerTest", "[ANNLayerTest]") -{ - arma::mat output, input, delta; - Linear<> module(10, 10); - module.Parameters().randu(); - module.Reset(); +// /** +// * Simple linear module test. +// */ +// TEST_CASE("SimpleLinearLayerTest", "[ANNLayerTest]") +// { +// arma::mat output, input, delta; +// Linear<> module(10, 10); +// module.Parameters().randu(); +// module.Reset(); - // Test the Forward function. - input = arma::zeros(10, 1); - module.Forward(input, output); - REQUIRE(arma::accu(module.Parameters().submat(100, - 0, module.Parameters().n_elem - 1, 0)) == - Approx(arma::accu(output)).epsilon(1e-5)); +// // Test the Forward function. +// input = arma::zeros(10, 1); +// module.Forward(input, output); +// REQUIRE(arma::accu(module.Parameters().submat(100, +// 0, module.Parameters().n_elem - 1, 0)) == +// Approx(arma::accu(output)).epsilon(1e-5)); - // Test the Backward function. - module.Backward(input, input, delta); - REQUIRE(arma::accu(delta) == 0); -} +// // Test the Backward function. +// module.Backward(input, input, delta); +// REQUIRE(arma::accu(delta) == 0); +// } -/** - * Jacobian linear module test. - */ -TEST_CASE("JacobianLinearLayerTest", "[ANNLayerTest]") -{ - for (size_t i = 0; i < 5; ++i) - { - const size_t inputElements = math::RandInt(2, 1000); - const size_t outputElements = math::RandInt(2, 1000); +// /** +// * Jacobian linear module test. +// */ +// TEST_CASE("JacobianLinearLayerTest", "[ANNLayerTest]") +// { +// for (size_t i = 0; i < 5; ++i) +// { +// const size_t inputElements = math::RandInt(2, 1000); +// const size_t outputElements = math::RandInt(2, 1000); - arma::mat input; - input.set_size(inputElements, 1); +// arma::mat input; +// input.set_size(inputElements, 1); - Linear<> module(inputElements, outputElements); - module.Parameters().randu(); +// Linear<> module(inputElements, outputElements); +// module.Parameters().randu(); - double error = JacobianTest(module, input); - REQUIRE(error <= 1e-5); - } -} +// double error = JacobianTest(module, input); +// REQUIRE(error <= 1e-5); +// } +// } -/** - * Linear layer numerical gradient test. - */ -TEST_CASE("GradientLinearLayerTest", "[ANNLayerTest]") -{ - // Linear function gradient instantiation. - struct GradientFunction - { - GradientFunction() : - input(arma::randu(10, 1)), - target(arma::mat("0")) - { - model = new FFN, NguyenWidrowInitialization>(); - model->Predictors() = input; - model->Responses() = target; - model->Add >(); - model->Add >(10, 10); - model->Add >(10, 2); - model->Add >(); - } +// /** +// * Linear layer numerical gradient test. +// */ +// TEST_CASE("GradientLinearLayerTest", "[ANNLayerTest]") +// { +// // Linear function gradient instantiation. +// struct GradientFunction +// { +// GradientFunction() : +// input(arma::randu(10, 1)), +// target(arma::mat("1")) +// { +// model = new FFN(); +// model->ResetData(input, target); +// model->Add >(); +// model->Add >(10, 10); +// model->Add >(10, 2); +// model->Add >(); +// } - ~GradientFunction() - { - delete model; - } +// ~GradientFunction() +// { +// delete model; +// } - double Gradient(arma::mat& gradient) const - { - double error = model->Evaluate(model->Parameters(), 0, 1); - model->Gradient(model->Parameters(), 0, gradient, 1); - return error; - } +// double Gradient(arma::mat& gradient) const +// { +// double error = model->Evaluate(model->Parameters(), 0, 1); +// model->Gradient(model->Parameters(), 0, gradient, 1); +// return error; +// } - arma::mat& Parameters() { return model->Parameters(); } +// arma::mat& Parameters() { return model->Parameters(); } - FFN, NguyenWidrowInitialization>* model; - arma::mat input, target; - } function; +// FFN* model; +// arma::mat input, target; +// } function; - REQUIRE(CheckGradient(function) <= 1e-4); -} +// REQUIRE(CheckGradient(function) <= 1e-4); +// } /** * Simple Linear3D layer test. @@ -506,18 +505,26 @@ TEST_CASE("SimpleLinear3DLayerTest", "[ANNLayerTest]") const size_t batchSize = 1; arma::mat input, output, delta; - Linear3D<> module(inSize, outSize); - module.Reset(); + // Create a Linear3D layer outside of a network, and then set its memory. + Linear3D module(outSize); + module.InputDimensions() = std::vector({ 4, 2 }); + module.ComputeOutputDimensions(); + arma::mat weights(module.WeightSize(), 1); + module.SetWeights(weights.memptr()); + module.Parameters().randu(); // Test the Forward function. input = arma::zeros(inSize * nPoints, batchSize); + output.set_size(outSize * nPoints, batchSize); module.Forward(input, output); REQUIRE(arma::accu(module.Bias()) == Approx(arma::accu(output) / (nPoints * batchSize)).epsilon(1e-3)); // Test the Backward function. - module.Backward(input, input, delta); + delta.set_size(input.n_rows, input.n_cols); + output.zeros(); + module.Backward(input, output, delta); REQUIRE(arma::accu(delta) == 0); } @@ -536,7 +543,13 @@ TEST_CASE("JacobianLinear3DLayerTest", "[ANNLayerTest]") arma::mat input; input.set_size(inSize * nPoints, batchSize); - Linear3D<> module(inSize, outSize); + // Create a Linear3D layer outside a network and initialize its memory. + Linear3D module(outSize); + module.InputDimensions() = std::vector({ inSize, nPoints }); + module.ComputeOutputDimensions(); + arma::mat weights(module.WeightSize(), 1); + module.SetWeights(weights.memptr()); + module.Parameters().randu(); double error = JacobianTest(module, input); @@ -565,11 +578,10 @@ TEST_CASE("GradientLinear3DLayerTest", "[ANNLayerTest]") target(1, 1) = 1; target(1, 2) = 1; - model = new FFN, RandomInitialization>(); - model->Predictors() = input; - model->Responses() = target; - model->Add>(); - model->Add>(inSize, outSize); + model = new FFN(); + model->ResetData(input, target); + model->Add(outSize); + model->InputDimensions() = std::vector{ 4, 2 }; } ~GradientFunction() @@ -586,7 +598,7 @@ TEST_CASE("GradientLinear3DLayerTest", "[ANNLayerTest]") arma::mat& Parameters() { return model->Parameters(); } - FFN, RandomInitialization>* model; + FFN* model; arma::mat input, target; const size_t inSize; const size_t outSize; @@ -597,80 +609,79 @@ TEST_CASE("GradientLinear3DLayerTest", "[ANNLayerTest]") REQUIRE(CheckGradient(function) <= 1e-7); } -/** - * Simple noisy linear module test. - */ -TEST_CASE("SimpleNoisyLinearLayerTest", "[ANNLayerTest]") -{ - arma::mat output, input, delta; - NoisyLinear<> module(10, 10); - module.Parameters().randu(); - module.Reset(); +// /** +// * Simple noisy linear module test. +// */ +// TEST_CASE("SimpleNoisyLinearLayerTest", "[ANNLayerTest]") +// { +// arma::mat output, input, delta; +// NoisyLinear<> module(10, 10); +// module.Parameters().randu(); +// module.Reset(); - // Test the Backward function. - module.Backward(input, input, delta); - REQUIRE(arma::accu(delta) == 0); -} +// // Test the Backward function. +// module.Backward(input, input, delta); +// REQUIRE(arma::accu(delta) == 0); +// } -/** - * Jacobian noisy linear module test. - */ -TEST_CASE("JacobianNoisyLinearLayerTest", "[ANNLayerTest]") -{ - const size_t inputElements = math::RandInt(2, 1000); - const size_t outputElements = math::RandInt(2, 1000); +// /** +// * Jacobian noisy linear module test. +// */ +// TEST_CASE("JacobianNoisyLinearLayerTest", "[ANNLayerTest]") +// { +// const size_t inputElements = math::RandInt(2, 1000); +// const size_t outputElements = math::RandInt(2, 1000); - arma::mat input; - input.set_size(inputElements, 1); +// arma::mat input; +// input.set_size(inputElements, 1); - NoisyLinear<> module(inputElements, outputElements); - module.Parameters().randu(); +// NoisyLinear<> module(inputElements, outputElements); +// module.Parameters().randu(); - double error = JacobianTest(module, input); - REQUIRE(error <= 1e-5); -} +// double error = JacobianTest(module, input); +// REQUIRE(error <= 1e-5); +// } -/** - * Noisy Linear layer numerical gradient test. - */ -TEST_CASE("GradientNoisyLinearLayerTest", "[ANNLayerTest]") -{ - // Noisy linear function gradient instantiation. - struct GradientFunction - { - GradientFunction() : - input(arma::randu(10, 1)), - target(arma::mat("0")) - { - model = new FFN, NguyenWidrowInitialization>(); - model->Predictors() = input; - model->Responses() = target; - model->Add >(); - model->Add >(10, 10); - model->Add >(10, 2); - model->Add >(); - } +// /** +// * Noisy Linear layer numerical gradient test. +// */ +// TEST_CASE("GradientNoisyLinearLayerTest", "[ANNLayerTest]") +// { +// // Noisy linear function gradient instantiation. +// struct GradientFunction +// { +// GradientFunction() : +// input(arma::randu(10, 1)), +// target(arma::mat("1")) +// { +// model = new FFN(); +// model->ResetData(input, target); +// model->Add >(); +// model->Add >(10, 10); +// model->Add >(10, 2); +// model->Add >(); +// } - ~GradientFunction() - { - delete model; - } +// ~GradientFunction() +// { +// delete model; +// } - double Gradient(arma::mat& gradient) const - { - double error = model->Evaluate(model->Parameters(), 0, 1); - model->Gradient(model->Parameters(), 0, gradient, 1); - return error; - } +// double Gradient(arma::mat& gradient) const +// { +// double error = model->Evaluate(model->Parameters(), 0, 1); +// model->Gradient(model->Parameters(), 0, gradient, 1); +// return error; +// } - arma::mat& Parameters() { return model->Parameters(); } +// arma::mat& Parameters() { return model->Parameters(); } - FFN, NguyenWidrowInitialization>* model; - arma::mat input, target; - } function; +// FFN* model; +// arma::mat input, target; +// } function; - REQUIRE(CheckGradient(function) <= 1e-4); -} +// REQUIRE(CheckGradient(function) <= 1e-4); +// } /** * Simple linear no bias module test. @@ -678,9 +689,13 @@ TEST_CASE("GradientNoisyLinearLayerTest", "[ANNLayerTest]") TEST_CASE("SimpleLinearNoBiasLayerTest", "[ANNLayerTest]") { arma::mat output, input, delta; - LinearNoBias<> module(10, 10); + LinearNoBias module(10); + arma::mat weights(10 * 10, 1); + module.InputDimensions() = std::vector({ 10 }); + module.ComputeOutputDimensions(); + module.SetWeights(weights.memptr()); + module.Parameters().randu(); - module.Reset(); // Test the Forward function. input = arma::zeros(10, 1); @@ -688,7 +703,7 @@ TEST_CASE("SimpleLinearNoBiasLayerTest", "[ANNLayerTest]") REQUIRE(0 == arma::accu(output)); // Test the Backward function. - module.Backward(input, input, delta); + module.Backward(input, output, delta); REQUIRE(arma::accu(delta) == 0); } @@ -697,39 +712,61 @@ TEST_CASE("SimpleLinearNoBiasLayerTest", "[ANNLayerTest]") */ TEST_CASE("SimplePaddingLayerTest", "[ANNLayerTest]") { - arma::mat output, input, delta, input1, output1; - Padding<> module(1, 2, 3, 4); + arma::mat output, input, delta; + Padding module(1, 2, 3, 4); + module.InputDimensions() = std::vector({ 2, 5 }); + module.ComputeOutputDimensions(); // Test the Forward function. input = arma::randu(10, 1); + size_t totalOutputDimensions = module.OutputDimensions()[0]; + for (size_t i = 1; i < module.OutputDimensions().size(); ++i) + totalOutputDimensions *= module.OutputDimensions()[i]; + output.set_size(totalOutputDimensions, input.n_cols); + output.randu(); module.Forward(input, output); - REQUIRE(arma::accu(input) == arma::accu(output)); - REQUIRE(output.n_rows == input.n_rows + 3); - REQUIRE(output.n_cols == input.n_cols + 7); + REQUIRE(arma::accu(input) == Approx(arma::accu(output))); + REQUIRE(output.n_rows == (9 * 8)); // 2x5 --> 9x8 // Test the Backward function. + delta.set_size(input.n_rows, input.n_cols); module.Backward(input, output, delta); CheckMatrices(delta, input); // Test forward function for multiple filters. // Here it's 3 filters with height = 224, width = 224 // the output should be [226 * 226 * 3, 1] with 1 padding. - Padding<> module1(1, 1, 1, 1, 224, 224); - input1 = arma::randu(224 * 224 * 3, 1); - module1.Forward(input1, output1); - REQUIRE(arma::accu(input1) == arma::accu(output1)); - REQUIRE(output1.n_rows == (226 * 226 * 3)); - REQUIRE(output1.n_cols == 1); + module = Padding(1, 1, 1, 1); + module.InputDimensions() = std::vector({ 224, 224, 3 }); + module.ComputeOutputDimensions(); + + input = arma::randu(224 * 224 * 3, 1); + totalOutputDimensions = module.OutputDimensions()[0]; + for (size_t i = 1; i < module.OutputDimensions().size(); ++i) + totalOutputDimensions *= module.OutputDimensions()[i]; + output.set_size(totalOutputDimensions, input.n_cols); + output.randu(); + module.Forward(input, output); + REQUIRE(arma::accu(input) == Approx(arma::accu(output))); + REQUIRE(output.n_rows == (226 * 226 * 3)); + REQUIRE(output.n_cols == 1); // Test forward function for multiple batches with multiple filters. // Here it's 3 filters with height = 244, width = 244 // the output should be [246 * 246 * 3, 3] with 1 padding. - Padding<> module2(1, 1, 1, 1, 244, 244); - input1 = arma::randu(244 * 244 * 3, 3); - module2.Forward(input1, output1); - REQUIRE(arma::accu(input1) == arma::accu(output1)); - REQUIRE(output1.n_rows == (246 * 246 * 3)); - REQUIRE(output1.n_cols == 3); + module.InputDimensions() = std::vector({ 244, 244, 3 }); + module.ComputeOutputDimensions(); + totalOutputDimensions = module.OutputDimensions()[0]; + for (size_t i = 1; i < module.OutputDimensions().size(); ++i) + totalOutputDimensions *= module.OutputDimensions()[i]; + + input = arma::randu(244 * 244 * 3, 3); + output.set_size(totalOutputDimensions, input.n_cols); + output.randu(); + module.Forward(input, output); + REQUIRE(output.n_rows == (246 * 246 * 3)); + REQUIRE(output.n_cols == 3); + REQUIRE(arma::accu(input) == Approx(arma::accu(output))); } /** @@ -745,7 +782,12 @@ TEST_CASE("JacobianLinearNoBiasLayerTest", "[ANNLayerTest]") arma::mat input; input.set_size(inputElements, 1); - LinearNoBias<> module(inputElements, outputElements); + LinearNoBias module(outputElements); + arma::mat weights(inputElements * outputElements, 1); + module.InputDimensions() = std::vector({ inputElements }); + module.ComputeOutputDimensions(); + module.SetWeights(weights.memptr()); + module.Parameters().randu(); double error = JacobianTest(module, input); @@ -765,13 +807,11 @@ TEST_CASE("GradientLinearNoBiasLayerTest", "[ANNLayerTest]") input(arma::randu(10, 1)), target(arma::mat("0")) { - model = new FFN, NguyenWidrowInitialization>(); - model->Predictors() = input; - model->Responses() = target; - model->Add >(); - model->Add >(10, 10); - model->Add >(10, 2); - model->Add >(); + model = new FFN(); + model->ResetData(input, target); + model->Add(10); + model->Add(2); + model->Add(); } ~GradientFunction() @@ -788,37 +828,37 @@ TEST_CASE("GradientLinearNoBiasLayerTest", "[ANNLayerTest]") arma::mat& Parameters() { return model->Parameters(); } - FFN, NguyenWidrowInitialization>* model; + FFN* model; arma::mat input, target; } function; REQUIRE(CheckGradient(function) <= 1e-4); } -/** - * Jacobian negative log likelihood module test. - */ -TEST_CASE("JacobianNegativeLogLikelihoodLayerTest", "[ANNLayerTest]") -{ - for (size_t i = 0; i < 5; ++i) - { - NegativeLogLikelihood<> module; - const size_t inputElements = math::RandInt(5, 100); - arma::mat input; - RandomInitialization init(0, 1); - init.Initialize(input, inputElements, 1); +// /** +// * Jacobian negative log likelihood module test. +// */ +// TEST_CASE("JacobianNegativeLogLikelihoodLayerTest", "[ANNLayerTest]") +// { +// for (size_t i = 0; i < 5; ++i) +// { +// NegativeLogLikelihood module; +// const size_t inputElements = math::RandInt(5, 100); +// arma::mat input; +// RandomInitialization init(0, 1); +// init.Initialize(input, inputElements, 1); - arma::mat target(1, 1); - target(0) = math::RandInt(0, inputElements - 2); +// arma::mat target(1, 1); +// target(0) = math::RandInt(0, inputElements - 2); - double error = JacobianPerformanceTest(module, input, target); - REQUIRE(error <= 1e-5); - } -} +// double error = JacobianPerformanceTest(module, input, target); +// REQUIRE(error <= 1e-5); +// } +// } /** * Jacobian LeakyReLU module test. - */ + * TEST_CASE("JacobianLeakyReLULayerTest", "[ANNLayerTest]") { for (size_t i = 0; i < 5; ++i) @@ -828,16 +868,17 @@ TEST_CASE("JacobianLeakyReLULayerTest", "[ANNLayerTest]") arma::mat input; input.set_size(inputElements, 1); - LeakyReLU<> module; + LeakyReLU module; double error = JacobianTest(module, input); REQUIRE(error <= 1e-5); } } +*/ /** * Jacobian FlexibleReLU module test. - */ + * TEST_CASE("JacobianFlexibleReLULayerTest", "[ANNLayerTest]") { for (size_t i = 0; i < 5; ++i) @@ -847,16 +888,17 @@ TEST_CASE("JacobianFlexibleReLULayerTest", "[ANNLayerTest]") arma::mat input; input.set_size(inputElements, 1); - FlexibleReLU<> module; + FlexibleReLU module; double error = JacobianTest(module, input); REQUIRE(error <= 1e-5); } } +*/ /** * Flexible ReLU layer numerical gradient test. - */ + * TEST_CASE("GradientFlexibleReLULayerTest", "[ANNLayerTest]") { // Add function gradient instantiation. @@ -866,15 +908,14 @@ TEST_CASE("GradientFlexibleReLULayerTest", "[ANNLayerTest]") input(arma::randu(2, 1)), target(arma::mat("0")) { - model = new FFN, RandomInitialization>( - NegativeLogLikelihood<>(), RandomInitialization(0.1, 0.5)); + model = new FFN( + NegativeLogLikelihood(), RandomInitialization(0.1, 0.5)); - model->Predictors() = input; - model->Responses() = target; - model->Add >(2, 2); - model->Add >(2, 5); - model->Add >(0.05); - model->Add >(); + model->ResetData(input, target); + model->Add(2, 2); + model->Add(2, 5); + model->Add(0.05); + model->Add(); } ~GradientFunction() @@ -891,16 +932,17 @@ TEST_CASE("GradientFlexibleReLULayerTest", "[ANNLayerTest]") arma::mat& Parameters() { return model->Parameters(); } - FFN, RandomInitialization>* model; + FFN* model; arma::mat input, target; } function; REQUIRE(CheckGradient(function) <= 1e-4); } +*/ /** * Jacobian MultiplyConstant module test. - */ + * TEST_CASE("JacobianMultiplyConstantLayerTest", "[ANNLayerTest]") { for (size_t i = 0; i < 5; ++i) @@ -910,49 +952,50 @@ TEST_CASE("JacobianMultiplyConstantLayerTest", "[ANNLayerTest]") arma::mat input; input.set_size(inputElements, 1); - MultiplyConstant<> module(3.0); + MultiplyConstant module(3.0); double error = JacobianTest(module, input); REQUIRE(error <= 1e-5); } } +*/ /** * Check whether copying and moving network with MultiplyConstant is working or * not. */ -TEST_CASE("CheckCopyMoveMultiplyConstantTest", "[ANNLayerTest]") -{ - arma::mat input(2, 1000); - input.randu(); - - arma::mat output1; - arma::mat output2; - arma::mat output3; - arma::mat output4; - - MultiplyConstant<> *module1 = new MultiplyConstant<>(3.0); - module1->Forward(input, output1); - - MultiplyConstant<> module2 = *module1; - delete module1; - - module2.Forward(input, output2); - CheckMatrices(output1, output2); - - MultiplyConstant<> *module3 = new MultiplyConstant<>(3.0); - module3->Forward(input, output3); - - MultiplyConstant<> module4(std::move(*module3)); - delete module3; - - module4.Forward(input, output4); - CheckMatrices(output3, output4); -} +// TEST_CASE("CheckCopyMoveMultiplyConstantTest", "[ANNLayerTest]") +// { +// arma::mat input(2, 1000); +// input.randu(); +// +// arma::mat output1; +// arma::mat output2; +// arma::mat output3; +// arma::mat output4; +// +// MultiplyConstant<> *module1 = new MultiplyConstant<>(3.0); +// module1->Forward(input, output1); +// +// MultiplyConstant<> module2 = *module1; +// delete module1; +// +// module2.Forward(input, output2); +// CheckMatrices(output1, output2); +// +// MultiplyConstant<> *module3 = new MultiplyConstant<>(3.0); +// module3->Forward(input, output3); +// +// MultiplyConstant<> module4(std::move(*module3)); +// delete module3; +// +// module4.Forward(input, output4); +// CheckMatrices(output3, output4); +// } /** * Jacobian HardTanH module test. - */ + * TEST_CASE("JacobianHardTanHLayerTest", "[ANNLayerTest]") { for (size_t i = 0; i < 5; ++i) @@ -962,18 +1005,20 @@ TEST_CASE("JacobianHardTanHLayerTest", "[ANNLayerTest]") arma::mat input; input.set_size(inputElements, 1); - HardTanH<> module; + HardTanH module; double error = JacobianTest(module, input); REQUIRE(error <= 1e-5); } } +*/ /** * Simple select module test. - */ + * TEST_CASE("SimpleSelectLayerTest", "[ANNLayerTest]") { + // TODO: this needs to be adapted arma::mat outputA, outputB, input, delta; input = arma::ones(10, 5); @@ -983,12 +1028,12 @@ TEST_CASE("SimpleSelectLayerTest", "[ANNLayerTest]") } // Test the Forward function. - Select<> moduleA(3); + Select moduleA(3); moduleA.Forward(input, outputA); REQUIRE(30 == arma::accu(outputA)); // Test the Forward function. - Select<> moduleB(3, 5); + Select moduleB(3, 5); moduleB.Forward(input, outputB); REQUIRE(15 == arma::accu(outputB)); @@ -1000,31 +1045,33 @@ TEST_CASE("SimpleSelectLayerTest", "[ANNLayerTest]") moduleB.Backward(input, outputA, delta); REQUIRE(15 == arma::accu(delta)); } +*/ /** * Test that the functions that can access the parameters of the * Select layer work. - */ + * TEST_CASE("SelectLayerParametersTest", "[ANNLayerTest]") { // Parameter order : index, elements. - Select<> layer(3, 5); + Select layer(3, 5); // Make sure we can get the parameters successfully. REQUIRE(layer.Index() == 3); REQUIRE(layer.NumElements() == 5); } +*/ /** * Simple join module test. - */ + * TEST_CASE("SimpleJoinLayerTest", "[ANNLayerTest]") { arma::mat output, input, delta; input = arma::ones(10, 5); // Test the Forward function. - Join<> module; + Join module; module.Forward(input, output); REQUIRE(50 == arma::accu(output)); @@ -1038,635 +1085,1234 @@ TEST_CASE("SimpleJoinLayerTest", "[ANNLayerTest]") b = delta.n_rows == input.n_rows && input.n_cols; REQUIRE(b == true); } +*/ + +// /** +// * Simple add merge module test. +// */ +// TEST_CASE("SimpleAddMergeLayerTest", "[ANNLayerTest]") +// { +// arma::mat output, input, delta; +// input = arma::ones(10, 1); + +// for (size_t i = 0; i < 5; ++i) +// { +// AddMerge<> module(false, false); +// const size_t numMergeModules = math::RandInt(2, 10); +// for (size_t m = 0; m < numMergeModules; ++m) +// { +// IdentityLayer<> identityLayer; +// identityLayer.Forward(input, identityLayer.OutputParameter()); + +// module.Add >(identityLayer); +// } + +// // Test the Forward function. +// module.Forward(input, output); +// REQUIRE(10 * numMergeModules == arma::accu(output)); + +// // Test the Backward function. +// module.Backward(input, output, delta); +// REQUIRE(arma::accu(output) == arma::accu(delta)); +// } +// } + +// /** +// * Test the LSTM layer with a user defined rho parameter and without. +// */ +// TEST_CASE("LSTMRrhoTest", "[ANNLayerTest]") +// { +// const size_t rho = 5; +// arma::cube input = arma::randu(1, 1, 5); +// arma::cube target = arma::ones(1, 1, 5); +// RandomInitialization init(0.5, 0.5); + +// // Create model with user defined rho parameter. +// RNN modelA( +// rho, false, NegativeLogLikelihood(), init); +// modelA.Add >(); +// modelA.Add >(1, 10); + +// // Use LSTM layer with rho. +// modelA.Add >(10, 3, rho); +// modelA.Add >(); + +// // Create model without user defined rho parameter. +// RNN modelB( +// rho, false, NegativeLogLikelihood(), init); +// modelB.Add >(); +// modelB.Add >(1, 10); + +// // Use LSTM layer with rho = MAXSIZE. +// modelB.Add >(10, 3); +// modelB.Add >(); + +// ens::StandardSGD opt(0.1, 1, 5, -100, false); +// modelA.Train(input, target, opt); +// modelB.Train(input, target, opt); + +// CheckMatrices(modelB.Parameters(), modelA.Parameters()); +// } + +// /** +// * LSTM layer numerical gradient test. +// */ +// TEST_CASE("GradientLSTMLayerTest", "[ANNLayerTest]") +// { +// // LSTM function gradient instantiation. +// struct GradientFunction +// { +// GradientFunction() : +// input(arma::randu(1, 1, 5)), +// target(arma::ones(1, 1, 5)) +// { +// const size_t rho = 5; + +// model = new RNN(rho); +// model->ResetData(input, target); +// model->Add >(); +// model->Add >(1, 10); +// model->Add >(10, 3, rho); +// model->Add >(); +// } + +// ~GradientFunction() +// { +// delete model; +// } + +// double Gradient(arma::mat& gradient) const +// { +// double error = model->Evaluate(model->Parameters(), 0, 1); +// model->Gradient(model->Parameters(), 0, gradient, 1); +// return error; +// } + +// arma::mat& Parameters() { return model->Parameters(); } + +// RNN* model; +// arma::cube input, target; +// } function; + +// REQUIRE(CheckGradient(function) <= 1e-4); +// } + +// /** +// * Test that the functions that can modify and access the parameters of the +// * LSTM layer work. +// */ +// TEST_CASE("LSTMLayerParametersTest", "[ANNLayerTest]") +// { +// // Parameter order : inSize, outSize, rho. +// LSTM<> layer1(1, 2, 3); +// LSTM<> layer2(1, 2, 4); + +// // Make sure we can get the parameters successfully. +// REQUIRE(layer1.InSize() == 1); +// REQUIRE(layer1.OutSize() == 2); +// REQUIRE(layer1.Rho() == 3); + +// // Now modify the parameters to match the second layer. +// layer1.Rho() = 4; + +// // Now ensure all the results are the same. +// REQUIRE(layer1.InSize() == layer2.InSize()); +// REQUIRE(layer1.OutSize() == layer2.OutSize()); +// REQUIRE(layer1.Rho() == layer2.Rho()); +// } + +// /** +// * Test the FastLSTM layer with a user defined rho parameter and without. +// */ +// TEST_CASE("FastLSTMRrhoTest", "[ANNLayerTest]") +// { +// const size_t rho = 5; +// arma::cube input = arma::randu(1, 1, 5); +// arma::cube target = arma::ones(1, 1, 5); +// RandomInitialization init(0.5, 0.5); + +// // Create model with user defined rho parameter. +// RNN modelA( +// rho, false, NegativeLogLikelihood(), init); +// modelA.Add >(); +// modelA.Add >(1, 10); + +// // Use FastLSTM layer with rho. +// modelA.Add >(10, 3, rho); +// modelA.Add >(); + +// // Create model without user defined rho parameter. +// RNN modelB( +// rho, false, NegativeLogLikelihood(), init); +// modelB.Add >(); +// modelB.Add >(1, 10); + +// // Use FastLSTM layer with rho = MAXSIZE. +// modelB.Add >(10, 3); +// modelB.Add >(); + +// ens::StandardSGD opt(0.1, 1, 5, -100, false); +// modelA.Train(input, target, opt); +// modelB.Train(input, target, opt); + +// CheckMatrices(modelB.Parameters(), modelA.Parameters()); +// } + +// /** +// * FastLSTM layer numerical gradient test. +// */ +// TEST_CASE("GradientFastLSTMLayerTest", "[ANNLayerTest]") +// { +// // Fast LSTM function gradient instantiation. +// struct GradientFunction +// { +// GradientFunction() : +// input(arma::randu(1, 1, 5)), +// target(arma::ones(1, 1, 5)) +// { +// const size_t rho = 5; + +// model = new RNN(rho); +// model->ResetData(input, target); +// model->Add >(); +// model->Add >(1, 10); +// model->Add >(10, 3, rho); +// model->Add >(); +// } + +// ~GradientFunction() +// { +// delete model; +// } + +// double Gradient(arma::mat& gradient) const +// { +// double error = model->Evaluate(model->Parameters(), 0, 1); +// model->Gradient(model->Parameters(), 0, gradient, 1); +// return error; +// } + +// arma::mat& Parameters() { return model->Parameters(); } + +// RNN* model; +// arma::cube input, target; +// } function; + +// // The threshold should be << 0.1 but since the Fast LSTM layer uses an +// // approximation of the sigmoid function the estimated gradient is not +// // correct. +// REQUIRE(CheckGradient(function) <= 0.2); +// } + +// /** +// * Test that the functions that can modify and access the parameters of the +// * Fast LSTM layer work. +// */ +// TEST_CASE("FastLSTMLayerParametersTest", "[ANNLayerTest]") +// { +// // Parameter order : inSize, outSize, rho. +// FastLSTM<> layer1(1, 2, 3); +// FastLSTM<> layer2(1, 2, 4); + +// // Make sure we can get the parameters successfully. +// REQUIRE(layer1.InSize() == 1); +// REQUIRE(layer1.OutSize() == 2); +// REQUIRE(layer1.Rho() == 3); + +// // Now modify the parameters to match the second layer. +// layer1.Rho() = 4; + +// // Now ensure all the results are the same. +// REQUIRE(layer1.InSize() == layer2.InSize()); +// REQUIRE(layer1.OutSize() == layer2.OutSize()); +// REQUIRE(layer1.Rho() == layer2.Rho()); +// } + +// /** +// * Check whether copying and moving network with FastLSTM is working or not. +// */ +// TEST_CASE("CheckCopyMoveFastLSTMTest", "[ANNLayerTest]") +// { +// arma::cube input = arma::randu(1, 1, 5); +// arma::cube target = arma::ones(1, 1, 5); +// const size_t rho = 5; + +// RNN *model1 = +// new RNN(rho); +// model1->ResetData(input, target); +// model1->Add >(); +// model1->Add >(1, 10); +// model1->Add >(10, 3, rho); +// model1->Add >(); + +// RNN *model2 = +// new RNN(rho); +// model2->ResetData(input, target); +// model2->Add >(); +// model2->Add >(1, 10); +// model2->Add >(10, 3, rho); +// model2->Add >(); + +// // Check whether copy constructor is working or not. +// CheckRNNCopyFunction<>(model1, input, target, 1); + +// // Check whether move constructor is working or not. +// CheckRNNMoveFunction<>(model2, input, target, 1); +// } + +// /** +// * Check whether copying and moving network with LSTM is working or not. +// */ +// TEST_CASE("CheckCopyMoveLSTMTest", "[ANNLayerTest]") +// { +// arma::cube input = arma::randu(1, 1, 5); +// arma::cube target = arma::ones(1, 1, 5); +// const size_t rho = 5; + +// RNN *model1 = +// new RNN(rho); +// model1->ResetData(input, target); +// model1->Add >(); +// model1->Add >(1, 10); +// model1->Add >(10, 3, rho); +// model1->Add >(); + +// RNN *model2 = +// new RNN(rho); +// model2->ResetData(input, target); +// model2->Add >(); +// model2->Add >(1, 10); +// model2->Add >(10, 3, rho); +// model2->Add >(); + +// // Check whether copy constructor is working or not. +// CheckRNNCopyFunction<>(model1, input, target, 1); + +// // Check whether move constructor is working or not. +// CheckRNNMoveFunction<>(model2, input, target, 1); +// } + +// /** +// * Testing the overloaded Forward() of the LSTM layer, for retrieving the cell +// * state. Besides output, the overloaded function provides read access to cell +// * state of the LSTM layer. +// */ +// TEST_CASE("ReadCellStateParamLSTMLayerTest", "[ANNLayerTest]") +// { +// const size_t rho = 5, inputSize = 3, outputSize = 2; + +// // Provide input of all ones. +// arma::cube input = arma::ones(inputSize, outputSize, rho); + +// arma::mat inputGate, forgetGate, outputGate, hidden; +// arma::mat outLstm, cellLstm; + +// // LSTM layer. +// LSTM<> lstm(inputSize, outputSize, rho); +// lstm.Reset(); +// lstm.ResetCell(rho); + +// // Initialize the weights to all ones. +// lstm.Parameters().ones(); + +// arma::mat inputWeight = arma::ones(outputSize, inputSize); +// arma::mat outputWeight = arma::ones(outputSize, outputSize); +// arma::mat bias = arma::ones(outputSize, input.n_cols); +// arma::mat cellCalc = arma::zeros(outputSize, input.n_cols); +// arma::mat outCalc = arma::zeros(outputSize, input.n_cols); + +// for (size_t seqNum = 0; seqNum < rho; ++seqNum) +// { +// // Wrap a matrix around our data to avoid a copy. +// arma::mat stepData(input.slice(seqNum).memptr(), +// input.n_rows, input.n_cols, false, true); + +// // Apply Forward() on LSTM layer. +// lstm.Forward(stepData, // Input. +// outLstm, // Output. +// cellLstm, // Cell state. +// false); // Don't write into the cell state. + +// // Compute the value of cell state and output. +// // i = sigmoid(W.dot(x) + W.dot(h) + W.dot(c) + b). +// inputGate = 1.0 /(1 + arma::exp(-(inputWeight * stepData + +// outputWeight * outCalc + outputWeight % cellCalc + bias))); + +// // f = sigmoid(W.dot(x) + W.dot(h) + W.dot(c) + b). +// forgetGate = 1.0 /(1 + arma::exp(-(inputWeight * stepData + +// outputWeight * outCalc + outputWeight % cellCalc + bias))); + +// // z = tanh(W.dot(x) + W.dot(h) + b). +// hidden = arma::tanh(inputWeight * stepData + +// outputWeight * outCalc + bias); + +// // c = f * c + i * z. +// cellCalc = forgetGate % cellCalc + inputGate % hidden; + +// // o = sigmoid(W.dot(x) + W.dot(h) + W.dot(c) + b). +// outputGate = 1.0 /(1 + arma::exp(-(inputWeight * stepData + +// outputWeight * outCalc + outputWeight % cellCalc + bias))); + +// // h = o * tanh(c). +// outCalc = outputGate % arma::tanh(cellCalc); + +// CheckMatrices(outLstm, outCalc, 1e-12); +// CheckMatrices(cellLstm, cellCalc, 1e-12); +// } +// } + +// /** +// * Testing the overloaded Forward() of the LSTM layer, for retrieving the cell +// * state. Besides output, the overloaded function provides write access to cell +// * state of the LSTM layer. +// */ +// TEST_CASE("WriteCellStateParamLSTMLayerTest", "[ANNLayerTest]") +// { +// const size_t rho = 5, inputSize = 3, outputSize = 2; + +// // Provide input of all ones. +// arma::cube input = arma::ones(inputSize, outputSize, rho); + +// arma::mat inputGate, forgetGate, outputGate, hidden; +// arma::mat outLstm, cellLstm; +// arma::mat cellCalc; + +// // LSTM layer. +// LSTM<> lstm(inputSize, outputSize, rho); +// lstm.Reset(); +// lstm.ResetCell(rho); + +// // Initialize the weights to all ones. +// lstm.Parameters().ones(); + +// arma::mat inputWeight = arma::ones(outputSize, inputSize); +// arma::mat outputWeight = arma::ones(outputSize, outputSize); +// arma::mat bias = arma::ones(outputSize, input.n_cols); +// arma::mat outCalc = arma::zeros(outputSize, input.n_cols); + +// for (size_t seqNum = 0; seqNum < rho; ++seqNum) +// { +// // Wrap a matrix around our data to avoid a copy. +// arma::mat stepData(input.slice(seqNum).memptr(), +// input.n_rows, input.n_cols, false, true); + +// if (cellLstm.is_empty()) +// { +// // Set the cell state to zeros. +// cellLstm = arma::zeros(outputSize, input.n_cols); +// cellCalc = arma::zeros(outputSize, input.n_cols); +// } +// else +// { +// // Set the cell state to zeros. +// cellLstm = arma::zeros(cellLstm.n_rows, cellLstm.n_cols); +// cellCalc = arma::zeros(cellCalc.n_rows, cellCalc.n_cols); +// } + +// // Apply Forward() on the LSTM layer. +// lstm.Forward(stepData, // Input. +// outLstm, // Output. +// cellLstm, // Cell state. +// true); // Write into cell state. + +// // Compute the value of cell state and output. +// // i = sigmoid(W.dot(x) + W.dot(h) + W.dot(c) + b). +// inputGate = 1.0 /(1 + arma::exp(-(inputWeight * stepData + +// outputWeight * outCalc + outputWeight % cellCalc + bias))); + +// // f = sigmoid(W.dot(x) + W.dot(h) + W.dot(c) + b). +// forgetGate = 1.0 /(1 + arma::exp(-(inputWeight * stepData + +// outputWeight * outCalc + outputWeight % cellCalc + bias))); + +// // z = tanh(W.dot(x) + W.dot(h) + b). +// hidden = arma::tanh(inputWeight * stepData + +// outputWeight * outCalc + bias); + +// // c = f * c + i * z. +// cellCalc = forgetGate % cellCalc + inputGate % hidden; + +// // o = sigmoid(W.dot(x) + W.dot(h) + W.dot(c) + b). +// outputGate = 1.0 /(1 + arma::exp(-(inputWeight * stepData + +// outputWeight * outCalc + outputWeight % cellCalc + bias))); + +// // h = o * tanh(c). +// outCalc = outputGate % arma::tanh(cellCalc); + +// CheckMatrices(outLstm, outCalc, 1e-12); +// CheckMatrices(cellLstm, cellCalc, 1e-12); +// } + +// // Attempting to write empty matrix into cell state. +// lstm.Reset(); +// lstm.ResetCell(rho); +// arma::mat stepData(input.slice(0).memptr(), +// input.n_rows, input.n_cols, false, true); + +// lstm.Forward(stepData, // Input. +// outLstm, // Output. +// cellLstm, // Cell state. +// true); // Write into cell state. + +// for (size_t seqNum = 1; seqNum < rho; ++seqNum) +// { +// arma::mat empty; +// // Should throw error. +// REQUIRE_THROWS_AS(lstm.Forward(stepData, // Input. +// outLstm, // Output. +// empty, // Cell state. +// true), // Write into cell state. +// std::runtime_error); +// } +// } + +// /** +// * Test that the functions that can modify and access the parameters of the +// * GRU layer work. +// */ +// TEST_CASE("GRULayerParametersTest", "[ANNLayerTest]") +// { +// // Parameter order : inSize, outSize, rho. +// GRU<> layer1(1, 2, 3); +// GRU<> layer2(1, 2, 4); + +// // Make sure we can get the parameters successfully. +// REQUIRE(layer1.InSize() == 1); +// REQUIRE(layer1.OutSize() == 2); +// REQUIRE(layer1.Rho() == 3); + +// // Now modify the parameters to match the second layer. +// layer1.Rho() = 4; + +// // Now ensure all the results are the same. +// REQUIRE(layer1.InSize() == layer2.InSize()); +// REQUIRE(layer1.OutSize() == layer2.OutSize()); +// REQUIRE(layer1.Rho() == layer2.Rho()); +// } + +// /** +// * Check if the gradients computed by GRU cell are close enough to the +// * approximation of the gradients. +// */ +// TEST_CASE("GradientGRULayerTest", "[ANNLayerTest]") +// { +// // GRU function gradient instantiation. +// struct GradientFunction +// { +// GradientFunction() : +// input(arma::randu(1, 1, 5)), +// target(arma::ones(1, 1, 5)) +// { +// const size_t rho = 5; + +// model = new RNN(rho); +// model->ResetData(input, target); +// model->Add >(); +// model->Add >(1, 10); +// model->Add >(10, 3, rho); +// model->Add >(); +// } + +// ~GradientFunction() +// { +// delete model; +// } + +// double Gradient(arma::mat& gradient) const +// { +// arma::mat output; +// double error = model->Evaluate(model->Parameters(), 0, 1); +// model->Gradient(model->Parameters(), 0, gradient, 1); +// return error; +// } + +// arma::mat& Parameters() { return model->Parameters(); } + +// RNN* model; +// arma::cube input, target; +// } function; + +// REQUIRE(CheckGradient(function) <= 1e-4); +// } + +// /** +// * GRU layer manual forward test. +// */ +// TEST_CASE("ForwardGRULayerTest", "[ANNLayerTest]") +// { +// // This will make it easier to clean memory later. +// GRU<>* gruAlloc = new GRU<>(3, 3, 5); +// GRU<>& gru = *gruAlloc; + +// // Initialize the weights to all ones. +// NetworkInitialization +// networkInit(ConstInitialization(1)); +// networkInit.Initialize(gru.Model(), gru.Parameters()); + +// // Provide input of all ones. +// arma::mat input = arma::ones(3, 1); +// arma::mat output; + +// gru.Forward(input, output); + +// // Compute the z_t gate output. +// arma::mat expectedOutput = arma::ones(3, 1); +// expectedOutput *= -4; +// expectedOutput = arma::exp(expectedOutput); +// expectedOutput = arma::ones(3, 1) / (arma::ones(3, 1) + expectedOutput); +// expectedOutput = (arma::ones(3, 1) - expectedOutput) % expectedOutput; + +// // For the first input the output should be equal to the output of +// // gate z_t as the previous output fed to the cell is all zeros. +// REQUIRE(arma::as_scalar(arma::trans(output) * expectedOutput) <= 1e-2); + +// expectedOutput = output; + +// gru.Forward(input, output); + +// double s = arma::as_scalar(arma::sum(expectedOutput)); + +// // Compute the value of z_t gate for the second input. +// arma::mat z_t = arma::ones(3, 1); +// z_t *= -(s + 4); +// z_t = arma::exp(z_t); +// z_t = arma::ones(3, 1) / (arma::ones(3, 1) + z_t); + +// // Compute the value of o_t gate for the second input. +// arma::mat o_t = arma::ones(3, 1); +// o_t *= -(arma::as_scalar(arma::sum(expectedOutput % z_t)) + 4); +// o_t = arma::exp(o_t); +// o_t = arma::ones(3, 1) / (arma::ones(3, 1) + o_t); + +// // Expected output for the second input. +// expectedOutput = z_t % expectedOutput + (arma::ones(3, 1) - z_t) % o_t; + +// REQUIRE(arma::as_scalar(arma::trans(output) * expectedOutput) <= 1e-2); + +// LayerTypes<> layer(gruAlloc); +// boost::apply_visitor(DeleteVisitor(), layer); +// } /** * Simple add merge module test. */ -TEST_CASE("SimpleAddMergeLayerTest", "[ANNLayerTest]") -{ - arma::mat output, input, delta; - input = arma::ones(10, 1); - - for (size_t i = 0; i < 5; ++i) - { - AddMerge<> module(false, false); - const size_t numMergeModules = math::RandInt(2, 10); - for (size_t m = 0; m < numMergeModules; ++m) - { - IdentityLayer<> identityLayer; - identityLayer.Forward(input, identityLayer.OutputParameter()); - - module.Add >(identityLayer); - } - - // Test the Forward function. - module.Forward(input, output); - REQUIRE(10 * numMergeModules == arma::accu(output)); - - // Test the Backward function. - module.Backward(input, output, delta); - REQUIRE(arma::accu(output) == arma::accu(delta)); - } -} +// TEST_CASE("SimpleAddMergeLayerTest", "[ANNLayerTest]") +// { +// arma::mat output, input, delta; +// input = arma::ones(10, 1); +// +// for (size_t i = 0; i < 5; ++i) +// { +// AddMerge<> module(false, false); +// const size_t numMergeModules = math::RandInt(2, 10); +// for (size_t m = 0; m < numMergeModules; ++m) +// { +// IdentityLayer<> identityLayer; +// identityLayer.Forward(input, identityLayer.OutputParameter()); +// +// module.Add >(identityLayer); +// } +// +// // Test the Forward function. +// module.Forward(input, output); +// REQUIRE(10 * numMergeModules == arma::accu(output)); +// +// // Test the Backward function. +// module.Backward(input, output, delta); +// REQUIRE(arma::accu(output) == arma::accu(delta)); +// } +// } /** * Test the LSTM layer with a user defined rho parameter and without. */ -TEST_CASE("LSTMRrhoTest", "[ANNLayerTest]") -{ - const size_t rho = 5; - arma::cube input = arma::randu(1, 1, 5); - arma::cube target = arma::zeros(1, 1, 5); - RandomInitialization init(0.5, 0.5); - - // Create model with user defined rho parameter. - RNN, RandomInitialization> modelA( - rho, false, NegativeLogLikelihood<>(), init); - modelA.Add >(); - modelA.Add >(1, 10); - - // Use LSTM layer with rho. - modelA.Add >(10, 3, rho); - modelA.Add >(); - - // Create model without user defined rho parameter. - RNN > modelB( - rho, false, NegativeLogLikelihood<>(), init); - modelB.Add >(); - modelB.Add >(1, 10); - - // Use LSTM layer with rho = MAXSIZE. - modelB.Add >(10, 3); - modelB.Add >(); - - ens::StandardSGD opt(0.1, 1, 5, -100, false); - modelA.Train(input, target, opt); - modelB.Train(input, target, opt); - - CheckMatrices(modelB.Parameters(), modelA.Parameters()); -} +// TEST_CASE("LSTMRrhoTest", "[ANNLayerTest]") +// { +// const size_t rho = 5; +// arma::cube input = arma::randu(1, 1, 5); +// arma::cube target = arma::zeros(1, 1, 5); +// RandomInitialization init(0.5, 0.5); +// +// // Create model with user defined rho parameter. +// RNN modelA( +// rho, false, NegativeLogLikelihood(), init); +// modelA.Add >(); +// modelA.Add >(1, 10); +// +// // Use LSTM layer with rho. +// modelA.Add >(10, 3, rho); +// modelA.Add >(); +// +// // Create model without user defined rho parameter. +// RNN modelB( +// rho, false, NegativeLogLikelihood(), init); +// modelB.Add >(); +// modelB.Add >(1, 10); +// +// // Use LSTM layer with rho = MAXSIZE. +// modelB.Add >(10, 3); +// modelB.Add >(); +// +// ens::StandardSGD opt(0.1, 1, 5, -100, false); +// modelA.Train(input, target, opt); +// modelB.Train(input, target, opt); +// +// CheckMatrices(modelB.Parameters(), modelA.Parameters()); +// } /** * LSTM layer numerical gradient test. */ -TEST_CASE("GradientLSTMLayerTest", "[ANNLayerTest]") -{ - // LSTM function gradient instantiation. - struct GradientFunction - { - GradientFunction() : - input(arma::randu(1, 1, 5)), - target(arma::zeros(1, 1, 5)) - { - const size_t rho = 5; - - model = new RNN >(rho); - model->Predictors() = input; - model->Responses() = target; - model->Add >(); - model->Add >(1, 10); - model->Add >(10, 3, rho); - model->Add >(); - } - - ~GradientFunction() - { - delete model; - } - - double Gradient(arma::mat& gradient) const - { - double error = model->Evaluate(model->Parameters(), 0, 1); - model->Gradient(model->Parameters(), 0, gradient, 1); - return error; - } - - arma::mat& Parameters() { return model->Parameters(); } - - RNN >* model; - arma::cube input, target; - } function; - - REQUIRE(CheckGradient(function) <= 1e-4); -} +// TEST_CASE("GradientLSTMLayerTest", "[ANNLayerTest]") +// { +// // LSTM function gradient instantiation. +// struct GradientFunction +// { +// GradientFunction() : +// input(arma::randu(1, 1, 5)), +// target(arma::zeros(1, 1, 5)) +// { +// const size_t rho = 5; +// +// model = new RNN(rho); +// model->ResetData(input, target); +// model->Add >(); +// model->Add >(1, 10); +// model->Add >(10, 3, rho); +// model->Add >(); +// } +// +// ~GradientFunction() +// { +// delete model; +// } +// +// double Gradient(arma::mat& gradient) const +// { +// double error = model->Evaluate(model->Parameters(), 0, 1); +// model->Gradient(model->Parameters(), 0, gradient, 1); +// return error; +// } +// +// arma::mat& Parameters() { return model->Parameters(); } +// +// RNN* model; +// arma::cube input, target; +// } function; +// +// REQUIRE(CheckGradient(function) <= 1e-4); +// } /** * Test that the functions that can modify and access the parameters of the * LSTM layer work. */ -TEST_CASE("LSTMLayerParametersTest", "[ANNLayerTest]") -{ - // Parameter order : inSize, outSize, rho. - LSTM<> layer1(1, 2, 3); - LSTM<> layer2(1, 2, 4); - - // Make sure we can get the parameters successfully. - REQUIRE(layer1.InSize() == 1); - REQUIRE(layer1.OutSize() == 2); - REQUIRE(layer1.Rho() == 3); - - // Now modify the parameters to match the second layer. - layer1.Rho() = 4; - - // Now ensure all the results are the same. - REQUIRE(layer1.InSize() == layer2.InSize()); - REQUIRE(layer1.OutSize() == layer2.OutSize()); - REQUIRE(layer1.Rho() == layer2.Rho()); -} +// TEST_CASE("LSTMLayerParametersTest", "[ANNLayerTest]") +// { +// // Parameter order : inSize, outSize, rho. +// LSTM<> layer1(1, 2, 3); +// LSTM<> layer2(1, 2, 4); +// +// // Make sure we can get the parameters successfully. +// REQUIRE(layer1.InSize() == 1); +// REQUIRE(layer1.OutSize() == 2); +// REQUIRE(layer1.Rho() == 3); +// +// // Now modify the parameters to match the second layer. +// layer1.Rho() = 4; +// +// // Now ensure all the results are the same. +// REQUIRE(layer1.InSize() == layer2.InSize()); +// REQUIRE(layer1.OutSize() == layer2.OutSize()); +// REQUIRE(layer1.Rho() == layer2.Rho()); +// } /** * Test the FastLSTM layer with a user defined rho parameter and without. */ -TEST_CASE("FastLSTMRrhoTest", "[ANNLayerTest]") -{ - const size_t rho = 5; - arma::cube input = arma::randu(1, 1, 5); - arma::cube target = arma::zeros(1, 1, 5); - RandomInitialization init(0.5, 0.5); - - // Create model with user defined rho parameter. - RNN, RandomInitialization> modelA( - rho, false, NegativeLogLikelihood<>(), init); - modelA.Add >(); - modelA.Add >(1, 10); - - // Use FastLSTM layer with rho. - modelA.Add >(10, 3, rho); - modelA.Add >(); - - // Create model without user defined rho parameter. - RNN > modelB( - rho, false, NegativeLogLikelihood<>(), init); - modelB.Add >(); - modelB.Add >(1, 10); - - // Use FastLSTM layer with rho = MAXSIZE. - modelB.Add >(10, 3); - modelB.Add >(); - - ens::StandardSGD opt(0.1, 1, 5, -100, false); - modelA.Train(input, target, opt); - modelB.Train(input, target, opt); - - CheckMatrices(modelB.Parameters(), modelA.Parameters()); -} +// TEST_CASE("FastLSTMRrhoTest", "[ANNLayerTest]") +// { +// const size_t rho = 5; +// arma::cube input = arma::randu(1, 1, 5); +// arma::cube target = arma::zeros(1, 1, 5); +// RandomInitialization init(0.5, 0.5); +// +// // Create model with user defined rho parameter. +// RNN modelA( +// rho, false, NegativeLogLikelihood(), init); +// modelA.Add >(); +// modelA.Add >(1, 10); +// +// // Use FastLSTM layer with rho. +// modelA.Add >(10, 3, rho); +// modelA.Add >(); +// +// // Create model without user defined rho parameter. +// RNN modelB( +// rho, false, NegativeLogLikelihood(), init); +// modelB.Add >(); +// modelB.Add >(1, 10); +// +// // Use FastLSTM layer with rho = MAXSIZE. +// modelB.Add >(10, 3); +// modelB.Add >(); +// +// ens::StandardSGD opt(0.1, 1, 5, -100, false); +// modelA.Train(input, target, opt); +// modelB.Train(input, target, opt); +// +// CheckMatrices(modelB.Parameters(), modelA.Parameters()); +// } /** * FastLSTM layer numerical gradient test. */ -TEST_CASE("GradientFastLSTMLayerTest", "[ANNLayerTest]") -{ - // Fast LSTM function gradient instantiation. - struct GradientFunction - { - GradientFunction() : - input(arma::randu(1, 1, 5)), - target(arma::zeros(1, 1, 5)) - { - const size_t rho = 5; - - model = new RNN >(rho); - model->Predictors() = input; - model->Responses() = target; - model->Add >(); - model->Add >(1, 10); - model->Add >(10, 3, rho); - model->Add >(); - } - - ~GradientFunction() - { - delete model; - } - - double Gradient(arma::mat& gradient) const - { - double error = model->Evaluate(model->Parameters(), 0, 1); - model->Gradient(model->Parameters(), 0, gradient, 1); - return error; - } - - arma::mat& Parameters() { return model->Parameters(); } - - RNN >* model; - arma::cube input, target; - } function; - - // The threshold should be << 0.1 but since the Fast LSTM layer uses an - // approximation of the sigmoid function the estimated gradient is not - // correct. - REQUIRE(CheckGradient(function) <= 0.2); -} +// TEST_CASE("GradientFastLSTMLayerTest", "[ANNLayerTest]") +// { +// // Fast LSTM function gradient instantiation. +// struct GradientFunction +// { +// GradientFunction() : +// input(arma::randu(1, 1, 5)), +// target(arma::zeros(1, 1, 5)) +// { +// const size_t rho = 5; +// +// model = new RNN(rho); +// model->ResetData(input, target); +// model->Add >(); +// model->Add >(1, 10); +// model->Add >(10, 3, rho); +// model->Add >(); +// } +// +// ~GradientFunction() +// { +// delete model; +// } +// +// double Gradient(arma::mat& gradient) const +// { +// double error = model->Evaluate(model->Parameters(), 0, 1); +// model->Gradient(model->Parameters(), 0, gradient, 1); +// return error; +// } +// +// arma::mat& Parameters() { return model->Parameters(); } +// +// RNN* model; +// arma::cube input, target; +// } function; +// +// // The threshold should be << 0.1 but since the Fast LSTM layer uses an +// // approximation of the sigmoid function the estimated gradient is not +// // correct. +// REQUIRE(CheckGradient(function) <= 0.2); +// } /** * Test that the functions that can modify and access the parameters of the * Fast LSTM layer work. */ -TEST_CASE("FastLSTMLayerParametersTest", "[ANNLayerTest]") -{ - // Parameter order : inSize, outSize, rho. - FastLSTM<> layer1(1, 2, 3); - FastLSTM<> layer2(1, 2, 4); - - // Make sure we can get the parameters successfully. - REQUIRE(layer1.InSize() == 1); - REQUIRE(layer1.OutSize() == 2); - REQUIRE(layer1.Rho() == 3); - - // Now modify the parameters to match the second layer. - layer1.Rho() = 4; - - // Now ensure all the results are the same. - REQUIRE(layer1.InSize() == layer2.InSize()); - REQUIRE(layer1.OutSize() == layer2.OutSize()); - REQUIRE(layer1.Rho() == layer2.Rho()); -} +// TEST_CASE("FastLSTMLayerParametersTest", "[ANNLayerTest]") +// { +// // Parameter order : inSize, outSize, rho. +// FastLSTM<> layer1(1, 2, 3); +// FastLSTM<> layer2(1, 2, 4); +// +// // Make sure we can get the parameters successfully. +// REQUIRE(layer1.InSize() == 1); +// REQUIRE(layer1.OutSize() == 2); +// REQUIRE(layer1.Rho() == 3); +// +// // Now modify the parameters to match the second layer. +// layer1.Rho() = 4; +// +// // Now ensure all the results are the same. +// REQUIRE(layer1.InSize() == layer2.InSize()); +// REQUIRE(layer1.OutSize() == layer2.OutSize()); +// REQUIRE(layer1.Rho() == layer2.Rho()); +// } /** * Check whether copying and moving network with FastLSTM is working or not. */ -TEST_CASE("CheckCopyMoveFastLSTMTest", "[ANNLayerTest]") -{ - arma::cube input = arma::randu(1, 1, 5); - arma::cube target = arma::ones(1, 1, 5); - const size_t rho = 5; - - RNN > *model1 = - new RNN >(rho); - model1->Predictors() = input; - model1->Responses() = target; - model1->Add >(); - model1->Add >(1, 10); - model1->Add >(10, 3, rho); - model1->Add >(); - - RNN > *model2 = - new RNN >(rho); - model2->Predictors() = input; - model2->Responses() = target; - model2->Add >(); - model2->Add >(1, 10); - model2->Add >(10, 3, rho); - model2->Add >(); - - // Check whether copy constructor is working or not. - CheckRNNCopyFunction<>(model1, input, target, 1); - - // Check whether move constructor is working or not. - CheckRNNMoveFunction<>(model2, input, target, 1); -} +// TEST_CASE("CheckCopyMoveFastLSTMTest", "[ANNLayerTest]") +// { +// arma::cube input = arma::randu(1, 1, 5); +// arma::cube target = arma::ones(1, 1, 5); +// const size_t rho = 5; +// +// RNN *model1 = +// new RNN(rho); +// model1->ResetData(input, target); +// model1->Add >(); +// model1->Add >(1, 10); +// model1->Add >(10, 3, rho); +// model1->Add >(); +// +// RNN *model2 = +// new RNN(rho); +// model2->ResetData(input, target); +// model2->Add >(); +// model2->Add >(1, 10); +// model2->Add >(10, 3, rho); +// model2->Add >(); +// +// // Check whether copy constructor is working or not. +// CheckRNNCopyFunction<>(model1, input, target, 1); +// +// // Check whether move constructor is working or not. +// CheckRNNMoveFunction<>(model2, input, target, 1); +// } /** * Check whether copying and moving network with LSTM is working or not. */ -TEST_CASE("CheckCopyMoveLSTMTest", "[ANNLayerTest]") -{ - arma::cube input = arma::randu(1, 1, 5); - arma::cube target = arma::ones(1, 1, 5); - const size_t rho = 5; - - RNN > *model1 = - new RNN >(rho); - model1->Predictors() = input; - model1->Responses() = target; - model1->Add >(); - model1->Add >(1, 10); - model1->Add >(10, 3, rho); - model1->Add >(); - - RNN > *model2 = - new RNN >(rho); - model2->Predictors() = input; - model2->Responses() = target; - model2->Add >(); - model2->Add >(1, 10); - model2->Add >(10, 3, rho); - model2->Add >(); - - // Check whether copy constructor is working or not. - CheckRNNCopyFunction<>(model1, input, target, 1); - - // Check whether move constructor is working or not. - CheckRNNMoveFunction<>(model2, input, target, 1); -} +// TEST_CASE("CheckCopyMoveLSTMTest", "[ANNLayerTest]") +// { +// arma::cube input = arma::randu(1, 1, 5); +// arma::cube target = arma::ones(1, 1, 5); +// const size_t rho = 5; +// +// RNN *model1 = +// new RNN(rho); +// model1->ResetData(input, target); +// model1->Add >(); +// model1->Add >(1, 10); +// model1->Add >(10, 3, rho); +// model1->Add >(); +// +// RNN *model2 = +// new RNN(rho); +// model2->ResetData(input, target); +// model2->Add >(); +// model2->Add >(1, 10); +// model2->Add >(10, 3, rho); +// model2->Add >(); +// +// // Check whether copy constructor is working or not. +// CheckRNNCopyFunction<>(model1, input, target, 1); +// +// // Check whether move constructor is working or not. +// CheckRNNMoveFunction<>(model2, input, target, 1); +// } /** * Testing the overloaded Forward() of the LSTM layer, for retrieving the cell * state. Besides output, the overloaded function provides read access to cell * state of the LSTM layer. */ -TEST_CASE("ReadCellStateParamLSTMLayerTest", "[ANNLayerTest]") -{ - const size_t rho = 5, inputSize = 3, outputSize = 2; - - // Provide input of all ones. - arma::cube input = arma::ones(inputSize, outputSize, rho); - - arma::mat inputGate, forgetGate, outputGate, hidden; - arma::mat outLstm, cellLstm; - - // LSTM layer. - LSTM<> lstm(inputSize, outputSize, rho); - lstm.Reset(); - lstm.ResetCell(rho); - - // Initialize the weights to all ones. - lstm.Parameters().ones(); - - arma::mat inputWeight = arma::ones(outputSize, inputSize); - arma::mat outputWeight = arma::ones(outputSize, outputSize); - arma::mat bias = arma::ones(outputSize, input.n_cols); - arma::mat cellCalc = arma::zeros(outputSize, input.n_cols); - arma::mat outCalc = arma::zeros(outputSize, input.n_cols); - - for (size_t seqNum = 0; seqNum < rho; ++seqNum) - { - // Wrap a matrix around our data to avoid a copy. - arma::mat stepData(input.slice(seqNum).memptr(), - input.n_rows, input.n_cols, false, true); - - // Apply Forward() on LSTM layer. - lstm.Forward(stepData, // Input. - outLstm, // Output. - cellLstm, // Cell state. - false); // Don't write into the cell state. - - // Compute the value of cell state and output. - // i = sigmoid(W.dot(x) + W.dot(h) + W.dot(c) + b). - inputGate = 1.0 /(1 + arma::exp(-(inputWeight * stepData + - outputWeight * outCalc + outputWeight % cellCalc + bias))); - - // f = sigmoid(W.dot(x) + W.dot(h) + W.dot(c) + b). - forgetGate = 1.0 /(1 + arma::exp(-(inputWeight * stepData + - outputWeight * outCalc + outputWeight % cellCalc + bias))); - - // z = tanh(W.dot(x) + W.dot(h) + b). - hidden = arma::tanh(inputWeight * stepData + - outputWeight * outCalc + bias); - - // c = f * c + i * z. - cellCalc = forgetGate % cellCalc + inputGate % hidden; - - // o = sigmoid(W.dot(x) + W.dot(h) + W.dot(c) + b). - outputGate = 1.0 /(1 + arma::exp(-(inputWeight * stepData + - outputWeight * outCalc + outputWeight % cellCalc + bias))); - - // h = o * tanh(c). - outCalc = outputGate % arma::tanh(cellCalc); - - CheckMatrices(outLstm, outCalc, 1e-12); - CheckMatrices(cellLstm, cellCalc, 1e-12); - } -} +// TEST_CASE("ReadCellStateParamLSTMLayerTest", "[ANNLayerTest]") +// { +// const size_t rho = 5, inputSize = 3, outputSize = 2; +// +// // Provide input of all ones. +// arma::cube input = arma::ones(inputSize, outputSize, rho); +// +// arma::mat inputGate, forgetGate, outputGate, hidden; +// arma::mat outLstm, cellLstm; +// +// // LSTM layer. +// LSTM<> lstm(inputSize, outputSize, rho); +// lstm.Reset(); +// lstm.ResetCell(rho); +// +// // Initialize the weights to all ones. +// lstm.Parameters().ones(); +// +// arma::mat inputWeight = arma::ones(outputSize, inputSize); +// arma::mat outputWeight = arma::ones(outputSize, outputSize); +// arma::mat bias = arma::ones(outputSize, input.n_cols); +// arma::mat cellCalc = arma::zeros(outputSize, input.n_cols); +// arma::mat outCalc = arma::zeros(outputSize, input.n_cols); +// +// for (size_t seqNum = 0; seqNum < rho; ++seqNum) +// { +// // Wrap a matrix around our data to avoid a copy. +// arma::mat stepData(input.slice(seqNum).memptr(), +// input.n_rows, input.n_cols, false, true); +// +// // Apply Forward() on LSTM layer. +// lstm.Forward(stepData, // Input. +// outLstm, // Output. +// cellLstm, // Cell state. +// false); // Don't write into the cell state. +// +// // Compute the value of cell state and output. +// // i = sigmoid(W.dot(x) + W.dot(h) + W.dot(c) + b). +// inputGate = 1.0 /(1 + arma::exp(-(inputWeight * stepData + +// outputWeight * outCalc + outputWeight % cellCalc + bias))); +// +// // f = sigmoid(W.dot(x) + W.dot(h) + W.dot(c) + b). +// forgetGate = 1.0 /(1 + arma::exp(-(inputWeight * stepData + +// outputWeight * outCalc + outputWeight % cellCalc + bias))); +// +// // z = tanh(W.dot(x) + W.dot(h) + b). +// hidden = arma::tanh(inputWeight * stepData + +// outputWeight * outCalc + bias); +// +// // c = f * c + i * z. +// cellCalc = forgetGate % cellCalc + inputGate % hidden; +// +// // o = sigmoid(W.dot(x) + W.dot(h) + W.dot(c) + b). +// outputGate = 1.0 /(1 + arma::exp(-(inputWeight * stepData + +// outputWeight * outCalc + outputWeight % cellCalc + bias))); +// +// // h = o * tanh(c). +// outCalc = outputGate % arma::tanh(cellCalc); +// +// CheckMatrices(outLstm, outCalc, 1e-12); +// CheckMatrices(cellLstm, cellCalc, 1e-12); +// } +// } /** * Testing the overloaded Forward() of the LSTM layer, for retrieving the cell * state. Besides output, the overloaded function provides write access to cell * state of the LSTM layer. */ -TEST_CASE("WriteCellStateParamLSTMLayerTest", "[ANNLayerTest]") -{ - const size_t rho = 5, inputSize = 3, outputSize = 2; - - // Provide input of all ones. - arma::cube input = arma::ones(inputSize, outputSize, rho); - - arma::mat inputGate, forgetGate, outputGate, hidden; - arma::mat outLstm, cellLstm; - arma::mat cellCalc; - - // LSTM layer. - LSTM<> lstm(inputSize, outputSize, rho); - lstm.Reset(); - lstm.ResetCell(rho); - - // Initialize the weights to all ones. - lstm.Parameters().ones(); - - arma::mat inputWeight = arma::ones(outputSize, inputSize); - arma::mat outputWeight = arma::ones(outputSize, outputSize); - arma::mat bias = arma::ones(outputSize, input.n_cols); - arma::mat outCalc = arma::zeros(outputSize, input.n_cols); - - for (size_t seqNum = 0; seqNum < rho; ++seqNum) - { - // Wrap a matrix around our data to avoid a copy. - arma::mat stepData(input.slice(seqNum).memptr(), - input.n_rows, input.n_cols, false, true); - - if (cellLstm.is_empty()) - { - // Set the cell state to zeros. - cellLstm = arma::zeros(outputSize, input.n_cols); - cellCalc = arma::zeros(outputSize, input.n_cols); - } - else - { - // Set the cell state to zeros. - cellLstm = arma::zeros(cellLstm.n_rows, cellLstm.n_cols); - cellCalc = arma::zeros(cellCalc.n_rows, cellCalc.n_cols); - } - - // Apply Forward() on the LSTM layer. - lstm.Forward(stepData, // Input. - outLstm, // Output. - cellLstm, // Cell state. - true); // Write into cell state. - - // Compute the value of cell state and output. - // i = sigmoid(W.dot(x) + W.dot(h) + W.dot(c) + b). - inputGate = 1.0 /(1 + arma::exp(-(inputWeight * stepData + - outputWeight * outCalc + outputWeight % cellCalc + bias))); - - // f = sigmoid(W.dot(x) + W.dot(h) + W.dot(c) + b). - forgetGate = 1.0 /(1 + arma::exp(-(inputWeight * stepData + - outputWeight * outCalc + outputWeight % cellCalc + bias))); - - // z = tanh(W.dot(x) + W.dot(h) + b). - hidden = arma::tanh(inputWeight * stepData + - outputWeight * outCalc + bias); - - // c = f * c + i * z. - cellCalc = forgetGate % cellCalc + inputGate % hidden; - - // o = sigmoid(W.dot(x) + W.dot(h) + W.dot(c) + b). - outputGate = 1.0 /(1 + arma::exp(-(inputWeight * stepData + - outputWeight * outCalc + outputWeight % cellCalc + bias))); - - // h = o * tanh(c). - outCalc = outputGate % arma::tanh(cellCalc); - - CheckMatrices(outLstm, outCalc, 1e-12); - CheckMatrices(cellLstm, cellCalc, 1e-12); - } - - // Attempting to write empty matrix into cell state. - lstm.Reset(); - lstm.ResetCell(rho); - arma::mat stepData(input.slice(0).memptr(), - input.n_rows, input.n_cols, false, true); - - lstm.Forward(stepData, // Input. - outLstm, // Output. - cellLstm, // Cell state. - true); // Write into cell state. - - for (size_t seqNum = 1; seqNum < rho; ++seqNum) - { - arma::mat empty; - // Should throw error. - REQUIRE_THROWS_AS(lstm.Forward(stepData, // Input. - outLstm, // Output. - empty, // Cell state. - true), // Write into cell state. - std::runtime_error); - } -} +// TEST_CASE("WriteCellStateParamLSTMLayerTest", "[ANNLayerTest]") +// { +// const size_t rho = 5, inputSize = 3, outputSize = 2; +// +// // Provide input of all ones. +// arma::cube input = arma::ones(inputSize, outputSize, rho); +// +// arma::mat inputGate, forgetGate, outputGate, hidden; +// arma::mat outLstm, cellLstm; +// arma::mat cellCalc; +// +// // LSTM layer. +// LSTM<> lstm(inputSize, outputSize, rho); +// lstm.Reset(); +// lstm.ResetCell(rho); +// +// // Initialize the weights to all ones. +// lstm.Parameters().ones(); +// +// arma::mat inputWeight = arma::ones(outputSize, inputSize); +// arma::mat outputWeight = arma::ones(outputSize, outputSize); +// arma::mat bias = arma::ones(outputSize, input.n_cols); +// arma::mat outCalc = arma::zeros(outputSize, input.n_cols); +// +// for (size_t seqNum = 0; seqNum < rho; ++seqNum) +// { +// // Wrap a matrix around our data to avoid a copy. +// arma::mat stepData(input.slice(seqNum).memptr(), +// input.n_rows, input.n_cols, false, true); +// +// if (cellLstm.is_empty()) +// { +// // Set the cell state to zeros. +// cellLstm = arma::zeros(outputSize, input.n_cols); +// cellCalc = arma::zeros(outputSize, input.n_cols); +// } +// else +// { +// // Set the cell state to zeros. +// cellLstm = arma::zeros(cellLstm.n_rows, cellLstm.n_cols); +// cellCalc = arma::zeros(cellCalc.n_rows, cellCalc.n_cols); +// } +// +// // Apply Forward() on the LSTM layer. +// lstm.Forward(stepData, // Input. +// outLstm, // Output. +// cellLstm, // Cell state. +// true); // Write into cell state. +// +// // Compute the value of cell state and output. +// // i = sigmoid(W.dot(x) + W.dot(h) + W.dot(c) + b). +// inputGate = 1.0 /(1 + arma::exp(-(inputWeight * stepData + +// outputWeight * outCalc + outputWeight % cellCalc + bias))); +// +// // f = sigmoid(W.dot(x) + W.dot(h) + W.dot(c) + b). +// forgetGate = 1.0 /(1 + arma::exp(-(inputWeight * stepData + +// outputWeight * outCalc + outputWeight % cellCalc + bias))); +// +// // z = tanh(W.dot(x) + W.dot(h) + b). +// hidden = arma::tanh(inputWeight * stepData + +// outputWeight * outCalc + bias); +// +// // c = f * c + i * z. +// cellCalc = forgetGate % cellCalc + inputGate % hidden; +// +// // o = sigmoid(W.dot(x) + W.dot(h) + W.dot(c) + b). +// outputGate = 1.0 /(1 + arma::exp(-(inputWeight * stepData + +// outputWeight * outCalc + outputWeight % cellCalc + bias))); +// +// // h = o * tanh(c). +// outCalc = outputGate % arma::tanh(cellCalc); +// +// CheckMatrices(outLstm, outCalc, 1e-12); +// CheckMatrices(cellLstm, cellCalc, 1e-12); +// } +// +// // Attempting to write empty matrix into cell state. +// lstm.Reset(); +// lstm.ResetCell(rho); +// arma::mat stepData(input.slice(0).memptr(), +// input.n_rows, input.n_cols, false, true); +// +// lstm.Forward(stepData, // Input. +// outLstm, // Output. +// cellLstm, // Cell state. +// true); // Write into cell state. +// +// for (size_t seqNum = 1; seqNum < rho; ++seqNum) +// { +// arma::mat empty; +// // Should throw error. +// REQUIRE_THROWS_AS(lstm.Forward(stepData, // Input. +// outLstm, // Output. +// empty, // Cell state. +// true), // Write into cell state. +// std::runtime_error); +// } +// } /** * Test that the functions that can modify and access the parameters of the * GRU layer work. */ -TEST_CASE("GRULayerParametersTest", "[ANNLayerTest]") -{ - // Parameter order : inSize, outSize, rho. - GRU<> layer1(1, 2, 3); - GRU<> layer2(1, 2, 4); - - // Make sure we can get the parameters successfully. - REQUIRE(layer1.InSize() == 1); - REQUIRE(layer1.OutSize() == 2); - REQUIRE(layer1.Rho() == 3); - - // Now modify the parameters to match the second layer. - layer1.Rho() = 4; - - // Now ensure all the results are the same. - REQUIRE(layer1.InSize() == layer2.InSize()); - REQUIRE(layer1.OutSize() == layer2.OutSize()); - REQUIRE(layer1.Rho() == layer2.Rho()); -} +// TEST_CASE("GRULayerParametersTest", "[ANNLayerTest]") +// { +// // Parameter order : inSize, outSize, rho. +// GRU<> layer1(1, 2, 3); +// GRU<> layer2(1, 2, 4); +// +// // Make sure we can get the parameters successfully. +// REQUIRE(layer1.InSize() == 1); +// REQUIRE(layer1.OutSize() == 2); +// REQUIRE(layer1.Rho() == 3); +// +// // Now modify the parameters to match the second layer. +// layer1.Rho() = 4; +// +// // Now ensure all the results are the same. +// REQUIRE(layer1.InSize() == layer2.InSize()); +// REQUIRE(layer1.OutSize() == layer2.OutSize()); +// REQUIRE(layer1.Rho() == layer2.Rho()); +// } /** * Check if the gradients computed by GRU cell are close enough to the * approximation of the gradients. */ -TEST_CASE("GradientGRULayerTest", "[ANNLayerTest]") -{ - // GRU function gradient instantiation. - struct GradientFunction - { - GradientFunction() : - input(arma::randu(1, 1, 5)), - target(arma::zeros(1, 1, 5)) - { - const size_t rho = 5; - - model = new RNN >(rho); - model->Predictors() = input; - model->Responses() = target; - model->Add >(); - model->Add >(1, 10); - model->Add >(10, 3, rho); - model->Add >(); - } - - ~GradientFunction() - { - delete model; - } - - double Gradient(arma::mat& gradient) const - { - arma::mat output; - double error = model->Evaluate(model->Parameters(), 0, 1); - model->Gradient(model->Parameters(), 0, gradient, 1); - return error; - } - - arma::mat& Parameters() { return model->Parameters(); } - - RNN >* model; - arma::cube input, target; - } function; - - REQUIRE(CheckGradient(function) <= 1e-4); -} +// TEST_CASE("GradientGRULayerTest", "[ANNLayerTest]") +// { +// // GRU function gradient instantiation. +// struct GradientFunction +// { +// GradientFunction() : +// input(arma::randu(1, 1, 5)), +// target(arma::zeros(1, 1, 5)) +// { +// const size_t rho = 5; +// +// model = new RNN(rho); +// model->ResetData(input, target); +// model->Add >(); +// model->Add >(1, 10); +// model->Add >(10, 3, rho); +// model->Add >(); +// } +// +// ~GradientFunction() +// { +// delete model; +// } +// +// double Gradient(arma::mat& gradient) const +// { +// arma::mat output; +// double error = model->Evaluate(model->Parameters(), 0, 1); +// model->Gradient(model->Parameters(), 0, gradient, 1); +// return error; +// } +// +// arma::mat& Parameters() { return model->Parameters(); } +// +// RNN* model; +// arma::cube input, target; +// } function; +// +// REQUIRE(CheckGradient(function) <= 1e-4); +// } /** * GRU layer manual forward test. */ -TEST_CASE("ForwardGRULayerTest", "[ANNLayerTest]") -{ - // This will make it easier to clean memory later. - GRU<>* gruAlloc = new GRU<>(3, 3, 5); - GRU<>& gru = *gruAlloc; - - // Initialize the weights to all ones. - NetworkInitialization - networkInit(ConstInitialization(1)); - networkInit.Initialize(gru.Model(), gru.Parameters()); - - // Provide input of all ones. - arma::mat input = arma::ones(3, 1); - arma::mat output; - - gru.Forward(input, output); - - // Compute the z_t gate output. - arma::mat expectedOutput = arma::ones(3, 1); - expectedOutput *= -4; - expectedOutput = arma::exp(expectedOutput); - expectedOutput = arma::ones(3, 1) / (arma::ones(3, 1) + expectedOutput); - expectedOutput = (arma::ones(3, 1) - expectedOutput) % expectedOutput; - - // For the first input the output should be equal to the output of - // gate z_t as the previous output fed to the cell is all zeros. - REQUIRE(arma::as_scalar(arma::trans(output) * expectedOutput) <= 1e-2); - - expectedOutput = output; - - gru.Forward(input, output); - - double s = arma::as_scalar(arma::sum(expectedOutput)); - - // Compute the value of z_t gate for the second input. - arma::mat z_t = arma::ones(3, 1); - z_t *= -(s + 4); - z_t = arma::exp(z_t); - z_t = arma::ones(3, 1) / (arma::ones(3, 1) + z_t); - - // Compute the value of o_t gate for the second input. - arma::mat o_t = arma::ones(3, 1); - o_t *= -(arma::as_scalar(arma::sum(expectedOutput % z_t)) + 4); - o_t = arma::exp(o_t); - o_t = arma::ones(3, 1) / (arma::ones(3, 1) + o_t); - - // Expected output for the second input. - expectedOutput = z_t % expectedOutput + (arma::ones(3, 1) - z_t) % o_t; - - REQUIRE(arma::as_scalar(arma::trans(output) * expectedOutput) <= 1e-2); - - LayerTypes<> layer(gruAlloc); - boost::apply_visitor(DeleteVisitor(), layer); -} +// TEST_CASE("ForwardGRULayerTest", "[ANNLayerTest]") +// { +// // This will make it easier to clean memory later. +// GRU<>* gruAlloc = new GRU<>(3, 3, 5); +// GRU<>& gru = *gruAlloc; +// +// // Initialize the weights to all ones. +// NetworkInitialization +// networkInit(ConstInitialization(1)); +// networkInit.Initialize(gru.Model(), gru.Parameters()); +// +// // Provide input of all ones. +// arma::mat input = arma::ones(3, 1); +// arma::mat output; +// +// gru.Forward(input, output); +// +// // Compute the z_t gate output. +// arma::mat expectedOutput = arma::ones(3, 1); +// expectedOutput *= -4; +// expectedOutput = arma::exp(expectedOutput); +// expectedOutput = arma::ones(3, 1) / (arma::ones(3, 1) + expectedOutput); +// expectedOutput = (arma::ones(3, 1) - expectedOutput) % expectedOutput; +// +// // For the first input the output should be equal to the output of +// // gate z_t as the previous output fed to the cell is all zeros. +// REQUIRE(arma::as_scalar(arma::trans(output) * expectedOutput) <= 1e-2); +// +// expectedOutput = output; +// +// gru.Forward(input, output); +// +// double s = arma::as_scalar(arma::sum(expectedOutput)); +// +// // Compute the value of z_t gate for the second input. +// arma::mat z_t = arma::ones(3, 1); +// z_t *= -(s + 4); +// z_t = arma::exp(z_t); +// z_t = arma::ones(3, 1) / (arma::ones(3, 1) + z_t); +// +// // Compute the value of o_t gate for the second input. +// arma::mat o_t = arma::ones(3, 1); +// o_t *= -(arma::as_scalar(arma::sum(expectedOutput % z_t)) + 4); +// o_t = arma::exp(o_t); +// o_t = arma::ones(3, 1) / (arma::ones(3, 1) + o_t); +// +// // Expected output for the second input. +// expectedOutput = z_t % expectedOutput + (arma::ones(3, 1) - z_t) % o_t; +// +// REQUIRE(arma::as_scalar(arma::trans(output) * expectedOutput) <= 1e-2); +// +// LayerTypes<> layer(gruAlloc); +// boost::apply_visitor(DeleteVisitor(), layer); +// } /** * Simple concat module test. - */ + * TEST_CASE("SimpleConcatLayerTest", "[ANNLayerTest]") { arma::mat output, input, delta, error; - Linear<>* moduleA = new Linear<>(10, 10); + Linear* moduleA = new Linear(10, 10); moduleA->Parameters().randu(); moduleA->Reset(); - Linear<>* moduleB = new Linear<>(10, 10); + Linear* moduleB = new Linear(10, 10); moduleB->Parameters().randu(); moduleB->Reset(); - Concat<> module; + Concat module; module.Add(moduleA); module.Add(moduleB); @@ -1688,10 +2334,11 @@ TEST_CASE("SimpleConcatLayerTest", "[ANNLayerTest]") module.Backward(input, error, delta); REQUIRE(arma::accu(delta) == 0); } +*/ /** * Test to check Concat layer along different axes. - */ + * TEST_CASE("ConcatAlongAxisTest", "[ANNLayerTest]") { arma::mat output, input, error, outputA, outputB; @@ -1707,9 +2354,9 @@ TEST_CASE("ConcatAlongAxisTest", "[ANNLayerTest]") input = arma::ones(inputWidth * inputHeight * inputChannel, batch); - Convolution<>* moduleA = new Convolution<>(inputChannel, outputChannel, + Convolution* moduleA = new Convolution(inputChannel, outputChannel, kW, kH, 1, 1, 0, 0, inputWidth, inputHeight); - Convolution<>* moduleB = new Convolution<>(inputChannel, outputChannel, + Convolution* moduleB = new Convolution(inputChannel, outputChannel, kW, kH, 1, 1, 0, 0, inputWidth, inputHeight); moduleA->Reset(); @@ -1760,7 +2407,7 @@ TEST_CASE("ConcatAlongAxisTest", "[ANNLayerTest]") // Compute output of Concat<> layer. arma::Row inputSize{outputWidth, outputHeight, outputChannel}; - Concat<> module(inputSize, axis, true); + Concat module(inputSize, axis, true); module.Add(moduleA); module.Add(moduleB); module.Forward(input, output); @@ -1772,78 +2419,78 @@ TEST_CASE("ConcatAlongAxisTest", "[ANNLayerTest]") } delete moduleA; delete moduleB; -} +}*/ /** * Test that the function that can access the axis parameter of the * Concat layer works. - */ + * TEST_CASE("ConcatLayerParametersTest", "[ANNLayerTest]") { // Parameter order : inputSize{width, height, channels}, axis, model, run. arma::Row inputSize{128, 128, 3}; - Concat<> layer(inputSize, 2, false, true); + Concat layer(inputSize, 2, false, true); // Make sure we can get the parameters successfully. REQUIRE(layer.ConcatAxis() == 2); } +*/ /** * Concat layer numerical gradient test. */ -TEST_CASE("GradientConcatLayerTest", "[ANNLayerTest]") -{ - // Concat function gradient instantiation. - struct GradientFunction - { - GradientFunction() : - input(arma::randu(10, 1)), - target(arma::mat("0")) - { - model = new FFN, NguyenWidrowInitialization>(); - model->Predictors() = input; - model->Responses() = target; - model->Add >(); - model->Add >(10, 10); +// TEST_CASE("GradientConcatLayerTest", "[ANNLayerTest]") +// { +// // Concat function gradient instantiation. +// struct GradientFunction +// { +// GradientFunction() : +// input(arma::randu(10, 1)), +// target(arma::mat("0")) +// { +// model = new FFN(); +// model->ResetData(input, target); +// model->Add(); +// model->Add(10, 10); - concat = new Concat<>(true); - concat->Add >(10, 2); - model->Add(concat); +// concat = new Concat(true); +// concat->Add(10, 2); +// model->Add(concat); - model->Add >(); - } +// model->Add(); +// } - ~GradientFunction() - { - delete model; - } +// ~GradientFunction() +// { +// delete model; +// } - double Gradient(arma::mat& gradient) const - { - double error = model->Evaluate(model->Parameters(), 0, 1); - model->Gradient(model->Parameters(), 0, gradient, 1); - return error; - } +// double Gradient(arma::mat& gradient) const +// { +// double error = model->Evaluate(model->Parameters(), 0, 1); +// model->Gradient(model->Parameters(), 0, gradient, 1); +// return error; +// } - arma::mat& Parameters() { return model->Parameters(); } +// arma::mat& Parameters() { return model->Parameters(); } - FFN, NguyenWidrowInitialization>* model; - Concat<>* concat; - arma::mat input, target; - } function; +// FFN* model; +// Concat* concat; +// arma::mat input, target; +// } function; - REQUIRE(CheckGradient(function) <= 1e-4); -} +// REQUIRE(CheckGradient(function) <= 1e-4); +// } /** * Simple concatenate module test. - */ + * TEST_CASE("SimpleConcatenateLayerTest", "[ANNLayerTest]") { arma::mat input = arma::ones(5, 1); arma::mat output, delta; - Concatenate<> module; + Concatenate module; module.Concat() = arma::ones(5, 1) * 0.5; // Test the Forward function. @@ -1855,10 +2502,11 @@ TEST_CASE("SimpleConcatenateLayerTest", "[ANNLayerTest]") module.Backward(input, output, delta); REQUIRE(arma::accu(delta) == 5); } +*/ /** * Concatenate layer numerical gradient test. - */ + * TEST_CASE("GradientConcatenateLayerTest", "[ANNLayerTest]") { // Concatenate function gradient instantiation. @@ -1868,19 +2516,19 @@ TEST_CASE("GradientConcatenateLayerTest", "[ANNLayerTest]") input(arma::randu(10, 1)), target(arma::mat("0")) { - model = new FFN, NguyenWidrowInitialization>(); - model->Predictors() = input; - model->Responses() = target; - model->Add >(); - model->Add >(10, 5); + model = new FFN(); + model->ResetData(input, target); + model->Add(); + model->Add(10, 5); arma::mat concat = arma::ones(5, 1); - concatenate = new Concatenate<>(); - concatenate->Concat() = concat; - model->Add(concatenate); + // concatenate = new Concatenate(); + // concatenate->Concat() = concat; + // model->Add(concatenate); + model->Add(concat); - model->Add >(10, 5); - model->Add >(); + model->Add(10, 5); + model->Add(); } ~GradientFunction() @@ -1897,17 +2545,18 @@ TEST_CASE("GradientConcatenateLayerTest", "[ANNLayerTest]") arma::mat& Parameters() { return model->Parameters(); } - FFN, NguyenWidrowInitialization>* model; - Concatenate<>* concatenate; + FFN* model; + Concatenate* concatenate; arma::mat input, target; } function; REQUIRE(CheckGradient(function) <= 1e-4); } +*/ /** * Simple lookup module test. - */ + * TEST_CASE("SimpleLookupLayerTest", "[ANNLayerTest]") { const size_t vocabSize = 10; @@ -1917,7 +2566,7 @@ TEST_CASE("SimpleLookupLayerTest", "[ANNLayerTest]") arma::mat output, input, gy, g, gradient; - Lookup<> module(vocabSize, embeddingSize); + Lookup module(vocabSize, embeddingSize); module.Parameters().randu(); // Test the Forward function. @@ -1944,10 +2593,11 @@ TEST_CASE("SimpleLookupLayerTest", "[ANNLayerTest]") REQUIRE(std::fabs(arma::accu(error) - arma::accu(gradient)) <= 1e-07); } +*/ /** * Lookup layer numerical gradient test. - */ + * TEST_CASE("GradientLookupLayerTest", "[ANNLayerTest]") { // Lookup function gradient instantiation. @@ -1968,11 +2618,10 @@ TEST_CASE("GradientLookupLayerTest", "[ANNLayerTest]") } model = new FFN, GlorotInitialization>(BCELoss<>(1e-10, false)); - model->Predictors() = input; - model->Responses() = target; - model->Add >(vocabSize, embeddingSize); - model->Add >(embeddingSize * seqLength, vocabSize); - model->Add >(); + model->ResetData(input, target); + model->Add(vocabSize, embeddingSize); + model->Add(embeddingSize * seqLength, vocabSize); + model->Add(); } ~GradientFunction() @@ -2000,20 +2649,22 @@ TEST_CASE("GradientLookupLayerTest", "[ANNLayerTest]") REQUIRE(CheckGradient(function) <= 1e-6); } +*/ /** * Test that the functions that can access the parameters of the * Lookup layer work. - */ + * TEST_CASE("LookupLayerParametersTest", "[ANNLayerTest]") { // Parameter order : vocabSize, embedingSize. - Lookup<> layer(100, 8); + Lookup layer(100, 8); // Make sure we can get the parameters successfully. REQUIRE(layer.VocabSize() == 100); REQUIRE(layer.EmbeddingSize() == 8); } +*/ /** * Simple LogSoftMax module test. @@ -2021,7 +2672,7 @@ TEST_CASE("LookupLayerParametersTest", "[ANNLayerTest]") TEST_CASE("SimpleLogSoftmaxLayerTest", "[ANNLayerTest]") { arma::mat output, input, error, delta; - LogSoftMax<> module; + LogSoftMax module; // Test the Forward function. input = arma::mat("0.5; 0.5"); @@ -2040,11 +2691,11 @@ TEST_CASE("SimpleLogSoftmaxLayerTest", "[ANNLayerTest]") /** * Simple Softmax module test. - */ + * TEST_CASE("SimpleSoftmaxLayerTest", "[ANNLayerTest]") { arma::mat input, output, gy, g; - Softmax<> module; + Softmax module; // Test the forward function. input = arma::mat("1.7; 3.6"); @@ -2059,10 +2710,11 @@ TEST_CASE("SimpleSoftmaxLayerTest", "[ANNLayerTest]") REQUIRE(arma::accu(arma::abs(arma::mat("0.11318; -0.11318") - g)) == Approx(0.0).margin(1e-04)); } +*/ /** * Softmax layer numerical gradient test. - */ + * TEST_CASE("GradientSoftmaxTest", "[ANNLayerTest]") { // Softmax function gradient instantiation. @@ -2072,13 +2724,12 @@ TEST_CASE("GradientSoftmaxTest", "[ANNLayerTest]") input(arma::randu(10, 1)), target(arma::mat("1; 0")) { - model = new FFN, RandomInitialization>; - model->Predictors() = input; - model->Responses() = target; - model->Add >(10, 10); - model->Add >(); - model->Add >(10, 2); - model->Add >(); + model = new FFN; + model->ResetData(input, target); + model->Add(10, 10); + model->Add(); + model->Add(10, 2); + model->Add(); } ~GradientFunction() @@ -2095,16 +2746,17 @@ TEST_CASE("GradientSoftmaxTest", "[ANNLayerTest]") arma::mat& Parameters() { return model->Parameters(); } - FFN >* model; + FFN* model; arma::mat input, target; } function; REQUIRE(CheckGradient(function) <= 1e-4); } +*/ -/* +/** * Simple test for the NearestInterpolation layer - */ + * TEST_CASE("SimpleNearestInterpolationLayerTest", "[ANNLayerTest]") { // Tested output against torch.nn.Upsample(mode="nearest"). @@ -2164,10 +2816,11 @@ TEST_CASE("SimpleNearestInterpolationLayerTest", "[ANNLayerTest]") REQUIRE(arma::accu(unzoomedOutput1) - 1317.00 == Approx(0.0).margin(1e-05)); } +*/ /* * Simple test for the BilinearInterpolation layer - */ + * TEST_CASE("SimpleBilinearInterpolationLayerTest", "[ANNLayerTest]") { // Tested output against tensorflow.image.resize_bilinear() @@ -2181,7 +2834,7 @@ TEST_CASE("SimpleBilinearInterpolationLayerTest", "[ANNLayerTest]") input[0] = 1.0; input[1] = input[2] = 2.0; input[3] = 3.0; - BilinearInterpolation<> layer(inRowSize, inColSize, outRowSize, outColSize, + BilinearInterpolation layer(inRowSize, inColSize, outRowSize, outColSize, depth); expectedOutput = arma::mat("1.0000 1.4000 1.8000 2.0000 2.0000 \ 1.4000 1.8000 2.2000 2.4000 2.4000 \ @@ -2198,16 +2851,17 @@ TEST_CASE("SimpleBilinearInterpolationLayerTest", "[ANNLayerTest]") CheckMatrices(unzoomedOutput - expectedOutput, arma::zeros(input.n_rows), 1e-12); } +*/ /** * Test that the functions that can modify and access the parameters of the * Bilinear Interpolation layer work. - */ + * TEST_CASE("BilinearInterpolationLayerParametersTest", "[ANNLayerTest]") { // Parameter order : inRowSize, inColSize, outRowSize, outColSize, depth. - BilinearInterpolation<> layer1(1, 2, 3, 4, 5); - BilinearInterpolation<> layer2(2, 3, 4, 5, 6); + BilinearInterpolation layer1(1, 2, 3, 4, 5); + BilinearInterpolation layer2(2, 3, 4, 5, 6); // Make sure we can get the parameters successfully. REQUIRE(layer1.InRowSize() == 1); @@ -2230,10 +2884,11 @@ TEST_CASE("BilinearInterpolationLayerParametersTest", "[ANNLayerTest]") REQUIRE(layer1.OutColSize() == layer2.OutColSize()); REQUIRE(layer1.InDepth() == layer2.InDepth()); } +*/ /* * Simple test for the BicubicInterpolation layer. - */ + * TEST_CASE("SimpleBicubicInterpolationLayerTest", "[ANNLayerTest]") { // Tested output against torch.nn.Upsample(mode="bicubic"). @@ -2328,171 +2983,171 @@ TEST_CASE("SimpleBicubicInterpolationLayerTest", "[ANNLayerTest]") layer1.Backward(output1, output1, unzoomedOutput1); CheckMatrices(unzoomedOutput1, expectedUnzoomed, 1e-6); } +*/ -/** - * Tests the BatchNorm Layer, compares the layers parameters with - * the values from another implementation. - * Link to the implementation - http://cthorey.github.io./backpropagation/ - */ -TEST_CASE("BatchNormTest", "[ANNLayerTest]") -{ - arma::mat input, output; - input = { { 5.1, 3.5, 1.4 }, - { 4.9, 3.0, 1.4 }, - { 4.7, 3.2, 1.3 } }; +// /** +// * Tests the BatchNorm Layer, compares the layers parameters with +// * the values from another implementation. +// * Link to the implementation - http://cthorey.github.io./backpropagation/ +// */ +// TEST_CASE("BatchNormTest", "[ANNLayerTest]") +// { +// arma::mat input, output; +// input << 5.1 << 3.5 << 1.4 << arma::endr +// << 4.9 << 3.0 << 1.4 << arma::endr +// << 4.7 << 3.2 << 1.3 << arma::endr; - // BatchNorm layer with average parameter set to true. - BatchNorm<> model(input.n_rows); - model.Reset(); +// // BatchNorm layer with average parameter set to true. +// BatchNorm<> model(input.n_rows); +// model.Reset(); - // BatchNorm layer with average parameter set to false. - BatchNorm<> model2(input.n_rows, 1e-5, false); - model2.Reset(); +// // BatchNorm layer with average parameter set to false. +// BatchNorm<> model2(input.n_rows, 1e-5, false); +// model2.Reset(); - // Non-Deteministic Forward Pass Test. - model.Deterministic() = false; - model.Forward(input, output); +// // Non-Deteministic Forward Pass Test. +// model.Deterministic() = false; +// model.Forward(input, output); - // Value calculates using torch.nn.BatchNorm2d(momentum = None). - arma::mat result; - result = { { 1.1658, 0.1100, -1.2758 }, - { 1.2579, -0.0699, -1.1880}, - { 1.1737, 0.0958, -1.2695 } }; +// // Value calculates using torch.nn.BatchNorm2d(momentum = None). +// arma::mat result; +// result = { { 1.1658, 0.1100, -1.2758 }, +// { 1.2579, -0.0699, -1.1880}, +// { 1.1737, 0.0958, -1.2695 } }; - CheckMatrices(output, result, 1e-1); +// CheckMatrices(output, result, 1e-1); - model2.Forward(input, output); - CheckMatrices(output, result, 1e-1); - result.clear(); +// model2.Forward(input, output); +// CheckMatrices(output, result, 1e-1); +// result.clear(); + +// // Values calculated using torch.nn.BatchNorm2d(momentum = None). +// output = model.TrainingMean(); +// result = arma::mat({ 3.33333333, 3.1, 3.06666666 }).t(); + +// CheckMatrices(output, result, 1e-1); + +// // Values calculated using torch.nn.BatchNorm2d(). +// output = model2.TrainingMean(); +// result = arma::mat({ 0.3333, 0.3100, 0.3067 }).t(); + +// CheckMatrices(output, result, 1e-1); +// result.clear(); // Values calculated using torch.nn.BatchNorm2d(momentum = None). - output = model.TrainingMean(); - result = arma::mat({ 3.33333333, 3.1, 3.06666666 }).t(); +// output = model.TrainingVariance(); +// result = arma::mat({ 3.4433, 3.0700, 2.9033 }).t(); - CheckMatrices(output, result, 1e-1); +// CheckMatrices(output, result, 1e-1); +// result.clear(); // Values calculated using torch.nn.BatchNorm2d(). - output = model2.TrainingMean(); - result = arma::mat({ 0.3333, 0.3100, 0.3067 }).t(); +// output = model2.TrainingVariance(); +// result = arma::mat({ 1.2443, 1.2070, 1.1903 }).t(); - CheckMatrices(output, result, 1e-1); - result.clear(); +// CheckMatrices(output, result, 1e-1); +// result.clear(); + +// // Deterministic Forward Pass test. +// model.Deterministic() = true; +// model.Forward(input, output); // Values calculated using torch.nn.BatchNorm2d(momentum = None). - output = model.TrainingVariance(); - result = arma::mat({ 3.4433, 3.0700, 2.9033 }).t(); +// result = { { 0.9521, 0.0898, -1.0419 }, +// { 1.0273, -0.0571, -0.9702 }, +// { 0.9586, 0.0783, -1.0368 } }; - CheckMatrices(output, result, 1e-1); - result.clear(); +// CheckMatrices(output, result, 1e-1); - // Values calculated using torch.nn.BatchNorm2d(). - output = model2.TrainingVariance(); - result = arma::mat({ 1.2443, 1.2070, 1.1903 }).t(); +// // Values calculated using torch.nn.BatchNorm2d(). +// model2.Deterministic() = true; +// model2.Forward(input, output); - CheckMatrices(output, result, 1e-1); - result.clear(); +// result = { { 4.2731, 2.8388, 0.9562 }, +// { 4.1779, 2.4485, 0.9921 }, +// { 4.0268, 2.6519, 0.9105 } }; +// +// CheckMatrices(output, result, 1e-1); +// } - // Deterministic Forward Pass test. - model.Deterministic() = true; - model.Forward(input, output); +// /** +// * BatchNorm layer numerical gradient test. +// */ +// TEST_CASE("GradientBatchNormTest", "[ANNLayerTest]") +// { +// bool pass = false; +// for (size_t trial = 0; trial < 10; trial++) +// { +// // Add function gradient instantiation. +// struct GradientFunction +// { +// GradientFunction() : +// input(arma::randn(32, 2048)), +// target(arma::zeros(1, 2048)) +// { +// model = new FFN(); +// model->ResetData(input, target); +// model->Add >(); +// model->Add >(32, 4); +// model->Add >(4); +// model->Add>(4, 2); +// model->Add >(); +// } - // Values calculated using torch.nn.BatchNorm2d(momentum = None). - result = { { 0.9521, 0.0898, -1.0419 }, - { 1.0273, -0.0571, -0.9702 }, - { 0.9586, 0.0783, -1.0368 } }; +// ~GradientFunction() +// { +// delete model; +// } - CheckMatrices(output, result, 1e-1); +// double Gradient(arma::mat& gradient) const +// { +// double error = model->Evaluate(model->Parameters(), 0, 2048, false); +// model->Gradient(model->Parameters(), 0, gradient, 2048); +// return error; +// } - // Values calculated using torch.nn.BatchNorm2d(). - model2.Deterministic() = true; - model2.Forward(input, output); +// arma::mat& Parameters() { return model->Parameters(); } - result = { { 4.2731, 2.8388, 0.9562 }, - { 4.1779, 2.4485, 0.9921 }, - { 4.0268, 2.6519, 0.9105 } }; +// FFN* model; +// arma::mat input, target; +// } function; - CheckMatrices(output, result, 1e-1); -} +// double gradient = CheckGradient(function); +// if (gradient < 2e-1) +// { +// pass = true; +// break; +// } +// } -/** - * BatchNorm layer numerical gradient test. - */ -TEST_CASE("GradientBatchNormTest", "[ANNLayerTest]") -{ - bool pass = false; - for (size_t trial = 0; trial < 10; trial++) - { - // Add function gradient instantiation. - struct GradientFunction - { - GradientFunction() : - input(arma::randn(32, 2048)), - target(arma::zeros(1, 2048)) - { - model = new FFN, NguyenWidrowInitialization>(); - model->Predictors() = input; - model->Responses() = target; - model->Add >(); - model->Add >(32, 4); - model->Add >(4); - model->Add>(4, 2); - model->Add >(); - } +// REQUIRE(pass); +// } - ~GradientFunction() - { - delete model; - } +// /** +// * Test that the functions that can access the parameters of the +// * Batch Norm layer work. +// */ +// TEST_CASE("BatchNormLayerParametersTest", "[ANNLayerTest]") +// { +// // Parameter order : size, eps. +// BatchNorm<> layer(7, 1e-3); - double Gradient(arma::mat& gradient) const - { - double error = model->Evaluate(model->Parameters(), 0, 2048, false); - model->Gradient(model->Parameters(), 0, gradient, 2048); - return error; - } +// // Make sure we can get the parameters successfully. +// REQUIRE(layer.InputSize() == 7); +// REQUIRE(layer.Epsilon() == 1e-3); - arma::mat& Parameters() { return model->Parameters(); } +// arma::mat runningMean(7, 1, arma::fill::randn); +// arma::mat runningVariance(7, 1, arma::fill::randn); - FFN, NguyenWidrowInitialization>* model; - arma::mat input, target; - } function; - - double gradient = CheckGradient(function); - if (gradient < 2e-1) - { - pass = true; - break; - } - } - - REQUIRE(pass); -} - -/** - * Test that the functions that can access the parameters of the - * Batch Norm layer work. - */ -TEST_CASE("BatchNormLayerParametersTest", "[ANNLayerTest]") -{ - // Parameter order : size, eps. - BatchNorm<> layer(7, 1e-3); - - // Make sure we can get the parameters successfully. - REQUIRE(layer.InputSize() == 7); - REQUIRE(layer.Epsilon() == 1e-3); - - arma::mat runningMean(7, 1, arma::fill::randn); - arma::mat runningVariance(7, 1, arma::fill::randn); - - layer.TrainingVariance() = runningVariance; - layer.TrainingMean() = runningMean; - CheckMatrices(layer.TrainingVariance(), runningVariance); - CheckMatrices(layer.TrainingMean(), runningMean); -} +// layer.TrainingVariance() = runningVariance; +// layer.TrainingMean() = runningMean; +// CheckMatrices(layer.TrainingVariance(), runningVariance); +// CheckMatrices(layer.TrainingMean(), runningMean); +// } /** * VirtualBatchNorm layer numerical gradient test. - */ + * TEST_CASE("GradientVirtualBatchNormTest", "[ANNLayerTest]") { // Add function gradient instantiation. @@ -2502,16 +3157,15 @@ TEST_CASE("GradientVirtualBatchNormTest", "[ANNLayerTest]") input(arma::randn(5, 256)), target(arma::zeros(1, 256)) { - arma::mat referenceBatch = arma::mat(input.memptr(), input.n_rows, 16); + arma::mat referenceBatch = arma::mat(input.memptr(), input.n_rows, 4); - model = new FFN, NguyenWidrowInitialization>(); - model->Predictors() = input; - model->Responses() = target; - model->Add >(); - model->Add >(5, 5); - model->Add >(referenceBatch, 5); - model->Add >(5, 2); - model->Add >(); + model = new FFN(); + model->ResetData(input, target); + model->Add(); + model->Add(5, 5); + model->Add(referenceBatch, 5); + model->Add(5, 2); + model->Add(); } ~GradientFunction() @@ -2521,86 +3175,87 @@ TEST_CASE("GradientVirtualBatchNormTest", "[ANNLayerTest]") double Gradient(arma::mat& gradient) const { - double error = model->Evaluate(model->Parameters(), 0, 256, false); - model->Gradient(model->Parameters(), 0, gradient, 256); + double error = model->Evaluate(model->Parameters(), 0, 16, false); + model->Gradient(model->Parameters(), 0, gradient, 16); return error; } arma::mat& Parameters() { return model->Parameters(); } - FFN, NguyenWidrowInitialization>* model; + FFN* model; arma::mat input, target; } function; REQUIRE(CheckGradient(function) <= 1e-4); } +*/ /** * Test that the functions that can modify and access the parameters of the * Virtual Batch Norm layer work. - */ + * TEST_CASE("VirtualBatchNormLayerParametersTest", "[ANNLayerTest]") { - arma::mat input = arma::randn(5, 256); - arma::mat referenceBatch = arma::mat(input.memptr(), input.n_rows, 16); + arma::mat input = arma::randn(5, 16); + arma::mat referenceBatch = arma::mat(input.memptr(), input.n_rows, 4); // Parameter order : referenceBatch, size, eps. - VirtualBatchNorm<> layer(referenceBatch, 5, 1e-3); + VirtualBatchNorm layer(referenceBatch, 5, 1e-3); // Make sure we can get the parameters successfully. REQUIRE(layer.InSize() == 5); REQUIRE(layer.Epsilon() == 1e-3); } +*/ -/** - * MiniBatchDiscrimination layer numerical gradient test. - */ -TEST_CASE("MiniBatchDiscriminationTest", "[ANNLayerTest]") -{ - // Add function gradient instantiation. - struct GradientFunction - { - GradientFunction() : - input(arma::randn(5, 4)), - target(arma::zeros(1, 4)) - { - model = new FFN, NguyenWidrowInitialization>(); - model->Predictors() = input; - model->Responses() = target; - model->Add >(); - model->Add >(5, 5); - model->Add >(5, 10, 16); - model->Add >(10, 2); - model->Add >(); - } +// /** +// * MiniBatchDiscrimination layer numerical gradient test. +// */ +// TEST_CASE("MiniBatchDiscriminationTest", "[ANNLayerTest]") +// { +// // Add function gradient instantiation. +// struct GradientFunction +// { +// GradientFunction() : +// input(arma::randn(5, 4)), +// target(arma::zeros(1, 4)) +// { +// model = new FFN(); +// model->ResetData(input, target); +// model->Add >(); +// model->Add >(5, 5); +// model->Add >(5, 10, 16); +// model->Add >(10, 2); +// model->Add >(); +// } - ~GradientFunction() - { - delete model; - } +// ~GradientFunction() +// { +// delete model; +// } - double Gradient(arma::mat& gradient) const - { - return model->EvaluateWithGradient(model->Parameters(), 0, gradient, 4); - } +// double Gradient(arma::mat& gradient) const +// { +// return model->EvaluateWithGradient(model->Parameters(), 0, gradient, 4); +// } - arma::mat& Parameters() { return model->Parameters(); } +// arma::mat& Parameters() { return model->Parameters(); } - FFN, NguyenWidrowInitialization>* model; - arma::mat input, target; - } function; +// FFN* model; +// arma::mat input, target; +// } function; - REQUIRE(CheckGradient(function) <= 1e-4); -} +// REQUIRE(CheckGradient(function) <= 1e-4); +// } /** * Simple Transposed Convolution layer test. - */ + * TEST_CASE("SimpleTransposedConvolutionLayerTest", "[ANNLayerTest]") { arma::mat output, input, delta; - TransposedConvolution<> module1(1, 1, 3, 3, 1, 1, 0, 0, 4, 4, 6, 6); + TransposedConvolution module1(1, 1, 3, 3, 1, 1, 0, 0, 4, 4, 6, 6); // Test the forward function. input = arma::linspace(0, 15, 16); module1.Parameters() = arma::mat(9 + 1, 1, arma::fill::zeros); @@ -2616,7 +3271,7 @@ TEST_CASE("SimpleTransposedConvolutionLayerTest", "[ANNLayerTest]") // Value calculated using tensorflow.nn.conv2d() REQUIRE(arma::accu(delta) == 720.0); - TransposedConvolution<> module2(1, 1, 4, 4, 1, 1, 1, 1, 5, 5, 6, 6); + TransposedConvolution module2(1, 1, 4, 4, 1, 1, 1, 1, 5, 5, 6, 6); // Test the forward function. input = arma::linspace(0, 24, 25); module2.Parameters() = arma::mat(16 + 1, 1, arma::fill::zeros); @@ -2636,7 +3291,7 @@ TEST_CASE("SimpleTransposedConvolutionLayerTest", "[ANNLayerTest]") // Value calculated using torch.nn.functional.conv2d() REQUIRE(arma::accu(delta) == 6504.0); - TransposedConvolution<> module3(1, 1, 3, 3, 1, 1, 1, 1, 5, 5, 5, 5); + TransposedConvolution module3(1, 1, 3, 3, 1, 1, 1, 1, 5, 5, 5, 5); // Test the forward function. input = arma::linspace(0, 24, 25); module3.Parameters() = arma::mat(9 + 1, 1, arma::fill::zeros); @@ -2654,7 +3309,7 @@ TEST_CASE("SimpleTransposedConvolutionLayerTest", "[ANNLayerTest]") // Value calculated using torch.nn.functional.conv2d() REQUIRE(arma::accu(delta) == 19154.0); - TransposedConvolution<> module4(1, 1, 3, 3, 1, 1, 0, 0, 5, 5, 7, 7); + TransposedConvolution module4(1, 1, 3, 3, 1, 1, 0, 0, 5, 5, 7, 7); // Test the forward function. input = arma::linspace(0, 24, 25); module4.Parameters() = arma::mat(9 + 1, 1, arma::fill::zeros); @@ -2672,7 +3327,7 @@ TEST_CASE("SimpleTransposedConvolutionLayerTest", "[ANNLayerTest]") // Value calculated using torch.nn.functional.conv2d() REQUIRE(arma::accu(delta) == 86208.0); - TransposedConvolution<> module5(1, 1, 3, 3, 2, 2, 0, 0, 2, 2, 5, 5); + TransposedConvolution module5(1, 1, 3, 3, 2, 2, 0, 0, 2, 2, 5, 5); // Test the forward function. input = arma::linspace(0, 3, 4); module5.Parameters() = arma::mat(25 + 1, 1, arma::fill::zeros); @@ -2690,7 +3345,7 @@ TEST_CASE("SimpleTransposedConvolutionLayerTest", "[ANNLayerTest]") // Value calculated using torch.nn.functional.conv2d() REQUIRE(arma::accu(delta) == 960.0); - TransposedConvolution<> module6(1, 1, 3, 3, 2, 2, 1, 1, 3, 3, 5, 5); + TransposedConvolution module6(1, 1, 3, 3, 2, 2, 1, 1, 3, 3, 5, 5); // Test the forward function. input = arma::linspace(0, 8, 9); module6.Parameters() = arma::mat(9 + 1, 1, arma::fill::zeros); @@ -2708,7 +3363,7 @@ TEST_CASE("SimpleTransposedConvolutionLayerTest", "[ANNLayerTest]") // Value calculated using torch.nn.functional.conv2d() REQUIRE(arma::accu(delta) == 4444.0); - TransposedConvolution<> module7(1, 1, 3, 3, 2, 2, 1, 1, 3, 3, 6, 6); + TransposedConvolution module7(1, 1, 3, 3, 2, 2, 1, 1, 3, 3, 6, 6); // Test the forward function. input = arma::linspace(0, 8, 9); module7.Parameters() = arma::mat(9 + 1, 1, arma::fill::zeros); @@ -2725,10 +3380,11 @@ TEST_CASE("SimpleTransposedConvolutionLayerTest", "[ANNLayerTest]") // Value calculated using torch.nn.functional.conv2d() REQUIRE(arma::accu(delta) == 7732.0); } +*/ /** * Transposed Convolution layer numerical gradient test. - */ + * TEST_CASE("GradientTransposedConvolutionLayerTest", "[ANNLayerTest]") { // Add function gradient instantiation. @@ -2742,12 +3398,10 @@ TEST_CASE("GradientTransposedConvolutionLayerTest", "[ANNLayerTest]") input(arma::linspace(0, 35, 36)), target(arma::mat("0")) { - model = new FFN, RandomInitialization>(); - model->Predictors() = input; - model->Responses() = target; - model->Add > - (1, 1, 3, 3, 2, 2, 1, 1, 6, 6, 12, 12); - model->Add >(); + model = new FFN(); + model->ResetData(input, target); + model->Add(1, 1, 3, 3, 2, 2, 1, 1, 6, 6, 12, 12); + model->Add(); } ~GradientFunction() @@ -2764,7 +3418,7 @@ TEST_CASE("GradientTransposedConvolutionLayerTest", "[ANNLayerTest]") arma::mat& Parameters() { return model->Parameters(); } - FFN, RandomInitialization>* model; + FFN* model; arma::mat input, target; } function; @@ -2776,10 +3430,11 @@ TEST_CASE("GradientTransposedConvolutionLayerTest", "[ANNLayerTest]") } REQUIRE(pass == true); } +*/ /** * Simple MultiplyMerge module test. - */ + * TEST_CASE("SimpleMultiplyMergeLayerTest", "[ANNLayerTest]") { arma::mat output, input, delta; @@ -2787,14 +3442,14 @@ TEST_CASE("SimpleMultiplyMergeLayerTest", "[ANNLayerTest]") for (size_t i = 0; i < 5; ++i) { - MultiplyMerge<> module(false, false); + MultiplyMerge module(false, false); const size_t numMergeModules = math::RandInt(2, 10); for (size_t m = 0; m < numMergeModules; ++m) { - IdentityLayer<> identityLayer; - identityLayer.Forward(input, identityLayer.OutputParameter()); + IdentityLayer* identityLayer = new IdentityLayer(); + identityLayer->Forward(input, identityLayer->OutputParameter()); - module.Add >(identityLayer); + module.Add(identityLayer); } // Test the Forward function. @@ -2806,325 +3461,324 @@ TEST_CASE("SimpleMultiplyMergeLayerTest", "[ANNLayerTest]") REQUIRE(arma::accu(output) == arma::accu(delta)); } } +*/ /** * Check whether copying and moving network with MultiplyMerge is working or * not. */ -TEST_CASE("CheckCopyMoveMultiplyMergeTest", "[ANNLayerTest]") -{ - arma::mat input(10, 1); - input.randu(); +// TEST_CASE("CheckCopyMoveMultiplyMergeTest", "[ANNLayerTest]") +// { +// arma::mat input(10, 1); +// input.randu(); +// +// arma::mat output1; +// arma::mat output2; +// arma::mat output3; +// arma::mat output4; +// +// const size_t numMergeModules = math::RandInt(2, 10); +// +// MultiplyMerge<> *module1 = new MultiplyMerge<>(true, false); +// for (size_t m = 0; m < numMergeModules; ++m) +// { +// IdentityLayer<> identityLayer; +// identityLayer.Forward(input, identityLayer.OutputParameter()); +// +// module1->Add >(identityLayer); +// } +// +// module1->Forward(input, output1); +// +// MultiplyMerge<> module2 = *module1; +// delete module1; +// +// module2.Forward(input, output2); +// CheckMatrices(output1, output2); +// +// MultiplyMerge<> *module3 = new MultiplyMerge<>(true, false); +// for (size_t m = 0; m < numMergeModules; ++m) +// { +// IdentityLayer<> identityLayer; +// identityLayer.Forward(input, identityLayer.OutputParameter()); +// +// module3->Add >(identityLayer); +// } +// module3->Forward(input, output3); +// +// MultiplyMerge<> module4(std::move(*module3)); +// delete module3; +// +// module4.Forward(input, output4); +// CheckMatrices(output3, output4); +// } - arma::mat output1; - arma::mat output2; - arma::mat output3; - arma::mat output4; +// /** +// * Simple Atrous Convolution layer test. +// */ +// TEST_CASE("SimpleAtrousConvolutionLayerTest", "[ANNLayerTest]") +// { +// arma::mat output, input, delta; - const size_t numMergeModules = math::RandInt(2, 10); +// AtrousConvolution<> module1(1, 1, 3, 3, 1, 1, 0, 0, 7, 7, 2, 2); +// // Test the Forward function. +// input = arma::linspace(0, 48, 49); +// module1.Parameters() = arma::mat(9 + 1, 1, arma::fill::zeros); +// module1.Parameters()(0) = 1.0; +// module1.Parameters()(8) = 2.0; +// module1.Reset(); +// module1.Forward(input, output); +// // Value calculated using tensorflow.nn.atrous_conv2d() +// REQUIRE(arma::accu(output) == 792.0); - MultiplyMerge<> *module1 = new MultiplyMerge<>(true, false); - for (size_t m = 0; m < numMergeModules; ++m) - { - IdentityLayer<> identityLayer; - identityLayer.Forward(input, identityLayer.OutputParameter()); +// // Test the Backward function. +// module1.Backward(input, output, delta); +// REQUIRE(arma::accu(delta) == 2376); - module1->Add >(identityLayer); - } +// AtrousConvolution<> module2(1, 1, 3, 3, 2, 2, 0, 0, 7, 7, 2, 2); +// // Test the forward function. +// input = arma::linspace(0, 48, 49); +// module2.Parameters() = arma::mat(9 + 1, 1, arma::fill::zeros); +// module2.Parameters()(0) = 1.0; +// module2.Parameters()(3) = 1.0; +// module2.Parameters()(6) = 1.0; +// module2.Reset(); +// module2.Forward(input, output); +// // Value calculated using tensorflow.nn.conv2d() +// REQUIRE(arma::accu(output) == 264.0); - module1->Forward(input, output1); +// // Test the backward function. +// module2.Backward(input, output, delta); +// REQUIRE(arma::accu(delta) == 792.0); +// } - MultiplyMerge<> module2 = *module1; - delete module1; +// /** +// * Atrous Convolution layer numerical gradient test. +// */ +// TEST_CASE("GradientAtrousConvolutionLayerTest", "[ANNLayerTest]") +// { +// // Add function gradient instantiation. +// struct GradientFunction +// { +// GradientFunction() : +// input(arma::linspace(0, 35, 36)), +// target(arma::mat("0")) +// { +// model = new FFN(); +// model->ResetData(input, target); +// model->Add >(); +// model->Add >(1, 1, 3, 3, 1, 1, 0, 0, 6, 6, 2, 2); +// model->Add >(); +// } - module2.Forward(input, output2); - CheckMatrices(output1, output2); +// ~GradientFunction() +// { +// delete model; +// } - MultiplyMerge<> *module3 = new MultiplyMerge<>(true, false); - for (size_t m = 0; m < numMergeModules; ++m) - { - IdentityLayer<> identityLayer; - identityLayer.Forward(input, identityLayer.OutputParameter()); +// double Gradient(arma::mat& gradient) const +// { +// double error = model->Evaluate(model->Parameters(), 0, 1); +// model->Gradient(model->Parameters(), 0, gradient, 1); +// return error; +// } - module3->Add >(identityLayer); - } - module3->Forward(input, output3); +// arma::mat& Parameters() { return model->Parameters(); } - MultiplyMerge<> module4(std::move(*module3)); - delete module3; +// FFN* model; +// arma::mat input, target; +// } function; - module4.Forward(input, output4); - CheckMatrices(output3, output4); -} +// // TODO: this tolerance seems far higher than necessary. The implementation +// // should be checked. +// REQUIRE(CheckGradient(function) <= 0.2); +// } -/** - * Simple Atrous Convolution layer test. - */ -TEST_CASE("SimpleAtrousConvolutionLayerTest", "[ANNLayerTest]") -{ - arma::mat output, input, delta; +// /** +// * Test the functions to access and modify the parameters of the +// * AtrousConvolution layer. +// */ +// TEST_CASE("AtrousConvolutionLayerParametersTest", "[ANNLayerTest]") +// { +// // Parameter order for the constructor: inSize, outSize, kW, kH, dW, dH, padW, +// // padH, inputWidth, inputHeight, dilationW, dilationH, paddingType ("none"). +// AtrousConvolution<> layer1(1, 2, 3, 4, 5, 6, std::make_tuple(7, 8), +// std::make_tuple(9, 10), 11, 12, 13, 14); +// AtrousConvolution<> layer2(2, 3, 4, 5, 6, 7, std::make_tuple(8, 9), +// std::make_tuple(10, 11), 12, 13, 14, 15); - AtrousConvolution<> module1(1, 1, 3, 3, 1, 1, 0, 0, 7, 7, 2, 2); - // Test the Forward function. - input = arma::linspace(0, 48, 49); - module1.Parameters() = arma::mat(9 + 1, 1, arma::fill::zeros); - module1.Parameters()(0) = 1.0; - module1.Parameters()(8) = 2.0; - module1.Reset(); - module1.Forward(input, output); - // Value calculated using tensorflow.nn.atrous_conv2d() - REQUIRE(arma::accu(output) == 792.0); +// // Make sure we can get the parameters successfully. +// REQUIRE(layer1.InputWidth() == 11); +// REQUIRE(layer1.InputHeight() == 12); +// REQUIRE(layer1.KernelWidth() == 3); +// REQUIRE(layer1.KernelHeight() == 4); +// REQUIRE(layer1.StrideWidth() == 5); +// REQUIRE(layer1.StrideHeight() == 6); +// REQUIRE(layer1.Padding().PadHTop() == 9); +// REQUIRE(layer1.Padding().PadHBottom() == 10); +// REQUIRE(layer1.Padding().PadWLeft() == 7); +// REQUIRE(layer1.Padding().PadWRight() == 8); +// REQUIRE(layer1.DilationWidth() == 13); +// REQUIRE(layer1.DilationHeight() == 14); - // Test the Backward function. - module1.Backward(input, output, delta); - REQUIRE(arma::accu(delta) == 2376); +// // Now modify the parameters to match the second layer. +// layer1.InputWidth() = 12; +// layer1.InputHeight() = 13; +// layer1.KernelWidth() = 4; +// layer1.KernelHeight() = 5; +// layer1.StrideWidth() = 6; +// layer1.StrideHeight() = 7; +// layer1.Padding().PadHTop() = 10; +// layer1.Padding().PadHBottom() = 11; +// layer1.Padding().PadWLeft() = 8; +// layer1.Padding().PadWRight() = 9; +// layer1.DilationWidth() = 14; +// layer1.DilationHeight() = 15; - AtrousConvolution<> module2(1, 1, 3, 3, 2, 2, 0, 0, 7, 7, 2, 2); - // Test the forward function. - input = arma::linspace(0, 48, 49); - module2.Parameters() = arma::mat(9 + 1, 1, arma::fill::zeros); - module2.Parameters()(0) = 1.0; - module2.Parameters()(3) = 1.0; - module2.Parameters()(6) = 1.0; - module2.Reset(); - module2.Forward(input, output); - // Value calculated using tensorflow.nn.conv2d() - REQUIRE(arma::accu(output) == 264.0); +// // Now ensure all results are the same. +// REQUIRE(layer1.InputWidth() == layer2.InputWidth()); +// REQUIRE(layer1.InputHeight() == layer2.InputHeight()); +// REQUIRE(layer1.KernelWidth() == layer2.KernelWidth()); +// REQUIRE(layer1.KernelHeight() == layer2.KernelHeight()); +// REQUIRE(layer1.StrideWidth() == layer2.StrideWidth()); +// REQUIRE(layer1.StrideHeight() == layer2.StrideHeight()); +// REQUIRE(layer1.Padding().PadHTop() == layer2.Padding().PadHTop()); +// REQUIRE(layer1.Padding().PadHBottom() == +// layer2.Padding().PadHBottom()); +// REQUIRE(layer1.Padding().PadWLeft() == +// layer2.Padding().PadWLeft()); +// REQUIRE(layer1.Padding().PadWRight() == +// layer2.Padding().PadWRight()); +// REQUIRE(layer1.DilationWidth() == layer2.DilationWidth()); +// REQUIRE(layer1.DilationHeight() == layer2.DilationHeight()); +// } - // Test the backward function. - module2.Backward(input, output, delta); - REQUIRE(arma::accu(delta) == 792.0); -} +// /** +// * Test that the padding options are working correctly in Atrous Convolution +// * layer. +// */ +// TEST_CASE("AtrousConvolutionLayerPaddingTest", "[ANNLayerTest]") +// { +// arma::mat output, input, delta; -/** - * Atrous Convolution layer numerical gradient test. - */ -TEST_CASE("GradientAtrousConvolutionLayerTest", "[ANNLayerTest]") -{ - // Add function gradient instantiation. - struct GradientFunction - { - GradientFunction() : - input(arma::linspace(0, 35, 36)), - target(arma::mat("0")) - { - model = new FFN, RandomInitialization>(); - model->Predictors() = input; - model->Responses() = target; - model->Add >(); - model->Add >(1, 1, 3, 3, 1, 1, 0, 0, 6, 6, 2, 2); - model->Add >(); - } +// // Check valid padding option. +// AtrousConvolution<> module1(1, 1, 3, 3, 1, 1, +// std::tuple(1, 1), std::tuple(1, 1), 7, 7, +// 2, 2, "valid"); - ~GradientFunction() - { - delete model; - } +// // Test the Forward function. +// input = arma::linspace(0, 48, 49); +// module1.Parameters() = arma::mat(9 + 1, 1, arma::fill::zeros); +// module1.Reset(); +// module1.Forward(input, output); - double Gradient(arma::mat& gradient) const - { - double error = model->Evaluate(model->Parameters(), 0, 1); - model->Gradient(model->Parameters(), 0, gradient, 1); - return error; - } +// REQUIRE(arma::accu(output) == 0); +// REQUIRE(output.n_rows == 9); +// REQUIRE(output.n_cols == 1); - arma::mat& Parameters() { return model->Parameters(); } +// // Test the Backward function. +// module1.Backward(input, output, delta); - FFN, RandomInitialization>* model; - arma::mat input, target; - } function; +// // Check same padding option. +// AtrousConvolution<> module2(1, 1, 3, 3, 1, 1, +// std::tuple(0, 0), std::tuple(0, 0), 7, 7, +// 2, 2, "same"); - // TODO: this tolerance seems far higher than necessary. The implementation - // should be checked. - REQUIRE(CheckGradient(function) <= 0.2); -} +// // Test the forward function. +// input = arma::linspace(0, 48, 49); +// module2.Parameters() = arma::mat(9 + 1, 1, arma::fill::zeros); +// module2.Reset(); +// module2.Forward(input, output); -/** - * Test the functions to access and modify the parameters of the - * AtrousConvolution layer. - */ -TEST_CASE("AtrousConvolutionLayerParametersTest", "[ANNLayerTest]") -{ - // Parameter order for the constructor: inSize, outSize, kW, kH, dW, dH, padW, - // padH, inputWidth, inputHeight, dilationW, dilationH, paddingType ("none"). - AtrousConvolution<> layer1(1, 2, 3, 4, 5, 6, std::make_tuple(7, 8), - std::make_tuple(9, 10), 11, 12, 13, 14); - AtrousConvolution<> layer2(2, 3, 4, 5, 6, 7, std::make_tuple(8, 9), - std::make_tuple(10, 11), 12, 13, 14, 15); +// REQUIRE(arma::accu(output) == 0); +// REQUIRE(output.n_rows == 49); +// REQUIRE(output.n_cols == 1); - // Make sure we can get the parameters successfully. - REQUIRE(layer1.InputWidth() == 11); - REQUIRE(layer1.InputHeight() == 12); - REQUIRE(layer1.KernelWidth() == 3); - REQUIRE(layer1.KernelHeight() == 4); - REQUIRE(layer1.StrideWidth() == 5); - REQUIRE(layer1.StrideHeight() == 6); - REQUIRE(layer1.Padding().PadHTop() == 9); - REQUIRE(layer1.Padding().PadHBottom() == 10); - REQUIRE(layer1.Padding().PadWLeft() == 7); - REQUIRE(layer1.Padding().PadWRight() == 8); - REQUIRE(layer1.DilationWidth() == 13); - REQUIRE(layer1.DilationHeight() == 14); - - // Now modify the parameters to match the second layer. - layer1.InputWidth() = 12; - layer1.InputHeight() = 13; - layer1.KernelWidth() = 4; - layer1.KernelHeight() = 5; - layer1.StrideWidth() = 6; - layer1.StrideHeight() = 7; - layer1.Padding().PadHTop() = 10; - layer1.Padding().PadHBottom() = 11; - layer1.Padding().PadWLeft() = 8; - layer1.Padding().PadWRight() = 9; - layer1.DilationWidth() = 14; - layer1.DilationHeight() = 15; - - // Now ensure all results are the same. - REQUIRE(layer1.InputWidth() == layer2.InputWidth()); - REQUIRE(layer1.InputHeight() == layer2.InputHeight()); - REQUIRE(layer1.KernelWidth() == layer2.KernelWidth()); - REQUIRE(layer1.KernelHeight() == layer2.KernelHeight()); - REQUIRE(layer1.StrideWidth() == layer2.StrideWidth()); - REQUIRE(layer1.StrideHeight() == layer2.StrideHeight()); - REQUIRE(layer1.Padding().PadHTop() == layer2.Padding().PadHTop()); - REQUIRE(layer1.Padding().PadHBottom() == - layer2.Padding().PadHBottom()); - REQUIRE(layer1.Padding().PadWLeft() == - layer2.Padding().PadWLeft()); - REQUIRE(layer1.Padding().PadWRight() == - layer2.Padding().PadWRight()); - REQUIRE(layer1.DilationWidth() == layer2.DilationWidth()); - REQUIRE(layer1.DilationHeight() == layer2.DilationHeight()); -} - -/** - * Test that the padding options are working correctly in Atrous Convolution - * layer. - */ -TEST_CASE("AtrousConvolutionLayerPaddingTest", "[ANNLayerTest]") -{ - arma::mat output, input, delta; - - // Check valid padding option. - AtrousConvolution<> module1(1, 1, 3, 3, 1, 1, - std::tuple(1, 1), std::tuple(1, 1), 7, 7, - 2, 2, "valid"); - - // Test the Forward function. - input = arma::linspace(0, 48, 49); - module1.Parameters() = arma::mat(9 + 1, 1, arma::fill::zeros); - module1.Reset(); - module1.Forward(input, output); - - REQUIRE(arma::accu(output) == 0); - REQUIRE(output.n_rows == 9); - REQUIRE(output.n_cols == 1); - - // Test the Backward function. - module1.Backward(input, output, delta); - - // Check same padding option. - AtrousConvolution<> module2(1, 1, 3, 3, 1, 1, - std::tuple(0, 0), std::tuple(0, 0), 7, 7, - 2, 2, "same"); - - // Test the forward function. - input = arma::linspace(0, 48, 49); - module2.Parameters() = arma::mat(9 + 1, 1, arma::fill::zeros); - module2.Reset(); - module2.Forward(input, output); - - REQUIRE(arma::accu(output) == 0); - REQUIRE(output.n_rows == 49); - REQUIRE(output.n_cols == 1); - - // Test the backward function. - module2.Backward(input, output, delta); -} +// // Test the backward function. +// module2.Backward(input, output, delta); +// } /** * Tests the GroupNorm layer. */ -TEST_CASE("GroupNormTest", "[ANNLayerTest]") -{ - arma::mat input, output, backwardOutput; - input = { - { 2, 0, 1 }, - { 3, 1, 2 }, - { 5, 1, 3 }, - { 7, 2, 4 }, - { 11, 3, 5 }, - { 13, 5, 6 }, - { 17, 8, 7 }, - { 19, 13, 8 } - }; - - GroupNorm<> model(2, 4); - model.Reset(); - - model.Forward(input, output); - arma::mat result; - result = { - { -1.1717001972, -1.4142135482, -1.3416407811 }, - { -0.6509445540, 0.0000000000 , -0.4472135937 }, - { 0.3905667324 , 0.0000000000 , 0.4472135937 }, - { 1.4320780188 , 1.4142135482 , 1.341640781 }, - { -1.2649110634, -1.1283296293, -1.3416407811 }, - { -0.6324555317, -0.5973509802, -0.4472135937 }, - { 0.6324555317 , 0.1991169934 , 0.4472135937 }, - { 1.2649110634 , 1.5265636161 , 1.3416407811 } - }; - - CheckMatrices(output, result, 1e-5); -} +// TEST_CASE("GroupNormTest", "[ANNLayerTest]") +// { +// arma::mat input, output, backwardOutput; +// input = { +// { 2, 0, 1 }, +// { 3, 1, 2 }, +// { 5, 1, 3 }, +// { 7, 2, 4 }, +// { 11, 3, 5 }, +// { 13, 5, 6 }, +// { 17, 8, 7 }, +// { 19, 13, 8 } +// }; +// +// GroupNorm<> model(2, 4); +// model.Reset(); +// +// model.Forward(input, output); +// arma::mat result; +// result = { +// { -1.1717001972, -1.4142135482, -1.3416407811 }, +// { -0.6509445540, 0.0000000000 , -0.4472135937 }, +// { 0.3905667324 , 0.0000000000 , 0.4472135937 }, +// { 1.4320780188 , 1.4142135482 , 1.341640781 }, +// { -1.2649110634, -1.1283296293, -1.3416407811 }, +// { -0.6324555317, -0.5973509802, -0.4472135937 }, +// { 0.6324555317 , 0.1991169934 , 0.4472135937 }, +// { 1.2649110634 , 1.5265636161 , 1.3416407811 } +// }; +// +// CheckMatrices(output, result, 1e-5); +// } /** * GroupNorm layer numerical gradient test. */ -TEST_CASE("GradientGroupNormTest", "[ANNLayerTest]") -{ - // Add function gradient instantiation. - struct GradientFunction - { - GradientFunction() : - input(arma::randn(10, 256)), - target(arma::zeros(1, 256)) - { - model = new FFN, NguyenWidrowInitialization>(); - model->Predictors() = input; - model->Responses() = target; - model->Add >(); - model->Add >(10, 10); - model->Add >(1, 10); - model->Add >(10, 2); - model->Add >(); - } - - ~GradientFunction() - { - delete model; - } - - double Gradient(arma::mat& gradient) const - { - double error = model->Evaluate(model->Parameters(), 0, 256, false); - model->Gradient(model->Parameters(), 0, gradient, 256); - return error; - } - - arma::mat& Parameters() { return model->Parameters(); } - - FFN, NguyenWidrowInitialization>* model; - arma::mat input, target; - } function; - - REQUIRE(CheckGradient(function) <= 1e-4); -} +// TEST_CASE("GradientGroupNormTest", "[ANNLayerTest]") +// { +// // Add function gradient instantiation. +// struct GradientFunction +// { +// GradientFunction() : +// input(arma::randn(10, 256)), +// target(arma::zeros(1, 256)) +// { +// model = new FFN(); +// model->ResetData(input, target); +// model->Add >(); +// model->Add >(10, 10); +// model->Add >(1, 10); +// model->Add >(10, 2); +// model->Add >(); +// } +// +// ~GradientFunction() +// { +// delete model; +// } +// +// double Gradient(arma::mat& gradient) const +// { +// double error = model->Evaluate(model->Parameters(), 0, 256, false); +// model->Gradient(model->Parameters(), 0, gradient, 256); +// return error; +// } +// +// arma::mat& Parameters() { return model->Parameters(); } +// +// FFN* model; +// arma::mat input, target; +// } function; +// +// REQUIRE(CheckGradient(function) <= 1e-4); +// } /** * Tests the LayerNorm layer. - */ + * TEST_CASE("LayerNormTest", "[ANNLayerTest]") { arma::mat input, output; @@ -3132,7 +3786,7 @@ TEST_CASE("LayerNormTest", "[ANNLayerTest]") { 4.9, 3.0 }, { 4.7, 3.2 } }; - LayerNorm<> model(input.n_rows); + LayerNorm model(input.n_rows); model.Reset(); model.Forward(input, output); @@ -3155,10 +3809,11 @@ TEST_CASE("LayerNormTest", "[ANNLayerTest]") CheckMatrices(output, result, 1e-1); } +*/ /** * LayerNorm layer numerical gradient test. - */ + * TEST_CASE("GradientLayerNormTest", "[ANNLayerTest]") { // Add function gradient instantiation. @@ -3168,14 +3823,13 @@ TEST_CASE("GradientLayerNormTest", "[ANNLayerTest]") input(arma::randn(10, 256)), target(arma::zeros(1, 256)) { - model = new FFN, NguyenWidrowInitialization>(); - model->Predictors() = input; - model->Responses() = target; - model->Add >(); - model->Add >(10, 10); - model->Add >(10); - model->Add >(10, 2); - model->Add >(); + model = new FFN(); + model->ResetData(input, target); + model->Add(); + model->Add(10, 10); + model->Add(10); + model->Add(10, 2); + model->Add(); } ~GradientFunction() @@ -3185,77 +3839,79 @@ TEST_CASE("GradientLayerNormTest", "[ANNLayerTest]") double Gradient(arma::mat& gradient) const { - double error = model->Evaluate(model->Parameters(), 0, 256, false); - model->Gradient(model->Parameters(), 0, gradient, 256); + double error = model->Evaluate(model->Parameters(), 0, 16, false); + model->Gradient(model->Parameters(), 0, gradient, 16); return error; } arma::mat& Parameters() { return model->Parameters(); } - FFN, NguyenWidrowInitialization>* model; + FFN* model; arma::mat input, target; } function; REQUIRE(CheckGradient(function) <= 1e-4); } +*/ /** * Test that the functions that can access the parameters of the * Layer Norm layer work. - */ + * TEST_CASE("LayerNormLayerParametersTest", "[ANNLayerTest]") { // Parameter order : size, eps. - LayerNorm<> layer(5, 1e-3); + LayerNorm layer(5, 1e-3); // Make sure we can get the parameters successfully. REQUIRE(layer.InSize() == 5); REQUIRE(layer.Epsilon() == 1e-3); } +*/ -/** - * Test if the AddMerge layer is able to forward the - * Forward/Backward/Gradient calls. - */ -TEST_CASE("AddMergeRunTest", "[ANNLayerTest]") -{ - arma::mat output, input, delta, error; +// /** +// * Test if the AddMerge layer is able to forward the +// * Forward/Backward/Gradient calls. +// */ +// TEST_CASE("AddMergeRunTest", "[ANNLayerTest]") +// { +// arma::mat output, input, delta, error; - AddMerge<> module(true, true); +// AddMerge<> module(true, true); - Linear<>* linear = new Linear<>(10, 10); - module.Add(linear); +// Linear<>* linear = new Linear<>(10, 10); +// module.Add(linear); - linear->Parameters().randu(); - linear->Reset(); +// linear->Parameters().randu(); +// linear->Reset(); - input = arma::zeros(10, 1); - module.Forward(input, output); +// input = arma::zeros(10, 1); +// module.Forward(input, output); - double parameterSum = arma::accu(linear->Parameters().submat( - 100, 0, linear->Parameters().n_elem - 1, 0)); +// double parameterSum = arma::accu(linear->Parameters().submat( +// 100, 0, linear->Parameters().n_elem - 1, 0)); - // Test the Backward function. - module.Backward(input, input, delta); +// // Test the Backward function. +// module.Backward(input, input, delta); - // Clean up before we break, - delete linear; +// // Clean up before we break, +// delete linear; - REQUIRE(parameterSum == Approx(arma::accu(output)).epsilon(1e-5)); - REQUIRE(arma::accu(delta) == 0); -} +// REQUIRE(parameterSum == Approx(arma::accu(output)).epsilon(1e-5)); +// REQUIRE(arma::accu(delta) == 0); +// } /** * Test if the MultiplyMerge layer is able to forward the * Forward/Backward/Gradient calls. - */ + * TEST_CASE("MultiplyMergeRunTest", "[ANNLayerTest]") { arma::mat output, input, delta, error; - MultiplyMerge<> module(true, true); + MultiplyMerge module(true, true); - Linear<>* linear = new Linear<>(10, 10); + Linear* linear = new Linear(10, 10); module.Add(linear); linear->Parameters().randu(); @@ -3276,21 +3932,22 @@ TEST_CASE("MultiplyMergeRunTest", "[ANNLayerTest]") REQUIRE(parameterSum == Approx(arma::accu(output)).epsilon(1e-5)); REQUIRE(arma::accu(delta) == 0); } +*/ /** * Simple subview module test. - */ + * TEST_CASE("SimpleSubviewLayerTest", "[ANNLayerTest]") { arma::mat output, input, delta, outputMat; - Subview<> moduleRow(1, 10, 19); + Subview moduleRow(1, 10, 19); // Test the Forward function for a vector. input = arma::ones(20, 1); moduleRow.Forward(input, output); REQUIRE(output.n_rows == 10); - Subview<> moduleMat(4, 3, 6, 0, 2); + Subview moduleMat(4, 3, 6, 0, 2); // Test the Forward function for a matrix. input = arma::ones(20, 8); @@ -3303,46 +3960,48 @@ TEST_CASE("SimpleSubviewLayerTest", "[ANNLayerTest]") REQUIRE(accu(delta) == 160); REQUIRE(delta.n_rows == 20); } +*/ /** * Subview index test. - */ + * TEST_CASE("SubviewIndexTest", "[ANNLayerTest]") { arma::mat outputEnd, outputMid, outputStart, input, delta; input = arma::linspace(1, 20, 20); // Slicing from the initial indices. - Subview<> moduleStart(1, 0, 9); + Subview moduleStart(1, 0, 9); arma::mat subStart = arma::linspace(1, 10, 10); moduleStart.Forward(input, outputStart); CheckMatrices(outputStart, subStart); // Slicing from the mid indices. - Subview<> moduleMid(1, 6, 15); + Subview moduleMid(1, 6, 15); arma::mat subMid = arma::linspace(7, 16, 10); moduleMid.Forward(input, outputMid); CheckMatrices(outputMid, subMid); // Slicing from the end indices. - Subview<> moduleEnd(1, 10, 19); + Subview moduleEnd(1, 10, 19); arma::mat subEnd = arma::linspace(11, 20, 10); moduleEnd.Forward(input, outputEnd); CheckMatrices(outputEnd, subEnd); } +*/ /** * Subview batch test. - */ + * TEST_CASE("SubviewBatchTest", "[ANNLayerTest]") { arma::mat output, input, outputCol, outputMat, outputDef; // All rows selected. - Subview<> moduleCol(1, 0, 19); + Subview moduleCol(1, 0, 19); // Test with inSize 1. input = arma::ones(20, 8); @@ -3350,7 +4009,7 @@ TEST_CASE("SubviewBatchTest", "[ANNLayerTest]") CheckMatrices(outputCol, input); // Few rows and columns selected. - Subview<> moduleMat(4, 3, 6, 0, 2); + Subview moduleMat(4, 3, 6, 0, 2); // Test with inSize greater than 1. moduleMat.Forward(input, outputMat); @@ -3358,23 +4017,24 @@ TEST_CASE("SubviewBatchTest", "[ANNLayerTest]") CheckMatrices(outputMat, output); // endCol changed to 3 by default. - Subview<> moduleDef(4, 1, 6, 0, 4); + Subview moduleDef(4, 1, 6, 0, 4); // Test with inSize greater than 1 and endCol >= inSize. moduleDef.Forward(input, outputDef); output = arma::ones(24, 2); CheckMatrices(outputDef, output); } +*/ /** * Test that the functions that can modify and access the parameters of the * Subview layer work. - */ + * TEST_CASE("SubviewLayerParametersTest", "[ANNLayerTest]") { // Parameter order : inSize, beginRow, endRow, beginCol, endCol. - Subview<> layer1(1, 2, 3, 4, 5); - Subview<> layer2(1, 3, 4, 5, 6); + Subview layer1(1, 2, 3, 4, 5); + Subview layer2(1, 3, 4, 5, 6); // Make sure we can get the parameters correctly. REQUIRE(layer1.InSize() == 1); @@ -3396,14 +4056,15 @@ TEST_CASE("SubviewLayerParametersTest", "[ANNLayerTest]") REQUIRE(layer1.BeginCol() == layer2.BeginCol()); REQUIRE(layer1.EndCol() == layer2.EndCol()); } +*/ /* * Simple Reparametrization module test. - */ + * TEST_CASE("SimpleReparametrizationLayerTest", "[ANNLayerTest]") { arma::mat input, output, delta; - Reparametrization<> module(5); + Reparametrization module(5); // Test the Forward function. As the mean is zero and the standard // deviation is small, after multiplying the gaussian sample, the @@ -3418,14 +4079,15 @@ TEST_CASE("SimpleReparametrizationLayerTest", "[ANNLayerTest]") module.Backward(input, gy, delta); REQUIRE(arma::accu(delta) != 0); // klBackward will be added. } +*/ /** * Reparametrization module stochastic boolean test. - */ + * TEST_CASE("ReparametrizationLayerStochasticTest", "[ANNLayerTest]") { arma::mat input, outputA, outputB; - Reparametrization<> module(5, false); + Reparametrization module(5, false); input = join_cols(arma::ones(5, 1), arma::zeros(5, 1)); @@ -3436,14 +4098,15 @@ TEST_CASE("ReparametrizationLayerStochasticTest", "[ANNLayerTest]") CheckMatrices(outputA, outputB); } +*/ /** * Reparametrization module includeKl boolean test. - */ + * TEST_CASE("ReparametrizationLayerIncludeKlTest", "[ANNLayerTest]") { arma::mat input, output, gy, delta; - Reparametrization<> module(5, true, false); + Reparametrization module(5, true, false); input = join_cols(arma::ones(5, 1), arma::zeros(5, 1)); @@ -3456,29 +4119,31 @@ TEST_CASE("ReparametrizationLayerIncludeKlTest", "[ANNLayerTest]") REQUIRE(arma::accu(delta) == 0); } +*/ /** * Jacobian Reparametrization module test. - */ + * TEST_CASE("JacobianReparametrizationLayerTest", "[ANNLayerTest]") { for (size_t i = 0; i < 5; ++i) { - const size_t inputElementsHalf = math::RandInt(2, 1000); + const size_t inputElementsHalf = math::RandInt(2, 10); arma::mat input; input.set_size(inputElementsHalf * 2, 1); - Reparametrization<> module(inputElementsHalf, false, false); + Reparametrization module(inputElementsHalf, false, false); double error = JacobianTest(module, input); REQUIRE(error <= 1e-5); } } +*/ /** * Reparametrization layer numerical gradient test. - */ + * TEST_CASE("GradientReparametrizationLayerTest", "[ANNLayerTest]") { // Linear function gradient instantiation. @@ -3488,14 +4153,13 @@ TEST_CASE("GradientReparametrizationLayerTest", "[ANNLayerTest]") input(arma::randu(10, 1)), target(arma::mat("0")) { - model = new FFN, NguyenWidrowInitialization>(); - model->Predictors() = input; - model->Responses() = target; - model->Add >(); - model->Add >(10, 6); - model->Add >(3, false, true, 1); - model->Add >(3, 2); - model->Add >(); + model = new FFN(); + model->ResetData(input, target); + model->Add(); + model->Add(10, 6); + model->Add(3, false, true, 1); + model->Add(3, 2); + model->Add(); } ~GradientFunction() @@ -3512,16 +4176,17 @@ TEST_CASE("GradientReparametrizationLayerTest", "[ANNLayerTest]") arma::mat& Parameters() { return model->Parameters(); } - FFN, NguyenWidrowInitialization>* model; + FFN* model; arma::mat input, target; } function; - REQUIRE(CheckGradient(function) <= 1e-4); + // REQUIRE(CheckGradient(function) <= 1e-4); } +*/ /** * Reparametrization layer beta numerical gradient test. - */ + * TEST_CASE("GradientReparametrizationLayerBetaTest", "[ANNLayerTest]") { // Linear function gradient instantiation. @@ -3531,15 +4196,14 @@ TEST_CASE("GradientReparametrizationLayerBetaTest", "[ANNLayerTest]") input(arma::randu(10, 2)), target(arma::mat("0 0")) { - model = new FFN, NguyenWidrowInitialization>(); - model->Predictors() = input; - model->Responses() = target; - model->Add >(); - model->Add >(10, 6); + model = new FFN(); + model->ResetData(input, target); + model->Add(); + model->Add(10, 6); // Use a value of beta not equal to 1. - model->Add >(3, false, true, 2); - model->Add >(3, 2); - model->Add >(); + model->Add(3, false, true, 2); + model->Add(3, 2); + model->Add(); } ~GradientFunction() @@ -3556,21 +4220,22 @@ TEST_CASE("GradientReparametrizationLayerBetaTest", "[ANNLayerTest]") arma::mat& Parameters() { return model->Parameters(); } - FFN, NguyenWidrowInitialization>* model; + FFN* model; arma::mat input, target; } function; - REQUIRE(CheckGradient(function) <= 1e-4); + // REQUIRE(CheckGradient(function) <= 1e-4); } +*/ /** * Test that the functions that can access the parameters of the * Reparametrization layer work. - */ + * TEST_CASE("ReparametrizationLayerParametersTest", "[ANNLayerTest]") { // Parameter order : latentSize, stochastic, includeKL, beta. - Reparametrization<> layer(5, false, false, 2); + Reparametrization layer(5, false, false, 2); // Make sure we can get the parameters successfully. REQUIRE(layer.OutputSize() == 5); @@ -3578,21 +4243,22 @@ TEST_CASE("ReparametrizationLayerParametersTest", "[ANNLayerTest]") REQUIRE(layer.IncludeKL() == false); REQUIRE(layer.Beta() == 2); } +*/ /** * Simple residual module test. - */ + * TEST_CASE("SimpleResidualLayerTest", "[ANNLayerTest]") { arma::mat outputA, outputB, input, deltaA, deltaB; - Sequential<>* sequential = new Sequential<>(true); - Residual<>* residual = new Residual<>(true); + Sequential* sequential = new Sequential(true); + Residual* residual = new Residual(true); - Linear<>* linearA = new Linear<>(10, 10); + Linear* linearA = new Linear(10, 10); linearA->Parameters().randu(); linearA->Reset(); - Linear<>* linearB = new Linear<>(10, 10); + Linear* linearB = new Linear(10, 10); linearB->Parameters().randu(); linearB->Reset(); @@ -3622,22 +4288,23 @@ TEST_CASE("SimpleResidualLayerTest", "[ANNLayerTest]") delete linearA; delete linearB; } +*/ /** * Simple Highway module test. - */ + * TEST_CASE("SimpleHighwayLayerTest", "[ANNLayerTest]") { arma::mat outputA, outputB, input, deltaA, deltaB; - Sequential<>* sequential = new Sequential<>(true); - Highway<>* highway = new Highway<>(10, true); + Sequential* sequential = new Sequential(true); + Highway* highway = new Highway(10, true); highway->Parameters().zeros(); highway->Reset(); - Linear<>* linearA = new Linear<>(10, 10); + Linear* linearA = new Linear(10, 10); linearA->Parameters().randu(); linearA->Reset(); - Linear<>* linearB = new Linear<>(10, 10); + Linear* linearB = new Linear(10, 10); linearB->Parameters().randu(); linearB->Reset(); @@ -3660,249 +4327,247 @@ TEST_CASE("SimpleHighwayLayerTest", "[ANNLayerTest]") delete linearA; delete linearB; } +*/ /** * Test that the function that can access the inSize parameter of the * Highway layer works. - */ + * TEST_CASE("HighwayLayerParametersTest", "[ANNLayerTest]") { // Parameter order : inSize, model. - Highway<> layer(1, true); + Highway layer(1, true); // Make sure we can get the parameter successfully. REQUIRE(layer.InSize() == 1); } +*/ + +// /** +// * Sequential layer numerical gradient test. +// */ +// TEST_CASE("GradientHighwayLayerTest", "[ANNLayerTest]") +// { +// // Linear function gradient instantiation. +// struct GradientFunction +// { +// GradientFunction() : +// input(arma::randu(5, 1)), +// target(arma::mat("0")) +// { +// model = new FFN(); +// model->ResetData(input, target); +// model->Add(); +// model->Add(5, 10); + +// highway = new Highway(10); +// highway->Add(10, 10); +// highway->Add(); +// highway->Add(10, 10); +// highway->Add(); + +// model->Add(highway); +// model->Add(10, 2); +// model->Add(); +// } + +// ~GradientFunction() +// { +// delete model; +// } + +// double Gradient(arma::mat& gradient) const +// { +// double error = model->Evaluate(model->Parameters(), 0, 1); +// model->Gradient(model->Parameters(), 0, gradient, 1); +// return error; +// } + +// arma::mat& Parameters() { return model->Parameters(); } + +// FFN* model; +// Highway* highway; +// arma::mat input, target; +// } function; + +// REQUIRE(CheckGradient(function) <= 1e-4); +// } /** * Sequential layer numerical gradient test. */ -TEST_CASE("GradientHighwayLayerTest", "[ANNLayerTest]") -{ - // Linear function gradient instantiation. - struct GradientFunction - { - GradientFunction() : - input(arma::randu(5, 1)), - target(arma::mat("0")) - { - model = new FFN, NguyenWidrowInitialization>(); - model->Predictors() = input; - model->Responses() = target; - model->Add >(); - model->Add >(5, 10); +// TEST_CASE("GradientSequentialLayerTest", "[ANNLayerTest]") +// { +// // Linear function gradient instantiation. +// struct GradientFunction +// { +// GradientFunction() : +// input(arma::randu(10, 1)), +// target(arma::mat("0")) +// { +// model = new FFN(); +// model->ResetData(input, target); +// model->Add(); +// model->Add(10, 10); +// sequential = new Sequential(); +// sequential->Add(10, 10); +// sequential->Add(); +// sequential->Add(10, 5); +// sequential->Add(); - highway = new Highway<>(10); - highway->Add >(10, 10); - highway->Add >(); - highway->Add >(10, 10); - highway->Add >(); +// model->Add(sequential); +// model->Add(5, 2); +// model->Add(); +// } - model->Add(highway); - model->Add >(10, 2); - model->Add >(); - } +// ~GradientFunction() +// { +// delete model; +// } - ~GradientFunction() - { - delete model; - } +// double Gradient(arma::mat& gradient) const +// { +// double error = model->Evaluate(model->Parameters(), 0, 1); +// model->Gradient(model->Parameters(), 0, gradient, 1); +// return error; +// } - double Gradient(arma::mat& gradient) const - { - double error = model->Evaluate(model->Parameters(), 0, 1); - model->Gradient(model->Parameters(), 0, gradient, 1); - return error; - } +// arma::mat& Parameters() { return model->Parameters(); } - arma::mat& Parameters() { return model->Parameters(); } +// FFN* model; +// Sequential* sequential; +// arma::mat input, target; +// } function; - FFN, NguyenWidrowInitialization>* model; - Highway<>* highway; - arma::mat input, target; - } function; +// REQUIRE(CheckGradient(function) <= 1e-4); +// } - REQUIRE(CheckGradient(function) <= 1e-4); -} +// /** +// * WeightNorm layer numerical gradient test. +// */ +// TEST_CASE("GradientWeightNormLayerTest", "[ANNLayerTest]") +// { +// // Linear function gradient instantiation. +// struct GradientFunction +// { +// GradientFunction() : +// input(arma::randu(10, 1)), +// target(arma::mat("0")) +// { +// model = new FFN(); +// model->ResetData(input, target); +// model->Add(10, 10); -/** - * Sequential layer numerical gradient test. - */ -TEST_CASE("GradientSequentialLayerTest", "[ANNLayerTest]") -{ - // Linear function gradient instantiation. - struct GradientFunction - { - GradientFunction() : - input(arma::randu(10, 1)), - target(arma::mat("0")) - { - model = new FFN, NguyenWidrowInitialization>(); - model->Predictors() = input; - model->Responses() = target; - model->Add >(); - model->Add >(10, 10); - sequential = new Sequential<>(); - sequential->Add >(10, 10); - sequential->Add >(); - sequential->Add >(10, 5); - sequential->Add >(); +// Linear* linear = new Linear(10, 2); +// weightNorm = new WeightNorm(linear); - model->Add(sequential); - model->Add >(5, 2); - model->Add >(); - } +// model->Add(weightNorm); +// model->Add(); +// } - ~GradientFunction() - { - delete model; - } +// ~GradientFunction() +// { +// delete model; +// } - double Gradient(arma::mat& gradient) const - { - double error = model->Evaluate(model->Parameters(), 0, 1); - model->Gradient(model->Parameters(), 0, gradient, 1); - return error; - } +// double Gradient(arma::mat& gradient) const +// { +// double error = model->Evaluate(model->Parameters(), 0, 1); +// model->Gradient(model->Parameters(), 0, gradient, 1); +// return error; +// } - arma::mat& Parameters() { return model->Parameters(); } +// arma::mat& Parameters() { return model->Parameters(); } - FFN, NguyenWidrowInitialization>* model; - Sequential<>* sequential; - arma::mat input, target; - } function; +// FFN* model; +// WeightNorm* weightNorm; +// arma::mat input, target; +// } function; - REQUIRE(CheckGradient(function) <= 1e-4); -} +// REQUIRE(CheckGradient(function) <= 1e-4); +// } -/** - * WeightNorm layer numerical gradient test. - */ -TEST_CASE("GradientWeightNormLayerTest", "[ANNLayerTest]") -{ - // Linear function gradient instantiation. - struct GradientFunction - { - GradientFunction() : - input(arma::randu(10, 1)), - target(arma::mat("0")) - { - model = new FFN, NguyenWidrowInitialization>(); - model->Predictors() = input; - model->Responses() = target; - model->Add >(10, 10); +// /** +// * Test if the WeightNorm layer is able to forward the +// * Forward/Backward/Gradient calls. +// */ +// TEST_CASE("WeightNormRunTest", "[ANNLayerTest]") +// { +// arma::mat output, input, delta, error; +// Linear* linear = new Linear(10, 10); - Linear<>* linear = new Linear<>(10, 2); - weightNorm = new WeightNorm<>(linear); +// WeightNorm module(linear); - model->Add(weightNorm); - model->Add >(); - } +// module.Parameters().randu(); +// module.Reset(); - ~GradientFunction() - { - delete model; - } +// linear->Bias().zeros(); - double Gradient(arma::mat& gradient) const - { - double error = model->Evaluate(model->Parameters(), 0, 1); - model->Gradient(model->Parameters(), 0, gradient, 1); - return error; - } +// input = arma::zeros(10, 1); +// module.Forward(input, output); - arma::mat& Parameters() { return model->Parameters(); } +// // Test the Backward function. +// module.Backward(input, input, delta); - FFN, NguyenWidrowInitialization>* model; - WeightNorm<>* weightNorm; - arma::mat input, target; - } function; +// REQUIRE(0 == arma::accu(output)); +// REQUIRE(arma::accu(delta) == 0); +// } - REQUIRE(CheckGradient(function) <= 1e-4); -} +// // General ANN serialization test. +// template +// void ANNLayerSerializationTest(LayerType& layer) +// { +// arma::mat input(5, 100, arma::fill::randu); +// arma::mat output(5, 100, arma::fill::randu); -/** - * Test if the WeightNorm layer is able to forward the - * Forward/Backward/Gradient calls. - */ -TEST_CASE("WeightNormRunTest", "[ANNLayerTest]") -{ - arma::mat output, input, delta, error; +// FFN model; +// model.Add>(input.n_rows, 10); +// model.Add(layer); +// model.Add>(); +// model.Add>(10, output.n_rows); +// model.Add>(); - Linear<>* linear = new Linear<>(10, 10); +// ens::StandardSGD opt(0.1, 1, 5, -100, false); +// model.Train(input, output, opt); - WeightNorm<> module(linear); +// arma::mat originalOutput; +// model.Predict(input, originalOutput); - module.Parameters().randu(); - module.Reset(); +// // Now serialize the model. +// FFN xmlModel, jsonModel, +// binaryModel; +// SerializeObjectAll(model, xmlModel, jsonModel, binaryModel); - linear->Bias().zeros(); +// // Ensure that predictions are the same. +// arma::mat modelOutput, xmlOutput, jsonOutput, binaryOutput; +// model.Predict(input, modelOutput); +// xmlModel.Predict(input, xmlOutput); +// jsonModel.Predict(input, jsonOutput); +// binaryModel.Predict(input, binaryOutput); - input = arma::zeros(10, 1); - module.Forward(input, output); +// CheckMatrices(originalOutput, modelOutput, 1e-5); +// CheckMatrices(originalOutput, xmlOutput, 1e-5); +// CheckMatrices(originalOutput, jsonOutput, 1e-5); +// CheckMatrices(originalOutput, binaryOutput, 1e-5); +// } - // Test the Backward function. - module.Backward(input, input, delta); +// /** +// * Simple serialization test for batch normalization layer. +// */ +// TEST_CASE("BatchNormSerializationTest", "[ANNLayerTest]") +// { +// BatchNorm<> layer(10); +// ANNLayerSerializationTest(layer); +// } - REQUIRE(0 == arma::accu(output)); - REQUIRE(arma::accu(delta) == 0); -} - -// General ANN serialization test. -template -void ANNLayerSerializationTest(LayerType& layer) -{ - arma::mat input(5, 100, arma::fill::randu); - arma::mat output(5, 100, arma::fill::randu); - - FFN, ann::RandomInitialization> model; - model.Add>(input.n_rows, 10); - model.Add(layer); - model.Add>(); - model.Add>(10, output.n_rows); - model.Add>(); - - ens::StandardSGD opt(0.1, 1, 5, -100, false); - model.Train(input, output, opt); - - arma::mat originalOutput; - model.Predict(input, originalOutput); - - // Now serialize the model. - FFN, ann::RandomInitialization> xmlModel, jsonModel, - binaryModel; - SerializeObjectAll(model, xmlModel, jsonModel, binaryModel); - - // Ensure that predictions are the same. - arma::mat modelOutput, xmlOutput, jsonOutput, binaryOutput; - model.Predict(input, modelOutput); - xmlModel.Predict(input, xmlOutput); - jsonModel.Predict(input, jsonOutput); - binaryModel.Predict(input, binaryOutput); - - CheckMatrices(originalOutput, modelOutput, 1e-5); - CheckMatrices(originalOutput, xmlOutput, 1e-5); - CheckMatrices(originalOutput, jsonOutput, 1e-5); - CheckMatrices(originalOutput, binaryOutput, 1e-5); -} - -/** - * Simple serialization test for batch normalization layer. - */ -TEST_CASE("BatchNormSerializationTest", "[ANNLayerTest]") -{ - BatchNorm<> layer(10); - ANNLayerSerializationTest(layer); -} - -/** - * Simple serialization test for layer normalization layer. - */ -TEST_CASE("LayerNormSerializationTest", "[ANNLayerTest]") -{ - LayerNorm<> layer(10); - ANNLayerSerializationTest(layer); -} +// /** +// * Simple serialization test for layer normalization layer. +// */ +// TEST_CASE("LayerNormSerializationTest", "[ANNLayerTest]") +// { +// LayerNorm<> layer(10); +// ANNLayerSerializationTest(layer); +// } /** * Test that the functions that can modify and access the parameters of the @@ -3910,16 +4575,13 @@ TEST_CASE("LayerNormSerializationTest", "[ANNLayerTest]") */ TEST_CASE("ConvolutionLayerParametersTest", "[ANNLayerTest]") { - // Parameter order: inSize, outSize, kW, kH, dW, dH, padW, padH, inputWidth, - // inputHeight, paddingType. - Convolution<> layer1(1, 2, 3, 4, 5, 6, std::tuple(7, 8), - std::tuple(9, 10), 11, 12, "none"); - Convolution<> layer2(2, 3, 4, 5, 6, 7, std::tuple(8, 9), - std::tuple(10, 11), 12, 13, "none"); + // Parameter order: outSize, kW, kH, dW, dH, padW, padH, paddingType. + Convolution layer1(2, 3, 4, 5, 6, std::tuple(7, 8), + std::tuple(9, 10), "none"); + Convolution layer2(3, 4, 5, 6, 7, std::tuple(8, 9), + std::tuple(10, 11), "none"); // Make sure we can get the parameters successfully. - REQUIRE(layer1.InputWidth() == 11); - REQUIRE(layer1.InputHeight() == 12); REQUIRE(layer1.KernelWidth() == 3); REQUIRE(layer1.KernelHeight() == 4); REQUIRE(layer1.StrideWidth() == 5); @@ -3930,8 +4592,6 @@ TEST_CASE("ConvolutionLayerParametersTest", "[ANNLayerTest]") REQUIRE(layer1.PadHBottom() == 10); // Now modify the parameters to match the second layer. - layer1.InputWidth() = 12; - layer1.InputHeight() = 13; layer1.KernelWidth() = 4; layer1.KernelHeight() = 5; layer1.StrideWidth() = 6; @@ -3942,8 +4602,6 @@ TEST_CASE("ConvolutionLayerParametersTest", "[ANNLayerTest]") layer1.PadHBottom() = 11; // Now ensure all results are the same. - REQUIRE(layer1.InputWidth() == layer2.InputWidth()); - REQUIRE(layer1.InputHeight() == layer2.InputHeight()); REQUIRE(layer1.KernelWidth() == layer2.KernelWidth()); REQUIRE(layer1.KernelHeight() == layer2.KernelHeight()); REQUIRE(layer1.StrideWidth() == layer2.StrideWidth()); @@ -3962,13 +4620,18 @@ TEST_CASE("ConvolutionLayerPaddingTest", "[ANNLayerTest]") arma::mat output, input, delta; // Check valid padding option. - Convolution<> module1(1, 1, 3, 3, 1, 1, std::tuple(1, 1), - std::tuple(1, 1), 7, 7, "valid"); + Convolution module1(1, 3, 3, 1, 1, std::tuple(1, 1), + std::tuple(1, 1), "valid"); + module1.InputDimensions() = std::vector({ 7, 7 }); + module1.ComputeOutputDimensions(); + arma::mat weights1(module1.WeightSize(), 1); + REQUIRE(weights1.n_elem == 10); + module1.SetWeights(weights1.memptr()); // Test the Forward function. input = arma::linspace(0, 48, 49); - module1.Parameters() = arma::mat(9 + 1, 1, arma::fill::zeros); - module1.Reset(); + output.set_size(module1.OutputSize(), 1); + module1.Parameters().zeros(); module1.Forward(input, output); REQUIRE(arma::accu(output) == 0); @@ -3976,16 +4639,22 @@ TEST_CASE("ConvolutionLayerPaddingTest", "[ANNLayerTest]") REQUIRE(output.n_cols == 1); // Test the Backward function. + delta.set_size(arma::size(input)); module1.Backward(input, output, delta); // Check same padding option. - Convolution<> module2(1, 1, 3, 3, 1, 1, std::tuple(0, 0), - std::tuple(0, 0), 7, 7, "same"); + Convolution module2(1, 3, 3, 1, 1, std::tuple(0, 0), + std::tuple(0, 0), "same"); + module2.InputDimensions() = std::vector({ 7, 7 }); + module2.ComputeOutputDimensions(); + arma::mat weights2(module2.WeightSize(), 1); + REQUIRE(weights2.n_elem == 10); + module2.SetWeights(weights2.memptr()); // Test the forward function. input = arma::linspace(0, 48, 49); - module2.Parameters() = arma::mat(9 + 1, 1, arma::fill::zeros); - module2.Reset(); + output.set_size(module2.OutputSize(), 1); + module2.Parameters().zeros(); module2.Forward(input, output); REQUIRE(arma::accu(output) == 0); @@ -3993,17 +4662,59 @@ TEST_CASE("ConvolutionLayerPaddingTest", "[ANNLayerTest]") REQUIRE(output.n_cols == 1); // Test the backward function. + delta.set_size(arma::size(input)); module2.Backward(input, output, delta); } /** - * Test that the padding options in Transposed Convolution layer. + * Convolution layer numerical gradient test. */ +TEST_CASE("GradientConvolutionLayerTest", "[ANNLayerTest]") +{ + struct GradientFunction + { + GradientFunction() : + input(arma::linspace(0, 35, 36)), + target(arma::mat("1")) + { + model = new FFN(); + model->ResetData(input, target); + model->Add(1, 3, 3, 1, 1, std::tuple(0, 0), + std::tuple(0, 0), "same"); + model->Add(); + + model->InputDimensions() = std::vector({ 6, 6 }); + } + + ~GradientFunction() + { + delete model; + } + + double Gradient(arma::mat& gradient) const + { + double error = model->Evaluate(model->Parameters(), 0, 1); + model->Gradient(model->Parameters(), 0, gradient, 1); + return error; + } + + arma::mat& Parameters() { return model->Parameters(); } + + FFN* model; + arma::mat input, target; + } function; + + REQUIRE(CheckGradient(function) < 1e3); +} + +/** + * Test that the padding options in Transposed Convolution layer. + * TEST_CASE("TransposedConvolutionLayerPaddingTest", "[ANNLayerTest]") { arma::mat output, input, delta; - TransposedConvolution<> module1(1, 1, 3, 3, 1, 1, 0, 0, 4, 4, 6, 6, "VALID"); + TransposedConvolution module1(1, 1, 3, 3, 1, 1, 0, 0, 4, 4, 6, 6, "VALID"); // Test the forward function. // Valid Should give the same result. input = arma::linspace(0, 15, 16); @@ -4018,7 +4729,7 @@ TEST_CASE("TransposedConvolutionLayerPaddingTest", "[ANNLayerTest]") REQUIRE(arma::accu(delta) == 0.0); // Test Valid for non zero padding. - TransposedConvolution<> module2(1, 1, 3, 3, 2, 2, + TransposedConvolution module2(1, 1, 3, 3, 2, 2, std::tuple(0, 0), std::tuple(0, 0), 2, 2, 5, 5, "VALID"); // Test the forward function. @@ -4038,7 +4749,7 @@ TEST_CASE("TransposedConvolutionLayerPaddingTest", "[ANNLayerTest]") REQUIRE(arma::accu(delta) == 960.0); // Test for same padding type. - TransposedConvolution<> module3(1, 1, 3, 3, 2, 2, 0, 0, 3, 3, 3, 3, "SAME"); + TransposedConvolution module3(1, 1, 3, 3, 2, 2, 0, 0, 3, 3, 3, 3, "SAME"); // Test the forward function. input = arma::linspace(0, 8, 9); module3.Parameters() = arma::mat(9 + 1, 1, arma::fill::zeros); @@ -4053,7 +4764,7 @@ TEST_CASE("TransposedConvolutionLayerPaddingTest", "[ANNLayerTest]") REQUIRE(arma::accu(delta) == 0.0); // Output shape should equal input. - TransposedConvolution<> module4(1, 1, 3, 3, 1, 1, + TransposedConvolution module4(1, 1, 3, 3, 1, 1, std::tuple(2, 2), std::tuple(2, 2), 5, 5, 5, 5, "SAME"); // Test the forward function. @@ -4069,7 +4780,7 @@ TEST_CASE("TransposedConvolutionLayerPaddingTest", "[ANNLayerTest]") module4.Backward(input, output, delta); REQUIRE(arma::accu(delta) == 0.0); - TransposedConvolution<> module5(1, 1, 3, 3, 2, 2, 0, 0, 2, 2, 2, 2, "SAME"); + TransposedConvolution module5(1, 1, 3, 3, 2, 2, 0, 0, 2, 2, 2, 2, "SAME"); // Test the forward function. input = arma::linspace(0, 3, 4); module5.Parameters() = arma::mat(25 + 1, 1, arma::fill::zeros); @@ -4083,7 +4794,7 @@ TEST_CASE("TransposedConvolutionLayerPaddingTest", "[ANNLayerTest]") module5.Backward(input, output, delta); REQUIRE(arma::accu(delta) == 0.0); - TransposedConvolution<> module6(1, 1, 4, 4, 1, 1, 1, 1, 5, 5, 5, 5, "SAME"); + TransposedConvolution module6(1, 1, 4, 4, 1, 1, 1, 1, 5, 5, 5, 5, "SAME"); // Test the forward function. input = arma::linspace(0, 24, 25); module6.Parameters() = arma::mat(16 + 1, 1, arma::fill::zeros); @@ -4097,102 +4808,103 @@ TEST_CASE("TransposedConvolutionLayerPaddingTest", "[ANNLayerTest]") module6.Backward(input, output, delta); REQUIRE(arma::accu(delta) == 0.0); } +*/ /** * Simple test for Lp Pooling layer. */ -TEST_CASE("LpMaxPoolingTestCase", "[ANNLayerTest]") -{ - // For rectangular input to pooling layers. - arma::mat input = arma::mat(8, 1); - arma::mat output; - input.zeros(); - input(0) = input(6) = 30; - input(1) = input(7) = 120; - input(2) = input(4) = 272; - input(3) = input(5) = 315; - // Output-Size should be 1 x 2. - // Square output. - LpPooling<> module1(4, 2, 2, 2, 2); - module1.InputHeight() = 2; - module1.InputWidth() = 4; - module1.Forward(input, output); - // Calculated using torch.nn.LPPool2d(). - REQUIRE(arma::accu(output) - 706.0 == Approx(0.0).margin(2e-5)); - REQUIRE(output.n_elem == 2); - - // For Square input. - input = arma::mat(16, 1); - input.zeros(); - input(0) = 4; - input(1) = 3; - input(3) = 12; - input(7) = 35; - input(8) = 6; - input(11) = 7; - input(12) = 8; - input(15) = 24; - // Output-Size should be 2 x 2. - // Square output. - LpPooling<> module3(2, 2, 2, 2, 2); - module3.InputHeight() = 4; - module3.InputWidth() = 4; - module3.Forward(input, output); - // Calculated using torch.nn.LPPool2d(). - REQUIRE(arma::accu(output) - 77.0 == Approx(0.0).margin(2e-5)); - REQUIRE(output.n_elem == 4); -} +// TEST_CASE("LpMaxPoolingTestCase", "[ANNLayerTest]") +// { +// // For rectangular input to pooling layers. +// arma::mat input = arma::mat(8, 1); +// arma::mat output; +// input.zeros(); +// input(0) = input(6) = 30; +// input(1) = input(7) = 120; +// input(2) = input(4) = 272; +// input(3) = input(5) = 315; +// // Output-Size should be 1 x 2. +// // Square output. +// LpPooling<> module1(4, 2, 2, 2, 2); +// module1.InputHeight() = 2; +// module1.InputWidth() = 4; +// module1.Forward(input, output); +// // Calculated using torch.nn.LPPool2d(). +// REQUIRE(arma::accu(output) - 706.0 == Approx(0.0).margin(2e-5)); +// REQUIRE(output.n_elem == 2); +// +// // For Square input. +// input = arma::mat(16, 1); +// input.zeros(); +// input(0) = 4; +// input(1) = 3; +// input(3) = 12; +// input(7) = 35; +// input(8) = 6; +// input(11) = 7; +// input(12) = 8; +// input(15) = 24; +// // Output-Size should be 2 x 2. +// // Square output. +// LpPooling<> module3(2, 2, 2, 2, 2); +// module3.InputHeight() = 4; +// module3.InputWidth() = 4; +// module3.Forward(input, output); +// // Calculated using torch.nn.LPPool2d(). +// REQUIRE(arma::accu(output) - 77.0 == Approx(0.0).margin(2e-5)); +// REQUIRE(output.n_elem == 4); +// } /** * Simple test for Mean Pooling layer. */ -TEST_CASE("MeanPoolingTestCase", "[ANNLayerTest]") -{ - // For rectangular input to pooling layers. - arma::mat input = arma::mat(28, 1); - input.zeros(); - input(0) = input(16) = 1; - input(1) = input(17) = 2; - input(2) = input(18) = 3; - input(3) = input(19) = 4; - input(4) = input(20) = 5; - input(5) = input(23) = 6; - input(6) = input(24) = 7; - input(14) = input(25) = 8; - input(15) = input(26) = 9; - - MeanPooling<> module1(2, 2, 2, 2, false); - MeanPooling<> module2(2, 2, 2, 2, true); - module1.InputWidth() = 7; - module1.InputHeight() = 4; - module2.InputWidth() = 7; - module2.InputHeight() = 4; - - // Calculated using torch.nn.MeanPool2d(). - arma::mat result1, result2; - result1 << 0.7500 << 4.2500 << arma::endr - << 1.7500 << 4.0000 << arma::endr - << 2.7500 << 6.0000 << arma::endr - << 3.5000 << 2.5000 << arma::endr; - - result2 << 0.7500 << 4.2500 << arma::endr - << 1.7500 << 4.0000 << arma::endr - << 2.7500 << 6.0000 << arma::endr; - - arma::mat output1, output2; - module1.Forward(input, output1); - module2.Forward(input, output2); - output1.reshape(4, 2); - output2.reshape(3, 2); - CheckMatrices(output1, result1, 1e-1); - CheckMatrices(output2, result2, 1e-1); - - arma::mat delta1, delta2; - module1.Backward(input, output1, delta1); - REQUIRE(arma::accu(delta1) == 25.5); - module2.Backward(input, output2, delta2); - REQUIRE(arma::accu(delta2) == 19.5); -} +// TEST_CASE("MeanPoolingTestCase", "[ANNLayerTest]") +// { +// // For rectangular input to pooling layers. +// arma::mat input = arma::mat(28, 1); +// input.zeros(); +// input(0) = input(16) = 1; +// input(1) = input(17) = 2; +// input(2) = input(18) = 3; +// input(3) = input(19) = 4; +// input(4) = input(20) = 5; +// input(5) = input(23) = 6; +// input(6) = input(24) = 7; +// input(14) = input(25) = 8; +// input(15) = input(26) = 9; +// +// MeanPooling<> module1(2, 2, 2, 2, false); +// MeanPooling<> module2(2, 2, 2, 2, true); +// module1.InputWidth() = 7; +// module1.InputHeight() = 4; +// module2.InputWidth() = 7; +// module2.InputHeight() = 4; +// +// // Calculated using torch.nn.MeanPool2d(). +// arma::mat result1, result2; +// result1 << 0.7500 << 4.2500 << arma::endr +// << 1.7500 << 4.0000 << arma::endr +// << 2.7500 << 6.0000 << arma::endr +// << 3.5000 << 2.5000 << arma::endr; +// +// result2 << 0.7500 << 4.2500 << arma::endr +// << 1.7500 << 4.0000 << arma::endr +// << 2.7500 << 6.0000 << arma::endr; +// +// arma::mat output1, output2; +// module1.Forward(input, output1); +// module2.Forward(input, output2); +// output1.reshape(4, 2); +// output2.reshape(3, 2); +// CheckMatrices(output1, result1, 1e-1); +// CheckMatrices(output2, result2, 1e-1); +// +// arma::mat delta1, delta2; +// module1.Backward(input, output1, delta1); +// REQUIRE(arma::accu(delta1) == 25.5); +// module2.Backward(input, output2, delta2); +// REQUIRE(arma::accu(delta2) == 19.5); +// } /** * Simple test for Max Pooling layer. @@ -4213,10 +4925,12 @@ TEST_CASE("MaxPoolingTestCase", "[ANNLayerTest]") input(10) = 8; input(11) = 9; // Output-Size should be 2 x 2. + output.set_size(4, 1); + // Square output. - MaxPooling<> module1(2, 2, 2, 1); - module1.InputHeight() = 3; - module1.InputWidth() = 4; + MaxPooling module1(2, 2, 2, 1); + module1.InputDimensions() = std::vector({ 4, 3 }); + module1.ComputeOutputDimensions(); module1.Forward(input, output); // Calculated using torch.nn.MaxPool2d(). REQUIRE(arma::accu(output) == 28); @@ -4232,10 +4946,12 @@ TEST_CASE("MaxPoolingTestCase", "[ANNLayerTest]") input(3) = 3; input(6) = 3; // Output-Size should be 1 x 2. + output.set_size(2, 1); + // Rectangular output. - MaxPooling<> module2(3, 2, 3, 1); - module2.InputHeight() = 3; - module2.InputWidth() = 3; + MaxPooling module2(3, 2, 3, 1); + module2.InputDimensions() = std::vector({ 3, 3 }); + module2.ComputeOutputDimensions(); module2.Forward(input, output); // Calculated using torch.nn.MaxPool2d(). REQUIRE(arma::accu(output) == 12.0); @@ -4251,10 +4967,12 @@ TEST_CASE("MaxPoolingTestCase", "[ANNLayerTest]") input(4) = 3; input(8) = 3; // Output-Size should be 3 x 3. + output.set_size(9, 1); + // Square output. - MaxPooling<> module3(2, 2, 1, 1); - module3.InputHeight() = 4; - module3.InputWidth() = 4; + MaxPooling module3(2, 2, 1, 1); + module3.InputDimensions() = std::vector({ 4, 4 }); + module3.ComputeOutputDimensions(); module3.Forward(input, output); // Calculated using torch.nn.MaxPool2d(). REQUIRE(arma::accu(output) == 30.0); @@ -4268,10 +4986,12 @@ TEST_CASE("MaxPoolingTestCase", "[ANNLayerTest]") input(1) = 1; input(3) = 1; // Output-Size should be 2 x 2. + output.set_size(4, 1); + // Square output. - MaxPooling<> module4(2, 1, 1, 1); - module4.InputHeight() = 2; - module4.InputWidth() = 3; + MaxPooling module4(2, 1, 1, 1); + module4.InputDimensions() = std::vector({ 3, 2 }); + module4.ComputeOutputDimensions(); module4.Forward(input, output); // Calculated using torch.nn.MaxPool2d(). REQUIRE(arma::accu(output) == 3); @@ -4282,12 +5002,12 @@ TEST_CASE("MaxPoolingTestCase", "[ANNLayerTest]") /** * Test that the functions that can modify and access the parameters of the * Glimpse layer work. - */ + * TEST_CASE("GlimpseLayerParametersTest", "[ANNLayerTest]") { // Parameter order : inSize, size, depth, scale, inputWidth, inputHeight. - Glimpse<> layer1(1, 2, 3, 4, 5, 6); - Glimpse<> layer2(1, 2, 3, 4, 6, 7); + Glimpse layer1(1, 2, 3, 4, 5, 6); + Glimpse layer2(1, 2, 3, 4, 6, 7); // Make sure we can get the parameters successfully. REQUIRE(layer1.InputHeight() == 6); @@ -4309,37 +5029,25 @@ TEST_CASE("GlimpseLayerParametersTest", "[ANNLayerTest]") REQUIRE(layer1.GlimpseSize() == layer2.GlimpseSize()); REQUIRE(layer1.InSize() == layer2.InSize()); } +*/ /** * Test that the function that can access the stdev parameter of the * Reinforce Normal layer works. - */ + * TEST_CASE("ReinforceNormalLayerParametersTest", "[ANNLayerTest]") { // Parameter : stdev. - ReinforceNormal<> layer(4.0); + ReinforceNormal layer(4.0); // Make sure we can get the parameter successfully. REQUIRE(layer.StandardDeviation() == 4.0); } - -/** - * Test that the function that can access the parameters of the - * VR Class Reward layer works. - */ -TEST_CASE("VRClassRewardLayerParametersTest", "[ANNLayerTest]") -{ - // Parameter order : scale, sizeAverage. - VRClassReward<> layer(2, false); - - // Make sure we can get the parameters successfully. - REQUIRE(layer.Scale() == 2); - REQUIRE(layer.SizeAverage() == false); -} +*/ /** * Simple test for Adaptive pooling for Max Pooling layer. - */ + * TEST_CASE("AdaptiveMaxPoolingTestCase", "[ANNLayerTest]") { // For rectangular input. @@ -4358,7 +5066,7 @@ TEST_CASE("AdaptiveMaxPoolingTestCase", "[ANNLayerTest]") input(11) = 9; // Output-Size should be 2 x 2. // Square output. - AdaptiveMaxPooling<> module1(2, 2); + AdaptiveMaxPooling module1(2, 2); module1.InputHeight() = 3; module1.InputWidth() = 4; module1.Forward(input, output); @@ -4380,7 +5088,7 @@ TEST_CASE("AdaptiveMaxPoolingTestCase", "[ANNLayerTest]") input(6) = 3; // Output-Size should be 1 x 2. // Rectangular output. - AdaptiveMaxPooling<> module2(2, 1); + AdaptiveMaxPooling module2(2, 1); module2.InputHeight() = 3; module2.InputWidth() = 3; module2.Forward(input, output); @@ -4402,7 +5110,7 @@ TEST_CASE("AdaptiveMaxPoolingTestCase", "[ANNLayerTest]") input(8) = 3; // Output-Size should be 3 x 3. // Square output. - AdaptiveMaxPooling<> module3(std::tuple(3, 3)); + AdaptiveMaxPooling module3(std::tuple(3, 3)); module3.InputHeight() = 4; module3.InputWidth() = 4; module3.Forward(input, output); @@ -4422,7 +5130,7 @@ TEST_CASE("AdaptiveMaxPoolingTestCase", "[ANNLayerTest]") input(3) = 1; // Output-Size should be 2 x 2. // Square output. - AdaptiveMaxPooling<> module4(std::tuple(2, 2)); + AdaptiveMaxPooling module4(std::tuple(2, 2)); module4.InputHeight() = 4; module4.InputWidth() = 5; module4.Forward(input, output); @@ -4434,10 +5142,11 @@ TEST_CASE("AdaptiveMaxPoolingTestCase", "[ANNLayerTest]") module4.Backward(input, output, delta); REQUIRE(arma::accu(delta) == 2.0); } +*/ /** * Simple test for Adaptive pooling for Mean Pooling layer. - */ + * TEST_CASE("AdaptiveMeanPoolingTestCase", "[ANNLayerTest]") { // For rectangular input. @@ -4456,7 +5165,7 @@ TEST_CASE("AdaptiveMeanPoolingTestCase", "[ANNLayerTest]") input(11) = 9; // Output-Size should be 2 x 2. // Square output. - AdaptiveMeanPooling<> module1(2, 2); + AdaptiveMeanPooling module1(2, 2); module1.InputHeight() = 3; module1.InputWidth() = 4; module1.Forward(input, output); @@ -4478,7 +5187,7 @@ TEST_CASE("AdaptiveMeanPoolingTestCase", "[ANNLayerTest]") input(6) = 3; // Output-Size should be 1 x 2. // Rectangular output. - AdaptiveMeanPooling<> module2(1, 2); + AdaptiveMeanPooling module2(1, 2); module2.InputHeight() = 3; module2.InputWidth() = 3; module2.Forward(input, output); @@ -4500,7 +5209,7 @@ TEST_CASE("AdaptiveMeanPoolingTestCase", "[ANNLayerTest]") input(8) = 3; // Output-Size should be 3 x 3. // Square output. - AdaptiveMeanPooling<> module3(std::tuple(3, 3)); + AdaptiveMeanPooling module3(std::tuple(3, 3)); module3.InputHeight() = 4; module3.InputWidth() = 4; module3.Forward(input, output); @@ -4520,7 +5229,7 @@ TEST_CASE("AdaptiveMeanPoolingTestCase", "[ANNLayerTest]") input(4) = 3; // Output-Size should be 3 x 3. // Square output. - AdaptiveMeanPooling<> module4(std::tuple(3, 3)); + AdaptiveMeanPooling module4(std::tuple(3, 3)); module4.InputHeight() = 4; module4.InputWidth() = 6; module4.Forward(input, output); @@ -4532,224 +5241,226 @@ TEST_CASE("AdaptiveMeanPoolingTestCase", "[ANNLayerTest]") module4.Backward(input, output, delta); REQUIRE(arma::accu(delta) == 2.25); } +*/ +/* TEST_CASE("TransposedConvolutionalLayerOptionalParameterTest", "[ANNLayerTest]") { - Sequential<>* decoder = new Sequential<>(); + Sequential* decoder = new Sequential(); // Check if we can create an object without specifying output. - REQUIRE_NOTHROW(decoder->Add>(24, 16, + REQUIRE_NOTHROW(decoder->Add(24, 16, 5, 5, 1, 1, 0, 0, 10, 10)); - REQUIRE_NOTHROW(decoder->Add>(16, 1, + REQUIRE_NOTHROW(decoder->Add(16, 1, 15, 15, 1, 1, 1, 1, 14, 14)); delete decoder; } +*/ -TEST_CASE("BatchNormWithMinBatchesTest", "[ANNLayerTest]") -{ - arma::mat input, output, result, runningMean, runningVar, delta; +// TEST_CASE("BatchNormWithMinBatchesTest", "[ANNLayerTest]") +// { +// arma::mat input, output, result, runningMean, runningVar, delta; - // The input test matrix is of the form 3 x 2 x 4 x 1 where - // number of images are 3 and number of feature maps are 2. - input = { { 1, 446, 42 }, - { 2, 16, 63 }, - { 3, 13, 63 }, - { 4, 21, 21 }, - { 1, 13, 11 }, - { 32, 45, 42 }, - { 22, 16, 63 }, - { 32, 13, 42 } }; +// // The input test matrix is of the form 3 x 2 x 4 x 1 where +// // number of images are 3 and number of feature maps are 2. +// input = { { 1, 446, 42 }, +// { 2, 16, 63 }, +// { 3, 13, 63 }, +// { 4, 21, 21 }, +// { 1, 13, 11 }, +// { 32, 45, 42 }, +// { 22, 16, 63 }, +// { 32, 13, 42 } }; +// +// // Output calculated using torch.nn.BatchNorm2d(). +// result = { { -0.4786, 3.2634, -0.1338 }, +// { -0.4702, -0.3525, 0.0427 }, +// { -0.4618, -0.3777, 0.0427 }, +// { -0.4534, -0.3104, -0.3104 }, +// { -1.5429, -0.8486, -0.9643 }, +// { 0.2507, 1.0029, 0.8293 }, +// { -0.3279, -0.675, 2.0443 }, +// { 0.2507 , -0.8486 , 0.8293 } }; - // Output calculated using torch.nn.BatchNorm2d(). - result = { { -0.4786, 3.2634, -0.1338 }, - { -0.4702, -0.3525, 0.0427 }, - { -0.4618, -0.3777, 0.0427 }, - { -0.4534, -0.3104, -0.3104 }, - { -1.5429, -0.8486, -0.9643 }, - { 0.2507, 1.0029, 0.8293 }, - { -0.3279, -0.675, 2.0443 }, - { 0.2507 , -0.8486 , 0.8293 } }; +// // Check correctness of batch normalization. +// BatchNorm<> module1(2, 1e-5, false, 0.1); +// module1.Reset(); +// module1.Forward(input, output); +// CheckMatrices(output, result, 1e-1); - // Check correctness of batch normalization. - BatchNorm<> module1(2, 1e-5, false, 0.1); - module1.Reset(); - module1.Forward(input, output); - CheckMatrices(output, result, 1e-1); +// // Check backward function. +// module1.Backward(input, output, delta); +// REQUIRE(arma::accu(delta) == Approx(0.0102676).epsilon(1e-5)); - // Check backward function. - module1.Backward(input, output, delta); - REQUIRE(arma::accu(delta) == Approx(0.0102676).epsilon(1e-5)); +// // Check values for running mean and running variance. +// // Calculated using torch.nn.BatchNorm2d(). +// runningMean = arma::mat(2, 1); +// runningVar = arma::mat(2, 1); +// runningMean(0) = 5.7917; +// runningMean(1) = 2.76667; +// runningVar(0) = 1543.6545; +// runningVar(1) = 33.488; - // Check values for running mean and running variance. - // Calculated using torch.nn.BatchNorm2d(). - runningMean = arma::mat(2, 1); - runningVar = arma::mat(2, 1); - runningMean(0) = 5.7917; - runningMean(1) = 2.76667; - runningVar(0) = 1543.6545; - runningVar(1) = 33.488; +// CheckMatrices(runningMean, module1.TrainingMean(), 1e-3); +// CheckMatrices(runningVar, module1.TrainingVariance(), 1e-2); - CheckMatrices(runningMean, module1.TrainingMean(), 1e-3); - CheckMatrices(runningVar, module1.TrainingVariance(), 1e-2); +// // Check correctness of layer when running mean and variance +// // are updated using cumulative average. +// BatchNorm<> module2(2); +// module2.Reset(); +// module2.Forward(input, output); +// CheckMatrices(output, result, 1e-1); - // Check correctness of layer when running mean and variance - // are updated using cumulative average. - BatchNorm<> module2(2); - module2.Reset(); - module2.Forward(input, output); - CheckMatrices(output, result, 1e-1); +// // Check values for running mean and running variance. +// // Calculated using torch.nn.BatchNorm2d(). +// runningMean(0) = 57.9167; +// runningMean(1) = 27.6667; +// runningVar(0) = 15427.5380; +// runningVar(1) = 325.8787; - // Check values for running mean and running variance. - // Calculated using torch.nn.BatchNorm2d(). - runningMean(0) = 57.9167; - runningMean(1) = 27.6667; - runningVar(0) = 15427.5380; - runningVar(1) = 325.8787; +// CheckMatrices(runningMean, module2.TrainingMean(), 1e-2); +// CheckMatrices(runningVar, module2.TrainingVariance(), 1e-2); - CheckMatrices(runningMean, module2.TrainingMean(), 1e-2); - CheckMatrices(runningVar, module2.TrainingVariance(), 1e-2); +// // Check correctness when model is testing. +// arma::mat deterministicOutput; +// module1.Deterministic() = true; +// module1.Forward(input, deterministicOutput); - // Check correctness when model is testing. - arma::mat deterministicOutput; - module1.Deterministic() = true; - module1.Forward(input, deterministicOutput); +// result.clear(); +// result = { { -0.12195, 11.20426, 0.92158 }, +// { -0.0965, 0.259824, 1.4560 }, +// { -0.071054, 0.183567, 1.45607 }, +// { -0.045601, 0.3870852, 0.38708 }, +// { -0.305288, 1.7683, 1.4227 }, +// { 5.05166, 7.29812, 6.7797 }, +// { 3.323614, 2.2867, 10.4086 }, +// { 5.05166, 1.7683, 6.7797 } }; - result.clear(); - result = { { -0.12195, 11.20426, 0.92158 }, - { -0.0965, 0.259824, 1.4560 }, - { -0.071054, 0.183567, 1.45607 }, - { -0.045601, 0.3870852, 0.38708 }, - { -0.305288, 1.7683, 1.4227 }, - { 5.05166, 7.29812, 6.7797 }, - { 3.323614, 2.2867, 10.4086 }, - { 5.05166, 1.7683, 6.7797 } }; +// CheckMatrices(result, deterministicOutput, 1e-1); - CheckMatrices(result, deterministicOutput, 1e-1); +// // Check correctness by updating the running mean and variance again. +// module1.Deterministic() = false; - // Check correctness by updating the running mean and variance again. - module1.Deterministic() = false; +// // Clean up. +// output.clear(); +// input.clear(); - // Clean up. - output.clear(); - input.clear(); +// // The input test matrix is of the form 2 x 2 x 3 x 1 where +// // number of images are 2 and number of feature maps are 2. +// input = { { 12, 443 }, +// { 134, 45 }, +// { 11, 13 }, +// { 14, 55 }, +// { 110, 4 }, +// { 1, 45 } }; +// +// result = { { -0.629337, 2.14791 }, +// { 0.156797, -0.416694 }, +// { -0.63578, -0.622893 }, +// { -0.637481, 0.4440386 }, +// { 1.894857, -0.901267 }, +// { -0.980402, 0.180253 } }; - // The input test matrix is of the form 2 x 2 x 3 x 1 where - // number of images are 2 and number of feature maps are 2. - input = { { 12, 443 }, - { 134, 45 }, - { 11, 13 }, - { 14, 55 }, - { 110, 4 }, - { 1, 45 } }; +// module1.Forward(input, output); +// CheckMatrices(result, output, 1e-3); - result = { { -0.629337, 2.14791 }, - { 0.156797, -0.416694 }, - { -0.63578, -0.622893 }, - { -0.637481, 0.4440386 }, - { 1.894857, -0.901267 }, - { -0.980402, 0.180253 } }; +// // Check correctness for the second module as well. +// module2.Forward(input, output); +// CheckMatrices(result, output, 1e-3); - module1.Forward(input, output); - CheckMatrices(result, output, 1e-3); +// // Calculated using torch.nn.BatchNorm2d(). +// runningMean(0) = 16.1792; +// runningMean(1) = 6.30667; +// runningVar(0) = 4276.5849; +// runningVar(1) = 202.595; - // Check correctness for the second module as well. - module2.Forward(input, output); - CheckMatrices(result, output, 1e-3); +// CheckMatrices(runningMean, module1.TrainingMean(), 1e-3); +// CheckMatrices(runningVar, module1.TrainingVariance(), 1e-1); - // Calculated using torch.nn.BatchNorm2d(). - runningMean(0) = 16.1792; - runningMean(1) = 6.30667; - runningVar(0) = 4276.5849; - runningVar(1) = 202.595; +// // Check correctness of running mean and variance when their +// // values are updated using cumulative average. +// runningMean(0) = 83.79166; +// runningMean(1) = 32.9166; +// runningVar(0) = 22164.1035; +// runningVar(1) = 1025.2227; - CheckMatrices(runningMean, module1.TrainingMean(), 1e-3); - CheckMatrices(runningVar, module1.TrainingVariance(), 1e-1); +// CheckMatrices(runningMean, module2.TrainingMean(), 1e-3); +// CheckMatrices(runningVar, module2.TrainingVariance(), 1e-3); - // Check correctness of running mean and variance when their - // values are updated using cumulative average. - runningMean(0) = 83.79166; - runningMean(1) = 32.9166; - runningVar(0) = 22164.1035; - runningVar(1) = 1025.2227; +// // Check backward function. +// module1.Backward(input, output, delta); - CheckMatrices(runningMean, module2.TrainingMean(), 1e-3); - CheckMatrices(runningVar, module2.TrainingVariance(), 1e-3); +// deterministicOutput.clear(); +// module1.Deterministic() = true; +// module1.Forward(input, deterministicOutput); - // Check backward function. - module1.Backward(input, output, delta); +// result.clear(); +// result = { { -0.06388436, 6.524754114 }, +// { 1.799655281, 0.44047968 }, +// { -0.07913291, -0.04784981 }, +// { 0.5405045, 3.4210097 }, +// { 7.2851023, -0.1620577 }, +// { -0.37282639, 2.7184474 } }; - deterministicOutput.clear(); - module1.Deterministic() = true; - module1.Forward(input, deterministicOutput); +// // Calculated using torch.nn.BatchNorm2d(). +// CheckMatrices(result, deterministicOutput, 1e-1); +// } - result.clear(); - result = { { -0.06388436, 6.524754114 }, - { 1.799655281, 0.44047968 }, - { -0.07913291, -0.04784981 }, - { 0.5405045, 3.4210097 }, - { 7.2851023, -0.1620577 }, - { -0.37282639, 2.7184474 } }; +// /** +// * Batch Normalization layer numerical gradient test. +// */ +// TEST_CASE("GradientBatchNormWithMiniBatchesTest", "[ANNLayerTest]") +// { +// // Add function gradient instantiation. +// // To make this test robust, check it ten times. +// bool pass = false; +// for (size_t trial = 0; trial < 10; trial++) +// { +// struct GradientFunction +// { +// GradientFunction() : +// input(arma::randn(16, 1024)), +// target(arma::zeros(1, 1024)) +// { +// model = new FFN(); +// model->ResetData(input, target); +// model->Add>(); +// model->Add>(1, 2, 3, 3, 1, 1, 0, 0, 4, 4); +// model->Add>(2); +// model->Add>(2 * 2 * 2, 2); +// model->Add>(); +// } - // Calculated using torch.nn.BatchNorm2d(). - CheckMatrices(result, deterministicOutput, 1e-1); -} +// ~GradientFunction() +// { +// delete model; +// } -/** - * Batch Normalization layer numerical gradient test. - */ -TEST_CASE("GradientBatchNormWithMiniBatchesTest", "[ANNLayerTest]") -{ - // Add function gradient instantiation. - // To make this test robust, check it ten times. - bool pass = false; - for (size_t trial = 0; trial < 10; trial++) - { - struct GradientFunction - { - GradientFunction() : - input(arma::randn(16, 1024)), - target(arma::zeros(1, 1024)) - { - model = new FFN, NguyenWidrowInitialization>(); - model->Predictors() = input; - model->Responses() = target; - model->Add>(); - model->Add>(1, 2, 3, 3, 1, 1, 0, 0, 4, 4); - model->Add>(2); - model->Add>(2 * 2 * 2, 2); - model->Add>(); - } +// double Gradient(arma::mat& gradient) const +// { +// double error = model->Evaluate(model->Parameters(), 0, 1024, false); +// model->Gradient(model->Parameters(), 0, gradient, 1024); +// return error; +// } - ~GradientFunction() - { - delete model; - } +// arma::mat& Parameters() { return model->Parameters(); } - double Gradient(arma::mat& gradient) const - { - double error = model->Evaluate(model->Parameters(), 0, 1024, false); - model->Gradient(model->Parameters(), 0, gradient, 1024); - return error; - } +// FFN* model; +// arma::mat input, target; +// } function; - arma::mat& Parameters() { return model->Parameters(); } +// double gradient = CheckGradient(function); +// if (gradient < 1e-1) +// { +// pass = true; +// break; +// } +// } - FFN, NguyenWidrowInitialization>* model; - arma::mat input, target; - } function; - - double gradient = CheckGradient(function); - if (gradient < 1e-1) - { - pass = true; - break; - } - } - - REQUIRE(pass); -} +// REQUIRE(pass); +// } TEST_CASE("ConvolutionLayerTestCase", "[ANNLayerTest]") { @@ -4766,98 +5477,100 @@ TEST_CASE("ConvolutionLayerTestCase", "[ANNLayerTest]") { 22, 16 , 63 }, { 32, 13 , 42 } }; - Convolution<> layer(2, 4, 1, 1, 1, 1, 0, 0, 4, 1); - layer.Reset(); + Convolution layer(4, 1, 1, 1, 1, 0, 0); + layer.InputDimensions() = std::vector({ 4, 1, 2 }); + layer.ComputeOutputDimensions(); + arma::mat layerWeights(layer.WeightSize(), 1); + layer.SetWeights(layerWeights.memptr()); + output.set_size(layer.OutputSize(), 3); // Set weights to 1.0 and bias to 0.0. - layer.Parameters().zeros(); - arma::mat weight(2 * 4, 1); - weight.fill(1.0); - layer.Parameters().submat(arma::span(0, 2 * 4 - 1), arma::span()) = weight; + layer.Weight().fill(1.0); + layer.Bias().zeros(); layer.Forward(input, output); // Value calculated using torch.nn.Conv2d(). REQUIRE(arma::accu(output) == 4108); // Set bias to one. - layer.Parameters().fill(1.0); + layer.Bias().fill(1.0); layer.Forward(input, output); // Value calculated using torch.nn.Conv2d(). REQUIRE(arma::accu(output) == 4156); } -TEST_CASE("BatchNormDeterministicTest", "[ANNLayerTest]") -{ - FFN<> module; - module.Add>(2, 1e-5, false); - module.Add>(); +// TEST_CASE("BatchNormDeterministicTest", "[ANNLayerTest]") +// { +// FFN<> module; +// module.Add>(2, 1e-5, false); +// module.Add>(); - arma::mat input(4, 3), output; - module.ResetParameters(); +// arma::mat input(4, 3), output; +// module.ResetParameters(); - // The model should switch to Deterministic mode for predicting. - module.Predict(input, output); - REQUIRE(boost::get*>(module.Model()[0])->Deterministic() == true); +// // The model should switch to Deterministic mode for predicting. +// module.Predict(input, output); +// REQUIRE(boost::get*>(module.Model()[0])->Deterministic() == true); - output.ones(); - module.Train(input, output); - // The model should switch to training mode for predicting. - REQUIRE(boost::get*>(module.Model()[0])->Deterministic() == 0); -} +// output.ones(); +// module.Train(input, output); +// // The model should switch to training mode for predicting. +// REQUIRE(boost::get*>(module.Model()[0])->Deterministic() == 0); +// } -/** - * Linear module weight initialization test. - */ -TEST_CASE("LinearLayerWeightInitializationTest", "[ANNLayerTest]") -{ - size_t inSize = 10, outSize = 4; - Linear<> linear = Linear<>(inSize, outSize); - linear.Reset(); - RandomInitialization().Initialize(linear.Weight()); - linear.Bias().ones(); +// /** +// * Linear module weight initialization test. +// */ +// TEST_CASE("LinearLayerWeightInitializationTest", "[ANNLayerTest]") +// { +// size_t inSize = 10, outSize = 4; +// Linear<> linear = Linear<>(inSize, outSize); +// linear.Reset(); +// RandomInitialization().Initialize(linear.Weight()); +// linear.Bias().ones(); - REQUIRE(std::equal(linear.Weight().begin(), - linear.Weight().end(), linear.Parameters().begin())); +// REQUIRE(std::equal(linear.Weight().begin(), +// linear.Weight().end(), linear.Parameters().begin())); - REQUIRE(std::equal(linear.Bias().begin(), - linear.Bias().end(), linear.Parameters().begin() + inSize * outSize)); +// REQUIRE(std::equal(linear.Bias().begin(), +// linear.Bias().end(), linear.Parameters().begin() + inSize * outSize)); - REQUIRE(linear.Weight().n_rows == outSize); - REQUIRE(linear.Weight().n_cols == inSize); - REQUIRE(linear.Bias().n_rows == outSize); - REQUIRE(linear.Bias().n_cols == 1); - REQUIRE(linear.Parameters().n_rows == inSize * outSize + outSize); -} +// REQUIRE(linear.Weight().n_rows == outSize); +// REQUIRE(linear.Weight().n_cols == inSize); +// REQUIRE(linear.Bias().n_rows == outSize); +// REQUIRE(linear.Bias().n_cols == 1); +// REQUIRE(linear.Parameters().n_rows == inSize * outSize + outSize); +// } -/** - * Atrous Convolution module weight initialization test. - */ -TEST_CASE("AtrousConvolutionLayerWeightInitializationTest", "[ANNLayerTest]") -{ - size_t inSize = 2, outSize = 3; - size_t kernelWidth = 4, kernelHeight = 5; - AtrousConvolution<> module = AtrousConvolution<>(inSize, outSize, - kernelWidth, kernelHeight, 6, 7, std::make_tuple(8, 9), - std::make_tuple(10, 11), 12, 13, 14, 15); - module.Reset(); - RandomInitialization().Initialize(module.Weight()); - module.Bias().ones(); +// /** +// * Atrous Convolution module weight initialization test. +// */ +// TEST_CASE("AtrousConvolutionLayerWeightInitializationTest", "[ANNLayerTest]") +// { +// size_t inSize = 2, outSize = 3; +// size_t kernelWidth = 4, kernelHeight = 5; +// AtrousConvolution<> module = AtrousConvolution<>(inSize, outSize, +// kernelWidth, kernelHeight, 6, 7, std::make_tuple(8, 9), +// std::make_tuple(10, 11), 12, 13, 14, 15); +// module.Reset(); +// RandomInitialization().Initialize(module.Weight()); +// module.Bias().ones(); - REQUIRE(std::equal(module.Weight().begin(), - module.Weight().end(), module.Parameters().begin())); +// REQUIRE(std::equal(module.Weight().begin(), +// module.Weight().end(), module.Parameters().begin())); - REQUIRE(std::equal(module.Bias().begin(), - module.Bias().end(), module.Parameters().end() - outSize)); +// REQUIRE(std::equal(module.Bias().begin(), +// module.Bias().end(), module.Parameters().end() - outSize)); - REQUIRE(module.Weight().n_rows == kernelWidth); - REQUIRE(module.Weight().n_cols == kernelHeight); - REQUIRE(module.Weight().n_slices == inSize * outSize); - REQUIRE(module.Bias().n_rows == outSize); - REQUIRE(module.Bias().n_cols == 1); - REQUIRE(module.Parameters().n_rows - == (outSize * inSize * kernelWidth * kernelHeight) + outSize); -} +// REQUIRE(module.Weight().n_rows == kernelWidth); +// REQUIRE(module.Weight().n_cols == kernelHeight); +// REQUIRE(module.Weight().n_slices == inSize * outSize); +// REQUIRE(module.Bias().n_rows == outSize); +// REQUIRE(module.Bias().n_cols == 1); +// REQUIRE(module.Parameters().n_rows +// == (outSize * inSize * kernelWidth * kernelHeight) + outSize); +// } /** * Convolution module weight initialization test. @@ -4866,10 +5579,14 @@ TEST_CASE("ConvolutionLayerWeightInitializationTest", "[ANNLayerTest]") { size_t inSize = 2, outSize = 3; size_t kernelWidth = 4, kernelHeight = 5; - Convolution<> module = Convolution<>(inSize, outSize, + Convolution module = Convolution(outSize, kernelWidth, kernelHeight, 6, 7, std::tuple(8, 9), - std::tuple(10, 11), 12, 13, "none"); - module.Reset(); + std::tuple(10, 11), "none"); + module.InputDimensions() = std::vector({ 12, 13, 2 }); + module.ComputeOutputDimensions(); + arma::mat weights(module.WeightSize(), 1); + module.SetWeights(weights.memptr()); + RandomInitialization().Initialize(module.Weight()); module.Bias().ones(); @@ -4881,7 +5598,7 @@ TEST_CASE("ConvolutionLayerWeightInitializationTest", "[ANNLayerTest]") REQUIRE(module.Weight().n_rows == kernelWidth); REQUIRE(module.Weight().n_cols == kernelHeight); - REQUIRE(module.Weight().n_slices == inSize * outSize); + REQUIRE(module.Weight().n_slices == outSize * inSize); REQUIRE(module.Bias().n_rows == outSize); REQUIRE(module.Bias().n_cols == 1); REQUIRE(module.Parameters().n_rows @@ -4890,12 +5607,12 @@ TEST_CASE("ConvolutionLayerWeightInitializationTest", "[ANNLayerTest]") /** * Transposed Convolution module weight initialization test. - */ + * TEST_CASE("TransposedConvolutionWeightInitializationTest", "[ANNLayerTest]") { size_t inSize = 3, outSize = 3; size_t kernelWidth = 4, kernelHeight = 4; - TransposedConvolution<> module = TransposedConvolution<>(inSize, outSize, + TransposedConvolution module = TransposedConvolution(inSize, outSize, kernelWidth, kernelHeight, 1, 1, 1, 1, 5, 5, 6, 6); module.Reset(); RandomInitialization().Initialize(module.Weight()); @@ -4915,262 +5632,263 @@ TEST_CASE("TransposedConvolutionWeightInitializationTest", "[ANNLayerTest]") REQUIRE(module.Parameters().n_rows == (outSize * inSize * kernelWidth * kernelHeight) + outSize); } +*/ /** * Simple Test for ChannelShuffle layer. */ -TEST_CASE("ChannelShuffleLayerTest", "[ANNLayerTest]") -{ - arma::mat input1, output1, outputExpected1, outputBackward1; - ChannelShuffle<> module1(2, 2, 6, 2); - - input1 << 1 << 13 << arma::endr - << 2 << 14 << arma::endr - << 3 << 15 << arma::endr - << 4 << 16 << arma::endr - << 5 << 17 << arma::endr - << 6 << 18 << arma::endr - << 7 << 19 << arma::endr - << 8 << 20 << arma::endr - << 9 << 21 << arma::endr - << 10 << 22 << arma::endr - << 11 << 23 << arma::endr - << 12 << 24 << arma::endr; - input1.reshape(24, 1); - // Value calculated using torch.nn.ChannelShuffle(). - outputExpected1 << 1 << 17 << arma::endr - << 2 << 18 << arma::endr - << 3 << 19 << arma::endr - << 4 << 20 << arma::endr - << 13 << 9 << arma::endr - << 14 << 10 << arma::endr - << 15 << 11 << arma::endr - << 16 << 12 << arma::endr - << 5 << 21 << arma::endr - << 6 << 22 << arma::endr - << 7 << 23 << arma::endr - << 8 << 24 << arma::endr; - outputExpected1.reshape(24, 1); - // Check the Forward pass of the layer. - module1.Forward(input1, output1); - CheckMatrices(output1, outputExpected1); - - // Check the Backward pass of the layer. - module1.Backward(output1, output1, outputBackward1); - CheckMatrices(input1, outputBackward1); - -} +// TEST_CASE("ChannelShuffleLayerTest", "[ANNLayerTest]") +// { +// arma::mat input1, output1, outputExpected1, outputBackward1; +// ChannelShuffle<> module1(2, 2, 6, 2); +// +// input1 << 1 << 13 << arma::endr +// << 2 << 14 << arma::endr +// << 3 << 15 << arma::endr +// << 4 << 16 << arma::endr +// << 5 << 17 << arma::endr +// << 6 << 18 << arma::endr +// << 7 << 19 << arma::endr +// << 8 << 20 << arma::endr +// << 9 << 21 << arma::endr +// << 10 << 22 << arma::endr +// << 11 << 23 << arma::endr +// << 12 << 24 << arma::endr; +// input1.reshape(24, 1); +// // Value calculated using torch.nn.ChannelShuffle(). +// outputExpected1 << 1 << 17 << arma::endr +// << 2 << 18 << arma::endr +// << 3 << 19 << arma::endr +// << 4 << 20 << arma::endr +// << 13 << 9 << arma::endr +// << 14 << 10 << arma::endr +// << 15 << 11 << arma::endr +// << 16 << 12 << arma::endr +// << 5 << 21 << arma::endr +// << 6 << 22 << arma::endr +// << 7 << 23 << arma::endr +// << 8 << 24 << arma::endr; +// outputExpected1.reshape(24, 1); +// // Check the Forward pass of the layer. +// module1.Forward(input1, output1); +// CheckMatrices(output1, outputExpected1); +// +// // Check the Backward pass of the layer. +// module1.Backward(output1, output1, outputBackward1); +// CheckMatrices(input1, outputBackward1); +// +// } /** * Simple Test for PixelShuffle layer. */ -TEST_CASE("PixelShuffleLayerTest", "[ANNLayerTest]") -{ - arma::mat input1, output1, gy1, g1, outputExpected1, gExpected1; - arma::mat input2, output2, gy2, g2, outputExpected2, gExpected2; - PixelShuffle<> module1(2, 2, 2, 4); - PixelShuffle<> module2(2, 2, 2, 4); - - // Input is a single image, of size (2,2) and having 4 channels. - input1 << 1 << 3 << 2 << 4 << 0 << 0 << 0 << 0 << 0 << 0 << 0 << 0 << 0 << 0 - << 0 << 0 << arma::endr; - gy1 << 1 << 5 << 9 << 13 << 2 << 6 << 10 << 14 << 3 << 7 << 11 << 15 << 4 << 8 - << 12 << 16 << arma::endr; - - // Calculated using torch.nn.PixelShuffle(). - outputExpected1 << 1 << 0 << 3 << 0 << 0 << 0 << 0 << 0 << 2 << 0 << 4 << 0 - << 0 << 0 << 0 << 0 << arma::endr; - gExpected1 << 1 << 9 << 3 << 11 << 5 << 13 << 7 << 15 << 2 << 10 << 4 << 12 - << 6 << 14 << 8 << 16 << arma::endr; - - input1 = input1.t(); - outputExpected1 = outputExpected1.t(); - gy1 = gy1.t(); - gExpected1 = gExpected1.t(); - - // Check the Forward pass of the layer. - module1.Forward(input1, output1); - CheckMatrices(output1, outputExpected1); - - // Check the Backward pass of the layer. - module1.Backward(input1, gy1, g1); - CheckMatrices(g1, gExpected1); - - // Input is a batch of 2 images, each of size (2,2) and having 4 channels. - input2 << 1 << 3 << 2 << 4 << 0 << 0 << 0 << 0 << 0 << 0 << 0 << 0 << 0 << 0 - << 0 << 0 << arma::endr << 5 << 7 << 6 << 8 << 0 << 0 << 0 << 0 << 0 << 0 - << 0 << 0 << 0 << 0 << 0 << 0 << arma::endr; - gy2 << 1 << 5 << 9 << 13 << 2 << 6 << 10 << 14 << 3 << 7 << 11 << 15 << 4 << 8 - << 12 << 16 << arma::endr << 17 << 21 << 25 << 29 << 18 << 22 << 26 << 30 - << 19 << 23 << 27 << 31 << 20 << 24 << 28 << 32 << arma::endr; - - // Calculated using torch.nn.PixelShuffle(). - outputExpected2 << 1 << 0 << 3 << 0 << 0 << 0 << 0 << 0 << 2 << 0 << 4 << 0 - << 0 << 0 << 0 << 0 << arma::endr << 5 << 0 << 7 << 0 << 0 << 0 << 0 << 0 - << 6 << 0 << 8 << 0 << 0 << 0 << 0 << 0 << arma::endr; - gExpected2 << 1 << 9 << 3 << 11 << 5 << 13 << 7 << 15 << 2 << 10 << 4 << 12 - << 6 << 14 << 8 << 16 << arma::endr << 17 << 25 << 19 << 27 << 21 << 29 - << 23 << 31 << 18 << 26 << 20 << 28 << 22 << 30 << 24 << 32 << arma::endr; - - input2 = input2.t(); - outputExpected2 = outputExpected2.t(); - gy2 = gy2.t(); - gExpected2 = gExpected2.t(); - - // Check the Forward pass of the layer. - module2.Forward(input2, output2); - CheckMatrices(output2, outputExpected2); - - // Check the Backward pass of the layer. - module2.Backward(input2, gy2, g2); - CheckMatrices(g2, gExpected2); -} +// TEST_CASE("PixelShuffleLayerTest", "[ANNLayerTest]") +// { +// arma::mat input1, output1, gy1, g1, outputExpected1, gExpected1; +// arma::mat input2, output2, gy2, g2, outputExpected2, gExpected2; +// PixelShuffle<> module1(2, 2, 2, 4); +// PixelShuffle<> module2(2, 2, 2, 4); +// +// // Input is a single image, of size (2,2) and having 4 channels. +// input1 << 1 << 3 << 2 << 4 << 0 << 0 << 0 << 0 << 0 << 0 << 0 << 0 << 0 << 0 +// << 0 << 0 << arma::endr; +// gy1 << 1 << 5 << 9 << 13 << 2 << 6 << 10 << 14 << 3 << 7 << 11 << 15 << 4 << 8 +// << 12 << 16 << arma::endr; +// +// // Calculated using torch.nn.PixelShuffle(). +// outputExpected1 << 1 << 0 << 3 << 0 << 0 << 0 << 0 << 0 << 2 << 0 << 4 << 0 +// << 0 << 0 << 0 << 0 << arma::endr; +// gExpected1 << 1 << 9 << 3 << 11 << 5 << 13 << 7 << 15 << 2 << 10 << 4 << 12 +// << 6 << 14 << 8 << 16 << arma::endr; +// +// input1 = input1.t(); +// outputExpected1 = outputExpected1.t(); +// gy1 = gy1.t(); +// gExpected1 = gExpected1.t(); +// +// // Check the Forward pass of the layer. +// module1.Forward(input1, output1); +// CheckMatrices(output1, outputExpected1); +// +// // Check the Backward pass of the layer. +// module1.Backward(input1, gy1, g1); +// CheckMatrices(g1, gExpected1); +// +// // Input is a batch of 2 images, each of size (2,2) and having 4 channels. +// input2 << 1 << 3 << 2 << 4 << 0 << 0 << 0 << 0 << 0 << 0 << 0 << 0 << 0 << 0 +// << 0 << 0 << arma::endr << 5 << 7 << 6 << 8 << 0 << 0 << 0 << 0 << 0 << 0 +// << 0 << 0 << 0 << 0 << 0 << 0 << arma::endr; +// gy2 << 1 << 5 << 9 << 13 << 2 << 6 << 10 << 14 << 3 << 7 << 11 << 15 << 4 << 8 +// << 12 << 16 << arma::endr << 17 << 21 << 25 << 29 << 18 << 22 << 26 << 30 +// << 19 << 23 << 27 << 31 << 20 << 24 << 28 << 32 << arma::endr; +// +// // Calculated using torch.nn.PixelShuffle(). +// outputExpected2 << 1 << 0 << 3 << 0 << 0 << 0 << 0 << 0 << 2 << 0 << 4 << 0 +// << 0 << 0 << 0 << 0 << arma::endr << 5 << 0 << 7 << 0 << 0 << 0 << 0 << 0 +// << 6 << 0 << 8 << 0 << 0 << 0 << 0 << 0 << arma::endr; +// gExpected2 << 1 << 9 << 3 << 11 << 5 << 13 << 7 << 15 << 2 << 10 << 4 << 12 +// << 6 << 14 << 8 << 16 << arma::endr << 17 << 25 << 19 << 27 << 21 << 29 +// << 23 << 31 << 18 << 26 << 20 << 28 << 22 << 30 << 24 << 32 << arma::endr; +// +// input2 = input2.t(); +// outputExpected2 = outputExpected2.t(); +// gy2 = gy2.t(); +// gExpected2 = gExpected2.t(); +// +// // Check the Forward pass of the layer. +// module2.Forward(input2, output2); +// CheckMatrices(output2, outputExpected2); +// +// // Check the Backward pass of the layer. +// module2.Backward(input2, gy2, g2); +// CheckMatrices(g2, gExpected2); +// } /** * Test that the function that can access the parameters of the * PixelShuffle layer works. */ -TEST_CASE("PixelShuffleLayerParametersTest", "[ANNLayerTest]") -{ - // Create the layer using the empty constructor. - PixelShuffle<> layer; +// TEST_CASE("PixelShuffleLayerParametersTest", "[ANNLayerTest]") +// { +// // Create the layer using the empty constructor. +// PixelShuffle<> layer; +// +// // Set the different input parameters of the layer. +// layer.UpscaleFactor() = 2; +// layer.InputHeight() = 2; +// layer.InputWidth() = 2; +// layer.InputChannels() = 4; +// +// // Make sure we can get the parameters successfully. +// REQUIRE(layer.UpscaleFactor() == 2); +// REQUIRE(layer.InputHeight() == 2); +// REQUIRE(layer.InputWidth() == 2); +// REQUIRE(layer.InputChannels() == 4); +// +// arma::mat input, output; +// // Input is a batch of 2 images, each of size (2,2) and having 4 channels. +// input << 1 << 3 << 2 << 4 << 0 << 0 << 0 << 0 << 0 << 0 << 0 << 0 << 0 << 0 +// << 0 << 0 << arma::endr << 5 << 7 << 6 << 8 << 0 << 0 << 0 << 0 << 0 << 0 +// << 0 << 0 << 0 << 0 << 0 << 0 << arma::endr; +// input = input.t(); +// layer.Forward(input, output); +// +// // Check whether output parameters are returned correctly. +// REQUIRE(layer.OutputHeight() == 4); +// REQUIRE(layer.OutputWidth() == 4); +// REQUIRE(layer.OutputChannels() == 1); +// } - // Set the different input parameters of the layer. - layer.UpscaleFactor() = 2; - layer.InputHeight() = 2; - layer.InputWidth() = 2; - layer.InputChannels() = 4; +// /** +// * Simple Test for SpatialDropout layer. +// */ +// TEST_CASE("SpatialDropoutLayerTest", "[ANNLayerTest]") +// { +// arma::mat input, output, gy, g, temp; +// arma::mat outputsExpected = arma::zeros(8, 12); +// arma::mat gsExpected = arma::zeros(8, 12); - // Make sure we can get the parameters successfully. - REQUIRE(layer.UpscaleFactor() == 2); - REQUIRE(layer.InputHeight() == 2); - REQUIRE(layer.InputWidth() == 2); - REQUIRE(layer.InputChannels() == 4); +// // Set the seed to a random value. +// arma::arma_rng::set_seed_random(); +// SpatialDropout<> module(3, 0.2); - arma::mat input, output; - // Input is a batch of 2 images, each of size (2,2) and having 4 channels. - input << 1 << 3 << 2 << 4 << 0 << 0 << 0 << 0 << 0 << 0 << 0 << 0 << 0 << 0 - << 0 << 0 << arma::endr << 5 << 7 << 6 << 8 << 0 << 0 << 0 << 0 << 0 << 0 - << 0 << 0 << 0 << 0 << 0 << 0 << arma::endr; - input = input.t(); - layer.Forward(input, output); +// // Input is a batch of 2 images, each of size (2,2) and having 4 channels. +// input = { 0.4963, 0.0885, 0.7682, 0.1320, 0.3074, 0.4901, 0.6341, 0.8964, +// 0.4556, 0.3489, 0.6323, 0.4017 }; +// +// gy = { 1, 3, 2, 4, 5, 7, 6, 8, 9, 11, 10, 12 }; +// +// // Following values have been calculated using torch.nn.Dropout2d(p=0.2). +// temp = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }; +// outputsExpected.row(0) = temp; +// temp = { 0, 0, 0, 0, 0.3842, 0.6126, 0.7926, 1.1205, 0.5695, 0.4361, 0.7904, +// 0.5021 }; +// outputsExpected.row(1) = temp; +// temp = { 0.6204, 0.1106, 0.9603, 0.1650, 0, 0, 0, 0, 0.5695, 0.4361, +// 0.7904, 0.5021 }; +// outputsExpected.row(2) = temp; +// temp = { 0.6204, 0.1106, 0.9603, 0.1650, 0.3842, 0.6126, 0.7926, 1.1205, 0, +// 0, 0, 0 }; +// outputsExpected.row(3) = temp; +// temp = { 0, 0, 0, 0, 0, 0, 0, 0, 0.5695, 0.4361, 0.7904, 0.5021 }; +// outputsExpected.row(4) = temp; +// temp = { 0, 0, 0, 0, 0.3842, 0.6126, 0.7926, 1.1205, 0, 0, 0, 0 }; +// outputsExpected.row(5) = temp; +// temp = { 0.6204, 0.1106, 0.9603, 0.1650, 0, 0, 0, 0, 0, 0, 0, 0 }; +// outputsExpected.row(6) = temp; +// temp = { 0.6204, 0.1106, 0.9603, 0.1650, 0.3842, 0.6126, 0.7926, 1.1205, +// 0.5695, 0.4361, 0.7904, 0.5021 }; +// outputsExpected.row(7) = temp; +// temp = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }; +// gsExpected.row(0) = temp; +// temp = { 0, 0, 0, 0, 6.2500, 8.7500, 7.5000, 10.0000, 11.2500, 13.7500, +// 12.5000, 15.0000 }; +// gsExpected.row(1) = temp; +// temp = { 1.2500, 3.7500, 2.5000, 5.0000, 0, 0, 0, 0, 11.2500, 13.7500, +// 12.5000, 15.0000 }; +// gsExpected.row(2) = temp; +// temp = { 1.2500, 3.7500, 2.5000, 5.0000, 6.2500, 8.7500, 7.5000, 10.0000, 0, +// 0, 0, 0 }; +// gsExpected.row(3) = temp; +// temp = { 0, 0, 0, 0, 0, 0, 0, 0, 11.2500, 13.7500, 12.5000, 15.0000 }; +// gsExpected.row(4) = temp; +// temp = { 0, 0, 0, 0, 6.2500, 8.7500, 7.5000, 10.0000, 0, 0, 0, 0 }; +// gsExpected.row(5) = temp; +// temp = { 1.2500, 3.7500, 2.5000, 5.0000, 0, 0, 0, 0, 0, 0, 0, 0 }; +// gsExpected.row(6) = temp; +// temp = { 1.2500, 3.7500, 2.5000, 5.0000, 6.2500, 8.7500, 7.5000, 10.0000, +// 11.2500, 13.7500, 12.5000, 15.0000 }; +// gsExpected.row(7) = temp; - // Check whether output parameters are returned correctly. - REQUIRE(layer.OutputHeight() == 4); - REQUIRE(layer.OutputWidth() == 4); - REQUIRE(layer.OutputChannels() == 1); -} +// input = input.t(); +// gy = gy.t(); +// outputsExpected = outputsExpected.t(); +// gsExpected = gsExpected.t(); -/* - * Simple Test for SpatialDropout layer. - */ -TEST_CASE("SpatialDropoutLayerTest", "[ANNLayerTest]") -{ - arma::mat input, output, gy, g, temp; - arma::mat outputsExpected = arma::zeros(8, 12); - arma::mat gsExpected = arma::zeros(8, 12); +// // Compute the Forward and Backward passes and store the results. +// module.Forward(input, output); +// module.Backward(input, gy, g); - // Set the seed to a random value. - arma::arma_rng::set_seed_random(); - SpatialDropout<> module(3, 0.2); +// // Check through all possible cases, to find a match and then compare results. +// for (size_t i = 0; i < outputsExpected.n_cols; ++i) +// { +// if (arma::approx_equal(outputsExpected.col(i), output, "absdiff", 1e-1)) +// { +// // Check the correctness of the Forward pass of the layer. +// CheckMatrices(output, outputsExpected.col(i), 1e-1); +// // Check the correctness of the Backward pass of the layer. +// CheckMatrices(g, gsExpected.col(i), 1e-1); +// } +// } - // Input is a batch of 2 images, each of size (2,2) and having 4 channels. - input = { 0.4963, 0.0885, 0.7682, 0.1320, 0.3074, 0.4901, 0.6341, 0.8964, - 0.4556, 0.3489, 0.6323, 0.4017 }; +// // Check if the output is same as input when using deterministic mode. +// module.Deterministic() = true; +// output.clear(); +// module.Forward(input, output); +// CheckMatrices(output, input, 1e-1); +// } - gy = { 1, 3, 2, 4, 5, 7, 6, 8, 9, 11, 10, 12 }; +// /** +// * Test that the function that can access the parameters of the +// * SpatialDropout layer works. +// */ +// TEST_CASE("SpatialDropoutLayerParametersTest", "[ANNLayerTest]") +// { +// // Create the layer using the empty constructor. +// SpatialDropout<> layer; - // Following values have been calculated using torch.nn.Dropout2d(p=0.2). - temp = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }; - outputsExpected.row(0) = temp; - temp = { 0, 0, 0, 0, 0.3842, 0.6126, 0.7926, 1.1205, 0.5695, 0.4361, 0.7904, - 0.5021 }; - outputsExpected.row(1) = temp; - temp = { 0.6204, 0.1106, 0.9603, 0.1650, 0, 0, 0, 0, 0.5695, 0.4361, - 0.7904, 0.5021 }; - outputsExpected.row(2) = temp; - temp = { 0.6204, 0.1106, 0.9603, 0.1650, 0.3842, 0.6126, 0.7926, 1.1205, 0, - 0, 0, 0 }; - outputsExpected.row(3) = temp; - temp = { 0, 0, 0, 0, 0, 0, 0, 0, 0.5695, 0.4361, 0.7904, 0.5021 }; - outputsExpected.row(4) = temp; - temp = { 0, 0, 0, 0, 0.3842, 0.6126, 0.7926, 1.1205, 0, 0, 0, 0 }; - outputsExpected.row(5) = temp; - temp = { 0.6204, 0.1106, 0.9603, 0.1650, 0, 0, 0, 0, 0, 0, 0, 0 }; - outputsExpected.row(6) = temp; - temp = { 0.6204, 0.1106, 0.9603, 0.1650, 0.3842, 0.6126, 0.7926, 1.1205, - 0.5695, 0.4361, 0.7904, 0.5021 }; - outputsExpected.row(7) = temp; - temp = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }; - gsExpected.row(0) = temp; - temp = { 0, 0, 0, 0, 6.2500, 8.7500, 7.5000, 10.0000, 11.2500, 13.7500, - 12.5000, 15.0000 }; - gsExpected.row(1) = temp; - temp = { 1.2500, 3.7500, 2.5000, 5.0000, 0, 0, 0, 0, 11.2500, 13.7500, - 12.5000, 15.0000 }; - gsExpected.row(2) = temp; - temp = { 1.2500, 3.7500, 2.5000, 5.0000, 6.2500, 8.7500, 7.5000, 10.0000, 0, - 0, 0, 0 }; - gsExpected.row(3) = temp; - temp = { 0, 0, 0, 0, 0, 0, 0, 0, 11.2500, 13.7500, 12.5000, 15.0000 }; - gsExpected.row(4) = temp; - temp = { 0, 0, 0, 0, 6.2500, 8.7500, 7.5000, 10.0000, 0, 0, 0, 0 }; - gsExpected.row(5) = temp; - temp = { 1.2500, 3.7500, 2.5000, 5.0000, 0, 0, 0, 0, 0, 0, 0, 0 }; - gsExpected.row(6) = temp; - temp = { 1.2500, 3.7500, 2.5000, 5.0000, 6.2500, 8.7500, 7.5000, 10.0000, - 11.2500, 13.7500, 12.5000, 15.0000 }; - gsExpected.row(7) = temp; +// // Set the input parameters. +// layer.Size() = 3; +// layer.Ratio(0.2); - input = input.t(); - gy = gy.t(); - outputsExpected = outputsExpected.t(); - gsExpected = gsExpected.t(); - - // Compute the Forward and Backward passes and store the results. - module.Forward(input, output); - module.Backward(input, gy, g); - - // Check through all possible cases, to find a match and then compare results. - for (size_t i = 0; i < outputsExpected.n_cols; ++i) - { - if (arma::approx_equal(outputsExpected.col(i), output, "absdiff", 1e-1)) - { - // Check the correctness of the Forward pass of the layer. - CheckMatrices(output, outputsExpected.col(i), 1e-1); - // Check the correctness of the Backward pass of the layer. - CheckMatrices(g, gsExpected.col(i), 1e-1); - } - } - - // Check if the output is same as input when using deterministic mode. - module.Deterministic() = true; - output.clear(); - module.Forward(input, output); - CheckMatrices(output, input, 1e-1); -} - -/** - * Test that the function that can access the parameters of the - * SpatialDropout layer works. - */ -TEST_CASE("SpatialDropoutLayerParametersTest", "[ANNLayerTest]") -{ - // Create the layer using the empty constructor. - SpatialDropout<> layer; - - // Set the input parameters. - layer.Size() = 3; - layer.Ratio(0.2); - - // Check whether the input parameters have been set correctly. - REQUIRE(layer.Size() == 3); - REQUIRE(layer.Ratio() == 0.2); -} +// // Check whether the input parameters have been set correctly. +// REQUIRE(layer.Size() == 3); +// REQUIRE(layer.Ratio() == 0.2); +// } /** * Simple Positional Encoding layer test. - */ + * TEST_CASE("SimplePositionalEncodingTest", "[ANNLayerTest]") { const size_t seqLength = 5; @@ -5181,7 +5899,7 @@ TEST_CASE("SimplePositionalEncodingTest", "[ANNLayerTest]") arma::mat gy = 0.01 * arma::randu(embedDim * seqLength, batchSize); arma::mat output, g; - PositionalEncoding<> module(embedDim, seqLength); + PositionalEncoding module(embedDim, seqLength); // Check Forward function. module.Forward(input, output); @@ -5192,10 +5910,11 @@ TEST_CASE("SimplePositionalEncodingTest", "[ANNLayerTest]") module.Backward(input, gy, g); REQUIRE(std::equal(gy.begin(), gy.end(), g.begin())); } +*/ /** * Jacobian test for Positional Encoding layer. - */ + * TEST_CASE("JacobianPositionalEncodingTest", "[ANNLayerTest]") { for (size_t i = 0; i < 5; ++i) @@ -5205,16 +5924,17 @@ TEST_CASE("JacobianPositionalEncodingTest", "[ANNLayerTest]") arma::mat input; input.set_size(embedDim * seqLength, 1); - PositionalEncoding<> module(embedDim, seqLength); + PositionalEncoding module(embedDim, seqLength); double error = JacobianTest(module, input); REQUIRE(error <= 1e-5); } } +*/ /** * Simple Multihead Attention test. - */ + * TEST_CASE("SimpleMultiheadAttentionTest", "[ANNLayerTest]") { size_t tLen = 5; @@ -5239,7 +5959,7 @@ TEST_CASE("SimpleMultiheadAttentionTest", "[ANNLayerTest]") arma::mat keyPaddingMask = arma::zeros(1, sLen); keyPaddingMask(sLen - 1) = std::numeric_limits::lowest(); - MultiheadAttention<> module(tLen, sLen, embedDim, numHeads); + MultiheadAttention module(tLen, sLen, embedDim, numHeads); module.AttentionMask() = attnMask; module.KeyPaddingMask() = keyPaddingMask; module.Reset(); @@ -5266,10 +5986,11 @@ TEST_CASE("SimpleMultiheadAttentionTest", "[ANNLayerTest]") REQUIRE(gradient.n_rows == module.Parameters().n_rows); REQUIRE(gradient.n_cols == module.Parameters().n_cols); } +*/ /** * Jacobian MultiheadAttention module test. - */ + * TEST_CASE("JacobianMultiheadAttentionTest", "[ANNLayerTest]") { // Check when query = key = value. @@ -5283,7 +6004,7 @@ TEST_CASE("JacobianMultiheadAttentionTest", "[ANNLayerTest]") arma::mat query = arma::randu(embedDim * tgtSeqLen, batchSize); arma::mat input = arma::join_cols(arma::join_cols(query, query), query); - MultiheadAttention<> module(tgtSeqLen, tgtSeqLen, embedDim, nHeads); + MultiheadAttention module(tgtSeqLen, tgtSeqLen, embedDim, nHeads); module.Parameters().randu(); double error = CustomJacobianTest(module, input); @@ -5303,7 +6024,7 @@ TEST_CASE("JacobianMultiheadAttentionTest", "[ANNLayerTest]") arma::mat key = 0.091 * arma::randu(embedDim * srcSeqLen, batchSize); arma::mat input = arma::join_cols(arma::join_cols(query, key), key); - MultiheadAttention<> module(tgtSeqLen, srcSeqLen, embedDim, nHeads); + MultiheadAttention module(tgtSeqLen, srcSeqLen, embedDim, nHeads); module.Parameters().randu(); double error = CustomJacobianTest(module, input); @@ -5324,17 +6045,18 @@ TEST_CASE("JacobianMultiheadAttentionTest", "[ANNLayerTest]") arma::mat value = 0.045 * arma::randu(embedDim * srcSeqLen, batchSize); arma::mat input = arma::join_cols(arma::join_cols(query, key), value); - MultiheadAttention<> module(tgtSeqLen, srcSeqLen, embedDim, nHeads); + MultiheadAttention module(tgtSeqLen, srcSeqLen, embedDim, nHeads); module.Parameters().randu(); double error = JacobianTest(module, input); REQUIRE(error <= 1e-5); } } +*/ /** * Numerical gradient test for MultiheadAttention layer. - */ + * TEST_CASE("GradientMultiheadAttentionTest", "[ANNLayerTest]") { struct GradientFunction @@ -5368,16 +6090,17 @@ TEST_CASE("GradientMultiheadAttentionTest", "[ANNLayerTest]") keyPaddingMask = arma::zeros(1, srcSeqLen); keyPaddingMask(srcSeqLen - 1) = std::numeric_limits::lowest(); - model = new FFN, XavierInitialization>(); - model->Predictors() = input; - model->Responses() = target; - attnModule = new MultiheadAttention<>(tgtSeqLen, srcSeqLen, - embedDim, nHeads); - attnModule->AttentionMask() = attnMask; - attnModule->KeyPaddingMask() = keyPaddingMask; - model->Add(attnModule); - model->Add>(embedDim * tgtSeqLen, vocabSize); - model->Add>(); + model = new FFN(); + model->ResetData(input, target); + // attnModule = new MultiheadAttention(tgtSeqLen, srcSeqLen, embedDim, + // nHeads); + // attnModule->AttentionMask() = attnMask; + // attnModule->KeyPaddingMask() = keyPaddingMask; + // model->Add(attnModule); + model->Add(tgtSeqLen, srcSeqLen, embedDim, nHeads, + attnMask, keyPaddingMask); + model->Add(embedDim * tgtSeqLen, vocabSize); + model->Add(); } ~GradientFunction() @@ -5394,8 +6117,8 @@ TEST_CASE("GradientMultiheadAttentionTest", "[ANNLayerTest]") arma::mat& Parameters() { return model->Parameters(); } - FFN, XavierInitialization>* model; - MultiheadAttention<>* attnModule; + FFN* model; + // MultiheadAttention* attnModule; arma::mat input, target, attnMask, keyPaddingMask; const size_t tgtSeqLen; @@ -5408,10 +6131,11 @@ TEST_CASE("GradientMultiheadAttentionTest", "[ANNLayerTest]") REQUIRE(CheckGradient(function) <= 3e-06); } +*/ /** * Simple tests for instance normalization layer. - */ + * TEST_CASE("InstanceNormLayerTest", "[ANNLayerTest]") { arma::mat input, result, output, delta, deltaExpected; @@ -5526,11 +6250,12 @@ TEST_CASE("InstanceNormLayerTest", "[ANNLayerTest]") CheckMatrices(output, result, 1e-1); } +*/ /** * Test that the functions that can access the parameters of the * Instance Norm layer work. - */ + * TEST_CASE("InstanceNormLayerParametersTest", "[ANNLayerTest]") { // Parameter order : size, eps. @@ -5548,10 +6273,11 @@ TEST_CASE("InstanceNormLayerParametersTest", "[ANNLayerTest]") CheckMatrices(layer.TrainingVariance(), runningVariance); CheckMatrices(layer.TrainingMean(), runningMean); } +*/ /** * Instance Norm layer numerical gradient test. - */ + * TEST_CASE("GradientInstanceNormLayerTest", "[ANNLayerTest]") { // Add function gradient instantiation. @@ -5567,9 +6293,8 @@ TEST_CASE("GradientInstanceNormLayerTest", "[ANNLayerTest]") arma::mat target; target.ones(1, 1024); - model = new FFN, NguyenWidrowInitialization>(); - model->Predictors() = input; - model->Responses() = target; + model = new FFN(); + model->ResetData(input, target); model->Add >(); model->Add >(1, 2, 3, 3, 1, 1, 0, 0, 4, 4); model->Add > (2, 1024); @@ -5591,7 +6316,7 @@ TEST_CASE("GradientInstanceNormLayerTest", "[ANNLayerTest]") arma::mat& Parameters() { return model->Parameters(); } - FFN, NguyenWidrowInitialization>* model; + FFN* model; arma::mat input, target; } function; @@ -5605,3 +6330,4 @@ TEST_CASE("GradientInstanceNormLayerTest", "[ANNLayerTest]") REQUIRE(pass); } +*/ diff --git a/src/mlpack/tests/ann_test_tools.hpp b/src/mlpack/tests/ann_test_tools.hpp index dff9bbf5a9..d9f485484e 100644 --- a/src/mlpack/tests/ann_test_tools.hpp +++ b/src/mlpack/tests/ann_test_tools.hpp @@ -17,41 +17,24 @@ using namespace mlpack; using namespace mlpack::ann; -// Helper function which calls the Reset function of the given module. -template -void ResetFunction( - T& layer, - typename std::enable_if::value>::type* = 0) -{ - layer.Reset(); -} - -template -void ResetFunction( - T& /* layer */, - typename std::enable_if::value>::type* = 0) -{ - /* Nothing to do here */ -} - // Approximate Jacobian and supposedly-true Jacobian, then compare them // similarly to before. template double JacobianTest(ModuleType& module, - arma::mat& input, - const double minValue = -2, - const double maxValue = -1, - const double perturbation = 1e-6) + arma::mat& input, + const double minValue = -2, + const double maxValue = -1, + const double perturbation = 1e-6) { arma::mat output, outputA, outputB, jacobianA, jacobianB; + output.set_size(module.OutputSize(), input.n_cols); + outputA.set_size(module.OutputSize(), input.n_cols); + outputB.set_size(module.OutputSize(), input.n_cols); // Initialize the input matrix. RandomInitialization init(minValue, maxValue); init.Initialize(input, input.n_rows, input.n_cols); - // Initialize the module parameters. - ResetFunction(module); - // Initialize the jacobian matrix. module.Forward(input, output); jacobianA = arma::zeros(input.n_elem, output.n_elem); @@ -89,7 +72,7 @@ double JacobianTest(ModuleType& module, deriv.zeros(); derivTemp(i) = 1; - arma::mat delta; + arma::mat delta(input.n_rows, input.n_cols); module.Backward(input, deriv, delta); jacobianB.col(i) = delta; @@ -107,9 +90,9 @@ double CustomJacobianTest(ModuleType& module, const double perturbation = 1e-6) { arma::mat output, outputA, outputB, jacobianA, jacobianB; - - // Initialize the module parameters. - ResetFunction(module); + output.set_size(module.OutputSize(), input.n_cols); + outputA.set_size(module.OutputSize(), input.n_cols); + outputB.set_size(module.OutputSize(), input.n_cols); // Initialize the jacobian matrix. module.Forward(input, output); @@ -140,7 +123,7 @@ double CustomJacobianTest(ModuleType& module, deriv.zeros(); deriv(i) = 1; - arma::mat delta; + arma::mat delta(input.n_rows, input.n_cols); module.Backward(input, deriv, delta); jacobianB.col(i) = delta; diff --git a/src/mlpack/tests/ann_visitor_test.cpp b/src/mlpack/tests/ann_visitor_test.cpp deleted file mode 100644 index 29f376611e..0000000000 --- a/src/mlpack/tests/ann_visitor_test.cpp +++ /dev/null @@ -1,235 +0,0 @@ -/** - * @file tests/ann_visitor_test.cpp - * - * Tests for testing visitors in ANN's of mlpack. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#include -#include -#include -#include -#include -#include - -#include "catch.hpp" -#include "test_catch_tools.hpp" - -using namespace mlpack; -using namespace mlpack::ann; - -/** - * Test that the BiasSetVisitor works properly. - */ -TEST_CASE("BiasSetVisitorTest", "[ANNVisitorTest]") -{ - LayerTypes<> linear = new Linear<>(10, 10); - - arma::mat layerWeights(110, 1); - layerWeights.zeros(); - - ResetVisitor resetVisitor; - - boost::apply_visitor(WeightSetVisitor(layerWeights, 0), linear); - - boost::apply_visitor(resetVisitor, linear); - - arma::mat weight = {"1 2 3 4 5 6 7 8 9 10"}; - - size_t biasSize = boost::apply_visitor(BiasSetVisitor(weight, 0), linear); - - REQUIRE(biasSize == 10); - - arma::mat input(10, 1), output; - input.randu(); - - boost::apply_visitor(ForwardVisitor(input, output), linear); - - REQUIRE(arma::accu(output) == 55); - - boost::apply_visitor(DeleteVisitor(), linear); -} - -/** - * Check correctness of WeightSize() for a layer. - */ -void CheckCorrectnessOfWeightSize(LayerTypes<>& layer) -{ - size_t weightSize = boost::apply_visitor(WeightSizeVisitor(), - layer); - - arma::mat parameters; - boost::apply_visitor(ParametersVisitor(parameters), layer); - - REQUIRE(weightSize == parameters.n_elem); -} - -/** - * Test that WeightSetVisitor works properly. - */ -TEST_CASE("WeightSetVisitorTest", "[ANNVisitorTest]") -{ - size_t randomSize = arma::randi(arma::distr_param(1, 100)); - - LayerTypes<> linear = new Linear<>(randomSize, randomSize); - - arma::mat layerWeights(randomSize * randomSize + randomSize, 1); - layerWeights.zeros(); - - size_t setWeights = boost::apply_visitor(WeightSetVisitor(layerWeights, 0), - linear); - - REQUIRE(setWeights == randomSize * randomSize + randomSize); -} - -/** - * Test that WeightSizeVisitor works properly for linear layer. - */ -TEST_CASE("WeightSizeVisitorTestForLinearLayer", "[ANNVisitorTest]") -{ - size_t randomInSize = arma::randi(arma::distr_param(1, 100)); - size_t randomOutSize = arma::randi(arma::distr_param(1, 100)); - - LayerTypes<> linearLayer = new Linear<>(randomInSize, randomOutSize); - - CheckCorrectnessOfWeightSize(linearLayer); -} - -/** - * Test that WeightSizeVisitor works properly for concat layer. - */ -TEST_CASE("WeightSizeVisitorTestForConcatLayer", "[ANNVisitorTest]") -{ - LayerTypes<> concatLayer = new Concat<>(); - - CheckCorrectnessOfWeightSize(concatLayer); -} - -/** - * Test that WeightSizeVisitor works properly for fast lstm layer. - */ -TEST_CASE("WeightSizeVisitorTestForFastLSTMLayer", "[ANNVisitorTest]") -{ - size_t randomInSize = arma::randi(arma::distr_param(1, 100)); - size_t randomOutSize = arma::randi(arma::distr_param(1, 100)); - - LayerTypes<> fastLSTMLayer = new FastLSTM<>(randomInSize, randomOutSize); - - CheckCorrectnessOfWeightSize(fastLSTMLayer); -} - -/** - * Test that WeightSizeVisitor works properly for Add layer. - */ -TEST_CASE("WeightSizeVisitorTestForAddLayer", "[ANNVisitorTest]") -{ - size_t randomOutSize = arma::randi(arma::distr_param(1, 100)); - - LayerTypes<> addLayer = new Add<>(randomOutSize); - - CheckCorrectnessOfWeightSize(addLayer); -} - -/** - * Test that WeightSizeVisitor works properly for Atrous Convolution Layer. - */ -TEST_CASE("WeightSizeVisitorTestForAtrousConvolutionLayer", "[ANNVisitorTest]") -{ - size_t randomInSize = arma::randi(arma::distr_param(1, 100)); - size_t randomOutSize = arma::randi(arma::distr_param(1, 100)); - size_t randomKernelWidth = arma::randi(arma::distr_param(1, 100)); - size_t randomKernelHeight = arma::randi(arma::distr_param(1, 100)); - - LayerTypes<> atrousConvLayer = new AtrousConvolution<>(randomInSize, - randomOutSize, randomKernelWidth, randomKernelHeight); - - CheckCorrectnessOfWeightSize(atrousConvLayer); -} - - -/** - * Test that WeightSizeVisitor works properly for Convolution layer. - */ -TEST_CASE("WeightSizeVisitorTestForConvLayer", "[ANNVisitorTest]") -{ - size_t randomInSize = arma::randi(arma::distr_param(1, 100)); - size_t randomOutSize = arma::randi(arma::distr_param(1, 100)); - size_t randomKernelWidth = arma::randi(arma::distr_param(1, 100)); - size_t randomKernelHeight = arma::randi(arma::distr_param(1, 100)); - - LayerTypes<> convLayer = new Convolution<>(randomInSize, randomOutSize, - randomKernelWidth, randomKernelHeight); - CheckCorrectnessOfWeightSize(convLayer); -} - -/** - * Test that WeightSizeVisitor works properly for BatchNorm layer. - */ -TEST_CASE("WeightSizeVisitorTestForBatchNormLayer", "[ANNVisitorTest]") -{ - size_t randomSize = arma::randi(arma::distr_param(1, 100)); - - LayerTypes<> batchNorm = new BatchNorm<>(randomSize); - CheckCorrectnessOfWeightSize(batchNorm); -} - -/** - * Test that WeightSizeVisitor works properly for LSTM layer. - */ -TEST_CASE("WeightSizeVisitorTestForLSTMLayer", "[ANNVisitorTest]") -{ - size_t randomInSize = arma::randi(arma::distr_param(1, 100)); - size_t randomOutSize = arma::randi(arma::distr_param(1, 100)); - - LayerTypes<> lstm = new LSTM<>(randomInSize, randomOutSize); - CheckCorrectnessOfWeightSize(lstm); -} - -/** - * Test that WeightSizeVisitor works properly for Transposed Convolution layer. - */ -TEST_CASE("WeightSizeVisitorTestForTransposedConvLayer", "[ANNVisitorTest]") -{ - size_t randomInSize = arma::randi(arma::distr_param(1, 100)); - size_t randomOutSize = arma::randi(arma::distr_param(1, 100)); - size_t randomKernelWidth = arma::randi(arma::distr_param(1, 100)); - size_t randomKernelHeight = arma::randi(arma::distr_param(1, 100)); - - LayerTypes<> transposedConvLayer = new TransposedConvolution<>(randomInSize, - randomOutSize, randomKernelWidth, randomKernelHeight); - - CheckCorrectnessOfWeightSize(transposedConvLayer); -} - -/** - * Test that WeightSizeVisitor works properly for noisy linear layer. - */ -TEST_CASE("WeightSizeVisitorTestForNoisyLinearLayer", "[ANNVisitorTest]") -{ - size_t randomInSize = arma::randi(arma::distr_param(1, 100)); - size_t randomOutSize = arma::randi(arma::distr_param(1, 100)); - - LayerTypes<> noisyLinearLayer = new NoisyLinear<>(randomInSize, - randomOutSize); - - CheckCorrectnessOfWeightSize(noisyLinearLayer); -} - -/** - * Test that WeightSizeVisitor works properly for Multihead Attention layer. - */ -TEST_CASE("WeightSizeVisitorTestForMultiheadAttentionLayer", "[ANNVisitorTest]") -{ - size_t randomtgtSeqLen = arma::randi(arma::distr_param(1, 100)); - size_t randomsrcSeqLen = arma::randi(arma::distr_param(1, 100)); - size_t randomembedDim = 768; - size_t randomnumHeads = 12; - - LayerTypes<> MultiheadAttentionLayer = new MultiheadAttention<>( - randomtgtSeqLen, randomsrcSeqLen, randomembedDim, randomnumHeads); - - CheckCorrectnessOfWeightSize(MultiheadAttentionLayer); -} diff --git a/src/mlpack/tests/async_learning_test.cpp b/src/mlpack/tests/async_learning_test.cpp index 4e8f03e9e3..d788386c4f 100644 --- a/src/mlpack/tests/async_learning_test.cpp +++ b/src/mlpack/tests/async_learning_test.cpp @@ -13,7 +13,7 @@ #include #include -#include +#include #include #include #include @@ -48,11 +48,11 @@ TEST_CASE("OneStepQLearningTest", "[AsyncLearningTest]") // Set up the network. FFN, GaussianInitialization> model(MeanSquaredError<>(), GaussianInitialization(0, 0.001)); - model.Add>(4, 20); - model.Add>(); - model.Add>(20, 20); - model.Add>(); - model.Add>(20, 2); + model.Add(20); + model.Add(); + model.Add(20); + model.Add(); + model.Add(2); // Set up the policy. using Policy = GreedyPolicy; @@ -124,11 +124,11 @@ TEST_CASE("OneStepSarsaTest", "[AsyncLearningTest]") // Set up the network. FFN, GaussianInitialization> model(MeanSquaredError<>(), GaussianInitialization(0, 0.001)); - model.Add>(4, 20); - model.Add>(); - model.Add>(20, 20); - model.Add>(); - model.Add>(20, 2); + model.Add(20); + model.Add(); + model.Add(20); + model.Add(); + model.Add(2); // Set up the policy. using Policy = GreedyPolicy; @@ -199,11 +199,11 @@ TEST_CASE("NStepQLearningTest", "[AsyncLearningTest]") // Set up the network. FFN, GaussianInitialization> model(MeanSquaredError<>(), GaussianInitialization(0, 0.001)); - model.Add>(4, 20); - model.Add>(); - model.Add>(20, 20); - model.Add>(); - model.Add>(20, 2); + model.Add(20); + model.Add(); + model.Add(20); + model.Add(); + model.Add(2); // Set up the policy. using Policy = GreedyPolicy; diff --git a/src/mlpack/tests/callback_test.cpp b/src/mlpack/tests/callback_test.cpp index aeeed3fe26..203ce3d9a5 100644 --- a/src/mlpack/tests/callback_test.cpp +++ b/src/mlpack/tests/callback_test.cpp @@ -13,7 +13,8 @@ #include #include #include -#include +#include +//#include #include #include #include @@ -47,12 +48,12 @@ TEST_CASE("FFNCallbackTest", "[CallbackTest]") if (!data::Load("lab3.csv", labels)) FAIL("Cannot load test dataset lab3.csv!"); - FFN, RandomInitialization> model; + FFN model; - model.Add>(1, 2); - model.Add>(); - model.Add>(2, 1); - model.Add>(); + model.Add(2); + model.Add(); + model.Add(1); + model.Add(); std::stringstream stream; model.Train(data, labels, ens::PrintLoss(stream)); @@ -73,12 +74,12 @@ TEST_CASE("FFNWithOptimizerCallbackTest", "[CallbackTest]") if (!data::Load("lab3.csv", labels)) FAIL("Cannot load test dataset lab3.csv!"); - FFN, RandomInitialization> model; + FFN model; - model.Add>(1, 2); - model.Add>(); - model.Add>(2, 1); - model.Add>(); + model.Add(2); + model.Add(); + model.Add(1); + model.Add(); std::stringstream stream; ens::StandardSGD opt(0.1, 1, 5); @@ -98,14 +99,13 @@ TEST_CASE("RNNCallbackTest", "[CallbackTest]") RandomInitialization init(0.5, 0.5); // Create model with user defined rho parameter. - RNN, RandomInitialization> model( - rho, false, NegativeLogLikelihood<>(), init); - model.Add>(); - model.Add>(1, 10); + RNN model( + rho, false, NegativeLogLikelihood(), init); + model.Add(10); - // Use LSTM layer with rho. - model.Add>(10, 3, rho); - model.Add>(); + // Use LSTM layer with 3 units. + model.Add(3); + model.Add(); std::stringstream stream; model.Train(input, target, ens::PrintLoss(stream)); @@ -124,14 +124,13 @@ TEST_CASE("RNNWithOptimizerCallbackTest", "[CallbackTest]") RandomInitialization init(0.5, 0.5); // Create model with user defined rho parameter. - RNN, RandomInitialization> model( - rho, false, NegativeLogLikelihood<>(), init); - model.Add>(); - model.Add>(1, 10); + RNN model( + rho, false, NegativeLogLikelihood(), init); + model.Add(10); - // Use LSTM layer with rho. - model.Add>(10, 3, rho); - model.Add>(); + // Use LSTM layer with 3 units. + model.Add(3); + model.Add(); std::stringstream stream; ens::StandardSGD opt(0.1, 1, 5); @@ -234,7 +233,7 @@ TEST_CASE("SRWithOptimizerCallback", "[CallbackTest]") /* * Tests the RBM Implementation with PrintLoss callback. - */ + * TEST_CASE("RBMCallbackTest", "[CallbackTest]") { // Normalised dataset. @@ -259,7 +258,7 @@ TEST_CASE("RBMCallbackTest", "[CallbackTest]") double objVal = model.Train(msgd, ens::ProgressBar(70, stream)); REQUIRE(!std::isnan(objVal)); REQUIRE(stream.str().length() > 0); -} +}*/ /** * Tests the SparseAutoencoder implementation with diff --git a/src/mlpack/tests/catch.hpp b/src/mlpack/tests/catch.hpp index db1fed3b98..d2a12427b2 100644 --- a/src/mlpack/tests/catch.hpp +++ b/src/mlpack/tests/catch.hpp @@ -1,6 +1,6 @@ /* - * Catch v2.13.8 - * Generated: 2022-01-03 21:20:09.589503 + * Catch v2.13.9 + * Generated: 2022-04-12 22:37:23.260201 * ---------------------------------------------------------- * This file has been merged from multiple headers. Please don't edit it directly * Copyright (c) 2022 Two Blue Cubes Ltd. All rights reserved. @@ -15,7 +15,7 @@ #define CATCH_VERSION_MAJOR 2 #define CATCH_VERSION_MINOR 13 -#define CATCH_VERSION_PATCH 8 +#define CATCH_VERSION_PATCH 9 #ifdef __clang__ # pragma clang system_header @@ -13392,6 +13392,10 @@ namespace Catch { filename.erase(0, lastSlash); filename[0] = '#'; } + else + { + filename.insert(0, "#"); + } auto lastDot = filename.find_last_of('.'); if (lastDot != std::string::npos) { @@ -15387,7 +15391,7 @@ namespace Catch { } Version const& libraryVersion() { - static Version version( 2, 13, 8, "", 0 ); + static Version version( 2, 13, 9, "", 0 ); return version; } @@ -17890,7 +17894,7 @@ using Catch::Detail::Approx; #define INFO( msg ) (void)(0) #define UNSCOPED_INFO( msg ) (void)(0) #define WARN( msg ) (void)(0) -#define CAPTURE( msg ) (void)(0) +#define CAPTURE( ... ) (void)(0) #define TEST_CASE( ... ) INTERNAL_CATCH_TESTCASE_NO_REGISTRATION(INTERNAL_CATCH_UNIQUE_NAME( C_A_T_C_H_T_E_S_T_ )) #define TEST_CASE_METHOD( className, ... ) INTERNAL_CATCH_TESTCASE_NO_REGISTRATION(INTERNAL_CATCH_UNIQUE_NAME( C_A_T_C_H_T_E_S_T_ )) diff --git a/src/mlpack/tests/convolution_test.cpp b/src/mlpack/tests/convolution_test.cpp index c2798090ce..1893804aa5 100644 --- a/src/mlpack/tests/convolution_test.cpp +++ b/src/mlpack/tests/convolution_test.cpp @@ -54,6 +54,46 @@ void Convolution2DMethodTest(const arma::mat input, REQUIRE(*outputPtr == Approx(*convOutputPtr).epsilon(1e-5)); } +/* + * Implementation of the convolution function test with custom stride and + * dilation. This does not work for every convolution type. + * + * @param input Input used to perform the convolution. + * @param filter Filter used to perform the convolution. + * @param output The reference output data that contains the results of the + * convolution. + * @param strideH Height stride parameter. + * @param strideW Width stride parameter. + * @param dilationH Height dilation parameter. + * @param dilationW Width dilation parameter. + * + * @tparam ConvolutionFunction Convolution function used for the check. + */ +template +void Convolution2DMethodTest(const arma::mat input, + const arma::mat filter, + const arma::mat output, + const size_t strideW, + const size_t strideH, + const size_t dilationW, + const size_t dilationH) +{ + arma::mat convOutput; + ConvolutionFunction::Convolution(input, filter, convOutput, strideW, strideH, + dilationW, dilationH); + + // Check the output dimension. + bool b = (convOutput.n_rows == output.n_rows) && + (convOutput.n_cols == output.n_cols); + REQUIRE(b == 1); + + const double* outputPtr = output.memptr(); + const double* convOutputPtr = convOutput.memptr(); + + for (size_t i = 0; i < output.n_elem; ++i, outputPtr++, convOutputPtr++) + REQUIRE(*outputPtr == Approx(*convOutputPtr).epsilon(1e-5)); +} + /* * Implementation of the convolution function test using 3rd order tensors. * @@ -368,3 +408,163 @@ TEST_CASE("FullConvolutionBatchTest", "[ConvolutionTest]") ConvolutionMethodBatchTest >(input, filterCube, outputCube); } + +/** + * Test that non-stride-1 convolution works the same as stride-1 convolution on + * a smaller matrix. + */ +TEST_CASE("Stride2ConvolutionTest", "[ConvolutionTest]") +{ + // Generate dataset. + arma::mat input, filter, output; + input = { { 1, 2, 3, 4 }, + { 4, 1, 2, 3 }, + { 3, 4, 1, 2 }, + { 2, 3, 4, 1 } }; + + filter = { { 1, -1 }, + { -1, 1 } }; + + output = { { 1, 1, -4 }, + { -1, -4, 1 }, + { -2, -1, 1 } }; + + // Perform the naive convolution approach. + Convolution2DMethodTest >(input, filter, + output, 2, 2, 1, 1); +} + +TEST_CASE("Stride3ConvolutionTest", "[ConvolutionTest]") +{ + // Generate dataset. + arma::mat input, filter, output; + input = { { 1, 2, 3, 4 }, + { 4, 1, 2, 3 }, + { 3, 4, 1, 2 }, + { 2, 3, 4, 1 } }; + + filter = { { 1, -1 }, + { -1, 1 } }; + + output = { { 1, 1 }, + { -1, -4 } }; + + // Perform the naive convolution approach. + Convolution2DMethodTest >(input, filter, + output, 3, 3, 1, 1); +} + +TEST_CASE("UnequalStrideConvolutionTest", "[ConvolutionTest]") +{ + // Generate dataset. + arma::mat input, filter, output; + input = { { 1, 2, 3, 4 }, + { 4, 1, 2, 3 }, + { 3, 4, 1, 2 }, + { 2, 3, 4, 1 } }; + + filter = { { 1, -1 }, + { -1, 1 } }; + + output = { { 1, 1 }, + { -1, 0 }, + { -2, 3 } }; + + // Perform the naive convolution approach. + Convolution2DMethodTest >(input, filter, + output, 3, 2, 1, 1); +} + +TEST_CASE("Dilation2ConvolutionTest", "[ConvolutionTest]") +{ + // Generate dataset. + arma::mat input, filter, output; + input = { { 1, 2, 3, 4 }, + { 4, 1, 2, 3 }, + { 3, 4, 1, 2 }, + { 2, 3, 4, 1 } }; + + filter = { { 1, -1 }, + { -1, 1 } }; + + output = { { 1, 2, 2, 2, -3, -4 }, + { 4, 1, -2, 2, -2, -3 }, + { 2, 2, -4, -4, 2, 2 }, + { -2, 2, 4, -4, -2, 2 }, + { -3, -4, 2, 2, 1, 2 }, + { -2, -3, -2, 2, 4, 1 } }; + + // Perform the naive convolution approach. + Convolution2DMethodTest >(input, filter, + output, 1, 1, 2, 2); +} + +TEST_CASE("Dilation3ConvolutionTest", "[ConvolutionTest]") +{ + // Generate dataset. + arma::mat input, filter, output; + input = { { 1, 2, 3, 4 }, + { 4, 1, 2, 3 }, + { 3, 4, 1, 2 }, + { 2, 3, 4, 1 } }; + + filter = { { 1, -1 }, + { -1, 1 } }; + + output = { { 1, 2, 3, 3, -2, -3, -4 }, + { 4, 1, 2, -1, -1, -2, -3 }, + { 3, 4, 1, -1, -4, -1, -2 }, + { 1, 1, 1, -4, -1, -1, 3 }, + { -4, -1, -2, 1, 1, 2, 3 }, + { -3, -4, -1, 1, 4, 1, 2 }, + { -2, -3, -4, 1, 3, 4, 1 } }; + + // Perform the naive convolution approach. + Convolution2DMethodTest >(input, filter, + output, 1, 1, 3, 3); +} + +TEST_CASE("UnequalDilationConvolutionTest", "[ConvolutionTest]") +{ + // Generate dataset. + arma::mat input, filter, output; + input = { { 1, 2, 3, 4 }, + { 4, 1, 2, 3 }, + { 3, 4, 1, 2 }, + { 2, 3, 4, 1 } }; + + filter = { { 1, -1 }, + { -1, 1 } }; + + output = { { 1, 2, 3, 3, -2, -3, -4 }, + { 4, 1, 2, -1, -1, -2, -3 }, + { 2, 2, -2, -4, -2, 2, 2 }, + { -2, 2, 2, 0, -2, -2, 2 }, + { -3, -4, -1, 1, 4, 1, 2 }, + { -2, -3, -4, 1, 3, 4, 1 } }; + + // Perform the naive convolution approach. + Convolution2DMethodTest >(input, filter, + output, 1, 1, 3, 2); +} + +TEST_CASE("DilationAndStrideConvolutionTest", "[ConvolutionTest]") +{ + // Generate dataset. + arma::mat input, filter, output; + input = { { 1, 2, 3, 4 }, + { 4, 1, 2, 3 }, + { 3, 4, 1, 2 }, + { 2, 3, 4, 1 } }; + + filter = { { 1, -1 }, + { -1, 1 } }; + + output = { { 1, 2, -3 }, + { 2, -4, 2 }, + { -3, 2, 1 } }; + + // Perform the naive convolution approach. + Convolution2DMethodTest >(input, filter, + output, 2, 2, 2, 2); +} diff --git a/src/mlpack/tests/convolutional_network_test.cpp b/src/mlpack/tests/convolutional_network_test.cpp index 43eb1bb0d2..91994e6b8c 100644 --- a/src/mlpack/tests/convolutional_network_test.cpp +++ b/src/mlpack/tests/convolutional_network_test.cpp @@ -14,6 +14,7 @@ #include #include +#include #include #include @@ -70,6 +71,84 @@ void CheckMoveFunction(ModelType* network1, CheckMatrices(predictions1, predictions2); } +/** + * Build a trivial network with a single padding layer, and make sure it + * successfully pads the input. + */ +TEST_CASE("PaddingTest", "[ConvolutionalNetworktest]") +{ + arma::mat X; + X.load("mnist_first250_training_4s_and_9s.arm"); + + // Create the network. + FFN model; + + model.Add(1, 2, 3, 4); + + // Now, pass the data through. + arma::mat results; + model.InputDimensions() = std::vector({ 28, 28 }); + model.Forward(X, results); + + // Ensure that things are correctly padded. + arma::cube reshapedResults(results.memptr(), 35, 31, results.n_cols, false, + true); + + for (size_t i = 0; i < reshapedResults.n_slices; ++i) + { + // Check left. + for (size_t j = 0; j < reshapedResults.n_rows; ++j) + REQUIRE(reshapedResults(j, 0, i) == 0.0); + + // Check top. + for (size_t j = 0; j < 3; ++j) + for (size_t k = 0; k < reshapedResults.n_cols; ++k) + REQUIRE(reshapedResults(j, k, i) == 0.0); + + // Check bottom. + for (size_t j = 31; j < reshapedResults.n_rows; ++j) + for (size_t k = 0; k < reshapedResults.n_cols; ++k) + REQUIRE(reshapedResults(j, k, i) == 0.0); + + // Check right. + for (size_t j = 0; j < reshapedResults.n_rows; ++j) + for (size_t k = 29; k < reshapedResults.n_cols; ++k) + REQUIRE(reshapedResults(j, k, i) == 0.0); + } +} + +/** + * Build a trivial network with a MaxPooling layer, and make sure it + * successfully does the max-pool operation. + */ +TEST_CASE("MaxPoolingTest", "[ConvolutionalNetworkTest]") +{ + arma::mat X(8, 3); + X.col(0) = arma::vec("1, 2, 3, 4, 5, 6, 7, 8"); + X.col(1) = arma::vec("5, 7, 6, 8, 4, 3, 1, 2"); + X.col(2) = arma::vec("3, 4, 1, -1, 5, 5, 5, 5"); + + // Create the network. + FFN model; + model.Add(2, 2); + + arma::mat results; + model.InputDimensions() = std::vector({ 2, 4 }); + model.Forward(X, results); + + REQUIRE(results.n_rows == 3); + REQUIRE(results.n_cols == 3); + REQUIRE(results(0, 0) == 4); + REQUIRE(results(1, 0) == 6); + REQUIRE(results(2, 0) == 8); + REQUIRE(results(0, 1) == 8); + REQUIRE(results(1, 1) == 8); + REQUIRE(results(2, 1) == 4); + REQUIRE(results(0, 2) == 4); + REQUIRE(results(1, 2) == 5); + REQUIRE(results(2, 2) == 5); +} + /** * Train the vanilla network on a larger dataset. */ @@ -125,25 +204,27 @@ TEST_CASE("VanillaNetworkTest", "[ConvolutionalNetworkTest]") bool success = false; for (size_t trial = 0; trial < 5; ++trial) { - FFN, RandomInitialization> model; + FFN model; - model.Add >(1, 8, 5, 5, 1, 1, 0, 0, 28, 28); - model.Add >(); - model.Add >(8, 8, 2, 2); - model.Add >(8, 12, 2, 2); - model.Add >(); - model.Add >(2, 2, 2, 2); - model.Add >(192, 20); - model.Add >(); - model.Add >(20, 10); - model.Add >(); - model.Add >(10, 2); - model.Add >(); + model.Add(8, 5, 5, 1, 1, 0, 0); + model.Add(); + model.Add(2, 2); + model.Add(12, 2, 2); + model.Add(); + model.Add(2, 2); + model.Add(20); + model.Add(); + model.Add(10); + model.Add(); + model.Add(2); + model.Add(); + + model.InputDimensions() = std::vector({ 28, 28 }); // Train for only 8 epochs. ens::RMSProp opt(0.001, 1, 0.88, 1e-8, 8 * nPoints, -1); - double objVal = model.Train(X, Y, opt); + double objVal = model.Train(X, Y, opt, ens::PrintLoss()); // Test that objective value returned by FFN::Train() is finite. REQUIRE(std::isfinite(objVal) == true); @@ -170,6 +251,103 @@ TEST_CASE("VanillaNetworkTest", "[ConvolutionalNetworkTest]") REQUIRE(success == true); } +TEST_CASE("VanillaNetworkBatchSizeTest", "[ConvolutionalNetworkTest]") +{ + FFN model; + + model.Add(8, 5, 5, 1, 1, 0, 0); + model.Add(); + model.Add(2, 2); + model.Add(12, 2, 2); + model.Add(); + model.Add(2, 2); + model.Add(20); + model.Add(); + model.Add(10); + model.Add(); + model.Add(2); + model.Add(); + + model.InputDimensions() = std::vector({ 28, 28 }); + + arma::mat X; + X.load("mnist_first250_training_4s_and_9s.arm"); + + // Normalize each point since these are images. + arma::uword nPoints = X.n_cols; + for (arma::uword i = 0; i < nPoints; ++i) + { + X.col(i) /= norm(X.col(i), 2); + } + + // Build the target matrix. + arma::mat Y = arma::zeros(1, nPoints); + for (size_t i = 0; i < nPoints; ++i) + { + if (i < nPoints / 2) + { + // Assign label "1" to all samples with digit = 4 + Y(i) = 1; + } + else + { + // Assign label "0" to all samples with digit = 9 + Y(i) = 0; + } + } + + // Perform one epoch of training to get the weights to somewhere reasonable. + ens::RMSProp opt(0.001, 1, 0.88, 1e-8, nPoints, -1); + model.Train(X, Y, opt); + + size_t trials = 7; + for (size_t trial = 0; trial < trials; ++trial) + { + const size_t batchSize = std::pow(2.0, (double) trial + 1.0); + + // Check the forward pass, and then call EvaluateWithGradient() to compute + // the gradient. + arma::mat results; + arma::mat batchData = X.cols(0, batchSize - 1); + arma::mat batchResponses = Y.cols(0, batchSize - 1); + model.ResetData(std::move(batchData), std::move(batchResponses)); + model.Forward(X.cols(0, batchSize - 1), results); + + arma::mat gradient(1, model.WeightSize()); + const double obj = model.EvaluateWithGradient(model.Parameters(), gradient); + + REQUIRE(results.n_cols == batchSize); + + // Now compute results with a batch size of 1. + arma::mat singleResults(results.n_rows, results.n_cols); + arma::mat singleGradient(gradient.n_rows, gradient.n_cols); + double singleObj = 0.0; + + for (size_t i = 0; i < batchSize; ++i) + { + arma::mat tmpResult; + arma::mat singleData = X.cols(i, i); + arma::mat singleResponses = Y.cols(i, i); + model.ResetData(std::move(singleData), std::move(singleResponses)); + model.Forward(X.cols(i, i), tmpResult); + REQUIRE(tmpResult.n_cols == 1); + singleResults.col(i) = tmpResult; + + arma::mat tmpGradient(1, model.WeightSize()); + singleObj += model.EvaluateWithGradient(model.Parameters(), tmpGradient); + + singleGradient += tmpGradient; + } + + // Check the forward pass results. + CheckMatrices(results, singleResults); + + // Now, check EvaluateWithGradient()'s results. + REQUIRE(obj == Approx(singleObj)); + CheckMatrices(gradient, singleGradient); + } +} + /** * Train the vanilla network on a larger dataset. */ @@ -222,39 +400,132 @@ TEST_CASE("CheckCopyVanillaNetworkTest", "[ConvolutionalNetworkTest]") // of iterations using random weights. If this works 1 of 5 times, I'm fine // with that. All I want to know is that the network is able to escape from // local minima and to solve the task. - FFN, RandomInitialization> *model = new FFN, RandomInitialization>; + FFN *model = + new FFN; - model->Add >(1, 8, 5, 5, 1, 1, 0, 0, 28, 28); - model->Add >(); - model->Add >(8, 8, 2, 2); - model->Add >(8, 12, 2, 2); - model->Add >(); - model->Add >(2, 2, 2, 2); - model->Add >(192, 20); - model->Add >(); - model->Add >(20, 10); - model->Add >(); - model->Add >(10, 2); - model->Add >(); + model->Add(8, 5, 5, 1, 1, 0, 0); + model->Add(); + model->Add(2, 2); + model->Add(12, 2, 2); + model->Add(); + model->Add(2, 2); + model->Add(20); + model->Add(); + model->Add(10); + model->Add(); + model->Add(2); + model->Add(); + model->InputDimensions() = std::vector({ 28, 28 }); - FFN, RandomInitialization> *model1 = new FFN, RandomInitialization>; + FFN *model1 = + new FFN; + + model1->Add(8, 5, 5, 1, 1, 0, 0); + model1->Add(); + model1->Add(2, 2); + model1->Add(12, 2, 2); + model1->Add(); + model1->Add(2, 2); + model1->Add(20); + model1->Add(); + model1->Add(10); + model1->Add(); + model1->Add(2); + model1->Add(); + model1->InputDimensions() = std::vector({ 28, 28 }); - model1->Add >(1, 8, 5, 5, 1, 1, 0, 0, 28, 28); - model1->Add >(); - model1->Add >(8, 8, 2, 2); - model1->Add >(8, 12, 2, 2); - model1->Add >(); - model1->Add >(2, 2, 2, 2); - model1->Add >(192, 20); - model1->Add >(); - model1->Add >(20, 10); - model1->Add >(); - model1->Add >(10, 2); - model1->Add >(); - // Check whether copy constructor is working or not. CheckCopyFunction<>(model, X, Y, 8); // Check whether move constructor is working or not. CheckMoveFunction<>(model1, X, Y, 8); } + +TEST_CASE("Issue2986", "[ConvolutionalNetworkTest]") +{ + // Ensure that the code snippet in issue #2986 succeeds without any issues. + arma::mat input, output, delta; + input.ones(36, 1); + + // Note that the stride here is 2, not 1. + Convolution c(1, 3, 3, 2, 2, 0, 0); + + // Set up the layer without an enclosing FFN. + c.InputDimensions() = std::vector({ 6, 6 }); + c.ComputeOutputDimensions(); + arma::mat weights(c.WeightSize(), 1, arma::fill::randu); + c.SetWeights(weights.memptr()); + + output.set_size(c.OutputSize(), 1); + delta.set_size(input.size()); + + REQUIRE_NOTHROW(c.Forward(input, output)); + REQUIRE_NOTHROW(c.Backward(input, output, delta)); + + // Now test with a stride of 3. + c = Convolution(1, 3, 3, 3, 3, 0, 0); + + // Set up the layer without an enclosing FFN. + c.InputDimensions() = std::vector({ 6, 6 }); + c.ComputeOutputDimensions(); + weights.set_size(c.WeightSize(), 1); + weights.randu(); + c.SetWeights(weights.memptr()); + + output.set_size(c.OutputSize(), 1); + delta.set_size(input.size()); + + REQUIRE_NOTHROW(c.Forward(input, output)); + REQUIRE_NOTHROW(c.Backward(input, output, delta)); + + // Now test with different strides for height and width. + c = Convolution(1, 3, 3, 2, 3, 0, 0); + + // Set up the layer without an enclosing FFN. + c.InputDimensions() = std::vector({ 6, 6 }); + c.ComputeOutputDimensions(); + weights.set_size(c.WeightSize(), 1); + weights.randu(); + c.SetWeights(weights.memptr()); + + output.set_size(c.OutputSize(), 1); + delta.set_size(input.size()); + + REQUIRE_NOTHROW(c.Forward(input, output)); + REQUIRE_NOTHROW(c.Backward(input, output, delta)); +} + +// Test that the Convolution layer gives reasonable output when a non-zero +// padding size is used. +TEST_CASE("CustomPaddingTest", "[ConvolutionalNetworkTest]") +{ + arma::mat input, output, delta, weights; + input.ones(36, 1); + + Convolution c = Convolution(1, 3, 3, 1, 1, { 1, 2 }, { 3, 4 }, "none"); + + c.InputDimensions() = std::vector({ 6, 6 }); + c.ComputeOutputDimensions(); + + // First, check that the output dimensions are reasonable. + REQUIRE(c.OutputDimensions().size() == 3); + REQUIRE(c.OutputDimensions()[0] == 7); + REQUIRE(c.OutputDimensions()[1] == 11); + REQUIRE(c.OutputDimensions()[2] == 1); + + weights.set_size(c.WeightSize(), 1); + weights.ones(); + c.SetWeights(weights.memptr()); + + // Now make sure that the forward pass returns the correct output. + output.set_size(c.OutputSize(), 1); + REQUIRE_NOTHROW(c.Forward(input, output)); + REQUIRE(output.n_rows == c.OutputSize()); + REQUIRE(output.n_cols == 1); + // The lower right corner's convolution entry should only touch one input + // value (and everything else padding). + REQUIRE(output(output.n_rows - 1, 0) == 1.0); + + delta.set_size(input.size()); + REQUIRE_NOTHROW(c.Backward(input, output, delta)); +} diff --git a/src/mlpack/tests/custom_layer.hpp b/src/mlpack/tests/custom_layer.hpp index 593496bb50..3a3eaa78ad 100644 --- a/src/mlpack/tests/custom_layer.hpp +++ b/src/mlpack/tests/custom_layer.hpp @@ -2,7 +2,7 @@ * @file tests/custom_layer.hpp * @author Projyal Dev * - * A simple custom layer mimicing SigmoidLayer for testing if custom + * A simple custom layer mimicking SigmoidLayer for testing if custom * layers work. * * mlpack is free software; you may redistribute it and/or modify it under the @@ -15,20 +15,20 @@ #include #include - +#include +#include namespace mlpack { namespace ann { - /** - * Standard Sigmoid layer. - */ - template < - class ActivationFunction = LogisticFunction, - typename InputDataType = arma::mat, - typename OutputDataType = arma::mat - > - using CustomLayer = BaseLayer< - ActivationFunction, InputDataType, OutputDataType>; + +/** + * Standard Sigmoid layer. + */ +template < + class ActivationFunction = LogisticFunction, + typename MatType = arma::mat +> +using CustomLayer = BaseLayer; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/tests/cv_test.cpp b/src/mlpack/tests/cv_test.cpp index aafee5db11..1ee34b0566 100644 --- a/src/mlpack/tests/cv_test.cpp +++ b/src/mlpack/tests/cv_test.cpp @@ -24,7 +24,7 @@ #include #include #include -#include +#include #include #include #include @@ -33,6 +33,7 @@ #include #include #include +#include #include #include #include @@ -44,6 +45,7 @@ using namespace mlpack; using namespace mlpack::ann; using namespace mlpack::cv; using namespace mlpack::naive_bayes; +using namespace mlpack::perceptron; using namespace mlpack::regression; using namespace mlpack::tree; using namespace mlpack::data; @@ -301,10 +303,9 @@ TEST_CASE("MSEMatResponsesTest", "[CVTest]") arma::mat data("1 2"); arma::mat trainingResponses("1 2; 3 4"); - FFN, ConstInitialization> ffn(MeanSquaredError<>(), + FFN ffn(MeanSquaredError(), ConstInitialization(0)); - ffn.Add>(1, 2); - ffn.Add>(); + ffn.Add(2); ens::RMSProp opt(0.2); opt.BatchSize() = 1; @@ -612,6 +613,33 @@ TEST_CASE("KFoldCVAccuracyTest", "[CVTest]") REQUIRE_NOTHROW(cv.Model()); } +/** + * Test k-fold cross-validation with the perceptron. + */ +TEST_CASE("KFoldCVPerceptronTest", "[CVTest]") +{ + // The same as the test above (for Naive Bayes), but with the perceptron. + + // Making a 10-points dataset. The last point should be classified wrong when + // it is tested separately. + arma::mat data("0 1 2 3 100 101 102 103 104 5"); + arma::Row labels("0 0 0 0 1 1 1 1 1 1"); + size_t numClasses = 2; + + // 10-fold cross-validation, no shuffling. + KFoldCV, Accuracy> cv(10, data, labels, numClasses); + + // We should succeed in classifying separately the first nine samples, and + // fail with the remaining one. + double expectedAccuracy = (9 * 1.0 + 0.0) / 10; + + REQUIRE(cv.Evaluate() == Approx(expectedAccuracy).epsilon(1e-7)); + + // Assert we can access a trained model without the exception of + // uninitialization. + REQUIRE_NOTHROW(cv.Model()); +} + /** * Test k-fold cross-validation with weighted linear regression. */ diff --git a/src/mlpack/tests/feedforward_network_2_test.cpp b/src/mlpack/tests/feedforward_network_2_test.cpp index 90ba3c3fbd..a115468305 100644 --- a/src/mlpack/tests/feedforward_network_2_test.cpp +++ b/src/mlpack/tests/feedforward_network_2_test.cpp @@ -14,6 +14,7 @@ #include #include +#include #include #include #include @@ -101,9 +102,9 @@ TEST_CASE("RBFNetworkTest", "[FeedForwardNetworkTest]") KMeans<> kmeans; kmeans.Cluster(trainData, 8, centroids); - FFN > model; - model.Add >(trainData.n_rows, 8, centroids); - model.Add >(8, 3); + FFN model; + model.Add(8, centroids); + model.Add(3); // RBFN neural net with MeanSquaredError. TestNetwork<>(model, trainData, trainLabels1, testData, testLabels, 10, 0.1); @@ -133,9 +134,9 @@ TEST_CASE("RBFNetworkTest", "[FeedForwardNetworkTest]") KMeans<> kmeans1; kmeans1.Cluster(dataset, 140, centroids1); - FFN > model1; - model1.Add >(dataset.n_rows, 140, centroids1, 4.1); - model1.Add >(140, 2); + FFN model1; + model1.Add(140, centroids1, 4.1); + model1.Add(2); // RBFN neural net with MeanSquaredError. TestNetwork<>(model1, dataset, labels1, dataset, labels, 10, 0.1); diff --git a/src/mlpack/tests/feedforward_network_test.cpp b/src/mlpack/tests/feedforward_network_test.cpp index 6fd5af4e30..613d0e56e8 100644 --- a/src/mlpack/tests/feedforward_network_test.cpp +++ b/src/mlpack/tests/feedforward_network_test.cpp @@ -12,7 +12,7 @@ */ #include -#include +#include #include #include @@ -20,7 +20,7 @@ #include "catch.hpp" #include "serialization.hpp" -#include "custom_layer.hpp" +//#include "custom_layer.hpp" using namespace mlpack; using namespace mlpack::ann; @@ -37,7 +37,7 @@ void TestNetwork(ModelType& model, const size_t maxEpochs, const double classificationErrorThreshold) { - ens::RMSProp opt(0.01, 32, 0.88, 1e-8, maxEpochs * trainData.n_cols, -1); + ens::RMSProp opt(0.01, 32, 0.88, 1e-8, trainData.n_cols * maxEpochs, -100); model.Train(trainData, trainLabels, opt); MatType predictionTemp; @@ -51,6 +51,7 @@ void TestNetwork(ModelType& model, } size_t correct = arma::accu(prediction == testLabels); + double classificationError = 1 - double(correct) / testData.n_cols; REQUIRE(classificationError <= classificationErrorThreshold); } @@ -59,14 +60,14 @@ void TestNetwork(ModelType& model, template void CheckCopyFunction(ModelType* network1, MatType& trainData, - MatType& trainLabels, - const size_t maxEpochs) + MatType& trainLabels) { - ens::RMSProp opt(0.01, 32, 0.88, 1e-8, maxEpochs * trainData.n_cols, -1); + ens::RMSProp opt(0.01, 32, 0.88, 1e-8, trainData.n_cols, -1); network1->Train(trainData, trainLabels, opt); arma::mat predictions1; network1->Predict(trainData, predictions1); + FFN<> network2; network2 = *network1; delete network1; @@ -85,7 +86,7 @@ void CheckMoveFunction(ModelType* network1, MatType& trainLabels, const size_t maxEpochs) { - ens::RMSProp opt(0.01, 32, 0.88, 1e-8, maxEpochs * trainData.n_cols, -1); + ens::RMSProp opt(0.01, 32, 0.88, 1e-8, trainData.n_cols, -1); network1->Train(trainData, trainLabels, opt); arma::mat predictions1; @@ -136,28 +137,28 @@ TEST_CASE("CheckCopyMovingVanillaNetworkTest", "[FeedForwardNetworkTest]") * +-----+ +-----+ */ - FFN > *model = new FFN >; - model->Add >(trainData.n_rows, 8); - model->Add >(); - model->Add >(8, 3); - model->Add >(); + FFN *model = new FFN; + model->Add(8); + model->Add(); + model->Add(3); + model->Add(); - FFN > *model1 = new FFN >; - model1->Add >(trainData.n_rows, 8); - model1->Add >(); - model1->Add >(8, 3); - model1->Add >(); + FFN *model1 = new FFN; + model1->Add(8); + model1->Add(); + model1->Add(3); + model1->Add(); // Check whether copy constructor is working or not. - CheckCopyFunction<>(model, trainData, trainLabels, 1); + CheckCopyFunction(model, trainData, trainLabels); // Check whether move constructor is working or not. - CheckMoveFunction<>(model1, trainData, trainLabels, 1); + CheckMoveFunction(model1, trainData, trainLabels, 1); } /** * Check whether copying and moving network with Reparametrization is working or not. - */ + * TEST_CASE("CheckCopyMovingReparametrizationNetworkTest", "[FeedForwardNetworkTest]") { @@ -165,31 +166,64 @@ TEST_CASE("CheckCopyMovingReparametrizationNetworkTest", arma::mat trainData; data::Load("thyroid_train.csv", trainData, true); - // Normalize labels to [0, 2]. - arma::mat trainLabels = trainData.row(trainData.n_rows - 1) - 1; + arma::mat trainLabels = trainData.row(trainData.n_rows - 1); trainData.shed_row(trainData.n_rows - 1); - /* - * Construct a feed forward network with trainData.n_rows input nodes, - * followed by a linear layer and then a reparametrization layer. - */ + // Construct a feed forward network with trainData.n_rows input nodes, + // followed by a linear layer and then a reparametrization layer. + FFN *model = new FFN; + model->Add(8); + model->Add(false, true, 1); + model->Add(); - FFN > *model = new FFN >; - model->Add >(trainData.n_rows, 8); - model->Add >(4, false, true, 1); - model->Add >(); - - FFN > *model1 = new FFN >; - model1->Add >(trainData.n_rows, 8); - model1->Add >(4, false, true, 1); - model1->Add >(); + FFN *model1 = new FFN; + model1->Add(8); + model1->Add(false, true, 1); + model1->Add(); // Check whether copy constructor is working or not. - CheckCopyFunction<>(model, trainData, trainLabels, 1); + CheckCopyFunction(model, trainData, trainLabels); // Check whether move constructor is working or not. - CheckMoveFunction<>(model1, trainData, trainLabels, 1); + CheckMoveFunction(model1, trainData, trainLabels, 1); } +*/ + +/** + * Check whether copying and moving network with Reparametrization is working or not. + */ +// TEST_CASE("CheckCopyMovingReparametrizationNetworkTest", +// "[FeedForwardNetworkTest]") +// { +// // Load the dataset. +// arma::mat trainData; +// data::Load("thyroid_train.csv", trainData, true); +// +// // Normalize labels to [0, 2]. +// arma::mat trainLabels = trainData.row(trainData.n_rows - 1) - 1; +// trainData.shed_row(trainData.n_rows - 1); +// +// /* +// * Construct a feed forward network with trainData.n_rows input nodes, +// * followed by a linear layer and then a reparametrization layer. +// */ +// +// FFN *model = new FFN; +// model->Add >(trainData.n_rows, 8); +// model->Add >(4, false, true, 1); +// model->Add >(); +// +// FFN *model1 = new FFN; +// model1->Add >(trainData.n_rows, 8); +// model1->Add >(4, false, true, 1); +// model1->Add >(); +// +// // Check whether copy constructor is working or not. +// CheckCopyFunction<>(model, trainData, trainLabels, 1); +// +// // Check whether move constructor is working or not. +// CheckMoveFunction<>(model1, trainData, trainLabels, 1); +// } /** * Check whether copying and moving network with linear3d is working or not. @@ -204,45 +238,25 @@ TEST_CASE("CheckCopyMovingLinear3DNetworkTest", "[FeedForwardNetworkTest]") arma::mat trainLabels = trainData.row(trainData.n_rows - 1) - 1; trainData.shed_row(trainData.n_rows - 1); - /* - * Construct a feed forward network with trainData.n_rows input nodes, - * hiddenLayerSize hidden nodes and trainLabels.n_rows output nodes. The - * network structure looks like: - * - * Input Hidden Output - * Layer Layer Layer - * +-----+ +-----+ +-----+ - * | | | | | | - * | +------>| +------>| | - * | | +>| | +>| | - * +-----+ | +--+--+ | +-----+ - * | | - * Bias | Bias | - * Layer | Layer | - * +-----+ | +-----+ | - * | | | | | | - * | +-----+ | +-----+ - * | | | | - * +-----+ +-----+ - */ + // Construct a feed forward network with trainData.n_rows input nodes, + // followed by a linear layer and then a Linear3D layer. + FFN *model = new FFN; + model->Add(8); + model->Add(); + model->Add(3); + model->Add(); - FFN > *model = new FFN >; - model->Add >(trainData.n_rows, 8); - model->Add >(); - model->Add >(8, 3); - model->Add >(); - - FFN > *model1 = new FFN >; - model1->Add >(trainData.n_rows, 8); - model1->Add >(); - model1->Add >(8, 3); - model1->Add >(); + FFN *model1 = new FFN; + model1->Add(8); + model1->Add(); + model1->Add(3); + model1->Add(); // Check whether copy constructor is working or not. - CheckCopyFunction<>(model, trainData, trainLabels, 1); + CheckCopyFunction(model, trainData, trainLabels); // Check whether move constructor is working or not. - CheckMoveFunction<>(model1, trainData, trainLabels, 1); + CheckMoveFunction(model1, trainData, trainLabels, 1); } /** @@ -256,28 +270,24 @@ TEST_CASE("CheckCopyMovingNoisyLinearTest", "[FeedForwardNetworkTest]") arma::mat output = arma::mat("0"); // Check copying constructor. - FFN> *model1 = new FFN>(); - model1->Predictors() = input; - model1->Responses() = output; - model1->Add>(); - model1->Add>(10, 5); - model1->Add >(5, 1); - model1->Add>(); + FFN *model1 = new FFN(); + model1->ResetData(input, output); + model1->Add(5); + model1->Add(1); + model1->Add(); // Check whether copy constructor is working or not. - CheckCopyFunction<>(model1, input, output, 1); + CheckCopyFunction(model1, input, output); // Check moving constructor. - FFN> *model2 = new FFN>(); - model2->Predictors() = input; - model2->Responses() = output; - model2->Add>(); - model2->Add>(10, 5); - model2->Add >(5, 1); - model2->Add>(); + FFN *model2 = new FFN(); + model2->ResetData(input, output); + model2->Add(5); + model2->Add(1); + model2->Add(); // Check whether move constructor is working or not. - CheckMoveFunction<>(model2, input, output, 1); + CheckMoveFunction(model2, input, output, 1); } /** @@ -291,43 +301,39 @@ TEST_CASE("CheckCopyMovingConcatenateTest", "[FeedForwardNetworkTest]") arma::mat output = arma::mat("1"); // Check copying constructor. - FFN> *model1 = new FFN>(); - model1->Predictors() = input; - model1->Responses() = output; - model1->Add>(); - model1->Add>(10, 5); + FFN *model1 = new FFN(); + model1->ResetData(input, output); + model1->Add(5); // Create concatenate layer. arma::mat concatMatrix = arma::ones(5, 1); - Concatenate<>* concatLayer = new Concatenate<>(); + Concatenate* concatLayer = new Concatenate(); concatLayer->Concat() = concatMatrix; // Add concatenate layer to the current network. model1->Add(concatLayer); - model1->Add >(10, 5); - model1->Add>(); + model1->Add(5); + model1->Add(); // Check whether copy constructor is working or not. - CheckCopyFunction<>(model1, input, output, 1); + CheckCopyFunction(model1, input, output); // Check moving constructor. - FFN> *model2 = new FFN>(); - model2->Predictors() = input; - model2->Responses() = output; - model2->Add>(); - model2->Add>(10, 5); + FFN *model2 = new FFN(); + model2->ResetData(input, output); + model2->Add(5); // Create new concat layer. - Concatenate<>* concatLayer2 = new Concatenate<>(); + Concatenate* concatLayer2 = new Concatenate(); concatLayer2->Concat() = concatMatrix; // Add concatenate layer to the current network. model2->Add(concatLayer2); - model2->Add >(10, 5); - model2->Add>(); + model2->Add(5); + model2->Add(); // Check whether move constructor is working or not. - CheckMoveFunction<>(model2, input, output, 1); + CheckMoveFunction(model2, input, output, 1); } /** @@ -343,47 +349,25 @@ TEST_CASE("CheckCopyMovingDropoutNetworkTest", "[FeedForwardNetworkTest]") arma::mat trainLabels = trainData.row(trainData.n_rows - 1) - 1; trainData.shed_row(trainData.n_rows - 1); - /* - * Construct a feed forward network with trainData.n_rows input nodes, - * hiddenLayerSize hidden nodes and trainLabels.n_rows output nodes. The - * network structure looks like: - * - * Input Hidden Output - * Layer Layer Layer - * +-----+ +-----+ +-----+ - * | | | | | | - * | +------>| +------>| | - * | | +>| | +>| | - * +-----+ | +--+--+ | +-----+ - * | | - * Bias | Bias | - * Layer | Layer | - * +-----+ | +-----+ | - * | | | | | | - * | +-----+ | +-----+ - * | | | | - * +-----+ +-----+ - */ + FFN *model = new FFN; + model->Add(8); + model->Add(); + model->Add(0.3); + model->Add(3); + model->Add(); - FFN > *model = new FFN >; - model->Add >(trainData.n_rows, 8); - model->Add >(); - model->Add >(0.3); - model->Add >(8, 3); - model->Add >(); - - FFN > *model1 = new FFN >; - model1->Add >(trainData.n_rows, 8); - model1->Add >(); - model1->Add >(0.3); - model1->Add >(8, 3); - model1->Add >(); + FFN *model1 = new FFN; + model1->Add(8); + model1->Add(); + model1->Add(0.3); + model1->Add(3); + model1->Add(); // Check whether copy constructor is working or not. - CheckCopyFunction<>(model, trainData, trainLabels, 1); + CheckCopyFunction(model, trainData, trainLabels); // Check whether move constructor is working or not. - CheckMoveFunction<>(model1, trainData, trainLabels, 1); + CheckMoveFunction(model1, trainData, trainLabels, 1); } /** @@ -414,20 +398,20 @@ TEST_CASE("CheckCopyMovingVanillaNetworkTestNoBias", "[FeedForwardNetworkTest]") * +-----+ +--+--+ +-----+ */ - FFN > *model = new FFN >; - model->Add >(trainData.n_rows, 8); - model->Add >(); - model->Add >(8, 3); - model->Add >(); + FFN *model = new FFN; + model->Add(8); + model->Add(); + model->Add(3); + model->Add(); - FFN > *model1 = new FFN >; - model1->Add >(trainData.n_rows, 8); - model1->Add >(); - model1->Add >(8, 3); - model1->Add >(); + FFN *model1 = new FFN; + model1->Add(8); + model1->Add(); + model1->Add(3); + model1->Add(); // Check whether copy constructor is working or not. - CheckCopyFunction<>(model, trainData, trainLabels, 1); + CheckCopyFunction<>(model, trainData, trainLabels); // Check whether move constructor is working or not. CheckMoveFunction<>(model1, trainData, trainLabels, 1); @@ -436,38 +420,38 @@ TEST_CASE("CheckCopyMovingVanillaNetworkTestNoBias", "[FeedForwardNetworkTest]") /** * Check whether copying and moving network with Reparametrization is working or not. */ -TEST_CASE("CheckCopyMovingReparametrizationNetworkTestNoBias", - "[FeedForwardNetworkTest]") -{ - // Load the dataset. - arma::mat trainData; - data::Load("thyroid_train.csv", trainData, true); - - // Normalize labels to [0, 2]. - arma::mat trainLabels = trainData.row(trainData.n_rows - 1) - 1; - trainData.shed_row(trainData.n_rows - 1); - - /* - * Construct a feed forward network with trainData.n_rows input nodes, - * followed by a linear layer and then a reparametrization layer. - */ - - FFN > *model = new FFN >; - model->Add >(trainData.n_rows, 8); - model->Add >(4, false, true, 1); - model->Add >(); - - FFN > *model1 = new FFN >; - model1->Add >(trainData.n_rows, 8); - model1->Add >(4, false, true, 1); - model1->Add >(); - - // Check whether copy constructor is working or not. - CheckCopyFunction<>(model, trainData, trainLabels, 1); - - // Check whether move constructor is working or not. - CheckMoveFunction<>(model1, trainData, trainLabels, 1); -} +// TEST_CASE("CheckCopyMovingReparametrizationNetworkTestNoBias", +// "[FeedForwardNetworkTest]") +// { +// // Load the dataset. +// arma::mat trainData; +// data::Load("thyroid_train.csv", trainData, true); +// +// // Normalize labels to [0, 2]. +// arma::mat trainLabels = trainData.row(trainData.n_rows - 1) - 1; +// trainData.shed_row(trainData.n_rows - 1); +// +// /* +// * Construct a feed forward network with trainData.n_rows input nodes, +// * followed by a linear layer and then a reparametrization layer. +// */ +// +// FFN *model = new FFN; +// model->Add >(trainData.n_rows, 8); +// model->Add >(4, false, true, 1); +// model->Add >(); +// +// FFN *model1 = new FFN; +// model1->Add >(trainData.n_rows, 8); +// model1->Add >(4, false, true, 1); +// model1->Add >(); +// +// // Check whether copy constructor is working or not. +// CheckCopyFunction<>(model, trainData, trainLabels, 1); +// +// // Check whether move constructor is working or not. +// CheckMoveFunction<>(model1, trainData, trainLabels, 1); +// } /** * Train the vanilla network on a larger dataset. @@ -513,11 +497,11 @@ TEST_CASE("FFVanillaNetworkTest", "[FeedForwardNetworkTest]") * +-----+ +-----+ */ - FFN > model; - model.Add >(trainData.n_rows, 8); - model.Add >(); - model.Add >(8, 3); - model.Add >(); + FFN model; + model.Add(8); + model.Add(); + model.Add(3); + model.Add(); // Vanilla neural net with logistic activation function. // Because 92% of the patients are not hyperthyroid the neural @@ -534,13 +518,13 @@ TEST_CASE("FFVanillaNetworkTest", "[FeedForwardNetworkTest]") arma::mat labels = arma::zeros(1, dataset.n_cols); labels.submat(0, labels.n_cols / 2, 0, labels.n_cols - 1).fill(1); - FFN > model1; - model1.Add >(dataset.n_rows, 10); - model1.Add >(); - model1.Add >(10, 2); - model1.Add >(); + FFN model1; + model1.Add(10); + model1.Add(); + model1.Add(2); + model1.Add(); // Vanilla neural net with logistic activation function. - TestNetwork<>(model1, dataset, labels, dataset, labels, 10, 0.2); + TestNetwork(model1, dataset, labels, dataset, labels, 10, 0.2); } TEST_CASE("ForwardBackwardTest", "[FeedForwardNetworkTest]") @@ -555,14 +539,13 @@ TEST_CASE("ForwardBackwardTest", "[FeedForwardNetworkTest]") arma::mat labels = arma::zeros(1, dataset.n_cols); labels.submat(0, labels.n_cols / 2, 0, labels.n_cols - 1).fill(1); - FFN > model; - model.Add >(dataset.n_rows, 50); - model.Add >(); - model.Add >(50, 10); - model.Add >(); + FFN model; + model.Add(50); + model.Add(); + model.Add(10); + model.Add(); ens::VanillaUpdate opt; - model.ResetParameters(); #if ENS_VERSION_MAJOR == 1 opt.Initialize(model.Parameters().n_rows, model.Parameters().n_cols); #else @@ -662,12 +645,12 @@ TEST_CASE("DropoutNetworkTest", "[FeedForwardNetworkTest]") * +-----+ */ - FFN > model; - model.Add >(trainData.n_rows, 8); - model.Add >(); - model.Add >(); - model.Add >(8, 3); - model.Add >(); + FFN model; + model.Add(8); + model.Add(); + model.Add(); + model.Add(3); + model.Add(); // Vanilla neural net with logistic activation function. // Because 92% of the patients are not hyperthyroid the neural @@ -685,19 +668,19 @@ TEST_CASE("DropoutNetworkTest", "[FeedForwardNetworkTest]") arma::mat labels = arma::zeros(1, dataset.n_cols); labels.submat(0, labels.n_cols / 2, 0, labels.n_cols - 1).fill(1); - FFN > model1; - model1.Add >(dataset.n_rows, 10); - model1.Add >(); - model.Add >(); - model1.Add >(10, 2); - model1.Add >(); + FFN model1; + model1.Add(10); + model1.Add(); + model.Add(); + model1.Add(2); + model1.Add(); // Vanilla neural net with logistic activation function. - TestNetwork<>(model1, dataset, labels, dataset, labels, 10, 0.2); + TestNetwork(model1, dataset, labels, dataset, labels, 10, 0.2); } /** * Train the highway network on a larger dataset. - */ + * TEST_CASE("HighwayNetworkTest", "[FeedForwardNetworkTest]") { arma::mat dataset; @@ -710,16 +693,16 @@ TEST_CASE("HighwayNetworkTest", "[FeedForwardNetworkTest]") arma::mat labels = arma::zeros(1, dataset.n_cols); labels.submat(0, labels.n_cols / 2, 0, labels.n_cols - 1).fill(1); - FFN > model; - model.Add >(dataset.n_rows, 10); - Highway<>* highway = new Highway<>(10, true); - highway->Add >(10, 10); - highway->Add >(); + FFN model; + model.Add(10); + Highway* highway = new Highway(); + highway->Add(10); + highway->Add(); model.Add(highway); // This takes ownership of the memory. - model.Add >(10, 2); - model.Add >(); - TestNetwork<>(model, dataset, labels, dataset, labels, 10, 0.2); -} + model.Add(2); + model.Add(); + TestNetwork(model, dataset, labels, dataset, labels, 10, 0.2); +}*/ /** * Train the DropConnect network on a larger dataset. @@ -767,16 +750,16 @@ TEST_CASE("DropConnectNetworkTest", "[FeedForwardNetworkTest]") * */ - FFN > model; - model.Add >(trainData.n_rows, 8); - model.Add >(); - model.Add >(8, 3); - model.Add >(); + FFN model; + model.Add(8); + model.Add(); + model.Add(3); + model.Add(); // Vanilla neural net with logistic activation function. // Because 92% of the patients are not hyperthyroid the neural // network must be significant better than 92%. - TestNetwork<>(model, trainData, trainLabels, testData, testLabels, 10, 0.1); + TestNetwork(model, trainData, trainLabels, testData, testLabels, 10, 0.1); arma::mat dataset; dataset.load("mnist_first250_training_4s_and_9s.arm"); @@ -788,13 +771,14 @@ TEST_CASE("DropConnectNetworkTest", "[FeedForwardNetworkTest]") arma::mat labels = arma::zeros(1, dataset.n_cols); labels.submat(0, labels.n_cols / 2, 0, labels.n_cols - 1).fill(1); - FFN > model1; - model1.Add >(dataset.n_rows, 10); - model1.Add >(); - model1.Add >(10, 2); - model1.Add >(); + FFN model1; + model1.Add(10); + model1.Add(); + model1.Add(2); + model1.Add(); + // Vanilla neural net with logistic activation function. - TestNetwork<>(model1, dataset, labels, dataset, labels, 10, 0.2); + TestNetwork(model1, dataset, labels, dataset, labels, 10, 0.2); } /** @@ -803,9 +787,9 @@ TEST_CASE("DropConnectNetworkTest", "[FeedForwardNetworkTest]") */ TEST_CASE("FFNMiscTest", "[FeedForwardNetworkTest]") { - FFN> model; - model.Add>(2, 3); - model.Add>(); + FFN model; + model.Add(3); + model.Add(); auto copiedModel(model); copiedModel = model; @@ -838,19 +822,19 @@ TEST_CASE("FFSerializationTest", "[FeedForwardNetworkTest]") // Vanilla neural net with logistic activation function. // Because 92% of the patients are not hyperthyroid the neural // network must be significant better than 92%. - FFN > model; - model.Add >(trainData.n_rows, 8); - model.Add >(); - model.Add >(); - model.Add >(8, 3); - model.Add >(); + FFN model; + model.Add(8); + model.Add(); + model.Add(); + model.Add(3); + model.Add(); ens::RMSProp opt(0.01, 32, 0.88, 1e-8, trainData.n_cols /* 1 epoch */, -1); model.Train(trainData, trainLabels, opt); - FFN> xmlModel, jsonModel, binaryModel; - xmlModel.Add>(10, 10); // Layer that will get removed. + FFN xmlModel, jsonModel, binaryModel; + xmlModel.Add(10); // Layer that will get removed. // Serialize into other models. SerializeObjectAll(model, xmlModel, jsonModel, binaryModel); @@ -859,7 +843,7 @@ TEST_CASE("FFSerializationTest", "[FeedForwardNetworkTest]") model.Predict(testData, predictions); xmlModel.Predict(testData, xmlPredictions); jsonModel.Predict(testData, jsonPredictions); - jsonModel.Predict(testData, binaryPredictions); + binaryModel.Predict(testData, binaryPredictions); CheckMatrices(predictions, xmlPredictions, jsonPredictions, binaryPredictions); @@ -868,110 +852,112 @@ TEST_CASE("FFSerializationTest", "[FeedForwardNetworkTest]") /** * Test that serialization works ok for PReLU. */ -TEST_CASE("PReLUSerializationTest", "[FeedForwardNetworkTest]") -{ - // Load the dataset. - arma::mat trainData; - if (!data::Load("thyroid_train.csv", trainData)) - FAIL("Cannot open thyroid_train.csv"); - - arma::mat trainLabels = trainData.row(trainData.n_rows - 1); - trainData.shed_row(trainData.n_rows - 1); - trainLabels -= 1; // The labels should be between 0 and numClasses - 1. - - arma::mat testData; - if (!data::Load("thyroid_test.csv", testData)) - FAIL("Cannot load dataset thyroid_test.csv"); - - arma::mat testLabels = testData.row(testData.n_rows - 1); - testData.shed_row(testData.n_rows - 1); - testLabels -= 1; // The labels should be between 0 and numClasses - 1. - - // Vanilla neural net with logistic activation function. - // Because 92% of the patients are not hyperthyroid the neural - // network must be significant better than 92%. - FFN > model; - model.Add >(trainData.n_rows, 8); - model.Add >(); - model.Add >(); - model.Add >(8, 3); - model.Add >(); - - ens::RMSProp opt(0.01, 32, 0.88, 1e-8, trainData.n_cols /* 1 epoch */, -1); - - model.Train(trainData, trainLabels, opt); - - FFN> xmlModel, jsonModel, binaryModel; - xmlModel.Add>(10, 10); // Layer that will get removed. - - // Serialize into other models. - SerializeObjectAll(model, xmlModel, jsonModel, binaryModel); - - arma::mat predictions, xmlPredictions, jsonPredictions, binaryPredictions; - model.Predict(testData, predictions); - xmlModel.Predict(testData, xmlPredictions); - jsonModel.Predict(testData, jsonPredictions); - jsonModel.Predict(testData, binaryPredictions); - - CheckMatrices(predictions, xmlPredictions, jsonPredictions, - binaryPredictions); -} +// TEST_CASE("PReLUSerializationTest", "[FeedForwardNetworkTest]") +// { +// // Load the dataset. +// arma::mat trainData; +// if (!data::Load("thyroid_train.csv", trainData)) +// FAIL("Cannot open thyroid_train.csv"); +// +// arma::mat trainLabels = trainData.row(trainData.n_rows - 1); +// trainData.shed_row(trainData.n_rows - 1); +// trainLabels -= 1; // The labels should be between 0 and numClasses - 1. +// +// arma::mat testData; +// if (!data::Load("thyroid_test.csv", testData)) +// FAIL("Cannot load dataset thyroid_test.csv"); +// +// arma::mat testLabels = testData.row(testData.n_rows - 1); +// testData.shed_row(testData.n_rows - 1); +// testLabels -= 1; // The labels should be between 0 and numClasses - 1. +// +// // Vanilla neural net with logistic activation function. +// // Because 92% of the patients are not hyperthyroid the neural +// // network must be significant better than 92%. +// FFN model; +// model.Add >(trainData.n_rows, 8); +// model.Add >(); +// model.Add >(); +// model.Add >(8, 3); +// model.Add >(); +// +// ens::RMSProp opt(0.01, 32, 0.88, 1e-8, trainData.n_cols /* 1 epoch */, -1); +// +// model.Train(trainData, trainLabels, opt); +// +// FFN xmlModel, jsonModel, binaryModel; +// xmlModel.Add>(10, 10); // Layer that will get removed. +// +// // Serialize into other models. +// SerializeObjectAll(model, xmlModel, jsonModel, binaryModel); +// +// arma::mat predictions, xmlPredictions, jsonPredictions, binaryPredictions; +// model.Predict(testData, predictions); +// xmlModel.Predict(testData, xmlPredictions); +// jsonModel.Predict(testData, jsonPredictions); +// jsonModel.Predict(testData, binaryPredictions); +// +// CheckMatrices(predictions, xmlPredictions, jsonPredictions, +// binaryPredictions); +// } /** * Test if the custom layers work. The target is to see if the code compiles * when the Train and Prediction are called. */ -TEST_CASE("CustomLayerTest", "[FeedForwardNetworkTest]") -{ - // Load the dataset. - arma::mat trainData; - if (!data::Load("thyroid_train.csv", trainData)) - FAIL("Cannot open thyroid_train.csv"); - - arma::mat trainLabels = trainData.row(trainData.n_rows - 1); - trainData.shed_row(trainData.n_rows - 1); - trainLabels -= 1; // The labels should be between 0 and numClasses - 1. - - arma::mat testData; - if (!data::Load("thyroid_test.csv", testData)) - FAIL("Cannot load dataset thyroid_test.csv"); - - arma::mat testLabels = testData.row(testData.n_rows - 1); - testData.shed_row(testData.n_rows - 1); - testLabels -= 1; // The labels should be between 0 and numClasses - 1. - - FFN, RandomInitialization, CustomLayer<> > model; - model.Add >(trainData.n_rows, 8); - model.Add >(); - model.Add >(8, 3); - model.Add >(); - - ens::RMSProp opt(0.01, 32, 0.88, 1e-8, 15, -1); - model.Train(trainData, trainLabels, opt); - - arma::mat predictionTemp; - model.Predict(testData, predictionTemp); - arma::mat prediction = arma::zeros(1, predictionTemp.n_cols); -} +// TEST_CASE("CustomLayerTest", "[FeedForwardNetworkTest]") +// { +// // Load the dataset. +// arma::mat trainData; +// if (!data::Load("thyroid_train.csv", trainData)) +// FAIL("Cannot open thyroid_train.csv"); +// +// arma::mat trainLabels = trainData.row(trainData.n_rows - 1); +// trainData.shed_row(trainData.n_rows - 1); +// trainLabels -= 1; // The labels should be between 0 and numClasses - 1. +// +// arma::mat testData; +// if (!data::Load("thyroid_test.csv", testData)) +// FAIL("Cannot load dataset thyroid_test.csv"); +// +// arma::mat testLabels = testData.row(testData.n_rows - 1); +// testData.shed_row(testData.n_rows - 1); +// testLabels -= 1; // The labels should be between 0 and numClasses - 1. +// +// FFN > model; +// model.Add >(trainData.n_rows, 8); +// model.Add >(); +// model.Add >(8, 3); +// model.Add >(); +// +// ens::RMSProp opt(0.01, 32, 0.88, 1e-8, 15, -1); +// model.Train(trainData, trainLabels, opt); +// +// arma::mat predictionTemp; +// model.Predict(testData, predictionTemp); +// arma::mat prediction = arma::zeros(1, predictionTemp.n_cols); +// } /** * Test the overload of Forward function which allows partial forward pass. */ TEST_CASE("PartialForwardTest", "[FeedForwardNetworkTest]") { - FFN, RandomInitialization> model; - model.Add >(5, 10); + FFN model; + model.Add(10); - // Add a new Add<> module which adds a constant term to the input. - Add<>* addModule = new Add<>(10); + // Add a new Add<> module which adds a (learnable) constant term to the input. + Add* addModule = new Add(); model.Add(addModule); - LinearNoBias<>* linearNoBiasModule = new LinearNoBias<>(10, 10); + LinearNoBias* linearNoBiasModule = new LinearNoBias(10); model.Add(linearNoBiasModule); - model.Add >(10, 10); + model.Add(10); + + // Set up the network for inputs of dimensionality 10. + model.Reset(10); - model.ResetParameters(); // Set the parameters of the Add<> module to a matrix of ones. addModule->Parameters() = arma::ones(10, 1); // Set the parameters of the LinearNoBias<> module to a matrix of ones. @@ -1026,12 +1012,12 @@ TEST_CASE("FFNTrainReturnObjective", "[FeedForwardNetworkTest]") // Vanilla neural net with logistic activation function. // Because 92% of the patients are not hyperthyroid the neural // network must be significantly better than 92%. - FFN > model; - model.Add >(trainData.n_rows, 8); - model.Add >(); - model.Add >(); - model.Add >(8, 3); - model.Add >(); + FFN model; + model.Add(8); + model.Add(); + model.Add(); + model.Add(3); + model.Add(); ens::RMSProp opt(0.01, 32, 0.88, 1e-8, trainData.n_cols /* 1 epoch */, -1); @@ -1046,14 +1032,14 @@ TEST_CASE("FFNTrainReturnObjective", "[FeedForwardNetworkTest]") TEST_CASE("FFNReturnModel", "[FeedForwardNetworkTest]") { // Create dummy network. - FFN > model; - Linear<>* linearA = new Linear<>(3, 3); + FFN model; + Linear* linearA = new Linear(3); model.Add(linearA); - Linear<>* linearB = new Linear<>(3, 4); + Linear* linearB = new Linear(4); model.Add(linearB); - // Initialize network parameter. - model.ResetParameters(); + // Initialize network parameters, with a new input size of 3. + model.Reset(3); // Set all network parameter to one. model.Parameters().ones(); @@ -1063,9 +1049,8 @@ TEST_CASE("FFNReturnModel", "[FeedForwardNetworkTest]") // Get the layer parameter from layer A and layer B and store them in // parameterA and parameterB. - arma::mat parameterA, parameterB; - boost::apply_visitor(ParametersVisitor(parameterA), model.Model()[0]); - boost::apply_visitor(ParametersVisitor(parameterB), model.Model()[1]); + const arma::mat parameterA = model.Network()[0]->Parameters(); + const arma::mat parameterB = model.Network()[1]->Parameters(); CheckMatrices(parameterA, arma::ones(3 * 3 + 3, 1)); CheckMatrices(parameterB, arma::zeros(3 * 4 + 4, 1)); @@ -1097,11 +1082,10 @@ TEST_CASE("OptimizerTest", "[FeedForwardNetworkTest]") testData.shed_row(testData.n_rows - 1); testLabels -= 1; // The labels should be between 0 and numClasses. - FFN, RandomInitialization, CustomLayer<> > model; - model.Add >(trainData.n_rows, 8); - model.Add >(); - model.Add >(8, 3); - model.Add >(); + FFN model; + model.Add(8); + model.Add(3); + model.Add(); ens::DE opt(200, 1000, 0.6, 0.8, 1e-5); model.Train(trainData, trainLabels, opt); @@ -1125,23 +1109,18 @@ TEST_CASE("FFNCheckInputShapeTest", "[FeedForwardNetworkTest]") arma::mat testData; data::Load("thyroid_test.csv", testData, true); - arma::mat testLabels = testData.row(testData.n_rows - 1) - 1; + arma::mat testLabels = testData.row(testData.n_rows - 1); testData.shed_row(testData.n_rows - 1); - FFN, RandomInitialization, CustomLayer<> > model; - // Purposely putting wrong input shape so that error is thrown. - model.Add >(trainData.n_rows - 3, 8); - model.Add >(); - model.Add >(8, 3); - model.Add >(); - - std::string expectedMsg = "FFN<>::Train(): "; - expectedMsg += "the first layer of the network expects "; - expectedMsg += std::to_string(trainData.n_rows - 3) + " elements, "; - expectedMsg += "but the input has " + std::to_string(trainData.n_rows) + - " dimensions! "; + FFN model; + model.Add(8); + model.Add(3); + model.Add(); ens::DE opt(200, 1000, 0.6, 0.8, 1e-5); + // Now set up the input incorrectly. + model.InputDimensions() = std::vector({ 1, 2, 3 }); + REQUIRE_THROWS_AS(model.Train(trainData, trainLabels, opt), std::logic_error); } diff --git a/src/mlpack/tests/init_rules_test.cpp b/src/mlpack/tests/init_rules_test.cpp index 46c76e32f1..5d56e904b8 100644 --- a/src/mlpack/tests/init_rules_test.cpp +++ b/src/mlpack/tests/init_rules_test.cpp @@ -214,18 +214,17 @@ TEST_CASE("NetworkInitTest", "[InitRulesTest]") { arma::mat input = arma::ones(5, 1); arma::mat response; - NegativeLogLikelihood<> outputLayer; + NegativeLogLikelihood outputLayer; // Create a simple network and use the RandomInitialization rule to // initialize the network parameters. RandomInitialization randomInit(0.5, 0.5); - FFN, RandomInitialization> randomModel( + FFN randomModel( std::move(outputLayer), randomInit); - randomModel.Add >(); - randomModel.Add >(5, 5); - randomModel.Add >(5, 2); - randomModel.Add >(); + randomModel.Add(5); + randomModel.Add(2); + randomModel.Add(); randomModel.Predict(input, response); bool b = arma::all(arma::vectorise(randomModel.Parameters()) == 0.5); @@ -234,23 +233,21 @@ TEST_CASE("NetworkInitTest", "[InitRulesTest]") // Create a simple network and use the OrthogonalInitialization rule to // initialize the network parameters. - FFN, OrthogonalInitialization> orthogonalModel; - orthogonalModel.Add >(); - orthogonalModel.Add >(5, 5); - orthogonalModel.Add >(5, 2); - orthogonalModel.Add >(); + FFN orthogonalModel; + orthogonalModel.Add(5); + orthogonalModel.Add(2); + orthogonalModel.Add(); orthogonalModel.Predict(input, response); REQUIRE(orthogonalModel.Parameters().n_elem == 42); // Create a simple network and use the ZeroInitialization rule to // initialize the network parameters. - FFN, ConstInitialization> - zeroModel(NegativeLogLikelihood<>(), ConstInitialization(0)); - zeroModel.Add >(); - zeroModel.Add >(5, 5); - zeroModel.Add >(5, 2); - zeroModel.Add >(); + FFN + zeroModel(NegativeLogLikelihood(), ConstInitialization(0)); + zeroModel.Add(5); + zeroModel.Add(2); + zeroModel.Add(); zeroModel.Predict(input, response); REQUIRE(arma::accu(zeroModel.Parameters()) == 0); @@ -261,34 +258,31 @@ TEST_CASE("NetworkInitTest", "[InitRulesTest]") // parameters. KathirvalavakumarSubavathiInitialization kathirvalavakumarSubavathiInit( input, 1.5); - FFN, KathirvalavakumarSubavathiInitialization> + FFN ksModel(std::move(outputLayer), kathirvalavakumarSubavathiInit); - ksModel.Add >(); - ksModel.Add >(5, 5); - ksModel.Add >(5, 2); - ksModel.Add >(); + ksModel.Add(5); + ksModel.Add(2); + ksModel.Add(); ksModel.Predict(input, response); REQUIRE(ksModel.Parameters().n_elem == 42); // Create a simple network and use the OivsInitialization rule to // initialize the network parameters. - FFN, OivsInitialization<> > oivsModel; - oivsModel.Add >(); - oivsModel.Add >(5, 5); - oivsModel.Add >(5, 2); - oivsModel.Add >(); + FFN > oivsModel; + oivsModel.Add(5); + oivsModel.Add(2); + oivsModel.Add(); oivsModel.Predict(input, response); REQUIRE(oivsModel.Parameters().n_elem == 42); // Create a simple network and use the GaussianInitialization rule to // initialize the network parameters. - FFN, GaussianInitialization> gaussianModel; - gaussianModel.Add >(); - gaussianModel.Add >(5, 5); - gaussianModel.Add >(5, 2); - gaussianModel.Add >(); + FFN gaussianModel; + gaussianModel.Add(5); + gaussianModel.Add(2); + gaussianModel.Add(); gaussianModel.Predict(input, response); REQUIRE(gaussianModel.Parameters().n_elem == 42); diff --git a/src/mlpack/tests/ksinit_test.cpp b/src/mlpack/tests/ksinit_test.cpp index 930df617e4..15a9dec67c 100644 --- a/src/mlpack/tests/ksinit_test.cpp +++ b/src/mlpack/tests/ksinit_test.cpp @@ -15,7 +15,7 @@ #include #include -#include +#include #include #include #include @@ -75,12 +75,12 @@ void BuildVanillaNetwork(MatType& trainData, // Cauchy’s Inequality Based on Sensitivity Analysis" paper. KathirvalavakumarSubavathiInitialization init(trainData, 4.59); - FFN, KathirvalavakumarSubavathiInitialization> - model(MeanSquaredError<>(), init); + FFN + model(MeanSquaredError(), init); - model.Add >(trainData.n_rows, hiddenLayerSize); - model.Add >(); - model.Add >(hiddenLayerSize, outputSize); + model.Add(hiddenLayerSize); + model.Add(); + model.Add(outputSize); ens::RMSProp opt(0.01, 1, 0.88, 1e-8, maxEpochs * trainData.n_cols, 1e-18); diff --git a/src/mlpack/tests/layer_names_test.cpp b/src/mlpack/tests/layer_names_test.cpp deleted file mode 100644 index 9d94f0ff67..0000000000 --- a/src/mlpack/tests/layer_names_test.cpp +++ /dev/null @@ -1,160 +0,0 @@ -/** - * @file tests/layer_names_test.cpp - * @author Sreenik Seal - * - * Tests for testing the string representation of - * layers in mlpack's ANN module. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#include -#include -#include -#include - -#include "catch.hpp" - -using namespace mlpack; -using namespace ann; - -/** - * Test if the LayerNameVisitor works properly. - */ -TEST_CASE("LayerNameVisitorTest", "[LayerNamesTest]") -{ - LayerTypes<> atrousConvolution = new AtrousConvolution<>(); - LayerTypes<> alphaDropout = new AlphaDropout<>(); - LayerTypes<> batchNorm = new BatchNorm<>(); - LayerTypes<> constant = new Constant<>(); - LayerTypes<> convolution = new Convolution<>(); - LayerTypes<> dropConnect = new DropConnect<>(); - LayerTypes<> dropout = new Dropout<>(); - LayerTypes<> flexibleReLU = new FlexibleReLU<>(); - LayerTypes<> layerNorm = new LayerNorm<>(); - LayerTypes<> linear = new Linear<>(); - LayerTypes<> linearNoBias = new LinearNoBias<>(); - LayerTypes<> maxPooling = new MaxPooling<>(); - LayerTypes<> meanPooling = new MeanPooling<>(); - LayerTypes<> multiplyConstant = new MultiplyConstant<>(); - LayerTypes<> reLULayer = new ReLULayer<>(); - LayerTypes<> transposedConvolution = new TransposedConvolution<>(); - LayerTypes<> identityLayer = new IdentityLayer<>(); - LayerTypes<> tanHLayer = new TanHLayer<>(); - LayerTypes<> eLU = new ELU<>(); - LayerTypes<> hardTanH = new HardTanH<>(); - LayerTypes<> leakyReLU = new LeakyReLU<>(); - LayerTypes<> pReLU = new PReLU<>(); - LayerTypes<> sigmoidLayer = new SigmoidLayer<>(); - LayerTypes<> logSoftMax = new LogSoftMax<>(); - LayerTypes<> lstmLayer = new LSTM<>(100, 10); - LayerTypes<> creluLayer = new CReLU<>(); - LayerTypes<> highwayLayer = new Highway<>(); - LayerTypes<> gruLayer = new GRU<>(); - LayerTypes<> glimpseLayer = new Glimpse<>(); - LayerTypes<> fastlstmLayer = new FastLSTM<>(); - LayerTypes<> weightnormLayer = new WeightNorm<>(new IdentityLayer<>()); - - // Bilinear interpolation is not yet supported by the string converter. - LayerTypes<> unsupportedLayer = new BilinearInterpolation<>(); - - REQUIRE(boost::apply_visitor(LayerNameVisitor(), - atrousConvolution) == "atrousconvolution"); - REQUIRE(boost::apply_visitor(LayerNameVisitor(), - alphaDropout) == "alphadropout"); - REQUIRE(boost::apply_visitor(LayerNameVisitor(), - batchNorm) == "batchnorm"); - REQUIRE(boost::apply_visitor(LayerNameVisitor(), - constant) == "constant"); - REQUIRE(boost::apply_visitor(LayerNameVisitor(), - convolution) == "convolution"); - REQUIRE(boost::apply_visitor(LayerNameVisitor(), - dropConnect) == "dropconnect"); - REQUIRE(boost::apply_visitor(LayerNameVisitor(), - dropout) == "dropout"); - REQUIRE(boost::apply_visitor(LayerNameVisitor(), - flexibleReLU) == "flexiblerelu"); - REQUIRE(boost::apply_visitor(LayerNameVisitor(), - layerNorm) == "layernorm"); - REQUIRE(boost::apply_visitor(LayerNameVisitor(), - linear) == "linear"); - REQUIRE(boost::apply_visitor(LayerNameVisitor(), - linearNoBias) == "linearnobias"); - REQUIRE(boost::apply_visitor(LayerNameVisitor(), - maxPooling) == "maxpooling"); - REQUIRE(boost::apply_visitor(LayerNameVisitor(), - meanPooling) == "meanpooling"); - REQUIRE(boost::apply_visitor(LayerNameVisitor(), - multiplyConstant) == "multiplyconstant"); - REQUIRE(boost::apply_visitor(LayerNameVisitor(), - reLULayer) == "relu"); - REQUIRE(boost::apply_visitor(LayerNameVisitor(), - transposedConvolution) == "transposedconvolution"); - REQUIRE(boost::apply_visitor(LayerNameVisitor(), - identityLayer) == "identity"); - REQUIRE(boost::apply_visitor(LayerNameVisitor(), - tanHLayer) == "tanh"); - REQUIRE(boost::apply_visitor(LayerNameVisitor(), - eLU) == "elu"); - REQUIRE(boost::apply_visitor(LayerNameVisitor(), - hardTanH) == "hardtanh"); - REQUIRE(boost::apply_visitor(LayerNameVisitor(), - leakyReLU) == "leakyrelu"); - REQUIRE(boost::apply_visitor(LayerNameVisitor(), - pReLU) == "prelu"); - REQUIRE(boost::apply_visitor(LayerNameVisitor(), - sigmoidLayer) == "sigmoid"); - REQUIRE(boost::apply_visitor(LayerNameVisitor(), - logSoftMax) == "logsoftmax"); - REQUIRE(boost::apply_visitor(LayerNameVisitor(), - unsupportedLayer) == "unsupported"); - REQUIRE(boost::apply_visitor(LayerNameVisitor(), - lstmLayer) == "lstm"); - REQUIRE(boost::apply_visitor(LayerNameVisitor(), - creluLayer) == "crelu"); - REQUIRE(boost::apply_visitor(LayerNameVisitor(), - highwayLayer) == "highway"); - REQUIRE(boost::apply_visitor(LayerNameVisitor(), - gruLayer) == "gru"); - REQUIRE(boost::apply_visitor(LayerNameVisitor(), - glimpseLayer) == "glimpse"); - REQUIRE(boost::apply_visitor(LayerNameVisitor(), - fastlstmLayer) == "fastlstm"); - REQUIRE(boost::apply_visitor(LayerNameVisitor(), - weightnormLayer) == "weightnorm"); - // Delete all instances. - boost::apply_visitor(DeleteVisitor(), atrousConvolution); - boost::apply_visitor(DeleteVisitor(), alphaDropout); - boost::apply_visitor(DeleteVisitor(), batchNorm); - boost::apply_visitor(DeleteVisitor(), constant); - boost::apply_visitor(DeleteVisitor(), convolution); - boost::apply_visitor(DeleteVisitor(), dropConnect); - boost::apply_visitor(DeleteVisitor(), dropout); - boost::apply_visitor(DeleteVisitor(), flexibleReLU); - boost::apply_visitor(DeleteVisitor(), layerNorm); - boost::apply_visitor(DeleteVisitor(), linear); - boost::apply_visitor(DeleteVisitor(), linearNoBias); - boost::apply_visitor(DeleteVisitor(), maxPooling); - boost::apply_visitor(DeleteVisitor(), meanPooling); - boost::apply_visitor(DeleteVisitor(), multiplyConstant); - boost::apply_visitor(DeleteVisitor(), reLULayer); - boost::apply_visitor(DeleteVisitor(), transposedConvolution); - boost::apply_visitor(DeleteVisitor(), identityLayer); - boost::apply_visitor(DeleteVisitor(), tanHLayer); - boost::apply_visitor(DeleteVisitor(), eLU); - boost::apply_visitor(DeleteVisitor(), hardTanH); - boost::apply_visitor(DeleteVisitor(), leakyReLU); - boost::apply_visitor(DeleteVisitor(), pReLU); - boost::apply_visitor(DeleteVisitor(), sigmoidLayer); - boost::apply_visitor(DeleteVisitor(), logSoftMax); - boost::apply_visitor(DeleteVisitor(), unsupportedLayer); - boost::apply_visitor(DeleteVisitor(), lstmLayer); - boost::apply_visitor(DeleteVisitor(), creluLayer); - boost::apply_visitor(DeleteVisitor(), highwayLayer); - boost::apply_visitor(DeleteVisitor(), gruLayer); - boost::apply_visitor(DeleteVisitor(), glimpseLayer); - boost::apply_visitor(DeleteVisitor(), fastlstmLayer); - boost::apply_visitor(DeleteVisitor(), weightnormLayer); -} diff --git a/src/mlpack/tests/loss_functions_test.cpp b/src/mlpack/tests/loss_functions_test.cpp index 2c800b1e21..f7699b4fc9 100644 --- a/src/mlpack/tests/loss_functions_test.cpp +++ b/src/mlpack/tests/loss_functions_test.cpp @@ -14,7 +14,7 @@ */ #include -#include +#include #include #include #include @@ -34,6 +34,7 @@ #include #include #include +#include #include #include #include @@ -54,7 +55,7 @@ TEST_CASE("HuberLossTest", "[LossFunctionsTest]") arma::mat input, target, output; arma::mat expectedOutput; double loss; - HuberLoss<> module; + HuberLoss module; // Test for sum reduction. input = arma::mat("-0.0494 -1.1958 -1.0486 -0.2121 1.6028 0.0737 -0.7091 " @@ -108,10 +109,10 @@ TEST_CASE("PoissonNLLLossTest", "[LossFunctionsTest]") arma::mat input, target, input4, target4; arma::mat output1, output2, output3, output4; arma::mat expOutput1, expOutput2, expOutput3, expOutput4; - PoissonNLLLoss<> module1(true, false, 1e-8, false); - PoissonNLLLoss<> module2(true, true, 1e-08, true); - PoissonNLLLoss<> module3(true, true, 1e-08, false); - PoissonNLLLoss<> module4(false, true, 1e-08, false); + PoissonNLLLoss module1(true, false, 1e-8, false); + PoissonNLLLoss module2(true, true, 1e-08, true); + PoissonNLLLoss module3(true, true, 1e-08, false); + PoissonNLLLoss module4(false, true, 1e-08, false); // Test the Forward function on a user generated input. input = arma::mat("1.0 1.0 1.9 1.6 -1.9 3.7 -1.0 0.5"); @@ -175,7 +176,7 @@ TEST_CASE("SimpleKLDivergenceTest", "[LossFunctionsTest]") arma::mat input, target, output; arma::mat expectedOutput; double loss; - KLDivergence<> module; + KLDivergence module; // Test for sum reduction. input = arma::mat("-0.7007 -2.0247 -0.7132 -0.4584 -0.2637 -1.1795 -0.1093 " @@ -224,9 +225,9 @@ TEST_CASE("SimpleKLDivergenceTest", "[LossFunctionsTest]") */ TEST_CASE("SimpleMeanSquaredLogarithmicErrorTest", "[LossFunctionsTest]") { - arma::mat input, target, output, expectedOutput; + arma::mat input, target, output, expectedOutput; double loss; - MeanSquaredLogarithmicError<> module; + MeanSquaredLogarithmicError module; // Test for sum reduction. input = arma::mat("-0.0494 1.1958 1.0486 -0.2121 1.6028 0.0737 -0.7091 " @@ -243,30 +244,24 @@ TEST_CASE("SimpleMeanSquaredLogarithmicErrorTest", "[LossFunctionsTest]") loss = module.Forward(input, target); REQUIRE(loss == Approx(13.2728).epsilon(1e-3)); - // Test the Backward function. - module.Backward(input, target, output); - REQUIRE(arma::as_scalar(arma::accu(output)) == Approx(-10.5619).epsilon(1e-3)); - REQUIRE(output.n_rows == input.n_rows); - REQUIRE(output.n_cols == input.n_cols); - CheckMatrices(output, expectedOutput, 0.1); - - // Test for mean reduction by modifying reduction parameter using accessor. - module.Reduction() = false; - expectedOutput = arma::mat("-0.0718 0.0585 0.1067 -0.2119 0.0348 -0.0907 " - "-0.9612 0.0482 0.0120 0.2070 0.0148 -0.0266"); - expectedOutput.reshape(4, 3); - - // Test the Forward function. Loss should be 1.10606. - loss = module.Forward(input, target); - REQUIRE(loss == Approx(1.10606).epsilon(1e-3)); - // Test the Backward function. module.Backward(input, target, output); REQUIRE(arma::as_scalar(arma::accu(output)) == - Approx(-0.880156).epsilon(1e-3)); + Approx(-10.5619).epsilon(1e-3)); REQUIRE(output.n_rows == input.n_rows); REQUIRE(output.n_cols == input.n_cols); CheckMatrices(output, expectedOutput, 0.1); + + // Test the error function on a single input. + input = arma::mat("2"); + target = arma::mat("3"); + loss = module.Forward(input, target); + REQUIRE(loss == Approx(0.082760974810151655).epsilon(1e-3)); + + // Test the Backward function on a single input. + module.Backward(input, target, output); + REQUIRE(arma::accu(output) == Approx(-0.1917880483011872).epsilon(1e-3)); + REQUIRE(output.n_elem == 1); } /* @@ -275,7 +270,7 @@ TEST_CASE("SimpleMeanSquaredLogarithmicErrorTest", "[LossFunctionsTest]") TEST_CASE("SimpleMeanSquaredErrorTest", "[LossFunctionsTest]") { arma::mat input, output, target; - MeanSquaredError<> module(false); + MeanSquaredError module(false); // Test the Forward function on a user generated input and compare it against // the manually calculated result. @@ -325,8 +320,8 @@ TEST_CASE("SimpleMeanSquaredErrorTest", "[LossFunctionsTest]") TEST_CASE("SimpleBinaryCrossEntropyLossTest", "[LossFunctionsTest]") { arma::mat input1, input2, input3, output, target1, target2, target3; - BCELoss<> module1(1e-6, true); - BCELoss<> module2(1e-6, false); + BCELoss module1(1e-6, true); + BCELoss module2(1e-6, false); // Test the Forward function on a user generator input and compare it against // the manually calculated result. input1 = arma::mat("0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5"); @@ -378,7 +373,7 @@ TEST_CASE("SimpleSigmoidCrossEntropyErrorTest", "[LossFunctionsTest]") { arma::mat input1, input2, input3, output, target1, target2, target3, expectedOutput; - SigmoidCrossEntropyError<> module; + SigmoidCrossEntropyError module; // Test the Forward function on a user generator input and compare it against // the calculated result. @@ -439,7 +434,7 @@ TEST_CASE("SimpleEarthMoverDistanceLayerTest", "[LossFunctionsTest]") arma::mat input1, input2, output, target1, target2, expectedOutput; arma::mat input3, target3; double loss; - EarthMoverDistance<> module; + EarthMoverDistance module; // Test the Forward function on a user generator input and compare it against // the manually calculated result. @@ -509,12 +504,10 @@ TEST_CASE("GradientMeanSquaredErrorTest", "[LossFunctionsTest]") input = arma::randu(10, 1); target = arma::randu(2, 1); - model = new FFN, NguyenWidrowInitialization>(); - model->Predictors() = input; - model->Responses() = target; - model->Add >(); - model->Add >(10, 2); - model->Add >(); + model = new FFN(); + model->ResetData(input, target); + model->Add(2); + model->Add(); } ~GradientFunction() @@ -532,7 +525,7 @@ TEST_CASE("GradientMeanSquaredErrorTest", "[LossFunctionsTest]") arma::mat& Parameters() { return model->Parameters(); } - FFN, NguyenWidrowInitialization>* model; + FFN* model; arma::mat input, target; } function; @@ -552,12 +545,10 @@ TEST_CASE("GradientReconstructionLossTest", "[LossFunctionsTest]") input = arma::randu(10, 1); target = arma::randu(2, 1); - model = new FFN, NguyenWidrowInitialization>(); - model->Predictors() = input; - model->Responses() = target; - model->Add >(); - model->Add >(10, 2); - model->Add >(); + model = new FFN(); + model->ResetData(input, target); + model->Add(2); + model->Add(); } ~GradientFunction() @@ -575,7 +566,7 @@ TEST_CASE("GradientReconstructionLossTest", "[LossFunctionsTest]") arma::mat& Parameters() { return model->Parameters(); } - FFN, NguyenWidrowInitialization>* model; + FFN* model; arma::mat input, target; } function; @@ -589,7 +580,7 @@ TEST_CASE("DiceLossTest", "[LossFunctionsTest]") { arma::mat input1, input2, target, output; double loss; - DiceLoss<> module; + DiceLoss module; // Test the Forward function. Loss should be 0 if input = target. input1 = arma::ones(10, 1); @@ -630,7 +621,7 @@ TEST_CASE("SimpleMeanBiasErrorTest", "[LossFunctionsTest]") { arma::mat input, target, output; double loss; - MeanBiasError<> module; + MeanBiasError module; // Test for sum reduction. input = arma::mat("-0.0494 -1.1958 -1.0486 -0.2121 1.6028 0.0737 -0.7091 " @@ -680,7 +671,7 @@ TEST_CASE("LogCoshLossTest", "[LossFunctionsTest]") { arma::mat input, target, output; double loss; - LogCoshLoss<> module(2); + LogCoshLoss module(2); // Test the Forward function. Loss should be 0 if input = target. input = arma::ones(10, 1); @@ -733,7 +724,7 @@ TEST_CASE("HingeEmbeddingLossTest", "[LossFunctionsTest]") { arma::mat input, target, output, expectedOutput; double loss; - HingeEmbeddingLoss<> module; + HingeEmbeddingLoss module; // Test for sum reduction input = arma::mat("0.1778 0.0957 0.1397 0.2256 0.1203 0.2403 0.1925 0.3144 " @@ -785,7 +776,7 @@ TEST_CASE("SimpleL1LossTest", "[LossFunctionsTest]") { arma::mat input, output, target; double loss; - L1Loss<> module(true); + L1Loss module(true); // Test the Forward function on a user generator input and compare it against // the manually calculated result. @@ -811,7 +802,7 @@ TEST_CASE("CosineEmbeddingLossTest", "[LossFunctionsTest]") { arma::mat input1, input2, y, output; double loss; - CosineEmbeddingLoss<> module; + CosineEmbeddingLoss module; // Test the Forward function. Loss should be 0 if input1 = input2 and y = 1. input1 = arma::mat(1, 10); @@ -851,44 +842,6 @@ TEST_CASE("CosineEmbeddingLossTest", "[LossFunctionsTest]") // Test the Backward function. module.Backward(input1, input2, output); REQUIRE(arma::accu(output) == Approx(0.06324556).epsilon(1e-3)); - - // Check for correctness for cube. - CosineEmbeddingLoss<> module2(0.5, true); - - arma::cube input3(3, 2, 2); - arma::cube input4(3, 2, 2); - input3.fill(1); - input4.fill(1); - input3(0) = 2; - input3(1) = 2; - input3(4) = 2; - input3(6) = 2; - input3(8) = 2; - input3(10) = 2; - input4(2) = 2; - input4(9) = 2; - input4(11) = 2; - loss = module2.Forward(input3, input4); - // Calculated using torch.nn.CosineEmbeddingLoss(). - REQUIRE(loss == Approx(0.55395).epsilon(1e-3)); - - // Test the Backward function. - module2.Backward(input3, input4, output); - REQUIRE(arma::accu(output) == Approx(-0.36649111).epsilon(1e-3)); - - // Check Output for mean type of reduction. - CosineEmbeddingLoss<> module3(0.0, true, false); - loss = module3.Forward(input3, input4); - REQUIRE(loss == Approx(0.092325).epsilon(1e-3)); - - // Check correctness for cube. - module3.Similarity() = false; - loss = module3.Forward(input3, input4); - REQUIRE(loss == Approx(0.90767498236).epsilon(1e-3)); - - // Test the Backward function. - module3.Backward(input3, input4, output); - REQUIRE(arma::accu(output) == Approx(0.0236749374).epsilon(1e-4)); } /* @@ -899,7 +852,7 @@ TEST_CASE("MarginRankingLossTest", "[LossFunctionsTest]") arma::mat input, input1, input2, target, output, expectedOutput; double loss; // Test sum reduction - MarginRankingLoss<> module; + MarginRankingLoss module; input1 = arma::mat("0.4287 -1.6208 -1.5006 -0.4473 1.5208 -4.5184 9.3574 " "-4.8090 4.3455 5.2070"); input2 = arma::mat("-4.5288 -9.2766 -0.5882 -5.6643 -6.0175 8.8506 3.4759 " @@ -925,7 +878,7 @@ TEST_CASE("MarginRankingLossTest", "[LossFunctionsTest]") // Test for mean reduction by modifying reduction parameter using accessor. module.Reduction() = false; - + // Test the forward function // loss should be 3.0353 // value calculated using torch.nn.MarginRankingLoss(margin=1.0,reduction='mean') @@ -950,8 +903,8 @@ TEST_CASE("SoftMarginLossTest", "[LossFunctionsTest]") { arma::mat input, target, output, expectedOutput; double loss; - SoftMarginLoss<> module1; - SoftMarginLoss<> module2(false); + SoftMarginLoss module1; + SoftMarginLoss module2(false); input = arma::mat("0.1778 0.0957 0.1397 0.1203 0.2403 0.1925 -0.2264 -0.3400 " "-0.3336"); @@ -1005,7 +958,7 @@ TEST_CASE("SoftMarginLossTest", "[LossFunctionsTest]") TEST_CASE("MeanAbsolutePercentageErrorTest", "[LossFunctionsTest]") { arma::mat input, target, output, expectedOutput; - MeanAbsolutePercentageError<> module; + MeanAbsolutePercentageError module; input = arma::mat("3 -0.5 2 7"); target = arma::mat("2.5 0.2 2 8"); @@ -1025,6 +978,20 @@ TEST_CASE("MeanAbsolutePercentageErrorTest", "[LossFunctionsTest]") CheckMatrices(output, expectedOutput, 0.1); } +/** + * Test that the function that can access the parameters of the + * VR Class Reward layer works. + */ +TEST_CASE("VRClassRewardLayerParametersTest", "[LossFunctionsTest]") +{ + // Parameter order : scale, sizeAverage. + VRClassReward layer(2, false); + + // Make sure we can get the parameters successfully. + REQUIRE(layer.Scale() == 2); + REQUIRE(layer.SizeAverage() == false); +} + /* * Simple test for the Triplet Margin Loss function. */ @@ -1032,7 +999,7 @@ TEST_CASE("TripletMarginLossTest") { arma::mat anchor, positive, negative; arma::mat input, target, output; - TripletMarginLoss<> module; + TripletMarginLoss module; // Test the Forward function on a user generated input and compare it against // the manually calculated result. @@ -1080,8 +1047,8 @@ TEST_CASE("HingeLossTest", "[LossFunctionsTest]") { arma::mat input, target, target_b, output; double loss, loss_b; - HingeLoss<> module1; - HingeLoss<> module2(false); + HingeLoss module1; + HingeLoss module2(false); // Test the Forward function. Loss should be 0 if input = target. input = arma::ones(10, 1); @@ -1157,8 +1124,8 @@ TEST_CASE("MultiLabelSoftMarginLossTest", "[LossFunctionsTest]") { arma::mat input, target, output, expectedOutput; double loss; - MultiLabelSoftMarginLoss<> module1; - MultiLabelSoftMarginLoss<> module2(false); + MultiLabelSoftMarginLoss module1; + MultiLabelSoftMarginLoss module2(false); input = arma::mat("0.1778 0.0957 0.1397 0.1203 0.2403 0.1925 -0.2264 -0.3400 " "-0.3336"); @@ -1216,8 +1183,8 @@ TEST_CASE("MultiLabelSoftMarginLossWeightedTest", "[LossFunctionsTest]") arma::rowvec weights; double loss; weights = arma::mat("1 2 3"); - MultiLabelSoftMarginLoss<> module1(true, weights); - MultiLabelSoftMarginLoss<> module2(false, weights); + MultiLabelSoftMarginLoss module1(true, weights); + MultiLabelSoftMarginLoss module2(false, weights); input = arma::mat("0.1778 0.0957 0.1397 0.2256 0.1203 0.2403 0.1925 0.3144 " "-0.2264 -0.3400 -0.3336 -0.8695"); @@ -1274,7 +1241,7 @@ TEST_CASE("NegativeLogLikelihoodLossTest", "[LossFunctionsTest]") arma::mat input, target, output; arma::mat expectedOutput; double loss; - NegativeLogLikelihood<> module; + NegativeLogLikelihood module; // Test for sum reduction. input = arma::mat("-0.1689 -2.0033 -3.8886 -0.2862 -1.9392 -2.2532" diff --git a/src/mlpack/tests/gan_test.cpp b/src/mlpack/tests/not_adapted/gan_test.cpp similarity index 100% rename from src/mlpack/tests/gan_test.cpp rename to src/mlpack/tests/not_adapted/gan_test.cpp diff --git a/src/mlpack/tests/rbm_network_test.cpp b/src/mlpack/tests/not_adapted/rbm_network_test.cpp similarity index 100% rename from src/mlpack/tests/rbm_network_test.cpp rename to src/mlpack/tests/not_adapted/rbm_network_test.cpp diff --git a/src/mlpack/tests/wgan_test.cpp b/src/mlpack/tests/not_adapted/wgan_test.cpp similarity index 100% rename from src/mlpack/tests/wgan_test.cpp rename to src/mlpack/tests/not_adapted/wgan_test.cpp diff --git a/src/mlpack/tests/perceptron_test.cpp b/src/mlpack/tests/perceptron_test.cpp index d53903c259..57422961fc 100644 --- a/src/mlpack/tests/perceptron_test.cpp +++ b/src/mlpack/tests/perceptron_test.cpp @@ -120,7 +120,7 @@ TEST_CASE("And", "[PerceptronTest]") mat testData; testData = { { 0, 1, 1, 0 }, { 1, 0, 1, 0 } }; - Row predictedLabels(testData.n_cols); + Row predictedLabels; p.Classify(testData, predictedLabels); CHECK(predictedLabels(0, 0) == 0); @@ -146,7 +146,7 @@ TEST_CASE("Or", "[PerceptronTest]") mat testData; testData = { { 0, 1, 1, 0 }, { 1, 0, 1, 0 } }; - Row predictedLabels(testData.n_cols); + Row predictedLabels; p.Classify(testData, predictedLabels); CHECK(predictedLabels(0, 0) == 1); @@ -173,7 +173,7 @@ TEST_CASE("Random3", "[PerceptronTest]") mat testData; testData = { { 0, 1, 1 }, { 1, 0, 1 } }; - Row predictedLabels(testData.n_cols); + Row predictedLabels; p.Classify(testData, predictedLabels); for (size_t i = 0; i < predictedLabels.n_cols; ++i) @@ -198,7 +198,7 @@ TEST_CASE("TwoPoints", "[PerceptronTest]") mat testData; testData = { { 0, 1 }, { 1, 0 } }; - Row predictedLabels(testData.n_cols); + Row predictedLabels; p.Classify(testData, predictedLabels); CHECK(predictedLabels(0, 0) == 0); @@ -223,7 +223,7 @@ TEST_CASE("NonLinearlySeparableDataset", "[PerceptronTest]") mat testData; testData = { { 3, 4, 5, 6 }, { 3, 2.3, 1.7, 1.5 } }; - Row predictedLabels(testData.n_cols); + Row predictedLabels; p.Classify(testData, predictedLabels); CHECK(predictedLabels(0, 0) == 0); @@ -244,4 +244,28 @@ TEST_CASE("SecondaryConstructor", "[PerceptronTest]") Perceptron<> p1(trainData, labels.row(0), 2, 1000); Perceptron<> p2(p1); + + REQUIRE(p1.Weights().n_elem > 0); + REQUIRE(p2.Weights().n_elem > 0); +} + +/** + * This tests that we can build the Perceptron when specifying instance weights. + */ +TEST_CASE("InstanceWeightsConstructor", "[PerceptronTest]") +{ + mat trainData; + trainData = { { 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8 }, + { 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2 } }; + + Mat labels; + labels = { 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1 }; + + rowvec instanceWeights; + instanceWeights = { 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 0.9, + 0.8, 0.7, 0.6, 0.5, 0.4 }; + + Perceptron<> p(trainData, labels.row(0), 2, instanceWeights, 1000); + + REQUIRE(p.Weights().n_elem > 0); } diff --git a/src/mlpack/tests/recurrent_network_test.cpp b/src/mlpack/tests/recurrent_network_test.cpp index 4b5aca4afb..a7d3c459c3 100644 --- a/src/mlpack/tests/recurrent_network_test.cpp +++ b/src/mlpack/tests/recurrent_network_test.cpp @@ -11,17 +11,16 @@ */ #include -#include -#include +#include #include +#include +#include #include -#include -#include -#include + +#include #include "catch.hpp" #include "serialization.hpp" -#include "custom_layer.hpp" using namespace mlpack; using namespace mlpack::ann; @@ -69,6 +68,32 @@ void GenerateNoisySines(arma::cube& data, } } +/** + * Construct dataset for sine wave prediction. + * + * @param data Input data used to store the noisy sines. + * @param labels Labels used to store the target class of the noisy sines. + * @param points Number of points/features in a single sequence. + * @param sequences Number of sequences for each class. + * @param noise The noise factor that influences the sines. + */ +void GenerateSines(arma::cube& data, + arma::cube& labels, + const size_t sequences, + const size_t len) +{ + arma::vec x = arma::sin(arma::linspace(0, + sequences + len, sequences + len)); + data.set_size(1, len, sequences); + labels.set_size(1, 1, sequences); + + for (size_t i = 0; i < sequences; ++i) + { + data.slice(i) = arma::reshape(x.subvec(i, i + len), 1, len); + labels.slice(i) = x(i + len); + } +} + /* * This sample is a simplified version of Derek D. Monner's Distracted Sequence * Recall task, which involves 10 symbols: @@ -124,150 +149,33 @@ void GenerateDistractedSequence(arma::mat& input, arma::mat& output) output.reshape(output.n_elem, 1); } -/** - * Train the specified network and the construct distracted sequence recall - * dataset. - */ -template -void DistractedSequenceRecallTestNetwork( - const size_t cellSize, const size_t hiddenSize) -{ - const size_t trainDistractedSequenceCount = 600; - const size_t testDistractedSequenceCount = 300; - - arma::field trainInput(1, trainDistractedSequenceCount); - arma::field trainLabels(1, trainDistractedSequenceCount); - arma::field testInput(1, testDistractedSequenceCount); - arma::field testLabels(1, testDistractedSequenceCount); - - // Generate the training data. - for (size_t i = 0; i < trainDistractedSequenceCount; ++i) - GenerateDistractedSequence(trainInput(0, i), trainLabels(0, i)); - - // Generate the test data. - for (size_t i = 0; i < testDistractedSequenceCount; ++i) - GenerateDistractedSequence(testInput(0, i), testLabels(0, i)); - - /* - * Construct a network with 10 input units, layerSize hidden units and 3 - * output units. The hidden layer is connected to itself. The network - * structure looks like: - * - * Input Recurrent Hidden Output - * Layer(10) Layer(cellSize) Layer(3) Layer(3) - * +-----+ +-----+ +-----+ +-----+ - * | | | | | | | | - * | +------>| +------>| |------>| | - * | | ..>| | | | | | - * +-----+ . +--+--+ +-----+ +-----+ - * . . - * . . - * ....... - */ - const size_t outputSize = 3; - const size_t inputSize = 10; - const size_t rho = trainInput.at(0, 0).n_elem / inputSize; - - // It isn't guaranteed that the recurrent network will converge in the - // specified number of iterations using random weights. If this works 1 of 5 - // times, I'm fine with that. All I want to know is that the network is able - // to escape from local minima and to solve the task. - size_t successes = 0; - size_t offset = 0; - for (size_t trial = 0; trial < 5; ++trial) - { - RNN > model(rho); - model.Add >(); - model.Add >(inputSize, cellSize); - model.Add(cellSize, hiddenSize); - model.Add >(hiddenSize, outputSize); - model.Add >(); - - StandardSGD opt(0.1, 50, 2, -50000); - - // We increase the number of iterations (training) if the first run didn't - // pass. - arma::cube inputTemp, labelsTemp; - for (size_t iteration = 0; iteration < (9 + offset); iteration++) - { - for (size_t j = 0; j < trainDistractedSequenceCount; ++j) - { - inputTemp = arma::cube(trainInput.at(0, j).memptr(), inputSize, 1, - trainInput.at(0, j).n_elem / inputSize, false, true); - labelsTemp = arma::cube(trainLabels.at(0, j).memptr(), outputSize, 1, - trainLabels.at(0, j).n_elem / outputSize, false, true); - - model.Train(inputTemp, labelsTemp, opt); - } - } - - double error = 0; - - // Ask the network to predict the targets in the given sequence at the - // prompts. - for (size_t i = 0; i < testDistractedSequenceCount; ++i) - { - arma::cube output; - arma::cube input(testInput.at(0, i).memptr(), inputSize, 1, - testInput.at(0, i).n_elem / inputSize, false, true); - - model.Predict(input, output); - for (size_t j = 0; j < output.n_slices; ++j) - { - arma::mat outputSlice = output.slice(j); - data::Binarize(outputSlice, outputSlice, 0.5); - output.slice(j) = outputSlice; - } - - arma::cube label(testLabels.at(0, i).memptr(), outputSize, 1, - testLabels.at(0, i).n_elem / outputSize, false, true); - if (arma::accu(arma::abs(label - output)) != 0) - error += 1; - } - - error /= testDistractedSequenceCount; - // Can we reproduce the results from the paper. They provide an 95% accuracy - // on a test set of 1000 randomly selected sequences. - // Ensure that this is within tolerance, which is at least as good as the - // paper's results (plus a little bit for noise). - if (error <= 0.3) - { - ++successes; - break; - } - - offset += 2; - } - - REQUIRE(successes >= 1); -} /** * Train the specified networks on the Derek D. Monner's distracted sequence * recall task. */ -TEST_CASE("LSTMDistractedSequenceRecallTest", "[RecurrentNetworkTest]") -{ - DistractedSequenceRecallTestNetwork >(4, 8); -} +/* TEST_CASE("LSTMDistractedSequenceRecallTest", "[RecurrentNetworkTest]") */ +/* { */ +/* DistractedSequenceRecallTestNetwork >(4, 8); */ +/* } */ /** * Train the specified networks on the Derek D. Monner's distracted sequence * recall task. */ -TEST_CASE("FastLSTMDistractedSequenceRecallTest", "[RecurrentNetworkTest]") -{ - DistractedSequenceRecallTestNetwork >(4, 8); -} +/* TEST_CASE("FastLSTMDistractedSequenceRecallTest", "[RecurrentNetworkTest]") */ +/* { */ +/* DistractedSequenceRecallTestNetwork >(4, 8); */ +/* } */ /** * Train the specified networks on the Derek D. Monner's distracted sequence * recall task. */ -TEST_CASE("GRUDistractedSequenceRecallTest", "[RecurrentNetworkTest]") -{ - DistractedSequenceRecallTestNetwork >(4, 8); -} +/* TEST_CASE("GRUDistractedSequenceRecallTest", "[RecurrentNetworkTest]") */ +/* { */ +/* DistractedSequenceRecallTestNetwork >(4, 8); */ +/* } */ /** * Create a simple recurrent neural network for the noisy sines task, and @@ -276,15 +184,16 @@ TEST_CASE("GRUDistractedSequenceRecallTest", "[RecurrentNetworkTest]") template void BatchSizeTest() { - const size_t rho = 10; + const size_t T = 50; + const size_t bpttTruncate = 10; // Generate 12 (2 * 6) noisy sines. A single sine contains rho // points/features. arma::cube input; arma::mat labelsTemp; - GenerateNoisySines(input, labelsTemp, rho, 6); + GenerateNoisySines(input, labelsTemp, 4, 5); - arma::cube labels = arma::zeros(1, labelsTemp.n_cols, rho); + arma::cube labels = arma::zeros(1, labelsTemp.n_cols, T); for (size_t i = 0; i < labelsTemp.n_cols; ++i) { const int value = arma::as_scalar(arma::find( @@ -292,15 +201,15 @@ void BatchSizeTest() labels.tube(0, i).fill(value); } - RNN<> model(rho); - model.Add>(1, 10); - model.Add>(); - model.Add(10, 10); - model.Add>(); - model.Add>(10, 10); - model.Add>(); + RNN<> model(bpttTruncate); + model.Add(100); + model.Add(); + model.Add(10); + model.Add(); + model.Add(10); + model.Add(); - model.Reset(); + model.Reset(1); arma::mat initParams = model.Parameters(); StandardSGD opt(1e-5, 1, 5, -100, false); @@ -309,13 +218,14 @@ void BatchSizeTest() // This is trained with one point. arma::mat outputParams = model.Parameters(); - model.Reset(); + model.Reset(1); model.Parameters() = initParams; opt.BatchSize() = 2; model.Train(input, labels, opt); CheckMatrices(outputParams, model.Parameters(), 1); + model.Reset(1); model.Parameters() = initParams; opt.BatchSize() = 5; model.Train(input, labels, opt); @@ -328,28 +238,28 @@ void BatchSizeTest() */ TEST_CASE("LSTMBatchSizeTest", "[RecurrentNetworkTest]") { - BatchSizeTest>(); + BatchSizeTest(); } /** * Ensure fast LSTMs work with larger batch sizes. */ -TEST_CASE("FastLSTMBatchSizeTest", "[RecurrentNetworkTest]") -{ - BatchSizeTest>(); -} +//TEST_CASE("FastLSTMBatchSizeTest", "[RecurrentNetworkTest]") +//{ +// BatchSizeTest>(); +//} /** * Ensure GRUs work with larger batch sizes. */ -TEST_CASE("GRUBatchSizeTest", "[RecurrentNetworkTest]") -{ - BatchSizeTest>(); -} +//TEST_CASE("GRUBatchSizeTest", "[RecurrentNetworkTest]") +//{ +// BatchSizeTest>(); +//} /** * Make sure the RNN can be properly serialized. - */ + * TEST_CASE("RNNSerializationTest", "[RecurrentNetworkTest]") { const size_t rho = 10; @@ -383,7 +293,7 @@ TEST_CASE("RNNSerializationTest", "[RecurrentNetworkTest]") * . . * . . * ....... - */ + * Add<> add(4); Linear<> lookup(1, 4); SigmoidLayer<> sigmoidLayer; @@ -397,7 +307,7 @@ TEST_CASE("RNNSerializationTest", "[RecurrentNetworkTest]") model.Add >(4, 10); model.Add >(); - StandardSGD opt(0.1, 1, input.n_cols /* 1 epoch */, -100); + StandardSGD opt(0.1, 1, input.n_cols /* 1 epoch *, -100); model.Train(input, labels, opt); // Serialize the network. @@ -413,10 +323,11 @@ TEST_CASE("RNNSerializationTest", "[RecurrentNetworkTest]") CheckMatrices(prediction, xmlPrediction, jsonPrediction, binaryPrediction); } +*/ /** * Train the BRNN on a larger dataset. - */ + * TEST_CASE("SequenceClassificationBRNNTest", "[RecurrentNetworkTest]") { // Using same test for RNN below. @@ -485,10 +396,11 @@ TEST_CASE("SequenceClassificationBRNNTest", "[RecurrentNetworkTest]") REQUIRE(successes >= 1); } +*/ /** * Train the vanilla network on a larger dataset. - */ + * TEST_CASE("SequenceClassificationTest", "[RecurrentNetworkTest]") { // It isn't guaranteed that the recurrent network will converge in the @@ -529,7 +441,7 @@ TEST_CASE("SequenceClassificationTest", "[RecurrentNetworkTest]") * . . * . . * ....... - */ + * Add<> add(4); Linear<> lookup(1, 4); SigmoidLayer<> sigmoidLayer; @@ -575,6 +487,7 @@ TEST_CASE("SequenceClassificationTest", "[RecurrentNetworkTest]") REQUIRE(successes >= 1); } +*/ /** * @brief Generates noisy sine wave and outputs the data and the labels that @@ -658,12 +571,10 @@ void GenerateNoisySinRNN(arma::cube& data, */ double RNNSineTest(size_t hiddenUnits, size_t rho, size_t numEpochs = 100) { - RNN > net(rho, true); - net.Add >(1, hiddenUnits); - net.Add >(hiddenUnits, hiddenUnits); - net.Add >(hiddenUnits, 1); - - RMSProp opt(0.005, 100, 0.9, 1e-08, 50000, 1e-5); + RNN net(rho, true); + net.Add(hiddenUnits); + net.Add(hiddenUnits); + net.Add(1); // Generate data arma::cube data; @@ -678,12 +589,12 @@ double RNNSineTest(size_t hiddenUnits, size_t rho, size_t numEpochs = 100) arma::cube testLabels = labels.subcube(0, labels.n_cols - testCols, 0, labels.n_rows - 1, labels.n_cols - 1, labels.n_slices - 1); - for (size_t i = 0; i < numEpochs; ++i) - { - net.Train(data.subcube(0, 0, 0, data.n_rows - 1, trainCols - 1, - data.n_slices - 1), labels.subcube(0, 0, 0, labels.n_rows - 1, - trainCols - 1, labels.n_slices - 1), opt); - } + RMSProp opt(0.005, 16, 0.9, 1e-08, trainCols * numEpochs, 1e-5); + + net.Train(data.subcube(0, 0, 0, data.n_rows - 1, trainCols - 1, + data.n_slices - 1), labels.subcube(0, 0, 0, labels.n_rows - 1, + trainCols - 1, labels.n_slices - 1), opt); + // Well now it should be trained. Do the test here. arma::cube prediction; net.Predict(testData, prediction); @@ -713,7 +624,7 @@ TEST_CASE("MultiTimestepTest", "[RecurrentNetworkTest]") /** * Test that RNN::Train() returns finite objective value. - */ + * TEST_CASE("RNNTrainReturnObjective", "[RecurrentNetworkTest]") { const size_t rho = 10; @@ -747,7 +658,7 @@ TEST_CASE("RNNTrainReturnObjective", "[RecurrentNetworkTest]") * . . * . . * ....... - */ + * Add<> add(4); Linear<> lookup(1, 4); SigmoidLayer<> sigmoidLayer; @@ -761,15 +672,16 @@ TEST_CASE("RNNTrainReturnObjective", "[RecurrentNetworkTest]") model.Add >(4, 10); model.Add >(); - StandardSGD opt(0.1, 1, input.n_cols /* 1 epoch */, -100); + StandardSGD opt(0.1, 1, input.n_cols /* 1 epoch *, -100); double objVal = model.Train(input, labels, opt); REQUIRE(std::isfinite(objVal) == true); } +*/ /** * Test that BRNN::Train() returns finite objective value. - */ + * TEST_CASE("BRNNTrainReturnObjective", "[RecurrentNetworkTest]") { const size_t rho = 10; @@ -805,6 +717,7 @@ TEST_CASE("BRNNTrainReturnObjective", "[RecurrentNetworkTest]") // Test that BRNN::Train() returns finite objective value. REQUIRE(std::isfinite(objVal) == true); } +*/ /** * Test that RNN::Train() does not give an error for large rho. @@ -822,10 +735,9 @@ TEST_CASE("LargeRhoValueRnnTest", "[RecurrentNetworkTest]") RNN<> model(rho); - model.Add>(); - model.Add>(numLetters, hiddenSize, rho); - model.Add>(0.1); - model.Add>(hiddenSize, numLetters); + model.Add(hiddenSize); + model.Add(0.1); + model.Add(numLetters); const auto makeInput = [numLetters](const char *line) -> MatType { @@ -864,7 +776,7 @@ TEST_CASE("LargeRhoValueRnnTest", "[RecurrentNetworkTest]") inputs[i] = makeInput(trainingData[i].c_str()); targets[i] = makeTarget(trainingData[i].c_str()); } - ens::SGD<> opt(0.01, 1, 100); + ens::StandardSGD opt(0.01, 1, 100); model.Train(inputs[0], targets[0], opt); INFO("Training over"); } @@ -872,7 +784,7 @@ TEST_CASE("LargeRhoValueRnnTest", "[RecurrentNetworkTest]") /** * Test to make sure that an error is thrown when input with * wrong input shape is provided to a RNN. - */ + * TEST_CASE("RNNCheckInputShapeTest", "[RecurrentNetworkTest]") { const size_t rho = 10; @@ -906,7 +818,7 @@ TEST_CASE("RNNCheckInputShapeTest", "[RecurrentNetworkTest]") * . . * . . * ....... - */ + * Add<> add(4); // Purposely providing wrong input shape of 3. // The correct input shape is 1. @@ -927,7 +839,41 @@ TEST_CASE("RNNCheckInputShapeTest", "[RecurrentNetworkTest]") expectedMsg += std::to_string(3) + " elements, "; expectedMsg += "but the input has " + std::to_string(1) + " dimensions! "; - StandardSGD opt(0.1, 1, input.n_cols /* 1 epoch */, -100); + StandardSGD opt(0.1, 1, input.n_cols /* 1 epoch *, -100); REQUIRE_THROWS_AS(model.Train(input, labels, opt), std::logic_error); } +*/ + +/** + * Test that a simple RNN with no recurrent components behaves the same as an + * FFN. + */ +TEST_CASE("RNNFFNTest", "[RecurrentNetworkTest]") +{ + // We'll create an RNN with *no* BPTT, just a simple single-layer linear + // network. + RNN rnn; + FFN ffn; + + rnn.Add(10); + rnn.Add(); + rnn.Add(1); + + ffn.Add(10); + ffn.Add(); + ffn.Add(1); + + // Now create some random data. + arma::cube data(20, 200, 1, arma::fill::randu); + arma::cube responses(1, 200, 1, arma::fill::randu); + + // Train the FFN. + ens::StandardSGD optimizer(1e-5, 1, 200, 1e-8, false); + + ffn.Train(data.slice(0), responses.slice(0), optimizer); + rnn.Train(data, responses, optimizer); + + // Now, the weights should be the same! + CheckMatrices(ffn.Parameters(), rnn.Parameters()); +} diff --git a/src/mlpack/tests/reward_clipping_test.cpp b/src/mlpack/tests/reward_clipping_test.cpp index d1fd873740..fe2381d4b9 100644 --- a/src/mlpack/tests/reward_clipping_test.cpp +++ b/src/mlpack/tests/reward_clipping_test.cpp @@ -22,7 +22,7 @@ #include #include -#include +#include #include #include #include @@ -62,7 +62,7 @@ TEST_CASE("RewardClippedAcrobotWithDQN", "[RewardClippingTest]") for (size_t trial = 0; trial < 3; ++trial) { // Set up the network. - SimpleDQN<> model(4, 64, 32, 3); + SimpleDQN<> model(64, 32, 3); // Set up the policy and replay method. GreedyPolicy> policy(1.0, 1000, 0.1, 0.99); diff --git a/src/mlpack/tests/serialization_test.cpp b/src/mlpack/tests/serialization_test.cpp index 17b51cc865..9d4beb8b94 100644 --- a/src/mlpack/tests/serialization_test.cpp +++ b/src/mlpack/tests/serialization_test.cpp @@ -38,7 +38,7 @@ #include #include #include -#include +//#include #include using namespace mlpack; @@ -1498,7 +1498,7 @@ TEST_CASE("HoeffdingTreeTest", "[SerializationTest]") /** * Build a Binary RBM, then save it and make sure the parameters of the * all the RBM are equal. - */ + * TEST_CASE("BinaryRBMTest", "[SerializationTest]") { arma::mat data; @@ -1531,11 +1531,12 @@ TEST_CASE("BinaryRBMTest", "[SerializationTest]") CheckMatrices(Rbm.Weight(), RbmText.Weight()); CheckMatrices(Rbm.Weight(), RbmBinary.Weight()); } +*/ /** * Build a ssRBM, then save it and make sure the parameters of the * all the RBM are equal. - */ + * TEST_CASE("ssRBMTest", "[SerializationTest]") { arma::mat data; @@ -1585,6 +1586,7 @@ TEST_CASE("ssRBMTest", "[SerializationTest]") CheckMatrices(Rbm.Weight(), RbmText.Weight()); CheckMatrices(Rbm.Weight(), RbmBinary.Weight()); } +*/ // Make sure serialization works for BayesianLinearRegression. TEST_CASE("BayesianLinearRegressionTest", "[SerializationTest]") From f9db8d46be58b4bb742576cc27f9deaa93fd29aa Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Tue, 10 May 2022 13:56:01 +0530 Subject: [PATCH 35/57] Revert "Squashed commit of the following:" This reverts commit 1ea72a60aba0045995fd651927f736334b910ad4. --- .ci/linux-steps.yaml | 1 - .ci/macos-steps.yaml | 1 - .github/workflows/main.yml | 1 - CMake/FindGo.cmake | 4 +- CMake/FindGonum.cmake | 11 +- COPYRIGHT.txt | 2 +- HISTORY.md | 2 - src/mlpack/bindings/python/copy_artifacts.py | 12 +- .../bindings/python/is_serializable.hpp | 4 +- src/mlpack/core/cereal/CMakeLists.txt | 2 + .../core/cereal/pointer_variant_wrapper.hpp | 159 + .../cereal/pointer_vector_variant_wrapper.hpp | 97 + src/mlpack/core/util/size_checks.hpp | 19 +- src/mlpack/methods/ann/CMakeLists.txt | 11 +- .../methods/ann/{not_adapted => }/brnn.hpp | 1 + .../ann/{not_adapted => }/brnn_impl.hpp | 0 .../convolution_rules/naive_convolution.hpp | 51 +- src/mlpack/methods/ann/ffn.hpp | 688 +- src/mlpack/methods/ann/ffn_impl.hpp | 1278 ++-- src/mlpack/methods/ann/forward_decls.hpp | 29 - .../ann/{not_adapted => }/gan/CMakeLists.txt | 0 .../methods/ann/{not_adapted => }/gan/gan.hpp | 0 .../ann/{not_adapted => }/gan/gan_impl.hpp | 0 .../{not_adapted => }/gan/gan_policies.hpp | 0 .../gan/metrics/CMakeLists.txt | 0 .../gan/metrics/inception_score.hpp | 0 .../gan/metrics/inception_score_impl.hpp | 0 .../ann/{not_adapted => }/gan/wgan_impl.hpp | 0 .../ann/{not_adapted => }/gan/wgangp_impl.hpp | 0 .../methods/ann/init_rules/const_init.hpp | 8 +- .../methods/ann/init_rules/glorot_init.hpp | 18 +- src/mlpack/methods/ann/init_rules/he_init.hpp | 6 - .../methods/ann/init_rules/network_init.hpp | 47 +- .../methods/ann/init_rules/oivs_init.hpp | 10 +- .../methods/ann/init_rules/random_init.hpp | 7 - src/mlpack/methods/ann/layer/CMakeLists.txt | 112 +- .../adaptive_max_pooling.hpp | 90 +- .../adaptive_max_pooling_impl.hpp | 42 +- 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353 +- src/mlpack/tests/custom_layer.hpp | 24 +- src/mlpack/tests/cv_test.cpp | 36 +- .../tests/feedforward_network_2_test.cpp | 13 +- src/mlpack/tests/feedforward_network_test.cpp | 713 ++- .../tests/{not_adapted => }/gan_test.cpp | 0 src/mlpack/tests/init_rules_test.cpp | 58 +- src/mlpack/tests/ksinit_test.cpp | 12 +- src/mlpack/tests/layer_names_test.cpp | 160 + src/mlpack/tests/loss_functions_test.cpp | 169 +- src/mlpack/tests/perceptron_test.cpp | 34 +- .../{not_adapted => }/rbm_network_test.cpp | 0 src/mlpack/tests/recurrent_network_test.cpp | 320 +- src/mlpack/tests/reward_clipping_test.cpp | 4 +- src/mlpack/tests/serialization_test.cpp | 8 +- .../tests/{not_adapted => }/wgan_test.cpp | 0 356 files changed, 22397 insertions(+), 16978 deletions(-) create mode 100644 src/mlpack/core/cereal/pointer_variant_wrapper.hpp create mode 100644 src/mlpack/core/cereal/pointer_vector_variant_wrapper.hpp rename src/mlpack/methods/ann/{not_adapted => }/brnn.hpp (99%) rename 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mode 100644 src/mlpack/methods/ann/visitor/add_visitor_impl.hpp create mode 100644 src/mlpack/methods/ann/visitor/backward_visitor.hpp create mode 100644 src/mlpack/methods/ann/visitor/backward_visitor_impl.hpp create mode 100644 src/mlpack/methods/ann/visitor/bias_set_visitor.hpp create mode 100644 src/mlpack/methods/ann/visitor/bias_set_visitor_impl.hpp create mode 100644 src/mlpack/methods/ann/visitor/copy_visitor.hpp create mode 100644 src/mlpack/methods/ann/visitor/copy_visitor_impl.hpp create mode 100644 src/mlpack/methods/ann/visitor/delete_visitor.hpp create mode 100644 src/mlpack/methods/ann/visitor/delete_visitor_impl.hpp create mode 100644 src/mlpack/methods/ann/visitor/delta_visitor.hpp create mode 100644 src/mlpack/methods/ann/visitor/delta_visitor_impl.hpp create mode 100644 src/mlpack/methods/ann/visitor/deterministic_set_visitor.hpp create mode 100644 src/mlpack/methods/ann/visitor/deterministic_set_visitor_impl.hpp create mode 100644 src/mlpack/methods/ann/visitor/forward_visitor.hpp create mode 100644 src/mlpack/methods/ann/visitor/forward_visitor_impl.hpp create mode 100644 src/mlpack/methods/ann/visitor/gradient_set_visitor.hpp create mode 100644 src/mlpack/methods/ann/visitor/gradient_set_visitor_impl.hpp create mode 100644 src/mlpack/methods/ann/visitor/gradient_update_visitor.hpp create mode 100644 src/mlpack/methods/ann/visitor/gradient_update_visitor_impl.hpp create mode 100644 src/mlpack/methods/ann/visitor/gradient_visitor.hpp create mode 100644 src/mlpack/methods/ann/visitor/gradient_visitor_impl.hpp create mode 100644 src/mlpack/methods/ann/visitor/gradient_zero_visitor.hpp create mode 100644 src/mlpack/methods/ann/visitor/gradient_zero_visitor_impl.hpp create mode 100644 src/mlpack/methods/ann/visitor/input_shape_visitor.hpp create mode 100644 src/mlpack/methods/ann/visitor/input_shape_visitor_impl.hpp create mode 100644 src/mlpack/methods/ann/visitor/load_output_parameter_visitor.hpp create mode 100644 src/mlpack/methods/ann/visitor/load_output_parameter_visitor_impl.hpp create mode 100644 src/mlpack/methods/ann/visitor/loss_visitor.hpp create mode 100644 src/mlpack/methods/ann/visitor/loss_visitor_impl.hpp create mode 100644 src/mlpack/methods/ann/visitor/output_height_visitor.hpp create mode 100644 src/mlpack/methods/ann/visitor/output_height_visitor_impl.hpp create mode 100644 src/mlpack/methods/ann/visitor/output_parameter_visitor.hpp create mode 100644 src/mlpack/methods/ann/visitor/output_parameter_visitor_impl.hpp create mode 100644 src/mlpack/methods/ann/visitor/output_width_visitor.hpp create mode 100644 src/mlpack/methods/ann/visitor/output_width_visitor_impl.hpp create mode 100644 src/mlpack/methods/ann/visitor/parameters_set_visitor.hpp create mode 100644 src/mlpack/methods/ann/visitor/parameters_set_visitor_impl.hpp create mode 100644 src/mlpack/methods/ann/visitor/parameters_visitor.hpp create mode 100644 src/mlpack/methods/ann/visitor/parameters_visitor_impl.hpp create mode 100644 src/mlpack/methods/ann/visitor/reset_cell_visitor.hpp create mode 100644 src/mlpack/methods/ann/visitor/reset_cell_visitor_impl.hpp create mode 100644 src/mlpack/methods/ann/visitor/reset_visitor.hpp create mode 100644 src/mlpack/methods/ann/visitor/reset_visitor_impl.hpp create mode 100644 src/mlpack/methods/ann/visitor/reward_set_visitor.hpp create mode 100644 src/mlpack/methods/ann/visitor/reward_set_visitor_impl.hpp create mode 100644 src/mlpack/methods/ann/visitor/run_set_visitor.hpp create mode 100644 src/mlpack/methods/ann/visitor/run_set_visitor_impl.hpp create mode 100644 src/mlpack/methods/ann/visitor/save_output_parameter_visitor.hpp create mode 100644 src/mlpack/methods/ann/visitor/save_output_parameter_visitor_impl.hpp create mode 100644 src/mlpack/methods/ann/visitor/set_input_height_visitor.hpp create mode 100644 src/mlpack/methods/ann/visitor/set_input_height_visitor_impl.hpp create mode 100644 src/mlpack/methods/ann/visitor/set_input_width_visitor.hpp create mode 100644 src/mlpack/methods/ann/visitor/set_input_width_visitor_impl.hpp create mode 100644 src/mlpack/methods/ann/visitor/weight_set_visitor.hpp create mode 100644 src/mlpack/methods/ann/visitor/weight_set_visitor_impl.hpp create mode 100644 src/mlpack/methods/ann/visitor/weight_size_visitor.hpp create mode 100644 src/mlpack/methods/ann/visitor/weight_size_visitor_impl.hpp create mode 100644 src/mlpack/tests/ann_visitor_test.cpp rename src/mlpack/tests/{not_adapted => }/gan_test.cpp (100%) create mode 100644 src/mlpack/tests/layer_names_test.cpp rename src/mlpack/tests/{not_adapted => }/rbm_network_test.cpp (100%) rename src/mlpack/tests/{not_adapted => }/wgan_test.cpp (100%) diff --git a/.ci/linux-steps.yaml b/.ci/linux-steps.yaml index a04e305bc3..5e87b118a5 100644 --- a/.ci/linux-steps.yaml +++ b/.ci/linux-steps.yaml @@ -80,7 +80,6 @@ steps: mkdir build && cd build if [ "$(binding)" == "go" ]; then export GOPATH=$PWD/src/mlpack/bindings/go - export GO111MODULE=off go get -u -t gonum.org/v1/gonum/... fi cmake $(CMakeArgs) -DPYTHON_EXECUTABLE=`which python` -DCEREAL_INCLUDE_DIR=/usr/include/ .. diff --git a/.ci/macos-steps.yaml b/.ci/macos-steps.yaml index ef9a736fac..60f5e4d3b6 100644 --- a/.ci/macos-steps.yaml +++ b/.ci/macos-steps.yaml @@ -33,7 +33,6 @@ steps: mkdir build && cd build if [ "$(binding)" == "go" ]; then export GOPATH=$PWD/src/mlpack/bindings/go - export GO111MODULE=off go get -u -t gonum.org/v1/gonum/... fi if [ "$(binding)" == "python" ]; then diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 8b6d7a7ffb..99e4039bfc 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -57,7 +57,6 @@ jobs: run: | remotes::install_deps(dependencies = TRUE) remotes::install_cran("roxygen2") - remotes::install_cran("pkgbuild") shell: Rscript {0} - name: CMake diff --git a/CMake/FindGo.cmake b/CMake/FindGo.cmake index 6d5011f1c8..39b93f69aa 100644 --- a/CMake/FindGo.cmake +++ b/CMake/FindGo.cmake @@ -14,8 +14,8 @@ if (GO_EXECUTABLE) RESULT_VARIABLE RESULT ) if (RESULT EQUAL 0) - string(REGEX MATCH "([0-9]+\\.[0-9]+\(\\.[0-9]+\)?)" - GO_VERSION_STRING "${GO_VERSION_STRING}") + string(REGEX REPLACE ".*([0-9]+\\.[0-9]+\(\\.[0-9]+\)?).*" "\\1" + GO_VERSION_STRING ${GO_VERSION_STRING}) endif() endif() diff --git a/CMake/FindGonum.cmake b/CMake/FindGonum.cmake index 3667625915..eb18819a85 100644 --- a/CMake/FindGonum.cmake +++ b/CMake/FindGonum.cmake @@ -4,21 +4,20 @@ if (GO_EXECUTABLE) execute_process( COMMAND ${GO_EXECUTABLE} list gonum.org/v1/gonum/mat - OUTPUT_VARIABLE GONUM_RAW_STRING + OUTPUT_VARIABLE GONUM_VERSION_STRING RESULT_VARIABLE RESULT ) if (RESULT EQUAL 0) + string(REGEX REPLACE ".*([0-9]+\\.[0-9]+\\.[0-9]+[\n]+).*" "\\1" + GONUM_VERSION_STRING ${GONUM_VERSION_STRING}) string(REGEX REPLACE "\n$" "" - GONUM_RAW_STRING ${GONUM_RAW_STRING}) - if ("${GONUM_RAW_STRING}" STREQUAL "gonum.org/v1/gonum/mat") - set(GONUM_FOUND 1) - endif() + GONUM_VERSION_STRING ${GONUM_VERSION_STRING}) endif() endif() include(FindPackageHandleStandardArgs) find_package_handle_standard_args( Gonum - REQUIRED_VARS GONUM_FOUND + REQUIRED_VARS GONUM_VERSION_STRING FAIL_MESSAGE "Gonum not found" ) diff --git a/COPYRIGHT.txt b/COPYRIGHT.txt index 8bd3ffc06b..b9b43bdc6d 100644 --- a/COPYRIGHT.txt +++ b/COPYRIGHT.txt @@ -7,7 +7,7 @@ Source: Files: * Copyright: - Copyright 2008-2022, Ryan Curtin + Copyright 2008-2021, Ryan Curtin Copyright 2008-2013, Bill March Copyright 2008-2012, Dongryeol Lee Copyright 2008-2013, Nishant Mehta diff --git a/HISTORY.md b/HISTORY.md index 605aaec568..c5c0146a13 100644 --- a/HISTORY.md +++ b/HISTORY.md @@ -1,7 +1,5 @@ ### mlpack ?.?.? ###### ????-??-?? - * Fix `Perceptron` to work with cross-validation framework (#3190). - * Migrate from boost tests to Catch2 framework (#2523), (#2584). * Bump minimum armadillo version from 8.400 to 9.800 (#3043), (#3048). diff --git a/src/mlpack/bindings/python/copy_artifacts.py b/src/mlpack/bindings/python/copy_artifacts.py index 969be65451..ab62a403c5 100644 --- a/src/mlpack/bindings/python/copy_artifacts.py +++ b/src/mlpack/bindings/python/copy_artifacts.py @@ -6,14 +6,18 @@ # terms of the 3-clause BSD license. You should have received a copy of the # 3-clause BSD license along with mlpack. If not, see # http://www.opensource.org/licenses/BSD-3-Clause for more information. +import sys import sysconfig import shutil import os -import glob -# Match any lib.$platform*/mlpack/ directory. -directory = glob.glob('build/lib.' + sysconfig.get_platform() + '*/mlpack/')[0] -directory = directory.replace('\\', '/') +directory = 'build/lib.' + \ + sysconfig.get_platform() + \ + '-' + \ + str(sys.version_info[0]) + \ + '.' + \ + str(sys.version_info[1]) + \ + '/mlpack/' # Now copy all the files from the directory to the desired location. for f in os.listdir(directory): diff --git a/src/mlpack/bindings/python/is_serializable.hpp b/src/mlpack/bindings/python/is_serializable.hpp index f57553939a..dcec2986f5 100644 --- a/src/mlpack/bindings/python/is_serializable.hpp +++ b/src/mlpack/bindings/python/is_serializable.hpp @@ -21,7 +21,7 @@ namespace python { template inline bool IsSerializable( util::ParamData& /* d */, - const typename std::enable_if::value>::type* = 0) + const typename boost::disable_if>::type* = 0) { return false; } @@ -29,7 +29,7 @@ inline bool IsSerializable( template inline bool IsSerializable( util::ParamData& /* d */, - const typename std::enable_if::value>::type* = 0) + const typename boost::enable_if>::type* = 0) { return true; } diff --git a/src/mlpack/core/cereal/CMakeLists.txt b/src/mlpack/core/cereal/CMakeLists.txt index cad97c39ea..e6acb18e3f 100644 --- a/src/mlpack/core/cereal/CMakeLists.txt +++ b/src/mlpack/core/cereal/CMakeLists.txt @@ -7,6 +7,8 @@ set(SOURCES pair_associative_container.hpp pointer_wrapper.hpp pointer_vector_wrapper.hpp + pointer_variant_wrapper.hpp + pointer_vector_variant_wrapper.hpp unordered_map.hpp ) diff --git a/src/mlpack/core/cereal/pointer_variant_wrapper.hpp b/src/mlpack/core/cereal/pointer_variant_wrapper.hpp new file mode 100644 index 0000000000..92ac65f7fc --- /dev/null +++ b/src/mlpack/core/cereal/pointer_variant_wrapper.hpp @@ -0,0 +1,159 @@ +/** + * @file core/cereal/pointer_variant_wrapper.hpp + * @author Omar Shrit + * + * Implementation of a boost::variant wrapper to enable the serialization of + * the pointers inside boost variant in cereal + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_CORE_CEREAL_POINTER_VARIANT_WRAPPER_HPP +#define MLPACK_CORE_CEREAL_POINTER_VARIANT_WRAPPER_HPP + +#include +#include +#include +#include + +#include +#include +#include + +#include "pointer_wrapper.hpp" + +namespace cereal { + +// Forward declaration. +template +class PointerVariantWrapper; + +/** + * Serialize a boost variant in which the variant it self is a raw pointer. + * This wrapper will wrap each variant independently by encapsulating each variant + * into the PoninterWrapper we have created already. + * + * @param t A reference to boost variant that holds raw pointer. + */ +template +inline PointerVariantWrapper +make_pointer_variant(boost::variant& t) +{ + return PointerVariantWrapper(t); +} + +template +struct save_visitor : public boost::static_visitor +{ + save_visitor(Archive& ar) : ar(ar) {} + + template + void operator()(const T* value) const + { + ar(CEREAL_POINTER(value)); + } + + template + void operator()(boost::variant& value) const + { + ar(make_pointer_variant(value)); + } + + Archive& ar; +}; + +template +struct load_visitor : public boost::static_visitor +{ + template + static void load_impl(Archive& ar, VariantType& variant, std::true_type) + { + // Note that T will be a pointer type. + T loadVariant; + ar(CEREAL_POINTER(loadVariant)); + variant = loadVariant; + } + + template + static void load_impl(Archive& ar, VariantType& value, std::false_type) + { + // This must be a nested boost::variant. + T loadVariant; + ar(make_pointer_variant(loadVariant)); + value = loadVariant; + } + + template + static void load(Archive& ar, VariantType& variant) + { + // Delegate to the proper load_impl() overload depending on whether T is a + // pointer type. If T is not a pointer type, then we expect it to be a + // nested boost::variant. + load_impl(ar, variant, typename std::is_pointer::type()); + } +}; + +/** + * The objective of this class is to create a wrapper for + * boost::variant. + * Cereal supports the serialization of boost::variant, but + * we need to serialize it if it holds a raw pointers. + * This class depeds on the PointerWrapper we have already created in which it is + * used to serialize each variant independently + */ +template +class PointerVariantWrapper +{ + public: + PointerVariantWrapper(boost::variant& pointerVar) : + pointerVariant(pointerVar) + {} + + template + void save(Archive& ar) const + { + // which represents the index in std::variant. + int which = pointerVariant.which(); + ar(CEREAL_NVP(which)); + save_visitor s(ar); + boost::apply_visitor(s, pointerVariant); + } + + template + void load(Archive& ar) + { + // Load the size of the serialized type. + int which; + ar(CEREAL_NVP(which)); + + // Create function pointers to each overload of load_visitor::load, for + // all T in VariantTypes. + using LoadFuncType = void(*)(Archive&, boost::variant&); + LoadFuncType loadFuncArray[] = { &load_visitor::load... }; + + if (which >= int(sizeof(loadFuncArray)/sizeof(loadFuncArray[0]))) + throw std::runtime_error("Invalid 'which' selector when" + "deserializing boost::variant"); + + loadFuncArray[which](ar, pointerVariant); + } + + private: + boost::variant& pointerVariant; +}; + +/** + * Cereal does not support the serialization of raw pointer. + * This macro enable developers to serialize boost::variant that holds raw + * pointers by using the above PointerVariantWrapper class which replace the + * internal raw pointers by smart pointer internally. + * + * @param T boost::variant that holds raw pointer to be serialized. + */ +#define CEREAL_VARIANT_POINTER(T) cereal::make_pointer_variant(T) + +} // namespace cereal + +#endif // CEREAL_POINTER_VARIANT_WRAPPER_HPP diff --git a/src/mlpack/core/cereal/pointer_vector_variant_wrapper.hpp b/src/mlpack/core/cereal/pointer_vector_variant_wrapper.hpp new file mode 100644 index 0000000000..76f035e7b5 --- /dev/null +++ b/src/mlpack/core/cereal/pointer_vector_variant_wrapper.hpp @@ -0,0 +1,97 @@ +/** + * @file core/cereal/pointer_vector_variant_wrapper.hpp + * @author Omar Shrit + * + * Implementation of a boost::variant wrapper to enable the serialization of + * the pointers inside boost variant in cereal + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_CORE_CEREAL_POINTER_VECTOR_VARIANT_WRAPPER_HPP +#define MLPACK_CORE_CEREAL_POINTER_VECTOR_VARIANT_WRAPPER_HPP + +#include "pointer_wrapper.hpp" +#include "pointer_variant_wrapper.hpp" +#include "pointer_vector_wrapper.hpp" + +namespace cereal { + +// Forward declaration +template +class PointerVectorVariantWrapper; + +/** + * Serialize a std::vector of boost variants in which the variant in each boost + * variant is a raw pointer. + * This wrapper will wrap each boost variant independently by encapsulating each + * boost variant into the PoninterVariantWrapper we have created already. + * + * @param t A reference to a vector of boost variants that holds raw pointer. + */ +template +inline PointerVectorVariantWrapper +make_vector_pointer_variant(std::vector>& t) +{ + return PointerVectorVariantWrapper(t); +} + +/** + * The objective of this class is to create a wrapper for + * a vector of boost::variant that holds pointer. + * Cereal supports the serialization of boost::variant, but + * we need to serialize it if it holds a vector of boost::variant that holds a + * pointers. + */ +template +class PointerVectorVariantWrapper +{ + public: + PointerVectorVariantWrapper( + std::vector>& vecPointerVar) + : vectorPointerVariant(vecPointerVar) + {} + + template + void save(Archive& ar) const + { + size_t vecSize = vectorPointerVariant.size(); + ar(CEREAL_NVP(vecSize)); + for (size_t i = 0; i < vectorPointerVariant.size(); ++i) + { + ar(CEREAL_VARIANT_POINTER(vectorPointerVariant.at(i))); + } + } + + template + void load(Archive& ar) + { + size_t vecSize = 0; + ar(CEREAL_NVP(vecSize)); + vectorPointerVariant.resize(vecSize); + for (size_t i = 0; i < vectorPointerVariant.size(); ++i) + { + ar(CEREAL_VARIANT_POINTER(vectorPointerVariant.at(i))); + } + } + + private: + std::vector>& vectorPointerVariant; +}; + +/** + * Cereal does not support the serialization of raw pointer. + * This macro enable developers to serialize a std vector that holds boost::variants + * that holds raw pointers by using the above PointerVectorVariantWrapper class + * which replace the internal raw pointers by smart pointer internally. + * + * @param T std::vector that holds raw pointer to be serialized. + */ +#define CEREAL_VECTOR_VARIANT_POINTER(T) cereal::make_vector_pointer_variant(T) + +} // namespace cereal + +#endif // CEREAL_POINTER_VECTOR_VARIANT_WRAPPER_HPP + diff --git a/src/mlpack/core/util/size_checks.hpp b/src/mlpack/core/util/size_checks.hpp index f5125a591b..c2d60459b8 100644 --- a/src/mlpack/core/util/size_checks.hpp +++ b/src/mlpack/core/util/size_checks.hpp @@ -26,27 +26,18 @@ namespace util { * error generation. * @param addInfo Name to use for labels for precise error generation. Default * is "labels"; for example, "weights" could also be used. - * @param isDataTranspose Bool parameter which can be set true to transpose data - * before size-check. Default is false. - * @param isLabelTranspose Bool parameter which can be set true to transpose label - * before size-check. Default is false. */ template inline void CheckSameSizes(const DataType& data, const LabelsType& label, const std::string& callerDescription, - const std::string& addInfo = "labels", - const bool& isDataTranspose = false, - const bool& isLabelTranspose = false) -{ - const size_t dataPoints = (isDataTranspose == true) ? data.n_rows : data.n_cols; - const size_t labelPoints = (isLabelTranspose == true) ? label.n_rows : label.n_cols; - - if (dataPoints != labelPoints) + const std::string& addInfo = "labels") +{ + if (data.n_cols != label.n_cols) { std::ostringstream oss; - oss << callerDescription << ": number of points (" << dataPoints << ") " - << "does not match number of " << addInfo << " (" << labelPoints + oss << callerDescription << ": number of points (" << data.n_cols << ") " + << "does not match number of " << addInfo << " (" << label.n_cols << ")!" << std::endl; throw std::invalid_argument(oss.str()); } diff --git a/src/mlpack/methods/ann/CMakeLists.txt b/src/mlpack/methods/ann/CMakeLists.txt index fb1101ec11..8888113548 100644 --- a/src/mlpack/methods/ann/CMakeLists.txt +++ b/src/mlpack/methods/ann/CMakeLists.txt @@ -3,17 +3,24 @@ set(SOURCES ffn.hpp ffn_impl.hpp - forward_decls.hpp - make_alias.hpp rnn.hpp rnn_impl.hpp + brnn.hpp + brnn_impl.hpp + layer_names.hpp ) +add_subdirectory(visitor) +add_subdirectory(activation_functions) add_subdirectory(init_rules) add_subdirectory(layer) add_subdirectory(loss_functions) add_subdirectory(convolution_rules) +add_subdirectory(gan) +add_subdirectory(rbm) +add_subdirectory(augmented) add_subdirectory(regularizer) +add_subdirectory(util) # Add directory name to sources. set(DIR_SRCS) diff --git a/src/mlpack/methods/ann/not_adapted/brnn.hpp b/src/mlpack/methods/ann/brnn.hpp similarity index 99% rename from src/mlpack/methods/ann/not_adapted/brnn.hpp rename to src/mlpack/methods/ann/brnn.hpp index ac1e77f3dc..e3170474b4 100644 --- a/src/mlpack/methods/ann/not_adapted/brnn.hpp +++ b/src/mlpack/methods/ann/brnn.hpp @@ -24,6 +24,7 @@ #include "init_rules/network_init.hpp" #include #include +#include #include #include diff --git a/src/mlpack/methods/ann/not_adapted/brnn_impl.hpp b/src/mlpack/methods/ann/brnn_impl.hpp similarity index 100% rename from src/mlpack/methods/ann/not_adapted/brnn_impl.hpp rename to src/mlpack/methods/ann/brnn_impl.hpp diff --git a/src/mlpack/methods/ann/convolution_rules/naive_convolution.hpp b/src/mlpack/methods/ann/convolution_rules/naive_convolution.hpp index c01fd6d749..d0bf1cadfd 100644 --- a/src/mlpack/methods/ann/convolution_rules/naive_convolution.hpp +++ b/src/mlpack/methods/ann/convolution_rules/naive_convolution.hpp @@ -57,15 +57,9 @@ class NaiveConvolution const size_t dilationW = 1, const size_t dilationH = 1) { - // Compute the output size. The filterRows and filterCols computation must - // take into account the fact that dilation only adds rows or columns - // *between* filter elements. So, e.g., a dilation of 2 on a kernel size of - // 3x3 means an effective kernel size of 5x5, *not* 6x6. - const size_t filterRows = filter.n_rows * dilationH - (dilationH - 1); - const size_t filterCols = filter.n_cols * dilationW - (dilationW - 1); - const size_t outputRows = (input.n_rows - filterRows + dH) / dH; - const size_t outputCols = (input.n_cols - filterCols + dW) / dW; - output.zeros(outputRows, outputCols); + output = arma::zeros >( + (input.n_rows - (filter.n_rows - 1) * dilationW - 1) / dW + 1, + (input.n_cols - (filter.n_cols - 1) * dilationH - 1) / dH + 1); // It seems to be about 3.5 times faster to use pointers instead of // filter(ki, kj) * input(leftInput + ki, topInput + kj) and output(i, j). @@ -109,22 +103,37 @@ class NaiveConvolution const size_t dilationW = 1, const size_t dilationH = 1) { - // First, compute the necessary padding for the full convolution. It is - // possible that this might be an overestimate. Note that these variables - // only hold the padding on one side of the input. - const size_t filterRows = filter.n_rows * dilationH - (dilationH - 1); - const size_t filterCols = filter.n_cols * dilationW - (dilationW - 1); - const size_t paddingRows = filterRows - 1; - const size_t paddingCols = filterCols - 1; + size_t outputRows = (input.n_rows - 1) * dW + 2 * (filter.n_rows - 1) + * dilationW + 1; + size_t outputCols = (input.n_cols - 1) * dH + 2 * (filter.n_cols - 1) + * dilationH + 1; + + for (size_t i = 0; i < dW; ++i) + { + if (((((i + outputRows - 2 * (filter.n_rows - 1) * dilationW - 1) % dW) + + dW) % dW) == i){ + outputRows += i; + break; + } + } + for (size_t i = 0; i < dH; ++i) + { + if (((((i + outputCols - 2 * (filter.n_cols - 1) * dilationH - 1) % dH) + + dH) % dH) == i){ + outputCols += i; + break; + } + } // Pad filter and input to the working output shape. - arma::Mat inputPadded(input.n_rows + 2 * paddingRows, - input.n_cols + 2 * paddingCols, arma::fill::zeros); - inputPadded.submat(paddingRows, paddingCols, paddingRows + input.n_rows - 1, - paddingCols + input.n_cols - 1) = input; + arma::Mat inputPadded = arma::zeros >(outputRows, + outputCols); + inputPadded.submat((filter.n_rows - 1) * dilationW, (filter.n_cols - 1) + * dilationH, (filter.n_rows - 1) * dilationW + input.n_rows - 1, + (filter.n_cols - 1) * dilationH + input.n_cols - 1) = input; NaiveConvolution::Convolution(inputPadded, filter, - output, dW, dH, dilationW, dilationH); + output, 1, 1, dilationW, dilationH); } /* diff --git a/src/mlpack/methods/ann/ffn.hpp b/src/mlpack/methods/ann/ffn.hpp index 0b6bde3648..1c65bbb749 100644 --- a/src/mlpack/methods/ann/ffn.hpp +++ b/src/mlpack/methods/ann/ffn.hpp @@ -15,49 +15,46 @@ #include -#include "forward_decls.hpp" +#include "visitor/delete_visitor.hpp" +#include "visitor/delta_visitor.hpp" +#include "visitor/output_height_visitor.hpp" +#include "visitor/output_parameter_visitor.hpp" +#include "visitor/output_width_visitor.hpp" +#include "visitor/reset_visitor.hpp" +#include "visitor/weight_size_visitor.hpp" +#include "visitor/copy_visitor.hpp" +#include "visitor/loss_visitor.hpp" + #include "init_rules/network_init.hpp" +#include #include -#include #include -#include +#include #include namespace mlpack { namespace ann /** Artificial Neural Network. */ { /** - * Implementation of a standard feed forward network. Any layer that inherits - * from the base `Layer` class can be added to this model. For recursive neural - * networks, see the `RNN` class. - * - * In general, a network can be created by using the `Add()` method to add - * layers to the network. Then, training can be performed with `Train()`, and - * data points can be passed through the trained network with `Predict()`. - * - * Although the actual types passed as input will be matrix objects with one - * data point per column, each data point can be a tensor of arbitrary shape. - * If data points are not 1-dimensional vectors, then set the shape of the input - * with `InputDimensions()` before calling `Train()`. - * - * More granular functionality is available with `Forward()`, Backward()`, and - * `Evaluate()`, or even by accessing the individual layers directly with - * `Network()`. + * Implementation of a standard feed forward network. * * @tparam OutputLayerType The output layer type used to evaluate the network. * @tparam InitializationRuleType Rule used to initialize the weight matrix. - * @tparam MatType Type of matrix to be given as input to the network. - * @tparam MatType Type of matrix to be produced as output from the last - * layer. + * @tparam CustomLayers Any set of custom layers that could be a part of the + * feed forward network. */ template< - typename OutputLayerType = NegativeLogLikelihood, - typename InitializationRuleType = RandomInitialization, - typename MatType = arma::mat> + typename OutputLayerType = NegativeLogLikelihood<>, + typename InitializationRuleType = RandomInitialization, + typename... CustomLayers +> class FFN { public: + //! Convenience typedef for the internal model construction. + using NetworkType = FFN; + /** * Create the FFN object. * @@ -75,73 +72,56 @@ class FFN InitializationRuleType initializeRule = InitializationRuleType()); //! Copy constructor. - FFN(const FFN& other); + FFN(const FFN&); + //! Move constructor. - FFN(FFN&& other); - //! Copy operator. - FFN& operator=(const FFN& other); - //! Move assignment operator. - FFN& operator=(FFN&& other); + FFN(FFN&&); + + //! Copy/move assignment operator. + FFN& operator = (FFN); + + //! Destructor to release allocated memory. + ~FFN(); /** - * Add a new layer to the model. + * Check if the optimizer has MaxIterations() parameter, if it does + * then check if it's value is less than the number of datapoints + * in the dataset. * - * @param args The layer parameter. + * @tparam OptimizerType Type of optimizer to use to train the model. + * @param optimizer optimizer used in the training process. + * @param samples Number of datapoints in the dataset. */ - template - void Add(Args... args) - { - network.template Add(args...); - inputDimensionsAreSet = false; - } + template + typename std::enable_if< + HasMaxIterations + ::value, void>::type + WarnMessageMaxIterations(OptimizerType& optimizer, size_t samples) const; /** - * Add a new layer to the model. Note that any trainable weights of this - * layer will be reset! (Any constant parameters are kept.) + * Check if the optimizer has MaxIterations() parameter, if it + * doesn't then simply return from the function. * - * @param layer The Layer to be added to the model. + * @tparam OptimizerType Type of optimizer to use to train the model. + * @param optimizer optimizer used in the training process. + * @param samples Number of datapoints in the dataset. */ - void Add(Layer* layer) - { - network.Add(layer); - inputDimensionsAreSet = false; - } - - //! Get the layers of the network. - const std::vector*>& Network() const - { - return network.Network(); - } - - /** - * Modify the network model. Be careful! If you change the structure of the - * network or parameters for layers, its state may become invalid, and the - * next time it is used for any operation the parameters will be reset. - * - * Don't add any layers like this; use `Add()` instead. - */ - std::vector*>& Network() - { - // We can no longer make any assumptions... the user may change anything. - inputDimensionsAreSet = false; - layerMemoryIsSet = false; - - return network.Network(); - } + template + typename std::enable_if< + !HasMaxIterations + ::value, void>::type + WarnMessageMaxIterations(OptimizerType& optimizer, size_t samples) const; /** * Train the feedforward network on the given input data using the given * optimizer. * - * If no parameters have ever been set (e.g. if `Parameters()` is an empty - * matrix), or if the parameters' size does not match the number of weights - * needed for the current input size (as given by `predictors` and optionally - * set further by `InputDimensions()`), then the network will be initialized - * using `InitializeRuleType`. + * This will use the existing model parameters as a starting point for the + * optimization. If this is not what you want, then you should access the + * parameters vector directly with Parameters() and modify it as desired. * - * If parameters are the right size for the given `predictors` and - * `InputDimensions()`, then the existing parameters will be used as a - * starting point. (If you want to reinitialize, first call `Reset()`.) + * If you want to pass in a parameter and discard the original parameter + * object, be sure to use std::move to avoid unnecessary copy. * * @tparam OptimizerType Type of optimizer to use to train the model. * @tparam CallbackTypes Types of Callback Functions. @@ -153,25 +133,22 @@ class FFN * @return The final objective of the trained model (NaN or Inf on error). */ template - typename MatType::elem_type Train(MatType predictors, - MatType responses, - OptimizerType& optimizer, - CallbackTypes&&... callbacks); + double Train(arma::mat predictors, + arma::mat responses, + OptimizerType& optimizer, + CallbackTypes&&... callbacks); /** * Train the feedforward network on the given input data. By default, the * RMSProp optimization algorithm is used, but others can be specified * (such as ens::SGD). * - * If no parameters have ever been set (e.g. if `Parameters()` is an empty - * matrix), or if the parameters' size does not match the number of weights - * needed for the current input size (as given by `predictors` and optionally - * set further by `InputDimensions()`), then the network will be initialized - * using `InitializeRuleType`. + * This will use the existing model parameters as a starting point for the + * optimization. If this is not what you want, then you should access the + * parameters vector directly with Parameters() and modify it as desired. * - * If parameters are the right size for the given `predictors` and - * `InputDimensions()`, then the existing parameters will be used as a - * starting point. (If you want to reinitialize, first call `Reset()`.) + * If you want to pass in a parameter and discard the original parameter + * object, be sure to use std::move to avoid unnecessary copy. * * @tparam OptimizerType Type of optimizer to use to train the model. * @param predictors Input training variables. @@ -182,123 +159,22 @@ class FFN * @return The final objective of the trained model (NaN or Inf on error). */ template - typename MatType::elem_type Train(MatType predictors, - MatType responses, - CallbackTypes&&... callbacks); + double Train(arma::mat predictors, + arma::mat responses, + CallbackTypes&&... callbacks); /** - * Predict the responses to a given set of predictors. The responses will be - * the output of the output layer when `predictors` is passed through the - * whole network (`OutputLayerType`). + * Predict the responses to a given set of predictors. The responses will + * reflect the output of the given output layer as returned by the + * output layer function. + * + * If you want to pass in a parameter and discard the original parameter + * object, be sure to use std::move to avoid unnecessary copy. * * @param predictors Input predictors. * @param results Matrix to put output predictions of responses into. - * @param batchSize Batch size to use for prediction. */ - void Predict(MatType predictors, - MatType& results, - const size_t batchSize = 128); - - // Return the number of weights in the model. - size_t WeightSize(); - - /** - * Set the logical dimensions of the input. `Train()` and `Predict()` expect - * data to be passed such that one point corresponds to one column, but this - * data is allowed to be an arbitrary higher-order tensor. - * - * So, if the input is meant to be 28x28x3 images, then the - * input data to `Train()` or `Predict()` should have 28*28*3 = 2352 rows, and - * `InputDimensions()` should be set to `{ 28, 28, 3 }`. Then, the layers of - * the network will interpret each input point as a 3-dimensional image - * instead of a 1-dimensional vector. - * - * If `InputDimensions()` is left unset before training, the data will be - * assumed to be a 1-dimensional vector. - */ - std::vector& InputDimensions() - { - // The user may change the input dimensions, so we will have to propagate - // these changes to the network. - inputDimensionsAreSet = false; - return inputDimensions; - } - //! Get the logical dimensions of the input. - const std::vector& InputDimensions() const { return inputDimensions; } - - //! Return the current set of weights. These are linearized: this contains - //! the weights of every layer. - const MatType& Parameters() const { return parameters; } - //! Modify the current set of weights. These are linearized: this contains - //! the weights of every layer. Be careful! If you change the shape of - //! `parameters` to something incorrect, it may be re-initialized the next - //! time a forward pass is done. - MatType& Parameters() { return parameters; } - - /** - * Reset the stored data of the network entirely. This resets all weights of - * each layer using `InitializationRuleType`, and prepares the network to - * accept a (flat 1-d) input size of `inputDimensionality` (if passed), or - * whatever input size has been set with `InputDimensions()`. - * - * This also resets the mode of the network to prediction mode (not training - * mode). See `SetNetworkMode()` for more information. - */ - void Reset(const size_t inputDimensionality = 0); - - /** - * Set all the layers in the network to training mode, if `training` is - * `true`, or set all the layers in the network to testing mode, if `training` - * is `false`. - */ - void SetNetworkMode(const bool training); - - /** - * Perform a manual forward pass of the data. - * - * `Forward()` and `Backward()` should be used as a pair, and they are - * designed mainly for advanced users. You should try to use `Predict()` and - * `Train()`, if you can. - * - * @param inputs The input data. - * @param results The predicted results. - */ - void Forward(const MatType& inputs, MatType& results); - - /** - * Perform a manual partial forward pass of the data. - * - * This function is meant for the cases when users require a forward pass only - * through certain layers and not the entire network. `Forward()` and - * `Backward()` should be used as a pair, and they are designed mainly for - * advanced users. You should try to use `Predict()` and `Train()`, if you - * can. - * - * @param inputs The input data for the specified first layer. - * @param results The predicted results from the specified last layer. - * @param begin The index of the first layer. - * @param end The index of the last layer. - */ - void Forward(const MatType& inputs, - MatType& results, - const size_t begin, - const size_t end); - - /** - * Perform a manual backward pass of the data. - * - * `Forward()` and `Backward()` should be used as a pair, and they are - * designed mainly for advanced users. You should try to use `Predict()` and - * `Train()` instead, if you can. - * - * @param inputs Inputs of current pass. - * @param targets The training target. - * @param gradients Computed gradients. - * @return Training error of the current pass. - */ - typename MatType::elem_type Backward(const MatType& inputs, - const MatType& targets, - MatType& gradients); + void Predict(arma::mat predictors, arma::mat& results); /** * Evaluate the feedforward network with the given predictors and responses. @@ -307,38 +183,41 @@ class FFN * @param predictors Input variables. * @param responses Target outputs for input variables. */ - typename MatType::elem_type Evaluate(const MatType& predictors, - const MatType& responses); - - //! Serialize the model. - template - void serialize(Archive& ar, const uint32_t /* version */); - - // - // Only ensmallen utility functions for training are found below here. - // They aren't generally useful otherwise. - // + template + double Evaluate(const PredictorsType& predictors, + const ResponsesType& responses); /** - * Note: this function is implemented so that it can be used by ensmallen's - * optimizers. It's not generally meant to be used otherwise. - * - * Evaluate the feedforward network with the given parameters. + * Evaluate the feedforward network with the given parameters. This function + * is usually called by the optimizer to train the model. * * @param parameters Matrix model parameters. */ - typename MatType::elem_type Evaluate(const MatType& parameters); + double Evaluate(const arma::mat& parameters); - /** - * Note: this function is implemented so that it can be used by ensmallen's - * optimizers. It's not generally meant to be used otherwise. - * + /** * Evaluate the feedforward network with the given parameters, but using only * a number of data points. This is useful for optimizers such as SGD, which * require a separable objective function. * - * Note that the network may return different results depending on the mode it - * is in (see `SetNetworkMode()`). + * @param parameters Matrix model parameters. + * @param begin Index of the starting point to use for objective function + * evaluation. + * @param batchSize Number of points to be passed at a time to use for + * objective function evaluation. + * @param deterministic Whether or not to train or test the model. Note some + * layer act differently in training or testing mode. + */ + double Evaluate(const arma::mat& parameters, + const size_t begin, + const size_t batchSize, + const bool deterministic); + + /** + * Evaluate the feedforward network with the given parameters, but using only + * a number of data points. This is useful for optimizers such as SGD, which + * require a separable objective function. This just calls the overload of + * Evaluate() with deterministic = true. * * @param parameters Matrix model parameters. * @param begin Index of the starting point to use for objective function @@ -346,14 +225,11 @@ class FFN * @param batchSize Number of points to be passed at a time to use for * objective function evaluation. */ - typename MatType::elem_type Evaluate(const MatType& parameters, - const size_t begin, - const size_t batchSize); + double Evaluate(const arma::mat& parameters, + const size_t begin, + const size_t batchSize); /** - * Note: this function is implemented so that it can be used by ensmallen's - * optimizers. It's not generally meant to be used otherwise. - * * Evaluate the feedforward network with the given parameters. * This function is usually called by the optimizer to train the model. * This just calls the overload of EvaluateWithGradient() with batchSize = 1. @@ -361,13 +237,10 @@ class FFN * @param parameters Matrix model parameters. * @param gradient Matrix to output gradient into. */ - typename MatType::elem_type EvaluateWithGradient(const MatType& parameters, - MatType& gradient); + template + double EvaluateWithGradient(const arma::mat& parameters, GradType& gradient); - /** - * Note: this function is implemented so that it can be used by ensmallen's - * optimizers. It's not generally meant to be used otherwise. - * + /** * Evaluate the feedforward network with the given parameters, but using only * a number of data points. This is useful for optimizers such as SGD, which * require a separable objective function. @@ -379,15 +252,13 @@ class FFN * @param batchSize Number of points to be passed at a time to use for * objective function evaluation. */ - typename MatType::elem_type EvaluateWithGradient(const MatType& parameters, - const size_t begin, - MatType& gradient, - const size_t batchSize); + template + double EvaluateWithGradient(const arma::mat& parameters, + const size_t begin, + GradType& gradient, + const size_t batchSize); /** - * Note: this function is implemented so that it can be used by ensmallen's - * optimizers. It's not generally meant to be used otherwise. - * * Evaluate the gradient of the feedforward network with the given parameters, * and with respect to only a number of points in the dataset. This is useful * for optimizers such as SGD, which require a separable objective function. @@ -399,156 +270,253 @@ class FFN * @param batchSize Number of points to be processed as a batch for objective * function gradient evaluation. */ - void Gradient(const MatType& parameters, + void Gradient(const arma::mat& parameters, const size_t begin, - MatType& gradient, + arma::mat& gradient, const size_t batchSize); /** - * Note: this function is implemented so that it can be used by ensmallen's - * optimizers. It's not generally meant to be used otherwise. - * - * Return the number of separable functions (the number of predictor points). - */ - size_t NumFunctions() const { return responses.n_cols; } - - /** - * Note: this function is implemented so that it can be used by ensmallen's - * optimizers. It's not generally meant to be used otherwise. - * - * Shuffle the order of function visitation. (This is equivalent to shuffling - * the dataset during training.) + * Shuffle the order of function visitation. This may be called by the + * optimizer. */ void Shuffle(); - /** - * Prepare the network for training on the given data. + /* + * Add a new module to the model. * - * This function won't actually trigger the training process, and is - * generally only useful internally. + * @param args The layer parameter. + */ + template + void Add(Args... args) { network.push_back(new LayerType(args...)); } + + /* + * Add a new module to the model. + * + * @param layer The Layer to be added to the model. + */ + void Add(LayerTypes layer) { network.push_back(layer); } + + //! Get the network model. + const std::vector >& Model() const + { + return network; + } + //! Modify the network model. Be careful! If you change the structure of the + //! network or parameters for layers, its state may become invalid, so be sure + //! to call ResetParameters() afterwards. + std::vector >& Model() { return network; } + + //! Return the number of separable functions (the number of predictor points). + size_t NumFunctions() const { return numFunctions; } + + //! Return the initial point for the optimization. + const arma::mat& Parameters() const { return parameter; } + //! Modify the initial point for the optimization. + arma::mat& Parameters() { return parameter; } + + //! Get the matrix of responses to the input data points. + const arma::mat& Responses() const { return responses; } + //! Modify the matrix of responses to the input data points. + arma::mat& Responses() { return responses; } + + //! Get the matrix of data points (predictors). + const arma::mat& Predictors() const { return predictors; } + //! Modify the matrix of data points (predictors). + arma::mat& Predictors() { return predictors; } + + /** + * Reset the module infomration (weights/parameters). + */ + void ResetParameters(); + + //! Serialize the model. + template + void serialize(Archive& ar, const uint32_t /* version */); + + /** + * Perform the forward pass of the data in real batch mode. + * + * Forward and Backward should be used as a pair, and they are designed mainly + * for advanced users. User should try to use Predict and Train unless those + * two functions can't satisfy some special requirements. + * + * @param inputs The input data. + * @param results The predicted results. + */ + template + void Forward(const PredictorsType& inputs, ResponsesType& results); + + /** + * Perform a partial forward pass of the data. + * + * This function is meant for the cases when users require a forward pass only + * through certain layers and not the entire network. + * + * @param inputs The input data for the specified first layer. + * @param results The predicted results from the specified last layer. + * @param begin The index of the first layer. + * @param end The index of the last layer. + */ + template + void Forward(const PredictorsType& inputs , + ResponsesType& results, + const size_t begin, + const size_t end); + + /** + * Perform the backward pass of the data in real batch mode. + * + * Forward and Backward should be used as a pair, and they are designed mainly + * for advanced users. User should try to use Predict and Train unless those + * two functions can't satisfy some special requirements. + * + * @param inputs Inputs of current pass. + * @param targets The training target. + * @param gradients Computed gradients. + * @return Training error of the current pass. + */ + template + double Backward(const PredictorsType& inputs, + const TargetsType& targets, + GradientsType& gradients); + + private: + // Helper functions. + /** + * The Forward algorithm (part of the Forward-Backward algorithm). Computes + * forward probabilities for each module. + * + * @param input Data sequence to compute probabilities for. + */ + template + void Forward(const InputType& input); + + /** + * Prepare the network for the given data. + * This function won't actually trigger training process. * * @param predictors Input data variables. * @param responses Outputs results from input data variables. */ - void ResetData(MatType predictors, MatType responses); - - private: - // Helper functions. - - //! Use the InitializationPolicy to initialize all the weights in the network. - void InitializeWeights(); - - //! Make the memory of each layer point to the right place, by calling - //! SetWeightPtr() on each layer. - void SetLayerMemory(); + void ResetData(arma::mat predictors, arma::mat responses); /** - * Ensure that all the locally-cached information about the network is valid, - * all parameter memory is initialized, and we can make forward and backward - * passes. + * The Backward algorithm (part of the Forward-Backward algorithm). Computes + * backward pass for module. + */ + void Backward(); + + /** + * Iterate through all layer modules and update the the gradient using the + * layer defined optimizer. + */ + template + void Gradient(const InputType& input); + + /** + * Reset the module status by setting the current deterministic parameter + * for all modules that implement the Deterministic function. + */ + void ResetDeterministic(); + + /** + * Reset the gradient for all modules that implement the Gradient function. + */ + void ResetGradients(arma::mat& gradient); + + /** + * Swap the content of this network with given network. * - * @param functionName Name of function to use if an exception is thrown. - * @param inputDimensionality Given dimensionality of the input data. - * @param setMode If true, the mode of the network will be set to the - * parameter given in `training`. Otherwise the mode of the network is - * left unmodified. - * @param training Mode to set the network to; `true` indicates the network - * should be set to training mode; `false` indicates testing mode. + * @param network Desired source network. */ - void CheckNetwork(const std::string& functionName, - const size_t inputDimensionality, - const bool setMode = false, - const bool training = false); + void Swap(FFN& network); - /** - * Set the input and output dimensions of each layer in the network correctly. - * The size of the input is taken, in case `inputDimensions` has not been set - * otherwise (e.g. via `InputDimensions()`). If `InputDimensions()` is not - * empty, then `inputDimensionality` is ignored. - */ - void UpdateDimensions(const std::string& functionName, - const size_t inputDimensionality = 0); - - /** - * Check if the optimizer has MaxIterations() parameter, if it does then check - * if its value is less than the number of datapoints in the dataset. - * - * @tparam OptimizerType Type of optimizer to use to train the model. - * @param optimizer optimizer used in the training process. - * @param samples Number of datapoints in the dataset. - */ - template - typename std::enable_if< - ens::traits::HasMaxIterationsSignature::value, void - >::type - WarnMessageMaxIterations(OptimizerType& optimizer, size_t samples) const; - - /** - * Check if the optimizer has MaxIterations() parameter; if it doesn't then - * simply return from the function. - * - * @tparam OptimizerType Type of optimizer to use to train the model. - * @param optimizer optimizer used in the training process. - * @param samples Number of datapoints in the dataset. - */ - template - typename std::enable_if< - !ens::traits::HasMaxIterationsSignature::value, void - >::type - WarnMessageMaxIterations(OptimizerType& optimizer, size_t samples) const; - - //! Instantiated output layer used to evaluate the network. + //! Instantiated outputlayer used to evaluate the network. OutputLayerType outputLayer; //! Instantiated InitializationRule object for initializing the network //! parameter. InitializationRuleType initializeRule; - //! All of the network is stored inside this multilayer. - MultiLayer network; + //! The input width. + size_t width; - /** - * Matrix of (trainable) parameters. Each weight here corresponds to a layer, - * and each layer's `parameters` member is an alias pointing to parameters in - * this matrix. - * - * Note: although each layer may have its own MatType and MatType, - * ensmallen optimization requires everything to be stored in one matrix - * object, so we have chosen MatType. This could be made more flexible - * with a "wrapper" class implementing the Armadillo API. - */ - MatType parameters; + //! The input height. + size_t height; - //! Dimensions of input data. - std::vector inputDimensions; + //! Indicator if we already trained the model. + bool reset; - //! The matrix of data points (predictors). This member is empty, except - //! during training---we must store a local copy of the training data since - //! the ensmallen optimizer will not provide training data. - MatType predictors; + //! Locally-stored model modules. + std::vector > network; - //! The matrix of responses to the input data points. This member is empty, - //! except during training. - MatType responses; + //! The matrix of data points (predictors). + arma::mat predictors; - //! Locally-stored output of the network from a forward pass; used by the - //! backward pass. - MatType networkOutput; - //! Locally-stored output of the backward pass; used by the gradient pass. - MatType networkDelta; - //! Locally-stored error of the backward pass; used by the gradient pass. - MatType error; + //! The matrix of responses to the input data points. + arma::mat responses; - //! If true, each layer has its memory properly set for a forward/backward - //! pass. - bool layerMemoryIsSet; + //! Matrix of (trained) parameters. + arma::mat parameter; - //! If true, each layer has its inputDimensions properly set, and - //! `totalInputSize` and `totalOutputSize` are valid. - bool inputDimensionsAreSet; + //! The number of separable functions (the number of predictor points). + size_t numFunctions; - // RNN will call `CheckNetwork()`, which is private. - friend class RNN; + //! The current error for the backward pass. + arma::mat error; + + //! Locally-stored delta visitor. + DeltaVisitor deltaVisitor; + + //! Locally-stored output parameter visitor. + OutputParameterVisitor outputParameterVisitor; + + //! Locally-stored weight size visitor. + WeightSizeVisitor weightSizeVisitor; + + //! Locally-stored output width visitor. + OutputWidthVisitor outputWidthVisitor; + + //! Locally-stored output height visitor. + OutputHeightVisitor outputHeightVisitor; + + //! Locally-stored loss visitor + LossVisitor lossVisitor; + + //! Locally-stored reset visitor. + ResetVisitor resetVisitor; + + //! Locally-stored delete visitor. + DeleteVisitor deleteVisitor; + + //! The current evaluation mode (training or testing). + bool deterministic; + + //! Locally-stored delta object. + arma::mat delta; + + //! Locally-stored input parameter object. + arma::mat inputParameter; + + //! Locally-stored output parameter object. + arma::mat outputParameter; + + //! Locally-stored gradient parameter. + arma::mat gradient; + + //! Locally-stored copy visitor + CopyVisitor copyVisitor; + + // The GAN class should have access to internal members. + template< + typename Model, + typename InitializerType, + typename NoiseType, + typename PolicyType + > + friend class GAN; }; // class FFN } // namespace ann diff --git a/src/mlpack/methods/ann/ffn_impl.hpp b/src/mlpack/methods/ann/ffn_impl.hpp index 734b961301..c30573ea9b 100644 --- a/src/mlpack/methods/ann/ffn_impl.hpp +++ b/src/mlpack/methods/ann/ffn_impl.hpp @@ -15,694 +15,662 @@ // In case it hasn't been included yet. #include "ffn.hpp" -#include "make_alias.hpp" +#include "visitor/forward_visitor.hpp" +#include "visitor/backward_visitor.hpp" +#include "visitor/deterministic_set_visitor.hpp" +#include "visitor/gradient_set_visitor.hpp" +#include "visitor/gradient_visitor.hpp" +#include "visitor/set_input_height_visitor.hpp" +#include "visitor/set_input_width_visitor.hpp" + +#include "util/check_input_shape.hpp" namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -FFN< - OutputLayerType, - InitializationRuleType, - MatType ->::FFN(OutputLayerType outputLayer, InitializationRuleType initializeRule) : + +template +FFN::FFN( + OutputLayerType outputLayer, InitializationRuleType initializeRule) : outputLayer(std::move(outputLayer)), initializeRule(std::move(initializeRule)), - layerMemoryIsSet(false), - inputDimensionsAreSet(false) + width(0), + height(0), + reset(false), + numFunctions(0), + deterministic(false) { /* Nothing to do here. */ } -template -FFN< - OutputLayerType, - InitializationRuleType, - MatType ->::FFN(const FFN& network): - outputLayer(network.outputLayer), - initializeRule(network.initializeRule), - network(network.network), - parameters(network.parameters), - inputDimensions(network.inputDimensions), - predictors(network.predictors), - responses(network.responses), - // These will be set correctly in the first Forward() call. - layerMemoryIsSet(false), - inputDimensionsAreSet(false) +template +FFN::~FFN() { - // Nothing to do. -}; - -template -FFN< - OutputLayerType, - InitializationRuleType, - MatType ->::FFN(FFN&& network): - outputLayer(std::move(network.outputLayer)), - initializeRule(std::move(network.initializeRule)), - network(std::move(network.network)), - parameters(std::move(network.parameters)), - inputDimensions(std::move(network.inputDimensions)), - predictors(std::move(network.predictors)), - responses(std::move(network.responses)), - // Aliases will not be correct after a std::move(), so we will manually - // reset them. - layerMemoryIsSet(false), - inputDimensionsAreSet(std::move(network.inputDimensionsAreSet)) -{ - // Nothing to do. -}; - -template -FFN& FFN< - OutputLayerType, - InitializationRuleType, - MatType ->::operator=(const FFN& other) -{ - if (this != &other) - { - outputLayer = other.outputLayer; - initializeRule = other.initializeRule; - network = other.network; - parameters = other.parameters; - inputDimensions = other.inputDimensions; - predictors = other.predictors; - responses = other.responses; - networkOutput = other.networkOutput; - networkDelta = other.networkDelta; - error = other.error; - inputDimensionsAreSet = other.inputDimensionsAreSet; - - // Copying will not preserve Armadillo aliases correctly, so we will reset - // those. - layerMemoryIsSet = false; - } - - return *this; + std::for_each(network.begin(), network.end(), + boost::apply_visitor(deleteVisitor)); } -template -FFN& FFN< - OutputLayerType, - InitializationRuleType, - MatType ->::operator=(FFN&& other) -{ - if (this != &other) - { - outputLayer = std::move(other.outputLayer); - initializeRule = std::move(other.initializeRule); - network = std::move(other.network); - parameters = std::move(other.parameters); - inputDimensions = std::move(other.inputDimensions); - predictors = std::move(other.predictors); - responses = std::move(other.responses); - networkOutput = std::move(other.networkOutput); - networkDelta = std::move(other.networkDelta); - error = std::move(other.error); - inputDimensionsAreSet = std::move(other.inputDimensionsAreSet); - layerMemoryIsSet = std::move(other.layerMemoryIsSet); - } - - return *this; -} - -template -template -typename MatType::elem_type FFN< - OutputLayerType, - InitializationRuleType, - MatType ->::Train(MatType predictors, - MatType responses, - OptimizerType& optimizer, - CallbackTypes&&... callbacks) -{ - ResetData(std::move(predictors), std::move(responses)); - - WarnMessageMaxIterations(optimizer, this->predictors.n_cols); - - // Ensure that the network can be used. - CheckNetwork("FFN::Train()", this->predictors.n_rows, true, true); - - // Train the model. - Timer::Start("ffn_optimization"); - const typename MatType::elem_type out = - optimizer.Optimize(*this, parameters, callbacks...); - Timer::Stop("ffn_optimization"); - - Log::Info << "FFN::Train(): final objective of trained model is " << out - << "." << std::endl; - return out; -} - -template -template -typename MatType::elem_type FFN< - OutputLayerType, - InitializationRuleType, - MatType ->::Train(MatType predictors, - MatType responses, - CallbackTypes&&... callbacks) -{ - OptimizerType optimizer; - return Train(std::move(predictors), std::move(responses), optimizer, - callbacks...); -} - -template -void FFN< - OutputLayerType, - InitializationRuleType, - MatType ->::Predict(MatType predictors, MatType& results, const size_t batchSize) -{ - // Ensure that the network is configured correctly. - CheckNetwork("FFN::Predict()", predictors.n_rows, true, false); - - results.set_size(network.OutputSize(), predictors.n_cols); - - for (size_t i = 0; i < predictors.n_cols; i += batchSize) - { - const size_t effectiveBatchSize = std::min(batchSize, - size_t(predictors.n_cols) - i); - - MatType predictorAlias(predictors.colptr(i), predictors.n_rows, - effectiveBatchSize, false, true); - MatType resultAlias(results.colptr(i), results.n_rows, - effectiveBatchSize, false, true); - - network.Forward(predictorAlias, resultAlias); - } -} - -template -size_t FFN< - OutputLayerType, - InitializationRuleType, - MatType ->::WeightSize() -{ - // If the input dimensions have not yet been propagated to the network, we - // must do that now. - UpdateDimensions("FFN::WeightSize()"); - return network.WeightSize(); -} - -template -void FFN< - OutputLayerType, - InitializationRuleType, - MatType ->::Reset(const size_t inputDimensionality) -{ - parameters.clear(); - - // If the user provided an input dimensionality, then we will take that as the - // new input size. Otherwise, whatever is currently specified in - // `InputDimensions()` will be used. - if (inputDimensionality != 0) - { - CheckNetwork("FFN::Reset()", inputDimensionality, true, false); - } - else - { - const size_t inputDims = std::accumulate(inputDimensions.begin(), - inputDimensions.end(), 0); - CheckNetwork("FFN::Reset()", inputDims, true, false); - } -} - -template -void FFN< - OutputLayerType, - InitializationRuleType, - MatType ->::SetNetworkMode(const bool training) -{ - network.Training() = training; -} - -template -void FFN< - OutputLayerType, - InitializationRuleType, - MatType ->::Forward(const MatType& inputs, MatType& results) -{ - Forward(inputs, results, 0, network.Network().size() - 1); -} - -template -void FFN< - OutputLayerType, - InitializationRuleType, - MatType ->::Forward(const MatType& inputs, - MatType& results, - const size_t begin, - const size_t end) -{ - // Sanity checking... - if (end < begin) - return; - - // Ensure the network is valid. - CheckNetwork("FFN::Forward()", inputs.n_rows); - - // We must always store a copy of the forward pass in `networkOutputs` in case - // we do a backward pass. - networkOutput.set_size(network.OutputSize(), inputs.n_cols); - network.Forward(inputs, networkOutput, begin, end); - - // It's possible the user passed `networkOutput` as `results`; in this case, - // we don't need to create an alias. - if (&results != &networkOutput) - results = networkOutput; -} - -template -typename MatType::elem_type FFN< - OutputLayerType, - InitializationRuleType, - MatType ->::Backward(const MatType& inputs, - const MatType& targets, - MatType& gradients) -{ - const typename MatType::elem_type res = - outputLayer.Forward(networkOutput, targets) + network.Loss(); - - // Compute the error of the output layer. - outputLayer.Backward(networkOutput, targets, error); - - // Perform the backward pass. - network.Backward(networkOutput, error, networkDelta); - - // Now compute the gradients. - // The gradient should have the same size as the parameters. - gradients.set_size(parameters.n_rows, parameters.n_cols); - network.Gradient(inputs, error, gradients); - - return res; -} - -template -typename MatType::elem_type FFN< - OutputLayerType, - InitializationRuleType, - MatType ->::Evaluate(const MatType& predictors, const MatType& responses) -{ - // Sanity check: ensure network is valid. - CheckNetwork("FFN::Evaluate()", predictors.n_rows); - - // Set networkOutput to the right size if needed, then perform the forward - // pass. - network.Forward(predictors, networkOutput); - - return outputLayer.Forward(networkOutput, responses) + network.Loss(); -} - -template -template -void FFN< - OutputLayerType, - InitializationRuleType, - MatType ->::serialize(Archive& ar, const uint32_t /* version */) -{ - // Serialize the output layer and initialization rule. - ar(CEREAL_NVP(outputLayer)); - ar(CEREAL_NVP(initializeRule)); - - // Serialize the network itself. - ar(CEREAL_NVP(network)); - ar(CEREAL_NVP(parameters)); - - // Serialize the expected input size. - ar(CEREAL_NVP(inputDimensions)); - - // If we are loading, we need to initialize the weights. - if (cereal::is_loading()) - { - // We can clear these members, since it's not possible to serialize in the - // middle of training and resume. - predictors.clear(); - responses.clear(); - - networkOutput.clear(); - networkDelta.clear(); - - layerMemoryIsSet = false; - inputDimensionsAreSet = false; - - // The weights in `parameters` will be correctly set for each layer in the - // first call to Forward(). - } -} - -template -typename MatType::elem_type FFN< - OutputLayerType, - InitializationRuleType, - MatType ->::Evaluate(const MatType& parameters) -{ - typename MatType::elem_type res = 0; - for (size_t i = 0; i < predictors.n_cols; ++i) - res += Evaluate(parameters, i, 1); - - return res; -} - -template -typename MatType::elem_type FFN< - OutputLayerType, - InitializationRuleType, - MatType ->::Evaluate(const MatType& /* parameters */, - const size_t begin, - const size_t batchSize) -{ - CheckNetwork("FFN::Evaluate()", predictors.n_rows); - - // Set networkOutput to the right size if needed, then perform the forward - // pass. - networkOutput.set_size(network.OutputSize(), batchSize); - network.Forward(predictors.cols(begin, begin + batchSize - 1), networkOutput); - - return outputLayer.Forward(networkOutput, - responses.cols(begin, begin + batchSize - 1)) + network.Loss(); -} - -template -typename MatType::elem_type FFN< - OutputLayerType, - InitializationRuleType, - MatType ->::EvaluateWithGradient(const MatType& parameters, MatType& gradient) -{ - typename MatType::elem_type res = 0; - res += EvaluateWithGradient(parameters, 0, gradient, 1); - for (size_t i = 1; i < predictors.n_cols; ++i) - { - arma::mat tmpGradient(gradient.n_rows, gradient.n_cols); - res += EvaluateWithGradient(parameters, i, tmpGradient, 1); - gradient += tmpGradient; - } - - return res; -} - -template -typename MatType::elem_type FFN< - OutputLayerType, - InitializationRuleType, - MatType ->::EvaluateWithGradient(const MatType& parameters, - const size_t begin, - MatType& gradient, - const size_t batchSize) -{ - CheckNetwork("FFN::EvaluateWithGradient()", predictors.n_rows); - - // Set networkOutput to the right size if needed, then perform the forward - // pass. - networkOutput.set_size(network.OutputSize(), batchSize); - - network.Forward(predictors.cols(begin, begin + batchSize - 1), networkOutput); - - const typename MatType::elem_type obj = outputLayer.Forward(networkOutput, - responses.cols(begin, begin + batchSize - 1)) + network.Loss(); - - // Now perform the backward pass. - outputLayer.Backward(networkOutput, - responses.cols(begin, begin + batchSize - 1), error); - - // The delta should have the same size as the input. - networkDelta.set_size(predictors.n_rows, batchSize); - network.Backward(networkOutput, error, networkDelta); - - // Now compute the gradients. - // The gradient should have the same size as the parameters. - gradient.set_size(parameters.n_rows, parameters.n_cols); - network.Gradient(predictors.cols(begin, begin + batchSize - 1), error, - gradient); - - return obj; -} - -template -void FFN< - OutputLayerType, - InitializationRuleType, - MatType ->::Gradient(const MatType& parameters, - const size_t begin, - MatType& gradient, - const size_t batchSize) -{ - this->EvaluateWithGradient(parameters, begin, gradient, batchSize); -} - -template -void FFN< - OutputLayerType, - InitializationRuleType, - MatType ->::Shuffle() -{ - math::ShuffleData(predictors, responses, predictors, responses); -} - -template -void FFN< - OutputLayerType, - InitializationRuleType, - MatType ->::ResetData(MatType predictors, MatType responses) +template +void FFN::ResetData( + arma::mat predictors, arma::mat responses) { + numFunctions = responses.n_cols; this->predictors = std::move(predictors); this->responses = std::move(responses); + this->deterministic = false; + ResetDeterministic(); - // Set the network to training mode. - SetNetworkMode(true); + if (!reset) + ResetParameters(); } -template -void FFN< - OutputLayerType, - InitializationRuleType, - MatType ->::InitializeWeights() -{ - // Set the network to testing mode. - SetNetworkMode(false); - - // Reset the network parameters with the given initialization rule. - NetworkInitialization networkInit(initializeRule); - networkInit.Initialize(network.Network(), parameters); -} - -template -void FFN< - OutputLayerType, - InitializationRuleType, - MatType ->::SetLayerMemory() -{ - size_t totalWeightSize = network.WeightSize(); - - Log::Assert(totalWeightSize == parameters.n_elem, - "FFN::SetLayerMemory(): total layer weight size does not match parameter " - "size!"); - - network.SetWeights(parameters.memptr()); - layerMemoryIsSet = true; -} - -template -void FFN< - OutputLayerType, - InitializationRuleType, - MatType ->::CheckNetwork(const std::string& functionName, - const size_t inputDimensionality, - const bool setMode, - const bool training) -{ - // If the network is empty, we can't do anything. - if (network.Network().size() == 0) - { - throw std::invalid_argument(functionName + ": cannot use network with no " - "layers!"); - } - - // Next, check that the input dimensions for each layer are correct. Note - // that this will throw an exception if the user has passed data that does not - // match this->inputDimensions. - if (!inputDimensionsAreSet) - UpdateDimensions(functionName, inputDimensionality); - - // We may need to initialize the `parameters` matrix if it is empty or the - // wrong size. - if (parameters.is_empty()) - { - InitializeWeights(); - } - else if (parameters.n_elem != network.WeightSize()) - { - parameters.clear(); - InitializeWeights(); - } - - // Make sure each layer is pointing at the right memory. - if (!layerMemoryIsSet) - SetLayerMemory(); - - // Finally, set the layers of the network to the right mode if the user - // requested it. - if (setMode) - SetNetworkMode(training); -} - -template -void FFN< - OutputLayerType, - InitializationRuleType, - MatType ->::UpdateDimensions(const std::string& functionName, - const size_t inputDimensionality) -{ - // If the input dimensions are completely unset, then assume our input is - // flat. - if (inputDimensions.size() == 0) - inputDimensions = { inputDimensionality }; - - size_t totalInputSize = 1; - for (size_t i = 0; i < inputDimensions.size(); ++i) - totalInputSize *= inputDimensions[i]; - - if (totalInputSize != inputDimensionality && inputDimensionality != 0) - { - throw std::logic_error(functionName + ": input size does not match expected" - " size set with InputDimensions()!"); - } - - // If the input dimensions have not changed from what has been computed - // before, we can terminate early---the network already has its dimensions - // set. - if (inputDimensions == network.InputDimensions()) - { - inputDimensionsAreSet = true; - return; - } - - network.InputDimensions() = inputDimensions; - network.ComputeOutputDimensions(); - inputDimensionsAreSet = true; -} - -template +template template typename std::enable_if< - ens::traits::HasMaxIterationsSignature::value, void ->::type -FFN< - OutputLayerType, - InitializationRuleType, - MatType ->::WarnMessageMaxIterations(OptimizerType& optimizer, size_t samples) const + HasMaxIterations + ::value, void>::type +FFN:: +WarnMessageMaxIterations(OptimizerType& optimizer, size_t samples) const { if (optimizer.MaxIterations() < samples && optimizer.MaxIterations() != 0) { - Log::Warn << "The optimizer's maximum number of iterations is less than the" - << " size of the dataset; the optimizer will not pass over the entire " - << "dataset. To fix this, modify the maximum number of iterations to be" - << " at least equal to the number of points of your dataset (" - << samples << ")." << std::endl; + Log::Warn << "The optimizer's maximum number of iterations " + << "is less than the size of the dataset; the " + << "optimizer will not pass over the entire " + << "dataset. To fix this, modify the maximum " + << "number of iterations to be at least equal " + << "to the number of points of your dataset " + << "(" << samples << ")." << std::endl; } } -template +template template typename std::enable_if< - !ens::traits::HasMaxIterationsSignature::value, void ->::type -FFN< - OutputLayerType, - InitializationRuleType, - MatType ->::WarnMessageMaxIterations(OptimizerType& /* optimizer */, - size_t /* samples */) const + !HasMaxIterations + ::value, void>::type +FFN:: +WarnMessageMaxIterations(OptimizerType& /* optimizer */, size_t /* samples */) + const { - // Nothing to do here. + return; } +template +template +double FFN::Train( + arma::mat predictors, + arma::mat responses, + OptimizerType& optimizer, + CallbackTypes&&... callbacks) +{ + CheckInputShape > >(network, + predictors.n_rows, + "FFN<>::Train()"); + + ResetData(std::move(predictors), std::move(responses)); + + WarnMessageMaxIterations(optimizer, this->predictors.n_cols); + + // Train the model. + const double out = optimizer.Optimize(*this, parameter, callbacks...); + + Log::Info << "FFN::FFN(): final objective of trained model is " << out + << "." << std::endl; + return out; +} + +template +template +double FFN::Train( + arma::mat predictors, + arma::mat responses, + CallbackTypes&&... callbacks) +{ + CheckInputShape > >(network, + predictors.n_rows, + "FFN<>::Train()"); + + ResetData(std::move(predictors), std::move(responses)); + + OptimizerType optimizer; + + WarnMessageMaxIterations(optimizer, this->predictors.n_cols); + + // Train the model. + const double out = optimizer.Optimize(*this, parameter, callbacks...); + + Log::Info << "FFN::FFN(): final objective of trained model is " << out + << "." << std::endl; + return out; +} + +template +template +void FFN::Forward( + const PredictorsType& inputs, ResponsesType& results) +{ + if (parameter.is_empty()) + ResetParameters(); + + Forward(inputs); + results = boost::apply_visitor(outputParameterVisitor, network.back()); +} + +template +template +void FFN::Forward( + const PredictorsType& inputs, + ResponsesType& results, + const size_t begin, + const size_t end) +{ + boost::apply_visitor(ForwardVisitor(inputs, + boost::apply_visitor(outputParameterVisitor, network[begin])), + network[begin]); + + for (size_t i = 1; i < end - begin + 1; ++i) + { + boost::apply_visitor(ForwardVisitor(boost::apply_visitor( + outputParameterVisitor, network[begin + i - 1]), + boost::apply_visitor(outputParameterVisitor, network[begin + i])), + network[begin + i]); + } + + results = boost::apply_visitor(outputParameterVisitor, network[end]); +} + +template +template +double FFN::Backward( + const PredictorsType& inputs, + const TargetsType& targets, + GradientsType& gradients) +{ + double res = outputLayer.Forward(boost::apply_visitor( + outputParameterVisitor, network.back()), targets); + + for (size_t i = 0; i < network.size(); ++i) + { + res += boost::apply_visitor(lossVisitor, network[i]); + } + + outputLayer.Backward(boost::apply_visitor(outputParameterVisitor, + network.back()), targets, error); + + gradients = arma::zeros(parameter.n_rows, parameter.n_cols); + + Backward(); + ResetGradients(gradients); + Gradient(inputs); + + return res; +} + +template +void FFN::Predict( + arma::mat predictors, arma::mat& results) +{ + CheckInputShape > >( + network, predictors.n_rows, "FFN<>::Predict()"); + + if (parameter.is_empty()) + ResetParameters(); + + if (!deterministic) + { + deterministic = true; + ResetDeterministic(); + } + + arma::mat resultsTemp; + Forward(arma::mat(predictors.colptr(0), predictors.n_rows, 1, false, true)); + resultsTemp = boost::apply_visitor(outputParameterVisitor, + network.back()).col(0); + + results = arma::mat(resultsTemp.n_elem, predictors.n_cols); + results.col(0) = resultsTemp.col(0); + + for (size_t i = 1; i < predictors.n_cols; ++i) + { + Forward(arma::mat(predictors.colptr(i), predictors.n_rows, 1, false, true)); + + resultsTemp = boost::apply_visitor(outputParameterVisitor, + network.back()); + results.col(i) = resultsTemp.col(0); + } +} + +template +template +double FFN::Evaluate( + const PredictorsType& predictors, const ResponsesType& responses) +{ + CheckInputShape > >( + network, predictors.n_rows, "FFN<>::Evaluate()"); + + if (parameter.is_empty()) + ResetParameters(); + + if (!deterministic) + { + deterministic = true; + ResetDeterministic(); + } + + Forward(predictors); + + double res = outputLayer.Forward(boost::apply_visitor( + outputParameterVisitor, network.back()), responses); + + for (size_t i = 0; i < network.size(); ++i) + { + res += boost::apply_visitor(lossVisitor, network[i]); + } + + return res; +} + +template +double FFN::Evaluate( + const arma::mat& parameters) +{ + double res = 0; + for (size_t i = 0; i < predictors.n_cols; ++i) + res += Evaluate(parameters, i, 1, true); + + return res; +} + +template +double FFN::Evaluate( + const arma::mat& /* parameters */, + const size_t begin, + const size_t batchSize, + const bool deterministic) +{ + if (parameter.is_empty()) + ResetParameters(); + + if (deterministic != this->deterministic) + { + this->deterministic = deterministic; + ResetDeterministic(); + } + + Forward(predictors.cols(begin, begin + batchSize - 1)); + double res = outputLayer.Forward( + boost::apply_visitor(outputParameterVisitor, network.back()), + responses.cols(begin, begin + batchSize - 1)); + + for (size_t i = 0; i < network.size(); ++i) + { + res += boost::apply_visitor(lossVisitor, network[i]); + } + + return res; +} + +template +double FFN::Evaluate( + const arma::mat& parameters, const size_t begin, const size_t batchSize) +{ + return Evaluate(parameters, begin, batchSize, true); +} + +template +template +double FFN:: +EvaluateWithGradient(const arma::mat& parameters, GradType& gradient) +{ + double res = 0; + for (size_t i = 0; i < predictors.n_cols; ++i) + res += EvaluateWithGradient(parameters, i, gradient, 1); + + return res; +} + +template +template +double FFN:: +EvaluateWithGradient(const arma::mat& /* parameters */, + const size_t begin, + GradType& gradient, + const size_t batchSize) +{ + if (gradient.is_empty()) + { + if (parameter.is_empty()) + ResetParameters(); + + gradient = arma::zeros(parameter.n_rows, parameter.n_cols); + } + else + { + gradient.zeros(); + } + + if (this->deterministic) + { + this->deterministic = false; + ResetDeterministic(); + } + + Forward(predictors.cols(begin, begin + batchSize - 1)); + double res = outputLayer.Forward( + boost::apply_visitor(outputParameterVisitor, network.back()), + responses.cols(begin, begin + batchSize - 1)); + + for (size_t i = 0; i < network.size(); ++i) + { + res += boost::apply_visitor(lossVisitor, network[i]); + } + + outputLayer.Backward( + boost::apply_visitor(outputParameterVisitor, network.back()), + responses.cols(begin, begin + batchSize - 1), + error); + + Backward(); + ResetGradients(gradient); + Gradient(predictors.cols(begin, begin + batchSize - 1)); + + return res; +} + +template +void FFN::Gradient( + const arma::mat& parameters, + const size_t begin, + arma::mat& gradient, + const size_t batchSize) +{ + this->EvaluateWithGradient(parameters, begin, gradient, batchSize); +} + +template +void FFN::Shuffle() +{ + math::ShuffleData(predictors, responses, predictors, responses); +} + +template +void FFN::ResetParameters() +{ + ResetDeterministic(); + + // Reset the network parameter with the given initialization rule. + NetworkInitialization networkInit(initializeRule); + networkInit.Initialize(network, parameter); +} + +template +void FFN::ResetDeterministic() +{ + DeterministicSetVisitor deterministicSetVisitor(deterministic); + std::for_each(network.begin(), network.end(), + boost::apply_visitor(deterministicSetVisitor)); +} + +template +void FFN::ResetGradients(arma::mat& gradient) +{ + size_t offset = 0; + for (size_t i = 0; i < network.size(); ++i) + { + offset += boost::apply_visitor(GradientSetVisitor(gradient, offset), + network[i]); + } +} + +template +template +void FFN::Forward(const InputType& input) +{ + boost::apply_visitor(ForwardVisitor(input, + boost::apply_visitor(outputParameterVisitor, network.front())), + network.front()); + + if (!reset) + { + if (boost::apply_visitor(outputWidthVisitor, network.front()) != 0) + { + width = boost::apply_visitor(outputWidthVisitor, network.front()); + } + + if (boost::apply_visitor(outputHeightVisitor, network.front()) != 0) + { + height = boost::apply_visitor(outputHeightVisitor, network.front()); + } + } + + for (size_t i = 1; i < network.size(); ++i) + { + if (!reset) + { + // Set the input width. + boost::apply_visitor(SetInputWidthVisitor(width), network[i]); + + // Set the input height. + boost::apply_visitor(SetInputHeightVisitor(height), network[i]); + } + + boost::apply_visitor(ForwardVisitor(boost::apply_visitor( + outputParameterVisitor, network[i - 1]), + boost::apply_visitor(outputParameterVisitor, network[i])), network[i]); + + if (!reset) + { + // Get the output width. + if (boost::apply_visitor(outputWidthVisitor, network[i]) != 0) + { + width = boost::apply_visitor(outputWidthVisitor, network[i]); + } + + // Get the output height. + if (boost::apply_visitor(outputHeightVisitor, network[i]) != 0) + { + height = boost::apply_visitor(outputHeightVisitor, network[i]); + } + } + } + + if (!reset) + reset = true; +} + +template +void FFN::Backward() +{ + boost::apply_visitor(BackwardVisitor(boost::apply_visitor( + outputParameterVisitor, network.back()), error, + boost::apply_visitor(deltaVisitor, network.back())), network.back()); + + for (size_t i = 2; i < network.size(); ++i) + { + boost::apply_visitor(BackwardVisitor(boost::apply_visitor( + outputParameterVisitor, network[network.size() - i]), + boost::apply_visitor(deltaVisitor, network[network.size() - i + 1]), + boost::apply_visitor(deltaVisitor, network[network.size() - i])), + network[network.size() - i]); + } +} + +template +template +void FFN::Gradient(const InputType& input) +{ + boost::apply_visitor(GradientVisitor(input, + boost::apply_visitor(deltaVisitor, network[1])), network.front()); + + for (size_t i = 1; i < network.size() - 1; ++i) + { + boost::apply_visitor(GradientVisitor(boost::apply_visitor( + outputParameterVisitor, network[i - 1]), + boost::apply_visitor(deltaVisitor, network[i + 1])), network[i]); + } + + boost::apply_visitor(GradientVisitor(boost::apply_visitor( + outputParameterVisitor, network[network.size() - 2]), error), + network[network.size() - 1]); +} + +template +template +void FFN::serialize( + Archive& ar, const uint32_t /* version */) +{ + ar(CEREAL_NVP(parameter)); + ar(CEREAL_NVP(width)); + ar(CEREAL_NVP(height)); + + ar(CEREAL_NVP(reset)); + + // Be sure to clear other layers before loading. + if (cereal::is_loading()) + { + std::for_each(network.begin(), network.end(), + boost::apply_visitor(deleteVisitor)); + network.clear(); + } + + ar(CEREAL_VECTOR_VARIANT_POINTER(network)); + + // If we are loading, we need to initialize the weights. + if (cereal::is_loading()) + { + size_t offset = 0; + for (size_t i = 0; i < network.size(); ++i) + { + offset += boost::apply_visitor(WeightSetVisitor(parameter, offset), + network[i]); + + boost::apply_visitor(resetVisitor, network[i]); + } + + deterministic = true; + ResetDeterministic(); + } +} + +template +void FFN::Swap(FFN& network) +{ + std::swap(outputLayer, network.outputLayer); + std::swap(initializeRule, network.initializeRule); + std::swap(width, network.width); + std::swap(height, network.height); + std::swap(reset, network.reset); + std::swap(this->network, network.network); + std::swap(predictors, network.predictors); + std::swap(responses, network.responses); + std::swap(parameter, network.parameter); + std::swap(numFunctions, network.numFunctions); + std::swap(error, network.error); + std::swap(deterministic, network.deterministic); + std::swap(delta, network.delta); + std::swap(inputParameter, network.inputParameter); + std::swap(outputParameter, network.outputParameter); + std::swap(gradient, network.gradient); +}; + +template +FFN::FFN( + const FFN& network): + outputLayer(network.outputLayer), + initializeRule(network.initializeRule), + width(network.width), + height(network.height), + reset(network.reset), + predictors(network.predictors), + responses(network.responses), + parameter(network.parameter), + numFunctions(network.numFunctions), + error(network.error), + deterministic(network.deterministic), + delta(network.delta), + inputParameter(network.inputParameter), + outputParameter(network.outputParameter), + gradient(network.gradient) +{ + // Build new layers according to source network + for (size_t i = 0; i < network.network.size(); ++i) + { + this->network.push_back(boost::apply_visitor(copyVisitor, + network.network[i])); + boost::apply_visitor(resetVisitor, this->network.back()); + } +}; + +template +FFN::FFN( + FFN&& network): + outputLayer(std::move(network.outputLayer)), + initializeRule(std::move(network.initializeRule)), + width(network.width), + height(network.height), + reset(network.reset), + predictors(std::move(network.predictors)), + responses(std::move(network.responses)), + parameter(std::move(network.parameter)), + numFunctions(network.numFunctions), + error(std::move(network.error)), + deterministic(network.deterministic), + delta(std::move(network.delta)), + inputParameter(std::move(network.inputParameter)), + outputParameter(std::move(network.outputParameter)), + gradient(std::move(network.gradient)) +{ + this->network = std::move(network.network); +}; + +template +FFN& +FFN::operator = (FFN network) +{ + Swap(network); + return *this; +}; + } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/forward_decls.hpp b/src/mlpack/methods/ann/forward_decls.hpp deleted file mode 100644 index e3bfc62d82..0000000000 --- a/src/mlpack/methods/ann/forward_decls.hpp +++ /dev/null @@ -1,29 +0,0 @@ -/** - * @file forward_decls.hpp - * @author Ryan Curtin - * - * Forward declarations of network types. This is needed for some `friend` - * functionality. - */ -#ifndef MLPACK_METHODS_ANN_FORWARD_DECLS_HPP -#define MLPACK_METHODS_ANN_FORWARD_DECLS_HPP - -namespace mlpack { -namespace ann { - -// See ffn.hpp. -template -class FFN; - -// See rnn.hpp. -template -class RNN; - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/not_adapted/gan/CMakeLists.txt b/src/mlpack/methods/ann/gan/CMakeLists.txt similarity index 100% rename from src/mlpack/methods/ann/not_adapted/gan/CMakeLists.txt rename to src/mlpack/methods/ann/gan/CMakeLists.txt diff --git a/src/mlpack/methods/ann/not_adapted/gan/gan.hpp b/src/mlpack/methods/ann/gan/gan.hpp similarity index 100% rename from src/mlpack/methods/ann/not_adapted/gan/gan.hpp rename to src/mlpack/methods/ann/gan/gan.hpp diff --git a/src/mlpack/methods/ann/not_adapted/gan/gan_impl.hpp b/src/mlpack/methods/ann/gan/gan_impl.hpp similarity index 100% rename from src/mlpack/methods/ann/not_adapted/gan/gan_impl.hpp rename to src/mlpack/methods/ann/gan/gan_impl.hpp diff --git a/src/mlpack/methods/ann/not_adapted/gan/gan_policies.hpp b/src/mlpack/methods/ann/gan/gan_policies.hpp similarity index 100% rename from src/mlpack/methods/ann/not_adapted/gan/gan_policies.hpp rename to src/mlpack/methods/ann/gan/gan_policies.hpp diff --git a/src/mlpack/methods/ann/not_adapted/gan/metrics/CMakeLists.txt b/src/mlpack/methods/ann/gan/metrics/CMakeLists.txt similarity index 100% rename from src/mlpack/methods/ann/not_adapted/gan/metrics/CMakeLists.txt rename to src/mlpack/methods/ann/gan/metrics/CMakeLists.txt diff --git a/src/mlpack/methods/ann/not_adapted/gan/metrics/inception_score.hpp b/src/mlpack/methods/ann/gan/metrics/inception_score.hpp similarity index 100% rename from src/mlpack/methods/ann/not_adapted/gan/metrics/inception_score.hpp rename to src/mlpack/methods/ann/gan/metrics/inception_score.hpp diff --git a/src/mlpack/methods/ann/not_adapted/gan/metrics/inception_score_impl.hpp b/src/mlpack/methods/ann/gan/metrics/inception_score_impl.hpp similarity index 100% rename from src/mlpack/methods/ann/not_adapted/gan/metrics/inception_score_impl.hpp rename to src/mlpack/methods/ann/gan/metrics/inception_score_impl.hpp diff --git a/src/mlpack/methods/ann/not_adapted/gan/wgan_impl.hpp b/src/mlpack/methods/ann/gan/wgan_impl.hpp similarity index 100% rename from src/mlpack/methods/ann/not_adapted/gan/wgan_impl.hpp rename to src/mlpack/methods/ann/gan/wgan_impl.hpp diff --git a/src/mlpack/methods/ann/not_adapted/gan/wgangp_impl.hpp b/src/mlpack/methods/ann/gan/wgangp_impl.hpp similarity index 100% rename from src/mlpack/methods/ann/not_adapted/gan/wgangp_impl.hpp rename to src/mlpack/methods/ann/gan/wgangp_impl.hpp diff --git a/src/mlpack/methods/ann/init_rules/const_init.hpp b/src/mlpack/methods/ann/init_rules/const_init.hpp index 3005da003c..ec1f24deea 100644 --- a/src/mlpack/methods/ann/init_rules/const_init.hpp +++ b/src/mlpack/methods/ann/init_rules/const_init.hpp @@ -98,13 +98,7 @@ class ConstInitialization //! Get the initialization value. double const& InitValue() const { return initVal; } //! Modify the initialization value. - double& InitValue() { return initVal; } - - template - void serialize(Archive& ar, const uint32_t /* version */) - { - ar(CEREAL_NVP(initVal)); - } + double& initValue() { return initVal; } private: //! Value to be initialized with diff --git a/src/mlpack/methods/ann/init_rules/glorot_init.hpp b/src/mlpack/methods/ann/init_rules/glorot_init.hpp index 8f1c9d51b6..f2d02bbb1c 100644 --- a/src/mlpack/methods/ann/init_rules/glorot_init.hpp +++ b/src/mlpack/methods/ann/init_rules/glorot_init.hpp @@ -104,19 +104,13 @@ class GlorotInitializationType */ template void Initialize(arma::Cube& W); - - /** - * Serialize the initialization. (Nothing to serialize for this one.) - */ - template - void serialize(Archive& /* ar */, const uint32_t /* version */) { } }; // class GlorotInitializationType -template<> +template <> template inline void GlorotInitializationType::Initialize(arma::Mat& W, - const size_t rows, - const size_t cols) + const size_t rows, + const size_t cols) { if (W.is_empty()) W.set_size(rows, cols); @@ -126,7 +120,7 @@ inline void GlorotInitializationType::Initialize(arma::Mat& W, normalInit.Initialize(W, rows, cols); } -template<> +template <> template inline void GlorotInitializationType::Initialize(arma::Mat& W) { @@ -138,7 +132,7 @@ inline void GlorotInitializationType::Initialize(arma::Mat& W) normalInit.Initialize(W); } -template<> +template <> template inline void GlorotInitializationType::Initialize(arma::Mat& W, const size_t rows, @@ -153,7 +147,7 @@ inline void GlorotInitializationType::Initialize(arma::Mat& W, randomInit.Initialize(W, rows, cols); } -template<> +template <> template inline void GlorotInitializationType::Initialize(arma::Mat& W) { diff --git a/src/mlpack/methods/ann/init_rules/he_init.hpp b/src/mlpack/methods/ann/init_rules/he_init.hpp index 2608b1740f..c82ecaec6c 100644 --- a/src/mlpack/methods/ann/init_rules/he_init.hpp +++ b/src/mlpack/methods/ann/init_rules/he_init.hpp @@ -136,12 +136,6 @@ class HeInitialization for (size_t i = 0; i < W.n_slices; ++i) Initialize(W.slice(i)); } - - template - void serialize(Archive& /* ar */, const uint32_t /* version */) - { - // Nothing to do. - } }; // class HeInitialization } // namespace ann diff --git a/src/mlpack/methods/ann/init_rules/network_init.hpp b/src/mlpack/methods/ann/init_rules/network_init.hpp index 8f51d243bc..5e811ef3a4 100644 --- a/src/mlpack/methods/ann/init_rules/network_init.hpp +++ b/src/mlpack/methods/ann/init_rules/network_init.hpp @@ -14,10 +14,14 @@ #define MLPACK_METHODS_ANN_INIT_RULES_NETWORK_INIT_HPP #include -#include +#include "../visitor/reset_visitor.hpp" +#include "../visitor/weight_size_visitor.hpp" +#include "../visitor/weight_set_visitor.hpp" #include "init_rules_traits.hpp" +#include + namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -25,7 +29,7 @@ namespace ann /** Artificial Neural Network. */ { * This class is used to initialize the network with the given initialization * rule. */ -template +template class NetworkInitialization { public: @@ -50,18 +54,16 @@ class NetworkInitialization * @param parameterOffset Offset for network paramater, default 0. */ template - void Initialize(const std::vector>*>& network, - arma::Mat& parameters, - size_t parameterOffset = 0) + void Initialize(const std::vector >& network, + arma::Mat& parameter, size_t parameterOffset = 0) { - // Determine the total number of parameters/weights of the given network. - if (parameters.is_empty()) + // Determine the number of parameter/weights of the given network. + if (parameter.is_empty()) { size_t weights = 0; for (size_t i = 0; i < network.size(); ++i) - weights += network[i]->WeightSize(); - - parameters.set_size(weights, 1); + weights += boost::apply_visitor(weightSizeVisitor, network[i]); + parameter.set_size(weights, 1); } // Initialize the network layer by layer or the complete network. @@ -71,8 +73,9 @@ class NetworkInitialization { // Initialize the layer with the specified parameter/weight // initialization rule. - const size_t weight = network[i]->WeightSize(); - arma::Mat tmp = arma::mat(parameters.memptr() + offset, + const size_t weight = boost::apply_visitor(weightSizeVisitor, + network[i]); + arma::Mat tmp = arma::mat(parameter.memptr() + offset, weight, 1, false, false); initializeRule.Initialize(tmp, tmp.n_elem, 1); @@ -82,7 +85,19 @@ class NetworkInitialization } else { - initializeRule.Initialize(parameters, parameters.n_elem, 1); + initializeRule.Initialize(parameter, parameter.n_elem, 1); + } + + // Note: We can't merge the for loop into the for loop above because + // WeightSetVisitor also sets the parameter/weights of the inner modules. + // Inner Modules are held by the parent module e.g. the concat module can + // hold various other modules. + for (size_t i = 0, offset = parameterOffset; i < network.size(); ++i) + { + offset += boost::apply_visitor(WeightSetVisitor(parameter, offset), + network[i]); + + boost::apply_visitor(resetVisitor, network[i]); } } @@ -90,6 +105,12 @@ class NetworkInitialization //! Instantiated InitializationRule object for initializing the network //! parameter. InitializationRuleType initializeRule; + + //! Locally-stored reset visitor. + ResetVisitor resetVisitor; + + //! Locally-stored weight size visitor. + WeightSizeVisitor weightSizeVisitor; }; // class NetworkInitialization } // namespace ann diff --git a/src/mlpack/methods/ann/init_rules/oivs_init.hpp b/src/mlpack/methods/ann/init_rules/oivs_init.hpp index 314eabf46f..1c34fbc80f 100644 --- a/src/mlpack/methods/ann/init_rules/oivs_init.hpp +++ b/src/mlpack/methods/ann/init_rules/oivs_init.hpp @@ -47,13 +47,15 @@ namespace ann /** Artificial Neural Network. */ { * w_i &=& \hat{w} \cdot \sqrt{a_i + 1} * @f} * - * Where f is the transfer function epsilon, k custom parameters, n the number - * of neurons in the outgoing layer and gamma a parameter that defines the - * random interval. + * Where f is the transfer function epsilon, k custom parameters, n the number of + * neurons in the outgoing layer and gamma a parameter that defines the random + * interval. * * @tparam ActivationFunction The activation function used for the oivs method. */ -template +template< + class ActivationFunction = LogisticFunction +> class OivsInitialization { public: diff --git a/src/mlpack/methods/ann/init_rules/random_init.hpp b/src/mlpack/methods/ann/init_rules/random_init.hpp index 22095d33a3..e48615f4ff 100644 --- a/src/mlpack/methods/ann/init_rules/random_init.hpp +++ b/src/mlpack/methods/ann/init_rules/random_init.hpp @@ -115,13 +115,6 @@ class RandomInitialization Initialize(W.slice(i)); } - template - void serialize(Archive& ar, const uint32_t /* version */) - { - ar(CEREAL_NVP(lowerBound)); - ar(CEREAL_NVP(upperBound)); - } - private: //! The number used as lower bound. double lowerBound; diff --git a/src/mlpack/methods/ann/layer/CMakeLists.txt b/src/mlpack/methods/ann/layer/CMakeLists.txt index e69e65e23d..c96c48799c 100644 --- a/src/mlpack/methods/ann/layer/CMakeLists.txt +++ b/src/mlpack/methods/ann/layer/CMakeLists.txt @@ -3,18 +3,67 @@ set(SOURCES add.hpp add_impl.hpp + add_merge.hpp + add_merge_impl.hpp + adaptive_max_pooling.hpp + adaptive_max_pooling_impl.hpp + adaptive_mean_pooling.hpp + adaptive_mean_pooling_impl.hpp alpha_dropout.hpp alpha_dropout_impl.hpp + atrous_convolution.hpp + atrous_convolution_impl.hpp base_layer.hpp + batch_norm.hpp + batch_norm_impl.hpp + bicubic_interpolation.hpp + bicubic_interpolation_impl.hpp + bilinear_interpolation.hpp + bilinear_interpolation_impl.hpp + channel_shuffle.hpp + channel_shuffle_impl.hpp + concat.hpp + concat_impl.hpp + concat_performance.hpp + concat_performance_impl.hpp concatenate.hpp concatenate_impl.hpp + constant.hpp + constant_impl.hpp convolution.hpp convolution_impl.hpp dropconnect.hpp dropconnect_impl.hpp dropout.hpp dropout_impl.hpp + elu.hpp + elu_impl.hpp + fast_lstm.hpp + fast_lstm_impl.hpp + flatten_t_swish.hpp + flatten_t_swish_impl.hpp + flexible_relu.hpp + flexible_relu_impl.hpp + glimpse.hpp + glimpse_impl.hpp + group_norm.hpp + group_norm_impl.hpp + gru.hpp + gru_impl.hpp + hard_tanh.hpp + hard_tanh_impl.hpp + highway.hpp + highway_impl.hpp + instance_norm.hpp + instance_norm_impl.hpp + isrlu.hpp + isrlu_impl.hpp + join.hpp + join_impl.hpp layer.hpp + layer_norm.hpp + layer_norm_impl.hpp + layer_traits.hpp layer_types.hpp leaky_relu.hpp leaky_relu_impl.hpp @@ -22,22 +71,73 @@ set(SOURCES linear_impl.hpp linear_no_bias.hpp linear_no_bias_impl.hpp - linear3d.hpp - linear3d_impl.hpp log_softmax.hpp log_softmax_impl.hpp + lookup.hpp + lookup_impl.hpp + lp_pooling.hpp + lp_pooling_impl.hpp lstm.hpp lstm_impl.hpp max_pooling.hpp max_pooling_impl.hpp - multi_layer.hpp - multi_layer_impl.hpp + mean_pooling.hpp + mean_pooling_impl.hpp + minibatch_discrimination.hpp + minibatch_discrimination_impl.hpp + multihead_attention_impl.hpp + multihead_attention.hpp + multiply_constant.hpp + multiply_constant_impl.hpp + multiply_merge.hpp + multiply_merge_impl.hpp + nearest_interpolation.hpp + nearest_interpolation_impl.hpp noisylinear.hpp noisylinear_impl.hpp - padding.hpp + parametric_relu.hpp + parametric_relu_impl.hpp + pixel_shuffle.hpp + pixel_shuffle_impl.hpp + positional_encoding.hpp + positional_encoding_impl.hpp + recurrent.hpp + recurrent_impl.hpp + recurrent_attention.hpp + recurrent_attention_impl.hpp + reinforce_normal.hpp + reinforce_normal_impl.hpp + relu6.hpp + relu6_impl.hpp + reparametrization.hpp + reparametrization_impl.hpp radial_basis_function.hpp radial_basis_function_impl.hpp - serialization.hpp + select.hpp + select_impl.hpp + sequential.hpp + sequential_impl.hpp + softmax_impl.hpp + softmax.hpp + spatial_dropout.hpp + spatial_dropout_impl.hpp + subview.hpp + transposed_convolution.hpp + transposed_convolution_impl.hpp + vr_class_reward.hpp + vr_class_reward_impl.hpp + c_relu.hpp + c_relu_impl.hpp + weight_norm.hpp + weight_norm_impl.hpp + hardshrink.hpp + hardshrink_impl.hpp + celu.hpp + celu_impl.hpp + softshrink.hpp + softshrink_impl.hpp + softmin.hpp + softmin_impl.hpp ) # Add directory name to sources. diff --git a/src/mlpack/methods/ann/layer/not_adapted/adaptive_max_pooling.hpp b/src/mlpack/methods/ann/layer/adaptive_max_pooling.hpp similarity index 62% rename from src/mlpack/methods/ann/layer/not_adapted/adaptive_max_pooling.hpp rename to src/mlpack/methods/ann/layer/adaptive_max_pooling.hpp index ad9219759b..fd080c42dd 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/adaptive_max_pooling.hpp +++ b/src/mlpack/methods/ann/layer/adaptive_max_pooling.hpp @@ -13,9 +13,7 @@ #define MLPACK_METHODS_ANN_LAYER_ADAPTIVE_MAX_POOLING_HPP #include - -#include "layer.hpp" -#include "max_pooling.hpp" +#include "layer_types.hpp" namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -23,17 +21,20 @@ namespace ann /** Artificial Neural Network. */ { /** * Implementation of the AdaptiveMaxPooling layer. * - * @tparam InputType The type of the layer's inputs. The layer automatically - * cast inputs to this type (Default: arma::mat). - * @tparam OutputType The type of the layer's Outputs. The layer automatically - * cast inputs to this type (Default: arma::mat). + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class AdaptiveMaxPoolingType : public Layer +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class AdaptiveMaxPooling { public: //! Create the AdaptiveMaxPooling object. - AdaptiveMaxPoolingType(); + AdaptiveMaxPooling(); /** * Create the AdaptiveMaxPooling object. @@ -41,16 +42,15 @@ class AdaptiveMaxPoolingType : public Layer * @param outputWidth Width of the output. * @param outputHeight Height of the output. */ - AdaptiveMaxPoolingType(const size_t outputWidth, - const size_t outputHeight); + AdaptiveMaxPooling(const size_t outputWidth, + const size_t outputHeight); /** * Create the AdaptiveMaxPooling object. * - * @param outputShape A two-value tuple indicating width and height of the - * output. + * @param outputShape A two-value tuple indicating width and height of the output. */ - AdaptiveMaxPoolingType(const std::tuple& outputShape); + AdaptiveMaxPooling(const std::tuple& outputShape); /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -59,7 +59,8 @@ class AdaptiveMaxPoolingType : public Layer * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - void Forward(const InputType& input, OutputType& output); + template + void Forward(const arma::Mat& input, arma::Mat& output); /** * Ordinary feed backward pass of a neural network, using 3rd-order tensors as @@ -70,30 +71,48 @@ class AdaptiveMaxPoolingType : public Layer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const InputType& input, - const OutputType& gy, - OutputType& g); + template + void Backward(const arma::Mat& input, + const arma::Mat& gy, + arma::Mat& g); + + //! Get the output parameter. + const OutputDataType& OutputParameter() const + { return poolingLayer.OutputParameter(); } + + //! Modify the output parameter. + OutputDataType& OutputParameter() { return poolingLayer.OutputParameter(); } + + //! Get the delta. + const OutputDataType& Delta() const { return poolingLayer.Delta(); } + //! Modify the delta. + OutputDataType& Delta() { return poolingLayer.Delta(); } + + //! Get the input width. + size_t InputWidth() const { return poolingLayer.InputWidth(); } + //! Modify the input width. + size_t& InputWidth() { return poolingLayer.InputWidth(); } + + //! Get the input height. + size_t InputHeight() const { return poolingLayer.InputHeight(); } + //! Modify the input height. + size_t& InputHeight() { return poolingLayer.InputHeight(); } //! Get the output width. - size_t const& OutputWidth() const { return outputWidth; } + size_t OutputWidth() const { return outputWidth; } //! Modify the output width. size_t& OutputWidth() { return outputWidth; } //! Get the output height. - size_t const& OutputHeight() const { return outputHeight; } + size_t OutputHeight() const { return outputHeight; } //! Modify the output height. size_t& OutputHeight() { return outputHeight; } - //! Get the number of trainable weights. - size_t WeightSize() const { return 0; } + //! Get the input size. + size_t InputSize() const { return poolingLayer.InputSize(); } - const std::vector& OutputDimensions() const - { - std::vector result(this->inputDimensions.size(), 1); - result[0] = outputWidth; - result[1] = outputHeight; - return result; - } + //! Get the output size. + size_t OutputSize() const { return poolingLayer.OutputSize(); } //! Get the size of the weights. size_t WeightSize() const { return 0; } @@ -108,7 +127,7 @@ class AdaptiveMaxPoolingType : public Layer /** * Initialize Kernel Size and Stride for Adaptive Pooling. */ - void InitializeAdaptivePadding() + void IntializeAdaptivePadding() { poolingLayer.StrideWidth() = std::floor(poolingLayer.InputWidth() / outputWidth); @@ -131,7 +150,7 @@ class AdaptiveMaxPoolingType : public Layer } //! Locally stored MaxPooling Object. - MaxPoolingType poolingLayer; + MaxPooling poolingLayer; //! Locally-stored output width. size_t outputWidth; @@ -141,12 +160,7 @@ class AdaptiveMaxPoolingType : public Layer //! Locally-stored reset parameter used to initialize the layer once. bool reset; -}; // class AdaptiveMaxPoolingType - -// Convenience typedefs. - -// Standard Adaptive max pooling layer. -typedef AdaptiveMaxPoolingType AdaptiveMaxPooling; +}; // class AdaptiveMaxPooling } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/not_adapted/adaptive_max_pooling_impl.hpp b/src/mlpack/methods/ann/layer/adaptive_max_pooling_impl.hpp similarity index 55% rename from src/mlpack/methods/ann/layer/not_adapted/adaptive_max_pooling_impl.hpp rename to src/mlpack/methods/ann/layer/adaptive_max_pooling_impl.hpp index f01f62bdc9..25cf195b6c 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/adaptive_max_pooling_impl.hpp +++ b/src/mlpack/methods/ann/layer/adaptive_max_pooling_impl.hpp @@ -18,61 +18,61 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -AdaptiveMaxPoolingType::AdaptiveMaxPoolingType() +template +AdaptiveMaxPooling::AdaptiveMaxPooling() { // Nothing to do here. } -template -AdaptiveMaxPoolingType::AdaptiveMaxPoolingType( +template +AdaptiveMaxPooling::AdaptiveMaxPooling( const size_t outputWidth, const size_t outputHeight) : - AdaptiveMaxPoolingType(std::tuple(outputWidth, outputHeight)) + AdaptiveMaxPooling(std::tuple(outputWidth, outputHeight)) { // Nothing to do here. } -template -AdaptiveMaxPoolingType::AdaptiveMaxPoolingType( +template +AdaptiveMaxPooling::AdaptiveMaxPooling( const std::tuple& outputShape): outputWidth(std::get<0>(outputShape)), outputHeight(std::get<1>(outputShape)), reset(false) { - poolingLayer = ann::MaxPoolingType(0, 0); + poolingLayer = ann::MaxPooling<>(0, 0); } -template -void AdaptiveMaxPoolingType::Forward( - const InputType& input, OutputType& output) +template +template +void AdaptiveMaxPooling::Forward( + const arma::Mat& input, arma::Mat& output) { if (!reset) { - InitializeAdaptivePadding(); + IntializeAdaptivePadding(); reset = true; } poolingLayer.Forward(input, output); } -template -void AdaptiveMaxPoolingType::Backward( - const InputType& input, - const OutputType& gy, - OutputType& g) +template +template +void AdaptiveMaxPooling::Backward( + const arma::Mat& input, + const arma::Mat& gy, + arma::Mat& g) { poolingLayer.Backward(input, gy, g); } -template +template template -void AdaptiveMaxPoolingType::serialize( +void AdaptiveMaxPooling::serialize( Archive& ar, const uint32_t /* version */) { - ar(cereal::base_class>(this)); - ar(CEREAL_NVP(outputWidth)); ar(CEREAL_NVP(outputHeight)); ar(CEREAL_NVP(reset)); diff --git a/src/mlpack/methods/ann/layer/not_adapted/adaptive_mean_pooling.hpp b/src/mlpack/methods/ann/layer/adaptive_mean_pooling.hpp similarity index 62% rename from src/mlpack/methods/ann/layer/not_adapted/adaptive_mean_pooling.hpp rename to src/mlpack/methods/ann/layer/adaptive_mean_pooling.hpp index 93f0095572..46a434ab54 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/adaptive_mean_pooling.hpp +++ b/src/mlpack/methods/ann/layer/adaptive_mean_pooling.hpp @@ -1,4 +1,3 @@ -// Maybe /** * @file methods/ann/layer/adaptive_mean_pooling.hpp * @author Kartik Dutt @@ -15,9 +14,7 @@ #define MLPACK_METHODS_ANN_LAYER_ADAPTIVE_MEAN_POOLING_HPP #include - -#include "layer.hpp" -#include "mean_pooling.hpp" +#include "layer_types.hpp" namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -25,17 +22,20 @@ namespace ann /** Artificial Neural Network. */ { /** * Implementation of the AdaptiveMeanPooling. * - * @tparam InputType The type of the layer's inputs. The layer automatically - * cast inputs to this type (Default: arma::mat). - * @tparam OutputType The type of the layer's Outputs. The layer automatically - * cast inputs to this type (Default: arma::mat). + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class AdaptiveMeanPoolingType : public Layer +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class AdaptiveMeanPooling { public: //! Create the AdaptiveMeanPooling object. - AdaptiveMeanPoolingType(); + AdaptiveMeanPooling(); /** * Create the AdaptiveMeanPooling object. @@ -43,16 +43,15 @@ class AdaptiveMeanPoolingType : public Layer * @param outputWidth Width of the output. * @param outputHeight Height of the output. */ - AdaptiveMeanPoolingType(const size_t outputWidth, - const size_t outputHeight); + AdaptiveMeanPooling(const size_t outputWidth, + const size_t outputHeight); /** * Create the AdaptiveMeanPooling object. * - * @param outputShape A two-value tuple indicating width and height of the - * output. + * @param outputShape A two-value tuple indicating width and height of the output. */ - AdaptiveMeanPoolingType(const std::tuple& outputShape); + AdaptiveMeanPooling(const std::tuple& outputShape); /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -61,7 +60,8 @@ class AdaptiveMeanPoolingType : public Layer * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - void Forward(const InputType& input, OutputType& output); + template + void Forward(const arma::Mat& input, arma::Mat& output); /** * Ordinary feed backward pass of a neural network, using 3rd-order tensors as @@ -72,30 +72,48 @@ class AdaptiveMeanPoolingType : public Layer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const InputType& input, - const OutputType& gy, - OutputType& g); + template + void Backward(const arma::Mat& input, + const arma::Mat& gy, + arma::Mat& g); + + //! Get the output parameter. + const OutputDataType& OutputParameter() const + { return poolingLayer.OutputParameter(); } + + //! Modify the output parameter. + OutputDataType& OutputParameter() { return poolingLayer.OutputParameter(); } + + //! Get the delta. + const OutputDataType& Delta() const { return poolingLayer.Delta(); } + //! Modify the delta. + OutputDataType& Delta() { return poolingLayer.Delta(); } + + //! Get the input width. + size_t InputWidth() const { return poolingLayer.InputWidth(); } + //! Modify the input width. + size_t& InputWidth() { return poolingLayer.InputWidth(); } + + //! Get the input height. + size_t InputHeight() const { return poolingLayer.InputHeight(); } + //! Modify the input height. + size_t& InputHeight() { return poolingLayer.InputHeight(); } //! Get the output width. - size_t const& OutputWidth() const { return outputWidth; } + size_t OutputWidth() const { return outputWidth; } //! Modify the output width. size_t& OutputWidth() { return outputWidth; } //! Get the output height. - size_t const& OutputHeight() const { return outputHeight; } + size_t OutputHeight() const { return outputHeight; } //! Modify the output height. size_t& OutputHeight() { return outputHeight; } - //! Get the number of trainable weights. - size_t WeightSize() const { return 0; } + //! Get the input size. + size_t InputSize() const { return poolingLayer.InputSize(); } - const std::vector& OutputDimensions() const - { - std::vector result(this->inputDimensions); - result[0] = outputWidth; - result[1] = outputHeight; - return result; - } + //! Get the output size. + size_t OutputSize() const { return poolingLayer.OutputSize(); } //! Get the size of the weights. size_t WeightSize() const { return 0; } @@ -110,7 +128,7 @@ class AdaptiveMeanPoolingType : public Layer /** * Initialize Kernel Size and Stride for Adaptive Pooling. */ - void InitializeAdaptivePadding() + void IntializeAdaptivePadding() { poolingLayer.StrideWidth() = std::floor(poolingLayer.InputWidth() / outputWidth); @@ -133,7 +151,7 @@ class AdaptiveMeanPoolingType : public Layer } //! Locally stored MeanPooling Object. - MeanPoolingType poolingLayer; + MeanPooling poolingLayer; //! Locally-stored output width. size_t outputWidth; @@ -143,12 +161,7 @@ class AdaptiveMeanPoolingType : public Layer //! Locally-stored reset parameter used to initialize the layer once. bool reset; -}; // class AdaptiveMeanPoolingType - -// Convenience typedefs. - -// Standard Adaptive mean pooling layer. -typedef AdaptiveMeanPoolingType AdaptiveMeanPooling; +}; // class AdaptiveMeanPooling } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/not_adapted/adaptive_mean_pooling_impl.hpp b/src/mlpack/methods/ann/layer/adaptive_mean_pooling_impl.hpp similarity index 55% rename from src/mlpack/methods/ann/layer/not_adapted/adaptive_mean_pooling_impl.hpp rename to src/mlpack/methods/ann/layer/adaptive_mean_pooling_impl.hpp index 34a5a5b27f..c930246a7f 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/adaptive_mean_pooling_impl.hpp +++ b/src/mlpack/methods/ann/layer/adaptive_mean_pooling_impl.hpp @@ -18,61 +18,61 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -AdaptiveMeanPoolingType::AdaptiveMeanPoolingType() +template +AdaptiveMeanPooling::AdaptiveMeanPooling() { // Nothing to do here. } -template -AdaptiveMeanPoolingType::AdaptiveMeanPoolingType( +template +AdaptiveMeanPooling::AdaptiveMeanPooling( const size_t outputWidth, const size_t outputHeight) : - AdaptiveMeanPoolingType(std::tuple(outputWidth, outputHeight)) + AdaptiveMeanPooling(std::tuple(outputWidth, outputHeight)) { // Nothing to do here. } -template -AdaptiveMeanPoolingType::AdaptiveMeanPoolingType( +template +AdaptiveMeanPooling::AdaptiveMeanPooling( const std::tuple& outputShape): outputWidth(std::get<0>(outputShape)), outputHeight(std::get<1>(outputShape)), reset(false) { - poolingLayer = ann::MeanPoolingType(0, 0); + poolingLayer = ann::MeanPooling<>(0, 0); } -template -void AdaptiveMeanPoolingType::Forward( - const InputType& input, OutputType& output) +template +template +void AdaptiveMeanPooling::Forward( + const arma::Mat& input, arma::Mat& output) { if (!reset) { - InitializeAdaptivePadding(); + IntializeAdaptivePadding(); reset = true; } poolingLayer.Forward(input, output); } -template -void AdaptiveMeanPoolingType::Backward( - const InputType& input, - const OutputType& gy, - OutputType& g) +template +template +void AdaptiveMeanPooling::Backward( + const arma::Mat& input, + const arma::Mat& gy, + arma::Mat& g) { poolingLayer.Backward(input, gy, g); } -template +template template -void AdaptiveMeanPoolingType::serialize( +void AdaptiveMeanPooling::serialize( Archive& ar, const uint32_t /* version */) { - ar(cereal::base_class>(this)); - ar(CEREAL_NVP(outputWidth)); ar(CEREAL_NVP(outputHeight)); ar(CEREAL_NVP(reset)); diff --git a/src/mlpack/methods/ann/layer/add.hpp b/src/mlpack/methods/ann/layer/add.hpp index 8645f38612..5fd498e704 100644 --- a/src/mlpack/methods/ann/layer/add.hpp +++ b/src/mlpack/methods/ann/layer/add.hpp @@ -13,90 +13,98 @@ #define MLPACK_METHODS_ANN_LAYER_ADD_HPP #include -#include "layer.hpp" +#include namespace mlpack { namespace ann /** Artificial Neural Network. */ { /** - * Implementation of the Add layer. The Add module applies a bias term to the - * incoming data. + * Implementation of the Add module class. The Add module applies a bias term + * to the incoming data. * - * @tparam MatType Matrix representation to accept as input and use for - * computation. + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class AddType : public Layer +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class Add { public: /** - * Create the AddType object. The output size of the layer will be the same - * as the input size. + * Create the Add object using the specified number of output units. + * + * @param outSize The number of output units. */ - AddType(); - - //! Clone the AddType object. This handles polymorphism correctly. - AddType* Clone() const { return new AddType(*this); } - - // Virtual destructor. - virtual ~AddType() { } - - //! Copy the given AddType layer. - AddType(const AddType& other); - //! Take ownership of the given AddType layer. - AddType(AddType&& other); - //! Copy the given AddType layer. - AddType& operator=(const AddType& other); - //! Take ownership of the given AddType layer. - AddType& operator=(AddType&& other); + Add(const size_t outSize = 0); /** - * Forward pass: add the bias to the input. + * Ordinary feed forward pass of a neural network, evaluating the function + * f(x) by propagating the activity forward through f. * * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - void Forward(const MatType& input, MatType& output); + template + void Forward(const arma::Mat& input, arma::Mat& output); /** - * Backward pass: send weights backwards (the bias does not affect anything). + * Ordinary feed backward pass of a neural network, calculating the function + * f(x) by propagating x backwards trough f. Using the results from the feed + * forward pass. * * @param * (input) The propagated input activation. * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const MatType& /* input */, - const MatType& gy, - MatType& g); + template + void Backward(const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g); /** - * Calculate the gradient using the output and the input activation. + * Calculate the gradient using the output delta and the input activation. * * @param * (input) The propagated input. * @param error The calculated error. * @param gradient The calculated gradient. */ - void Gradient(const MatType& /* input */, - const MatType& error, - MatType& gradient); + template + void Gradient(const arma::Mat& /* input */, + const arma::Mat& error, + arma::Mat& gradient); - //! Return the weights of the network. - const MatType& Parameters() const { return weights; } - //! Modify the weights of the network. - MatType& Parameters() { return weights; } + //! Get the parameters. + OutputDataType const& Parameters() const { return weights; } + //! Modify the parameters. + OutputDataType& Parameters() { return weights; } + + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } + + //! Get the gradient. + OutputDataType const& Gradient() const { return gradient; } + //! Modify the gradient. + OutputDataType& Gradient() { return gradient; } + + //! Get the output size. + size_t OutputSize() const { return outSize; } //! Get the size of weights. size_t WeightSize() const { return outSize; } - //! Compute the output dimensions of the layer, based on the internal values - //! of `InputDimensions()`. - void ComputeOutputDimensions(); - - //! Set the weights of the layer to use the given memory. - void SetWeights(typename MatType::elem_type* weightPtr); - /** - * Serialize the layer. + * Serialize the layer */ template void serialize(Archive& ar, const uint32_t /* version */); @@ -106,11 +114,17 @@ class AddType : public Layer size_t outSize; //! Locally-stored weight object. - MatType weights; -}; // class Add + OutputDataType weights; -// Standard Add layer. -typedef AddType Add; + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored gradient object. + OutputDataType gradient; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; +}; // class Add } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/add_impl.hpp b/src/mlpack/methods/ann/layer/add_impl.hpp index 8cd582c5ab..a268956fe8 100644 --- a/src/mlpack/methods/ann/layer/add_impl.hpp +++ b/src/mlpack/methods/ann/layer/add_impl.hpp @@ -19,103 +19,51 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -AddType::AddType() : outSize(0) +template +Add::Add(const size_t outSize) : + outSize(outSize) { - // Nothing to do. + weights.set_size(WeightSize(), 1); } -template -AddType::AddType(const AddType& other) : - Layer(other), - outSize(other.outSize) +template +template +void Add::Forward( + const arma::Mat& input, arma::Mat& output) { - // Nothing to do. + output = input; + output.each_col() += weights; } -template -AddType::AddType(AddType&& other) : - Layer(std::move(other)), - outSize(std::move(other.outSize)) -{ - // Nothing to do. -} - -template -AddType& -AddType::operator=(const AddType& other) -{ - if (&other != this) - { - Layer::operator=(other); - outSize = other.outSize; - } - - return *this; -} - -template -AddType& -AddType::operator=(AddType&& other) -{ - if (&other != this) - { - Layer::operator=(std::move(other)); - outSize = std::move(other.outSize); - } - - return *this; -} - -template -void AddType::Forward(const MatType& input, MatType& output) -{ - output = input + arma::repmat(arma::vectorise(weights), 1, input.n_cols); -} - -template -void AddType::Backward( - const MatType& /* input */, - const MatType& gy, - MatType& g) +template +template +void Add::Backward( + const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g) { g = gy; } -template -void AddType::Gradient( - const MatType& /* input */, - const MatType& error, - MatType& gradient) +template +template +void Add::Gradient( + const arma::Mat& /* input */, + const arma::Mat& error, + arma::Mat& gradient) { gradient = error; } -template -void AddType::SetWeights(typename MatType::elem_type* weightPtr) -{ - // Set the weights to wrap the given memory. - MakeAlias(weights, weightPtr, 1, outSize); -} - -template -void AddType::ComputeOutputDimensions() -{ - this->outputDimensions = this->inputDimensions; - - outSize = this->outputDimensions[0]; - for (size_t i = 1; i < this->outputDimensions.size(); ++i) - outSize *= this->outputDimensions[i]; -} - -template +template template -void AddType::serialize(Archive& ar, const uint32_t /* version */) +void Add::serialize( + Archive& ar, const uint32_t /* version */) { - ar(cereal::base_class>(this)); - ar(CEREAL_NVP(outSize)); - ar(CEREAL_NVP(weights)); + + if (cereal::is_loading()) + weights.set_size(outSize, 1); } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/not_adapted/add_merge.hpp b/src/mlpack/methods/ann/layer/add_merge.hpp similarity index 52% rename from src/mlpack/methods/ann/layer/not_adapted/add_merge.hpp rename to src/mlpack/methods/ann/layer/add_merge.hpp index bea6da12b5..1c0e02cebf 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/add_merge.hpp +++ b/src/mlpack/methods/ann/layer/add_merge.hpp @@ -1,4 +1,3 @@ -// Temporarily drop. /** * @file methods/ann/layer/add_merge.hpp * @author Marcus Edel @@ -16,6 +15,10 @@ #include +#include "../visitor/delete_visitor.hpp" +#include "../visitor/delta_visitor.hpp" +#include "../visitor/output_parameter_visitor.hpp" + #include "layer_types.hpp" namespace mlpack { @@ -25,17 +28,18 @@ namespace ann /** Artificial Neural Network. */ { * Implementation of the AddMerge module class. The AddMerge class accumulates * the output of various modules. * - * @tparam InputType Type of the input data (arma::colvec, arma::mat, + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). - * @tparam OutputType Type of the output data (arma::colvec, arma::mat, + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). * @tparam CustomLayers Additional custom layers that can be added. */ template< - typename InputType = arma::mat, - typename OutputType = arma::mat + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat, + typename... CustomLayers > -class AddMerge : public MultiLayer +class AddMerge { public: /** @@ -44,7 +48,7 @@ class AddMerge : public MultiLayer * @param model Expose all the network modules. * @param run Call the Forward/Backward method before the output is merged. */ - AddMerge(const bool run = true); + AddMerge(const bool model = false, const bool run = true); /** * Create the AddMerge object using the specified parameters. @@ -53,7 +57,7 @@ class AddMerge : public MultiLayer * @param run Call the Forward/Backward method before the output is merged. * @param ownsLayers Delete the layers when this is deallocated. */ - AddMerge(const bool run, const bool ownsLayers); + AddMerge(const bool model, const bool run, const bool ownsLayers); //! Destructor to release allocated memory. ~AddMerge(); @@ -65,7 +69,8 @@ class AddMerge : public MultiLayer * @param * (input) Input data used for evaluating the specified function. * @param output Resulting output activation. */ - void Forward(const InputType& input, OutputType& output); + template + void Forward(const InputType& /* input */, OutputType& output); /** * Ordinary feed backward pass of a neural network, calculating the function @@ -76,9 +81,10 @@ class AddMerge : public MultiLayer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const InputType& /* input */, - const OutputType& gy, - OutputType& g); + template + void Backward(const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g); /** * This is the overload of Backward() that runs only a specific layer with @@ -89,9 +95,10 @@ class AddMerge : public MultiLayer * @param g The calculated gradient. * @param index The index of the layer to run. */ - void Backward(const InputType& /* input */, - const OutputType& gy, - OutputType& g, + template + void Backward(const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g, const size_t index); /* @@ -101,9 +108,10 @@ class AddMerge : public MultiLayer * @param error The calculated error. * @param gradient The calculated gradient. */ - void Gradient(const InputType& input, - const OutputType& error, - OutputType& gradient); + template + void Gradient(const arma::Mat& input, + const arma::Mat& error, + arma::Mat& gradient); /* * This is the overload of Gradient() that runs a specific layer with the @@ -114,24 +122,63 @@ class AddMerge : public MultiLayer * @param gradient The calculated gradient. * @param The index of the layer to run. */ - void Gradient(const InputType& input, - const OutputType& error, - OutputType& gradient, + template + void Gradient(const arma::Mat& input, + const arma::Mat& error, + arma::Mat& gradient, const size_t index); + /* + * Add a new module to the model. + * + * @param args The layer parameter. + */ + template + void Add(Args... args) { network.push_back(new LayerType(args...)); } + + /* + * Add a new module to the model. + * + * @param layer The Layer to be added to the model. + */ + void Add(LayerTypes layer) { network.push_back(layer); } + + //! Get the input parameter. + InputDataType const& InputParameter() const { return inputParameter; } + //! Modify the input parameter. + InputDataType& InputParameter() { return inputParameter; } + + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } + + //! Return the model modules. + std::vector >& Model() + { + if (model) + { + return network; + } + + return empty; + } + + //! Get the parameters. + OutputDataType const& Parameters() const { return weights; } + //! Modify the parameters. + OutputDataType& Parameters() { return weights; } + //! Get the value of run parameter. bool Run() const { return run; } //! Modify the value of run parameter. bool& Run() { return run; } - const std::vector& OutputDimensions() const - { - // Propagate input size to child layers. - for (size_t i = 0; i < this->network.size(); ++i) - this->network[i]->InputDimensions() = this->inputDimensions; - return this->network.back()->OutputDimensions(); - } - /** * Serialize the layer. */ @@ -139,6 +186,9 @@ class AddMerge : public MultiLayer void serialize(Archive& ar, const uint32_t /* version */); private: + //! Parameter which indicates if the modules should be exposed. + bool model; + //! Parameter which indicates if the Forward/Backward method should be called //! before merging the output. bool run; @@ -146,6 +196,36 @@ class AddMerge : public MultiLayer //! We need this to know whether we should delete the internally-held layers //! in the destructor. bool ownsLayers; + + //! Locally-stored network modules. + std::vector > network; + + //! Locally-stored empty list of modules. + std::vector > empty; + + //! Locally-stored delete visitor module object. + DeleteVisitor deleteVisitor; + + //! Locally-stored output parameter visitor module object. + OutputParameterVisitor outputParameterVisitor; + + //! Locally-stored delta visitor module object. + DeltaVisitor deltaVisitor; + + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored gradient object. + OutputDataType gradient; + + //! Locally-stored input parameter object. + InputDataType inputParameter; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; + + //! Locally-stored weight object. + OutputDataType weights; }; // class AddMerge } // namespace ann diff --git a/src/mlpack/methods/ann/layer/add_merge_impl.hpp b/src/mlpack/methods/ann/layer/add_merge_impl.hpp new file mode 100644 index 0000000000..cd891088a4 --- /dev/null +++ b/src/mlpack/methods/ann/layer/add_merge_impl.hpp @@ -0,0 +1,168 @@ +/** + * @file methods/ann/layer/add_merge_impl.hpp + * @author Marcus Edel + * + * Definition of the AddMerge module which accumulates the output of the given + * modules. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_LAYER_ADD_MERGE_IMPL_HPP +#define MLPACK_METHODS_ANN_LAYER_ADD_MERGE_IMPL_HPP + +// In case it hasn't yet been included. +#include "add_merge.hpp" + +#include "../visitor/forward_visitor.hpp" +#include "../visitor/backward_visitor.hpp" +#include "../visitor/gradient_visitor.hpp" + +namespace mlpack { +namespace ann /** Artificial Neural Network. */ { + +template +AddMerge::AddMerge( + const bool model, const bool run) : + model(model), run(run), ownsLayers(!model) +{ + // Nothing to do here. +} + +template +AddMerge::AddMerge( + const bool model, const bool run, const bool ownsLayers) : + model(model), run(run), ownsLayers(ownsLayers) +{ + // Nothing to do here. +} + +template +AddMerge::~AddMerge() +{ + if (!model && ownsLayers) + { + std::for_each(network.begin(), network.end(), + boost::apply_visitor(deleteVisitor)); + } +} + +template +template +void AddMerge::Forward( + const InputType& input, OutputType& output) +{ + if (run) + { + for (size_t i = 0; i < network.size(); ++i) + { + boost::apply_visitor(ForwardVisitor(input, + boost::apply_visitor(outputParameterVisitor, network[i])), + network[i]); + } + } + + output = boost::apply_visitor(outputParameterVisitor, network.front()); + for (size_t i = 1; i < network.size(); ++i) + { + output += boost::apply_visitor(outputParameterVisitor, network[i]); + } +} + +template +template +void AddMerge::Backward( + const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g) +{ + if (run) + { + for (size_t i = 0; i < network.size(); ++i) + { + boost::apply_visitor(BackwardVisitor(boost::apply_visitor( + outputParameterVisitor, network[i]), gy, + boost::apply_visitor(deltaVisitor, network[i])), network[i]); + } + + g = boost::apply_visitor(deltaVisitor, network[0]); + for (size_t i = 1; i < network.size(); ++i) + { + g += boost::apply_visitor(deltaVisitor, network[i]); + } + } + else + g = gy; +} + +template +template +void AddMerge::Backward( + const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g, + const size_t index) +{ + boost::apply_visitor(BackwardVisitor(boost::apply_visitor( + outputParameterVisitor, network[index]), gy, + boost::apply_visitor(deltaVisitor, network[index])), network[index]); + g = boost::apply_visitor(deltaVisitor, network[index]); +} + +template +template +void AddMerge::Gradient( + const arma::Mat& input, + const arma::Mat& error, + arma::Mat& /* gradient */ ) +{ + if (run) + { + for (size_t i = 0; i < network.size(); ++i) + { + boost::apply_visitor(GradientVisitor(input, error), network[i]); + } + } +} + +template +template +void AddMerge::Gradient( + const arma::Mat& input, + const arma::Mat& error, + arma::Mat& /* gradient */, + const size_t index) +{ + boost::apply_visitor(GradientVisitor(input, error), network[index]); +} + +template +template +void AddMerge::serialize( + Archive& ar, const uint32_t /* version */) +{ + // Be sure to clear other layers before loading. + if (cereal::is_loading()) + network.clear(); + + ar(CEREAL_VECTOR_VARIANT_POINTER(network)); + ar(CEREAL_NVP(model)); + ar(CEREAL_NVP(run)); + ar(CEREAL_NVP(ownsLayers)); +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/layer/alpha_dropout.hpp b/src/mlpack/methods/ann/layer/alpha_dropout.hpp index b1e63870f6..abf75d87b8 100644 --- a/src/mlpack/methods/ann/layer/alpha_dropout.hpp +++ b/src/mlpack/methods/ann/layer/alpha_dropout.hpp @@ -17,7 +17,6 @@ #define MLPACK_METHODS_ANN_LAYER_ALPHA_DROPOUT_HPP #include -#include "layer.hpp" namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -41,11 +40,14 @@ namespace ann /** Artificial Neural Network. */ { * } * @endcode * - * @tparam MatType Matrix representation to accept as input and use for - * computation. + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class AlphaDropoutType : public Layer +template +class AlphaDropout { public: /** @@ -54,33 +56,17 @@ class AlphaDropoutType : public Layer * @param ratio The probability of setting a value to alphaDash. * @param alphaDash The dropout scaling parameter. */ - AlphaDropoutType(const double ratio = 0.5, - const double alphaDash = -alpha * lambda); + AlphaDropout(const double ratio = 0.5, + const double alphaDash = -alpha * lambda); /** - * Clone the AlphaDropoutType object. This handles polymorphism correctly. - */ - AlphaDropoutType* Clone() const { return new AlphaDropoutType(*this); } - - // Virtual destructor. - virtual ~AlphaDropoutType() { } - - //! Copy the given AlphaDropoutType layer. - AlphaDropoutType(const AlphaDropoutType& other); - //! Take ownership of the given AlphaDropoutType layer. - AlphaDropoutType(AlphaDropoutType&& other); - //! Copy the given AlphaDropoutType layer. - AlphaDropoutType& operator=(const AlphaDropoutType& other); - //! Take ownership of the given AlphaDropoutType layer. - AlphaDropoutType& operator=(AlphaDropoutType&& other); - - /** - * Ordinary feed forward pass of the AlphaDropout layer. + * Ordinary feed forward pass of the alpha_dropout layer. * * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - void Forward(const MatType& input, MatType& output); + template + void Forward(const arma::Mat& input, arma::Mat& output); /** * Ordinary feed backward pass of the alpha_dropout layer. @@ -89,7 +75,25 @@ class AlphaDropoutType : public Layer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const MatType& /* input */, const MatType& gy, MatType& g); + template + void Backward(const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g); + + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the detla. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } + + //! The value of the deterministic parameter. + bool Deterministic() const { return deterministic; } + //! Modify the value of the deterministic parameter. + bool& Deterministic() { return deterministic; } //! The probability of setting a value to alphaDash. double Ratio() const { return ratio; } @@ -101,10 +105,10 @@ class AlphaDropoutType : public Layer double B() const { return b; } //! Value of alphaDash. - double AlphaDash() const { return alphaDash; } + double AlphaDash() const {return alphaDash; } //! Get the mask. - const MatType& Mask() const { return mask; } + OutputDataType const& Mask() const {return mask;} //! Modify the probability of setting a value to alphaDash. As //! 'a' and 'b' depend on 'ratio', modify them as well. @@ -122,8 +126,14 @@ class AlphaDropoutType : public Layer void serialize(Archive& ar, const uint32_t /* version */); private: - //! Locally-stored mask object. - MatType mask; + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; + + //! Locally-stored mast object. + OutputDataType mask; //! The probability of setting a value to aplhaDash. double ratio; @@ -131,6 +141,9 @@ class AlphaDropoutType : public Layer //! The low variance value of SELU activation function. double alphaDash; + //! If true dropout and scaling is disabled, see notes above. + bool deterministic; + //! Value of alpha for normalized inputs (taken from SELU). static constexpr double alpha = 1.6732632423543772848170429916717; @@ -142,9 +155,7 @@ class AlphaDropoutType : public Layer //! Value to be added to a*x for affine transformation. double b; -}; // class AlphaDropoutType - -typedef AlphaDropoutType AlphaDropout; +}; // class AlphaDropout } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/alpha_dropout_impl.hpp b/src/mlpack/methods/ann/layer/alpha_dropout_impl.hpp index 91a150f410..34a1b44544 100644 --- a/src/mlpack/methods/ann/layer/alpha_dropout_impl.hpp +++ b/src/mlpack/methods/ann/layer/alpha_dropout_impl.hpp @@ -22,79 +22,25 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -AlphaDropoutType::AlphaDropoutType( +template +AlphaDropout::AlphaDropout( const double ratio, const double alphaDash) : ratio(ratio), - alphaDash(alphaDash) + alphaDash(alphaDash), + deterministic(false) { Ratio(ratio); } -template -AlphaDropoutType::AlphaDropoutType(const AlphaDropoutType& other) : - Layer(other), - mask(other.mask), - ratio(other.ratio), - alphaDash(other.alphaDash), - a(other.a), - b(other.b) +template +template +void AlphaDropout::Forward( + const arma::Mat& input, arma::Mat& output) { - // Nothing to do. -} - -template -AlphaDropoutType::AlphaDropoutType(AlphaDropoutType&& other) : - Layer(std::move(other)), - mask(std::move(other.mask)), - ratio(std::move(other.ratio)), - alphaDash(std::move(other.alphaDash)), - a(std::move(other.a)), - b(std::move(other.b)) -{ - // Nothing to do. -} - -template -AlphaDropoutType& -AlphaDropoutType::operator=(const AlphaDropoutType& other) -{ - if (&other != this) - { - Layer::operator=(other); - mask = other.mask; - ratio = other.ratio; - alphaDash = other.alphaDash; - a = other.a; - b = other.b; - } - - return *this; -} - -template -AlphaDropoutType& -AlphaDropoutType::operator=(AlphaDropoutType&& other) -{ - if (&other != this) - { - Layer::operator=(std::move(other)); - mask = std::move(other.mask); - ratio = std::move(other.ratio); - alphaDash = std::move(other.alphaDash); - a = std::move(other.a); - b = std::move(other.b); - } - - return *this; -} - -template -void AlphaDropoutType::Forward(const MatType& input, MatType& output) -{ - // The dropout mask will not be multiplied during testing. - if (!this->training) + // The dropout mask will not be multiplied in the deterministic mode + // (during testing). + if (deterministic) { output = input; } @@ -103,35 +49,29 @@ void AlphaDropoutType::Forward(const MatType& input, MatType& output) // Set values to alphaDash with probability ratio. Then apply affine // transformation so as to keep mean and variance of outputs to their // original values. - mask = arma::randu(input.n_rows, input.n_cols); + mask = arma::randu< arma::Mat >(input.n_rows, input.n_cols); mask.transform( [&](double val) { return (val > ratio); } ); output = (input % mask + alphaDash * (1 - mask)) * a + b; } } -template -void AlphaDropoutType::Backward( - const MatType& /* input */, const MatType& gy, MatType& g) +template +template +void AlphaDropout::Backward( + const arma::Mat& /* input */, const arma::Mat& gy, arma::Mat& g) { g = gy % mask * a; } -template +template template -void AlphaDropoutType::serialize( +void AlphaDropout::serialize( Archive& ar, const uint32_t /* version */) { - ar(cereal::base_class>(this)); - ar(CEREAL_NVP(ratio)); ar(CEREAL_NVP(alphaDash)); ar(CEREAL_NVP(a)); ar(CEREAL_NVP(b)); - - // No need to serialize the mask, since it will be recomputed on the next - // forward pass. But we should clear it if we are loading. - if (Archive::is_loading::value) - mask.clear(); } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/not_adapted/atrous_convolution.hpp b/src/mlpack/methods/ann/layer/atrous_convolution.hpp similarity index 82% rename from src/mlpack/methods/ann/layer/not_adapted/atrous_convolution.hpp rename to src/mlpack/methods/ann/layer/atrous_convolution.hpp index cc0bbcea9d..d6086de86f 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/atrous_convolution.hpp +++ b/src/mlpack/methods/ann/layer/atrous_convolution.hpp @@ -1,4 +1,3 @@ -// Temporarily drop. /** * @file methods/ann/layer/atrous_convolution.hpp * @author Aarush Gupta @@ -47,10 +46,10 @@ template < typename ForwardConvolutionRule = NaiveConvolution, typename BackwardConvolutionRule = NaiveConvolution, typename GradientConvolutionRule = NaiveConvolution, - typename InputType = arma::mat, - typename OutputType = arma::mat + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat > -class AtrousConvolution : public Layer +class AtrousConvolution { public: //! Create the AtrousConvolution object. @@ -140,7 +139,8 @@ class AtrousConvolution : public Layer * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - void Forward(const InputType& input, OutputType& output); + template + void Forward(const arma::Mat& input, arma::Mat& output); /** * Ordinary feed backward pass of a neural network, calculating the function @@ -151,9 +151,10 @@ class AtrousConvolution : public Layer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const InputType& /* input */, - const OutputType& gy, - OutputType& g); + template + void Backward(const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g); /* * Calculate the gradient using the output delta and the input activation. @@ -162,90 +163,101 @@ class AtrousConvolution : public Layer * @param error The calculated error. * @param gradient The calculated gradient. */ - void Gradient(const InputType& /* input */, - const OutputType& error, - OutputType& gradient); + template + void Gradient(const arma::Mat& /* input */, + const arma::Mat& error, + arma::Mat& gradient); //! Get the parameters. - OutputType const& Parameters() const { return weights; } + OutputDataType const& Parameters() const { return weights; } //! Modify the parameters. - OutputType& Parameters() { return weights; } + OutputDataType& Parameters() { return weights; } //! Get the weight of the layer. - const arma::Cube& Weight() const - { - return weight; - } + arma::cube const& Weight() const { return weight; } //! Modify the weight of the layer. - arma::Cube& Weight() { return weight; } - - const std::vector& OutputDimensions() const - { - std::vector result(inputDimensions.size(), 0); - result[0] = outputWidth; - result[1] = outputHeight; - return result; - } + arma::cube& Weight() { return weight; } //! Get the bias of the layer. - const OutputType& Bias() const { return bias; } + arma::mat const& Bias() const { return bias; } //! Modify the bias of the layer. - OutputType& Bias() { return bias; } + arma::mat& Bias() { return bias; } + + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } + + //! Get the gradient. + OutputDataType const& Gradient() const { return gradient; } + //! Modify the gradient. + OutputDataType& Gradient() { return gradient; } //! Get the input width. - const size_t& InputWidth() const { return inputWidth; } + size_t InputWidth() const { return inputWidth; } //! Modify input the width. size_t& InputWidth() { return inputWidth; } //! Get the input height. - const size_t& InputHeight() const { return inputHeight; } + size_t InputHeight() const { return inputHeight; } //! Modify the input height. size_t& InputHeight() { return inputHeight; } //! Get the output width. - const size_t& OutputWidth() const { return outputWidth; } + size_t OutputWidth() const { return outputWidth; } //! Modify the output width. size_t& OutputWidth() { return outputWidth; } //! Get the output height. - const size_t& OutputHeight() const { return outputHeight; } + size_t OutputHeight() const { return outputHeight; } //! Modify the output height. size_t& OutputHeight() { return outputHeight; } + //! Get the input size. + size_t InputSize() const { return inSize; } + + //! Get the output size. + size_t OutputSize() const { return outSize; } + //! Get the kernel width. - const size_t& KernelWidth() const { return kernelWidth; } + size_t KernelWidth() const { return kernelWidth; } //! Modify the kernel width. size_t& KernelWidth() { return kernelWidth; } //! Get the kernel height. - const size_t& KernelHeight() const { return kernelHeight; } + size_t KernelHeight() const { return kernelHeight; } //! Modify the kernel height. size_t& KernelHeight() { return kernelHeight; } //! Get the stride width. - const size_t& StrideWidth() const { return strideWidth; } + size_t StrideWidth() const { return strideWidth; } //! Modify the stride width. size_t& StrideWidth() { return strideWidth; } //! Get the stride height. - const size_t& StrideHeight() const { return strideHeight; } + size_t StrideHeight() const { return strideHeight; } //! Modify the stride height. size_t& StrideHeight() { return strideHeight; } //! Get the dilation rate on the X axis. - const size_t& DilationWidth() const { return dilationWidth; } + size_t DilationWidth() const { return dilationWidth; } //! Modify the dilation rate on the X axis. size_t& DilationWidth() { return dilationWidth; } //! Get the dilation rate on the Y axis. - const size_t& DilationHeight() const { return dilationHeight; } + size_t DilationHeight() const { return dilationHeight; } //! Modify the dilation rate on the Y axis. size_t& DilationHeight() { return dilationHeight; } //! Get the internal Padding layer. - PaddingType const& Padding() const { return padding; } + ann::Padding<> const& Padding() const { return padding; } //! Modify the internal Padding layer. - PaddingType& Padding() { return padding; } + ann::Padding<>& Padding() { return padding; } //! Get size of the weight matrix. size_t WeightSize() const @@ -346,13 +358,13 @@ class AtrousConvolution : public Layer size_t strideHeight; //! Locally-stored weight object. - OutputType weights; + OutputDataType weights; //! Locally-stored weight object. - arma::Cube weight; + arma::cube weight; //! Locally-stored bias term object. - OutputType bias; + arma::mat bias; //! Locally-stored input width. size_t inputWidth; @@ -373,19 +385,28 @@ class AtrousConvolution : public Layer size_t dilationHeight; //! Locally-stored transformed output parameter. - arma::Cube outputTemp; + arma::cube outputTemp; //! Locally-stored transformed padded input parameter. - arma::Cube inputPaddedTemp; + arma::cube inputPaddedTemp; //! Locally-stored transformed error parameter. - arma::Cube gTemp; + arma::cube gTemp; //! Locally-stored transformed gradient parameter. - arma::Cube gradientTemp; + arma::cube gradientTemp; //! Locally-stored padding layer. - PaddingType padding; + ann::Padding<> padding; + + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored gradient object. + OutputDataType gradient; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; }; // class AtrousConvolution } // namespace ann diff --git a/src/mlpack/methods/ann/layer/not_adapted/atrous_convolution_impl.hpp b/src/mlpack/methods/ann/layer/atrous_convolution_impl.hpp similarity index 81% rename from src/mlpack/methods/ann/layer/not_adapted/atrous_convolution_impl.hpp rename to src/mlpack/methods/ann/layer/atrous_convolution_impl.hpp index d18fd1f855..2377ddee00 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/atrous_convolution_impl.hpp +++ b/src/mlpack/methods/ann/layer/atrous_convolution_impl.hpp @@ -23,15 +23,15 @@ template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename InputType, - typename OutputType + typename InputDataType, + typename OutputDataType > AtrousConvolution< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - InputType, - OutputType + InputDataType, + OutputDataType >::AtrousConvolution() { // Nothing to do here. @@ -41,15 +41,15 @@ template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename InputType, - typename OutputType + typename InputDataType, + typename OutputDataType > AtrousConvolution< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - InputType, - OutputType + InputDataType, + OutputDataType >::AtrousConvolution( const size_t inSize, const size_t outSize, @@ -86,15 +86,15 @@ template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename InputType, - typename OutputType + typename InputDataType, + typename OutputDataType > AtrousConvolution< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - InputType, - OutputType + InputDataType, + OutputDataType >::AtrousConvolution( const size_t inSize, const size_t outSize, @@ -143,49 +143,49 @@ AtrousConvolution< InitializeSamePadding(padWLeft, padWRight, padHTop, padHBottom); } - padding = PaddingType(padWLeft, padWRight, padHTop, - padHBottom); + padding = ann::Padding<>(padWLeft, padWRight, padHTop, padHBottom); } template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename InputType, - typename OutputType + typename InputDataType, + typename OutputDataType > void AtrousConvolution< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - InputType, - OutputType ->::ResetWeights(typename OutputType::elem_type* weightsPtr) + InputDataType, + OutputDataType +>::Reset() { - weight = arma::Cube(weightsPtr, kernelWidth, - kernelHeight, outSize * inSize, false, true); - bias = OutputType(weightsPtr + weight.n_elem, outSize, 1, false, true); + weight = arma::cube(weights.memptr(), kernelWidth, kernelHeight, + outSize * inSize, false, false); + bias = arma::mat(weights.memptr() + weight.n_elem, + outSize, 1, false, false); } template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename InputType, - typename OutputType + typename InputDataType, + typename OutputDataType > +template void AtrousConvolution< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - InputType, - OutputType ->::Forward(const InputType& input, OutputType& output) + InputDataType, + OutputDataType +>::Forward(const arma::Mat& input, arma::Mat& output) { batchSize = input.n_cols; - arma::Cube inputTemp( - const_cast(input).memptr(), inputWidth, inputHeight, inSize * - batchSize, false, false); + arma::cube inputTemp(const_cast&>(input).memptr(), + inputWidth, inputHeight, inSize * batchSize, false, false); if (padding.PadWLeft() != 0 || padding.PadWRight() != 0 || padding.PadHTop() != 0 || padding.PadHBottom() != 0) @@ -207,8 +207,8 @@ void AtrousConvolution< padding.PadHTop(), padding.PadHBottom(), dilationHeight); output.set_size(wConv * hConv * outSize, batchSize); - outputTemp = arma::Cube(output.memptr(), - wConv, hConv, outSize * batchSize, false, false); + outputTemp = arma::Cube(output.memptr(), wConv, hConv, + outSize * batchSize, false, false); outputTemp.zeros(); for (size_t outMap = 0, outMapIdx = 0, batchCount = 0; outMap < @@ -222,7 +222,7 @@ void AtrousConvolution< for (size_t inMap = 0; inMap < inSize; inMap++, outMapIdx++) { - OutputType convOutput; + arma::Mat convOutput; if (padding.PadWLeft() != 0 || padding.PadWRight() != 0 || padding.PadHTop() != 0 || padding.PadHBottom() != 0) @@ -252,24 +252,25 @@ template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename InputType, - typename OutputType + typename InputDataType, + typename OutputDataType > +template void AtrousConvolution< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - InputType, - OutputType ->::Backward(const InputType& /* input */, const OutputType& gy, OutputType& g) + InputDataType, + OutputDataType +>::Backward( + const arma::Mat& /* input */, const arma::Mat& gy, arma::Mat& g) { - arma::Cube mappedError( - ((OutputType&) gy).memptr(), outputWidth, outputHeight, outSize * - batchSize, false, false); + arma::cube mappedError(((arma::Mat&) gy).memptr(), outputWidth, + outputHeight, outSize * batchSize, false, false); g.set_size(inputWidth * inputHeight * inSize, batchSize); - gTemp = arma::Cube(g.memptr(), inputWidth, - inputHeight, inSize * batchSize, false, false); + gTemp = arma::Cube(g.memptr(), inputWidth, inputHeight, + inSize * batchSize, false, false); gTemp.zeros(); for (size_t outMap = 0, outMapIdx = 0, batchCount = 0; outMap < @@ -283,7 +284,7 @@ void AtrousConvolution< for (size_t inMap = 0; inMap < inSize; inMap++, outMapIdx++) { - OutputType output, rotatedFilter; + arma::Mat output, rotatedFilter; Rotate180(weight.slice(outMapIdx), rotatedFilter); BackwardConvolutionRule::Convolution(mappedError.slice(outMap), @@ -310,30 +311,29 @@ template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename InputType, - typename OutputType + typename InputDataType, + typename OutputDataType > +template void AtrousConvolution< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - InputType, - OutputType + InputDataType, + OutputDataType >::Gradient( - const InputType& input, - const OutputType& error, - OutputType& gradient) + const arma::Mat& input, + const arma::Mat& error, + arma::Mat& gradient) { - arma::Cube mappedError( - ((OutputType&) error).memptr(), outputWidth, outputHeight, outSize * - batchSize, false, false); - arma::Cube inputTemp( - const_cast(input).memptr(), inputWidth, inputHeight, - inSize * batchSize, false, false); + arma::cube mappedError(((arma::Mat&) error).memptr(), outputWidth, + outputHeight, outSize * batchSize, false, false); + arma::cube inputTemp(const_cast&>(input).memptr(), + inputWidth, inputHeight, inSize * batchSize, false, false); gradient.set_size(weights.n_elem, 1); - gradientTemp = arma::Cube(gradient.memptr(), - weight.n_rows, weight.n_cols, weight.n_slices, false, false); + gradientTemp = arma::Cube(gradient.memptr(), weight.n_rows, + weight.n_cols, weight.n_slices, false, false); gradientTemp.zeros(); for (size_t outMap = 0, outMapIdx = 0, batchCount = 0; outMap < @@ -347,7 +347,7 @@ void AtrousConvolution< for (size_t inMap = 0; inMap < inSize; inMap++, outMapIdx++) { - InputType inputSlice; + arma::Mat inputSlice; if (padding.PadWLeft() != 0 || padding.PadWRight() != 0 || padding.PadHTop() != 0 || padding.PadHBottom() != 0) { @@ -358,9 +358,9 @@ void AtrousConvolution< inputSlice = inputTemp.slice(inMap + batchCount * inSize); } - OutputType deltaSlice = mappedError.slice(outMap); + arma::Mat deltaSlice = mappedError.slice(outMap); - OutputType output; + arma::Mat output; GradientConvolutionRule::Convolution(inputSlice, deltaSlice, output, strideWidth, strideHeight, 1, 1); @@ -404,20 +404,18 @@ template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename InputType, - typename OutputType + typename InputDataType, + typename OutputDataType > template void AtrousConvolution< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - InputType, - OutputType + InputDataType, + OutputDataType >::serialize(Archive& ar, const uint32_t /* version */) { - ar(cereal::base_class>(this)); - ar(CEREAL_NVP(inSize)); ar(CEREAL_NVP(outSize)); ar(CEREAL_NVP(batchSize)); @@ -432,22 +430,27 @@ void AtrousConvolution< ar(CEREAL_NVP(dilationWidth)); ar(CEREAL_NVP(dilationHeight)); ar(CEREAL_NVP(padding)); - ar(CEREAL_NVP(weights)); + + if (cereal::is_loading()) + { + weights.set_size((outSize * inSize * kernelWidth * kernelHeight) + outSize, + 1); + } } template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename InputType, - typename OutputType + typename InputDataType, + typename OutputDataType > void AtrousConvolution< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - InputType, - OutputType + InputDataType, + OutputDataType >::InitializeSamePadding(size_t& padWLeft, size_t& padWRight, size_t& padHTop, diff --git a/src/mlpack/methods/ann/layer/base_layer.hpp b/src/mlpack/methods/ann/layer/base_layer.hpp index 4762ee0b59..e2d7aaf809 100644 --- a/src/mlpack/methods/ann/layer/base_layer.hpp +++ b/src/mlpack/methods/ann/layer/base_layer.hpp @@ -30,7 +30,6 @@ #include #include #include -#include "layer.hpp" namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -41,83 +40,102 @@ namespace ann /** Artificial Neural Network. */ { * * A few convenience typedefs are given: * - * - Sigmoid - * - ReLU - * - TanH - * - Softplus - * - HardSigmoid - * - Swish - * - Mish - * - LiSHT - * - GELU - * - ELiSH - * - Elliot - * - Gaussian - * - HardSwish - * - TanhExp - * - SILU + * - SigmoidLayer + * - IdentityLayer + * - ReLULayer + * - TanHLayer + * - SoftplusLayer + * - HardSigmoidLayer + * - SwishLayer + * - MishLayer + * - LiSHTLayer + * - GELULayer + * - ELiSHLayer + * - ElliotLayer + * - GaussianLayer + * - HardSwishLayer + * - TanhExpLayer + * - SILULayer * * @tparam ActivationFunction Activation function used for the embedding layer. + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ template < class ActivationFunction = LogisticFunction, - typename MatType = arma::mat + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat > -class BaseLayer : public Layer +class BaseLayer { public: /** * Create the BaseLayer object. */ - BaseLayer() : Layer() + BaseLayer() { // Nothing to do here. } - // Virtual destructor. - virtual ~BaseLayer() { } - - // No copy constructor or operators needed here, since the class has no - // members. - - //! Clone the BaseLayer object. This handles polymorphism correctly. - BaseLayer* Clone() const { return new BaseLayer(*this); } - /** - * Forward pass: apply the activation to the inputs. + * Ordinary feed forward pass of a neural network, evaluating the function + * f(x) by propagating the activity forward through f. * * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - void Forward(const MatType& input, MatType& output) + template + void Forward(const InputType& input, OutputType& output) { ActivationFunction::Fn(input, output); } /** - * Backward pass: compute the function f(x) by propagating x backwards through - * f, using the results from the forward pass. + * Ordinary feed backward pass of a neural network, calculating the function + * f(x) by propagating x backwards trough f. Using the results from the feed + * forward pass. * * @param input The propagated input activation. * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const MatType& input, const MatType& gy, MatType& g) + template + void Backward(const arma::Mat& input, + const arma::Mat& gy, + arma::Mat& g) { - MatType derivative; + arma::Mat derivative; ActivationFunction::Deriv(input, derivative); g = gy % derivative; } + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } + /** * Serialize the layer. */ template - void serialize(Archive& ar, const uint32_t /* version */) + void serialize(Archive& /* ar */, const uint32_t /* version */) { - ar(cereal::base_class>(this)); - // Nothing to serialize. + /* Nothing to do here */ } + + private: + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; }; // class BaseLayer // Convenience typedefs. @@ -125,122 +143,179 @@ class BaseLayer : public Layer /** * Standard Sigmoid-Layer using the logistic activation function. */ -typedef BaseLayer Sigmoid; +template < + class ActivationFunction = LogisticFunction, + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +using SigmoidLayer = BaseLayer< + ActivationFunction, InputDataType, OutputDataType>; -template -using SigmoidType = BaseLayer; +/** + * Standard Identity-Layer using the identity activation function. + */ +template < + class ActivationFunction = IdentityFunction, + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +using IdentityLayer = BaseLayer< + ActivationFunction, InputDataType, OutputDataType>; /** * Standard rectified linear unit non-linearity layer. */ -typedef BaseLayer ReLU; - -template -using ReLUType = BaseLayer; +template < + class ActivationFunction = RectifierFunction, + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +using ReLULayer = BaseLayer< + ActivationFunction, InputDataType, OutputDataType>; /** * Standard hyperbolic tangent layer. */ -typedef BaseLayer TanH; - -template -using TanHType = BaseLayer; +template < + class ActivationFunction = TanhFunction, + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +using TanHLayer = BaseLayer< + ActivationFunction, InputDataType, OutputDataType>; /** * Standard Softplus-Layer using the Softplus activation function. */ -typedef BaseLayer SoftPlus; - -template -using SoftPlusType = BaseLayer; +template < + class ActivationFunction = SoftplusFunction, + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +using SoftPlusLayer = BaseLayer< + ActivationFunction, InputDataType, OutputDataType>; /** * Standard HardSigmoid-Layer using the HardSigmoid activation function. */ -typedef BaseLayer HardSigmoid; - -template -using HardSigmoidType = BaseLayer; +template < + class ActivationFunction = HardSigmoidFunction, + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +using HardSigmoidLayer = BaseLayer< + ActivationFunction, InputDataType, OutputDataType>; /** * Standard Swish-Layer using the Swish activation function. */ -typedef BaseLayer Swish; - -template -using SwishType = BaseLayer; +template < + class ActivationFunction = SwishFunction, + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +using SwishFunctionLayer = BaseLayer< + ActivationFunction, InputDataType, OutputDataType>; /** * Standard Mish-Layer using the Mish activation function. */ -typedef BaseLayer Mish; - -template -using MishType = BaseLayer; +template < + class ActivationFunction = MishFunction, + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +using MishFunctionLayer = BaseLayer< + ActivationFunction, InputDataType, OutputDataType>; /** * Standard LiSHT-Layer using the LiSHT activation function. */ -typedef BaseLayer LiSHT; - -template -using LiSHTType = BaseLayer; +template < + class ActivationFunction = LiSHTFunction, + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +using LiSHTFunctionLayer = BaseLayer< + ActivationFunction, InputDataType, OutputDataType>; /** * Standard GELU-Layer using the GELU activation function. */ -typedef BaseLayer GELU; - -template -using GELUType = BaseLayer; +template < + class ActivationFunction = GELUFunction, + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +using GELUFunctionLayer = BaseLayer< + ActivationFunction, InputDataType, OutputDataType>; /** * Standard Elliot-Layer using the Elliot activation function. */ -typedef BaseLayer Elliot; - -template -using ElliotType = BaseLayer; +template < + class ActivationFunction = ElliotFunction, + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +using ElliotFunctionLayer = BaseLayer< + ActivationFunction, InputDataType, OutputDataType>; /** * Standard ELiSH-Layer using the ELiSH activation function. */ -typedef BaseLayer Elish; - -template -using ElishType = BaseLayer; +template < + class ActivationFunction = ElishFunction, + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +using ElishFunctionLayer = BaseLayer< + ActivationFunction, InputDataType, OutputDataType>; /** * Standard Gaussian-Layer using the Gaussian activation function. */ -typedef BaseLayer Gaussian; - -template -using GaussianType = BaseLayer; +template < + class ActivationFunction = GaussianFunction, + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +using GaussianFunctionLayer = BaseLayer< + ActivationFunction, InputDataType, OutputDataType>; /** * Standard HardSwish-Layer using the HardSwish activation function. */ -typedef BaseLayer HardSwish; - -template -using HardSwishType = BaseLayer; +template < + class ActivationFunction = HardSwishFunction, + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +using HardSwishFunctionLayer = BaseLayer< + ActivationFunction, InputDataType, OutputDataType>; /** * Standard TanhExp-Layer using the TanhExp activation function. */ -typedef BaseLayer TanhExp; - -template -using TanhExpType = BaseLayer; +template < + class ActivationFunction = TanhExpFunction, + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +using TanhExpFunctionLayer = BaseLayer< + ActivationFunction, InputDataType, OutputDataType>; /** * Standard SILU-Layer using the SILU activation function. */ -typedef BaseLayer SILU; - -template -using SILUType = BaseLayer; +template < + class ActivationFunction = SILUFunction, + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +using SILUFunctionLayer = BaseLayer< + ActivationFunction, InputDataType, OutputDataType +>; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/not_adapted/batch_norm.hpp b/src/mlpack/methods/ann/layer/batch_norm.hpp similarity index 69% rename from src/mlpack/methods/ann/layer/not_adapted/batch_norm.hpp rename to src/mlpack/methods/ann/layer/batch_norm.hpp index 634fbe2c29..52df427dba 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/batch_norm.hpp +++ b/src/mlpack/methods/ann/layer/batch_norm.hpp @@ -50,10 +50,10 @@ namespace ann /** Artificial Neural Network. */ { * arma::sp_mat or arma::cube). */ template < - typename InputType = arma::mat, - typename OutputType = arma::mat + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat > -class BatchNorm : public Layer +class BatchNorm { public: //! Create the BatchNorm object. @@ -74,9 +74,9 @@ class BatchNorm : public Layer const double momentum = 0.1); /** - * Reset the layer parameters. + * Reset the layer parameters */ - void SetWeights(typename OutputType::elem_type* weightsPtr); + void Reset(); /** * Forward pass of the Batch Normalization layer. Transforms the input data @@ -86,7 +86,8 @@ class BatchNorm : public Layer * @param input Input data for the layer * @param output Resulting output activations. */ - void Forward(const InputType& input, OutputType& output); + template + void Forward(const arma::Mat& input, arma::Mat& output); /** * Backward pass through the layer. @@ -95,9 +96,10 @@ class BatchNorm : public Layer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const InputType& input, - const OutputType& gy, - OutputType& g); + template + void Backward(const arma::Mat& input, + const arma::Mat& gy, + arma::Mat& g); /** * Calculate the gradient using the output delta and the input activations. @@ -106,24 +108,45 @@ class BatchNorm : public Layer * @param error The calculated error * @param gradient The calculated gradient. */ - void Gradient(const InputType& input, - const OutputType& error, - OutputType& gradient); + template + void Gradient(const arma::Mat& input, + const arma::Mat& error, + arma::Mat& gradient); //! Get the parameters. - const OutputType& Parameters() const { return weights; } + OutputDataType const& Parameters() const { return weights; } //! Modify the parameters. - OutputType& Parameters() { return weights; } + OutputDataType& Parameters() { return weights; } + + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } + + //! Get the gradient. + OutputDataType const& Gradient() const { return gradient; } + //! Modify the gradient. + OutputDataType& Gradient() { return gradient; } + + //! Get the value of deterministic parameter. + bool Deterministic() const { return deterministic; } + //! Modify the value of deterministic parameter. + bool& Deterministic() { return deterministic; } //! Get the mean over the training data. - const OutputType& TrainingMean() const { return runningMean; } + OutputDataType const& TrainingMean() const { return runningMean; } //! Modify the mean over the training data. - OutputType& TrainingMean() { return runningMean; } + OutputDataType& TrainingMean() { return runningMean; } //! Get the variance over the training data. - const OutputType& TrainingVariance() const { return runningVariance; } + OutputDataType const& TrainingVariance() const { return runningVariance; } //! Modify the variance over the training data. - OutputType& TrainingVariance() { return runningVariance; } + OutputDataType& TrainingVariance() { return runningVariance; } //! Get the number of input units / channels. size_t InputSize() const { return size; } @@ -164,19 +187,25 @@ class BatchNorm : public Layer bool loading; //! Locally-stored scale parameter. - OutputType gamma; + OutputDataType gamma; //! Locally-stored shift parameter. - OutputType beta; + OutputDataType beta; //! Locally-stored mean object. - OutputType mean; + OutputDataType mean; //! Locally-stored variance object. - OutputType variance; + OutputDataType variance; //! Locally-stored parameters. - OutputType weights; + OutputDataType weights; + + /** + * If true then mean and variance over the training set will be considered + * instead of being calculated over the batch. + */ + bool deterministic; //! Locally-stored running mean/variance counter. size_t count; @@ -186,16 +215,25 @@ class BatchNorm : public Layer double averageFactor; //! Locally-stored mean object. - OutputType runningMean; + OutputDataType runningMean; //! Locally-stored variance object. - OutputType runningVariance; + OutputDataType runningVariance; + + //! Locally-stored gradient object. + OutputDataType gradient; + + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; //! Locally-stored normalized input. - arma::Cube normalized; + arma::cube normalized; //! Locally-stored zero mean input. - arma::Cube inputMean; + arma::cube inputMean; }; // class BatchNorm } // namespace ann diff --git a/src/mlpack/methods/ann/layer/not_adapted/batch_norm_impl.hpp b/src/mlpack/methods/ann/layer/batch_norm_impl.hpp similarity index 65% rename from src/mlpack/methods/ann/layer/not_adapted/batch_norm_impl.hpp rename to src/mlpack/methods/ann/layer/batch_norm_impl.hpp index dadc719117..1b6637928c 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/batch_norm_impl.hpp +++ b/src/mlpack/methods/ann/layer/batch_norm_impl.hpp @@ -21,21 +21,22 @@ namespace mlpack { namespace ann { /** Artificial Neural Network. */ -template -BatchNorm::BatchNorm() : +template +BatchNorm::BatchNorm() : size(0), eps(1e-8), average(true), momentum(0.0), loading(false), + deterministic(false), count(0), averageFactor(0.0) { // Nothing to do here. } -template -BatchNorm::BatchNorm( +template +BatchNorm::BatchNorm( const size_t size, const double eps, const bool average, @@ -45,6 +46,7 @@ BatchNorm::BatchNorm( average(average), momentum(momentum), loading(false), + deterministic(false), count(0), averageFactor(0.0) { @@ -53,14 +55,13 @@ BatchNorm::BatchNorm( runningVariance.ones(size, 1); } -template -void BatchNorm::SetWeights( - typename OutputType::elem_type* weightsPtr) +template +void BatchNorm::Reset() { // Gamma acts as the scaling parameters for the normalized output. - gamma = OutputType(weightsPtr, size, 1, false, false); + gamma = arma::mat(weights.memptr(), size, 1, false, false); // Beta acts as the shifting parameters for the normalized output. - beta = OutputType(weightsPtr + gamma.n_elem, size, 1, false, false); + beta = arma::mat(weights.memptr() + gamma.n_elem, size, 1, false, false); if (!loading) { @@ -68,16 +69,18 @@ void BatchNorm::SetWeights( beta.fill(0.0); } + deterministic = false; loading = false; } -template -void BatchNorm::Forward( - const InputType& input, - OutputType& output) +template +template +void BatchNorm::Forward( + const arma::Mat& input, + arma::Mat& output) { - Log::Assert(input.n_rows % size == 0, "Input features must be divisible " - "by feature maps."); + Log::Assert(input.n_rows % size == 0, "Input features must be divisible \ + by feature maps."); const size_t batchSize = input.n_cols; const size_t inputSize = input.n_rows / size; @@ -86,7 +89,7 @@ void BatchNorm::Forward( output.set_size(arma::size(input)); // We will calculate minibatch norm on each channel / feature map. - if (this->training) + if (!deterministic) { // Check only during training, batch-size can be one during inference. if (batchSize == 1 && inputSize == 1) @@ -98,14 +101,12 @@ void BatchNorm::Forward( // Input corresponds to output from convolution layer. // Use a cube for simplicity. - arma::Cube inputTemp( - const_cast(input).memptr(), inputSize, size, batchSize, - false, false); + arma::cube inputTemp(const_cast&>(input).memptr(), + inputSize, size, batchSize, false, false); // Initialize output to same size and values for convenience. - arma::Cube outputTemp( - const_cast(output).memptr(), inputSize, size, batchSize, - false, false); + arma::cube outputTemp(const_cast&>(output).memptr(), + inputSize, size, batchSize, false, false); outputTemp = inputTemp; // Calculate mean and variance over all channels. @@ -151,9 +152,8 @@ void BatchNorm::Forward( { // Normalize the input and scale and shift the output. output = input; - arma::Cube outputTemp( - const_cast(output).memptr(), input.n_rows / size, size, - batchSize, false, false); + arma::cube outputTemp(const_cast&>(output).memptr(), + input.n_rows / size, size, batchSize, false, false); outputTemp.each_slice() -= arma::repmat(runningMean.t(), input.n_rows / size, 1); @@ -166,29 +166,28 @@ void BatchNorm::Forward( } } -template -void BatchNorm::Backward( - const InputType& input, - const OutputType& gy, - OutputType& g) +template +template +void BatchNorm::Backward( + const arma::Mat& input, + const arma::Mat& gy, + arma::Mat& g) { const arma::mat stdInv = 1.0 / arma::sqrt(variance + eps); g.set_size(arma::size(input)); - arma::Cube gyTemp( - const_cast(gy).memptr(), input.n_rows / size, size, - input.n_cols, false, false); - arma::Cube gTemp( - const_cast(g).memptr(), input.n_rows / size, size, - input.n_cols, false, false); + arma::cube gyTemp(const_cast&>(gy).memptr(), + input.n_rows / size, size, input.n_cols, false, false); + arma::cube gTemp(const_cast&>(g).memptr(), + input.n_rows / size, size, input.n_cols, false, false); // Step 1: dl / dxhat. - arma::Cube norm = - gyTemp.each_slice() % arma::repmat(gamma.t(), input.n_rows / size, 1); + arma::cube norm = gyTemp.each_slice() % arma::repmat(gamma.t(), + input.n_rows / size, 1); // Step 2: sum dl / dxhat * (x - mu) * -0.5 * stdInv^3. - OutputType temp = arma::sum(norm % inputMean, 2); - OutputType vars = temp % arma::repmat(arma::pow(stdInv, 3), + arma::mat temp = arma::sum(norm % inputMean, 2); + arma::mat vars = temp % arma::repmat(arma::pow(stdInv, 3), input.n_rows / size, 1) * -0.5; // Step 3: dl / dxhat * 1 / stdInv + variance * 2 * (x - mu) / m + @@ -199,25 +198,25 @@ void BatchNorm::Backward( // Step 4: sum (dl / dxhat * -1 / stdInv) + variance * // (sum -2 * (x - mu)) / m. - OutputType normTemp = arma::sum(norm.each_slice() % + arma::mat normTemp = arma::sum(norm.each_slice() % arma::repmat(-stdInv, input.n_rows / size, 1) , 2) / input.n_cols; gTemp.each_slice() += normTemp; } -template -void BatchNorm::Gradient( - const InputType& /* input */, - const OutputType& error, - OutputType& gradient) +template +template +void BatchNorm::Gradient( + const arma::Mat& /* input */, + const arma::Mat& error, + arma::Mat& gradient) { gradient.set_size(size + size, 1); - arma::Cube errorTemp( - const_cast(error).memptr(), error.n_rows / size, size, - error.n_cols, false, false); + arma::cube errorTemp(const_cast&>(error).memptr(), + error.n_rows / size, size, error.n_cols, false, false); // Step 5: dl / dy * xhat. - OutputType temp = arma::sum(arma::sum(normalized % errorTemp, 0), 2); + arma::mat temp = arma::sum(arma::sum(normalized % errorTemp, 0), 2); gradient.submat(0, 0, gamma.n_elem - 1, 0) = temp.t(); // Step 6: dl / dy. @@ -225,27 +224,22 @@ void BatchNorm::Gradient( gradient.submat(gamma.n_elem, 0, gradient.n_elem - 1, 0) = temp.t(); } -template +template template -void BatchNorm::serialize( +void BatchNorm::serialize( Archive& ar, const uint32_t /* version */) { - ar(cereal::base_class>(this)); - ar(CEREAL_NVP(size)); + + if (cereal::is_loading()) + { + weights.set_size(size + size, 1); + loading = true; + } + ar(CEREAL_NVP(eps)); ar(CEREAL_NVP(gamma)); ar(CEREAL_NVP(beta)); - ar(CEREAL_NVP(weights)); - - if (Archive::is_loading::value) - { - // Gamma acts as the scaling parameters for the normalized output. - gamma = arma::mat(weights.memptr(), size, 1, false, false); - // Beta acts as the shifting parameters for the normalized output. - beta = arma::mat(weights.memptr() + gamma.n_elem, size, 1, false, false); - } - ar(CEREAL_NVP(count)); ar(CEREAL_NVP(averageFactor)); ar(CEREAL_NVP(momentum)); diff --git a/src/mlpack/methods/ann/layer/not_adapted/bicubic_interpolation.hpp b/src/mlpack/methods/ann/layer/bicubic_interpolation.hpp similarity index 100% rename from src/mlpack/methods/ann/layer/not_adapted/bicubic_interpolation.hpp rename to src/mlpack/methods/ann/layer/bicubic_interpolation.hpp diff --git a/src/mlpack/methods/ann/layer/not_adapted/bicubic_interpolation_impl.hpp b/src/mlpack/methods/ann/layer/bicubic_interpolation_impl.hpp similarity index 100% rename from src/mlpack/methods/ann/layer/not_adapted/bicubic_interpolation_impl.hpp rename to src/mlpack/methods/ann/layer/bicubic_interpolation_impl.hpp diff --git a/src/mlpack/methods/ann/layer/not_adapted/bilinear_interpolation.hpp b/src/mlpack/methods/ann/layer/bilinear_interpolation.hpp similarity index 55% rename from src/mlpack/methods/ann/layer/not_adapted/bilinear_interpolation.hpp rename to src/mlpack/methods/ann/layer/bilinear_interpolation.hpp index d8dfb3a286..8595bc4d57 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/bilinear_interpolation.hpp +++ b/src/mlpack/methods/ann/layer/bilinear_interpolation.hpp @@ -1,4 +1,3 @@ -// Temporarily drop. /** * @file methods/ann/layer/bilinear_interpolation.hpp * @author Kris Singh @@ -28,30 +27,35 @@ namespace ann /** Artificial Neural Network. */ { * different known points in the grid. This way, we represent any arbitrary * point, present within the grid, as a function of those four points. * - * @tparam InputType Type of the input data (arma::colvec, arma::mat, + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). - * @tparam OutputType Type of the output data (arma::colvec, arma::mat, + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). */ template < - typename InputType = arma::mat, - typename OutputType = arma::mat + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat > -class BilinearInterpolationType : public Layer +class BilinearInterpolation { public: - //! Create the BilinearInterpolationType object. - BilinearInterpolationType(); + //! Create the Bilinear Interpolation object. + BilinearInterpolation(); /** - * The constructor for the Bilinear Interpolation. The input size will be set - * by the given input when the layer is used. + * The constructor for the Bilinear Interpolation. * + * @param inRowSize Number of input rows. + * @param inColSize Number of input columns. * @param outRowSize Number of output rows. * @param outColSize Number of output columns. + * @param depth Number of input slices. */ - BilinearInterpolationType(const size_t outRowSize, - const size_t outColSize); + BilinearInterpolation(const size_t inRowSize, + const size_t inColSize, + const size_t outRowSize, + const size_t outColSize, + const size_t depth); /** * Forward pass through the layer. The layer interpolates @@ -60,7 +64,8 @@ class BilinearInterpolationType : public Layer * @param input The input matrix. * @param output The resulting interpolated output matrix. */ - void Forward(const InputType& input, OutputType& output); + template + void Forward(const arma::Mat& input, arma::Mat& output); /** * Ordinary feed backward pass of a neural network, calculating the function @@ -73,22 +78,30 @@ class BilinearInterpolationType : public Layer * @param gradient The computed backward gradient. * @param output The resulting down-sampled output. */ - void Backward(const InputType& /*input*/, - const OutputType& gradient, - OutputType& output); + template + void Backward(const arma::Mat& /*input*/, + const arma::Mat& gradient, + arma::Mat& output); - const std::vector& OutputDimensions() const - { - std::vector result(this->inputDimensions.size(), 0); - result[0] = outRowSize; - result[1] = outColSize; - if (result.size() > 2) - { - for (size_t i = 0; i < result.size(); ++i) - result[i] = this->inputDimensions[i]; - } - return result; - } + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } + + //! Get the row size of the input. + size_t const& InRowSize() const { return inRowSize; } + //! Modify the row size of the input. + size_t& InRowSize() { return inRowSize; } + + //! Get the column size of the input. + size_t const& InColSize() const { return inColSize; } + //! Modify the column size of the input. + size_t& InColSize() { return inColSize; } //! Get the row size of the output. size_t const& OutRowSize() const { return outRowSize; } @@ -100,6 +113,17 @@ class BilinearInterpolationType : public Layer //! Modify the column size of the output. size_t& OutColSize() { return outColSize; } + //! Get the depth of the input. + size_t const& InDepth() const { return depth; } + //! Modify the depth of the input. + size_t& InDepth() { return depth; } + + //! Get the shape of the input. + size_t InputShape() const + { + return inRowSize; + } + /** * Serialize the layer. */ @@ -107,16 +131,24 @@ class BilinearInterpolationType : public Layer void serialize(Archive& ar, const uint32_t /* version */); private: + //! Locally stored row size of the input. + size_t inRowSize; + //! Locally stored column size of the input. + size_t inColSize; //! Locally stored row size of the output. size_t outRowSize; - //! Locally stored column size of the input. size_t outColSize; + //! Locally stored depth of the input. + size_t depth; + //! Locally stored number of input points. + size_t batchSize; + //! Locally-stored delta object. + OutputDataType delta; + //! Locally-stored output parameter object. + OutputDataType outputParameter; }; // class BilinearInterpolation -// Standard BilinearInterpolation layer. -typedef BilinearInterpolationType BilinearInterpolation; - } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/not_adapted/bilinear_interpolation_impl.hpp b/src/mlpack/methods/ann/layer/bilinear_interpolation_impl.hpp similarity index 51% rename from src/mlpack/methods/ann/layer/not_adapted/bilinear_interpolation_impl.hpp rename to src/mlpack/methods/ann/layer/bilinear_interpolation_impl.hpp index 3fd621598c..3621f099b3 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/bilinear_interpolation_impl.hpp +++ b/src/mlpack/methods/ann/layer/bilinear_interpolation_impl.hpp @@ -19,54 +19,69 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -BilinearInterpolationType:: -BilinearInterpolationType(): + +template +BilinearInterpolation:: +BilinearInterpolation(): + inRowSize(0), + inColSize(0), outRowSize(0), - outColSize(0) + outColSize(0), + depth(0), + batchSize(0) { // Nothing to do here. } -template -BilinearInterpolationType:: -BilinearInterpolationType(const size_t outRowSize, - const size_t outColSize) : +template +BilinearInterpolation:: +BilinearInterpolation( + const size_t inRowSize, + const size_t inColSize, + const size_t outRowSize, + const size_t outColSize, + const size_t depth): + inRowSize(inRowSize), + inColSize(inColSize), outRowSize(outRowSize), - outColSize(outColSize) + outColSize(outColSize), + depth(depth), + batchSize(0) { // Nothing to do here. } -template -void BilinearInterpolationType::Forward( - const InputType& input, OutputType& output) +template +template +void BilinearInterpolation::Forward( + const arma::Mat& input, arma::Mat& output) { - const size_t batchSize = input.n_cols; - const size_t depth = this->inputDimensions.size() <= 2 ? 1 : - std::accumulate(this->inputDimensions.begin() + 2, this->inputDimensions.end(), 0); + batchSize = input.n_cols; + if (output.is_empty()) + output.set_size(outRowSize * outColSize * depth, batchSize); + else + { + assert(output.n_rows == outRowSize * outColSize * depth); + assert(output.n_cols == batchSize); + } - assert(output.n_rows == outRowSize * outColSize * depth); - assert(output.n_cols == batchSize); + assert(inRowSize >= 2); + assert(inColSize >= 2); - assert(this->inputDimensions[0] >= 2); - assert(this->inputDimensions[1] >= 2); + arma::cube inputAsCube(const_cast&>(input).memptr(), + inRowSize, inColSize, depth * batchSize, false, false); + arma::cube outputAsCube(output.memptr(), outRowSize, outColSize, + depth * batchSize, false, true); - arma::Cube inputAsCube( - const_cast(input).memptr(), this->inputDimensions[0], - this->inputDimensions[1], depth * batchSize, false, false); - arma::Cube outputAsCube( - output.memptr(), outRowSize, outColSize, depth * batchSize, false, true); - - double scaleRow = (double) this->inputDimensions[0] / (double) outRowSize; - double scaleCol = (double) this->inputDimensions[1] / (double) outColSize; + double scaleRow = (double) inRowSize / (double) outRowSize; + double scaleCol = (double) inColSize / (double) outColSize; arma::mat22 coeffs; for (size_t i = 0; i < outRowSize; ++i) { size_t rOrigin = (size_t) std::floor(i * scaleRow); - if (rOrigin > this->inputDimensions[0] - 2) - rOrigin = this->inputDimensions[0] - 2; + if (rOrigin > inRowSize - 2) + rOrigin = inRowSize - 2; // Scaled distance of the interpolated point from the topmost row. double deltaR = i * scaleRow - rOrigin; @@ -76,8 +91,8 @@ void BilinearInterpolationType::Forward( { // Scaled distance of the interpolated point from the leftmost column. size_t cOrigin = (size_t) std::floor(j * scaleCol); - if (cOrigin > this->inputDimensions[1] - 2) - cOrigin = this->inputDimensions[1] - 2; + if (cOrigin > inColSize - 2) + cOrigin = inColSize - 2; double deltaC = j * scaleCol - cOrigin; if (deltaC > 1) @@ -96,27 +111,28 @@ void BilinearInterpolationType::Forward( } } -template -void BilinearInterpolationType::Backward( - const InputType& /*input*/, - const OutputType& gradient, - OutputType& output) +template +template +void BilinearInterpolation::Backward( + const arma::Mat& /*input*/, + const arma::Mat& gradient, + arma::Mat& output) { - const size_t batchSize = output.n_cols; - const size_t depth = this->inputDimensions.size() <= 2 ? 1 : - std::accumulate(this->inputDimensions.begin() + 2, this->inputDimensions.end(), 0); - - assert(output.n_rows == this->inputDimensions[0] * this->inputDimensions[1] * depth); + if (output.is_empty()) + output.set_size(inRowSize * inColSize * depth, batchSize); + else + { + assert(output.n_rows == inRowSize * inColSize * depth); + assert(output.n_cols == batchSize); + } assert(outRowSize >= 2); assert(outColSize >= 2); - arma::Cube gradientAsCube( - ((OutputType&) gradient).memptr(), outRowSize, outColSize, depth * - batchSize, false, false); - arma::Cube outputAsCube( - output.memptr(), this->inputDimensions[0], this->inputDimensions[1], depth * batchSize, - false, true); + arma::cube gradientAsCube(((arma::Mat&) gradient).memptr(), outRowSize, + outColSize, depth * batchSize, false, false); + arma::cube outputAsCube(output.memptr(), inRowSize, inColSize, + depth * batchSize, false, true); if (gradient.n_elem == output.n_elem) { @@ -124,17 +140,17 @@ void BilinearInterpolationType::Backward( } else { - double scaleRow = (double)(outRowSize) / this->inputDimensions[0]; - double scaleCol = (double)(outColSize) / this->inputDimensions[1]; + double scaleRow = (double)(outRowSize) / inRowSize; + double scaleCol = (double)(outColSize) / inColSize; arma::mat22 coeffs; - for (size_t i = 0; i < this->inputDimensions[0]; ++i) + for (size_t i = 0; i < inRowSize; ++i) { size_t rOrigin = (size_t) std::floor(i * scaleRow); if (rOrigin > outRowSize - 2) rOrigin = outRowSize - 2; double deltaR = i * scaleRow - rOrigin; - for (size_t j = 0; j < this->inputDimensions[1]; ++j) + for (size_t j = 0; j < inColSize; ++j) { size_t cOrigin = (size_t) std::floor(j * scaleCol); @@ -157,15 +173,16 @@ void BilinearInterpolationType::Backward( } } -template +template template -void BilinearInterpolationType::serialize( +void BilinearInterpolation::serialize( Archive& ar, const uint32_t /* version */) { - ar(cereal::base_class>(this)); - + ar(CEREAL_NVP(inRowSize)); + ar(CEREAL_NVP(inColSize)); ar(CEREAL_NVP(outRowSize)); ar(CEREAL_NVP(outColSize)); + ar(CEREAL_NVP(depth)); } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/not_adapted/c_relu.hpp b/src/mlpack/methods/ann/layer/c_relu.hpp similarity index 65% rename from src/mlpack/methods/ann/layer/not_adapted/c_relu.hpp rename to src/mlpack/methods/ann/layer/c_relu.hpp index e273fb43e6..365111a7d7 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/c_relu.hpp +++ b/src/mlpack/methods/ann/layer/c_relu.hpp @@ -14,12 +14,10 @@ #include -#include "layer.hpp" - namespace mlpack { namespace ann /** Artificial Neural Network. */ { - /** + * * A concatenated ReLU has two outputs, one ReLU and one negative ReLU, * concatenated together. In other words, for positive x it produces [x, 0], * and for negative x it produces [0, x]. Because it has two outputs, @@ -40,21 +38,22 @@ namespace ann /** Artificial Neural Network. */ { * } * @endcode * - * @tparam InputType The type of the layer's inputs. The layer automatically - * cast inputs to this type (Default: arma::mat). - * @tparam OutputType The type of the computation which also causes the output - * to also be in this type. The type also allows the computation and weight - * type to differ from the input type (Default: arma::mat). + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class CReLUType : public Layer +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class CReLU { public: - //! Create the CReLU object. - CReLUType(); - - //! Clone the CReLUType object. This handles polymorphism correctly. - CReLUType* Clone() const { return new CReLUType(*this); } + /** + * Create the CReLU object. + */ + CReLU(); /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -64,6 +63,7 @@ class CReLUType : public Layer * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ + template void Forward(const InputType& input, OutputType& output); /** @@ -75,17 +75,35 @@ class CReLUType : public Layer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const InputType& input, const OutputType& gy, OutputType& g); + template + void Backward(const DataType& input, const DataType& gy, DataType& g); - //! Serialize the layer. + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } + + //! Get size of weights. + size_t WeightSize() const { return 0; } + + /** + * Serialize the layer. + */ template void serialize(Archive& /* ar */, const uint32_t /* version */); -}; // class CReLUType -// Convenience typedefs. + private: + //! Locally-stored delta object. + OutputDataType delta; -// Standard CReLU layer. -typedef CReLUType CReLU; + //! Locally-stored output parameter object. + OutputDataType outputParameter; +}; // class CReLU } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/not_adapted/c_relu_impl.hpp b/src/mlpack/methods/ann/layer/c_relu_impl.hpp similarity index 63% rename from src/mlpack/methods/ann/layer/not_adapted/c_relu_impl.hpp rename to src/mlpack/methods/ann/layer/c_relu_impl.hpp index 9cf536b9ef..dc2dbce4eb 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/c_relu_impl.hpp +++ b/src/mlpack/methods/ann/layer/c_relu_impl.hpp @@ -18,36 +18,39 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -CReLUType::CReLUType() +template +CReLU::CReLU() { // Nothing to do here. } +template template -void CReLUType::Forward( +void CReLU::Forward( const InputType& input, OutputType& output) { output = arma::join_cols(arma::max(input, 0.0 * input), arma::max( (-1 * input), 0.0 * input)); } -template -void CReLUType::Backward( - const InputType& input, const OutputType& gy, OutputType& g) +template +template +void CReLU::Backward( + const DataType& input, const DataType& gy, DataType& g) { - OutputType temp = gy % (input >= 0.0); + DataType temp; + temp = gy % (input >= 0.0); g = temp.rows(0, (input.n_rows / 2 - 1)) - temp.rows(input.n_rows / 2, (input.n_rows - 1)); } -template +template template -void CReLUType::serialize( - Archive& ar, +void CReLU::serialize( + Archive& /* ar */, const uint32_t /* version */) { - ar(cereal::base_class>(this)); + // Nothing to do here. } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/not_adapted/celu.hpp b/src/mlpack/methods/ann/layer/celu.hpp similarity index 64% rename from src/mlpack/methods/ann/layer/not_adapted/celu.hpp rename to src/mlpack/methods/ann/layer/celu.hpp index 7bc357ec75..ae508703ad 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/celu.hpp +++ b/src/mlpack/methods/ann/layer/celu.hpp @@ -25,8 +25,6 @@ #include -#include "layer.hpp" - namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -48,16 +46,18 @@ namespace ann /** Artificial Neural Network. */ { * \right. * @f} * - * When not in training mode, there is no computation of the derivative. + * In the deterministic mode, there is no computation of the derivative. * - * @tparam InputType The type of the layer's inputs. The layer automatically - * cast inputs to this type (Default: arma::mat). - * @tparam OutputType The type of the computation which also causes the output - * to also be in this type. The type also allows the computation and weight - * type to differ from the input type (Default: arma::mat). + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class CELUType : public Layer +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class CELU { public: /** @@ -67,10 +67,7 @@ class CELUType : public Layer * * @param alpha Scale parameter for the negative factor (default = 1.0). */ - CELUType(const double alpha = 1.0); - - //! Clone the CELUType object. This handles polymorphism correctly. - CELUType* Clone() const { return new CELUType(*this); } + CELU(const double alpha = 1.0); /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -79,6 +76,7 @@ class CELUType : public Layer * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ + template void Forward(const InputType& input, OutputType& output); /** @@ -90,29 +88,54 @@ class CELUType : public Layer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const InputType& input, const OutputType& gy, OutputType& g); + template + void Backward(const DataType& input, const DataType& gy, DataType& g); + + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } //! Get the non zero gradient. double const& Alpha() const { return alpha; } //! Modify the non zero gradient. double& Alpha() { return alpha; } - //! Serialize the layer. + //! Get the value of deterministic parameter. + bool Deterministic() const { return deterministic; } + //! Modify the value of deterministic parameter. + bool& Deterministic() { return deterministic; } + + //! Get size of weights. + size_t WeightSize() { return 0; } + + /** + * Serialize the layer. + */ template void serialize(Archive& ar, const uint32_t /* version */); private: + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; + //! Locally stored first derivative of the activation function. - OutputType derivative; + arma::mat derivative; //! CELU Hyperparameter (alpha > 0). double alpha; -}; // class CELUType -// Convenience typedefs. - -// Standard CELU layer. -typedef CELUType CELU; + //! If true the derivative computation is disabled, see notes above. + bool deterministic; +}; // class CELU } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/not_adapted/celu_impl.hpp b/src/mlpack/methods/ann/layer/celu_impl.hpp similarity index 66% rename from src/mlpack/methods/ann/layer/not_adapted/celu_impl.hpp rename to src/mlpack/methods/ann/layer/celu_impl.hpp index 76a001b091..3cfa9b6977 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/celu_impl.hpp +++ b/src/mlpack/methods/ann/layer/celu_impl.hpp @@ -18,9 +18,10 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -CELUType::CELUType(const double alpha) : - alpha(alpha) +template +CELU::CELU(const double alpha) : + alpha(alpha), + deterministic(false) { if (alpha == 0) { @@ -29,18 +30,19 @@ CELUType::CELUType(const double alpha) : } } +template template -void CELUType::Forward( +void CELU::Forward( const InputType& input, OutputType& output) { - output = arma::ones(arma::size(input)); + output = arma::ones(arma::size(input)); for (size_t i = 0; i < input.n_elem; ++i) { output(i) = (input(i) >= 0) ? input(i) : alpha * - (std::exp(input(i) / alpha) - 1); + (std::exp(input(i) / alpha) - 1); } - if (this->training) + if (!deterministic) { derivative.set_size(arma::size(input)); for (size_t i = 0; i < input.n_elem; ++i) @@ -51,24 +53,21 @@ void CELUType::Forward( } } -template -void CELUType::Backward( - const InputType& /* input */, const OutputType& gy, OutputType& g) +template +template +void CELU::Backward( + const DataType& /* input */, const DataType& gy, DataType& g) { g = gy % derivative; } -template +template template -void CELUType::serialize( +void CELU::serialize( Archive& ar, const uint32_t /* version */) { - ar(cereal::base_class>(this)); - ar(CEREAL_NVP(alpha)); - if (Archive::is_loading::value) - derivative.clear(); } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/not_adapted/channel_shuffle.hpp b/src/mlpack/methods/ann/layer/channel_shuffle.hpp similarity index 100% rename from src/mlpack/methods/ann/layer/not_adapted/channel_shuffle.hpp rename to src/mlpack/methods/ann/layer/channel_shuffle.hpp diff --git a/src/mlpack/methods/ann/layer/not_adapted/channel_shuffle_impl.hpp b/src/mlpack/methods/ann/layer/channel_shuffle_impl.hpp similarity index 100% rename from src/mlpack/methods/ann/layer/not_adapted/channel_shuffle_impl.hpp rename to src/mlpack/methods/ann/layer/channel_shuffle_impl.hpp diff --git a/src/mlpack/methods/ann/layer/concat.hpp b/src/mlpack/methods/ann/layer/concat.hpp new file mode 100644 index 0000000000..e693234f3e --- /dev/null +++ b/src/mlpack/methods/ann/layer/concat.hpp @@ -0,0 +1,263 @@ +/** + * @file methods/ann/layer/concat.hpp + * @author Marcus Edel + * @author Mehul Kumar Nirala + * + * Definition of the Concat class, which acts as a concatenation container. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_LAYER_CONCAT_HPP +#define MLPACK_METHODS_ANN_LAYER_CONCAT_HPP + +#include + +#include "../visitor/delete_visitor.hpp" +#include "../visitor/delta_visitor.hpp" +#include "../visitor/output_parameter_visitor.hpp" + +#include "layer_types.hpp" + +namespace mlpack { +namespace ann /** Artificial Neural Network. */ { + +/** + * Implementation of the Concat class. The Concat class works as a + * feed-forward fully connected network container which plugs various layers + * together. + * + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam CustomLayers Additional custom layers if required. + */ +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat, + typename... CustomLayers +> +class Concat +{ + public: + /** + * Create the Concat object using the specified parameters. + * + * @param model Expose all network modules. + * @param run Call the Forward/Backward method before the output is merged. + */ + Concat(const bool model = false, + const bool run = true); + + /** + * Create the Concat object using the specified parameters. + * + * @param inputSize A vector denoting input size of each layer added. + * @param axis Concat axis. + * @param model Expose all network modules. + * @param run Call the Forward/Backward method before the output is merged. + */ + Concat(arma::Row& inputSize, + const size_t axis, + const bool model = false, + const bool run = true); + + /** + * Destroy the layers held by the model. + */ + ~Concat(); + + /** + * Ordinary feed forward pass of a neural network, evaluating the function + * f(x) by propagating the activity forward through f. + * + * @param input Input data used for evaluating the specified function. + * @param output Resulting output activation. + */ + template + void Forward(const arma::Mat& input, arma::Mat& output); + + /** + * Ordinary feed backward pass of a neural network, using 3rd-order tensors as + * input, calculating the function f(x) by propagating x backwards through f. + * Using the results from the feed forward pass. + * + * @param * (input) The propagated input activation. + * @param gy The backpropagated error. + * @param g The calculated gradient. + */ + template + void Backward(const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g); + + /** + * This is the overload of Backward() that runs only a specific layer with + * the given input. + * + * @param * (input) The propagated input activation. + * @param gy The backpropagated error. + * @param g The calculated gradient. + * @param index The index of the layer to run. + */ + template + void Backward(const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g, + const size_t index); + + /* + * Calculate the gradient using the output delta and the input activation. + * + * @param input The input parameter used for calculating the gradient. + * @param error The calculated error. + * @param gradient The calculated gradient. + */ + template + void Gradient(const arma::Mat& /* input */, + const arma::Mat& error, + arma::Mat& /* gradient */); + + /* + * This is the overload of Gradient() that runs a specific layer with the + * given input. + * + * @param input The input parameter used for calculating the gradient. + * @param error The calculated error. + * @param gradient The calculated gradient. + * @param The index of the layer to run. + */ + template + void Gradient(const arma::Mat& input, + const arma::Mat& error, + arma::Mat& gradient, + const size_t index); + + /* + * Add a new module to the model. + * + * @param args The layer parameter. + */ + template + void Add(Args... args) { network.push_back(new LayerType(args...)); } + + /* + * Add a new module to the model. + * + * @param layer The Layer to be added to the model. + */ + void Add(LayerTypes layer) { network.push_back(layer); } + + //! Return the model modules. + std::vector >& Model() + { + if (model) + { + return network; + } + + return empty; + } + + //! Return the initial point for the optimization. + const arma::mat& Parameters() const { return weights; } + //! Modify the initial point for the optimization. + arma::mat& Parameters() { return weights; } + + //! Get the value of run parameter. + bool Run() const { return run; } + //! Modify the value of run parameter. + bool& Run() { return run; } + + arma::mat const& InputParameter() const { return inputParameter; } + //! Modify the input parameter. + arma::mat& InputParameter() { return inputParameter; } + + //! Get the output parameter. + arma::mat const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + arma::mat& OutputParameter() { return outputParameter; } + + //! Get the delta.e + arma::mat const& Delta() const { return delta; } + //! Modify the delta. + arma::mat& Delta() { return delta; } + + //! Get the gradient. + arma::mat const& Gradient() const { return gradient; } + //! Modify the gradient. + arma::mat& Gradient() { return gradient; } + + //! Get the axis of concatenation. + size_t const& ConcatAxis() const { return axis; } + + //! Get the size of the weight matrix. + size_t WeightSize() const { return 0; } + + /** + * Serialize the layer + */ + template + void serialize(Archive& ar, const uint32_t /* version */); + + private: + //! Parameter which indicates the input size of modules. + arma::Row inputSize; + + //! Parameter which indicates the axis of concatenation. + size_t axis; + + //! Parameter which indicates whether to use the axis of concatenation. + bool useAxis; + + //! Parameter which indicates if the modules should be exposed. + bool model; + + //! Parameter which indicates if the Forward/Backward method should be called + //! before merging the output. + bool run; + + //! Parameter to store channels. + size_t channels; + + //! Locally-stored network modules. + std::vector > network; + + //! Locally-stored model weights. + OutputDataType weights; + + //! Locally-stored delta visitor. + DeltaVisitor deltaVisitor; + + //! Locally-stored output parameter visitor. + OutputParameterVisitor outputParameterVisitor; + + //! Locally-stored delete visitor. + DeleteVisitor deleteVisitor; + + //! Locally-stored empty list of modules. + std::vector > empty; + + //! Locally-stored delta object. + arma::mat delta; + + //! Locally-stored input parameter object. + arma::mat inputParameter; + + //! Locally-stored output parameter object. + arma::mat outputParameter; + + //! Locally-stored gradient object. + arma::mat gradient; +}; // class Concat + +} // namespace ann +} // namespace mlpack + +// Include implementation. +#include "concat_impl.hpp" + +#endif diff --git a/src/mlpack/methods/ann/layer/concat_impl.hpp b/src/mlpack/methods/ann/layer/concat_impl.hpp new file mode 100644 index 0000000000..b410361295 --- /dev/null +++ b/src/mlpack/methods/ann/layer/concat_impl.hpp @@ -0,0 +1,293 @@ +/** + * @file methods/ann/layer/concat_impl.hpp + * @author Marcus Edel + * @author Mehul Kumar Nirala + * + * Implementation of the Concat class, which acts as a concatenation contain. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_LAYER_CONCAT_IMPL_HPP +#define MLPACK_METHODS_ANN_LAYER_CONCAT_IMPL_HPP + +// In case it hasn't yet been included. +#include "concat.hpp" + +#include "../visitor/forward_visitor.hpp" +#include "../visitor/backward_visitor.hpp" +#include "../visitor/gradient_visitor.hpp" + +namespace mlpack { +namespace ann /** Artificial Neural Network. */ { + +template +Concat::Concat( + const bool model, const bool run) : + axis(0), + useAxis(false), + model(model), + run(run), + channels(1) +{ + weights.set_size(0, 0); +} + +template +Concat::Concat( + arma::Row& inputSize, + const size_t axis, + const bool model, + const bool run) : + inputSize(inputSize), + axis(axis), + useAxis(true), + model(model), + run(run) +{ + weights.set_size(0, 0); + + // Parameters to help calculate the number of channels. + size_t oldColSize = 1, newColSize = 1; + // Axis is specified and useAxis is true. + if (useAxis) + { + // Axis is specified without input dimension. + // Throw an error. + if (inputSize.n_elem > 0) + { + // Calculate rowSize, newColSize based on the axis + // of concatenation. Finally concat along cols and + // reshape to original format i.e. (input, batch_size). + size_t i = std::min(axis + 1, (size_t) inputSize.n_elem); + for (; i < inputSize.n_elem; ++i) + { + newColSize *= inputSize[i]; + } + } + else + { + throw std::logic_error("Input dimensions not specified."); + } + } + else + { + channels = 1; + } + if (newColSize <= 0) + { + throw std::logic_error("Col size is zero."); + } + channels = newColSize / oldColSize; + inputSize.clear(); +} + +template +Concat::~Concat() +{ + if (!model) + { + // Clear memory. + std::for_each(network.begin(), network.end(), + boost::apply_visitor(deleteVisitor)); + } +} + +template +template +void Concat::Forward( + const arma::Mat& input, arma::Mat& output) +{ + if (run) + { + for (size_t i = 0; i < network.size(); ++i) + { + boost::apply_visitor(ForwardVisitor(input, + boost::apply_visitor(outputParameterVisitor, network[i])), + network[i]); + } + } + + output = boost::apply_visitor(outputParameterVisitor, network.front()); + + // Reshape output to incorporate the channels. + output.reshape(output.n_rows / channels, output.n_cols * channels); + + for (size_t i = 1; i < network.size(); ++i) + { + arma::Mat out = boost::apply_visitor(outputParameterVisitor, + network[i]); + + out.reshape(out.n_rows / channels, out.n_cols * channels); + + // Vertically concatentate output from each layer. + output = arma::join_cols(output, out); + } + // Reshape output to its original shape. + output.reshape(output.n_rows * channels, output.n_cols / channels); +} + +template +template +void Concat::Backward( + const arma::Mat& /* input */, const arma::Mat& gy, arma::Mat& g) +{ + size_t rowCount = 0; + if (run) + { + arma::Mat delta; + arma::Mat gyTmp(((arma::Mat&) gy).memptr(), gy.n_rows / channels, + gy.n_cols * channels, false, false); + for (size_t i = 0; i < network.size(); ++i) + { + // Use rows from the error corresponding to the output from each layer. + size_t rows = boost::apply_visitor( + outputParameterVisitor, network[i]).n_rows; + + // Extract from gy the parameters for the i-th network. + delta = gyTmp.rows(rowCount / channels, (rowCount + rows) / channels - 1); + delta.reshape(delta.n_rows * channels, delta.n_cols / channels); + + boost::apply_visitor(BackwardVisitor( + boost::apply_visitor(outputParameterVisitor, + network[i]), delta, + boost::apply_visitor(deltaVisitor, network[i])), network[i]); + rowCount += rows; + } + + g = boost::apply_visitor(deltaVisitor, network[0]); + for (size_t i = 1; i < network.size(); ++i) + { + g += boost::apply_visitor(deltaVisitor, network[i]); + } + } + else + { + g = gy; + } +} + +template +template +void Concat::Backward( + const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g, + const size_t index) +{ + size_t rowCount = 0, rows = 0; + + for (size_t i = 0; i < index; ++i) + { + rowCount += boost::apply_visitor( + outputParameterVisitor, network[i]).n_rows; + } + rows = boost::apply_visitor(outputParameterVisitor, network[index]).n_rows; + + // Reshape gy to extract the i-th layer gy. + arma::Mat gyTmp(((arma::Mat&) gy).memptr(), gy.n_rows / channels, + gy.n_cols * channels, false, false); + + arma::Mat delta = gyTmp.rows(rowCount / channels, (rowCount + rows) / + channels - 1); + delta.reshape(delta.n_rows * channels, delta.n_cols / channels); + + boost::apply_visitor(BackwardVisitor(boost::apply_visitor( + outputParameterVisitor, network[index]), delta, + boost::apply_visitor(deltaVisitor, network[index])), network[index]); + + g = boost::apply_visitor(deltaVisitor, network[index]); +} + +template +template +void Concat::Gradient( + const arma::Mat& input, + const arma::Mat& error, + arma::Mat& /* gradient */) +{ + if (run) + { + size_t rowCount = 0; + // Reshape error to extract the i-th layer error. + arma::Mat errorTmp(((arma::Mat&) error).memptr(), + error.n_rows / channels, error.n_cols * channels, false, false); + for (size_t i = 0; i < network.size(); ++i) + { + size_t rows = boost::apply_visitor( + outputParameterVisitor, network[i]).n_rows; + + // Extract from error the parameters for the i-th network. + arma::Mat err = errorTmp.rows(rowCount / channels, (rowCount + rows) / + channels - 1); + err.reshape(err.n_rows * channels, err.n_cols / channels); + + boost::apply_visitor(GradientVisitor(input, err), network[i]); + rowCount += rows; + } + } +} + +template +template +void Concat::Gradient( + const arma::Mat& input, + const arma::Mat& error, + arma::Mat& /* gradient */, + const size_t index) +{ + size_t rowCount = 0; + for (size_t i = 0; i < index; ++i) + { + rowCount += boost::apply_visitor(outputParameterVisitor, + network[i]).n_rows; + } + size_t rows = boost::apply_visitor( + outputParameterVisitor, network[index]).n_rows; + + arma::Mat errorTmp(((arma::Mat&) error).memptr(), + error.n_rows / channels, error.n_cols * channels, false, false); + arma::Mat err = errorTmp.rows(rowCount / channels, (rowCount + rows) / + channels - 1); + err.reshape(err.n_rows * channels, err.n_cols / channels); + + boost::apply_visitor(GradientVisitor(input, err), network[index]); +} + +template +template +void Concat::serialize( + Archive& ar, const uint32_t /* version */) +{ + ar(CEREAL_NVP(model)); + ar(CEREAL_NVP(run)); + + // Do we have to load or save a model? + if (model) + { + // Clear memory first, if needed. + if (cereal::is_loading()) + { + std::for_each(network.begin(), network.end(), + boost::apply_visitor(deleteVisitor)); + } + ar(CEREAL_VECTOR_VARIANT_POINTER(network)); + } +} + +} // namespace ann +} // namespace mlpack + + +#endif diff --git a/src/mlpack/methods/ann/layer/not_adapted/concat_performance.hpp b/src/mlpack/methods/ann/layer/concat_performance.hpp similarity index 67% rename from src/mlpack/methods/ann/layer/not_adapted/concat_performance.hpp rename to src/mlpack/methods/ann/layer/concat_performance.hpp index c565fa3eb0..b7ddbe1625 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/concat_performance.hpp +++ b/src/mlpack/methods/ann/layer/concat_performance.hpp @@ -24,17 +24,17 @@ namespace ann /** Artificial Neural Network. */ { * feed-forward fully connected network container which plugs performance layers * together. * - * @tparam InputType Type of the input data (arma::colvec, arma::mat, + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). - * @tparam OutputType Type of the output data (arma::colvec, arma::mat, + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). */ template < typename OutputLayerType = NegativeLogLikelihood<>, - typename InputType = arma::mat, - typename OutputType = arma::mat + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat > -class ConcatPerformance : public Layer +class ConcatPerformance { public: /** @@ -43,7 +43,8 @@ class ConcatPerformance : public Layer * @param inSize The number of inputs. * @param outputLayer Output layer used to evaluate the network. */ - ConcatPerformance(OutputLayerType&& outputLayer = OutputLayerType()); + ConcatPerformance(const size_t inSize = 0, + OutputLayerType&& outputLayer = OutputLayerType()); /* * Computes the Negative log likelihood. @@ -51,7 +52,8 @@ class ConcatPerformance : public Layer * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - void Forward(const InputType& input, OutputType& target); + template + double Forward(const arma::Mat& input, arma::Mat& target); /** * Ordinary feed backward pass of a neural network. The negative log @@ -64,29 +66,42 @@ class ConcatPerformance : public Layer * between 1 and the number of classes. * @param output The calculated error. */ - void Backward(const InputType& input, - const OutputType& target, - OutputType& output); + template + void Backward(const arma::Mat& input, + const arma::Mat& target, + arma::Mat& output); //! Get the output parameter. - OutputType& OutputParameter() const { return outputParameter; } + OutputDataType& OutputParameter() const { return outputParameter; } //! Modify the output parameter. - OutputType& OutputParameter() { return outputParameter; } + OutputDataType& OutputParameter() { return outputParameter; } //! Get the delta. - OutputType& Delta() const { return delta; } + OutputDataType& Delta() const { return delta; } //! Modify the delta. - OutputType& Delta() { return delta; } + OutputDataType& Delta() { return delta; } + + //! Get the number of inputs. + size_t InSize() const { return inSize; } /** - * Serialize the layer. + * Serialize the layer */ template void serialize(Archive& /* ar */, const uint32_t /* version */); private: + //! Locally-stored number of inputs. + size_t inSize; + //! Instantiated outputlayer used to evaluate the network. OutputLayerType outputLayer; + + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; }; // class ConcatPerformance } // namespace ann diff --git a/src/mlpack/methods/ann/layer/not_adapted/concat_performance_impl.hpp b/src/mlpack/methods/ann/layer/concat_performance_impl.hpp similarity index 61% rename from src/mlpack/methods/ann/layer/not_adapted/concat_performance_impl.hpp rename to src/mlpack/methods/ann/layer/concat_performance_impl.hpp index 60def206a9..ee4d9b8f4b 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/concat_performance_impl.hpp +++ b/src/mlpack/methods/ann/layer/concat_performance_impl.hpp @@ -20,14 +20,15 @@ namespace ann /** Artificial Neural Network. */ { template< typename OutputLayerType, - typename InputType, - typename OutputType + typename InputDataType, + typename OutputDataType > ConcatPerformance< OutputLayerType, - InputType, - OutputType ->::ConcatPerformance(OutputLayerType&& outputLayer) : + InputDataType, + OutputDataType +>::ConcatPerformance(const size_t inSize, OutputLayerType&& outputLayer) : + inSize(inSize), outputLayer(std::move(outputLayer)) { // Nothing to do here. @@ -35,51 +36,51 @@ ConcatPerformance< template< typename OutputLayerType, - typename InputType, - typename OutputType + typename InputDataType, + typename OutputDataType > -void ConcatPerformance< +template +double ConcatPerformance< OutputLayerType, - InputType, - OutputType ->::Forward(const InputType& input, OutputType& target) + InputDataType, + OutputDataType +>::Forward(const arma::Mat& input, arma::Mat& target) { - const size_t elements = input.n_elem / inputDimensions[0]; + const size_t elements = input.n_elem / inSize; double output = 0; - for (size_t i = 0; i < input.n_elem; i += elements) + for (size_t i = 0; i < input.n_elem; i+= elements) { - InputType subInput = input.submat(i, 0, i + elements - 1, 0); + arma::mat subInput = input.submat(i, 0, i + elements - 1, 0); output += outputLayer.Forward(subInput, target); } - // TODO: what to do with output? - //return output; - return; + return output; } template< typename OutputLayerType, - typename InputType, - typename OutputType + typename InputDataType, + typename OutputDataType > +template void ConcatPerformance< OutputLayerType, - InputType, - OutputType + InputDataType, + OutputDataType >::Backward( - const InputType& input, - const OutputType& target, - OutputType& output) + const arma::Mat& input, + const arma::Mat& target, + arma::Mat& output) { - const size_t elements = input.n_elem / inputDimensions[0]; + const size_t elements = input.n_elem / inSize; - InputType subInput = input.submat(0, 0, elements - 1, 0); - OutputType subOutput; + arma::mat subInput = input.submat(0, 0, elements - 1, 0); + arma::mat subOutput; outputLayer.Backward(subInput, target, subOutput); - output = arma::zeros(subOutput.n_elem, inputDimensions[0]); + output = arma::zeros(subOutput.n_elem, inSize); output.col(0) = subOutput; for (size_t i = elements, j = 0; i < input.n_elem; i+= elements, ++j) @@ -93,19 +94,17 @@ void ConcatPerformance< template< typename OutputLayerType, - typename InputType, - typename OutputType + typename InputDataType, + typename OutputDataType > template void ConcatPerformance< OutputLayerType, - InputType, - OutputType + InputDataType, + OutputDataType >::serialize(Archive& ar, const uint32_t /* version */) { - ar(cereal::base_class>(this)); - - ar(CEREAL_NVP(outputLayer)); + ar(CEREAL_NVP(inSize)); } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/concatenate.hpp b/src/mlpack/methods/ann/layer/concatenate.hpp index 7c21560465..561ecf2595 100644 --- a/src/mlpack/methods/ann/layer/concatenate.hpp +++ b/src/mlpack/methods/ann/layer/concatenate.hpp @@ -14,7 +14,7 @@ #define MLPACK_METHODS_ANN_LAYER_CONCATENATE_HPP #include -#include "layer.hpp" +#include namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -22,39 +22,36 @@ namespace ann /** Artificial Neural Network. */ { /** * Implementation of the Concatenate module class. The Concatenate module * concatenates a constant given matrix to the incoming data. + * Note: Users need to use the Concat() function to provide the concat matrix. * - * The Concat() function provides the concat matrix, or it can be passed to - * the constructor. - * - * After this layer is applied, the shape of the data will be a vector. - * - * @tparam MatType Matrix representation to accept as input and use for - * computation. + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class ConcatenateType : public Layer +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class Concatenate { public: /** - * Create the ConcatenateType object using the given constant matrix as the - * data to be concatenated to the output of the forward pass. + * Create the Concatenate object using the specified number of output units. */ - ConcatenateType(const MatType& concat = MatType()); + Concatenate(); - //! Clone the ConcatenateType object. This handles polymorphism correctly. - ConcatenateType* Clone() const { return new ConcatenateType(*this); } + //! Copy constructor. + Concatenate(const Concatenate& layer); - // Virtual destructor. - virtual ~ConcatenateType() { } + //! Move constructor. + Concatenate(Concatenate&& layer); - //! Copy the given ConcatenateType layer. - ConcatenateType(const ConcatenateType& other); - //! Take ownership of the given ConcatenateType layer. - ConcatenateType(ConcatenateType&& other); - //! Copy the given ConcatenateType layer. - ConcatenateType& operator=(const ConcatenateType& other); - //! Take ownership of the given ConcatenateType layer. - ConcatenateType& operator=(ConcatenateType&& other); + //! Operator= copy constructor. + Concatenate& operator=(const Concatenate& layer); + + //! Operator= move constructor. + Concatenate& operator=(Concatenate&& layer); /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -63,7 +60,8 @@ class ConcatenateType : public Layer * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - void Forward(const MatType& input, MatType& output); + template + void Forward(const arma::Mat& input, arma::Mat& output); /** * Ordinary feed backward pass of a neural network, calculating the function @@ -74,31 +72,57 @@ class ConcatenateType : public Layer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const MatType& /* input */, const MatType& gy, MatType& g); + template + void Backward(const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g); + + //! Get the parameters. + OutputDataType const& Parameters() const { return weights; } + //! Modify the parameters. + OutputDataType& Parameters() { return weights; } + + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } //! Get the concat matrix. - MatType const& Concat() const { return concat; } + OutputDataType const& Concat() const { return concat; } //! Modify the concat. - MatType& Concat() { return concat; } - - //! Compute the output dimensions of the layer based on `InputDimensions()`. - void ComputeOutputDimensions(); + OutputDataType& Concat() { return concat; } /** - * Serialize the layer. + * Serialize the layer */ template - void serialize(Archive& ar, const uint32_t /* version */); + void serialize(Archive& /* ar */, const uint32_t /* version */) + { + // Nothing to do here. + } private: - //! Matrix to be concatenated to input. - MatType concat; + //! Locally-stored number of input rows. + size_t inRows; + //! Locally-stored weight object. + OutputDataType weights; + + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; + + //! Locally-stored matrix to be concatenated to input. + OutputDataType concat; }; // class Concatenate -// Standard Concatenate layer. -typedef ConcatenateType Concatenate; - } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/concatenate_impl.hpp b/src/mlpack/methods/ann/layer/concatenate_impl.hpp index 8980e0c606..54b53471c8 100644 --- a/src/mlpack/methods/ann/layer/concatenate_impl.hpp +++ b/src/mlpack/methods/ann/layer/concatenate_impl.hpp @@ -19,106 +19,91 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -ConcatenateType:: -ConcatenateType(const MatType& concat) : - concat(concat) +template +Concatenate::Concatenate() : + inRows(0) { // Nothing to do here. } -template -ConcatenateType:: -ConcatenateType(const ConcatenateType& other) : - Layer(other), - concat(other.concat) +template +Concatenate::Concatenate( + const Concatenate& layer) : + inRows(layer.inRows), + weights(layer.weights), + delta(layer.delta), + concat(layer.concat) { - // Nothing to do. + // Nothing to to here. } -template -ConcatenateType:: -ConcatenateType(ConcatenateType&& other) : - Layer(std::move(other)), - concat(other.concat) +template +Concatenate::Concatenate(Concatenate&& layer) : + inRows(layer.inRows), + weights(std::move(layer.weights)), + delta(std::move(layer.delta)), + concat(std::move(layer.concat)) { - // Nothing to do. + // Nothing to do here. } -template -ConcatenateType& -ConcatenateType::operator=(const ConcatenateType& other) +template +Concatenate& +Concatenate:: +operator=(const Concatenate& layer) { - if (&other != this) + if (this != &layer) { - Layer::operator=(other); - concat = other.concat; + inRows = layer.inRows; + weights = layer.weights; + delta = layer.delta; + concat = layer.concat; } return *this; } -template -ConcatenateType& -ConcatenateType::operator=(ConcatenateType&& other) +template +Concatenate& +Concatenate:: +operator=(Concatenate&& layer) { - if (&other != this) + if (this != &layer) { - Layer::operator=(std::move(other)); - concat = std::move(other.concat); + inRows = layer.inRows; + weights = std::move(layer.weights); + delta = std::move(layer.delta); + concat = std::move(layer.concat); } - return *this; } -template -void ConcatenateType::Forward(const MatType& input, MatType& output) +template +template +void Concatenate::Forward( + const arma::Mat& input, arma::Mat& output) { if (concat.is_empty()) + Log::Warn << "The concat matrix has not been provided." << std::endl; + + if (input.n_cols != concat.n_cols) { - Log::Warn << "Concatenate::Forward(): the concat matrix is empty or was " - << "not provided." << std::endl; + Log::Fatal << "The number of columns of the concat matrix should be equal " + << "to the number of columns of input matrix." << std::endl; } - output.submat(0, 0, input.n_rows - 1, input.n_cols - 1) = input; - output.submat(input.n_rows, 0, output.n_rows - 1, input.n_cols - 1) = - arma::repmat(arma::vectorise(concat), 1, input.n_cols); + inRows = input.n_rows; + output = arma::join_cols(input, concat); } -template -void ConcatenateType::Backward( - const MatType& /* input */, - const MatType& gy, - MatType& g) +template +template +void Concatenate::Backward( + const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g) { - // Pass back the non-concatenated part. - g = gy.submat(0, 0, gy.n_rows - 1 - concat.n_elem, gy.n_cols - 1); -} - -template -void ConcatenateType::ComputeOutputDimensions() -{ - // This flattens the input. - size_t inSize = this->inputDimensions[0]; - for (size_t i = 1; i < this->inputDimensions.size(); ++i) - inSize *= this->inputDimensions[i]; - - this->outputDimensions = std::vector(this->inputDimensions.size(), - 1); - this->outputDimensions[0] = inSize + concat.n_elem; -} - -/** - * Serialize the layer. - */ -template -template -void ConcatenateType::serialize( - Archive& ar, const uint32_t /* version */) -{ - ar(cereal::base_class>(this)); - - ar(CEREAL_NVP(concat)); + g = gy.submat(0, 0, inRows - 1, concat.n_cols - 1); } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/not_adapted/constant.hpp b/src/mlpack/methods/ann/layer/constant.hpp similarity index 56% rename from src/mlpack/methods/ann/layer/not_adapted/constant.hpp rename to src/mlpack/methods/ann/layer/constant.hpp index bcfe997d16..9a0956ddf8 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/constant.hpp +++ b/src/mlpack/methods/ann/layer/constant.hpp @@ -15,8 +15,6 @@ #include -#include "layer.hpp" - namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -24,21 +22,18 @@ namespace ann /** Artificial Neural Network. */ { * Implementation of the constant layer. The constant layer outputs a given * constant value given any input value. * - * @tparam InputType The type of the layer's inputs. The layer automatically - * cast inputs to this type (Default: arma::mat). - * @tparam OutputType The type of the computation which also causes the output - * to also be in this type. The type also allows the computation and weight - * type to differ from the input type (Default: arma::mat). + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class ConstantType : public Layer +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class Constant { public: - /** - * Create an empty Constant layer. - */ - ConstantType(); - /** * Create the Constant object that outputs a given constant scalar value * given any input value. @@ -46,19 +41,7 @@ class ConstantType : public Layer * @param outSize The number of output units. * @param scalar The constant value used to create the constant output. */ - ConstantType(const size_t outSize, const double scalar = 0); - - //! Copy another ConstantType. - ConstantType(const ConstantType& layer); - //! Take ownership of another ConstantType. - ConstantType(ConstantType&& layer); - //! Copy another ConstantType. - ConstantType& operator=(const ConstantType& layer); - //! Take ownership of another ConstantType. - ConstantType& operator=(ConstantType&& layer); - - //! Clone the ConstantType object. This handles polymorphism correctly. - ConstantType* Clone() const { return new ConstantType(*this); } + Constant(const size_t outSize = 0, const double scalar = 0.0); /** * Ordinary feed forward pass of a neural network. The forward pass fills the @@ -67,6 +50,7 @@ class ConstantType : public Layer * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ + template void Forward(const InputType& input, OutputType& output); /** @@ -77,34 +61,52 @@ class ConstantType : public Layer * @param * (gy) The backpropagated error. * @param g The calculated gradient. */ - void Backward(const InputType& /* input */, - const OutputType& /* gy */, - OutputType& g); + template + void Backward(const DataType& /* input */, + const DataType& /* gy */, + DataType& g); + + //! Get the output parameter. + OutputDataType& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } //! Get the output size. - const std::vector& OutputDimensions() const + size_t OutSize() const { return outSize; } + + //! Get the size of the weights. + size_t WeightSize() const { - std::vector result(this->inputDimensions.size(), 0); - result[0] = outSize; - return result; + return 0; } - //! Serialize the layer. + /** + * Serialize the layer. + */ template void serialize(Archive& ar, const uint32_t /* version */); private: + //! Locally-stored number of input units. + size_t inSize; + //! Locally-stored number of output units. size_t outSize; //! Locally-stored constant output matrix. - OutputType constantOutput; -}; // class ConstantType + OutputDataType constantOutput; -// Convenience typedefs. + //! Locally-stored delta object. + OutputDataType delta; -// Standard HardShrink layer. -typedef ConstantType Constant; + //! Locally-stored output parameter object. + OutputDataType outputParameter; +}; // class ConstantLayer } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/constant_impl.hpp b/src/mlpack/methods/ann/layer/constant_impl.hpp new file mode 100644 index 0000000000..3f16311e46 --- /dev/null +++ b/src/mlpack/methods/ann/layer/constant_impl.hpp @@ -0,0 +1,65 @@ +/** + * @file methods/ann/layer/constant_impl.hpp + * @author Marcus Edel + * + * Implementation of the Constant class, which outputs a constant value given + * any input. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_LAYER_CONSTANT_IMPL_HPP +#define MLPACK_METHODS_ANN_LAYER_CONSTANT_IMPL_HPP + +// In case it hasn't yet been included. +#include "constant.hpp" + +namespace mlpack { +namespace ann /** Artificial Neural Network. */ { + +template +Constant::Constant( + const size_t outSize, + const double scalar) : + inSize(0), + outSize(outSize) +{ + constantOutput = OutputDataType(outSize, 1); + constantOutput.fill(scalar); +} + +template +template +void Constant::Forward( + const InputType& input, OutputType& output) +{ + if (inSize == 0) + { + inSize = input.n_elem; + } + + output = constantOutput; +} + +template +template +void Constant::Backward( + const DataType& /* input */, const DataType& /* gy */, DataType& g) +{ + g = arma::zeros(inSize, 1); +} + +template +template +void Constant::serialize( + Archive& ar, const uint32_t /* version */) +{ + ar(CEREAL_NVP(constantOutput)); +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/layer/convolution.hpp b/src/mlpack/methods/ann/layer/convolution.hpp index d6597edadb..c325eba95d 100644 --- a/src/mlpack/methods/ann/layer/convolution.hpp +++ b/src/mlpack/methods/ann/layer/convolution.hpp @@ -20,7 +20,7 @@ #include #include -#include "layer.hpp" +#include "layer_types.hpp" #include "padding.hpp" namespace mlpack { @@ -30,7 +30,7 @@ namespace ann /** Artificial Neural Network. */ { * Implementation of the Convolution class. The Convolution class represents a * single layer of a neural network. * Example usage: - * + * * Suppose we want to pass a matrix M (2744x100) to a `Convolution` layer; * in this example, `M` was obtained from "flattening" 100 images (or Mel * cepstral coefficients, if we talk about speech, or whatever you like) of @@ -62,95 +62,100 @@ namespace ann /** Artificial Neural Network. */ { * @tparam ForwardConvolutionRule Convolution to perform forward process. * @tparam BackwardConvolutionRule Convolution to perform backward process. * @tparam GradientConvolutionRule Convolution to calculate gradient. - * @tparam MatType Matrix representation to accept as input and use for - * computation. + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ template < typename ForwardConvolutionRule = NaiveConvolution, typename BackwardConvolutionRule = NaiveConvolution, typename GradientConvolutionRule = NaiveConvolution, - typename MatType = arma::mat + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat > -class ConvolutionType : public Layer +class Convolution { public: - //! Create the ConvolutionType object. - ConvolutionType(); + //! Create the Convolution object. + Convolution(); /** - * Create the ConvolutionType object using the specified number of output - * maps, filter size, stride and padding parameter. + * Create the Convolution object using the specified number of input maps, + * output maps, filter size, stride and padding parameter. * - * @param maps The number of output maps. + * @param inSize The number of input maps. + * @param outSize The number of output maps. * @param kernelWidth Width of the filter/kernel. * @param kernelHeight Height of the filter/kernel. * @param strideWidth Stride of filter application in the x direction. * @param strideHeight Stride of filter application in the y direction. * @param padW Padding width of the input. * @param padH Padding height of the input. - * @param paddingType The type of padding ("valid" or "same"). Defaults to - * "none". If not specified or "none", the values for `padW` and `padH` - * will be used. + * @param inputWidth The width of the input data. + * @param inputHeight The height of the input data. + * @param paddingType The type of padding (Valid or Same). Defaults to None. */ - ConvolutionType(const size_t maps, - const size_t kernelWidth, - const size_t kernelHeight, - const size_t strideWidth = 1, - const size_t strideHeight = 1, - const size_t padW = 0, - const size_t padH = 0, - const std::string& paddingType = "none"); + Convolution(const size_t inSize, + const size_t outSize, + const size_t kernelWidth, + const size_t kernelHeight, + const size_t strideWidth = 1, + const size_t strideHeight = 1, + const size_t padW = 0, + const size_t padH = 0, + const size_t inputWidth = 0, + const size_t inputHeight = 0, + const std::string& paddingType = "None"); /** * Create the Convolution object using the specified number of input maps, * output maps, filter size, stride and padding parameter. * - * @param maps The number of output maps. + * @param inSize The number of input maps. + * @param outSize The number of output maps. * @param kernelWidth Width of the filter/kernel. * @param kernelHeight Height of the filter/kernel. * @param strideWidth Stride of filter application in the x direction. * @param strideHeight Stride of filter application in the y direction. - * @param padW A two-value tuple indicating padding widths of the input. The - * first value is the padding for the left side; the second value is the - * padding on the right side. - * @param padH A two-value tuple indicating padding heights of the input. The - * first value is the padding for the top; the second value is the - * padding on the bottom. - * @param paddingType The type of padding ("valid" or "same"). Defaults to - * "none". If not specified or "none", the values for `padW` and `padH` - * will be used. + * @param padW A two-value tuple indicating padding widths of the input. + * First value is padding at left side. Second value is padding on + * right side. + * @param padH A two-value tuple indicating padding heights of the input. + * First value is padding at top. Second value is padding on + * bottom. + * @param inputWidth The width of the input data. + * @param inputHeight The height of the input data. + * @param paddingType The type of padding (Valid or Same). Defaults to None. */ - ConvolutionType(const size_t maps, - const size_t kernelWidth, - const size_t kernelHeight, - const size_t strideWidth, - const size_t strideHeight, - const std::tuple& padW, - const std::tuple& padH, - const std::string& paddingType = "none"); + Convolution(const size_t inSize, + const size_t outSize, + const size_t kernelWidth, + const size_t kernelHeight, + const size_t strideWidth, + const size_t strideHeight, + const std::tuple& padW, + const std::tuple& padH, + const size_t inputWidth = 0, + const size_t inputHeight = 0, + const std::string& paddingType = "None"); - //! Clone the ConvolutionType object. This handles polymorphism correctly. - ConvolutionType* Clone() const { return new ConvolutionType(*this); } + //! Copy constructor. + Convolution(const Convolution& layer); - //! Copy the given ConvolutionType (but not weights). - ConvolutionType(const ConvolutionType& layer); + //! Move constructor. + Convolution(Convolution&&); - //! Take ownership of the given ConvolutionType (but not weights). - ConvolutionType(ConvolutionType&&); + //! Copy assignment operator. + Convolution& operator=(const Convolution& layer); - //! Copy the given ConvolutionType (but not weights). - ConvolutionType& operator=(const ConvolutionType& layer); - - //! Take ownership of the given ConvolutionType (but not weights). - ConvolutionType& operator=(ConvolutionType&& layer); - - // Virtual destructor. - virtual ~ConvolutionType() { } + //! Move assignment operator. + Convolution& operator=(Convolution&& layer); /* * Set the weight and bias term. */ - void SetWeights(typename MatType::elem_type* weightsPtr); + void Reset(); /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -159,7 +164,8 @@ class ConvolutionType : public Layer * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - void Forward(const MatType& input, MatType& output); + template + void Forward(const arma::Mat& input, arma::Mat& output); /** * Ordinary feed backward pass of a neural network, calculating the function @@ -170,91 +176,135 @@ class ConvolutionType : public Layer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const MatType& /* input */, - const MatType& gy, - MatType& g); + template + void Backward(const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g); - /** + /* * Calculate the gradient using the output delta and the input activation. * * @param input The input parameter used for calculating the gradient. * @param error The calculated error. * @param gradient The calculated gradient. */ - void Gradient(const MatType& /* input */, - const MatType& error, - MatType& gradient); + template + void Gradient(const arma::Mat& /* input */, + const arma::Mat& error, + arma::Mat& gradient); //! Get the parameters. - MatType const& Parameters() const { return weights; } + OutputDataType const& Parameters() const { return weights; } //! Modify the parameters. - MatType& Parameters() { return weights; } + OutputDataType& Parameters() { return weights; } - //! Get the weight of the layer as a cube. - arma::Cube const& Weight() const - { - return weight; - } - //! Modify the weight of the layer as a cube. - arma::Cube& Weight() { return weight; } + //! Get the weight of the layer. + arma::cube const& Weight() const { return weight; } + //! Modify the weight of the layer. + arma::cube& Weight() { return weight; } //! Get the bias of the layer. - MatType const& Bias() const { return bias; } + arma::mat const& Bias() const { return bias; } //! Modify the bias of the layer. - MatType& Bias() { return bias; } + arma::mat& Bias() { return bias; } + + //! Get the input parameter. + InputDataType const& InputParameter() const { return inputParameter; } + //! Modify the input parameter. + InputDataType& InputParameter() { return inputParameter; } + + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } + + //! Get the gradient. + OutputDataType const& Gradient() const { return gradient; } + //! Modify the gradient. + OutputDataType& Gradient() { return gradient; } + + //! Get the input width. + size_t InputWidth() const { return inputWidth; } + //! Modify input the width. + size_t& InputWidth() { return inputWidth; } + + //! Get the input height. + size_t InputHeight() const { return inputHeight; } + //! Modify the input height. + size_t& InputHeight() { return inputHeight; } + + //! Get the output width. + size_t OutputWidth() const { return outputWidth; } + //! Modify the output width. + size_t& OutputWidth() { return outputWidth; } + + //! Get the output height. + size_t OutputHeight() const { return outputHeight; } + //! Modify the output height. + size_t& OutputHeight() { return outputHeight; } + + //! Get the number of input maps. + size_t InputSize() const { return inSize; } //! Get the number of output maps. - size_t const& Maps() const { return maps; } + size_t OutputSize() const { return outSize; } //! Get the kernel width. - size_t const& KernelWidth() const { return kernelWidth; } + size_t KernelWidth() const { return kernelWidth; } //! Modify the kernel width. size_t& KernelWidth() { return kernelWidth; } //! Get the kernel height. - size_t const& KernelHeight() const { return kernelHeight; } + size_t KernelHeight() const { return kernelHeight; } //! Modify the kernel height. size_t& KernelHeight() { return kernelHeight; } //! Get the stride width. - size_t const& StrideWidth() const { return strideWidth; } + size_t StrideWidth() const { return strideWidth; } //! Modify the stride width. size_t& StrideWidth() { return strideWidth; } //! Get the stride height. - size_t const& StrideHeight() const { return strideHeight; } + size_t StrideHeight() const { return strideHeight; } //! Modify the stride height. size_t& StrideHeight() { return strideHeight; } //! Get the top padding height. - size_t const& PadHTop() const { return padHTop; } + size_t PadHTop() const { return padHTop; } //! Modify the top padding height. size_t& PadHTop() { return padHTop; } //! Get the bottom padding height. - size_t const& PadHBottom() const { return padHBottom; } + size_t PadHBottom() const { return padHBottom; } //! Modify the bottom padding height. size_t& PadHBottom() { return padHBottom; } //! Get the left padding width. - size_t const& PadWLeft() const { return padWLeft; } + size_t PadWLeft() const { return padWLeft; } //! Modify the left padding width. size_t& PadWLeft() { return padWLeft; } //! Get the right padding width. - size_t const& PadWRight() const { return padWRight; } + size_t PadWRight() const { return padWRight; } //! Modify the right padding width. size_t& PadWRight() { return padWRight; } //! Get size of weights for the layer. size_t WeightSize() const { - return (maps * inMaps * higherInDimensions * kernelWidth * kernelHeight) + - maps; + return (outSize * inSize * kernelWidth * kernelHeight) + outSize; } - //! Compute the output dimensions of the layer based on `InputDimensions()`. - void ComputeOutputDimensions(); + //! Get the shape of the input. + size_t InputShape() const + { + return inputHeight * inputWidth * inSize; + } /** * Serialize the layer. @@ -263,7 +313,7 @@ class ConvolutionType : public Layer void serialize(Archive& ar, const uint32_t /* version */); private: - /** + /* * Return the convolution output size. * * @param size The size of the input (row or column). @@ -282,12 +332,12 @@ class ConvolutionType : public Layer return std::floor(size + pSideOne + pSideTwo - k) / s + 1; } - /** + /* * Function to assign padding such that output size is same as input size. */ void InitializeSamePadding(); - /** + /* * Rotates a 3rd-order tensor counterclockwise by 180 degrees. * * @param input The input data to be rotated. @@ -303,7 +353,7 @@ class ConvolutionType : public Layer output.slice(s) = arma::fliplr(arma::flipud(input.slice(s))); } - /** + /* * Rotates a dense matrix counterclockwise by 180 degrees. * * @param input The input data to be rotated. @@ -316,8 +366,11 @@ class ConvolutionType : public Layer output = arma::fliplr(arma::flipud(input)); } + //! Locally-stored number of input channels. + size_t inSize; + //! Locally-stored number of output channels. - size_t maps; + size_t outSize; //! Locally-stored number of input units. size_t batchSize; @@ -347,46 +400,54 @@ class ConvolutionType : public Layer size_t padHTop; //! Locally-stored weight object. - MatType weights; + OutputDataType weights; //! Locally-stored weight object. - arma::Cube weight; + arma::cube weight; //! Locally-stored bias term object. - MatType bias; + arma::mat bias; + + //! Locally-stored input width. + size_t inputWidth; + + //! Locally-stored input height. + size_t inputHeight; + + //! Locally-stored output width. + size_t outputWidth; + + //! Locally-stored output height. + size_t outputHeight; //! Locally-stored transformed output parameter. - arma::Cube outputTemp; + arma::cube outputTemp; //! Locally-stored transformed padded input parameter. - MatType inputPadded; + arma::cube inputPaddedTemp; //! Locally-stored transformed error parameter. - arma::Cube gTemp; + arma::cube gTemp; //! Locally-stored transformed gradient parameter. - arma::Cube gradientTemp; + arma::cube gradientTemp; //! Locally-stored padding layer. - ann::Padding padding; + ann::Padding<> padding; - //! Type of padding. - std::string paddingType; + //! Locally-stored delta object. + OutputDataType delta; - //! Locally-cached number of input maps. - size_t inMaps; - //! Locally-cached higher-order input dimensions. - size_t higherInDimensions; + //! Locally-stored gradient object. + OutputDataType gradient; + + //! Locally-stored input parameter object. + InputDataType inputParameter; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; }; // class Convolution -// Standard Convolution layer. -typedef ConvolutionType< - NaiveConvolution, - NaiveConvolution, - NaiveConvolution, - arma::mat -> Convolution; - } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/convolution_impl.hpp b/src/mlpack/methods/ann/layer/convolution_impl.hpp index 5e4f2c618e..ba957b6ead 100644 --- a/src/mlpack/methods/ann/layer/convolution_impl.hpp +++ b/src/mlpack/methods/ann/layer/convolution_impl.hpp @@ -22,14 +22,16 @@ template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename MatType + typename InputDataType, + typename OutputDataType > -ConvolutionType< +Convolution< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - MatType ->::ConvolutionType() + InputDataType, + OutputDataType +>::Convolution() { // Nothing to do here. } @@ -38,30 +40,38 @@ template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename MatType + typename InputDataType, + typename OutputDataType > -ConvolutionType< +Convolution< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - MatType ->::ConvolutionType( - const size_t maps, + InputDataType, + OutputDataType +>::Convolution( + const size_t inSize, + const size_t outSize, const size_t kernelWidth, const size_t kernelHeight, const size_t strideWidth, const size_t strideHeight, const size_t padW, const size_t padH, + const size_t inputWidth, + const size_t inputHeight, const std::string& paddingType) : - ConvolutionType( - maps, + Convolution( + inSize, + outSize, kernelWidth, kernelHeight, strideWidth, strideHeight, std::tuple(padW, padW), std::tuple(padH, padH), + inputWidth, + inputHeight, paddingType) { // Nothing to do here. @@ -71,23 +81,29 @@ template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename MatType + typename InputDataType, + typename OutputDataType > -ConvolutionType< +Convolution< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - MatType ->::ConvolutionType( - const size_t maps, + InputDataType, + OutputDataType +>::Convolution( + const size_t inSize, + const size_t outSize, const size_t kernelWidth, const size_t kernelHeight, const size_t strideWidth, const size_t strideHeight, const std::tuple& padW, const std::tuple& padH, - const std::string& paddingTypeIn) : - maps(maps), + const size_t inputWidth, + const size_t inputHeight, + const std::string& paddingType) : + inSize(inSize), + outSize(outSize), kernelWidth(kernelWidth), kernelHeight(kernelHeight), strideWidth(strideWidth), @@ -95,484 +111,443 @@ ConvolutionType< padWLeft(std::get<0>(padW)), padWRight(std::get<1>(padW)), padHBottom(std::get<1>(padH)), - padHTop(std::get<0>(padH)) + padHTop(std::get<0>(padH)), + inputWidth(inputWidth), + inputHeight(inputHeight), + outputWidth(0), + outputHeight(0) { + weights.set_size(WeightSize(), 1); + // Transform paddingType to lowercase. - this->paddingType = util::ToLower(paddingTypeIn); -} + const std::string paddingTypeLow = util::ToLower(paddingType); -template< - typename ForwardConvolutionRule, - typename BackwardConvolutionRule, - typename GradientConvolutionRule, - typename MatType -> -ConvolutionType< - ForwardConvolutionRule, - BackwardConvolutionRule, - GradientConvolutionRule, - MatType ->::ConvolutionType(const ConvolutionType& other) : - Layer(other), - maps(other.maps), - kernelWidth(other.kernelWidth), - kernelHeight(other.kernelHeight), - strideWidth(other.strideWidth), - strideHeight(other.strideHeight), - padWLeft(other.padWLeft), - padWRight(other.padWRight), - padHBottom(other.padHBottom), - padHTop(other.padHTop), - padding(other.padding), - paddingType(other.paddingType), - inMaps(other.inMaps), - higherInDimensions(other.higherInDimensions) -{ - // Nothing to do. -} - -template< - typename ForwardConvolutionRule, - typename BackwardConvolutionRule, - typename GradientConvolutionRule, - typename MatType -> -ConvolutionType< - ForwardConvolutionRule, - BackwardConvolutionRule, - GradientConvolutionRule, - MatType ->::ConvolutionType(ConvolutionType&& other) : - Layer(std::move(other)), - maps(std::move(other.maps)), - kernelWidth(std::move(other.kernelWidth)), - kernelHeight(std::move(other.kernelHeight)), - strideWidth(std::move(other.strideWidth)), - strideHeight(std::move(other.strideHeight)), - padWLeft(std::move(other.padWLeft)), - padWRight(std::move(other.padWRight)), - padHBottom(std::move(other.padHBottom)), - padHTop(std::move(other.padHTop)), - padding(std::move(other.padding)), - paddingType(std::move(other.paddingType)), - inMaps(std::move(other.inMaps)), - higherInDimensions(std::move(other.higherInDimensions)) -{ - // Nothing to do. -} - -template< - typename ForwardConvolutionRule, - typename BackwardConvolutionRule, - typename GradientConvolutionRule, - typename MatType -> -ConvolutionType< - ForwardConvolutionRule, - BackwardConvolutionRule, - GradientConvolutionRule, - MatType ->& -ConvolutionType< - ForwardConvolutionRule, - BackwardConvolutionRule, - GradientConvolutionRule, - MatType ->::operator=(const ConvolutionType& other) -{ - if (&other != this) - { - Layer::operator=(other); - maps = other.maps; - kernelWidth = other.kernelWidth; - kernelHeight = other.kernelHeight; - strideWidth = other.strideWidth; - strideHeight = other.strideHeight; - padWLeft = other.padWLeft; - padWRight = other.padWRight; - padHBottom = other.padHBottom; - padHTop = other.padHTop; - padding = other.padding; - paddingType = other.paddingType; - inMaps = other.inMaps; - higherInDimensions = other.higherInDimensions; - } - - return *this; -} - -template< - typename ForwardConvolutionRule, - typename BackwardConvolutionRule, - typename GradientConvolutionRule, - typename MatType -> -ConvolutionType< - ForwardConvolutionRule, - BackwardConvolutionRule, - GradientConvolutionRule, - MatType ->& -ConvolutionType< - ForwardConvolutionRule, - BackwardConvolutionRule, - GradientConvolutionRule, - MatType ->::operator=(ConvolutionType&& other) -{ - if (&other != this) - { - Layer::operator=(std::move(other)); - maps = std::move(other.maps); - kernelWidth = std::move(other.kernelWidth); - kernelHeight = std::move(other.kernelHeight); - strideWidth = std::move(other.strideWidth); - strideHeight = std::move(other.strideHeight); - padWLeft = std::move(other.padWLeft); - padWRight = std::move(other.padWRight); - padHBottom = std::move(other.padHBottom); - padHTop = std::move(other.padHTop); - padding = std::move(other.padding); - paddingType = std::move(other.paddingType); - inMaps = std::move(other.inMaps); - higherInDimensions = std::move(other.higherInDimensions); - } - - return *this; -} - -template< - typename ForwardConvolutionRule, - typename BackwardConvolutionRule, - typename GradientConvolutionRule, - typename MatType -> -void ConvolutionType< - ForwardConvolutionRule, - BackwardConvolutionRule, - GradientConvolutionRule, - MatType ->::SetWeights(typename MatType::elem_type* weightPtr) -{ - MakeAlias(weight, weightPtr, kernelWidth, kernelHeight, maps * inMaps); - MakeAlias(bias, weightPtr + weight.n_elem, maps, 1); - MakeAlias(weights, weightPtr, weight.n_elem + bias.n_elem, 1); -} - -template< - typename ForwardConvolutionRule, - typename BackwardConvolutionRule, - typename GradientConvolutionRule, - typename MatType -> -void ConvolutionType< - ForwardConvolutionRule, - BackwardConvolutionRule, - GradientConvolutionRule, - MatType ->::Forward(const MatType& input, MatType& output) -{ - batchSize = input.n_cols; - - // First, perform any padding if necessary. - const bool usingPadding = - (padWLeft != 0 || padWRight != 0 || padHTop != 0 || padHBottom != 0); - const size_t paddedRows = this->inputDimensions[0] + padWLeft + padWRight; - const size_t paddedCols = this->inputDimensions[1] + padHTop + padHBottom; - if (usingPadding) - { - inputPadded.set_size(paddedRows * paddedCols * inMaps * higherInDimensions, - input.n_cols); - padding.Forward(input, inputPadded); - } - - arma::Cube inputTemp; - MakeAlias(inputTemp, - const_cast(usingPadding ? inputPadded : input).memptr(), - paddedRows, paddedCols, inMaps * higherInDimensions * batchSize); - - MakeAlias(outputTemp, output.memptr(), this->outputDimensions[0], - this->outputDimensions[1], maps * higherInDimensions * batchSize); - outputTemp.zeros(); - - // We "ignore" dimensions higher than the third---that means that we just pass - // them through and treat them like different input points. - // - // If we eventually have a way to do convolutions for a single kernel - // in-batch, then this strategy may not be the most efficient solution. - for (size_t offset = 0; offset < (higherInDimensions * batchSize); ++offset) - { - const size_t fullInputOffset = offset * inMaps; - const size_t fullOutputOffset = offset * maps; - - // Iterate over output maps. - for (size_t outMap = 0; outMap < maps; ++outMap) - { - // Iterate over input maps (we will apply the filter and sum). - for (size_t inMap = 0; inMap < inMaps; ++inMap) - { - MatType convOutput; - - ForwardConvolutionRule::Convolution( - inputTemp.slice(inMap + fullInputOffset), - weight.slice(outMap), - convOutput, - strideWidth, - strideHeight); - - outputTemp.slice(outMap + fullOutputOffset) += convOutput; - } - - // Make sure to add the bias. - outputTemp.slice(outMap + fullOutputOffset) += bias(outMap); - } - } -} - -template< - typename ForwardConvolutionRule, - typename BackwardConvolutionRule, - typename GradientConvolutionRule, - typename MatType -> -void ConvolutionType< - ForwardConvolutionRule, - BackwardConvolutionRule, - GradientConvolutionRule, - MatType ->::Backward( - const MatType& /* input */, const MatType& gy, MatType& g) -{ - arma::Cube mappedError; - MakeAlias(mappedError, ((MatType&) gy).memptr(), this->outputDimensions[0], - this->outputDimensions[1], higherInDimensions * maps * batchSize); - - MakeAlias(gTemp, g.memptr(), this->inputDimensions[0], - this->inputDimensions[1], inMaps * higherInDimensions * batchSize); - gTemp.zeros(); - - const bool usingPadding = - (padWLeft != 0 || padWRight != 0 || padHTop != 0 || padHBottom != 0); - - // To perform the backward pass, we need to rotate all the filters. - arma::Cube rotatedFilters(weight.n_cols, - weight.n_rows, weight.n_slices); - for (size_t map = 0; map < maps; ++map) - { - Rotate180(weight.slice(map), rotatedFilters.slice(map)); - } - - // See Forward() for the overall iteration strategy. - for (size_t offset = 0; offset < (higherInDimensions * batchSize); ++offset) - { - const size_t fullInputOffset = offset * inMaps; - const size_t fullOutputOffset = offset * maps; - - // Iterate over input maps. - for (size_t inMap = 0; inMap < inMaps; ++inMap) - { - // Iterate over output maps. - for (size_t outMap = 0; outMap < maps; ++outMap) - { - MatType output; - - BackwardConvolutionRule::Convolution( - mappedError.slice(outMap + fullOutputOffset), - rotatedFilters.slice(outMap), - output, - strideHeight, - strideWidth); - - // If the stride width or height is greater than 1, then we have to - // insert columns and rows into the convolution output. - if (strideWidth == 1 && strideHeight == 1) - { - if (usingPadding) - { - gTemp.slice(inMap + fullInputOffset) += output.submat( - padWLeft, - padHTop, - padWLeft + gTemp.n_rows - 1, - padHTop + gTemp.n_cols - 1); - } - else - { - gTemp.slice(inMap + fullInputOffset) += output; - } - } - else - { - // We must iterate over each element of the output and manually - // re-insert the stride. - size_t col = padWLeft; - for (size_t i = 0; i < output.n_cols; ++i) - { - size_t row = padHTop; - for (size_t j = 0; j < output.n_rows; ++j) - { - gTemp(row, col, inMap + fullInputOffset) += output(j, i); - row += strideHeight; - } - col += strideWidth; - } - } - } - } - } -} - -template< - typename ForwardConvolutionRule, - typename BackwardConvolutionRule, - typename GradientConvolutionRule, - typename MatType -> -void ConvolutionType< - ForwardConvolutionRule, - BackwardConvolutionRule, - GradientConvolutionRule, - MatType ->::Gradient( - const MatType& input, - const MatType& error, - MatType& gradient) -{ - arma::Cube mappedError; - MakeAlias(mappedError, ((MatType&) error).memptr(), - this->outputDimensions[0], this->outputDimensions[1], - higherInDimensions * maps * batchSize); - - // We are depending here on `inputPadded` being properly set from a call to - // Forward(). - const bool usingPadding = - (padWLeft != 0 || padWRight != 0 || padHTop != 0 || padHBottom != 0); - const size_t paddedRows = this->inputDimensions[0] + padWLeft + padWRight; - const size_t paddedCols = this->inputDimensions[1] + padHTop + padHBottom; - - arma::Cube inputTemp( - const_cast(usingPadding ? inputPadded : input).memptr(), - paddedRows, paddedCols, inMaps * batchSize, false, false); - - // We will make an alias for the gradient, but note that this is only for the - // convolution map weights! The bias will be handled by direct accesses into - // `gradient`. - gradient.zeros(); - MakeAlias(gradientTemp, gradient.memptr(), weight.n_rows, weight.n_cols, - weight.n_slices); - - // See Forward() for our iteration strategy. - for (size_t offset = 0; offset < higherInDimensions * batchSize; ++offset) - { - const size_t fullInputOffset = offset * inMaps; - const size_t fullOutputOffset = offset * maps; - - for (size_t outMap = 0; outMap < maps; ++outMap) - { - for (size_t inMap = 0; inMap < inMaps; ++inMap) - { - MatType output; - GradientConvolutionRule::Convolution( - inputTemp.slice(inMap + fullInputOffset), - mappedError.slice(outMap + fullOutputOffset), - output, - strideWidth, - strideHeight); - - // TODO: understand this conditional. Is it needed? - if (gradientTemp.n_rows < output.n_rows || - gradientTemp.n_cols < output.n_cols) - { - gradientTemp.slice(outMap) += output.submat(0, 0, - gradientTemp.n_rows - 1, gradientTemp.n_cols - 1); - } - else if (gradientTemp.n_rows > output.n_rows || - gradientTemp.n_cols > output.n_cols) - { - gradientTemp.slice(outMap).submat(0, 0, output.n_rows - 1, - output.n_cols - 1) += output; - } - else - { - gradientTemp.slice(outMap) += output; - } - } - - gradient[weight.n_elem + outMap] += arma::accu(mappedError.slice(outMap + - fullOutputOffset)); - } - } -} - -template< - typename ForwardConvolutionRule, - typename BackwardConvolutionRule, - typename GradientConvolutionRule, - typename MatType -> -void ConvolutionType< - ForwardConvolutionRule, - BackwardConvolutionRule, - GradientConvolutionRule, - MatType ->::ComputeOutputDimensions() -{ - // First, we must make sure the padding sizes are up to date, which we can - // now do since inputDimensions is set correctly. - if (paddingType == "valid") + if (paddingTypeLow == "valid") { padWLeft = 0; padWRight = 0; padHTop = 0; padHBottom = 0; } - else if (paddingType == "same") + else if (paddingTypeLow == "same") { InitializeSamePadding(); } - padding = ann::Padding(padWLeft, padWRight, padHTop, padHBottom); - padding.InputDimensions() = this->inputDimensions; - padding.ComputeOutputDimensions(); - - // We must ensure that the output has at least 3 dimensions, since we will - // be adding some number of maps to the output. - this->outputDimensions = std::vector( - std::max(this->inputDimensions.size(), size_t(3)), 1); - this->outputDimensions[0] = ConvOutSize(this->inputDimensions[0], - kernelWidth, strideWidth, padWLeft, padWRight); - this->outputDimensions[1] = ConvOutSize(this->inputDimensions[1], - kernelHeight, strideHeight, padHTop, padHBottom); - - inMaps = (this->inputDimensions.size() >= 3) ? this->inputDimensions[2] : 1; - - // Compute and cache the total number of input maps. - higherInDimensions = 1; - for (size_t i = 3; i < this->inputDimensions.size(); ++i) - { - higherInDimensions *= this->inputDimensions[i]; - this->outputDimensions[i] = this->inputDimensions[i]; - } - - this->outputDimensions[2] = maps; + padding = ann::Padding<>(padWLeft, padWRight, padHTop, padHBottom); } template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename MatType + typename InputDataType, + typename OutputDataType > -template -void ConvolutionType< +Convolution< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - MatType + InputDataType, + OutputDataType +>::Convolution( + const Convolution& layer) : + inSize(layer.inSize), + outSize(layer.outSize), + kernelWidth(layer.kernelWidth), + kernelHeight(layer.kernelHeight), + strideWidth(layer.strideWidth), + strideHeight(layer.strideHeight), + padWLeft(layer.padWLeft), + padWRight(layer.padWRight), + padHBottom(layer.padHBottom), + padHTop(layer.padHTop), + weights(layer.weights), + inputWidth(layer.inputWidth), + inputHeight(layer.inputHeight), + outputWidth(layer.outputWidth), + outputHeight(layer.outputHeight), + padding(layer.padding) +{ + // Nothing to do here. +} + +template< + typename ForwardConvolutionRule, + typename BackwardConvolutionRule, + typename GradientConvolutionRule, + typename InputDataType, + typename OutputDataType +> +Convolution< + ForwardConvolutionRule, + BackwardConvolutionRule, + GradientConvolutionRule, + InputDataType, + OutputDataType +>::Convolution( + Convolution&& layer) : + inSize(0), + outSize(0), + kernelWidth(layer.kernelWidth), + kernelHeight(layer.kernelHeight), + strideWidth(layer.strideWidth), + strideHeight(layer.strideHeight), + padWLeft(layer.padWLeft), + padWRight(layer.padWRight), + padHBottom(layer.padHBottom), + padHTop(layer.padHTop), + weights(std::move(layer.weights)), + inputWidth(layer.inputWidth), + inputHeight(layer.inputHeight), + outputWidth(layer.outputWidth), + outputHeight(layer.outputHeight), + padding(std::move(layer.padding)) +{ + // Nothing to do here. +} + +template< + typename ForwardConvolutionRule, + typename BackwardConvolutionRule, + typename GradientConvolutionRule, + typename InputDataType, + typename OutputDataType +> +Convolution< + ForwardConvolutionRule, + BackwardConvolutionRule, + GradientConvolutionRule, + InputDataType, + OutputDataType +>& +Convolution< + ForwardConvolutionRule, + BackwardConvolutionRule, + GradientConvolutionRule, + InputDataType, + OutputDataType +>:: +operator=(const Convolution& layer) +{ + if (this != &layer) + { + inSize = layer.inSize; + outSize = layer.outSize; + kernelWidth = layer.kernelWidth; + kernelHeight = layer.kernelHeight; + strideWidth = layer.strideWidth; + strideHeight = layer.strideHeight; + padWLeft = layer.padWLeft; + padWRight = layer.padWRight; + padHBottom = layer.padHBottom; + padHTop = layer.padHTop; + inputWidth = layer.inputWidth; + inputHeight = layer.inputHeight; + outputWidth = layer.outputWidth; + outputHeight = layer.outputHeight; + padding = layer.padding; + weights = layer.weights; + } + + return *this; +} +template< + typename ForwardConvolutionRule, + typename BackwardConvolutionRule, + typename GradientConvolutionRule, + typename InputDataType, + typename OutputDataType +> +Convolution< + ForwardConvolutionRule, + BackwardConvolutionRule, + GradientConvolutionRule, + InputDataType, + OutputDataType +>& +Convolution< + ForwardConvolutionRule, + BackwardConvolutionRule, + GradientConvolutionRule, + InputDataType, + OutputDataType +>:: +operator=(Convolution&& layer) +{ + if (this != &layer) + { + inSize = layer.inSize; + outSize = layer.outSize; + kernelWidth = layer.kernelWidth; + kernelHeight = layer.kernelHeight; + strideWidth = layer.strideWidth; + strideHeight = layer.strideHeight; + padWLeft = layer.padWLeft; + padWRight = layer.padWRight; + padHBottom = layer.padHBottom; + padHTop = layer.padHTop; + inputWidth = layer.inputWidth; + inputHeight = layer.inputHeight; + outputWidth = layer.outputWidth; + outputHeight = layer.outputHeight; + padding = std::move(layer.padding); + weights = std::move(layer.weights); + } + + return *this; +} + +template< + typename ForwardConvolutionRule, + typename BackwardConvolutionRule, + typename GradientConvolutionRule, + typename InputDataType, + typename OutputDataType +> +void Convolution< + ForwardConvolutionRule, + BackwardConvolutionRule, + GradientConvolutionRule, + InputDataType, + OutputDataType +>::Reset() +{ + weight = arma::cube(weights.memptr(), kernelWidth, kernelHeight, + outSize * inSize, false, false); + bias = arma::mat(weights.memptr() + weight.n_elem, + outSize, 1, false, false); +} + +template< + typename ForwardConvolutionRule, + typename BackwardConvolutionRule, + typename GradientConvolutionRule, + typename InputDataType, + typename OutputDataType +> +template +void Convolution< + ForwardConvolutionRule, + BackwardConvolutionRule, + GradientConvolutionRule, + InputDataType, + OutputDataType +>::Forward(const arma::Mat& input, arma::Mat& output) +{ + batchSize = input.n_cols; + arma::cube inputTemp(const_cast&>(input).memptr(), + inputWidth, inputHeight, inSize * batchSize, false, false); + + if (padWLeft != 0 || padWRight != 0 || padHTop != 0 || padHBottom != 0) + { + inputPaddedTemp.set_size(inputTemp.n_rows + padWLeft + padWRight, + inputTemp.n_cols + padHTop + padHBottom, inputTemp.n_slices); + + for (size_t i = 0; i < inputTemp.n_slices; ++i) + { + padding.Forward(inputTemp.slice(i), inputPaddedTemp.slice(i)); + } + } + + size_t wConv = ConvOutSize(inputWidth, kernelWidth, strideWidth, padWLeft, + padWRight); + size_t hConv = ConvOutSize(inputHeight, kernelHeight, strideHeight, padHTop, + padHBottom); + + output.set_size(wConv * hConv * outSize, batchSize); + outputTemp = arma::Cube(output.memptr(), wConv, hConv, + outSize * batchSize, false, false); + outputTemp.zeros(); + + for (size_t outMap = 0, outMapIdx = 0, batchCount = 0; outMap < + outSize * batchSize; outMap++) + { + if (outMap != 0 && outMap % outSize == 0) + { + batchCount++; + outMapIdx = 0; + } + + for (size_t inMap = 0; inMap < inSize; inMap++, outMapIdx++) + { + arma::Mat convOutput; + + if (padWLeft != 0 || padWRight != 0 || padHTop != 0 || padHBottom != 0) + { + ForwardConvolutionRule::Convolution(inputPaddedTemp.slice(inMap + + batchCount * inSize), weight.slice(outMapIdx), convOutput, + strideWidth, strideHeight); + } + else + { + ForwardConvolutionRule::Convolution(inputTemp.slice(inMap + + batchCount * inSize), weight.slice(outMapIdx), convOutput, + strideWidth, strideHeight); + } + + outputTemp.slice(outMap) += convOutput; + } + + outputTemp.slice(outMap) += bias(outMap % outSize); + } + + outputWidth = outputTemp.n_rows; + outputHeight = outputTemp.n_cols; +} + +template< + typename ForwardConvolutionRule, + typename BackwardConvolutionRule, + typename GradientConvolutionRule, + typename InputDataType, + typename OutputDataType +> +template +void Convolution< + ForwardConvolutionRule, + BackwardConvolutionRule, + GradientConvolutionRule, + InputDataType, + OutputDataType +>::Backward( + const arma::Mat& /* input */, const arma::Mat& gy, arma::Mat& g) +{ + arma::cube mappedError(((arma::Mat&) gy).memptr(), outputWidth, + outputHeight, outSize * batchSize, false, false); + + g.set_size(inputWidth * inputHeight * inSize, batchSize); + gTemp = arma::Cube(g.memptr(), inputWidth, inputHeight, + inSize * batchSize, false, false); + gTemp.zeros(); + + for (size_t outMap = 0, outMapIdx = 0, batchCount = 0; outMap < + outSize * batchSize; outMap++) + { + if (outMap != 0 && outMap % outSize == 0) + { + batchCount++; + outMapIdx = 0; + } + + for (size_t inMap = 0; inMap < inSize; inMap++, outMapIdx++) + { + arma::Mat output, rotatedFilter; + Rotate180(weight.slice(outMapIdx), rotatedFilter); + + BackwardConvolutionRule::Convolution(mappedError.slice(outMap), + rotatedFilter, output, strideWidth, strideHeight); + + if (padWLeft != 0 || padWRight != 0 || padHTop != 0 || padHBottom != 0) + { + gTemp.slice(inMap + batchCount * inSize) += output.submat(padWLeft, + padHTop, padWLeft + gTemp.n_rows - 1, padHTop + gTemp.n_cols - 1); + } + else + { + gTemp.slice(inMap + batchCount * inSize) += output; + } + } + } +} + +template< + typename ForwardConvolutionRule, + typename BackwardConvolutionRule, + typename GradientConvolutionRule, + typename InputDataType, + typename OutputDataType +> +template +void Convolution< + ForwardConvolutionRule, + BackwardConvolutionRule, + GradientConvolutionRule, + InputDataType, + OutputDataType +>::Gradient( + const arma::Mat& input, + const arma::Mat& error, + arma::Mat& gradient) +{ + arma::cube mappedError(((arma::Mat&) error).memptr(), outputWidth, + outputHeight, outSize * batchSize, false, false); + arma::cube inputTemp(((arma::Mat&) input).memptr(), inputWidth, + inputHeight, inSize * batchSize, false, false); + + gradient.set_size(weights.n_elem, 1); + gradientTemp = arma::Cube(gradient.memptr(), weight.n_rows, + weight.n_cols, weight.n_slices, false, false); + gradientTemp.zeros(); + + for (size_t outMap = 0, outMapIdx = 0, batchCount = 0; outMap < + outSize * batchSize; outMap++) + { + if (outMap != 0 && outMap % outSize == 0) + { + batchCount++; + outMapIdx = 0; + } + + for (size_t inMap = 0; inMap < inSize; inMap++, outMapIdx++) + { + arma::Mat inputSlice; + if (padWLeft != 0 || padWRight != 0 || padHTop != 0 || padHBottom != 0) + { + inputSlice = inputPaddedTemp.slice(inMap + batchCount * inSize); + } + else + { + inputSlice = inputTemp.slice(inMap + batchCount * inSize); + } + + arma::Mat deltaSlice = mappedError.slice(outMap); + + arma::Mat output; + GradientConvolutionRule::Convolution(inputSlice, deltaSlice, + output, strideWidth, strideHeight); + + if (gradientTemp.n_rows < output.n_rows || + gradientTemp.n_cols < output.n_cols) + { + gradientTemp.slice(outMapIdx) += output.submat(0, 0, + gradientTemp.n_rows - 1, gradientTemp.n_cols - 1); + } + else if (gradientTemp.n_rows > output.n_rows || + gradientTemp.n_cols > output.n_cols) + { + gradientTemp.slice(outMapIdx).submat(0, 0, output.n_rows - 1, + output.n_cols - 1) += output; + } + else + { + gradientTemp.slice(outMapIdx) += output; + } + } + + gradient.submat(weight.n_elem + (outMap % outSize), 0, weight.n_elem + + (outMap % outSize), 0) = arma::accu(mappedError.slice(outMap)); + } +} + +template< + typename ForwardConvolutionRule, + typename BackwardConvolutionRule, + typename GradientConvolutionRule, + typename InputDataType, + typename OutputDataType +> +template +void Convolution< + ForwardConvolutionRule, + BackwardConvolutionRule, + GradientConvolutionRule, + InputDataType, + OutputDataType >::serialize(Archive& ar, const uint32_t /* version*/) { - ar(cereal::base_class>(this)); - - ar(CEREAL_NVP(maps)); + ar(CEREAL_NVP(inSize)); + ar(CEREAL_NVP(outSize)); ar(CEREAL_NVP(batchSize)); ar(CEREAL_NVP(kernelWidth)); ar(CEREAL_NVP(kernelHeight)); @@ -582,31 +557,41 @@ void ConvolutionType< ar(CEREAL_NVP(padWRight)); ar(CEREAL_NVP(padHBottom)); ar(CEREAL_NVP(padHTop)); + ar(CEREAL_NVP(inputWidth)); + ar(CEREAL_NVP(inputHeight)); + ar(CEREAL_NVP(outputWidth)); + ar(CEREAL_NVP(outputHeight)); ar(CEREAL_NVP(padding)); - ar(CEREAL_NVP(inMaps)); - ar(CEREAL_NVP(higherInDimensions)); + + if (cereal::is_loading()) + { + weights.set_size((outSize * inSize * kernelWidth * kernelHeight) + outSize, + 1); + } } template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename MatType + typename InputDataType, + typename OutputDataType > -void ConvolutionType< +void Convolution< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - MatType + InputDataType, + OutputDataType >::InitializeSamePadding() { /* * Using O = (W - F + 2P) / s + 1; */ - size_t totalVerticalPadding = (strideWidth - 1) * this->inputDimensions[0] + - kernelWidth - strideWidth; - size_t totalHorizontalPadding = (strideHeight - 1) * this->inputDimensions[1] - + kernelHeight - strideHeight; + size_t totalVerticalPadding = (strideWidth - 1) * inputWidth + kernelWidth - + strideWidth; + size_t totalHorizontalPadding = (strideHeight - 1) * inputHeight + + kernelHeight - strideHeight; padWLeft = totalVerticalPadding / 2; padWRight = totalVerticalPadding - totalVerticalPadding / 2; diff --git a/src/mlpack/methods/ann/layer/dropconnect.hpp b/src/mlpack/methods/ann/layer/dropconnect.hpp index c00a2d2129..db8d76aa4b 100644 --- a/src/mlpack/methods/ann/layer/dropconnect.hpp +++ b/src/mlpack/methods/ann/layer/dropconnect.hpp @@ -16,7 +16,10 @@ #include -#include "layer.hpp" +#include "layer_types.hpp" +#include "add_merge.hpp" +#include "linear.hpp" +#include "sequential.hpp" namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -25,58 +28,55 @@ namespace ann /** Artificial Neural Network. */ { * The DropConnect layer is a regularizer that randomly with probability * ratio sets the connection values to zero and scales the remaining * elements by factor 1 /(1 - ratio). The output is scaled with 1 / (1 - p) - * when in training mode. During testing, the layer just computes the output. - * The output is computed according to the input layer. If no input layer is - * given, it will take a linear layer as default. + * when deterministic is false. In the deterministic mode(during testing), + * the layer just computes the output. The output is computed according + * to the input layer. If no input layer is given, it will take a linear layer + * as default. * - * For more information, see the following. + * Note: + * During training you should set deterministic to false and during testing + * you should set deterministic to true. + * + * For more information, see the following. * * @code * @inproceedings{WanICML2013, - * title = {Regularization of Neural Networks using DropConnect}, + * title={Regularization of Neural Networks using DropConnect}, * booktitle = {Proceedings of the 30th International Conference on Machine * Learning(ICML - 13)}, - * author = {Li Wan and Matthew Zeiler and Sixin Zhang and Yann L. Cun and - * Rob Fergus}, - * year = {2013}, - * url = {http://proceedings.mlr.press/v28/wan13.pdf} + * author = {Li Wan and Matthew Zeiler and Sixin Zhang and Yann L. Cun and + * Rob Fergus}, + * year = {2013}, + * url = {http://proceedings.mlr.press/v28/wan13.pdf} * } * @endcode * - * @tparam MatType Matrix representation to accept as input and use for - * computation. + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class DropConnectType : public Layer +template< + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class DropConnect { public: //! Create the DropConnect object. - DropConnectType(); + DropConnect(); /** - * Creates the DropConnect Layer as a Linear Object that takes the number of - * output units and a ratio as parameter. + * Creates the DropConnect Layer as a Linear Object that takes input size, + * output size and ratio as parameter. * + * @param inSize The number of input units. * @param outSize The number of output units. * @param ratio The probability of setting a value to zero. */ - DropConnectType(const size_t outSize, - const double ratio = 0.5); - - //! Clone the DropConnectType object. This handles polymorphism correctly. - DropConnectType* Clone() const { return new DropConnectType(*this); } - - // Virtual destructor. - virtual ~DropConnectType(); - - //! Copy the given DropConnectType (except for weights). - DropConnectType(const DropConnectType& other); - //! Take ownership of the given DropConnectType (except for weights). - DropConnectType(DropConnectType&& other); - //! Copy the given DropConnectType (except for weights). - DropConnectType& operator=(const DropConnectType& other); - //! Take ownership of the given DropConnectType (except for weights). - DropConnectType& operator=(DropConnectType&& other); + DropConnect(const size_t inSize, + const size_t outSize, + const double ratio = 0.5); /** * Ordinary feed forward pass of the DropConnect layer. @@ -84,7 +84,8 @@ class DropConnectType : public Layer * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - void Forward(const MatType& input, MatType& output); + template + void Forward(const arma::Mat& input, arma::Mat& output); /** * Ordinary feed backward pass of the DropConnect layer. @@ -93,7 +94,10 @@ class DropConnectType : public Layer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const MatType& input, const MatType& gy, MatType& g); + template + void Backward(const arma::Mat& input, + const arma::Mat& gy, + arma::Mat& g); /** * Calculate the gradient using the output delta and the input activation. @@ -102,7 +106,39 @@ class DropConnectType : public Layer * @param error The calculated error. * @param * (gradient) The calculated gradient. */ - void Gradient(const MatType& input, const MatType& error, MatType& gradient); + template + void Gradient(const arma::Mat& input, + const arma::Mat& error, + arma::Mat& /* gradient */); + + //! Get the model modules. + std::vector >& Model() { return network; } + + //! Get the parameters. + OutputDataType const& Parameters() const { return weights; } + //! Modify the parameters. + OutputDataType& Parameters() { return weights; } + + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } + + //! Get the gradient. + OutputDataType const& Gradient() const { return gradient; } + //! Modify the gradient. + OutputDataType& Gradient() { return gradient; } + + //! The value of the deterministic parameter. + bool Deterministic() const { return deterministic; } + + //! Modify the value of the deterministic parameter. + bool &Deterministic() { return deterministic; } //! The probability of setting a value to zero. double Ratio() const { return ratio; } @@ -114,14 +150,8 @@ class DropConnectType : public Layer scale = 1.0 / (1.0 - ratio); } - //! Compute the output dimensions of the layer based on `InputDimensions()`. - void ComputeOutputDimensions(); - - //! Return the size of the weights. - size_t WeightSize() const { return baseLayer->WeightSize(); } - - // Set the weights to use the given memory `weightsPtr`. - void SetWeights(typename MatType::elem_type* weightsPtr); + //! Return the size of the weight matrix. + size_t WeightSize() const { return 0; } /** * Serialize the layer. @@ -136,21 +166,34 @@ class DropConnectType : public Layer //! The scale fraction. double scale; + //! Locally-stored weight object. + OutputDataType weights; + + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored gradient object. + OutputDataType gradient; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; + //! Locally-stored mask object. - MatType mask; + OutputDataType mask; + + //! If true dropout and scaling is disabled, see notes above. + bool deterministic; //! Denoise mask for the weights. - MatType denoise; + OutputDataType denoise; //! Locally-stored layer module. - Layer* baseLayer; + LayerTypes<> baseLayer; + + //! Locally-stored network modules. + std::vector > network; }; // class DropConnect. -// Convenience typedefs. - -// Standard DropConnect layer. -typedef DropConnectType DropConnect; - } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/dropconnect_impl.hpp b/src/mlpack/methods/ann/layer/dropconnect_impl.hpp index 9eb8bb91a4..9b22c91951 100644 --- a/src/mlpack/methods/ann/layer/dropconnect_impl.hpp +++ b/src/mlpack/methods/ann/layer/dropconnect_impl.hpp @@ -17,160 +17,112 @@ // In case it hasn't yet been included. #include "dropconnect.hpp" -#include "linear.hpp" +#include "../visitor/delete_visitor.hpp" +#include "../visitor/forward_visitor.hpp" +#include "../visitor/backward_visitor.hpp" +#include "../visitor/gradient_visitor.hpp" +#include "../visitor/parameters_set_visitor.hpp" +#include "../visitor/parameters_visitor.hpp" namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -DropConnectType::DropConnectType() : - Layer(), +template +DropConnect::DropConnect() : ratio(0.5), scale(2.0), - baseLayer(new LinearType(0)) + deterministic(true) { // Nothing to do here. } -template -DropConnectType::DropConnectType( +template +DropConnect::DropConnect( + const size_t inSize, const size_t outSize, const double ratio) : - Layer(), ratio(ratio), scale(1.0 / (1 - ratio)), - baseLayer(new LinearType(outSize)) + baseLayer(new Linear(inSize, outSize)) { - // Nothing to do. + network.push_back(baseLayer); } -template -DropConnectType::~DropConnectType() +template +template +void DropConnect::Forward( + const arma::Mat& input, + arma::Mat& output) { - delete baseLayer; -} - -template -DropConnectType::DropConnectType(const DropConnectType& other) : - Layer(other), - ratio(other.ratio), - scale(other.scale), - baseLayer(other.baseLayer->Clone()) -{ - // Nothing to do. -} - -template -DropConnectType::DropConnectType(DropConnectType&& other) : - Layer(std::move(other)), - ratio(std::move(other.ratio)), - scale(std::move(other.scale)), - baseLayer(std::move(other.baseLayer)) -{ - // Nothing to do. -} - -template -DropConnectType& -DropConnectType::operator=(const DropConnectType& other) -{ - if (&other != this) + // The DropConnect mask will not be multiplied in the deterministic mode + // (during testing). + if (deterministic) { - Layer::operator=(other); - ratio = other.ratio; - scale = other.scale; - baseLayer = other.baseLayer->Clone(); - } - - return *this; -} - -template -DropConnectType& -DropConnectType::operator=(DropConnectType&& other) -{ - if (&other != this) - { - Layer::operator=(std::move(other)); - ratio = std::move(other.ratio); - scale = std::move(other.scale); - baseLayer = std::move(other.baseLayer); - } - - return *this; -} - -template -void DropConnectType::Forward(const MatType& input, MatType& output) -{ - // The DropConnect mask will not be multiplied in testing mode. - if (!this->training) - { - baseLayer->Forward(input, output); + boost::apply_visitor(ForwardVisitor(input, output), baseLayer); } else { // Save weights for denoising. - denoise = baseLayer->Parameters(); + boost::apply_visitor(ParametersVisitor(denoise), baseLayer); // Scale with input / (1 - ratio) and set values to zero with // probability ratio. - mask = arma::randu(denoise.n_rows, denoise.n_cols); + mask = arma::randu >(denoise.n_rows, denoise.n_cols); mask.transform([&](double val) { return (val > ratio); }); - baseLayer->Parameters() = denoise % mask; - baseLayer->Forward(input, output); + arma::mat tmp = denoise % mask; + boost::apply_visitor(ParametersSetVisitor(tmp), baseLayer); + + boost::apply_visitor(ForwardVisitor(input, output), baseLayer); output = output * scale; } } -template -void DropConnectType::Backward( - const MatType& input, - const MatType& gy, - MatType& g) +template +template +void DropConnect::Backward( + const arma::Mat& input, + const arma::Mat& gy, + arma::Mat& g) { - baseLayer->Backward(input, gy, g); + boost::apply_visitor(BackwardVisitor(input, gy, g), baseLayer); } -template -void DropConnectType::Gradient( - const MatType& input, - const MatType& error, - MatType& gradient) +template +template +void DropConnect::Gradient( + const arma::Mat& input, + const arma::Mat& error, + arma::Mat& /* gradient */) { - baseLayer->Gradient(input, error, gradient); + boost::apply_visitor(GradientVisitor(input, error), + baseLayer); // Denoise the weights. - baseLayer->Parameters() = denoise; + boost::apply_visitor(ParametersSetVisitor(denoise), baseLayer); } -template -void DropConnectType::ComputeOutputDimensions() -{ - // Propagate input dimensions to the base layer. - baseLayer->InputDimensions() = this->inputDimensions; - this->outputDimensions = baseLayer->OutputDimensions(); -} - -template -void DropConnectType::SetWeights( - typename MatType::elem_type* weightsPtr) -{ - baseLayer->SetWeights(weightsPtr); -} - -template +template template -void DropConnectType::serialize( +void DropConnect::serialize( Archive& ar, const uint32_t /* version */) { - ar(cereal::base_class>(this)); + // Delete the old network first, if needed. + if (cereal::is_loading()) + { + boost::apply_visitor(DeleteVisitor(), baseLayer); + } ar(CEREAL_NVP(ratio)); ar(CEREAL_NVP(scale)); - ar(CEREAL_POINTER(baseLayer)); + ar(CEREAL_VARIANT_POINTER(baseLayer)); + + if (cereal::is_loading()) + { + network.clear(); + network.push_back(baseLayer); + } } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/dropout.hpp b/src/mlpack/methods/ann/layer/dropout.hpp index f03250145f..f3fb8f18b4 100644 --- a/src/mlpack/methods/ann/layer/dropout.hpp +++ b/src/mlpack/methods/ann/layer/dropout.hpp @@ -15,16 +15,18 @@ #include -#include "layer.hpp" - namespace mlpack { namespace ann /** Artificial Neural Network. */ { + /** * The dropout layer is a regularizer that randomly with probability 'ratio' * sets input values to zero and scales the remaining elements by factor 1 / * (1 - ratio) rather than during test time so as to keep the expected sum same. - * When the layer is in testing mode, there is no change in the input. + * In the deterministic mode (during testing), there is no change in the input. + * + * Note: During training you should set deterministic to false and during + * testing you should set deterministic to true. * * For more information, see the following. * @@ -41,11 +43,14 @@ namespace ann /** Artificial Neural Network. */ { * } * @endcode * - * @tparam MatType Matrix representation to accept as input and use for - * computation. + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class DropoutType : public Layer +template +class Dropout { public: /** @@ -53,22 +58,19 @@ class DropoutType : public Layer * * @param ratio The probability of setting a value to zero. */ - DropoutType(const double ratio = 0.5); + Dropout(const double ratio = 0.5); - //! Clone the DropoutType object. This handles polymorphism correctly. - DropoutType* Clone() const { return new DropoutType(*this); } + //! Copy Constructor + Dropout(const Dropout& layer); - // Virtual destructor. - virtual ~DropoutType() { } + //! Move Constructor + Dropout(const Dropout&&); - //! Copy the given DropoutType. - DropoutType(const DropoutType& other); - //! Take ownership of the given DropoutType. - DropoutType(DropoutType&& other); - //! Copy the given DropoutType. - DropoutType& operator=(const DropoutType& other); - //! Take ownership of the given DropoutType. - DropoutType& operator=(DropoutType&& other); + //! Copy assignment operator + Dropout& operator=(const Dropout& layer); + + //! Move assignment operator + Dropout& operator=(Dropout&& layer); /** * Ordinary feed forward pass of the dropout layer. @@ -76,7 +78,8 @@ class DropoutType : public Layer * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - void Forward(const MatType& input, MatType& output); + template + void Forward(const arma::Mat& input, arma::Mat& output); /** * Ordinary feed backward pass of the dropout layer. @@ -85,7 +88,25 @@ class DropoutType : public Layer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const MatType& /* input */, const MatType& gy, MatType& g); + template + void Backward(const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g); + + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the detla. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } + + //! The value of the deterministic parameter. + bool Deterministic() const { return deterministic; } + //! Modify the value of the deterministic parameter. + bool& Deterministic() { return deterministic; } //! The probability of setting a value to zero. double Ratio() const { return ratio; } @@ -104,20 +125,24 @@ class DropoutType : public Layer void serialize(Archive& ar, const uint32_t /* version */); private: - //! Locally-stored mask object. - MatType mask; + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; + + //! Locally-stored mast object. + OutputDataType mask; //! The probability of setting a value to zero. double ratio; //! The scale fraction. double scale; -}; // class DropoutType -// Convenience typedefs. - -// Standard Dropout layer. -typedef DropoutType Dropout; + //! If true dropout and scaling is disabled, see notes above. + bool deterministic; +}; // class Dropout } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/dropout_impl.hpp b/src/mlpack/methods/ann/layer/dropout_impl.hpp index 012de36f62..80f0d81fec 100644 --- a/src/mlpack/methods/ann/layer/dropout_impl.hpp +++ b/src/mlpack/methods/ann/layer/dropout_impl.hpp @@ -19,66 +19,73 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -DropoutType::DropoutType( +template +Dropout::Dropout( const double ratio) : ratio(ratio), - scale(1.0 / (1.0 - ratio)) + scale(1.0 / (1.0 - ratio)), + deterministic(false) { // Nothing to do here. } -template -DropoutType::DropoutType(const DropoutType& other) : - Layer(other), - ratio(other.ratio), - scale(other.scale) +template +Dropout::Dropout( + const Dropout& layer) : + ratio(layer.ratio), + scale(layer.scale), + deterministic(layer.deterministic) { - // Nothing to do. + // Nothing to do here. } -template -DropoutType::DropoutType(DropoutType&& other) : - Layer(std::move(other)), - ratio(std::move(other.ratio)), - scale(std::move(other.scale)) +template +Dropout::Dropout( + const Dropout&& layer) : + ratio(std::move(layer.ratio)), + scale(std::move(scale)), + deterministic(std::move(deterministic)) { - // Nothing to do. + // Nothing to do here. } -template -DropoutType& -DropoutType::operator=(const DropoutType& other) +template +Dropout& +Dropout:: +operator=(const Dropout& layer) { - if (&other != this) + if (this != &layer) { - Layer::operator=(other); - ratio = other.ratio; - scale = other.scale; + ratio = layer.ratio; + scale = layer.scale; + deterministic = layer.deterministic; } - return *this; } -template -DropoutType& -DropoutType::operator=(DropoutType&& other) +template +Dropout& +Dropout:: +operator=(Dropout&& layer) { - if (&other != this) + if (this != &layer) { - Layer::operator=(std::move(other)); - ratio = std::move(other.ratio); - scale = std::move(other.scale); + ratio = std::move(layer.ratio); + scale = std::move(layer.scale); + deterministic = std::move(layer.deterministic); } - return *this; } -template -void DropoutType::Forward(const MatType& input, MatType& output) +template +template +void Dropout::Forward( + const arma::Mat& input, + arma::Mat& output) { - // The dropout mask will not be multiplied in testing mode. - if (!this->training) + // The dropout mask will not be multiplied in the deterministic mode + // (during testing). + if (deterministic) { output = input; } @@ -86,29 +93,28 @@ void DropoutType::Forward(const MatType& input, MatType& output) { // Scale with input / (1 - ratio) and set values to zero with probability // 'ratio'. - mask = arma::randu(input.n_rows, input.n_cols); + mask = arma::randu >(input.n_rows, input.n_cols); mask.transform([&](double val) { return (val > ratio); }); output = input % mask * scale; } } -template -void DropoutType::Backward( - const MatType& /* input */, - const MatType& gy, - MatType& g) +template +template +void Dropout::Backward( + const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g) { g = gy % mask * scale; } -template +template template -void DropoutType::serialize( +void Dropout::serialize( Archive& ar, const uint32_t /* version */) { - ar(cereal::base_class>(this)); - ar(CEREAL_NVP(ratio)); // Reset scale. diff --git a/src/mlpack/methods/ann/layer/not_adapted/elu.hpp b/src/mlpack/methods/ann/layer/elu.hpp similarity index 70% rename from src/mlpack/methods/ann/layer/not_adapted/elu.hpp rename to src/mlpack/methods/ann/layer/elu.hpp index 292f61cc68..1b51455a93 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/elu.hpp +++ b/src/mlpack/methods/ann/layer/elu.hpp @@ -6,9 +6,9 @@ * Definition of the ELU activation function as described by Djork-Arne Clevert, * Thomas Unterthiner and Sepp Hochreiter. * - * Definition of the SELU function as introduced by Klambauer et. al. in Self - * Neural Networks. The SELU activation function keeps the mean and variance of - * the input invariant. + * Definition of the SELU function as introduced by + * Klambauer et. al. in Self Neural Networks. The SELU activation + * function keeps the mean and variance of the input invariant. * * In short, SELU = lambda * ELU, with 'alpha' and 'lambda' fixed for * normalized inputs. @@ -26,8 +26,6 @@ #include -#include "layer.hpp" - namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -63,6 +61,7 @@ namespace ann /** Artificial Neural Network. */ { * } * @endcode * + * * The SELU activation function is defined by * * @f{eqnarray*}{ @@ -93,19 +92,23 @@ namespace ann /** Artificial Neural Network. */ { * } * @endcode * - * In testing mode, there is no computation of the derivative. + * In the deterministic mode, there is no computation of the derivative. * + * @note During training deterministic should be set to false and during + * testing/inference deterministic should be set to true. * @note Make sure to use SELU activation function with normalized inputs and * weights initialized with Lecun Normal Initialization. * - * @tparam InputType The type of the layer's inputs. The layer automatically - * cast inputs to this type (Default: arma::mat). - * @tparam OutputType The type of the computation which also causes the output - * to also be in this type. The type also allows the computation and weight - * type to differ from the input type (Default: arma::mat). + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class ELUType : public Layer +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class ELU { public: /** @@ -113,7 +116,7 @@ class ELUType : public Layer * * NOTE: Use this constructor for SELU activation function. */ - ELUType(); + ELU(); /** * Create the ELU object using the specified parameter. The non zero @@ -123,10 +126,8 @@ class ELUType : public Layer * @note Use this constructor for ELU activation function. * @param alpha Scale parameter for the negative factor. */ - ELUType(const double alpha); + ELU(const double alpha); - //! Clone the ELUType object. This handles polymorphism correctly. - ELUType* Clone() const { return new ELUType(*this); } /** * Ordinary feed forward pass of a neural network, evaluating the function * f(x) by propagating the activity forward through f. @@ -134,6 +135,7 @@ class ELUType : public Layer * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ + template void Forward(const InputType& input, OutputType& output); /** @@ -145,13 +147,29 @@ class ELUType : public Layer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const InputType& input, const OutputType& gy, OutputType& g); + template + void Backward(const DataType& input, const DataType& gy, DataType& g); + + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } //! Get the non zero gradient. double const& Alpha() const { return alpha; } //! Modify the non zero gradient. double& Alpha() { return alpha; } + //! Get the value of deterministic parameter. + bool Deterministic() const { return deterministic; } + //! Modify the value of deterministic parameter. + bool& Deterministic() { return deterministic; } + //! Get the lambda parameter. double const& Lambda() const { return lambda; } @@ -162,27 +180,31 @@ class ELUType : public Layer void serialize(Archive& ar, const uint32_t /* version */); private: + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; + //! Locally stored first derivative of the activation function. - OutputType derivative; + arma::mat derivative; //! ELU Hyperparameter (0 < alpha) //! SELU parameter fixed to 1.6732632423543774 for normalized inputs. double alpha; - //! Lambda parameter used for multiplication of ELU function. + //! Lambda Parameter used for multiplication of ELU function. //! For ELU activation function, lambda = 1. //! For SELU activation function, lambda = 1.0507009873554802 for normalized //! inputs. double lambda; -}; // class ELUType -// Convenience typedefs. + //! If true the derivative computation is disabled, see notes above. + bool deterministic; +}; // class ELU -// Standard flexible ReLU layer. -typedef ELUType ELU; - -// Standard ELU layer. -typedef ELUType SELU; +// Template alias for SELU using ELU class. +using SELU = ELU; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/not_adapted/elu_impl.hpp b/src/mlpack/methods/ann/layer/elu_impl.hpp similarity index 53% rename from src/mlpack/methods/ann/layer/not_adapted/elu_impl.hpp rename to src/mlpack/methods/ann/layer/elu_impl.hpp index b81d2caf89..9615604c86 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/elu_impl.hpp +++ b/src/mlpack/methods/ann/layer/elu_impl.hpp @@ -26,59 +26,66 @@ namespace ann /** Artificial Neural Network. */ { // This constructor is called for SELU activation function. The values of // alpha and lambda are constant for normalized inputs. -template -ELUType::ELUType() : +template +ELU::ELU() : alpha(1.6732632423543774), - lambda(1.0507009873554802) + lambda(1.0507009873554802), + deterministic(false) { // Nothing to do here. } -// This constructor is called for ELU activation function. The value of lambda -// is fixed and equal to 1. 'alpha' is a hyperparameter. -template -ELUType::ELUType(const double alpha) : +// This constructor is called for ELU activation function. The value of lambda +// is fixed and equal to 1. 'alpha' is a hyperparameter. +template +ELU::ELU(const double alpha) : alpha(alpha), - lambda(1) + lambda(1), + deterministic(false) { // Nothing to do here. } +template template -void ELUType::Forward( +void ELU::Forward( const InputType& input, OutputType& output) { - output.ones(); + output = arma::ones(arma::size(input)); for (size_t i = 0; i < input.n_elem; ++i) { if (input(i) < DBL_MAX) { - output(i) = (input(i) > 0) ? lambda * input(i) : lambda * alpha * - (std::exp(input(i)) - 1); + output(i) = (input(i) > 0) ? lambda * input(i) : lambda * + alpha * (std::exp(input(i)) - 1); } } - if (!deterministic) - { - for (size_t i = 0; i < input.n_elem; ++i) - derivative(i) = (input(i) > 0) ? lambda : output(i) + lambda * alpha; - } + if (!deterministic) + { + derivative.set_size(arma::size(input)); + for (size_t i = 0; i < input.n_elem; ++i) + { + derivative(i) = (input(i) > 0) ? lambda : output(i) + + lambda * alpha; + } + } } -template -void ELUType::Backward( - const InputType& /* input */, const OutputType& gy, OutputType& g) +template +template +void ELU::Backward( + const DataType& /* input */, const DataType& gy, DataType& g) { g = gy % derivative; } -template +template template -void ELUType::serialize( - Archive& ar, const uint32_t /* version */) +void ELU::serialize( + Archive& ar, + const uint32_t /* version */) { - ar(cereal::base_class>(this)); - ar(CEREAL_NVP(alpha)); ar(CEREAL_NVP(lambda)); } diff --git a/src/mlpack/methods/ann/layer/not_adapted/fast_lstm.hpp b/src/mlpack/methods/ann/layer/fast_lstm.hpp similarity index 72% rename from src/mlpack/methods/ann/layer/not_adapted/fast_lstm.hpp rename to src/mlpack/methods/ann/layer/fast_lstm.hpp index 120442b145..80ddcabca6 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/fast_lstm.hpp +++ b/src/mlpack/methods/ann/layer/fast_lstm.hpp @@ -1,4 +1,3 @@ -// Temporarily drop. /** * @file methods/ann/layer/fast_lstm.hpp * @author Marcus Edel @@ -16,7 +15,6 @@ #include #include -#include "layer.hpp" namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -56,36 +54,36 @@ namespace ann /** Artificial Neural Network. */ { * * \see LSTM for a standard implementation of the LSTM layer. * - * @tparam InputType Type of the input data (arma::colvec, arma::mat, + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). - * @tparam OutputType Type of the output data (arma::colvec, arma::mat, + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). */ template < - typename InputType = arma::mat, - typename OutputType = arma::mat + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat > -class FastLSTMType : public Layer +class FastLSTM { public: // Convenience typedefs. - typedef typename InputType::elem_type InputET; - typedef typename OutputType::elem_type OutputET; + typedef typename InputDataType::elem_type InputElemType; + typedef typename OutputDataType::elem_type ElemType; - //! Create the FastLSTMType object. - FastLSTMType(); + //! Create the Fast LSTM object. + FastLSTM(); //! Copy Constructor - FastLSTMType(const FastLSTMType& layer); + FastLSTM(const FastLSTM& layer); //! Move Constructor - FastLSTMType(FastLSTMType&& layer); + FastLSTM(FastLSTM&& layer); //! Copy assignment operator - FastLSTMType& operator=(const FastLSTMType& layer); + FastLSTM& operator=(const FastLSTM& layer); //! Move assignment operator - FastLSTMType& operator=(FastLSTMType&& layer); + FastLSTM& operator=(FastLSTM&& layer); /** * Create the Fast LSTM layer object using the specified parameters. @@ -94,12 +92,9 @@ class FastLSTMType : public Layer * @param outSize The number of output units. * @param rho Maximum number of steps to backpropagate through time (BPTT). */ - FastLSTMType(const size_t inSize, - const size_t outSize, - const size_t rho = std::numeric_limits::max()); - - //! Clone the FastLSTMType object. This handles polymorphism correctly. - FastLSTMType* Clone() const { return new FastLSTMType(*this); } + FastLSTM(const size_t inSize, + const size_t outSize, + const size_t rho = std::numeric_limits::max()); /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -108,6 +103,7 @@ class FastLSTMType : public Layer * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ + template void Forward(const InputType& input, OutputType& output); /** @@ -119,16 +115,17 @@ class FastLSTMType : public Layer * @param gy The backpropagated error. * @param g The calculated gradient. */ + template void Backward(const InputType& input, - const OutputType& gy, - OutputType& g); + const ErrorType& gy, + GradientType& g); - /** + /* * Reset the layer parameter. */ void Reset(); - /** + /* * Resets the cell to accept a new input. This breaks the BPTT chain starts a * new one. * @@ -136,16 +133,17 @@ class FastLSTMType : public Layer */ void ResetCell(const size_t size); - /** + /* * Calculate the gradient using the output delta and the input activation. * * @param input The input parameter used for calculating the gradient. * @param error The calculated error. * @param gradient The calculated gradient. */ + template void Gradient(const InputType& input, - const OutputType& error, - OutputType& gradient); + const ErrorType& error, + GradientType& gradient); //! Get the maximum number of steps to backpropagate through time (BPTT). size_t Rho() const { return rho; } @@ -153,9 +151,24 @@ class FastLSTMType : public Layer size_t& Rho() { return rho; } //! Get the parameters. - OutputType const& Parameters() const { return weights; } + OutputDataType const& Parameters() const { return weights; } //! Modify the parameters. - OutputType& Parameters() { return weights; } + OutputDataType& Parameters() { return weights; } + + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } + + //! Get the gradient. + OutputDataType const& Gradient() const { return grad; } + //! Modify the gradient. + OutputDataType& Gradient() { return grad; } //! Get the number of input units. size_t InSize() const { return inSize; } @@ -169,15 +182,14 @@ class FastLSTMType : public Layer return 4 * outSize * inSize + 4 * outSize + 4 * outSize * outSize; } - const std::vector OutputDimensions() const + //! Get the shape of the input. + size_t InputShape() const { - std::vector result(inputDimensions.size(), 0); - result[0] = outSize; - return result; + return inSize; } /** - * Serialize the layer. + * Serialize the layer */ template void serialize(Archive& ar, const uint32_t /* version */); @@ -189,6 +201,7 @@ class FastLSTMType : public Layer * @param input The input data. * @param sigmoid The matrix to store the sigmoid approximation into. */ + template void FastSigmoid(const InputType& input, OutputType& sigmoids) { for (size_t i = 0; i < input.n_elem; ++i) @@ -201,10 +214,10 @@ class FastLSTMType : public Layer * @param data The given data sample for the sigmoid approximation. * @tparam The sigmoid approximation. */ - OutputET FastSigmoid(const InputET data) + ElemType FastSigmoid(const InputElemType data) { - OutputET x = 0.5 * data; - OutputET z; + ElemType x = 0.5 * data; + ElemType z; if (x >= 0) { if (x < 1.7) @@ -216,7 +229,7 @@ class FastLSTMType : public Layer } else { - OutputET xx = -x; + ElemType xx = -x; if (xx < 1.7) z = -(1.5 * xx / (1 + xx)); else if (xx < 3) @@ -247,10 +260,10 @@ class FastLSTMType : public Layer size_t gradientStep; //! Locally-stored weight object. - OutputType weights; + OutputDataType weights; //! Locally-stored previous output. - OutputType prevOutput; + OutputDataType prevOutput; //! Locally-stored batch size. size_t batchSize; @@ -263,50 +276,56 @@ class FastLSTMType : public Layer size_t gradientStepIdx; //! Locally-stored cell activation error. - OutputType cellActivationError; + OutputDataType cellActivationError; + + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored gradient object. + OutputDataType grad; //! Locally-stored output parameter object. - OutputType outputParameter; + OutputDataType outputParameter; //! Weights between the output and gate. - OutputType output2GateWeight; + OutputDataType output2GateWeight; //! Weights between the input and gate. - OutputType input2GateWeight; + OutputDataType input2GateWeight; //! Bias between the input and gate. - OutputType input2GateBias; + OutputDataType input2GateBias; //! Locally-stored gate parameter. - OutputType gate; + OutputDataType gate; //! Locally-stored gate activation. - OutputType gateActivation; + OutputDataType gateActivation; //! Locally-stored state activation. - OutputType stateActivation; + OutputDataType stateActivation; //! Locally-stored cell parameter. - OutputType cell; + OutputDataType cell; //! Locally-stored cell activation error. - OutputType cellActivation; + OutputDataType cellActivation; //! Locally-stored foget gate error. - OutputType forgetGateError; + OutputDataType forgetGateError; //! Locally-stored previous error. - OutputType prevError; + OutputDataType prevError; + + //! Locally-stored output parameters. + OutputDataType outParameter; //! Locally-stored current rho size. size_t rhoSize; //! Current backpropagate through time steps. size_t bpttSteps; -}; // class FastLSTMType. - -// Standard FastLSTM layer. -typedef FastLSTMType FastLSTM; +}; // class FastLSTM } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/not_adapted/fast_lstm_impl.hpp b/src/mlpack/methods/ann/layer/fast_lstm_impl.hpp similarity index 81% rename from src/mlpack/methods/ann/layer/not_adapted/fast_lstm_impl.hpp rename to src/mlpack/methods/ann/layer/fast_lstm_impl.hpp index 46b6824c0d..c72416bdeb 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/fast_lstm_impl.hpp +++ b/src/mlpack/methods/ann/layer/fast_lstm_impl.hpp @@ -19,14 +19,14 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -FastLSTMType::FastLSTMType() +template +FastLSTM::FastLSTM() { // Nothing to do here. } -template -FastLSTMType::FastLSTMType( +template +FastLSTM::FastLSTM( const size_t inSize, const size_t outSize, const size_t rho) : inSize(inSize), outSize(outSize), @@ -45,8 +45,8 @@ FastLSTMType::FastLSTMType( weights.set_size(WeightSize(), 1); } -template -FastLSTMType::FastLSTMType(const FastLSTMType& layer) : +template +FastLSTM::FastLSTM(const FastLSTM& layer) : inSize(layer.inSize), outSize(layer.outSize), rho(layer.rho), @@ -57,14 +57,15 @@ FastLSTMType::FastLSTMType(const FastLSTMType& layer) : batchSize(layer.batchSize), batchStep(layer.batchStep), gradientStepIdx(layer.gradientStepIdx), + grad(layer.grad), rhoSize(layer.rho), bpttSteps(layer.bpttSteps) { // Nothing to do here. } -template -FastLSTMType::FastLSTMType(FastLSTMType&& layer) : +template +FastLSTM::FastLSTM(FastLSTM&& layer) : inSize(std::move(layer.inSize)), outSize(std::move(layer.outSize)), rho(std::move(layer.rho)), @@ -75,15 +76,16 @@ FastLSTMType::FastLSTMType(FastLSTMType&& layer) : batchSize(std::move(layer.batchSize)), batchStep(std::move(layer.batchStep)), gradientStepIdx(std::move(layer.gradientStepIdx)), + grad(std::move(layer.grad)), rhoSize(std::move(layer.rho)), bpttSteps(std::move(layer.bpttSteps)) { // Nothing to do here. } -template -FastLSTMType& -FastLSTMType::operator=(const FastLSTMType& layer) +template +FastLSTM& +FastLSTM::operator=(const FastLSTM& layer) { if (this != &layer) { @@ -97,15 +99,16 @@ FastLSTMType::operator=(const FastLSTMType& layer) batchSize = layer.batchSize; batchStep = layer.batchStep; gradientStepIdx = layer.gradientStepIdx; + grad = layer.grad; rhoSize = layer.rho; bpttSteps = layer.bpttSteps; } return *this; } -template -FastLSTMType& -FastLSTMType::operator=(FastLSTMType&& layer) +template +FastLSTM& +FastLSTM::operator=(FastLSTM&& layer) { if (this != &layer) { @@ -119,30 +122,31 @@ FastLSTMType::operator=(FastLSTMType&& layer) batchSize = std::move(layer.batchSize); batchStep = std::move(layer.batchStep); gradientStepIdx = std::move(layer.gradientStepIdx); + grad = std::move(layer.grad); rhoSize = std::move(layer.rho); bpttSteps = std::move(layer.bpttSteps); } return *this; } -template -void FastLSTMType::Reset() +template +void FastLSTM::Reset() { // Set the weight parameter for the input to gate layer (linear layer) using // the overall layer parameter matrix. - input2GateWeight = OutputType(weights.memptr(), + input2GateWeight = OutputDataType(weights.memptr(), 4 * outSize, inSize, false, false); - input2GateBias = OutputType(weights.memptr() + input2GateWeight.n_elem, + input2GateBias = OutputDataType(weights.memptr() + input2GateWeight.n_elem, 4 * outSize, 1, false, false); // Set the weight parameter for the output to gate layer // (linear no bias layer) using the overall layer parameter matrix. - output2GateWeight = OutputType(weights.memptr() + input2GateWeight.n_elem + output2GateWeight = OutputDataType(weights.memptr() + input2GateWeight.n_elem + input2GateBias.n_elem, 4 * outSize, outSize, false, false); } -template -void FastLSTMType::ResetCell(const size_t size) +template +void FastLSTM::ResetCell(const size_t size) { if (size == std::numeric_limits::max()) return; @@ -175,8 +179,9 @@ void FastLSTMType::ResetCell(const size_t size) outParameter.zeros(outSize, (size + 1) * batchSize); } +template template -void FastLSTMType::Forward( +void FastLSTM::Forward( const InputType& input, OutputType& output) { // Check if the batch size changed, the number of cols is defines the input @@ -193,8 +198,8 @@ void FastLSTMType::Forward( forwardStep, forwardStep + batchStep); gate.cols(forwardStep, forwardStep + batchStep).each_col() += input2GateBias; - InputType sigmoidOut(gateActivation.colptr(forwardStep), - gateActivation.n_rows, batchStep, false, false); + arma::subview sigmoidOut = gateActivation.cols(forwardStep, + forwardStep + batchStep); FastSigmoid( gate.submat(0, forwardStep, 3 * outSize - 1, forwardStep + batchStep), sigmoidOut); @@ -242,19 +247,20 @@ void FastLSTMType::Forward( } } -template -void FastLSTMType::Backward( - const InputType& /* input */, const OutputType& gy, OutputType& g) +template +template +void FastLSTM::Backward( + const InputType& /* input */, const ErrorType& gy, GradientType& g) { - OutputType gyLocal; + ErrorType gyLocal; if (gradientStepIdx > 0) { gyLocal = gy + output2GateWeight.t() * prevError; } else { - gyLocal = OutputType(((OutputType&) gy).memptr(), gy.n_rows, gy.n_cols, - false, false); + gyLocal = ErrorType(((ErrorType&) gy).memptr(), gy.n_rows, gy.n_cols, false, + false); } cellActivationError = gyLocal % gateActivation.submat(outSize, @@ -313,11 +319,12 @@ void FastLSTMType::Backward( } } -template -void FastLSTMType::Gradient( +template +template +void FastLSTM::Gradient( const InputType& input, - const OutputType& /* error */, - OutputType& gradient) + const ErrorType& /* error */, + GradientType& gradient) { // Gradient of the input to gate layer. gradient.submat(0, 0, input2GateWeight.n_elem - 1, 0) = @@ -341,13 +348,11 @@ void FastLSTMType::Gradient( } } -template +template template -void FastLSTMType::serialize( +void FastLSTM::serialize( Archive& ar, const uint32_t /* version */) { - ar(cereal::base_class>(this)); - ar(CEREAL_NVP(weights)); ar(CEREAL_NVP(inSize)); ar(CEREAL_NVP(outSize)); @@ -366,10 +371,7 @@ void FastLSTMType::serialize( ar(CEREAL_NVP(cellActivation)); ar(CEREAL_NVP(forgetGateError)); ar(CEREAL_NVP(prevError)); - - // Restore aliases. - if (Archive::is_loading::value) - Reset(); + ar(CEREAL_NVP(outParameter)); } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/not_adapted/flatten_t_swish.hpp b/src/mlpack/methods/ann/layer/flatten_t_swish.hpp similarity index 100% rename from src/mlpack/methods/ann/layer/not_adapted/flatten_t_swish.hpp rename to src/mlpack/methods/ann/layer/flatten_t_swish.hpp diff --git a/src/mlpack/methods/ann/layer/not_adapted/flatten_t_swish_impl.hpp b/src/mlpack/methods/ann/layer/flatten_t_swish_impl.hpp similarity index 100% rename from src/mlpack/methods/ann/layer/not_adapted/flatten_t_swish_impl.hpp rename to src/mlpack/methods/ann/layer/flatten_t_swish_impl.hpp diff --git a/src/mlpack/methods/ann/layer/not_adapted/flexible_relu.hpp b/src/mlpack/methods/ann/layer/flexible_relu.hpp similarity index 50% rename from src/mlpack/methods/ann/layer/not_adapted/flexible_relu.hpp rename to src/mlpack/methods/ann/layer/flexible_relu.hpp index 279d00a5f7..7c2ea9e4dc 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/flexible_relu.hpp +++ b/src/mlpack/methods/ann/layer/flexible_relu.hpp @@ -3,9 +3,10 @@ * @author Aarush Gupta * @author Manthan-R-Sheth * - * Definition of the FlexibleReLU layer as described by Suo Qiu, Xiangmin Xu and - * Bolun Cai in "FReLU: Flexible Rectified Linear Units for Improving - * Convolutional Neural Networks". + * Definition of FlexibleReLU layer as described by + * Suo Qiu, Xiangmin Xu and Bolun Cai in + * "FReLU: Flexible Rectified Linear Units for Improving Convolutional + * Neural Networks", 2018 * * mlpack is free software; you may redistribute it and/or modify it under the * terms of the 3-clause BSD license. You should have received a copy of the @@ -17,8 +18,6 @@ #include -#include "layer.hpp" - namespace mlpack { namespace ann /**Artificial Neural Network*/ { @@ -28,10 +27,10 @@ namespace ann /**Artificial Neural Network*/ { * @f{eqnarray*}{ * f(x) &=& \max(0,x)+alpha \\ * f'(x) &=& \left\{ - * \begin{array}{lr} - * 1 & : x > 0 \\ - * 0 & : x \le 0 - * \end{array} + * \begin{array}{lr} + * 1 & : x > 0 \\ + * 0 & : x \le 0 + * \end{array} * \right. * @f} * @@ -48,33 +47,34 @@ namespace ann /**Artificial Neural Network*/ { * } * @endcode * - * @tparam InputType The type of the layer's inputs. The layer automatically - * cast inputs to this type (Default: arma::mat). - * @tparam OutputType The type of the computation which also causes the output - * to also be in this type. The type also allows the computation and weight - * type to differ from the input type (Default: arma::mat). + * @tparam InputDataType Type of the input data (arma::colvec, arma::mar, + * arma::sp_mat or arma::cube) + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube) */ -template -class FlexibleReLUType : public Layer +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class FlexibleReLU { public: /** - * Create the FlexibleReLU object using the specified alpha parameter. - * The trainable alpha parameter controls the range of the ReLU function. - * (Default alpha = 0). * - * @param alpha Parameter to adjust the range of the ReLU function. + * Create the FlexibleReLU object using the specified parameters. + * The non zero parameter can be adjusted by specifying the parameter + * alpha which controls the range of the relu function. (Default alpha = 0) + * This parameter is trainable. + * + * @param alpha Parameter for adjusting the range of the relu function. + * */ - FlexibleReLUType(const double alpha = 0); - - //! Clone the FlexibleReLUType object. This handles polymorphism correctly. - FlexibleReLUType* Clone() const { return new FlexibleReLUType(*this); } + FlexibleReLU(const double alpha = 0); /** - * Reset the layer parameter (alpha). The method is called to - * assign the allocated memory to the learnable layer parameter. + * Reset the layer parameter. */ - void SetWeights(typename OutputType::elem_type* weightsPtr); + void Reset(); /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -83,6 +83,7 @@ class FlexibleReLUType : public Layer * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ + template void Forward(const InputType& input, OutputType& output); /** @@ -94,7 +95,8 @@ class FlexibleReLUType : public Layer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const InputType& input, const OutputType& gy, OutputType& g); + template + void Backward(const DataType& input, const DataType& gy, DataType& g); /** * Calculate the gradient using the output delta and the input activation. @@ -103,22 +105,36 @@ class FlexibleReLUType : public Layer * @param error The calculated error. * @param gradient The calculated gradient. */ - void Gradient(const InputType& input, - const OutputType& error, - OutputType& gradient); + template + void Gradient(const arma::Mat& input, + const arma::Mat& error, + arma::Mat& gradient); //! Get the parameters. - OutputType const& Parameters() const { return alpha; } + OutputDataType const& Parameters() const { return alpha; } //! Modify the parameters. - OutputType& Parameters() { return alpha; } + OutputDataType& Parameters() { return alpha; } - //! Get the parameter controlling the range of the ReLU function. - const double& Alpha() const { return alpha; } - //! Modify the parameter controlling the range of the ReLU function. + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta;} + + //! Get the gradient. + OutputDataType const& Gradient() const { return gradient; } + //! Modify the gradient. + OutputDataType& Gradient() { return gradient; } + + //! Get the parameter controlling the range of the relu function. + double const& Alpha() const { return alpha; } + //! Modify the parameter controlling the range of the relu function. double& Alpha() { return alpha; } - const size_t WeightSize() const { return 1; } - /** * Serialize the layer. */ @@ -126,20 +142,21 @@ class FlexibleReLUType : public Layer void serialize(Archive& ar, const uint32_t /* version*/); private: + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; + //! Parameter object. - OutputType alpha; + OutputDataType alpha; - //! Parameter controlling the range of the ReLU function. + //! Locally-stored gradient object. + OutputDataType gradient; + + //! Parameter controlling the range of the rectifier function double userAlpha; - - //! Whether or not a forward pass has ever been performed. - bool initialized; -}; // class FlexibleReLUType - -// Convenience typedefs. - -// Standard flexible ReLU layer. -typedef FlexibleReLUType FlexibleReLU; +}; // class FlexibleReLU } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/not_adapted/flexible_relu_impl.hpp b/src/mlpack/methods/ann/layer/flexible_relu_impl.hpp similarity index 51% rename from src/mlpack/methods/ann/layer/not_adapted/flexible_relu_impl.hpp rename to src/mlpack/methods/ann/layer/flexible_relu_impl.hpp index 74af9b611e..ffd695b3e5 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/flexible_relu_impl.hpp +++ b/src/mlpack/methods/ann/layer/flexible_relu_impl.hpp @@ -18,67 +18,66 @@ #define MLPACK_METHODS_ANN_LAYER_FLEXIBLERELU_IMPL_HPP #include "flexible_relu.hpp" +#include namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -FlexibleReLUType::FlexibleReLUType(const double alpha) : - userAlpha(alpha), - initialized(false) +template +FlexibleReLU::FlexibleReLU( + const double alpha) : userAlpha(alpha) { this->alpha.set_size(1, 1); - this->alpha(0) = alpha; + this->alpha(0) = userAlpha; } -template -void FlexibleReLUType::SetWeights( - typename OutputType::elem_type* weightsPtr) +template +void FlexibleReLU::Reset() { - alpha = OutputType(weightsPtr, 1, 1, false, false); + //! Set value of alpha to the one given by user. + alpha(0) = userAlpha; } +template template -void FlexibleReLUType::Forward( +void FlexibleReLU::Forward( const InputType& input, OutputType& output) { - if (!initialized) - { - alpha[0] = userAlpha; - initialized = true; - } - output = arma::clamp(input, 0.0, DBL_MAX) + alpha(0); } -template -void FlexibleReLUType::Backward( - const InputType& input, const OutputType& gy, OutputType& g) +template +template +void FlexibleReLU::Backward( + const DataType& input, const DataType& gy, DataType& g) { - // Compute the first derivative of FlexibleReLU function. + //! Compute the first derivative of FlexibleReLU function. g = gy % arma::clamp(arma::sign(input), 0.0, 1.0); } -template -void FlexibleReLUType::Gradient( - const InputType& input, - const OutputType& error, - OutputType& gradient) +template +template +void FlexibleReLU::Gradient( + const arma::Mat& input, + const arma::Mat& error, + arma::Mat& gradient) { + if (gradient.n_elem == 0) + { + gradient.set_size(1, 1); + } + gradient(0) = arma::accu(error) / input.n_cols; } -template + +template template -void FlexibleReLUType::serialize( +void FlexibleReLU::serialize( Archive& ar, const uint32_t /* version*/) { - ar(cereal::base_class>(this)); - ar(CEREAL_NVP(alpha)); - ar(CEREAL_NVP(userAlpha)); - ar(CEREAL_NVP(initialized)); } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/not_adapted/glimpse.hpp b/src/mlpack/methods/ann/layer/glimpse.hpp similarity index 80% rename from src/mlpack/methods/ann/layer/not_adapted/glimpse.hpp rename to src/mlpack/methods/ann/layer/glimpse.hpp index ee25a0cbb6..99a268b6a8 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/glimpse.hpp +++ b/src/mlpack/methods/ann/layer/glimpse.hpp @@ -1,4 +1,3 @@ -// Temporarily drop. /** * @file methods/ann/layer/glimpse.hpp * @author Marcus Edel @@ -77,16 +76,16 @@ class MeanPoolingRule * (down-scaled cropped images) of increasing scale around a given location in a * given image. * - * @tparam InputType Type of the input data (arma::colvec, arma::mat, + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). - * @tparam OutputType Type of the output data (arma::colvec, arma::mat, + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). */ template < - typename InputType = arma::mat, - typename OutputType = arma::mat + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat > -class GlimpseType : public Layer +class Glimpse { public: /** @@ -101,12 +100,12 @@ class GlimpseType : public Layer * @param inputWidth The input width of the given input data. * @param inputHeight The input height of the given input data. */ - GlimpseType(const size_t inSize = 0, - const size_t size = 0, - const size_t depth = 3, - const size_t scale = 2, - const size_t inputWidth = 0, - const size_t inputHeight = 0); + Glimpse(const size_t inSize = 0, + const size_t size = 0, + const size_t depth = 3, + const size_t scale = 2, + const size_t inputWidth = 0, + const size_t inputHeight = 0); /** * Ordinary feed forward pass of the glimpse layer. @@ -114,7 +113,8 @@ class GlimpseType : public Layer * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - void Forward(const InputType& input, OutputType& output); + template + void Forward(const arma::Mat& input, arma::Mat& output); /** * Ordinary feed backward pass of the glimpse layer. @@ -123,13 +123,27 @@ class GlimpseType : public Layer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const InputType& /* input */, - const OutputType& gy, - OutputType& g); + template + void Backward(const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g); + + //! Get the output parameter. + OutputDataType& OutputParameter() const {return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the detla. + OutputDataType& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } //! Set the locationthe x and y coordinate of the center of the output //! glimpse. - void Location(const arma::mat& location) { this->location = location; } + void Location(const arma::mat& location) + { + this->location = location; + } //! Get the input width. size_t const& InputWidth() const { return inputWidth; } @@ -151,6 +165,11 @@ class GlimpseType : public Layer //! Modify the output height. size_t& OutputHeight() { return outputHeight; } + //! Get the value of the deterministic parameter. + bool Deterministic() const { return deterministic; } + //! Modify the value of the deterministic parameter. + bool& Deterministic() { return deterministic; } + //! Get the number of patches to crop per glimpse. size_t const& Depth() const { return depth; } @@ -163,14 +182,10 @@ class GlimpseType : public Layer //! Get the used glimpse size (height = width). size_t GlimpseSize() const { return size;} - const std::vector OutputDimensions() const + //! Get the shape of the input. + size_t InputShape() const { - std::vector result(inputDimensions.size(), 0); - result[0] = outputWidth; - result[1] = outputHeight; - for (size_t i = 2; i < inputDimensions.size(); ++i) - result[i] = inputDimensions[i]; - return result; + return inSize; } /** @@ -180,7 +195,7 @@ class GlimpseType : public Layer void serialize(Archive& ar, const uint32_t /* version */); private: - /** + /* * Transform the given input by changing rows to columns. * * @param w The input matrix used to perform the transformation. @@ -220,9 +235,10 @@ class GlimpseType : public Layer * @param input The input to be apply the pooling rule. * @param output The pooled result. */ + template void Pooling(const size_t kSize, - const InputType& input, - OutputType& output) + const arma::Mat& input, + arma::Mat& output) { const size_t rStep = kSize; const size_t cStep = kSize; @@ -244,20 +260,21 @@ class GlimpseType : public Layer * @param error The error used to perform the unpooling operation. * @param output The pooled result. */ - void Unpooling(const InputType& input, - const OutputType& error, - OutputType& output) + template + void Unpooling(const arma::Mat& input, + const arma::Mat& error, + arma::Mat& output) { const size_t rStep = input.n_rows / error.n_rows; const size_t cStep = input.n_cols / error.n_cols; - OutputType unpooledError; + arma::Mat unpooledError; for (size_t j = 0; j < input.n_cols; j += cStep) { for (size_t i = 0; i < input.n_rows; i += rStep) { - const InputType& inputArea = input(arma::span(i, i + rStep - 1), - arma::span(j, j + cStep - 1)); + const arma::Mat& inputArea = input(arma::span(i, i + rStep - 1), + arma::span(j, j + cStep - 1)); pooling.Unpooling(inputArea, error(i / rStep, j / cStep), unpooledError); @@ -275,7 +292,8 @@ class GlimpseType : public Layer * @param input The input to be apply the ReSampling rule. * @param output The pooled result. */ - void ReSampling(const InputType& input, OutputType& output) + template + void ReSampling(const arma::Mat& input, arma::Mat& output) { double wRatio = (double) (input.n_rows - 1) / (size - 1); double hRatio = (double) (input.n_cols - 1) / (size - 1); @@ -319,9 +337,10 @@ class GlimpseType : public Layer * @param error The error used to perform the DownwardReSampling operation. * @param output The DownwardReSampled result. */ - void DownwardReSampling(const InputType& input, - const OutputType& error, - OutputType& output) + template + void DownwardReSampling(const arma::Mat& input, + const arma::Mat& error, + arma::Mat& output) { double iWidth = input.n_rows - 1; double iHeight = input.n_cols - 1; @@ -385,30 +404,36 @@ class GlimpseType : public Layer //! Locally-stored output height. size_t outputHeight; + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; + //! Locally-stored depth of the input. size_t inputDepth; //! Locally-stored transformed input parameter. - arma::Cube inputTemp; + arma::cube inputTemp; //! Locally-stored transformed output parameter. - arma::Cube outputTemp; + arma::cube outputTemp; //! The x and y coordinate of the center of the output glimpse. - OutputType location; + arma::mat location; //! Locally-stored object to perform the mean pooling operation. MeanPoolingRule pooling; //! Location-stored module location parameter. - std::vector locationParameter; + std::vector locationParameter; //! Location-stored transformed gradient paramter. - arma::Cube gTemp; -}; // class GlimpseType + arma::cube gTemp; -// Standard Glimpse layer. -typedef GlimpseType Glimpse; + //! If true use maximum a posteriori during the forward pass. + bool deterministic; +}; // class GlimpseLayer } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/not_adapted/glimpse_impl.hpp b/src/mlpack/methods/ann/layer/glimpse_impl.hpp similarity index 76% rename from src/mlpack/methods/ann/layer/not_adapted/glimpse_impl.hpp rename to src/mlpack/methods/ann/layer/glimpse_impl.hpp index a76b77f70c..b66379bfd1 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/glimpse_impl.hpp +++ b/src/mlpack/methods/ann/layer/glimpse_impl.hpp @@ -20,8 +20,8 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -GlimpseType::GlimpseType( +template +Glimpse::Glimpse( const size_t inSize, const size_t size, const size_t depth, @@ -42,14 +42,13 @@ GlimpseType::GlimpseType( // Nothing to do here. } -template -void GlimpseType::Forward( - const InputType& input, OutputType& output) +template +template +void Glimpse::Forward( + const arma::Mat& input, arma::Mat& output) { - inputTemp = arma::Cube(input.colptr(0), - inputWidth, inputHeight, inSize); - outputTemp = arma::Cube(size, size, depth * - inputTemp.n_slices); + inputTemp = arma::cube(input.colptr(0), inputWidth, inputHeight, inSize); + outputTemp = arma::Cube(size, size, depth * inputTemp.n_slices); location = input.submat(0, 1, 1, 1); @@ -67,8 +66,7 @@ void GlimpseType::Forward( { size_t padSize = std::floor((glimpseSize - 1) / 2); - arma::Cube inputPadded = - arma::zeros>( + arma::Cube inputPadded = arma::zeros >( inputTemp.n_rows + padSize * 2, inputTemp.n_cols + padSize * 2, inputTemp.n_slices / inSize); @@ -100,8 +98,9 @@ void GlimpseType::Forward( for (size_t j = (inputIdx + depthIdx * (depth - 1)), paddedSlice = 0; j < outputTemp.n_slices; j += (inSize * depth), paddedSlice++) { - InputType poolingInput = inputPadded.subcube(x, y, paddedSlice, - x + glimpseSize - 1, y + glimpseSize - 1, paddedSlice); + arma::Mat poolingInput = inputPadded.subcube(x, y, + paddedSlice, x + glimpseSize - 1, y + glimpseSize - 1, + paddedSlice); if (scale == 2) { @@ -121,19 +120,19 @@ void GlimpseType::Forward( outputTemp.slice(i) = arma::trans(outputTemp.slice(i)); } - output = OutputType(outputTemp.memptr(), outputTemp.n_elem, 1); + output = arma::Mat(outputTemp.memptr(), outputTemp.n_elem, 1); outputWidth = outputTemp.n_rows; outputHeight = outputTemp.n_cols; } -template -void GlimpseType::Backward( - const InputType& /* input */, const OutputType& gy, OutputType& g) +template +template +void Glimpse::Backward( + const arma::Mat& /* input */, const arma::Mat& gy, arma::Mat& g) { // Generate a cube using the backpropagated error matrix. - arma::Cube mappedError = - arma::zeros>(outputWidth, + arma::Cube mappedError = arma::zeros(outputWidth, outputHeight, 1); location = locationParameter.back(); @@ -143,13 +142,13 @@ void GlimpseType::Backward( { for (size_t i = 0; i < gy.n_cols; ++i) { - mappedError.slice(s + i) = OutputType(gy.memptr(), + mappedError.slice(s + i) = arma::Mat(gy.memptr(), outputWidth, outputHeight); } } - gTemp = arma::zeros>( - inputTemp.n_rows, inputTemp.n_cols, inputTemp.n_slices); + gTemp = arma::zeros(inputTemp.n_rows, inputTemp.n_cols, + inputTemp.n_slices); for (size_t inputIdx = 0; inputIdx < inSize; inputIdx++) { @@ -158,8 +157,7 @@ void GlimpseType::Backward( { size_t padSize = std::floor((glimpseSize - 1) / 2); - arma::Cube inputPadded = - arma::zeros>( + arma::Cube inputPadded = arma::zeros >( inputTemp.n_rows + padSize * 2, inputTemp.n_cols + padSize * 2, inputTemp.n_slices / inSize); @@ -186,8 +184,9 @@ void GlimpseType::Backward( for (size_t j = (inputIdx + depthIdx * (depth - 1)), paddedSlice = 0; j < mappedError.n_slices; j += (inSize * depth), paddedSlice++) { - OutputType poolingOutput = inputPadded.subcube(x, y, paddedSlice, - x + glimpseSize - 1, y + glimpseSize - 1, paddedSlice); + arma::Mat poolingOutput = inputPadded.subcube(x, y, + paddedSlice, x + glimpseSize - 1, y + glimpseSize - 1, + paddedSlice); if (scale == 2) { @@ -212,16 +211,14 @@ void GlimpseType::Backward( } Transform(gTemp); - g = OutputType(gTemp.memptr(), gTemp.n_elem, 1); + g = arma::mat(gTemp.memptr(), gTemp.n_elem, 1); } -template +template template -void GlimpseType::serialize( +void Glimpse::serialize( Archive& ar, const uint32_t /* version */) { - ar(cereal::base_class>(this)); - ar(CEREAL_NVP(inSize)); ar(CEREAL_NVP(size)); ar(CEREAL_NVP(depth)); diff --git a/src/mlpack/methods/ann/layer/not_adapted/group_norm.hpp b/src/mlpack/methods/ann/layer/group_norm.hpp similarity index 100% rename from src/mlpack/methods/ann/layer/not_adapted/group_norm.hpp rename to src/mlpack/methods/ann/layer/group_norm.hpp diff --git a/src/mlpack/methods/ann/layer/not_adapted/group_norm_impl.hpp b/src/mlpack/methods/ann/layer/group_norm_impl.hpp similarity index 100% rename from src/mlpack/methods/ann/layer/not_adapted/group_norm_impl.hpp rename to src/mlpack/methods/ann/layer/group_norm_impl.hpp diff --git a/src/mlpack/methods/ann/layer/not_adapted/gru.hpp b/src/mlpack/methods/ann/layer/gru.hpp similarity index 67% rename from src/mlpack/methods/ann/layer/not_adapted/gru.hpp rename to src/mlpack/methods/ann/layer/gru.hpp index 290adb0276..c895ee7f81 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/gru.hpp +++ b/src/mlpack/methods/ann/layer/gru.hpp @@ -1,4 +1,3 @@ -// Temporarily drop. /** * @file methods/ann/layer/gru.hpp * @author Sumedh Ghaisas @@ -32,6 +31,9 @@ #include +#include "../visitor/delta_visitor.hpp" +#include "../visitor/output_parameter_visitor.hpp" + #include "layer_types.hpp" #include "add_merge.hpp" #include "sequential.hpp" @@ -44,16 +46,16 @@ namespace ann /** Artificial Neural Network. */ { * * This cell can be used in RNN networks. * - * @tparam InputType Type of the input data (arma::colvec, arma::mat, + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). - * @tparam OutputType Type of the output data (arma::colvec, arma::mat, + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). */ template < - typename InputType = arma::mat, - typename OutputType = arma::mat + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat > -class GRU : public Layer +class GRU { public: //! Create the GRU object. @@ -77,7 +79,8 @@ class GRU : public Layer * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - void Forward(const InputType& input, OutputType& output); + template + void Forward(const arma::Mat& input, arma::Mat& output); /** * Ordinary feed backward pass of a neural network, calculating the function @@ -88,9 +91,10 @@ class GRU : public Layer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const InputType& /* input */, - const OutputType& gy, - OutputType& g); + template + void Backward(const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g); /* * Calculate the gradient using the output delta and the input activation. @@ -99,9 +103,10 @@ class GRU : public Layer * @param error The calculated error. * @param gradient The calculated gradient. */ - void Gradient(const InputType& input, - const OutputType& /* error */, - OutputType& /* gradient */); + template + void Gradient(const arma::Mat& input, + const arma::Mat& /* error */, + arma::Mat& /* gradient */); /* * Resets the cell to accept a new input. This breaks the BPTT chain starts a @@ -111,18 +116,38 @@ class GRU : public Layer */ void ResetCell(const size_t size); + //! The value of the deterministic parameter. + bool Deterministic() const { return deterministic; } + //! Modify the value of the deterministic parameter. + bool& Deterministic() { return deterministic; } + //! Get the maximum number of steps to backpropagate through time (BPTT). size_t Rho() const { return rho; } //! Modify the maximum number of steps to backpropagate through time (BPTT). size_t& Rho() { return rho; } //! Get the parameters. - OutputType const& Parameters() const { return weights; } + OutputDataType const& Parameters() const { return weights; } //! Modify the parameters. - OutputType& Parameters() { return weights; } + OutputDataType& Parameters() { return weights; } + + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } + + //! Get the gradient. + OutputDataType const& Gradient() const { return gradient; } + //! Modify the gradient. + OutputDataType& Gradient() { return gradient; } //! Get the model modules. - std::vector*>& Model() { return network; } + std::vector >& Model() { return network; } //! Get the number of input units. size_t InSize() const { return inSize; } @@ -156,28 +181,37 @@ class GRU : public Layer size_t batchSize; //! Locally-stored weight object. - OutputType weights; + OutputDataType weights; //! Locally-stored input 2 gate module. - Layer* input2GateModule; + LayerTypes<> input2GateModule; //! Locally-stored output 2 gate module. - Layer* output2GateModule; + LayerTypes<> output2GateModule; //! Locally-stored output hidden state 2 gate module. - Layer* outputHidden2GateModule; + LayerTypes<> outputHidden2GateModule; //! Locally-stored input gate module. - Layer* inputGateModule; + LayerTypes<> inputGateModule; //! Locally-stored hidden state module. - Layer* hiddenStateModule; + LayerTypes<> hiddenStateModule; //! Locally-stored forget gate module. - Layer* forgetGateModule; + LayerTypes<> forgetGateModule; + + //! Locally-stored output parameter visitor. + OutputParameterVisitor outputParameterVisitor; + + //! Locally-stored delta visitor. + DeltaVisitor deltaVisitor; + + //! Locally-stored delete visitor. + DeleteVisitor deleteVisitor; //! Locally-stored list of network modules. - std::vector*> network; + std::vector > network; //! Locally-stored number of forward steps. size_t forwardStep; @@ -189,34 +223,34 @@ class GRU : public Layer size_t gradientStep; //! Locally-stored output parameters. - std::list outParameter; + std::list outParameter; //! Matrix of all zeroes to initialize the output - OutputType allZeros; + arma::mat allZeros; //! Iterator pointed to the last output produced by the cell - typename std::list::iterator prevOutput; + std::list::iterator prevOutput; //! Iterator pointed to the last output processed by backward - typename std::list::iterator backIterator; + std::list::iterator backIterator; //! Iterator pointed to the last output processed by gradient - typename std::list::iterator gradIterator; + std::list::iterator gradIterator; //! Locally-stored previous error. - OutputType prevError; + arma::mat prevError; //! If true dropout and scaling is disabled, see notes above. bool deterministic; //! Locally-stored delta object. - OutputType delta; + OutputDataType delta; //! Locally-stored gradient object. - OutputType gradient; + OutputDataType gradient; //! Locally-stored output parameter object. - OutputType outputParameter; + OutputDataType outputParameter; }; // class GRU } // namespace ann diff --git a/src/mlpack/methods/ann/layer/gru_impl.hpp b/src/mlpack/methods/ann/layer/gru_impl.hpp new file mode 100644 index 0000000000..e9b71ebb97 --- /dev/null +++ b/src/mlpack/methods/ann/layer/gru_impl.hpp @@ -0,0 +1,411 @@ +/** + * @file methods/ann/layer/gru_impl.hpp + * @author Sumedh Ghaisas + * + * Implementation of the GRU class, which implements a gru network + * layer. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_LAYER_GRU_IMPL_HPP +#define MLPACK_METHODS_ANN_LAYER_GRU_IMPL_HPP + +// In case it hasn't yet been included. +#include "gru.hpp" + +#include "../visitor/forward_visitor.hpp" +#include "../visitor/backward_visitor.hpp" +#include "../visitor/gradient_visitor.hpp" + +namespace mlpack { +namespace ann /** Artificial Neural Network. */ { + +template +GRU::GRU() +{ + // Nothing to do here. +} + +template +GRU::GRU( + const size_t inSize, + const size_t outSize, + const size_t rho) : + inSize(inSize), + outSize(outSize), + rho(rho), + batchSize(1), + forwardStep(0), + backwardStep(0), + gradientStep(0), + deterministic(false) +{ + // Input specific linear layers(for zt, rt, ot). + input2GateModule = new Linear<>(inSize, 3 * outSize); + + // Previous output gates (for zt and rt). + output2GateModule = new LinearNoBias<>(outSize, 2 * outSize); + + // Previous output gate for ot. + outputHidden2GateModule = new LinearNoBias<>(outSize, outSize); + + network.push_back(input2GateModule); + network.push_back(output2GateModule); + network.push_back(outputHidden2GateModule); + + inputGateModule = new SigmoidLayer<>(); + forgetGateModule = new SigmoidLayer<>(); + hiddenStateModule = new TanHLayer<>(); + + network.push_back(inputGateModule); + network.push_back(hiddenStateModule); + network.push_back(forgetGateModule); + + prevError = arma::zeros(3 * outSize, batchSize); + + allZeros = arma::zeros(outSize, batchSize); + + outParameter.emplace_back(allZeros.memptr(), + allZeros.n_rows, allZeros.n_cols, false, true); + + prevOutput = outParameter.begin(); + backIterator = outParameter.end(); + gradIterator = outParameter.end(); +} + +template +template +void GRU::Forward( + const arma::Mat& input, arma::Mat& output) +{ + if (input.n_cols != batchSize) + { + batchSize = input.n_cols; + prevError.resize(3 * outSize, batchSize); + allZeros.zeros(outSize, batchSize); + // Batch size better not change during an iteration... + if (outParameter.size() > 1) + { + Log::Fatal << "GRU<>::Forward(): batch size cannot change during a " + << "forward pass!" << std::endl; + } + + outParameter.clear(); + outParameter.emplace_back(allZeros.memptr(), + allZeros.n_rows, allZeros.n_cols, false, true); + + prevOutput = outParameter.begin(); + backIterator = outParameter.end(); + gradIterator = outParameter.end(); + } + + // Process the input linearly(zt, rt, ot). + boost::apply_visitor(ForwardVisitor(input, + boost::apply_visitor(outputParameterVisitor, input2GateModule)), + input2GateModule); + + // Process the output(zt, rt) linearly. + boost::apply_visitor(ForwardVisitor(*prevOutput, + boost::apply_visitor(outputParameterVisitor, output2GateModule)), + output2GateModule); + + // Merge the outputs(zt and rt). + output = (boost::apply_visitor(outputParameterVisitor, + input2GateModule).submat(0, 0, 2 * outSize - 1, batchSize - 1) + + boost::apply_visitor(outputParameterVisitor, output2GateModule)); + + // Pass the first outSize through inputGate(it). + boost::apply_visitor(ForwardVisitor(output.submat( + 0, 0, 1 * outSize - 1, batchSize - 1), boost::apply_visitor( + outputParameterVisitor, inputGateModule)), inputGateModule); + + // Pass the second through forgetGate. + boost::apply_visitor(ForwardVisitor(output.submat( + 1 * outSize, 0, 2 * outSize - 1, batchSize - 1), + boost::apply_visitor(outputParameterVisitor, forgetGateModule)), + forgetGateModule); + + arma::mat modInput = (boost::apply_visitor(outputParameterVisitor, + forgetGateModule) % *prevOutput); + + // Pass that through the outputHidden2GateModule. + boost::apply_visitor(ForwardVisitor(modInput, + boost::apply_visitor(outputParameterVisitor, outputHidden2GateModule)), + outputHidden2GateModule); + + // Merge for ot. + arma::mat outputH = boost::apply_visitor(outputParameterVisitor, + input2GateModule).submat(2 * outSize, 0, 3 * outSize - 1, batchSize - 1) + + boost::apply_visitor(outputParameterVisitor, outputHidden2GateModule); + + // Pass it through hiddenGate. + boost::apply_visitor(ForwardVisitor(outputH, + boost::apply_visitor(outputParameterVisitor, hiddenStateModule)), + hiddenStateModule); + + // Update the output (nextOutput): cmul1 + cmul2 + // Where cmul1 is input gate * prevOutput and + // cmul2 is (1 - input gate) * hidden gate. + output = (boost::apply_visitor(outputParameterVisitor, inputGateModule) + % (*prevOutput - boost::apply_visitor(outputParameterVisitor, + hiddenStateModule))) + boost::apply_visitor(outputParameterVisitor, + hiddenStateModule); + + forwardStep++; + if (forwardStep == rho) + { + forwardStep = 0; + if (!deterministic) + { + outParameter.emplace_back(allZeros.memptr(), + allZeros.n_rows, allZeros.n_cols, false, true); + prevOutput = --outParameter.end(); + } + else + { + *prevOutput = arma::mat(allZeros.memptr(), + allZeros.n_rows, allZeros.n_cols, false, true); + } + } + else if (!deterministic) + { + outParameter.push_back(output); + prevOutput = --outParameter.end(); + } + else + { + if (forwardStep == 1) + { + outParameter.clear(); + outParameter.push_back(output); + + prevOutput = outParameter.begin(); + } + else + { + *prevOutput = output; + } + } +} + +template +template +void GRU::Backward( + const arma::Mat& input, const arma::Mat& gy, arma::Mat& g) +{ + if (input.n_cols != batchSize) + { + batchSize = input.n_cols; + prevError.resize(3 * outSize, batchSize); + allZeros.zeros(outSize, batchSize); + // Batch size better not change during an iteration... + if (outParameter.size() > 1) + { + Log::Fatal << "GRU<>::Forward(): batch size cannot change during a " + << "forward pass!" << std::endl; + } + + outParameter.clear(); + outParameter.emplace_back(allZeros.memptr(), + allZeros.n_rows, allZeros.n_cols, false, true); + + prevOutput = outParameter.begin(); + backIterator = outParameter.end(); + gradIterator = outParameter.end(); + } + + arma::Mat gyLocal; + if ((outParameter.size() - backwardStep - 1) % rho != 0 && backwardStep != 0) + { + gyLocal = gy + boost::apply_visitor(deltaVisitor, output2GateModule); + } + else + { + gyLocal = arma::Mat(((arma::Mat&) gy).memptr(), gy.n_rows, + gy.n_cols, false, false); + } + + if (backIterator == outParameter.end()) + { + backIterator = --(--outParameter.end()); + } + + // Delta zt. + arma::mat dZt = gyLocal % (*backIterator - + boost::apply_visitor(outputParameterVisitor, + hiddenStateModule)); + + // Delta ot. + arma::mat dOt = gyLocal % (arma::ones(outSize, batchSize) - + boost::apply_visitor(outputParameterVisitor, inputGateModule)); + + // Delta of input gate. + boost::apply_visitor(BackwardVisitor(boost::apply_visitor( + outputParameterVisitor, inputGateModule), dZt, + boost::apply_visitor(deltaVisitor, inputGateModule)), + inputGateModule); + + // Delta of hidden gate. + boost::apply_visitor(BackwardVisitor(boost::apply_visitor( + outputParameterVisitor, hiddenStateModule), dOt, + boost::apply_visitor(deltaVisitor, hiddenStateModule)), + hiddenStateModule); + + // Delta of outputHidden2GateModule. + boost::apply_visitor(BackwardVisitor(boost::apply_visitor( + outputParameterVisitor, outputHidden2GateModule), + boost::apply_visitor(deltaVisitor, hiddenStateModule), + boost::apply_visitor(deltaVisitor, outputHidden2GateModule)), + outputHidden2GateModule); + + // Delta rt. + arma::mat dRt = boost::apply_visitor(deltaVisitor, outputHidden2GateModule) % + *backIterator; + + // Delta of forget gate. + boost::apply_visitor(BackwardVisitor(boost::apply_visitor( + outputParameterVisitor, forgetGateModule), dRt, + boost::apply_visitor(deltaVisitor, forgetGateModule)), + forgetGateModule); + + // Put delta zt. + prevError.submat(0, 0, 1 * outSize - 1, batchSize - 1) = boost::apply_visitor( + deltaVisitor, inputGateModule); + + // Put delta rt. + prevError.submat(1 * outSize, 0, 2 * outSize - 1, batchSize - 1) = + boost::apply_visitor(deltaVisitor, forgetGateModule); + + // Put delta ot. + prevError.submat(2 * outSize, 0, 3 * outSize - 1, batchSize - 1) = + boost::apply_visitor(deltaVisitor, hiddenStateModule); + + // Get delta ht - 1 for input gate and forget gate. + arma::mat prevErrorSubview = prevError.submat(0, 0, 2 * outSize - 1, + batchSize - 1); + boost::apply_visitor(BackwardVisitor(boost::apply_visitor( + outputParameterVisitor, input2GateModule), + prevErrorSubview, + boost::apply_visitor(deltaVisitor, output2GateModule)), + output2GateModule); + + // Add delta ht - 1 from hidden state. + boost::apply_visitor(deltaVisitor, output2GateModule) += + boost::apply_visitor(deltaVisitor, outputHidden2GateModule) % + boost::apply_visitor(outputParameterVisitor, forgetGateModule); + + // Add delta ht - 1 from ht. + boost::apply_visitor(deltaVisitor, output2GateModule) += gyLocal % + boost::apply_visitor(outputParameterVisitor, inputGateModule); + + // Get delta input. + boost::apply_visitor(BackwardVisitor(boost::apply_visitor( + outputParameterVisitor, input2GateModule), prevError, + boost::apply_visitor(deltaVisitor, input2GateModule)), + input2GateModule); + + backwardStep++; + backIterator--; + + g = boost::apply_visitor(deltaVisitor, input2GateModule); +} + +template +template +void GRU::Gradient( + const arma::Mat& input, + const arma::Mat& /* error */, + arma::Mat& /* gradient */) +{ + if (input.n_cols != batchSize) + { + batchSize = input.n_cols; + prevError.resize(3 * outSize, batchSize); + allZeros.zeros(outSize, batchSize); + // Batch size better not change during an iteration... + if (outParameter.size() > 1) + { + Log::Fatal << "GRU<>::Forward(): batch size cannot change during a " + << "forward pass!" << std::endl; + } + + outParameter.clear(); + outParameter.emplace_back(allZeros.memptr(), + allZeros.n_rows, allZeros.n_cols, false, true); + + prevOutput = outParameter.begin(); + backIterator = outParameter.end(); + gradIterator = outParameter.end(); + } + + if (gradIterator == outParameter.end()) + { + gradIterator = --(--outParameter.end()); + } + + boost::apply_visitor(GradientVisitor(input, prevError), input2GateModule); + + boost::apply_visitor(GradientVisitor( + *gradIterator, + prevError.submat(0, 0, 2 * outSize - 1, batchSize - 1)), + output2GateModule); + + boost::apply_visitor(GradientVisitor( + *gradIterator % boost::apply_visitor(outputParameterVisitor, + forgetGateModule), + prevError.submat(2 * outSize, 0, 3 * outSize - 1, batchSize - 1)), + outputHidden2GateModule); + + gradIterator--; +} + +template +void GRU::ResetCell(const size_t /* size */) +{ + outParameter.clear(); + outParameter.emplace_back(allZeros.memptr(), + allZeros.n_rows, allZeros.n_cols, false, true); + + prevOutput = outParameter.begin(); + backIterator = outParameter.end(); + gradIterator = outParameter.end(); + + forwardStep = 0; + backwardStep = 0; +} + +template +template +void GRU::serialize( + Archive& ar, const uint32_t /* version */) +{ + // If necessary, clean memory from the old model. + if (cereal::is_loading()) + { + boost::apply_visitor(deleteVisitor, input2GateModule); + boost::apply_visitor(deleteVisitor, output2GateModule); + boost::apply_visitor(deleteVisitor, outputHidden2GateModule); + boost::apply_visitor(deleteVisitor, inputGateModule); + boost::apply_visitor(deleteVisitor, forgetGateModule); + boost::apply_visitor(deleteVisitor, hiddenStateModule); + } + + ar(CEREAL_NVP(inSize)); + ar(CEREAL_NVP(outSize)); + ar(CEREAL_NVP(rho)); + + ar(CEREAL_VARIANT_POINTER(input2GateModule)); + ar(CEREAL_VARIANT_POINTER(output2GateModule)); + ar(CEREAL_VARIANT_POINTER(outputHidden2GateModule)); + ar(CEREAL_VARIANT_POINTER(inputGateModule)); + ar(CEREAL_VARIANT_POINTER(forgetGateModule)); + ar(CEREAL_VARIANT_POINTER(hiddenStateModule)); +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/layer/not_adapted/hard_tanh.hpp b/src/mlpack/methods/ann/layer/hard_tanh.hpp similarity index 71% rename from src/mlpack/methods/ann/layer/not_adapted/hard_tanh.hpp rename to src/mlpack/methods/ann/layer/hard_tanh.hpp index b214ff095b..3c49add924 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/hard_tanh.hpp +++ b/src/mlpack/methods/ann/layer/hard_tanh.hpp @@ -14,8 +14,6 @@ #include -#include "layer.hpp" - namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -39,14 +37,16 @@ namespace ann /** Artificial Neural Network. */ { * \right. * @f} * - * @tparam InputType The type of the layer's inputs. The layer automatically - * cast inputs to this type (Default: arma::mat). - * @tparam OutputType The type of the computation which also causes the output - * to also be in this type. The type also allows the computation and weight - * type to differ from the input type (Default: arma::mat). + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class HardTanHType : public Layer +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class HardTanH { public: /** @@ -57,10 +57,7 @@ class HardTanHType : public Layer * @param maxValue Range of the linear region maximum value. * @param minValue Range of the linear region minimum value. */ - HardTanHType(const double maxValue = 1, const double minValue = -1); - - //! Clone the HardTanHType object. This handles polymorphism correctly. - HardTanHType* Clone() const { return new HardTanHType(*this); } + HardTanH(const double maxValue = 1, const double minValue = -1); /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -69,6 +66,7 @@ class HardTanHType : public Layer * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ + template void Forward(const InputType& input, OutputType& output); /** @@ -80,7 +78,20 @@ class HardTanHType : public Layer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const InputType& input, const OutputType& gy, OutputType& g); + template + void Backward(const DataType& input, + const DataType& gy, + DataType& g); + + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } //! Get the maximum value. double const& MaxValue() const { return maxValue; } @@ -99,17 +110,18 @@ class HardTanHType : public Layer void serialize(Archive& ar, const uint32_t /* version */); private: + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; + //! Maximum value for the HardTanH function. double maxValue; //! Minimum value for the HardTanH function. double minValue; -}; // class HardTanHType - -// Convenience typedefs. - -// Standard HardTanH layer. -typedef HardTanHType HardTanH; +}; // class HardTanH } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/not_adapted/hard_tanh_impl.hpp b/src/mlpack/methods/ann/layer/hard_tanh_impl.hpp similarity index 71% rename from src/mlpack/methods/ann/layer/not_adapted/hard_tanh_impl.hpp rename to src/mlpack/methods/ann/layer/hard_tanh_impl.hpp index 8778412d77..30eb8c2a1f 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/hard_tanh_impl.hpp +++ b/src/mlpack/methods/ann/layer/hard_tanh_impl.hpp @@ -18,8 +18,8 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -HardTanHType::HardTanHType( +template +HardTanH::HardTanH( const double maxValue, const double minValue) : maxValue(maxValue), @@ -28,10 +28,12 @@ HardTanHType::HardTanHType( // Nothing to do here. } +template template -void HardTanHType::Forward( +void HardTanH::Forward( const InputType& input, OutputType& output) { + output = input; for (size_t i = 0; i < input.n_elem; ++i) { output(i) = (output(i) > maxValue ? maxValue : @@ -39,9 +41,10 @@ void HardTanHType::Forward( } } -template -void HardTanHType::Backward( - const InputType& input, const OutputType& gy, OutputType& g) +template +template +void HardTanH::Backward( + const DataType& input, const DataType& gy, DataType& g) { g = gy; for (size_t i = 0; i < input.n_elem; ++i) @@ -53,14 +56,12 @@ void HardTanHType::Backward( } } -template +template template -void HardTanHType::serialize( +void HardTanH::serialize( Archive& ar, const uint32_t /* version */) { - ar(cereal::base_class>(this)); - ar(CEREAL_NVP(maxValue)); ar(CEREAL_NVP(minValue)); } diff --git a/src/mlpack/methods/ann/layer/not_adapted/hardshrink.hpp b/src/mlpack/methods/ann/layer/hardshrink.hpp similarity index 67% rename from src/mlpack/methods/ann/layer/not_adapted/hardshrink.hpp rename to src/mlpack/methods/ann/layer/hardshrink.hpp index df6c61756c..5817acb713 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/hardshrink.hpp +++ b/src/mlpack/methods/ann/layer/hardshrink.hpp @@ -17,8 +17,6 @@ #include -#include "layer.hpp" - namespace mlpack { namespace ann /** Artifical Neural Network. */ { @@ -39,28 +37,23 @@ namespace ann /** Artifical Neural Network. */ { * \f} * * \f$\lambda\f$ is set to 0.5 by default. - * - * @tparam InputType The type of the layer's inputs. The layer automatically - * cast inputs to this type (Default: arma::mat). - * @tparam OutputType The type of the computation which also causes the output - * to also be in this type. The type also allows the computation and weight - * type to differ from the input type (Default: arma::mat). */ -template -class HardShrinkType : public Layer +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class HardShrink { public: /** * Create HardShrink object using specified hyperparameter lambda. * - * @param lambda Is calculated by multiplying the noise level sigma of the - * input(noisy image) and a coefficient 'a' which is one of the training - * parameters. Default value of lambda is 0.5. + * @param lambda Is calculated by multiplying the + * noise level sigma of the input(noisy image) and a + * coefficient 'a' which is one of the training parameters. + * Default value of lambda is 0.5. */ - HardShrinkType(const double lambda = 0.5); - - //! Clone the HardShrinkType object. This handles polymorphism correctly. - HardShrinkType* Clone() const { return new HardShrinkType(*this); } + HardShrink(const double lambda = 0.5); /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -69,6 +62,7 @@ class HardShrinkType : public Layer * @param input Input data used for evaluating the Hard Shrink function. * @param output Resulting output activation. */ + template void Forward(const InputType& input, OutputType& output); /** @@ -80,26 +74,42 @@ class HardShrinkType : public Layer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const InputType& input, const OutputType& gy, OutputType& g); + template + void Backward(const DataType& input, + DataType& gy, + DataType& g); + + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } //! Get the hyperparameter lambda. double const& Lambda() const { return lambda; } //! Modify the hyperparameter lambda. double& Lambda() { return lambda; } - //! Serialize the layer. + /** + * Serialize the layer. + */ template void serialize(Archive& ar, const uint32_t /* version */); private: + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; + //! Locally-stored hyperparameter lambda. double lambda; -}; // class HardShrinkType - -// Convenience typedefs. - -// Standard HardShrink layer. -typedef HardShrinkType HardShrink; +}; // class HardShrink } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/not_adapted/hardshrink_impl.hpp b/src/mlpack/methods/ann/layer/hardshrink_impl.hpp similarity index 63% rename from src/mlpack/methods/ann/layer/not_adapted/hardshrink_impl.hpp rename to src/mlpack/methods/ann/layer/hardshrink_impl.hpp index b1098cbb32..5969a281dd 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/hardshrink_impl.hpp +++ b/src/mlpack/methods/ann/layer/hardshrink_impl.hpp @@ -20,35 +20,37 @@ namespace ann /** Artificial Neural Network. */ { // This constructor is called for Hard Shrink activation function. // 'lambda' is a hyperparameter. -template -HardShrinkType::HardShrinkType(const double lambda) : +template +HardShrink::HardShrink(const double lambda) : lambda(lambda) { // Nothing to do here. } +template template -void HardShrinkType::Forward( +void HardShrink::Forward( const InputType& input, OutputType& output) { output = ((input > lambda) + (input < -lambda)) % input; } -template -void HardShrinkType::Backward( - const InputType& input, const OutputType& gy, OutputType& g) +template +template +void HardShrink::Backward( + const DataType& input, DataType& gy, DataType& g) { - g = gy % (arma::ones(arma::size(input)) - (input == 0)); + DataType derivative; + derivative = (arma::ones(arma::size(input)) - (input == 0)); + g = gy % derivative; } -template +template template -void HardShrinkType::serialize( +void HardShrink::serialize( Archive& ar, const uint32_t /* version */) { - ar(cereal::base_class>(this)); - ar(CEREAL_NVP(lambda)); } diff --git a/src/mlpack/methods/ann/layer/highway.hpp b/src/mlpack/methods/ann/layer/highway.hpp new file mode 100644 index 0000000000..526a434d23 --- /dev/null +++ b/src/mlpack/methods/ann/layer/highway.hpp @@ -0,0 +1,270 @@ +/** + * @file methods/ann/layer/highway.hpp + * @author Konstantin Sidorov + * @author Saksham Bansal + * + * Definition of the Highway layer. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_LAYER_HIGHWAY_HPP +#define MLPACK_METHODS_ANN_LAYER_HIGHWAY_HPP + +#include + +#include "../visitor/delete_visitor.hpp" +#include "../visitor/delta_visitor.hpp" +#include "../visitor/output_height_visitor.hpp" +#include "../visitor/output_parameter_visitor.hpp" +#include "../visitor/output_width_visitor.hpp" + +#include "layer_types.hpp" +#include "add_merge.hpp" + +namespace mlpack { +namespace ann /** Artificial Neural Network. */ { + +/** + * Implementation of the Highway layer. The Highway class can vary its behavior + * between that of feed-forward fully connected network container and that + * of a layer which simply passes its inputs through depending on the transform + * gate. Note that the size of the input and output matrices of this class + * should be equal. + * + * For more information, refer the following paper. + * + * @code + * @article{Srivastava2015, + * author = {Rupesh Kumar Srivastava, Klaus Greff, Jurgen Schmidhuber}, + * title = {Training Very Deep Networks}, + * journal = {Advances in Neural Information Processing Systems}, + * year = {2015}, + * url = {https://arxiv.org/abs/1507.06228}, + * } + * @endcode + * + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + */ +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat, + typename... CustomLayers> +class Highway +{ + public: + //! Create the Highway object. + Highway(); + + /** + * Create the Highway object. + * + * @param inSize The number of input units. + * @param model Expose all the network modules. + */ + Highway(const size_t inSize, const bool model = true); + + //! Destroy the Highway object. + ~Highway(); + + /** + * Reset the layer parameter. + */ + void Reset(); + + /** + * Ordinary feed-forward pass of a neural network, evaluating the function + * f(x) by propagating the activity forward through f. + * + * @param input Input data used for evaluating the specified function. + * @param output Resulting output activation. + */ + template + void Forward(const arma::Mat& input, arma::Mat& output); + + /** + * Ordinary feed-backward pass of a neural network, calculating the function + * f(x) by propagating x backwards through f. Using the results from the + * feed-forward pass. + * + * @param * (input) The propagated input activation. + * @param gy The backpropagated error. + * @param g The calculated gradient. + */ + template + void Backward(const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g); + + /** + * Calculate the gradient using the output delta and the input activation. + * + * @param input The input parameter used for calculating the gradient. + * @param error The calculated error. + * @param gradient The calculated gradient. + */ + template + void Gradient(const arma::Mat& input, + const arma::Mat& error, + arma::Mat& gradient); + + /** + * Add a new module to the model. + * + * @param args The layer parameter. + */ + template + void Add(Args... args) + { + network.push_back(new LayerType(args...)); + networkOwnerships.push_back(true); + } + + /** + * Add a new module to the model. + * + * @param layer The Layer to be added to the model. + */ + void Add(LayerTypes layer) + { + network.push_back(layer); + networkOwnerships.push_back(false); + } + + //! Return the modules of the model. + std::vector >& Model() + { + if (model) + { + return network; + } + + return empty; + } + + //! Get the parameters. + OutputDataType const& Parameters() const { return weights; } + //! Modify the parameters. + OutputDataType& Parameters() { return weights; } + + //! Get the input parameter. + InputDataType const& InputParameter() const { return inputParameter; } + //! Modify the input parameter. + InputDataType& InputParameter() { return inputParameter; } + + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } + + //! Get the gradient. + OutputDataType const& Gradient() const { return gradient; } + //! Modify the gradient. + OutputDataType& Gradient() { return gradient; } + + //! Get the number of input units. + size_t InSize() const { return inSize; } + + //! Get the shape of the input. + size_t InputShape() const + { + return inSize; + } + + /** + * Serialize the layer. + */ + template + void serialize(Archive& ar, const uint32_t /* version */); + + private: + //! Locally-stored number of input units. + size_t inSize; + + //! Parameter which indicates if the modules should be exposed. + bool model; + + //! Indicator if we already initialized the model. + bool reset; + + //! Locally-stored network modules. + std::vector > network; + + //! The list of network modules we are responsible for. + std::vector networkOwnerships; + + //! Locally-stored empty list of modules. + std::vector > empty; + + //! Locally-stored weight object. + OutputDataType weights; + + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored gradient object. + OutputDataType gradient; + + //! Weights for transformation of output. + OutputDataType transformWeight; + + //! Bias for transformation of output. + OutputDataType transformBias; + + //! Locally-stored transform gate parameters. + OutputDataType transformGate; + + //! Locally-stored transform gate activation. + OutputDataType transformGateActivation; + + //! Locally-stored transform gate error. + OutputDataType transformGateError; + + //! Locally-stored input parameter object. + InputDataType inputParameter; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; + + //! The input width. + size_t width; + + //! The input height. + size_t height; + + //! The normal output without highway network. + OutputDataType networkOutput; + + //! Locally-stored delta visitor. + DeltaVisitor deltaVisitor; + + //! Locally-stored output parameter visitor. + OutputParameterVisitor outputParameterVisitor; + + //! Locally-stored delete visitor. + DeleteVisitor deleteVisitor; + + //! Locally-stored output width visitor. + OutputWidthVisitor outputWidthVisitor; + + //! Locally-stored output height visitor. + OutputHeightVisitor outputHeightVisitor; +}; // class Highway + +} // namespace ann +} // namespace mlpack + +// Include implementation. +#include "highway_impl.hpp" + +#endif diff --git a/src/mlpack/methods/ann/layer/highway_impl.hpp b/src/mlpack/methods/ann/layer/highway_impl.hpp new file mode 100644 index 0000000000..00418e03a3 --- /dev/null +++ b/src/mlpack/methods/ann/layer/highway_impl.hpp @@ -0,0 +1,238 @@ +/** + * @file methods/ann/layer/highway_impl.hpp + * @author Konstantin Sidorov + * @author Saksham Bansal + * + * Implementation of Highway layer. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_LAYER_HIGHWAY_IMPL_HPP +#define MLPACK_METHODS_ANN_LAYER_HIGHWAY_IMPL_HPP + +// In case it hasn't yet been included. +#include "highway.hpp" + +#include "../visitor/forward_visitor.hpp" +#include "../visitor/backward_visitor.hpp" +#include "../visitor/gradient_visitor.hpp" +#include "../visitor/set_input_height_visitor.hpp" +#include "../visitor/set_input_width_visitor.hpp" + +namespace mlpack { +namespace ann /** Artificial Neural Network. */ { + +template +Highway::Highway() : + inSize(0), + model(true), + reset(false), + width(0), + height(0) +{ + // Nothing to do here. +} + +template< + typename InputDataType, typename OutputDataType, typename... CustomLayers> +Highway::Highway( + const size_t inSize, + const bool model) : + inSize(inSize), + model(model), + reset(false), + width(0), + height(0) +{ + weights.set_size(inSize * inSize + inSize, 1); +} + +template +Highway::~Highway() +{ + if (!model) + { + for (size_t i = 0; i < network.size(); ++i) + { + if (networkOwnerships[i]) + boost::apply_visitor(deleteVisitor, network[i]); + } + } +} + +template +void Highway::Reset() +{ + transformWeight = arma::mat(weights.memptr(), inSize, inSize, false, false); + transformBias = arma::mat(weights.memptr() + transformWeight.n_elem, + inSize, 1, false, false); +} + +template +template +void Highway::Forward( + const arma::Mat& input, arma::Mat& output) +{ + boost::apply_visitor(ForwardVisitor(input, + boost::apply_visitor(outputParameterVisitor, network.front())), + network.front()); + + if (!reset) + { + if (boost::apply_visitor(outputWidthVisitor, network.front()) != 0) + { + width = boost::apply_visitor(outputWidthVisitor, network.front()); + } + + if (boost::apply_visitor(outputHeightVisitor, network.front()) != 0) + { + height = boost::apply_visitor(outputHeightVisitor, network.front()); + } + } + + for (size_t i = 1; i < network.size(); ++i) + { + if (!reset) + { + // Set the input width. + boost::apply_visitor(SetInputWidthVisitor(width), network[i]); + + // Set the input height. + boost::apply_visitor(SetInputHeightVisitor(height), network[i]); + } + + boost::apply_visitor(ForwardVisitor(boost::apply_visitor( + outputParameterVisitor, network[i - 1]), + boost::apply_visitor(outputParameterVisitor, network[i])), + network[i]); + + if (!reset) + { + // Get the output width. + if (boost::apply_visitor(outputWidthVisitor, network[i]) != 0) + { + width = boost::apply_visitor(outputWidthVisitor, network[i]); + } + + // Get the output height. + if (boost::apply_visitor(outputHeightVisitor, network[i]) != 0) + { + height = boost::apply_visitor(outputHeightVisitor, network[i]); + } + } + } + if (!reset) + { + reset = true; + } + + output = boost::apply_visitor(outputParameterVisitor, network.back()); + + if (arma::size(output) != arma::size(input)) + { + Log::Fatal << "The sizes of the output and input matrices of the Highway" + << " network should be equal. Please examine the network layers."; + } + + transformGate = transformWeight * input; + transformGate.each_col() += transformBias; + transformGateActivation = 1.0 /(1 + arma::exp(-transformGate)); + inputParameter = input; + networkOutput = output; + output = (output % transformGateActivation) + + (input % (1 - transformGateActivation)); +} + +template +template +void Highway::Backward( + const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g) +{ + arma::Mat gyTransform = gy % transformGateActivation; + boost::apply_visitor(BackwardVisitor(boost::apply_visitor( + outputParameterVisitor, network.back()), + gyTransform, + boost::apply_visitor(deltaVisitor, network.back())), + network.back()); + + for (size_t i = 2; i < network.size() + 1; ++i) + { + boost::apply_visitor(BackwardVisitor(boost::apply_visitor( + outputParameterVisitor, network[network.size() - i]), + boost::apply_visitor(deltaVisitor, network[network.size() - i + 1]), + boost::apply_visitor(deltaVisitor, + network[network.size() - i])), network[network.size() - i]); + } + + g = boost::apply_visitor(deltaVisitor, network.front()); + + transformGateError = gy % (networkOutput - inputParameter) % + transformGateActivation % (1.0 - transformGateActivation); + g += transformWeight.t() * transformGateError; + g += gy % (1 - transformGateActivation); +} + +template +template +void Highway::Gradient( + const arma::Mat& input, + const arma::Mat& error, + arma::Mat& gradient) +{ + arma::Mat errorTransform = error % transformGateActivation; + boost::apply_visitor(GradientVisitor(boost::apply_visitor( + outputParameterVisitor, network[network.size() - 2]), + errorTransform), network.back()); + + for (size_t i = 2; i < network.size(); ++i) + { + boost::apply_visitor(GradientVisitor(boost::apply_visitor( + outputParameterVisitor, network[network.size() - i - 1]), + boost::apply_visitor(deltaVisitor, network[network.size() - i + 1])), + network[network.size() - i]); + } + + boost::apply_visitor(GradientVisitor(input, + boost::apply_visitor(deltaVisitor, network[1])), network.front()); + + gradient.submat(0, 0, transformWeight.n_elem - 1, 0) = arma::vectorise( + transformGateError * input.t()); + gradient.submat(transformWeight.n_elem, 0, gradient.n_elem - 1, 0) = + arma::sum(transformGateError, 1); +} + +template +template +void Highway::serialize( + Archive& ar, const uint32_t /* version */) +{ + // If loading, delete the old layers and set size for weights. + if (cereal::is_loading()) + { + for (LayerTypes& layer : network) + { + boost::apply_visitor(deleteVisitor, layer); + } + weights.set_size(inSize * inSize + inSize, 1); + } + + ar(CEREAL_NVP(model)); + ar(CEREAL_VECTOR_VARIANT_POINTER(network)); +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/layer/not_adapted/instance_norm.hpp b/src/mlpack/methods/ann/layer/instance_norm.hpp similarity index 100% rename from src/mlpack/methods/ann/layer/not_adapted/instance_norm.hpp rename to src/mlpack/methods/ann/layer/instance_norm.hpp diff --git a/src/mlpack/methods/ann/layer/not_adapted/instance_norm_impl.hpp b/src/mlpack/methods/ann/layer/instance_norm_impl.hpp similarity index 100% rename from src/mlpack/methods/ann/layer/not_adapted/instance_norm_impl.hpp rename to src/mlpack/methods/ann/layer/instance_norm_impl.hpp diff --git a/src/mlpack/methods/ann/layer/not_adapted/isrlu.hpp b/src/mlpack/methods/ann/layer/isrlu.hpp similarity index 100% rename from src/mlpack/methods/ann/layer/not_adapted/isrlu.hpp rename to src/mlpack/methods/ann/layer/isrlu.hpp diff --git a/src/mlpack/methods/ann/layer/not_adapted/isrlu_impl.hpp b/src/mlpack/methods/ann/layer/isrlu_impl.hpp similarity index 100% rename from src/mlpack/methods/ann/layer/not_adapted/isrlu_impl.hpp rename to src/mlpack/methods/ann/layer/isrlu_impl.hpp diff --git a/src/mlpack/methods/ann/layer/not_adapted/join.hpp b/src/mlpack/methods/ann/layer/join.hpp similarity index 61% rename from src/mlpack/methods/ann/layer/not_adapted/join.hpp rename to src/mlpack/methods/ann/layer/join.hpp index ee5a7acd00..38286a9508 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/join.hpp +++ b/src/mlpack/methods/ann/layer/join.hpp @@ -17,30 +17,24 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -// TODO: should we clarify the comments? This seems to join together points of -// a different batch -// TODO: I don't understand this layer well enough to update it... /** * Implementation of the Join module class. The Join class accumulates * the output of various modules. * - * @tparam InputType Type of the input data (arma::colvec, arma::mat, + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). - * @tparam OutputType Type of the output data (arma::colvec, arma::mat, + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). */ template< - typename InputType = arma::mat, - typename OutputType = arma::mat + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat > -class JoinType : public Layer +class Join { public: - //! Create the JoinType object. - JoinType(); - - //! Clone the JoinType object. This handles polymorphism correctly. - JoinType* Clone() const { return new JoinType(*this); } + //! Create the Join object. + Join(); /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -49,6 +43,7 @@ class JoinType : public Layer * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ + template void Forward(const InputType& input, OutputType& output); /** @@ -60,19 +55,20 @@ class JoinType : public Layer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const InputType& /* input */, - const OutputType& gy, - OutputType& g); + template + void Backward(const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g); - // This layer simply flattens its input into a vector. - const std::vector OutputDimensions() const - { - // TODO: it's not clear what to do here - std::vector result(inputDimensions.size(), 0); - result[0] = std::accumulate(inputDimensions.begin(), inputDimensions.end(), - 0); - return result; - } + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } /** * Serialize the layer. @@ -86,10 +82,13 @@ class JoinType : public Layer //! Locally-stored number of input cols. size_t inSizeCols; -}; // class JoinType -//Standard Join layer. -typedef JoinType Join; + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; +}; // class Join } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/not_adapted/join_impl.hpp b/src/mlpack/methods/ann/layer/join_impl.hpp similarity index 63% rename from src/mlpack/methods/ann/layer/not_adapted/join_impl.hpp rename to src/mlpack/methods/ann/layer/join_impl.hpp index aef5a9cc5a..a886d7ac44 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/join_impl.hpp +++ b/src/mlpack/methods/ann/layer/join_impl.hpp @@ -18,16 +18,17 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -JoinType::JoinType() : +template +Join::Join() : inSizeRows(0), inSizeCols(0) { // Nothing to do here. } +template template -void JoinType::Forward( +void Join::Forward( const InputType& input, OutputType& output) { inSizeRows = input.n_rows; @@ -35,24 +36,23 @@ void JoinType::Forward( output = arma::vectorise(input); } -template -void JoinType::Backward( - const InputType& /* input */, - const OutputType& gy, - OutputType& g) +template +template +void Join::Backward( + const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g) { - g = OutputType(((OutputType&) gy).memptr(), inSizeRows, inSizeCols, false, + g = arma::mat(((arma::Mat&) gy).memptr(), inSizeRows, inSizeCols, false, false); } -template +template template -void JoinType::serialize( +void Join::serialize( Archive& ar, const uint32_t /* version */) { - ar(cereal::base_class>(this)); - ar(CEREAL_NVP(inSizeRows)); ar(CEREAL_NVP(inSizeCols)); } diff --git a/src/mlpack/methods/ann/layer/layer.hpp b/src/mlpack/methods/ann/layer/layer.hpp index c3bfc6a593..8aca0ad270 100644 --- a/src/mlpack/methods/ann/layer/layer.hpp +++ b/src/mlpack/methods/ann/layer/layer.hpp @@ -2,7 +2,7 @@ * @file methods/ann/layer/layer.hpp * @author Marcus Edel * - * Base class for neural network layers. + * This includes various layers to construct a model. * * mlpack is free software; you may redistribute it and/or modify it under the * terms of the 3-clause BSD license. You should have received a copy of the @@ -12,312 +12,73 @@ #ifndef MLPACK_METHODS_ANN_LAYER_LAYER_HPP #define MLPACK_METHODS_ANN_LAYER_LAYER_HPP -namespace mlpack { -namespace ann { - -/** - * A layer is an abstract class implementing common neural networks operations, - * such as convolution, batch norm, etc. These operations require managing - * weights, losses, updates, and inter-layer connectivity. - * - * Users will just instantiate a layer by inherited from the abstract class and - * implement the layer specific methods. It is recommend that descendants of - * Layer implement the following methods: - * - * - Constructor: Defines custom layer attributes, and creates layer state - * variables. - * - * - Forward(input, output): Performs the forward logic of applying the layer - * to the input object and storing the result in the output object. - * - * - Backward(input, gy, g): Performs a backpropagation step through the layer, - * with respect to the given input. - * - * - Gradient(input, error, gradient): Computing the gradient of the layer with - * respect to its own input. - * - * The memory for the layer's parameters (weights and biases) is not allocated - * by the layer itself, instead it is allocated by the network that the layer - * belongs to, and passed to the layer when it needs to use it. - * - * See the linear layer implementation for a basic example. It's a layer with - * two variables, w and b, that returns y = w * x + b. It shows how to implement - * Forward(), Backward() and Gradient(). The weights of the layers are tracked - * in layer.Parameters(). - * - * @tparam MatType Matrix representation to accept as input and use for - * computation. - */ -template -class Layer -{ - public: - //! Default constructor. - Layer() : validOutputDimensions(false), training(false) - { /* Nothing to do here */ } - - //! Default deconstructor. - virtual ~Layer() { /* Nothing to do here */ } - - //! Copy constructor. This is not responsible for copying weights! - Layer(const Layer& layer) : - inputDimensions(layer.inputDimensions), - outputDimensions(layer.outputDimensions), - validOutputDimensions(layer.validOutputDimensions), - training(layer.training) - { /* Nothing to do here */ } - - //! Make a copy of the object. - virtual Layer* Clone() const = 0; - - //! Move constructor. This is not responsible for moving weights! - Layer(Layer&& layer) : - inputDimensions(std::move(layer.inputDimensions)), - outputDimensions(std::move(layer.outputDimensions)), - validOutputDimensions(std::move(layer.validOutputDimensions)), - training(std::move(layer.training)) - { /* Nothing to do here */ } - - //! Copy assignment operator. This is not responsible for copying weights! - virtual Layer& operator=(const Layer& layer) - { - if (&layer != this) - { - inputDimensions = layer.inputDimensions; - outputDimensions = layer.outputDimensions; - validOutputDimensions = layer.validOutputDimensions; - training = layer.training; - } - - return *this; - } - - //! Move assignment operator. This is not responsible for moving weights! - virtual Layer& operator=(Layer&& layer) - { - if (&layer != this) - { - inputDimensions = std::move(layer.inputDimensions); - outputDimensions = std::move(layer.outputDimensions); - validOutputDimensions = std::move(layer.validOutputDimensions); - training = std::move(layer.training); - } - - return *this; - } - - /** - * Takes an input object, and computes the corresponding output of the layer. - * In general input and output are matrices. However, some special layers like - * table layers might expect something else. Please, refer to each layer - * specification for further information. - * - * @param * (input) Input data used for evaluating the specified layer. - * @param * (output) Resulting output. - */ - virtual void Forward(const MatType& /* input */, - MatType& /* output */) - { /* Nothing to do here */ } - - /** - * Takes an input and output object, and computes the corresponding loss of - * the layer. In general input and output are matrices. However, some special - * layers like table layers might expect something else. Please, refer to each - * layer specification for further information. - * - * @param * (input) Input data used for evaluating the specified layer. - * @param * (output) Resulting output. - */ - virtual void Forward(const MatType& /* input */, - const MatType& /* output */) - { /* Nothing to do here */ } - - /** - * Performs a backpropagation step through the layer, with respect to the - * given input. In general this method makes the assumption Forward(input, - * output) has been called before, with the same input. If you do not respect - * this rule, Backward(input, gy, g) might compute incorrect results. - * - * In general input and gy and g are matrices. However, some special - * sub-classes like table layers might expect something else. Please, refer to - * each module specification for further information. - * - * A backpropagation step consist of computing of computing the gradient - * output input with respect to the output of the layer and given error. - * - * During the backward pass our goal is to use 'gy' in order to compute the - * downstream gradients (g). We assume that the upstream gradient (gy) has - * already been computed and is passed to the layer. - * - * @param * (input) The propagated input activation. - * @param * (gy) The backpropagated error. - * @param * (g) The calculated gradient. - */ - virtual void Backward(const MatType& /* input */, - const MatType& /* gy */, - MatType& /* g */) - { /* Nothing to do here */ } - - /** - * Computing the gradient of the layer with respect to its own input. This is - * returned in gradient. - * - * The layer parameters (weights and biases) are updated accordingly using the - * computed gradient not by the layer itself, instead they are updated by the - * network that holds the instantiated layer. - * - * @param * (input) The input parameter used for calculating the gradient. - * @param * (error) The calculated error. - * @param * (gradient) The calculated gradient. - */ - virtual void Gradient(const MatType& /* input */, - const MatType& /* error */, - MatType& /* gradient */) - { /* Nothing to do here */ } - - /** - * Reset the layer parameter. The method is called to assigned the allocated - * memory to the internal layer parameters like weights and biases. The method - * should be called before the first call of Forward(input, output). If you - * do not respect this rule, Forward(input, output) and Backward(input, gy, g) - * might compute incorrect results. - * - * @param weightsPtr This pointer should be used as the first element of the - * memory that is allocated for this layer. In general, SetWeights() - * implementations should use MakeAlias() with weightsPtr to wrap the - * weights of a layer. - */ - virtual void SetWeights(typename MatType::elem_type* /* weightsPtr */) { } - - /** - * Get the total number of trainable weights in the layer. - */ - virtual size_t WeightSize() const { return 0; } - - /** - * Get whether the layer is currently in training mode. - * - * @note During network training, this should be set to `true` for each layer - * in the network, and when predicting/testing the network, this should be set - * to `false`. (This is handled automatically by the `FFN` class and other - * related classes.) - */ - virtual bool const& Training() const { return training; } - - /** - * Modify whether the layer is currently in training mode. - * - * @note During network training, this should be set to `true` for each layer - * in the network, and when predicting/testing the network, this should be set - * to `false`. (This is handled automatically by the `FFN` class and other - * related classes.) - */ - virtual bool& Training() { return training; } - - //! Get the layer loss. Overload this if the layer should add any extra loss - //! to the loss function when computing the objective. (TODO: better comment) - virtual double Loss() { return 0; } - - //! Get the input dimensions. - const std::vector& InputDimensions() const { return inputDimensions; } - //! Modify the input dimensions. - std::vector& InputDimensions() - { - validOutputDimensions = false; - return inputDimensions; - } - - //! Get the output dimensions. - const std::vector& OutputDimensions() - { - if (!validOutputDimensions) - { - this->ComputeOutputDimensions(); - validOutputDimensions = true; - } - - return outputDimensions; - } - - //! Get the parameters. - virtual const MatType& Parameters() const - { - throw std::invalid_argument("Layer::Parameters(): cannot access parameters " - "of a layer with no weights!"); - } - //! Set the parameters. - virtual MatType& Parameters() - { - throw std::invalid_argument("Layer::Parameters(): cannot modify parameters " - "of a layer with no weights!"); - } - - //! Compute the output dimensions. This should be overloaded if the layer is - //! meant to work on higher-dimensional objects. When this is called, it is a - //! safe assumption that InputDimensions() is correct. - virtual void ComputeOutputDimensions() - { - // The default implementation is to assume that the output size is the same - // as the input. - outputDimensions = inputDimensions; - } - - //! Get the number of elements in the output from this layer. This cannot be - //! overloaded! Overload `ComputeOutputDimensions()` instead. - virtual size_t OutputSize() final - { - if (!validOutputDimensions) - { - this->ComputeOutputDimensions(); - validOutputDimensions = true; - } - - size_t outputSize = 1; - for (size_t i = 0; i < this->outputDimensions.size(); ++i) - outputSize *= this->outputDimensions[i]; - return outputSize; - } - - //! Serialize the layer. - template - void serialize(Archive& ar, const uint32_t /* version */) - { - ar(CEREAL_NVP(inputDimensions)); - ar(CEREAL_NVP(outputDimensions)); - ar(CEREAL_NVP(validOutputDimensions)); - ar(CEREAL_NVP(training)); - - // Note that layer weights are serialized by the FFN! - } - - protected: - /** - * Logical input dimensions of each point. Although each point given to ! - * `Forward()` will be represented as a column in a matrix, logically - * speaking it can be a higher-order tensor. So, for instance, if the point - * is 2-dimensional images of size 10x10, `Forward()` will contain columns - * with 100 rows, and `inputDimensions` will be `{10, 10}`. This generalizes - * to higher dimensions. - */ - std::vector inputDimensions; - - /** - * Logical output dimensions of each point. If the layer only performs - * elementwise operations, this is most likely equal to `inputDimensions`; but - * if the layer performs more complicated transformations, it may be - * different. - */ - std::vector outputDimensions; - - //! This is `true` if `ComputeOutputDimensions()` has been called, and - //! `outputDimensions` can be considered to be up-to-date. - bool validOutputDimensions; - - //! If true, the layer is in training mode; otherwise, it is in testing mode. - bool training; -}; - -} // namespace ann -} // namespace mlpack +#include "add.hpp" +#include "adaptive_max_pooling.hpp" +#include "adaptive_mean_pooling.hpp" +#include "add_merge.hpp" +#include "alpha_dropout.hpp" +#include "atrous_convolution.hpp" +#include "base_layer.hpp" +#include "batch_norm.hpp" +#include "bicubic_interpolation.hpp" +#include "bilinear_interpolation.hpp" +#include "c_relu.hpp" +#include "celu.hpp" +#include "concat_performance.hpp" +#include "concat.hpp" +#include "concatenate.hpp" +#include "constant.hpp" +#include "convolution.hpp" +#include "dropconnect.hpp" +#include "dropout.hpp" +#include "elu.hpp" +#include "fast_lstm.hpp" +#include "flatten_t_swish.hpp" +#include "flexible_relu.hpp" +#include "glimpse.hpp" +#include "gru.hpp" +#include "hard_tanh.hpp" +#include "hardshrink.hpp" +#include "highway.hpp" +#include "instance_norm.hpp" +#include "join.hpp" +#include "layer_norm.hpp" +#include "layer_types.hpp" +#include "leaky_relu.hpp" +#include "linear.hpp" +#include "linear_no_bias.hpp" +#include "linear3d.hpp" +#include "log_softmax.hpp" +#include "lookup.hpp" +#include "lp_pooling.hpp" +#include "lstm.hpp" +#include "max_pooling.hpp" +#include "mean_pooling.hpp" +#include "minibatch_discrimination.hpp" +#include "multihead_attention.hpp" +#include "multiply_constant.hpp" +#include "multiply_merge.hpp" +#include "nearest_interpolation.hpp" +#include "noisylinear.hpp" +#include "padding.hpp" +#include "parametric_relu.hpp" +#include "pixel_shuffle.hpp" +#include "positional_encoding.hpp" +#include "recurrent_attention.hpp" +#include "recurrent.hpp" +#include "reinforce_normal.hpp" +#include "relu6.hpp" +#include "reparametrization.hpp" +#include "select.hpp" +#include "sequential.hpp" +#include "softshrink.hpp" +#include "softmax.hpp" +#include "softmin.hpp" +#include "spatial_dropout.hpp" +#include "subview.hpp" +#include "transposed_convolution.hpp" +#include "virtual_batch_norm.hpp" +#include "vr_class_reward.hpp" +#include "weight_norm.hpp" #endif diff --git a/src/mlpack/methods/ann/layer/not_adapted/layer_norm.hpp b/src/mlpack/methods/ann/layer/layer_norm.hpp similarity index 67% rename from src/mlpack/methods/ann/layer/not_adapted/layer_norm.hpp rename to src/mlpack/methods/ann/layer/layer_norm.hpp index 822e09fc7d..c22408c221 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/layer_norm.hpp +++ b/src/mlpack/methods/ann/layer/layer_norm.hpp @@ -53,20 +53,20 @@ namespace ann /** Artificial Neural Network. */ { * } * @endcode * - * @tparam InputType Type of the input data (arma::colvec, arma::mat, + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). - * @tparam OutputType Type of the output data (arma::colvec, arma::mat, + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). */ template < - typename InputType = arma::mat, - typename OutputType = arma::mat + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat > -class LayerNormType : public Layer +class LayerNorm { public: - //! Create the LayerNormType object. - LayerNormType(); + //! Create the LayerNorm object. + LayerNorm(); /** * Create the LayerNorm object for a specified number of input units. @@ -74,10 +74,7 @@ class LayerNormType : public Layer * @param size The number of input units. * @param eps The epsilon added to variance to ensure numerical stability. */ - LayerNormType(const size_t size, const double eps = 1e-8); - - //! Clone the LayerNormType object. This handles polymorphism correctly. - LayerNormType* Clone() const { return new LayerNormType(*this); } + LayerNorm(const size_t size, const double eps = 1e-8); /** * Reset the layer parameters. @@ -92,7 +89,8 @@ class LayerNormType : public Layer * @param input Input data for the layer. * @param output Resulting output activations. */ - void Forward(const InputType& input, OutputType& output); + template + void Forward(const arma::Mat& input, arma::Mat& output); /** * Backward pass through the layer. @@ -101,9 +99,10 @@ class LayerNormType : public Layer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const InputType& input, - const OutputType& gy, - OutputType& g); + template + void Backward(const arma::Mat& input, + const arma::Mat& gy, + arma::Mat& g); /** * Calculate the gradient using the output delta and the input activations. @@ -112,20 +111,36 @@ class LayerNormType : public Layer * @param error The calculated error. * @param gradient The calculated gradient. */ - void Gradient(const InputType& input, - const OutputType& error, - OutputType& gradient); + template + void Gradient(const arma::Mat& input, + const arma::Mat& error, + arma::Mat& gradient); //! Get the parameters. - OutputType const& Parameters() const { return weights; } + OutputDataType const& Parameters() const { return weights; } //! Modify the parameters. - OutputType& Parameters() { return weights; } + OutputDataType& Parameters() { return weights; } + + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } + + //! Get the gradient. + OutputDataType const& Gradient() const { return gradient; } + //! Modify the gradient. + OutputDataType& Gradient() { return gradient; } //! Get the mean across single training data. - OutputType Mean() { return mean; } + OutputDataType Mean() { return mean; } //! Get the variance across single training data. - OutputType Variance() { return variance; } + OutputDataType Variance() { return variance; } //! Get the number of input units. size_t InSize() const { return size; } @@ -133,7 +148,11 @@ class LayerNormType : public Layer //! Get the value of epsilon. double Epsilon() const { return eps; } - const size_t WeightSize() const { return 2 * size; } + //! Get the shape of the input. + size_t InputShape() const + { + return size; + } /** * Serialize the layer. @@ -152,29 +171,35 @@ class LayerNormType : public Layer bool loading; //! Locally-stored scale parameter. - OutputType gamma; + OutputDataType gamma; //! Locally-stored shift parameter. - OutputType beta; + OutputDataType beta; //! Locally-stored parameters. - OutputType weights; + OutputDataType weights; //! Locally-stored mean object. - OutputType mean; + OutputDataType mean; //! Locally-stored variance object. - OutputType variance; + OutputDataType variance; + + //! Locally-stored gradient object. + OutputDataType gradient; + + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; //! Locally-stored normalized input. - OutputType normalized; + OutputDataType normalized; //! Locally-stored zero mean input. - OutputType inputMean; -}; // class LayerNormType - -// Standard LayerNorm type -typedef LayerNormType LayerNorm; + OutputDataType inputMean; +}; // class LayerNorm } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/not_adapted/layer_norm_impl.hpp b/src/mlpack/methods/ann/layer/layer_norm_impl.hpp similarity index 60% rename from src/mlpack/methods/ann/layer/not_adapted/layer_norm_impl.hpp rename to src/mlpack/methods/ann/layer/layer_norm_impl.hpp index 3381bc93fe..ae4ced5cf8 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/layer_norm_impl.hpp +++ b/src/mlpack/methods/ann/layer/layer_norm_impl.hpp @@ -20,8 +20,8 @@ namespace mlpack { namespace ann { /** Artificial Neural Network. */ -template -LayerNormType::LayerNormType() : +template +LayerNorm::LayerNorm() : size(0), eps(1e-8), loading(false) @@ -29,8 +29,8 @@ LayerNormType::LayerNormType() : // Nothing to do here. } -template -LayerNormType::LayerNormType( +template +LayerNorm::LayerNorm( const size_t size, const double eps) : size(size), eps(eps), @@ -39,12 +39,11 @@ LayerNormType::LayerNormType( weights.set_size(size + size, 1); } -template -void LayerNormType::SetWeights( - typename OutputType::elem_type* weightsPtr) +template +void LayerNorm::Reset() { - gamma = OutputType(weightsPtr, size, 1, false, false); - beta = OutputType(weightsPtr + gamma.n_elem, size, 1, false, false); + gamma = arma::mat(weights.memptr(), size, 1, false, false); + beta = arma::mat(weights.memptr() + gamma.n_elem, size, 1, false, false); if (!loading) { @@ -55,9 +54,10 @@ void LayerNormType::SetWeights( loading = false; } -template -void LayerNormType::Forward( - const InputType& input, OutputType& output) +template +template +void LayerNorm::Forward( + const arma::Mat& input, arma::Mat& output) { mean = arma::mean(input, 0); variance = arma::var(input, 1, 0); @@ -75,17 +75,18 @@ void LayerNormType::Forward( output.each_col() += beta; } -template -void LayerNormType::Backward( - const InputType& input, const OutputType& gy, OutputType& g) +template +template +void LayerNorm::Backward( + const arma::Mat& input, const arma::Mat& gy, arma::Mat& g) { - const OutputType stdInv = 1.0 / arma::sqrt(variance + eps); + const arma::mat stdInv = 1.0 / arma::sqrt(variance + eps); // dl / dxhat. - const OutputType norm = gy.each_col() % gamma; + const arma::mat norm = gy.each_col() % gamma; // sum dl / dxhat * (x - mu) * -0.5 * stdInv^3. - const OutputType var = arma::sum(norm % inputMean, 0) % + const arma::mat var = arma::sum(norm % inputMean, 0) % arma::pow(stdInv, 3.0) * -0.5; // dl / dxhat * 1 / stdInv + variance * 2 * (x - mu) / m + @@ -98,11 +99,12 @@ void LayerNormType::Backward( g.each_row() += arma::sum(norm.each_row() % -stdInv, 0) / input.n_rows; } -template -void LayerNormType::Gradient( - const InputType& /* input */, - const OutputType& error, - OutputType& gradient) +template +template +void LayerNorm::Gradient( + const arma::Mat& /* input */, + const arma::Mat& error, + arma::Mat& gradient) { gradient.set_size(size + size, 1); @@ -114,22 +116,22 @@ void LayerNormType::Gradient( arma::sum(error, 1); } -template +template template -void LayerNormType::serialize( +void LayerNorm::serialize( Archive& ar, const uint32_t /* version */) { - ar(cereal::base_class>(this)); - ar(CEREAL_NVP(size)); - ar(CEREAL_NVP(eps)); - // Ensure that we don't set the values of the weights if we have already - // learned them. if (cereal::is_loading()) { + weights.set_size(size + size, 1); loading = true; } + + ar(CEREAL_NVP(eps)); + ar(CEREAL_NVP(gamma)); + ar(CEREAL_NVP(beta)); } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/layer_traits.hpp b/src/mlpack/methods/ann/layer/layer_traits.hpp new file mode 100644 index 0000000000..a6a447f43d --- /dev/null +++ b/src/mlpack/methods/ann/layer/layer_traits.hpp @@ -0,0 +1,130 @@ +/** + * @file methods/ann/layer/layer_traits.hpp + * @author Marcus Edel + * + * This provides the LayerTraits class, a template class to get information + * about various layers. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_LAYER_LAYER_TRAITS_HPP +#define MLPACK_METHODS_ANN_LAYER_LAYER_TRAITS_HPP + +#include + +namespace mlpack { +namespace ann { + +/** + * This is a template class that can provide information about various layers. + * By default, this class will provide the weakest possible assumptions on + * layer, and each layer should override values as necessary. If a layer + * doesn't need to override a value, then there's no need to write a LayerTraits + * specialization for that class. + */ +template +class LayerTraits +{ + public: + /** + * This is true if the layer is a binary layer. + */ + static const bool IsBinary = false; + + /** + * This is true if the layer is an output layer. + */ + static const bool IsOutputLayer = false; + + /** + * This is true if the layer is a bias layer. + */ + static const bool IsBiasLayer = false; + + /* + * This is true if the layer is a LSTM layer. + **/ + static const bool IsLSTMLayer = false; + + /* + * This is true if the layer is a connection layer. + **/ + static const bool IsConnection = false; +}; + +// This gives us a HasGradientCheck type (where U is a function pointer) +// we can use with SFINAE to catch when a type has a Gradient(...) function. +HAS_MEM_FUNC(Gradient, HasGradientCheck); + +// This gives us a HasDeterministicCheck type (where U is a function +// pointer) we can use with SFINAE to catch when a type has a Deterministic() +// function. +HAS_MEM_FUNC(Deterministic, HasDeterministicCheck); + +// This gives us a HasParametersCheck type (where U is a function pointer) +// we can use with SFINAE to catch when a type has a Parameters() function. +HAS_MEM_FUNC(Parameters, HasParametersCheck); + +// This gives us a HasAddCheck type (where U is a function pointer) we +// can use with SFINAE to catch when a type has a Add() function. +HAS_MEM_FUNC(Add, HasAddCheck); + +// This gives us a HasModelCheck type we can use with SFINAE to catch when +// a type has a function named Model. +HAS_ANY_METHOD_FORM(Model, HasModelCheck); + +// This gives us a HasLocationCheck type (where U is a function pointer) +// we can use with SFINAE to catch when a type has a Location() function. +HAS_MEM_FUNC(Location, HasLocationCheck); + +// This gives us a HasResetCheck type (where U is a function pointer) +// we can use with SFINAE to catch when a type has a Reset() function. +HAS_MEM_FUNC(Reset, HasResetCheck); + +// This gives us a HasResetCheck type (where U is a function pointer) +// we can use with SFINAE to catch when a type has a ResetCell() function. +HAS_MEM_FUNC(ResetCell, HasResetCellCheck); + +// This gives us a HasRewardCheck type (where U is a function pointer) we +// can use with SFINAE to catch when a type has a Reward() function. +HAS_MEM_FUNC(Reward, HasRewardCheck); + +// This gives us a HasInputWidth type (where U is a function pointer) we +// can use with SFINAE to catch when a type has a InputWidth() function. +HAS_MEM_FUNC(InputWidth, HasInputWidth); + +// This gives us a HasInputHeight type (where U is a function pointer) we +// can use with SFINAE to catch when a type has a InputHeight() function. +HAS_MEM_FUNC(InputHeight, HasInputHeight); + +// This gives us a HasRho type (where U is a function pointer) we +// can use with SFINAE to catch when a type has a Rho() function. +HAS_MEM_FUNC(Rho, HasRho); + +// This gives us a HasLoss type (where U is a function pointer) we +// can use with SFINAE to catch when a type has a Loss() function. +HAS_MEM_FUNC(Loss, HasLoss); + +// This gives us a HasRunCheck type (where U is a function pointer) we +// can use with SFINAE to catch when a type has a Run() function. +HAS_MEM_FUNC(Run, HasRunCheck); + +// This gives us a HasBiasCheck type (where U is a function pointer) we +// can use with SFINAE to catch when a type has a Bias() function. +HAS_MEM_FUNC(Bias, HasBiasCheck); + +// This gives us a HasMaxIterationsC type (where U is a function pointer) +// we can use with SFINAE to catch when a type has a MaxIterations() function. +HAS_MEM_FUNC(MaxIterations, HasMaxIterations); + +// This gives us a HasInShapeCheck type we can use with SFINAE to catch when +// a type has a function named InputShape. +HAS_ANY_METHOD_FORM(InputShape, HasInputShapeCheck); + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/layer/layer_types.hpp b/src/mlpack/methods/ann/layer/layer_types.hpp index fb179b6457..a1970bd6e1 100644 --- a/src/mlpack/methods/ann/layer/layer_types.hpp +++ b/src/mlpack/methods/ann/layer/layer_types.hpp @@ -12,33 +12,62 @@ #ifndef MLPACK_METHODS_ANN_LAYER_LAYER_TYPES_HPP #define MLPACK_METHODS_ANN_LAYER_LAYER_TYPES_HPP -#include -#include -#include +#include -// Include each layer. +// Layer modules. #include #include #include +#include +#include +#include +#include +#include #include -#include -#include #include +#include +#include +#include +#include +#include +#include #include -#include +#include +#include #include #include #include -#include +#include +#include +#include #include +#include +#include +#include #include -#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include #include -// Convolution modes. +// Convolution modules. #include -#include #include +#include // Regularizers. #include @@ -46,7 +75,250 @@ // Loss function modules. #include -// Include definitions for polymorphic serialization. -#include +namespace mlpack { +namespace ann { + +template class BatchNorm; +template class DropConnect; +template class Glimpse; +template class LayerNorm; +template class LSTM; +template class GRU; +template class FastLSTM; +template class VRClassReward; +template class Concatenate; +template class Padding; +template class ReLU6; + +template +class Linear; + +template +class RBF; + +template +class LinearNoBias; + +template +class NoisyLinear; + +template +class Linear3D; + +template +class VirtualBatchNorm; + +template +class MiniBatchDiscrimination; + +template +class MultiheadAttention; + +template +class Reparametrization; + +template +class AddMerge; + +template +class Sequential; + +template +class Highway; + +template +class Recurrent; + +template +class Concat; + +template< + typename OutputLayerType, + typename InputDataType, + typename OutputDataType +> +class ConcatPerformance; + +template< + typename ForwardConvolutionRule, + typename BackwardConvolutionRule, + typename GradientConvolutionRule, + typename InputDataType, + typename OutputDataType +> +class Convolution; + +template< + typename ForwardConvolutionRule, + typename BackwardConvolutionRule, + typename GradientConvolutionRule, + typename InputDataType, + typename OutputDataType +> +class TransposedConvolution; + +template< + typename ForwardConvolutionRule, + typename BackwardConvolutionRule, + typename GradientConvolutionRule, + typename InputDataType, + typename OutputDataType +> +class AtrousConvolution; + +template< + typename InputDataType, + typename OutputDataType +> +class RecurrentAttention; + +template +class MultiplyMerge; + +template +class WeightNorm; + +template +class AdaptiveMaxPooling; + +template +class AdaptiveMeanPooling; + +using MoreTypes = boost::variant< + FlexibleReLU*, + Linear3D*, + LpPooling*, + PixelShuffle*, + ChannelShuffle*, + Glimpse*, + Highway*, + MultiheadAttention*, + Recurrent*, + RecurrentAttention*, + ReinforceNormal*, + ReLU6*, + Reparametrization*, + Select*, + SpatialDropout*, + Subview*, + VRClassReward*, + VirtualBatchNorm*, + RBF*, + BaseLayer*, + PositionalEncoding*, + ISRLU*, + BicubicInterpolation*, + NearestInterpolation*, + GroupNorm*, + InstanceNorm* +>; + +template +using LayerTypes = boost::variant< + AdaptiveMaxPooling*, + AdaptiveMeanPooling*, + Add*, + AddMerge*, + AlphaDropout*, + AtrousConvolution, + NaiveConvolution, + NaiveConvolution, + arma::mat, arma::mat>*, + BaseLayer*, + BaseLayer*, + BaseLayer*, + BaseLayer*, + BaseLayer*, + BatchNorm*, + BilinearInterpolation*, + CELU*, + Concat*, + Concatenate*, + ConcatPerformance, + arma::mat, arma::mat>*, + Constant*, + Convolution, + NaiveConvolution, + NaiveConvolution, arma::mat, arma::mat>*, + CReLU*, + DropConnect*, + Dropout*, + ELU*, + FastLSTM*, + GRU*, + HardTanH*, + Join*, + LayerNorm*, + LeakyReLU*, + Linear*, + LinearNoBias*, + LogSoftMax*, + Lookup*, + LSTM*, + MaxPooling*, + MeanPooling*, + MiniBatchDiscrimination*, + MultiplyConstant*, + MultiplyMerge*, + NegativeLogLikelihood*, + NoisyLinear*, + Padding*, + PReLU*, + Sequential*, + Sequential*, + Softmax*, + TransposedConvolution, + NaiveConvolution, + NaiveConvolution, arma::mat, arma::mat>*, + WeightNorm*, + MoreTypes, + CustomLayers*... +>; + +} // namespace ann +} // namespace mlpack #endif diff --git a/src/mlpack/methods/ann/layer/leaky_relu.hpp b/src/mlpack/methods/ann/layer/leaky_relu.hpp index b275371de6..52b2896fca 100644 --- a/src/mlpack/methods/ann/layer/leaky_relu.hpp +++ b/src/mlpack/methods/ann/layer/leaky_relu.hpp @@ -16,8 +16,6 @@ #include -#include "layer.hpp" - namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -34,11 +32,16 @@ namespace ann /** Artificial Neural Network. */ { * \right. * @f} * - * @tparam MatType Matrix representation to accept as input and use for - * computation. + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class LeakyReLUType : public Layer +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class LeakyReLU { public: /** @@ -46,24 +49,9 @@ class LeakyReLUType : public Layer * The non zero gradient can be adjusted by specifying the parameter * alpha in the range 0 to 1. Default (alpha = 0.03) * - * @param alpha Non zero gradient. + * @param alpha Non zero gradient */ - LeakyReLUType(const double alpha = 0.03); - - //! Clone the LeakyReLUType object. This handles polymorphism correctly. - LeakyReLUType* Clone() const { return new LeakyReLUType(*this); } - - // Virtual destructor. - virtual ~LeakyReLUType() { } - - //! Copy the given LeakyReLUType. - LeakyReLUType(const LeakyReLUType& other); - //! Take ownership of the given LeakyReLUType. - LeakyReLUType(LeakyReLUType&& other); - //! Copy the given LeakyReLUType. - LeakyReLUType& operator=(const LeakyReLUType& other); - //! Take ownership of the given LeakyReLUType. - LeakyReLUType& operator=(LeakyReLUType&& other); + LeakyReLU(const double alpha = 0.03); /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -72,7 +60,8 @@ class LeakyReLUType : public Layer * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - void Forward(const MatType& input, MatType& output); + template + void Forward(const InputType& input, OutputType& output); /** * Ordinary feed backward pass of a neural network, calculating the function @@ -83,27 +72,43 @@ class LeakyReLUType : public Layer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const MatType& input, const MatType& gy, MatType& g); + template + void Backward(const DataType& input, const DataType& gy, DataType& g); + + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } //! Get the non zero gradient. double const& Alpha() const { return alpha; } //! Modify the non zero gradient. double& Alpha() { return alpha; } - //! Serialize the layer. + //! Get size of weights. + size_t WeightSize() const { return 0; } + + /** + * Serialize the layer. + */ template void serialize(Archive& ar, const uint32_t /* version */); private: + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; + //! Leakyness Parameter in the range 0 LeakyReLU; - +}; // class LeakyReLU } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/leaky_relu_impl.hpp b/src/mlpack/methods/ann/layer/leaky_relu_impl.hpp index 7a6e8dce3a..d03009f348 100644 --- a/src/mlpack/methods/ann/layer/leaky_relu_impl.hpp +++ b/src/mlpack/methods/ann/layer/leaky_relu_impl.hpp @@ -20,68 +20,27 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -LeakyReLUType::LeakyReLUType(const double alpha) : - Layer(), - alpha(alpha) +template +LeakyReLU::LeakyReLU( + const double alpha) : alpha(alpha) { // Nothing to do here. } -template -LeakyReLUType::LeakyReLUType(const LeakyReLUType& other) : - Layer(other), - alpha(other.alpha) -{ - // Nothing to do. -} - -template -LeakyReLUType::LeakyReLUType( - LeakyReLUType&& other) : - Layer(std::move(other)), - alpha(std::move(other.alpha)) -{ - // Nothing to do. -} - -template -LeakyReLUType& -LeakyReLUType::operator=(const LeakyReLUType& other) -{ - if (&other != this) - { - Layer::operator=(other); - alpha = other.alpha; - } - - return *this; -} - -template -LeakyReLUType& -LeakyReLUType::operator=(LeakyReLUType&& other) -{ - if (&other != this) - { - Layer::operator=(std::move(other)); - alpha = std::move(other.alpha); - } - - return *this; -} - -template -void LeakyReLUType::Forward(const MatType& input, MatType& output) +template +template +void LeakyReLU::Forward( + const InputType& input, OutputType& output) { output = arma::max(input, alpha * input); } -template -void LeakyReLUType::Backward( - const MatType& input, const MatType& gy, MatType& g) +template +template +void LeakyReLU::Backward( + const DataType& input, const DataType& gy, DataType& g) { - MatType derivative; + DataType derivative; derivative.set_size(arma::size(input)); for (size_t i = 0; i < input.n_elem; ++i) derivative(i) = (input(i) >= 0) ? 1 : alpha; @@ -89,14 +48,12 @@ void LeakyReLUType::Backward( g = gy % derivative; } -template +template template -void LeakyReLUType::serialize( +void LeakyReLU::serialize( Archive& ar, const uint32_t /* version */) { - ar(cereal::base_class>(this)); - ar(CEREAL_NVP(alpha)); } diff --git a/src/mlpack/methods/ann/layer/linear.hpp b/src/mlpack/methods/ann/layer/linear.hpp index 7dca66ea01..5b9d23bd87 100644 --- a/src/mlpack/methods/ann/layer/linear.hpp +++ b/src/mlpack/methods/ann/layer/linear.hpp @@ -16,7 +16,7 @@ #include #include -#include "layer.hpp" +#include "layer_types.hpp" namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -25,120 +25,142 @@ namespace ann /** Artificial Neural Network. */ { * Implementation of the Linear layer class. The Linear class represents a * single layer of a neural network. * - * The linear layer applies a linear transformation to the incoming data - * (input), i.e. y = Ax + b. The input matrix given in Forward(input, output) - * must be either a vector or matrix. If the input is a matrix, then each column - * is assumed to be an input sample of given batch. - * - * @tparam MatType Matrix representation to accept as input and use for - * computation. - * @tparam RegularizerType Type of the regularizer to be used (Default no - * regularizer). + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template< - typename MatType = arma::mat, +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat, typename RegularizerType = NoRegularizer > -class LinearType : public Layer +class Linear { public: //! Create the Linear object. - LinearType(); + Linear(); /** - * Create the Linear layer object with the specified number of output - * dimensions. + * Create the Linear layer object using the specified number of units. * - * @param outSize The output dimension. - * @param regularizer The regularizer to use, optional (default: no - * regularizer). + * @param inSize The number of input units. + * @param outSize The number of output units. + * @param regularizer The regularizer to use, optional. */ - LinearType(const size_t outSize, - RegularizerType regularizer = RegularizerType()); + Linear(const size_t inSize, + const size_t outSize, + RegularizerType regularizer = RegularizerType()); - virtual ~LinearType() { } + //! Copy constructor. + Linear(const Linear& layer); - //! Clone the LinearType object. This handles polymorphism correctly. - LinearType* Clone() const { return new LinearType(*this); } + //! Move constructor. + Linear(Linear&&); - //! Copy the other Linear layer (but not weights). - LinearType(const LinearType& layer); + //! Copy assignment operator. + Linear& operator=(const Linear& layer); - //! Take ownership of the members of the other Linear layer (but not weights). - LinearType(LinearType&& layer); + //! Move assignment operator. + Linear& operator=(Linear&& layer); - //! Copy the other Linear layer (but not weights). - LinearType& operator=(const LinearType& layer); - - //! Take ownership of the members of the other Linear layer (but not weights). - LinearType& operator=(LinearType&& layer); - - /** - * Reset the layer parameter (weights and bias). The method is called to - * assign the allocated memory to the internal learnable parameters. + /* + * Reset the layer parameter. */ - void SetWeights(typename MatType::elem_type* weightsPtr); + void Reset(); /** * Ordinary feed forward pass of a neural network, evaluating the function * f(x) by propagating the activity forward through f. * - * f(x) is a linear transformation: Ax + b, where x is the given input, x are - * the layer weights and b is the layer bias. - * * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - void Forward(const MatType& input, MatType& output); + template + void Forward(const arma::Mat& input, arma::Mat& output); /** * Ordinary feed backward pass of a neural network, calculating the function * f(x) by propagating x backwards trough f. Using the results from the feed * forward pass. * - * To compute the downstream gradient (g) the chain rule is used. - * * @param * (input) The propagated input activation. * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const MatType& /* input */, - const MatType& gy, - MatType& g); + template + void Backward(const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g); - /** + /* * Calculate the gradient using the output delta and the input activation. * * @param input The input parameter used for calculating the gradient. * @param error The calculated error. * @param gradient The calculated gradient. */ - void Gradient(const MatType& input, - const MatType& error, - MatType& gradient); + template + void Gradient(const arma::Mat& input, + const arma::Mat& error, + arma::Mat& gradient); //! Get the parameters. - const MatType& Parameters() const { return weights; } + OutputDataType const& Parameters() const { return weights; } //! Modify the parameters. - MatType& Parameters() { return weights; } + OutputDataType& Parameters() { return weights; } + + //! Get the input parameter. + InputDataType const& InputParameter() const { return inputParameter; } + //! Modify the input parameter. + InputDataType& InputParameter() { return inputParameter; } + + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } + + //! Get the input size. + size_t InputSize() const { return inSize; } + + //! Get the output size. + size_t OutputSize() const { return outSize; } + + //! Get the gradient. + OutputDataType const& Gradient() const { return gradient; } + //! Modify the gradient. + OutputDataType& Gradient() { return gradient; } //! Get the weight of the layer. - MatType const& Weight() const { return weight; } + OutputDataType const& Weight() const { return weight; } //! Modify the weight of the layer. - MatType& Weight() { return weight; } + OutputDataType& Weight() { return weight; } //! Get the bias of the layer. - MatType const& Bias() const { return bias; } + OutputDataType const& Bias() const { return bias; } //! Modify the bias weights of the layer. - MatType& Bias() { return bias; } + OutputDataType& Bias() { return bias; } //! Get the size of the weights. - size_t WeightSize() const { return (inSize * outSize) + outSize; } + size_t WeightSize() const + { + return (inSize * outSize) + outSize; + } - //! Compute the output dimensions of the layer given `InputDimensions()`. - void ComputeOutputDimensions(); + //! Get the shape of the input. + size_t InputShape() const + { + return inSize; + } - //! Serialize the layer. + /** + * Serialize the layer + */ template void serialize(Archive& ar, const uint32_t /* version */); @@ -149,24 +171,30 @@ class LinearType : public Layer //! Locally-stored number of output units. size_t outSize; - //! Locally-stored weight object. This holds all the weights in a vectorized - //! form; i.e., the weights and the bias. - MatType weights; + //! Locally-stored weight object. + OutputDataType weights; //! Locally-stored weight parameters. - MatType weight; + OutputDataType weight; //! Locally-stored bias term parameters. - MatType bias; + OutputDataType bias; + + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored gradient object. + OutputDataType gradient; + + //! Locally-stored input parameter object. + InputDataType inputParameter; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; //! Locally-stored regularizer object. RegularizerType regularizer; -}; // class LinearType - -// Convenience typedefs. - -// Standard Linear layer using no regularization. -typedef LinearType Linear; +}; // class Linear } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/linear3d.hpp b/src/mlpack/methods/ann/layer/linear3d.hpp index 6b9b554ea7..24e3de56e6 100644 --- a/src/mlpack/methods/ann/layer/linear3d.hpp +++ b/src/mlpack/methods/ann/layer/linear3d.hpp @@ -14,10 +14,9 @@ #define MLPACK_METHODS_ANN_LAYER_LINEAR3D_HPP #include +#include #include -#include "layer.hpp" - namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -28,48 +27,49 @@ namespace ann /** Artificial Neural Network. */ { * Shape of input : (inSize * nPoints, batchSize) * Shape of output : (outSize * nPoints, batchSize) * - * @tparam MatType Matrix representation to accept as input and use for - * computation. + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template< - typename MatType = arma::mat, +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat, typename RegularizerType = NoRegularizer > -class Linear3DType : public Layer +class Linear3D { public: //! Create the Linear3D object. - Linear3DType(); + Linear3D(); /** - * Create the Linear3D layer object using the specified number of output - * units. + * Create the Linear3D layer object using the specified number of units. * + * @param inSize The number of input units. * @param outSize The number of output units. * @param regularizer The regularizer to use, optional. */ - Linear3DType(const size_t outSize, - RegularizerType regularizer = RegularizerType()); + Linear3D(const size_t inSize, + const size_t outSize, + RegularizerType regularizer = RegularizerType()); - //! Clone the Linear3DType object. This handles polymorphism correctly. - Linear3DType* Clone() const { return new Linear3DType(*this); } + //! Copy constructor. + Linear3D(const Linear3D& layer); - // Virtual destructor. - virtual ~Linear3DType() { } + //! Move constructor. + Linear3D(Linear3D&&); - //! Copy the given Linear3DType (but not weights). - Linear3DType(const Linear3DType& other); - //! Take ownership of the given Linear3DType (but not weights). - Linear3DType(Linear3DType&& other); - //! Copy the given Linear3DType (but not weights). - Linear3DType& operator=(const Linear3DType& other); - //! Take ownership of the given Linear3DType (but not weights). - Linear3DType& operator=(Linear3DType&& other); + //! Copy assignment operator. + Linear3D& operator=(const Linear3D& layer); + + //! Move assignment operator. + Linear3D& operator=(Linear3D&& layer); /* * Reset the layer parameter. */ - void SetWeights(typename MatType::elem_type* weightsPtr); + void Reset(); /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -78,7 +78,8 @@ class Linear3DType : public Layer * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - void Forward(const MatType& input, MatType& output); + template + void Forward(const arma::Mat& input, arma::Mat& output); /** * Ordinary feed backward pass of a neural network, calculating the function @@ -89,41 +90,69 @@ class Linear3DType : public Layer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const MatType& /* input */, - const MatType& gy, - MatType& g); + template + void Backward(const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g); - /** + /* * Calculate the gradient using the output delta and the input activation. * * @param input The input parameter used for calculating the gradient. * @param error The calculated error. * @param gradient The calculated gradient. */ - void Gradient(const MatType& input, - const MatType& error, - MatType& gradient); + template + void Gradient(const arma::Mat& input, + const arma::Mat& error, + arma::Mat& gradient); //! Get the parameters. - MatType const& Parameters() const { return weights; } + OutputDataType const& Parameters() const { return weights; } //! Modify the parameters. - MatType& Parameters() { return weights; } + OutputDataType& Parameters() { return weights; } + + //! Get the input parameter. + InputDataType const& InputParameter() const { return inputParameter; } + //! Modify the input parameter. + InputDataType& InputParameter() { return inputParameter; } + + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } + + //! Get the input size. + size_t InputSize() const { return inSize; } + + //! Get the output size. + size_t OutputSize() const { return outSize; } + + //! Get the gradient. + OutputDataType const& Gradient() const { return gradient; } + //! Modify the gradient. + OutputDataType& Gradient() { return gradient; } //! Get the weight of the layer. - MatType const& Weight() const { return weight; } + OutputDataType const& Weight() const { return weight; } //! Modify the weight of the layer. - MatType& Weight() { return weight; } + OutputDataType& Weight() { return weight; } //! Get the bias of the layer. - MatType const& Bias() const { return bias; } + OutputDataType const& Bias() const { return bias; } //! Modify the bias weights of the layer. - MatType& Bias() { return bias; } + OutputDataType& Bias() { return bias; } - //! Return the number of weight elements. - size_t WeightSize() const { return outSize * (this->inputDimensions[0] + 1); } - - //! Compute the output dimensions for the layer, using `InputDimensions()`. - void ComputeOutputDimensions(); + //! Get the shape of the input. + size_t InputShape() const + { + return inSize; + } /** * Serialize the layer @@ -132,25 +161,37 @@ class Linear3DType : public Layer void serialize(Archive& ar, const uint32_t /* version */); private: + //! Locally-stored number of input units. + size_t inSize; + //! Locally-stored number of output units. size_t outSize; //! Locally-stored weight object. - MatType weights; + OutputDataType weights; //! Locally-stored weight parameters. - MatType weight; + OutputDataType weight; //! Locally-stored bias term parameters. - MatType bias; + OutputDataType bias; + + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored gradient object. + OutputDataType gradient; + + //! Locally-stored input parameter object. + InputDataType inputParameter; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; //! Locally-stored regularizer object. RegularizerType regularizer; }; // class Linear -// Standard Linear3D layer. -typedef Linear3DType Linear3D; - } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/linear3d_impl.hpp b/src/mlpack/methods/ann/layer/linear3d_impl.hpp index cc4ae74375..37cc9e39e5 100644 --- a/src/mlpack/methods/ann/layer/linear3d_impl.hpp +++ b/src/mlpack/methods/ann/layer/linear3d_impl.hpp @@ -18,94 +18,116 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -Linear3DType::Linear3DType() : - Layer(), +template +Linear3D::Linear3D() : + inSize(0), outSize(0) { // Nothing to do here. } -template -Linear3DType::Linear3DType( +template +Linear3D::Linear3D( + const size_t inSize, const size_t outSize, RegularizerType regularizer) : - Layer(), + inSize(inSize), outSize(outSize), regularizer(regularizer) -{ } - -template -Linear3DType::Linear3DType( - const Linear3DType& other) : - Layer(other), - outSize(other.outSize), - regularizer(other.regularizer) { - // Nothing to do. + weights.set_size(outSize * inSize + outSize, 1); } -template -Linear3DType::Linear3DType( - Linear3DType&& other) : - Layer(std::move(other)), - outSize(std::move(other.outSize)), - regularizer(std::move(other.regularizer)) +template +Linear3D::Linear3D( + const Linear3D& layer) : + inSize(layer.inSize), + outSize(layer.outSize), + weights(layer.weights), + regularizer(layer.regularizer) { - // Nothing to do. + // Nothing to do here. } -template -Linear3DType& -Linear3DType::operator=( - const Linear3DType& other) +template +Linear3D::Linear3D( + Linear3D&& layer) : + inSize(0), + outSize(0), + weights(std::move(layer.weights)), + regularizer(std::move(layer.regularizer)) { - if (&other != this) + // Nothing to do here. +} + +template +Linear3D& +Linear3D:: +operator=(const Linear3D& layer) +{ + if (this != &layer) { - Layer::operator=(other); - outSize = other.outSize; - regularizer = other.regularizer; + inSize = layer.inSize; + outSize = layer.outSize; + weights = layer.weights; + regularizer = layer.regularizer; } - return *this; } -template -Linear3DType& -Linear3DType::operator=( - Linear3DType&& other) +template +Linear3D& +Linear3D:: +operator=(Linear3D&& layer) { - if (&other != this) + if (this != &layer) { - Layer::operator=(std::move(other)); - outSize = std::move(other.outSize); - regularizer = std::move(other.regularizer); + inSize = 0; + outSize = 0; + weights = std::move(layer.weights); + regularizer = std::move(layer.regularizer); } - return *this; } -template -void Linear3DType::SetWeights( - typename MatType::elem_type* weightsPtr) +template +void Linear3D::Reset() { - MakeAlias(weights, weightsPtr, outSize * this->inputDimensions[0] + outSize, - 1); - MakeAlias(weight, weightsPtr, outSize, this->inputDimensions[0]); - MakeAlias(bias, weightsPtr + weight.n_elem, outSize, 1); + typedef typename arma::Mat MatType; + + weight = MatType(weights.memptr(), outSize, inSize, false, false); + bias = MatType(weights.memptr() + weight.n_elem, outSize, 1, false, false); } -template -void Linear3DType::Forward( - const MatType& input, MatType& output) +template +template +void Linear3D::Forward( + const arma::Mat& input, arma::Mat& output) { - typedef typename arma::Cube CubeType; + typedef typename arma::Mat MatType; + typedef typename arma::Cube CubeType; - const size_t nPoints = input.n_rows / this->inputDimensions[0]; + if (input.n_rows % inSize != 0) + { + Log::Fatal << "Number of features in the input must be divisible by inSize." + << std::endl; + } + + const size_t nPoints = input.n_rows / inSize; const size_t batchSize = input.n_cols; - const CubeType inputTemp(const_cast(input).memptr(), - this->inputDimensions[0], nPoints, batchSize, false, false); + output.set_size(outSize * nPoints, batchSize); + + const CubeType inputTemp(const_cast(input).memptr(), inSize, + nPoints, batchSize, false, false); for (size_t i = 0; i < batchSize; ++i) { @@ -117,13 +139,16 @@ void Linear3DType::Forward( } } -template -void Linear3DType::Backward( - const MatType& /* input */, - const MatType& gy, - MatType& g) +template +template +void Linear3D::Backward( + const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g) { - typedef typename arma::Cube CubeType; + typedef typename arma::Mat MatType; + typedef typename arma::Cube CubeType; if (gy.n_rows % outSize != 0) { @@ -137,6 +162,8 @@ void Linear3DType::Backward( const CubeType gyTemp(const_cast(gy).memptr(), outSize, nPoints, batchSize, false, false); + g.set_size(inSize * nPoints, batchSize); + for (size_t i = 0; i < gyTemp.n_slices; ++i) { // Shape of weight : (outSize, inSize). @@ -145,26 +172,29 @@ void Linear3DType::Backward( } } -template -void Linear3DType::Gradient( - const MatType& input, - const MatType& error, - MatType& gradient) +template +template +void Linear3D::Gradient( + const arma::Mat& input, + const arma::Mat& error, + arma::Mat& gradient) { - typedef typename arma::Cube CubeType; + typedef typename arma::Mat MatType; + typedef typename arma::Cube CubeType; if (error.n_rows % outSize != 0) Log::Fatal << "Propagated error matrix has invalid dimension!" << std::endl; - const size_t nPoints = input.n_rows / this->inputDimensions[0]; + const size_t nPoints = input.n_rows / inSize; const size_t batchSize = input.n_cols; - const CubeType inputTemp(const_cast(input).memptr(), - this->inputDimensions[0], nPoints, batchSize, false, false); + const CubeType inputTemp(const_cast(input).memptr(), inSize, + nPoints, batchSize, false, false); const CubeType errorTemp(const_cast(error).memptr(), outSize, nPoints, batchSize, false, false); - CubeType dW(outSize, this->inputDimensions[0], batchSize); + CubeType dW(outSize, inSize, batchSize); for (size_t i = 0; i < batchSize; ++i) { // Shape of errorTemp : (outSize, nPoints, batchSize). @@ -172,6 +202,8 @@ void Linear3DType::Gradient( dW.slice(i) = errorTemp.slice(i) * inputTemp.slice(i).t(); } + gradient.set_size(arma::size(weights)); + gradient.submat(0, 0, weight.n_elem - 1, 0) = arma::vectorise(arma::sum(dW, 2)); @@ -181,27 +213,19 @@ void Linear3DType::Gradient( regularizer.Evaluate(weights, gradient); } -template -void Linear3DType< - MatType, RegularizerType ->::ComputeOutputDimensions() -{ - // The Linear3D layer shares weights for each row of the input, and - // duplicates it across the columns. Thus, we only change the number of - // rows. - this->outputDimensions = this->inputDimensions; - this->outputDimensions[0] = outSize; -} - -template +template template -void Linear3DType::serialize( +void Linear3D::serialize( Archive& ar, const uint32_t /* version */) { - ar(cereal::base_class>(this)); - + ar(CEREAL_NVP(inSize)); ar(CEREAL_NVP(outSize)); - ar(CEREAL_NVP(regularizer)); + + // This is inefficient, but we have to allocate this memory so that + // WeightSetVisitor gets the right size. + if (cereal::is_loading()) + weights.set_size(outSize * inSize + outSize, 1); } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/linear_impl.hpp b/src/mlpack/methods/ann/layer/linear_impl.hpp index fe7841e791..2edfb4802c 100644 --- a/src/mlpack/methods/ann/layer/linear_impl.hpp +++ b/src/mlpack/methods/ann/layer/linear_impl.hpp @@ -19,141 +19,136 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -LinearType::LinearType() : - Layer(), +template +Linear::Linear() : inSize(0), outSize(0) { // Nothing to do here. } -template -LinearType::LinearType( +template +Linear::Linear( + const size_t inSize, const size_t outSize, RegularizerType regularizer) : - Layer(), - inSize(0), // This will be computed in ComputeOutputDimensions(). + inSize(inSize), outSize(outSize), regularizer(regularizer) { weights.set_size(WeightSize(), 1); } -// Copy constructor. -template -LinearType::LinearType(const LinearType& layer) : - Layer(layer), +template +Linear::Linear( + const Linear& layer) : inSize(layer.inSize), outSize(layer.outSize), + weights(layer.weights), regularizer(layer.regularizer) { - // Nothing else to do. + // Nothing to do here. } -// Move constructor. -template -LinearType::LinearType(LinearType&& layer) : - Layer(std::move(layer)), - inSize(std::move(layer.inSize)), - outSize(std::move(layer.outSize)), +template +Linear::Linear( + Linear&& layer) : + inSize(0), + outSize(0), + weights(std::move(layer.weights)), regularizer(std::move(layer.regularizer)) { - // Nothing else to do. + // Nothing to do here. } -template -LinearType& -LinearType::operator=(const LinearType& layer) +template +Linear& +Linear:: +operator=(const Linear& layer) { - if (&layer != this) + if (this != &layer) { - Layer::operator=(layer); inSize = layer.inSize; outSize = layer.outSize; + weights = layer.weights; regularizer = layer.regularizer; } - return *this; } -template -LinearType& -LinearType::operator=( - LinearType&& layer) +template +Linear& +Linear:: +operator=(Linear&& layer) { - if (&layer != this) + if (this != &layer) { - Layer::operator=(std::move(layer)); - inSize = std::move(layer.inSize); - outSize = std::move(layer.outSize); + inSize = layer.inSize; + outSize = layer.outSize; + weights = std::move(layer.weights); regularizer = std::move(layer.regularizer); } - return *this; } -template -void LinearType::SetWeights( - typename MatType::elem_type* weightsPtr) +template +void Linear::Reset() { - MakeAlias(weights, weightsPtr, outSize * inSize + outSize, 1); - MakeAlias(weight, weightsPtr, outSize, inSize); - MakeAlias(bias, weightsPtr + weight.n_elem, outSize, 1); + weight = arma::mat(weights.memptr(), outSize, inSize, false, false); + bias = arma::mat(weights.memptr() + weight.n_elem, + outSize, 1, false, false); } -template -void LinearType::Forward( - const MatType& input, MatType& output) +template +template +void Linear::Forward( + const arma::Mat& input, arma::Mat& output) { output = weight * input; output.each_col() += bias; } -template -void LinearType::Backward( - const MatType& /* input */, const MatType& gy, MatType& g) +template +template +void Linear::Backward( + const arma::Mat& /* input */, const arma::Mat& gy, arma::Mat& g) { g = weight.t() * gy; } -template -void LinearType::Gradient( - const MatType& input, - const MatType& error, - MatType& gradient) +template +template +void Linear::Gradient( + const arma::Mat& input, + const arma::Mat& error, + arma::Mat& gradient) { gradient.submat(0, 0, weight.n_elem - 1, 0) = arma::vectorise( error * input.t()); gradient.submat(weight.n_elem, 0, gradient.n_elem - 1, 0) = arma::sum(error, 1); - regularizer.Evaluate(weights, gradient); } -template -void LinearType::ComputeOutputDimensions() -{ - inSize = this->inputDimensions[0]; - for (size_t i = 1; i < this->inputDimensions.size(); ++i) - inSize *= this->inputDimensions[i]; - this->outputDimensions = std::vector(this->inputDimensions.size(), - 1); - - // The Linear layer flattens its input. - this->outputDimensions[0] = outSize; -} - -template +template template -void LinearType::serialize( +void Linear::serialize( Archive& ar, const uint32_t /* version */) { - ar(cereal::base_class>(this)); - ar(CEREAL_NVP(inSize)); ar(CEREAL_NVP(outSize)); - ar(CEREAL_NVP(regularizer)); + ar(CEREAL_NVP(weights)); } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/linear_no_bias.hpp b/src/mlpack/methods/ann/layer/linear_no_bias.hpp index 00bf074d7a..1e85933135 100644 --- a/src/mlpack/methods/ann/layer/linear_no_bias.hpp +++ b/src/mlpack/methods/ann/layer/linear_no_bias.hpp @@ -16,7 +16,7 @@ #include #include -#include "layer.hpp" +#include "layer_types.hpp" namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -25,50 +25,48 @@ namespace ann /** Artificial Neural Network. */ { * Implementation of the LinearNoBias class. The LinearNoBias class represents a * single layer of a neural network. * - * @tparam MatType Matrix representation to accept as input and use for - * computation. - * @tparam RegularizerType Type of the regularizer to be used (Default no - * regularizer). + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template< - typename MatType = arma::mat, +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat, typename RegularizerType = NoRegularizer > -class LinearNoBiasType : public Layer +class LinearNoBias { public: //! Create the LinearNoBias object. - LinearNoBiasType(); - + LinearNoBias(); /** * Create the LinearNoBias object using the specified number of units. * + * @param inSize The number of input units. * @param outSize The number of output units. * @param regularizer The regularizer to use, optional. */ - LinearNoBiasType(const size_t outSize, - RegularizerType regularizer = RegularizerType()); - - //! Clone the LinearNoBiasType object. This handles polymorphism correctly. - LinearNoBiasType* Clone() const { return new LinearNoBiasType(*this); } - - //! Reset the layer parameter. - void SetWeights(typename MatType::elem_type* weightsPtr); + LinearNoBias(const size_t inSize, + const size_t outSize, + RegularizerType regularizer = RegularizerType()); //! Copy constructor. - LinearNoBiasType(const LinearNoBiasType& layer); + LinearNoBias(const LinearNoBias& layer); //! Move constructor. - LinearNoBiasType(LinearNoBiasType&&); + LinearNoBias(LinearNoBias&&); //! Copy assignment operator. - LinearNoBiasType& operator=(const LinearNoBiasType& layer); + LinearNoBias& operator=(const LinearNoBias& layer); //! Move assignment operator. - LinearNoBiasType& operator=(LinearNoBiasType&& layer); + LinearNoBias& operator=(LinearNoBias&& layer); - //! Virtual destructor. - virtual ~LinearNoBiasType() { } + /* + * Reset the layer parameter. + */ + void Reset(); /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -77,7 +75,8 @@ class LinearNoBiasType : public Layer * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - void Forward(const MatType& input, MatType& output); + template + void Forward(const arma::Mat& input, arma::Mat& output); /** * Ordinary feed backward pass of a neural network, calculating the function @@ -88,33 +87,69 @@ class LinearNoBiasType : public Layer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const MatType& /* input */, - const MatType& gy, - MatType& g); + template + void Backward(const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g); - /** + /* * Calculate the gradient using the output delta and the input activation. * * @param input The input parameter used for calculating the gradient. * @param error The calculated error. * @param gradient The calculated gradient. */ - void Gradient(const MatType& input, - const MatType& error, - MatType& gradient); + template + void Gradient(const arma::Mat& input, + const arma::Mat& error, + arma::Mat& gradient); //! Get the parameters. - const MatType& Parameters() const { return weight; } + OutputDataType const& Parameters() const { return weights; } //! Modify the parameters. - MatType& Parameters() { return weight; } + OutputDataType& Parameters() { return weights; } - //! Get the number of weights in the layer. - size_t WeightSize() const { return inSize * outSize; } + //! Get the input parameter. + InputDataType const& InputParameter() const { return inputParameter; } + //! Modify the input parameter. + InputDataType& InputParameter() { return inputParameter; } - //! Compute the output dimensions of the layer using `InputDimensions()`. - void ComputeOutputDimensions(); + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } - //! Serialize the layer. + //! Get the delta. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } + + //! Get the input size. + size_t InputSize() const { return inSize; } + + //! Get the output size. + size_t OutputSize() const { return outSize; } + + //! Get the gradient. + OutputDataType const& Gradient() const { return gradient; } + //! Modify the gradient. + OutputDataType& Gradient() { return gradient; } + + //! Get the size of the weights. + size_t WeightSize() const + { + return inSize * outSize; + } + + //! Get the shape of the input. + size_t InputShape() const + { + return inSize; + } + + /** + * Serialize the layer + */ template void serialize(Archive& ar, const uint32_t /* version */); @@ -125,17 +160,27 @@ class LinearNoBiasType : public Layer //! Locally-stored number of output units. size_t outSize; + //! Locally-stored weight object. + OutputDataType weights; + //! Locally-stored weight parameter. - MatType weight; + OutputDataType weight; + + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored gradient object. + OutputDataType gradient; + + //! Locally-stored input parameter object. + InputDataType inputParameter; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; //! Locally-stored regularizer object. RegularizerType regularizer; -}; // class LinearNoBiasType - -// Convenience typedefs. - -// Standard Linear without bias layer using no regularization. -typedef LinearNoBiasType LinearNoBias; +}; // class LinearNoBias } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/linear_no_bias_impl.hpp b/src/mlpack/methods/ann/layer/linear_no_bias_impl.hpp index bc2f18702c..ed26cd59fa 100644 --- a/src/mlpack/methods/ann/layer/linear_no_bias_impl.hpp +++ b/src/mlpack/methods/ann/layer/linear_no_bias_impl.hpp @@ -19,136 +19,135 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -LinearNoBiasType::LinearNoBiasType() : - Layer(), +template +LinearNoBias::LinearNoBias() : inSize(0), outSize(0) { // Nothing to do here. } -template -LinearNoBiasType::LinearNoBiasType( +template +LinearNoBias::LinearNoBias( + const size_t inSize, const size_t outSize, RegularizerType regularizer) : - Layer(), - inSize(0), // This will be set by ComputeOutputDimensions(). + inSize(inSize), outSize(outSize), regularizer(regularizer) { - // Nothing to do. + weights.set_size(WeightSize(), 1); } -template -LinearNoBiasType::LinearNoBiasType( - const LinearNoBiasType& layer) : - Layer(layer), +template +LinearNoBias::LinearNoBias( + const LinearNoBias& layer) : inSize(layer.inSize), outSize(layer.outSize), + weights(layer.weights), regularizer(layer.regularizer) { // Nothing to do here. } -template -LinearNoBiasType::LinearNoBiasType( - LinearNoBiasType&& layer) : - Layer(std::move(layer)), +template +LinearNoBias::LinearNoBias( + LinearNoBias&& layer) : inSize(0), outSize(0), + weights(std::move(layer.weights)), regularizer(std::move(layer.regularizer)) { // Nothing to do here. } -template -LinearNoBiasType& -LinearNoBiasType::operator=( - const LinearNoBiasType& layer) +template +LinearNoBias& +LinearNoBias:: +operator=(const LinearNoBias& layer) { if (this != &layer) { - Layer::operator=(layer); inSize = layer.inSize; outSize = layer.outSize; + weights = layer.weights; regularizer = layer.regularizer; } - return *this; } -template -LinearNoBiasType& -LinearNoBiasType::operator=( - LinearNoBiasType&& layer) +template +LinearNoBias& +LinearNoBias:: +operator=(LinearNoBias&& layer) { if (this != &layer) { - Layer::operator=(std::move(layer)); - inSize = std::move(layer.inSize); - outSize = std::move(layer.outSize); + inSize = layer.inSize; + outSize = layer.outSize; + weights = std::move(layer.weights); regularizer = std::move(layer.regularizer); } - return *this; } -template -void LinearNoBiasType::SetWeights( - typename MatType::elem_type* weightsPtr) +template +void LinearNoBias::Reset() { - MakeAlias(weight, weightsPtr, outSize, inSize); + weight = arma::mat(weights.memptr(), outSize, inSize, false, false); } -template -void LinearNoBiasType::Forward( - const MatType& input, MatType& output) +template +template +void LinearNoBias::Forward( + const arma::Mat& input, arma::Mat& output) { output = weight * input; } -template -void LinearNoBiasType::Backward( - const MatType& /* input */, const MatType& gy, MatType& g) +template +template +void LinearNoBias::Backward( + const arma::Mat& /* input */, const arma::Mat& gy, arma::Mat& g) { g = weight.t() * gy; } -template -void LinearNoBiasType::Gradient( - const MatType& input, - const MatType& error, - MatType& gradient) +template +template +void LinearNoBias::Gradient( + const arma::Mat& input, + const arma::Mat& error, + arma::Mat& gradient) { gradient.submat(0, 0, weight.n_elem - 1, 0) = arma::vectorise( error * input.t()); - regularizer.Evaluate(weight, gradient); + regularizer.Evaluate(weights, gradient); } -template -void LinearNoBiasType::ComputeOutputDimensions() -{ - inSize = this->inputDimensions[0]; - for (size_t i = 1; i < this->inputDimensions.size(); ++i) - inSize *= this->inputDimensions[i]; - - this->outputDimensions = std::vector(this->inputDimensions.size(), - 1); - - this->outputDimensions[0] = outSize; -} - -template +template template -void LinearNoBiasType::serialize( +void LinearNoBias::serialize( Archive& ar, const uint32_t /* version */) { - ar(cereal::base_class>(this)); - ar(CEREAL_NVP(inSize)); ar(CEREAL_NVP(outSize)); - ar(CEREAL_NVP(regularizer)); + + // This is inefficient, but necessary so that WeightSetVisitor sets the right + // size. + if (cereal::is_loading()) + weights.set_size(outSize * inSize, 1); } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/log_softmax.hpp b/src/mlpack/methods/ann/layer/log_softmax.hpp index a93edb6183..8c62fd2d9e 100644 --- a/src/mlpack/methods/ann/layer/log_softmax.hpp +++ b/src/mlpack/methods/ann/layer/log_softmax.hpp @@ -14,8 +14,6 @@ #include -#include "layer.hpp" - namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -26,32 +24,22 @@ namespace ann /** Artificial Neural Network. */ { * (NegativeLogLikelihoodLayer), which expects that the input contains * log-probabilities for each class. * - * @tparam MatType Matrix representation to accept as input and use for - * computation. + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class LogSoftMaxType : public Layer +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class LogSoftMax { public: /** - * Create the LogSoftmax layer. + * Create the LogSoftmax object. */ - LogSoftMaxType(); - - //! Clone the LogSoftMaxType object. This handles polymorphism correctly. - LogSoftMaxType* Clone() const { return new LogSoftMaxType(*this); } - - // Virtual destructor. - virtual ~LogSoftMaxType() { } - - //! Copy the given LogSoftMaxType. - LogSoftMaxType(const LogSoftMaxType& other); - //! Take ownership of the given LogSoftMaxType. - LogSoftMaxType(LogSoftMaxType&& other); - //! Copy the given LogSoftMaxType. - LogSoftMaxType& operator=(const LogSoftMaxType& other); - //! Take ownership of the given LogSoftMaxType. - LogSoftMaxType& operator=(LogSoftMaxType&& other); + LogSoftMax(); /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -60,7 +48,8 @@ class LogSoftMaxType : public Layer * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - void Forward(const MatType& input, MatType& output); + template + void Forward(const InputType& input, OutputType& output); /** * Ordinary feed backward pass of a neural network, calculating the function @@ -71,22 +60,34 @@ class LogSoftMaxType : public Layer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const MatType& input, const MatType& gy, MatType& g); + template + void Backward(const arma::Mat& input, + const arma::Mat& gy, + arma::Mat& g); + //! Get the output parameter. + OutputDataType& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + InputDataType& Delta() const { return delta; } + //! Modify the delta. + InputDataType& Delta() { return delta; } + + /** + * Serialize the layer. + */ template - void serialize(Archive& ar, const uint32_t /* version */) - { - ar(cereal::base_class>(this)); - // Nothing to do. - } + void serialize(Archive& /* ar */, const uint32_t /* version */); private: -}; // class LogSoftmaxType + //! Locally-stored delta object. + OutputDataType delta; -// Convenience typedefs. - -// Standard Linear layer using no regularization. -typedef LogSoftMaxType LogSoftMax; + //! Locally-stored output parameter object. + OutputDataType outputParameter; +}; // class LogSoftmax } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/log_softmax_impl.hpp b/src/mlpack/methods/ann/layer/log_softmax_impl.hpp index 37b2e296bd..4762662c2f 100644 --- a/src/mlpack/methods/ann/layer/log_softmax_impl.hpp +++ b/src/mlpack/methods/ann/layer/log_softmax_impl.hpp @@ -18,54 +18,18 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -LogSoftMaxType::LogSoftMaxType() +template +LogSoftMax::LogSoftMax() { // Nothing to do here. } -template -LogSoftMaxType::LogSoftMaxType(const LogSoftMaxType& other) : - Layer(other) +template +template +void LogSoftMax::Forward( + const InputType& input, OutputType& output) { - // Nothing to do here. -} - -template -LogSoftMaxType::LogSoftMaxType(LogSoftMaxType&& other) : - Layer(std::move(other)) -{ - // Nothing to do here. -} - -template -LogSoftMaxType& -LogSoftMaxType::operator=(const LogSoftMaxType& other) -{ - if (&other != this) - { - Layer::operator=(other); - } - - return *this; -} - -template -LogSoftMaxType& -LogSoftMaxType::operator=(LogSoftMaxType&& other) -{ - if (&other != this) - { - Layer::operator=(std::move(other)); - } - - return *this; -} - -template -void LogSoftMaxType::Forward(const MatType& input, MatType& output) -{ - MatType maxInput = arma::repmat(arma::max(input), input.n_rows, 1); + arma::mat maxInput = arma::repmat(arma::max(input), input.n_rows, 1); output = (maxInput - input); // Approximation of the base-e exponential function. The acuracy however is @@ -97,15 +61,25 @@ void LogSoftMaxType::Forward(const MatType& input, MatType& output) output = input - maxInput; } -template -void LogSoftMaxType::Backward( - const MatType& input, - const MatType& gy, - MatType& g) +template +template +void LogSoftMax::Backward( + const arma::Mat& input, + const arma::Mat& gy, + arma::Mat& g) { g = arma::exp(input) + gy; } +template +template +void LogSoftMax::serialize( + Archive& /* ar */, + const uint32_t /* version */) +{ + // Nothing to do here. +} + } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/not_adapted/lookup.hpp b/src/mlpack/methods/ann/layer/lookup.hpp similarity index 64% rename from src/mlpack/methods/ann/layer/not_adapted/lookup.hpp rename to src/mlpack/methods/ann/layer/lookup.hpp index 714924949d..6f6796f193 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/lookup.hpp +++ b/src/mlpack/methods/ann/layer/lookup.hpp @@ -14,7 +14,7 @@ #define MLPACK_METHODS_ANN_LAYER_LOOKUP_HPP #include -#include "layer.hpp" +#include namespace mlpack { namespace ann /* Artificial Neural Network. */ { @@ -29,16 +29,16 @@ namespace ann /* Artificial Neural Network. */ { * The input shape : (sequenceLength, batchSize). * The output shape : (embeddingSize, sequenceLength, batchSize). * - * @tparam InputType Type of the input data (arma::colvec, arma::mat, + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). - * @tparam OutputType Type of the output data (arma::colvec, arma::mat, + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). */ template < - typename InputType = arma::mat, - typename OutputType = arma::mat + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat > -class LookupType : public Layer +class Lookup { public: /** @@ -47,10 +47,7 @@ class LookupType : public Layer * @param vocabSize The size of the vocabulary. * @param embeddingSize The length of each embedding vector. */ - LookupType(const size_t vocabSize = 0, const size_t embeddingSize = 0); - - //! Clone the LookupType object. This handles polymorphism correctly. - LookupType* Clone() const { return new LookupType(*this); } + Lookup(const size_t vocabSize = 0, const size_t embeddingSize = 0); /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -59,7 +56,8 @@ class LookupType : public Layer * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - void Forward(const InputType& input, OutputType& output); + template + void Forward(const arma::Mat& input, arma::Mat& output); /** * Ordinary feed backward pass of a neural network, calculating the function @@ -70,9 +68,10 @@ class LookupType : public Layer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const InputType& /* input */, - const OutputType& gy, - OutputType& g); + template + void Backward(const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g); /** * Calculate the gradient using the output delta and the input activation. @@ -81,14 +80,30 @@ class LookupType : public Layer * @param error The calculated error. * @param gradient The calculated gradient. */ - void Gradient(const InputType& input, - const OutputType& error, - OutputType& gradient); + template + void Gradient(const arma::Mat& input, + const arma::Mat& error, + arma::Mat& gradient); //! Get the parameters. - OutputType const& Parameters() const { return weights; } + OutputDataType const& Parameters() const { return weights; } //! Modify the parameters. - OutputType& Parameters() { return weights; } + OutputDataType& Parameters() { return weights; } + + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } + + //! Get the gradient. + OutputDataType const& Gradient() const { return gradient; } + //! Modify the gradient. + OutputDataType& Gradient() { return gradient; } //! Get the size of the vocabulary. size_t VocabSize() const { return vocabSize; } @@ -96,19 +111,6 @@ class LookupType : public Layer //! Get the length of each embedding vector. size_t EmbeddingSize() const { return embeddingSize; } - //! Get the number of trainable parameters. - const size_t WeightSize() const { return embeddingSize * vocabSize; } - - //! Get the dimensions of the output. This layer adds an extra dimension for - //! the embedding. - const std::vector& OutputDimensions() const - { - std::vector result(inputDimensions.size() + 1, embeddingSize); - for (size_t i = 0; i < inputDimensions.size(); ++i) - result[i + 1] = inputDimensions[i]; - return result; - } - /** * Serialize the layer */ @@ -123,14 +125,21 @@ class LookupType : public Layer size_t embeddingSize; //! Locally-stored weight object. - OutputType weights; + OutputDataType weights; + + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored gradient object. + OutputDataType gradient; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; }; // class Lookup // Alias for using as embedding layer. -// template -// using Embedding = Lookup; -typedef LookupType Lookup; -typedef LookupType Embedding; +template +using Embedding = Lookup; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/not_adapted/lookup_impl.hpp b/src/mlpack/methods/ann/layer/lookup_impl.hpp similarity index 58% rename from src/mlpack/methods/ann/layer/not_adapted/lookup_impl.hpp rename to src/mlpack/methods/ann/layer/lookup_impl.hpp index d1e36d2f29..7755f83342 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/lookup_impl.hpp +++ b/src/mlpack/methods/ann/layer/lookup_impl.hpp @@ -19,30 +19,26 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -LookupType::LookupType( +template +Lookup::Lookup( const size_t vocabSize, const size_t embeddingSize) : vocabSize(vocabSize), embeddingSize(embeddingSize) { - // Nothing to do. + weights.set_size(embeddingSize, vocabSize); } -template -void LookupType::SetWeights( - typename OutputType::elem_type* weightsPtr) -{ - weights = OutputType(weightsPtr, embeddingSize, vocabSize, false, true); -} - -template -void LookupType::Forward( - const InputType& input, OutputType& output) +template +template +void Lookup::Forward( + const arma::Mat& input, arma::Mat& output) { const size_t seqLength = input.n_rows; const size_t batchSize = input.n_cols; + output.set_size(embeddingSize * seqLength, batchSize); + for (size_t i = 0; i < batchSize; ++i) { // ith column of output is a vectorized form of a matrix of shape @@ -53,29 +49,32 @@ void LookupType::Forward( } } -template -void LookupType::Backward( - const InputType& /* input */, - const OutputType& /* gy */, - OutputType& /* g */) +template +template +void Lookup::Backward( + const arma::Mat& /* input */, + const arma::Mat& /* gy */, + arma::Mat& /* g */) { Log::Fatal << "Lookup cannot be used as an intermediate layer." << std::endl; } -template -void LookupType::Gradient( - const InputType& input, - const OutputType& error, - OutputType& gradient) +template +template +void Lookup::Gradient( + const arma::Mat& input, + const arma::Mat& error, + arma::Mat& gradient) { - typedef typename arma::Cube CubeType; const size_t seqLength = input.n_rows; const size_t batchSize = input.n_cols; - const CubeType errorTemp(const_cast(error).memptr(), + arma::Cube errorTemp(const_cast&>(error).memptr(), embeddingSize, seqLength, batchSize, false, false); + gradient.set_size(arma::size(weights)); gradient.zeros(); + for (size_t i = 0; i < batchSize; ++i) { gradient.cols(arma::conv_to::from(input.col(i)) - 1) @@ -83,15 +82,18 @@ void LookupType::Gradient( } } -template +template template -void LookupType::serialize( +void Lookup::serialize( Archive& ar, const uint32_t /* version */) { - ar(cereal::base_class>(this)); - ar(CEREAL_NVP(vocabSize)); ar(CEREAL_NVP(embeddingSize)); + + // This is inefficient, but we have to allocate this memory so that + // WeightSetVisitor gets the right size. + if (cereal::is_loading()) + weights.set_size(embeddingSize, vocabSize); } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/not_adapted/lp_pooling.hpp b/src/mlpack/methods/ann/layer/lp_pooling.hpp similarity index 100% rename from src/mlpack/methods/ann/layer/not_adapted/lp_pooling.hpp rename to src/mlpack/methods/ann/layer/lp_pooling.hpp diff --git a/src/mlpack/methods/ann/layer/not_adapted/lp_pooling_impl.hpp b/src/mlpack/methods/ann/layer/lp_pooling_impl.hpp similarity index 100% rename from src/mlpack/methods/ann/layer/not_adapted/lp_pooling_impl.hpp rename to src/mlpack/methods/ann/layer/lp_pooling_impl.hpp diff --git a/src/mlpack/methods/ann/layer/lstm.hpp b/src/mlpack/methods/ann/layer/lstm.hpp index e2e297b2f2..effca7328e 100644 --- a/src/mlpack/methods/ann/layer/lstm.hpp +++ b/src/mlpack/methods/ann/layer/lstm.hpp @@ -15,8 +15,6 @@ #include #include -#include "layer.hpp" - namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -52,43 +50,43 @@ namespace ann /** Artificial Neural Network. */ { * \see FastLSTM for a faster LSTM version which combines the calculation of the * input, forget, output gates and hidden state in a single step. * - * @tparam MatType Matrix representation to accept as input and use for - * computation. + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class LSTMType : public RecurrentLayer +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class LSTM { public: //! Create the LSTM object. - LSTMType(); + LSTM(); /** * Create the LSTM layer object using the specified parameters. * + * @param inSize The number of input units. * @param outSize The number of output units. * @param rho Maximum number of steps to backpropagate through time (BPTT). */ - LSTMType(const size_t outSize); + LSTM(const size_t inSize, + const size_t outSize, + const size_t rho = std::numeric_limits::max()); - //! Clone the LSTMType object. This handles polymorphism correctly. - LSTMType* Clone() const { return new LSTMType(*this); } + //! Copy constructor. + LSTM(const LSTM& layer); - //! Copy the given LSTMType object. - LSTMType(const LSTMType& other); - //! Take ownership of the given LSTMType object's data. - LSTMType(LSTMType&& other); - //! Copy the given LSTMType object. - LSTMType& operator=(const LSTMType& other); - //! Take ownership of the given LSTMType object's data. - LSTMType& operator=(LSTMType&& other); + //! Move constructor. + LSTM(LSTM&&); - virtual ~LSTMType() { } + //! Copy assignment operator. + LSTM& operator=(const LSTM& layer); - /** - * Reset the layer parameter. The method is called to - * assign the allocated memory to the internal learnable parameters. - */ - void SetWeights(typename MatType::elem_type* weightsPtr); + //! Move assignment operator. + LSTM& operator=(LSTM&& layer); /** * Ordinary feed-forward pass of a neural network, evaluating the function @@ -97,7 +95,23 @@ class LSTMType : public RecurrentLayer * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - void Forward(const MatType& input, MatType& output); + template + void Forward(const InputType& input, OutputType& output); + + /** + * Ordinary feed-forward pass of a neural network, evaluating the function + * f(x) by propagating the activity forward through f. + * + * @param input Input data used for evaluating the specified function. + * @param output Resulting output activation. + * @param cellState Cell state of the LSTM. + * @param useCellState Use the cellState passed in the LSTM cell. + */ + template + void Forward(const InputType& input, + OutputType& output, + OutputType& cellState, + bool useCellState = false); /** * Ordinary feed backward pass of a neural network, calculating the function @@ -108,7 +122,23 @@ class LSTMType : public RecurrentLayer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const MatType& input, const MatType& gy, MatType& g); + template + void Backward(const InputType& input, + const ErrorType& gy, + GradientType& g); + + /* + * Reset the layer parameter. + */ + void Reset(); + + /* + * Resets the cell to accept a new input. This breaks the BPTT chain starts a + * new one. + * + * @param size The current maximum number of steps through time. + */ + void ResetCell(const size_t size); /* * Calculate the gradient using the output delta and the input activation. @@ -117,44 +147,56 @@ class LSTMType : public RecurrentLayer * @param error The calculated error. * @param gradient The calculated gradient. */ - void Gradient(const MatType& input, - const MatType& error, - MatType& gradient); + template + void Gradient(const InputType& input, + const ErrorType& error, + GradientType& gradient); - /** - * Reset the recurrent state of the LSTM layer, and allocate enough space to - * hold `bpttSteps` of previous passes with a batch size of `batchSize`. - * - * @param bpttSteps Number of steps of history to allocate space for. - * @param batchSize Batch size to prepare for. - */ - void ClearRecurrentState(const size_t bpttSteps, const size_t batchSize); + //! Get the maximum number of steps to backpropagate through time (BPTT). + size_t Rho() const { return rho; } + //! Modify the maximum number of steps to backpropagate through time (BPTT). + size_t& Rho() { return rho; } //! Get the parameters. - const MatType& Parameters() const { return weights; } + OutputDataType const& Parameters() const { return weights; } //! Modify the parameters. - MatType& Parameters() { return weights; } + OutputDataType& Parameters() { return weights; } - //! Get the total number of trainable parameters. + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } + + //! Get the gradient. + OutputDataType const& Gradient() const { return grad; } + //! Modify the gradient. + OutputDataType& Gradient() { return grad; } + + //! Get the number of input units. + size_t InSize() const { return inSize; } + + //! Get the number of output units. + size_t OutSize() const { return outSize; } + + //! Get the size of the weights. size_t WeightSize() const { return (4 * outSize * inSize + 7 * outSize + 4 * outSize * outSize); } - //! Given a properly set InputDimensions(), compute the output dimensions. - void ComputeOutputDimensions() + //! Get the shape of the input. + size_t InputShape() const { - inSize = std::accumulate(this->inputDimensions.begin(), - this->inputDimensions.end(), 0); - this->outputDimensions = std::vector(this->inputDimensions.size(), - 1); - - // The LSTM layer flattens its input. - this->outputDimensions[0] = outSize; + return inSize; } /** - * Serialize the layer. + * Serialize the layer */ template void serialize(Archive& ar, const uint32_t /* version */); @@ -166,111 +208,148 @@ class LSTMType : public RecurrentLayer //! Locally-stored number of output units. size_t outSize; + //! Number of steps to backpropagate through time (BPTT). + size_t rho; + + //! Locally-stored number of forward steps. + size_t forwardStep; + + //! Locally-stored number of backward steps. + size_t backwardStep; + + //! Locally-stored number of gradient steps. + size_t gradientStep; + //! Locally-stored weight object. - MatType weights; + OutputDataType weights; - //! Weights between the output and input gate. - MatType output2GateInputWeight; + //! Locally-stored previous output. + OutputDataType prevOutput; - //! Weights between the input and gate. - MatType input2GateInputWeight; + //! Locally-stored batch size. + size_t batchSize; - //! Bias between the input and input gate. - MatType input2GateInputBias; + //! Current batch step, alias for batchSize - 1. + size_t batchStep; - //! Weights between the cell and input gate. - MatType cell2GateInputWeight; - - //! Weights between the output and forget gate. - MatType output2GateForgetWeight; - - //! Weights between the input and gate. - MatType input2GateForgetWeight; - - //! Bias between the input and gate. - MatType input2GateForgetBias; - - //! Bias between the input and gate. - MatType cell2GateForgetWeight; - - //! Weights between the output and gate. - MatType output2GateOutputWeight; - - //! Weights between the input and gate. - MatType input2GateOutputWeight; - - //! Bias between the input and gate. - MatType input2GateOutputBias; - - //! Weights between cell and output gate. - MatType cell2GateOutputWeight; - - // Below here are recurrent state matrices. - - //! Locally-stored input gate parameter. - MatType inputGate; - - //! Locally-stored forget gate parameter. - MatType forgetGate; - - //! Locally-stored hidden layer parameter. - MatType hiddenLayer; - - //! Locally-stored output gate parameter. - MatType outputGate; - - //! Locally-stored input to hidden weight. - MatType input2HiddenWeight; - - //! Locally-stored input to hidden bias. - MatType input2HiddenBias; - - //! Locally-stored output to hidden weight. - MatType output2HiddenWeight; - - //! Locally-stored cell parameter. - arma::Cube cell; - - // These members store recurrent state. - - //! Locally-stored input gate activation. - arma::Cube inputGateActivation; - - //! Locally-stored forget gate activation. - arma::Cube forgetGateActivation; - - //! Locally-stored output gate activation. - arma::Cube outputGateActivation; - - //! Locally-stored hidden layer activation. - arma::Cube hiddenLayerActivation; + //! Current gradient step to keep track of the backpropagate through time + //! step. + size_t gradientStepIdx; //! Locally-stored cell activation error. - arma::Cube cellActivation; + OutputDataType cellActivationError; + + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored gradient object. + OutputDataType grad; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; + + //! Weights between the output and input gate. + OutputDataType output2GateInputWeight; + + //! Weights between the input and gate. + OutputDataType input2GateInputWeight; + + //! Bias between the input and input gate. + OutputDataType input2GateInputBias; + + //! Weights between the cell and input gate. + OutputDataType cell2GateInputWeight; + + //! Weights between the output and forget gate. + OutputDataType output2GateForgetWeight; + + //! Weights between the input and gate. + OutputDataType input2GateForgetWeight; + + //! Bias between the input and gate. + OutputDataType input2GateForgetBias; + + //! Bias between the input and gate. + OutputDataType cell2GateForgetWeight; + + //! Weights between the output and gate. + OutputDataType output2GateOutputWeight; + + //! Weights between the input and gate. + OutputDataType input2GateOutputWeight; + + //! Bias between the input and gate. + OutputDataType input2GateOutputBias; + + //! Weights between cell and output gate. + OutputDataType cell2GateOutputWeight; + + //! Locally-stored input gate parameter. + OutputDataType inputGate; + + //! Locally-stored forget gate parameter. + OutputDataType forgetGate; + + //! Locally-stored hidden layer parameter. + OutputDataType hiddenLayer; + + //! Locally-stored output gate parameter. + OutputDataType outputGate; + + //! Locally-stored input gate activation. + OutputDataType inputGateActivation; + + //! Locally-stored forget gate activation. + OutputDataType forgetGateActivation; + + //! Locally-stored output gate activation. + OutputDataType outputGateActivation; + + //! Locally-stored hidden layer activation. + OutputDataType hiddenLayerActivation; + + //! Locally-stored input to hidden weight. + OutputDataType input2HiddenWeight; + + //! Locally-stored input to hidden bias. + OutputDataType input2HiddenBias; + + //! Locally-stored output to hidden weight. + OutputDataType output2HiddenWeight; + + //! Locally-stored cell parameter. + OutputDataType cell; + + //! Locally-stored cell activation error. + OutputDataType cellActivation; //! Locally-stored forget gate error. - MatType forgetGateError; + OutputDataType forgetGateError; //! Locally-stored output gate error. - MatType outputGateError; + OutputDataType outputGateError; + + //! Locally-stored previous error. + OutputDataType prevError; //! Locally-stored output parameters. - arma::Cube outParameter; + OutputDataType outParameter; //! Locally-stored input cell error parameter. - MatType inputCellError; + OutputDataType inputCellError; //! Locally-stored input gate error. - MatType inputGateError; + OutputDataType inputGateError; //! Locally-stored hidden layer error. - MatType hiddenError; -}; // class LSTMType + OutputDataType hiddenError; -// Convenience typedefs. + //! Locally-stored current rho size. + size_t rhoSize; -// Standard LSTM layer. -typedef LSTMType LSTM; + //! Current backpropagate through time steps. + size_t bpttSteps; +}; // class LSTM } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/lstm_impl.hpp b/src/mlpack/methods/ann/layer/lstm_impl.hpp index 078bcb1a1b..2b298d18aa 100644 --- a/src/mlpack/methods/ann/layer/lstm_impl.hpp +++ b/src/mlpack/methods/ann/layer/lstm_impl.hpp @@ -18,296 +18,430 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -LSTMType::LSTMType() : - RecurrentLayer(), - outSize(0) +template +LSTM::LSTM() { // Nothing to do here. } -template -LSTMType::LSTMType(const size_t outSize) : - RecurrentLayer(), - outSize(outSize) +template +LSTM::LSTM( + const LSTM& layer) : + inSize(layer.inSize), + outSize(layer.outSize), + rho(layer.rho), + forwardStep(layer.forwardStep), + backwardStep(layer.backwardStep), + gradientStep(layer.gradientStep), + weights(layer.weights), + batchSize(layer.batchSize), + batchStep(layer.batchStep), + gradientStepIdx(layer.gradientStepIdx), + rhoSize(layer.rho), + bpttSteps(layer.bpttSteps) { // Nothing to do here. } -template -LSTMType::LSTMType(const LSTMType& layer) : - RecurrentLayer(layer) +template +LSTM::LSTM( + LSTM&& layer) : + inSize(std::move(layer.inSize)), + outSize(std::move(layer.outSize)), + rho(std::move(layer.rho)), + forwardStep(std::move(layer.forwardStep)), + backwardStep(std::move(layer.backwardStep)), + gradientStep(std::move(layer.gradientStep)), + weights(std::move(layer.weights)), + batchSize(std::move(layer.batchSize)), + batchStep(std::move(layer.batchStep)), + gradientStepIdx(std::move(layer.gradientStepIdx)), + rhoSize(std::move(layer.rho)), + bpttSteps(std::move(layer.bpttSteps)) { // Nothing to do here. } -template -LSTMType::LSTMType(LSTMType&& layer) : - RecurrentLayer(std::move(layer)) -{ - // Nothing to do here. -} - -template -LSTMType& LSTMType::operator=(const LSTMType& layer) +template +LSTM& +LSTM :: operator=(const LSTM& layer) { if (this != &layer) { - RecurrentLayer::operator=(layer); + inSize = layer.inSize; + outSize = layer.outSize; + rho = layer.rho; + forwardStep = layer.forwardStep; + backwardStep = layer.backwardStep; + gradientStep = layer.gradientStep; + weights = layer.weights; + batchSize = layer.batchSize; + batchStep = layer.batchStep; + gradientStepIdx = layer.gradientStepIdx; + grad = layer.grad; + rhoSize = layer.rho; + bpttSteps = layer.bpttSteps; } - return *this; } -template -LSTMType& LSTMType::operator=(LSTMType&& layer) +template +LSTM& +LSTM :: operator=(LSTM&& layer) { if (this != &layer) { - RecurrentLayer::operator=(std::move(layer)); + inSize = std::move(layer.inSize); + outSize = std::move(layer.outSize); + rho = std::move(layer.rho); + forwardStep = std::move(layer.forwardStep); + backwardStep = std::move(layer.backwardStep); + gradientStep = std::move(layer.gradientStep); + weights = std::move(layer.weights); + batchSize = std::move(layer.batchSize); + batchStep = std::move(layer.batchStep); + gradientStepIdx = std::move(layer.gradientStepIdx); + grad = std::move(layer.grad); + rhoSize = std::move(layer.rho); + bpttSteps = std::move(layer.bpttSteps); } - return *this; } -template -void LSTMType::ClearRecurrentState( - const size_t bpttSteps, const size_t batchSize) +template +LSTM::LSTM( + const size_t inSize, const size_t outSize, const size_t rho) : + inSize(inSize), + outSize(outSize), + rho(rho), + forwardStep(0), + backwardStep(0), + gradientStep(0), + batchSize(0), + batchStep(0), + gradientStepIdx(0), + rhoSize(rho), + bpttSteps(0) { + weights.set_size(WeightSize(), 1); +} + +template +void LSTM::ResetCell(const size_t size) +{ + if (size == std::numeric_limits::max()) + return; + + rhoSize = size; + + if (batchSize == 0) + return; + + bpttSteps = std::min(rho, rhoSize); + forwardStep = 0; + gradientStepIdx = 0; + backwardStep = batchSize * size - 1; + gradientStep = batchSize * size - 1; + + const size_t rhoBatchSize = size * batchSize; + // Make sure all of the different matrices we will use to hold parameters are // at least as large as we need. - inputGate.set_size(outSize, batchSize); - forgetGate.set_size(outSize, batchSize); - hiddenLayer.set_size(outSize, batchSize); - outputGate.set_size(outSize, batchSize); + inputGate.set_size(outSize, rhoBatchSize); + forgetGate.set_size(outSize, rhoBatchSize); + hiddenLayer.set_size(outSize, rhoBatchSize); + outputGate.set_size(outSize, rhoBatchSize); - inputGateActivation.set_size(outSize, batchSize, bpttSteps); - forgetGateActivation.set_size(outSize, batchSize, bpttSteps); - outputGateActivation.set_size(outSize, batchSize, bpttSteps); - hiddenLayerActivation.set_size(outSize, batchSize, bpttSteps); + inputGateActivation.set_size(outSize, rhoBatchSize); + forgetGateActivation.set_size(outSize, rhoBatchSize); + outputGateActivation.set_size(outSize, rhoBatchSize); + hiddenLayerActivation.set_size(outSize, rhoBatchSize); - cellActivation.set_size(outSize, batchSize, bpttSteps); - outParameter.set_size(outSize, batchSize, bpttSteps); + cellActivation.set_size(outSize, rhoBatchSize); + prevError.set_size(4 * outSize, batchSize); // Now reset recurrent values to 0. - cell.zeros(outSize, batchSize, bpttSteps); + cell.zeros(outSize, size * batchSize); + outParameter.zeros(outSize, (size + 1) * batchSize); } -template -void LSTMType::SetWeights( - typename MatType::elem_type* weightsPtr) +template +void LSTM::Reset() { // Set the weight parameter for the output gate. - MakeAlias(input2GateOutputWeight, weightsPtr, outSize, inSize); - size_t offset = input2GateOutputWeight.n_elem; - MakeAlias(input2GateOutputBias, weightsPtr + offset, outSize, 1); - offset += input2GateOutputBias.n_elem; + input2GateOutputWeight = OutputDataType(weights.memptr(), outSize, inSize, + false, false); + input2GateOutputBias = OutputDataType(weights.memptr() + + input2GateOutputWeight.n_elem, outSize, 1, false, false); + size_t offset = input2GateOutputWeight.n_elem + input2GateOutputBias.n_elem; // Set the weight parameter for the forget gate. - MakeAlias(input2GateForgetWeight, weightsPtr + offset, outSize, inSize); - offset += input2GateForgetWeight.n_elem; - MakeAlias(input2GateForgetBias, weightsPtr + offset, outSize, 1); - offset += input2GateForgetBias.n_elem; + input2GateForgetWeight = OutputDataType(weights.memptr() + offset, + outSize, inSize, false, false); + input2GateForgetBias = OutputDataType(weights.memptr() + + offset + input2GateForgetWeight.n_elem, outSize, 1, false, false); + offset += input2GateForgetWeight.n_elem + input2GateForgetBias.n_elem; // Set the weight parameter for the input gate. - MakeAlias(input2GateInputWeight, weightsPtr + offset, outSize, inSize); - offset += input2GateInputWeight.n_elem; - MakeAlias(input2GateInputBias, weightsPtr + offset, outSize, 1); - offset += input2GateInputBias.n_elem; + input2GateInputWeight = OutputDataType(weights.memptr() + + offset, outSize, inSize, false, false); + input2GateInputBias = OutputDataType(weights.memptr() + + offset + input2GateInputWeight.n_elem, outSize, 1, false, false); + offset += input2GateInputWeight.n_elem + input2GateInputBias.n_elem; // Set the weight parameter for the hidden gate. - MakeAlias(input2HiddenWeight, weightsPtr + offset, outSize, inSize); - offset += input2HiddenWeight.n_elem; - MakeAlias(input2HiddenBias, weightsPtr + offset, outSize, 1); - offset += input2HiddenBias.n_elem; + input2HiddenWeight = OutputDataType(weights.memptr() + + offset, outSize, inSize, false, false); + input2HiddenBias = OutputDataType(weights.memptr() + + offset + input2HiddenWeight.n_elem, outSize, 1, false, false); + offset += input2HiddenWeight.n_elem + input2HiddenBias.n_elem; // Set the weight parameter for the output multiplication. - MakeAlias(output2GateOutputWeight, weightsPtr + offset, outSize, outSize); + output2GateOutputWeight = OutputDataType(weights.memptr() + + offset, outSize, outSize, false, false); offset += output2GateOutputWeight.n_elem; // Set the weight parameter for the output multiplication. - MakeAlias(output2GateForgetWeight, weightsPtr + offset, outSize, outSize); + output2GateForgetWeight = OutputDataType(weights.memptr() + + offset, outSize, outSize, false, false); offset += output2GateForgetWeight.n_elem; // Set the weight parameter for the input multiplication. - MakeAlias(output2GateInputWeight, weightsPtr + offset, outSize, outSize); + output2GateInputWeight = OutputDataType(weights.memptr() + + offset, outSize, outSize, false, false); offset += output2GateInputWeight.n_elem; // Set the weight parameter for the hidden multiplication. - MakeAlias(output2HiddenWeight, weightsPtr + offset, outSize, outSize); + output2HiddenWeight = OutputDataType(weights.memptr() + + offset, outSize, outSize, false, false); offset += output2HiddenWeight.n_elem; // Set the weight parameter for the cell multiplication. - MakeAlias(cell2GateOutputWeight, weightsPtr + offset, outSize, 1); + cell2GateOutputWeight = OutputDataType(weights.memptr() + + offset, outSize, 1, false, false); offset += cell2GateOutputWeight.n_elem; // Set the weight parameter for the cell - forget gate multiplication. - MakeAlias(cell2GateForgetWeight, weightsPtr + offset, outSize, 1); + cell2GateForgetWeight = OutputDataType(weights.memptr() + + offset, outSize, 1, false, false); offset += cell2GateOutputWeight.n_elem; // Set the weight parameter for the cell - input gate multiplication. - MakeAlias(cell2GateInputWeight, weightsPtr + offset, outSize, 1); + cell2GateInputWeight = OutputDataType(weights.memptr() + + offset, outSize, 1, false, false); } // Forward when cellState is not needed. -template -void LSTMType::Forward(const MatType& input, MatType& output) +template +template +void LSTM::Forward( + const InputType& input, OutputType& output) { - // Convenience alias. - const size_t batchSize = input.n_cols; + //! Locally-stored cellState. + OutputType cellState; + Forward(input, output, cellState, false); +} - inputGate = input2GateInputWeight * input; - if (this->HasPreviousStep()) +// Forward when cellState is needed overloaded LSTM::Forward(). +template +template +void LSTM::Forward(const InputType& input, + OutputType& output, + OutputType& cellState, + bool useCellState) +{ + // Check if the batch size changed, the number of cols is defines the input + // batch size. + if (input.n_cols != batchSize) { - inputGate += - output2GateInputWeight * outParameter.slice(this->PreviousStep()); - } - inputGate.each_col() += input2GateInputBias; - - forgetGate = input2GateForgetWeight * input; - if (this->HasPreviousStep()) - { - forgetGate += output2GateForgetWeight * outParameter.slice( - this->PreviousStep()); - } - forgetGate.each_col() += input2GateForgetBias; - - if (this->HasPreviousStep()) - { - inputGate += arma::repmat(cell2GateInputWeight, 1, batchSize) % - cell.slice(this->PreviousStep()); - - forgetGate += arma::repmat(cell2GateForgetWeight, 1, batchSize) % - cell.slice(this->PreviousStep()); + batchSize = input.n_cols; + batchStep = batchSize - 1; + ResetCell(rhoSize); } - inputGateActivation.slice(this->CurrentStep()) = - 1.0 / (1.0 + arma::exp(-inputGate)); - forgetGateActivation.slice(this->CurrentStep()) = - 1.0 / (1.0 + arma::exp(-forgetGate)); + inputGate.cols(forwardStep, forwardStep + batchStep) = input2GateInputWeight * + input + output2GateInputWeight * outParameter.cols(forwardStep, + forwardStep + batchStep); + inputGate.cols(forwardStep, forwardStep + batchStep).each_col() += + input2GateInputBias; - hiddenLayer = input2HiddenWeight * input; - if (this->HasPreviousStep()) + forgetGate.cols(forwardStep, forwardStep + batchStep) = input2GateForgetWeight + * input + output2GateForgetWeight * outParameter.cols( + forwardStep, forwardStep + batchStep); + forgetGate.cols(forwardStep, forwardStep + batchStep).each_col() += + input2GateForgetBias; + + if (forwardStep > 0) { - hiddenLayer += output2HiddenWeight * - outParameter.slice(this->PreviousStep()); + if (useCellState) + { + if (!cellState.is_empty()) + { + cell.cols(forwardStep - batchSize, + forwardStep - batchSize + batchStep) = cellState; + } + else + { + throw std::runtime_error("Cell parameter is empty."); + } + } + inputGate.cols(forwardStep, forwardStep + batchStep) += + arma::repmat(cell2GateInputWeight, 1, batchSize) % + cell.cols(forwardStep - batchSize, forwardStep - batchSize + batchStep); + + forgetGate.cols(forwardStep, forwardStep + batchStep) += + arma::repmat(cell2GateForgetWeight, 1, batchSize) % + cell.cols(forwardStep - batchSize, forwardStep - batchSize + batchStep); } - hiddenLayer.each_col() += input2HiddenBias; - hiddenLayerActivation.slice(this->CurrentStep()) = arma::tanh(hiddenLayer); + inputGateActivation.cols(forwardStep, forwardStep + batchStep) = 1.0 / + (1 + arma::exp(-inputGate.cols(forwardStep, forwardStep + batchStep))); - if (!this->HasPreviousStep()) + forgetGateActivation.cols(forwardStep, forwardStep + batchStep) = 1.0 / + (1 + arma::exp(-forgetGate.cols(forwardStep, forwardStep + batchStep))); + + hiddenLayer.cols(forwardStep, forwardStep + batchStep) = input2HiddenWeight * + input + output2HiddenWeight * outParameter.cols( + forwardStep, forwardStep + batchStep); + + hiddenLayer.cols(forwardStep, forwardStep + batchStep).each_col() += + input2HiddenBias; + + hiddenLayerActivation.cols(forwardStep, forwardStep + batchStep) = + arma::tanh(hiddenLayer.cols(forwardStep, forwardStep + batchStep)); + + if (forwardStep == 0) { - cell.slice(this->CurrentStep()) = - inputGateActivation.slice(this->CurrentStep()) % - hiddenLayerActivation.slice(this->CurrentStep()); + cell.cols(forwardStep, forwardStep + batchStep) = + inputGateActivation.cols(forwardStep, forwardStep + batchStep) % + hiddenLayerActivation.cols(forwardStep, forwardStep + batchStep); } else { - cell.slice(this->CurrentStep()) = - forgetGateActivation.slice(this->CurrentStep()) % - cell.slice(this->PreviousStep()) + - inputGateActivation.slice(this->CurrentStep()) % - hiddenLayerActivation.slice(this->CurrentStep()); + cell.cols(forwardStep, forwardStep + batchStep) = + forgetGateActivation.cols(forwardStep, forwardStep + batchStep) % + cell.cols(forwardStep - batchSize, forwardStep - batchSize + batchStep) + + inputGateActivation.cols(forwardStep, forwardStep + batchStep) % + hiddenLayerActivation.cols(forwardStep, forwardStep + batchStep); } - outputGate = input2GateOutputWeight * input + - cell.slice(this->CurrentStep()).each_col() % cell2GateOutputWeight; - if (this->HasPreviousStep()) + outputGate.cols(forwardStep, forwardStep + batchStep) = input2GateOutputWeight + * input + output2GateOutputWeight * outParameter.cols( + forwardStep, forwardStep + batchStep) + cell.cols(forwardStep, + forwardStep + batchStep).each_col() % cell2GateOutputWeight; + + outputGate.cols(forwardStep, forwardStep + batchStep).each_col() += + input2GateOutputBias; + + outputGateActivation.cols(forwardStep, forwardStep + batchStep) = 1.0 / + (1 + arma::exp(-outputGate.cols(forwardStep, forwardStep + batchStep))); + + cellActivation.cols(forwardStep, forwardStep + batchStep) = + arma::tanh(cell.cols(forwardStep, forwardStep + batchStep)); + + outParameter.cols(forwardStep + batchSize, + forwardStep + batchSize + batchStep) = + cellActivation.cols(forwardStep, forwardStep + batchStep) % + outputGateActivation.cols(forwardStep, forwardStep + batchStep); + + output = OutputType(outParameter.memptr() + + (forwardStep + batchSize) * outSize, outSize, batchSize, false, false); + + cellState = OutputType(cell.memptr() + + forwardStep * outSize, outSize, batchSize, false, false); + + forwardStep += batchSize; + if ((forwardStep / batchSize) == bpttSteps) { - outputGate += - output2GateOutputWeight * outParameter.slice(this->PreviousStep()); + forwardStep = 0; } - outputGate.each_col() += input2GateOutputBias; - - outputGateActivation.slice(this->CurrentStep()) = - 1.0 / (1.0 + arma::exp(-outputGate)); - - cellActivation.slice(this->CurrentStep()) = - arma::tanh(cell.slice(this->CurrentStep())); - - // There's a bit of an issue here: we need to preserve the output for the next - // time step, but we also need to set `output` to that. Unfortunately for now - // we make a copy, but it's possible that we could instead use an alias here, - // or have `outParameter` hold a collection of aliases. - outParameter.slice(this->CurrentStep()) = - cellActivation.slice(this->CurrentStep()) % - outputGateActivation.slice(this->CurrentStep()); - - output = outParameter.slice(this->CurrentStep()); } -template -void LSTMType::Backward( - const MatType& /* input */, const MatType& gy, MatType& g) +template +template +void LSTM::Backward( + const InputType& /* input */, const ErrorType& gy, GradientType& g) { - MatType gyLocal; - if (this->HasPreviousStep()) + ErrorType gyLocal; + if (gradientStepIdx > 0) { - gyLocal = gy + output2GateOutputWeight.t() * outputGateError + - output2GateForgetWeight.t() * forgetGateError + - output2GateInputWeight.t() * inputGateError + - output2HiddenWeight.t() * hiddenError; + gyLocal = gy + prevError; } else { // Make an alias. - gyLocal = MatType(((MatType&) gy).memptr(), gy.n_rows, gy.n_cols, - false, false); + gyLocal = ErrorType(((ErrorType&) gy).memptr(), gy.n_rows, gy.n_cols, false, + false); } - outputGateError = gyLocal % cellActivation.slice(this->CurrentStep()) % - (outputGateActivation.slice(this->CurrentStep()) % - (1.0 - outputGateActivation.slice(this->CurrentStep()))); + outputGateError = + gyLocal % cellActivation.cols(backwardStep - batchStep, backwardStep) % + (outputGateActivation.cols(backwardStep - batchStep, backwardStep) % + (1.0 - outputGateActivation.cols(backwardStep - batchStep, + backwardStep))); - MatType cellError = gyLocal % - outputGateActivation.slice(this->CurrentStep()) % - (1 - arma::pow(cellActivation.slice(this->CurrentStep()), 2)) + - outputGateError.each_col() % cell2GateOutputWeight; + OutputDataType cellError = gyLocal % + outputGateActivation.cols(backwardStep - batchStep, backwardStep) % + (1 - arma::pow(cellActivation.cols(backwardStep - + batchStep, backwardStep), 2)) + outputGateError.each_col() % + cell2GateOutputWeight; - if (this->HasPreviousStep()) + if (gradientStepIdx > 0) { cellError += inputCellError; } - if (this->HasPreviousStep()) + if (backwardStep > batchStep) { - forgetGateError = cell.slice(this->PreviousStep()) % cellError % - (forgetGateActivation.slice(this->CurrentStep()) % - (1.0 - forgetGateActivation.slice(this->CurrentStep()))); + forgetGateError = cell.cols((backwardStep - batchSize) - batchStep, + (backwardStep - batchSize)) % cellError % (forgetGateActivation.cols( + backwardStep - batchStep, backwardStep) % (1.0 - + forgetGateActivation.cols(backwardStep - batchStep, backwardStep))); } else { - forgetGateError.zeros(forgetGateActivation.n_rows, - forgetGateActivation.n_cols); + forgetGateError.zeros(); } - inputGateError = hiddenLayerActivation.slice(this->CurrentStep()) % - cellError % (inputGateActivation.slice(this->CurrentStep()) % - (1.0 - inputGateActivation.slice(this->CurrentStep()))); + inputGateError = hiddenLayerActivation.cols(backwardStep - batchStep, + backwardStep) % cellError % + (inputGateActivation.cols(backwardStep - batchStep, backwardStep) % + (1.0 - inputGateActivation.cols(backwardStep - batchStep, backwardStep))); - hiddenError = inputGateActivation.slice(this->CurrentStep()) % cellError % - (1 - arma::pow(hiddenLayerActivation.slice(this->CurrentStep()), 2)); + hiddenError = inputGateActivation.cols(backwardStep - batchStep, + backwardStep) % cellError % (1 - arma::pow(hiddenLayerActivation.cols( + backwardStep - batchStep, backwardStep), 2)); - inputCellError = forgetGateActivation.slice(this->CurrentStep()) % cellError + - forgetGateError.each_col() % cell2GateForgetWeight + - inputGateError.each_col() % cell2GateInputWeight; + inputCellError = forgetGateActivation.cols(backwardStep - batchStep, + backwardStep) % cellError + forgetGateError.each_col() % + cell2GateForgetWeight + inputGateError.each_col() % cell2GateInputWeight; g = input2GateInputWeight.t() * inputGateError + input2HiddenWeight.t() * hiddenError + input2GateForgetWeight.t() * forgetGateError + input2GateOutputWeight.t() * outputGateError; + + prevError = output2GateOutputWeight.t() * outputGateError + + output2GateForgetWeight.t() * forgetGateError + + output2GateInputWeight.t() * inputGateError + + output2HiddenWeight.t() * hiddenError; + + backwardStep -= batchSize; + gradientStepIdx++; + if (gradientStepIdx == bpttSteps) + { + backwardStep = bpttSteps - 1; + gradientStepIdx = 0; + } } -template -void LSTMType::Gradient( - const MatType& input, - const MatType& /* error */, - MatType& gradient) +template +template +void LSTM::Gradient( + const InputType& input, + const ErrorType& /* error */, + GradientType& gradient) { - // This implementation depends on Gradient() being called just after - // Backward(), which is something we can safely assume. - // Input2GateOutputWeight and input2GateOutputBias gradients. gradient.submat(0, 0, input2GateOutputWeight.n_elem - 1, 0) = arma::vectorise(outputGateError * input.t()); @@ -316,7 +450,7 @@ void LSTMType::Gradient( arma::sum(outputGateError, 1); size_t offset = input2GateOutputWeight.n_elem + input2GateOutputBias.n_elem; - // input2GateForgetWeight and input2GateForgetBias gradients. + // Input2GateForgetWeight and input2GateForgetBias gradients. gradient.submat(offset, 0, offset + input2GateForgetWeight.n_elem - 1, 0) = arma::vectorise(forgetGateError * input.t()); gradient.submat(offset + input2GateForgetWeight.n_elem, 0, @@ -324,7 +458,7 @@ void LSTMType::Gradient( input2GateForgetBias.n_elem - 1, 0) = arma::sum(forgetGateError, 1); offset += input2GateForgetWeight.n_elem + input2GateForgetBias.n_elem; - // input2GateInputWeight and input2GateInputBias gradients. + // Input2GateInputWeight and input2GateInputBias gradients. gradient.submat(offset, 0, offset + input2GateInputWeight.n_elem - 1, 0) = arma::vectorise(inputGateError * input.t()); gradient.submat(offset + input2GateInputWeight.n_elem, 0, @@ -332,7 +466,7 @@ void LSTMType::Gradient( input2GateInputBias.n_elem - 1, 0) = arma::sum(inputGateError, 1); offset += input2GateInputWeight.n_elem + input2GateInputBias.n_elem; - // input2HiddenWeight and input2HiddenBias gradients. + // Input2HiddenWeight and input2HiddenBias gradients. gradient.submat(offset, 0, offset + input2HiddenWeight.n_elem - 1, 0) = arma::vectorise(hiddenError * input.t()); gradient.submat(offset + input2HiddenWeight.n_elem, 0, @@ -340,43 +474,48 @@ void LSTMType::Gradient( arma::sum(hiddenError, 1); offset += input2HiddenWeight.n_elem + input2HiddenBias.n_elem; - // output2GateOutputWeight gradients. + // Output2GateOutputWeight gradients. gradient.submat(offset, 0, offset + output2GateOutputWeight.n_elem - 1, 0) = arma::vectorise(outputGateError * - outParameter.slice(this->CurrentStep()).t()); + outParameter.cols(gradientStep - batchStep, gradientStep).t()); offset += output2GateOutputWeight.n_elem; - // output2GateForgetWeight gradients. + // Output2GateForgetWeight gradients. gradient.submat(offset, 0, offset + output2GateForgetWeight.n_elem - 1, 0) = arma::vectorise(forgetGateError * - outParameter.slice(this->CurrentStep()).t()); + outParameter.cols(gradientStep - batchStep, gradientStep).t()); offset += output2GateForgetWeight.n_elem; - // output2GateInputWeight gradients. + // Output2GateInputWeight gradients. gradient.submat(offset, 0, offset + output2GateInputWeight.n_elem - 1, 0) = arma::vectorise(inputGateError * - outParameter.slice(this->CurrentStep()).t()); + outParameter.cols(gradientStep - batchStep, gradientStep).t()); offset += output2GateInputWeight.n_elem; - // output2HiddenWeight gradients. + // Output2HiddenWeight gradients. gradient.submat(offset, 0, offset + output2HiddenWeight.n_elem - 1, 0) = arma::vectorise(hiddenError * - outParameter.slice(this->CurrentStep()).t()); + outParameter.cols(gradientStep - batchStep, gradientStep).t()); offset += output2HiddenWeight.n_elem; - // cell2GateOutputWeight gradients. + // Cell2GateOutputWeight gradients. gradient.submat(offset, 0, offset + cell2GateOutputWeight.n_elem - 1, 0) = - arma::sum(outputGateError % cell.slice(this->CurrentStep()), 1); + arma::sum(outputGateError % + cell.cols(gradientStep - batchStep, gradientStep), 1); offset += cell2GateOutputWeight.n_elem; - // cell2GateForgetWeight and cell2GateInputWeight gradients. - if (this->HasPreviousStep()) + // Cell2GateForgetWeight and cell2GateInputWeight gradients. + if (gradientStep > batchStep) { gradient.submat(offset, 0, offset + cell2GateForgetWeight.n_elem - 1, 0) = - arma::sum(forgetGateError % cell.slice(this->PreviousStep()), 1); + arma::sum(forgetGateError % + cell.cols((gradientStep - batchSize) - batchStep, + (gradientStep - batchSize)), 1); gradient.submat(offset + cell2GateForgetWeight.n_elem, 0, offset + cell2GateForgetWeight.n_elem + cell2GateInputWeight.n_elem - 1, 0) = - arma::sum(inputGateError % cell.slice(this->PreviousStep()), 1); + arma::sum(inputGateError % + cell.cols((gradientStep - batchSize) - batchStep, + (gradientStep - batchSize)), 1); } else { @@ -386,32 +525,41 @@ void LSTMType::Gradient( cell2GateForgetWeight.n_elem + cell2GateInputWeight.n_elem - 1, 0).zeros(); } + + if (gradientStep == 0) + { + gradientStep = batchSize * bpttSteps - 1; + } + else + { + gradientStep -= batchSize; + } } -template +template template -void LSTMType::serialize(Archive& ar, const uint32_t /* version */) +void LSTM::serialize( + Archive& ar, const uint32_t /* version */) { - ar(cereal::base_class>(this)); - + ar(CEREAL_NVP(weights)); ar(CEREAL_NVP(inSize)); ar(CEREAL_NVP(outSize)); - - // Clear recurrent state if we are loading. - if (Archive::is_loading::value) - { - inputGateActivation.clear(); - forgetGateActivation.clear(); - outputGateActivation.clear(); - hiddenLayerActivation.clear(); - cellActivation.clear(); - forgetGateError.clear(); - outputGateError.clear(); - outParameter.clear(); - inputCellError.clear(); - inputGateError.clear(); - hiddenError.clear(); - } + ar(CEREAL_NVP(rho)); + ar(CEREAL_NVP(bpttSteps)); + ar(CEREAL_NVP(batchSize)); + ar(CEREAL_NVP(batchStep)); + ar(CEREAL_NVP(forwardStep)); + ar(CEREAL_NVP(backwardStep)); + ar(CEREAL_NVP(gradientStep)); + ar(CEREAL_NVP(gradientStepIdx)); + ar(CEREAL_NVP(cell)); + ar(CEREAL_NVP(inputGateActivation)); + ar(CEREAL_NVP(forgetGateActivation)); + ar(CEREAL_NVP(outputGateActivation)); + ar(CEREAL_NVP(hiddenLayerActivation)); + ar(CEREAL_NVP(cellActivation)); + ar(CEREAL_NVP(prevError)); + ar(CEREAL_NVP(outParameter)); } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/max_pooling.hpp b/src/mlpack/methods/ann/layer/max_pooling.hpp index 25d25509e4..2547c5596a 100644 --- a/src/mlpack/methods/ann/layer/max_pooling.hpp +++ b/src/mlpack/methods/ann/layer/max_pooling.hpp @@ -15,8 +15,6 @@ #include -#include "layer.hpp" - namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -30,39 +28,32 @@ class MaxPoolingRule /* * Return the maximum value within the receptive block. * - * @param input Input used to perform the pooling operation. Could be an - * Armadillo subview. + * @param input Input used to perform the pooling operation. */ template - typename MatType::elem_type Pooling(const MatType& input) + size_t Pooling(const MatType& input) { - return arma::max(arma::vectorise(input)); - } - - template - std::tuple PoolingWithIndex( - const MatType& input) - { - const typename MatType::elem_type maxVal = - arma::max(arma::vectorise(input)); - const size_t index = arma::as_scalar(arma::find(input == maxVal, 1)); - - return std::tuple(index, maxVal); + return arma::as_scalar(arma::find(input.max() == input, 1)); } }; /** * Implementation of the MaxPooling layer. * - * @tparam MatType Matrix representation to accept as input and use for - * computation. + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class MaxPoolingType : public Layer +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class MaxPooling { public: //! Create the MaxPooling object. - MaxPoolingType(); + MaxPooling(); /** * Create the MaxPooling object using the specified number of units. @@ -73,25 +64,11 @@ class MaxPoolingType : public Layer * @param strideHeight Width of the stride operation. * @param floor Rounding operator (floor or ceil). */ - MaxPoolingType(const size_t kernelWidth, - const size_t kernelHeight, - const size_t strideWidth = 1, - const size_t strideHeight = 1, - const bool floor = true); - - // Virtual destructor. - virtual ~MaxPoolingType() { } - - //! Copy the given MaxPoolingType. - MaxPoolingType(const MaxPoolingType& other); - //! Take ownership of the given MaxPoolingType. - MaxPoolingType(MaxPoolingType&& other); - //! Copy the given MaxPoolingType. - MaxPoolingType& operator=(const MaxPoolingType& other); - //! Take ownership of the given MaxPoolingType. - MaxPoolingType& operator=(MaxPoolingType&& other); - - MaxPoolingType* Clone() const { return new MaxPoolingType(*this); } + MaxPooling(const size_t kernelWidth, + const size_t kernelHeight, + const size_t strideWidth = 1, + const size_t strideHeight = 1, + const bool floor = true); /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -100,7 +77,8 @@ class MaxPoolingType : public Layer * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - void Forward(const MatType& input, MatType& output); + template + void Forward(const arma::Mat& input, arma::Mat& output); /** * Ordinary feed backward pass of a neural network, using 3rd-order tensors as @@ -111,37 +89,79 @@ class MaxPoolingType : public Layer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const MatType& /* input */, - const MatType& gy, - MatType& g); + template + void Backward(const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g); + + //! Get the output parameter. + const OutputDataType& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + const OutputDataType& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } + + //! Get the input width. + size_t InputWidth() const { return inputWidth; } + //! Modify the input width. + size_t& InputWidth() { return inputWidth; } + + //! Get the input height. + size_t InputHeight() const { return inputHeight; } + //! Modify the input height. + size_t& InputHeight() { return inputHeight; } + + //! Get the output width. + size_t OutputWidth() const { return outputWidth; } + //! Modify the output width. + size_t& OutputWidth() { return outputWidth; } + + //! Get the output height. + size_t OutputHeight() const { return outputHeight; } + //! Modify the output height. + size_t& OutputHeight() { return outputHeight; } + + //! Get the input size. + size_t InputSize() const { return inSize; } + + //! Get the output size. + size_t OutputSize() const { return outSize; } //! Get the kernel width. - size_t const& KernelWidth() const { return kernelWidth; } + size_t KernelWidth() const { return kernelWidth; } //! Modify the kernel width. size_t& KernelWidth() { return kernelWidth; } //! Get the kernel height. - size_t const& KernelHeight() const { return kernelHeight; } + size_t KernelHeight() const { return kernelHeight; } //! Modify the kernel height. size_t& KernelHeight() { return kernelHeight; } //! Get the stride width. - size_t const& StrideWidth() const { return strideWidth; } + size_t StrideWidth() const { return strideWidth; } //! Modify the stride width. size_t& StrideWidth() { return strideWidth; } //! Get the stride height. - size_t const& StrideHeight() const { return strideHeight; } + size_t StrideHeight() const { return strideHeight; } //! Modify the stride height. size_t& StrideHeight() { return strideHeight; } //! Get the value of the rounding operation. - bool const& Floor() const { return floor; } + bool Floor() const { return floor; } //! Modify the value of the rounding operation. bool& Floor() { return floor; } - //! Compute the size of the output given `InputDimensions()`. - void ComputeOutputDimensions(); + //! Get the value of the deterministic parameter. + bool Deterministic() const { return deterministic; } + //! Modify the value of the deterministic parameter. + bool& Deterministic() { return deterministic; } + + //! Get the size of the weights. + size_t WeightSize() const { return 0; } /** * Serialize the layer. @@ -150,94 +170,62 @@ class MaxPoolingType : public Layer void serialize(Archive& ar, const uint32_t /* version */); private: - /** + /** * Apply pooling to the input and store the results. * * @param input The input to be apply the pooling rule. * @param output The pooled result. * @param poolingIndices The pooled indices. */ - void PoolingOperation( - const arma::Cube& input, - arma::Cube& output, - arma::Cube& poolingIndices) + template + void PoolingOperation(const arma::Mat& input, + arma::Mat& output, + arma::Mat& poolingIndices) { - // Iterate over all slices individually. - for (size_t s = 0; s < input.n_slices; ++s) + for (size_t j = 0, colidx = 0; j < output.n_cols; + ++j, colidx += strideHeight) { - for (size_t j = 0, colidx = 0; j < output.n_cols; - ++j, colidx += strideHeight) + for (size_t i = 0, rowidx = 0; i < output.n_rows; + ++i, rowidx += strideWidth) { - for (size_t i = 0, rowidx = 0; i < output.n_rows; - ++i, rowidx += strideWidth) + size_t rowEnd = rowidx + kernelWidth - 1; + size_t colEnd = colidx + kernelHeight - 1; + + if (rowEnd > input.n_rows - 1) + rowEnd = input.n_rows - 1; + if (colEnd > input.n_cols - 1) + colEnd = input.n_cols - 1; + + arma::mat subInput = input( + arma::span(rowidx, rowEnd), + arma::span(colidx, colEnd)); + + const size_t idx = pooling.Pooling(subInput); + output(i, j) = subInput(idx); + + if (!deterministic) { - const std::tuple poolResult = - pooling.PoolingWithIndex(input.slice(s).submat( - rowidx, - colidx, - rowidx + kernelWidth - 1 - offset, - colidx + kernelHeight - 1 - offset)); + arma::Mat subIndices = indices(arma::span(rowidx, rowEnd), + arma::span(colidx, colEnd)); - // Now map the returned pooling index, which corresponds to the - // submatrix we gave, back to its position in the (linearized) input. - const size_t poolIndex = std::get<0>(poolResult); - const size_t poolingCol = poolIndex / (kernelWidth - offset); - const size_t poolingRow = poolIndex % (kernelWidth - offset); - const size_t unmappedPoolingIndex = (rowidx + poolingRow) + - input.n_rows * (colidx + poolingCol) + - input.n_rows * input.n_cols * s; - - poolingIndices(i, j, s) = unmappedPoolingIndex; - output(i, j, s) = std::get<1>(poolResult); + poolingIndices(i, j) = subIndices(idx); } } } } /** - * Apply pooling to all slices of the input and store the results, but not the - * indices used. - * - * @param input The input to apply the pooling rule to. - * @param output The pooled result. - */ - void PoolingOperation( - const arma::Cube& input, - arma::Cube& output) - { - // Iterate over all slices individually. - for (size_t s = 0; s < input.n_slices; ++s) - { - for (size_t j = 0, colidx = 0; j < output.n_cols; - ++j, colidx += strideHeight) - { - for (size_t i = 0, rowidx = 0; i < output.n_rows; - ++i, rowidx += strideWidth) - { - output(i, j, s) = pooling.Pooling(input.slice(s).submat( - rowidx, - colidx, - rowidx + kernelWidth - 1 - offset, - colidx + kernelHeight - 1 - offset)); - } - } - } - } - - /** - * Apply unpooling to all slices of the input and store the results. + * Apply unpooling to the input and store the results. * * @param error The backward error. * @param output The pooled result. - * @param poolingIndices The pooled indices (from `PoolingOperation()`). + * @param poolingIndices The pooled indices. */ - void UnpoolingOperation( - const arma::Cube& error, - arma::Cube& output, - const arma::Cube& poolingIndices) + template + void Unpooling(const arma::Mat& error, + arma::Mat& output, + arma::Mat& poolingIndices) { - output.zeros(); - for (size_t i = 0; i < poolingIndices.n_elem; ++i) { output(poolingIndices(i)) += error(i); @@ -259,22 +247,64 @@ class MaxPoolingType : public Layer //! Rounding operation used. bool floor; - //! Locally-stored number of channels. - size_t channels; + //! Locally-stored number of input channels. + size_t inSize; - //! Locally-stored offset: indicates whether we take the first element or the - //! second element when pooling. Computed by `ComputeOutputDimensions()`. - size_t offset; + //! Locally-stored number of output channels. + size_t outSize; + + //! Locally-stored reset parameter used to initialize the module once. + bool reset; + + //! Locally-stored input width. + size_t inputWidth; + + //! Locally-stored input height. + size_t inputHeight; + + //! Locally-stored output width. + size_t outputWidth; + + //! Locally-stored output height. + size_t outputHeight; + + //! If true use maximum a posteriori during the forward pass. + bool deterministic; + + + //! Locally-stored number of input units. + size_t batchSize; + + //! Locally-stored output parameter. + arma::cube outputTemp; + + //! Locally-stored transformed input parameter. + arma::cube inputTemp; + + //! Locally-stored transformed output parameter. + arma::cube gTemp; //! Locally-stored pooling strategy. MaxPoolingRule pooling; - //! Locally-stored pooling indices. - arma::Cube poolingIndices; -}; // class MaxPoolingType + //! Locally-stored delta object. + OutputDataType delta; -// Standard MaxPooling layer. -typedef MaxPoolingType MaxPooling; + //! Locally-stored gradient object. + OutputDataType gradient; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; + + //! Locally-stored indices matrix parameter. + arma::Mat indices; + + //! Locally-stored indices column parameter. + arma::Col indicesCol; + + //! Locally-stored pooling indicies. + std::vector poolingIndices; +}; // class MaxPooling } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/max_pooling_impl.hpp b/src/mlpack/methods/ann/layer/max_pooling_impl.hpp index 29badb4e36..9650a5f2c3 100644 --- a/src/mlpack/methods/ann/layer/max_pooling_impl.hpp +++ b/src/mlpack/methods/ann/layer/max_pooling_impl.hpp @@ -19,201 +19,141 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -MaxPoolingType::MaxPoolingType() : - Layer() +template +MaxPooling::MaxPooling() { // Nothing to do here. } -template -MaxPoolingType::MaxPoolingType( +template +MaxPooling::MaxPooling( const size_t kernelWidth, const size_t kernelHeight, const size_t strideWidth, const size_t strideHeight, const bool floor) : - Layer(), kernelWidth(kernelWidth), kernelHeight(kernelHeight), strideWidth(strideWidth), strideHeight(strideHeight), floor(floor), - channels(0), - offset(0) + inSize(0), + outSize(0), + reset(false), + inputWidth(0), + inputHeight(0), + outputWidth(0), + outputHeight(0), + deterministic(false), + batchSize(0) { // Nothing to do here. } -template -MaxPoolingType::MaxPoolingType( - const MaxPoolingType& other) : - Layer(other), - kernelWidth(other.kernelWidth), - kernelHeight(other.kernelHeight), - strideWidth(other.strideWidth), - strideHeight(other.strideHeight), - floor(other.floor), - channels(other.channels), - offset(other.offset), - pooling(other.pooling) +template +template +void MaxPooling::Forward( + const arma::Mat& input, arma::Mat& output) { - // Nothing to do here. -} + batchSize = input.n_cols; + inSize = input.n_elem / (inputWidth * inputHeight * batchSize); + inputTemp = arma::cube(const_cast&>(input).memptr(), + inputWidth, inputHeight, batchSize * inSize, false, false); -template -MaxPoolingType::MaxPoolingType( - MaxPoolingType&& other) : - Layer(std::move(other)), - kernelWidth(std::move(other.kernelWidth)), - kernelHeight(std::move(other.kernelHeight)), - strideWidth(std::move(other.strideWidth)), - strideHeight(std::move(other.strideHeight)), - floor(std::move(other.floor)), - channels(std::move(other.channels)), - offset(std::move(other.offset)), - pooling(std::move(other.pooling)) -{ - // Nothing to do here. -} - -template -MaxPoolingType& -MaxPoolingType::operator=(const MaxPoolingType& other) -{ - if (&other != this) - { - Layer::operator=(other); - kernelWidth = other.kernelWidth; - kernelHeight = other.kernelHeight; - strideWidth = other.strideWidth; - strideHeight = other.strideHeight; - floor = other.floor; - channels = other.channels; - offset = other.offset; - pooling = other.pooling; - } - - return *this; -} - -template -MaxPoolingType& -MaxPoolingType::operator=(MaxPoolingType&& other) -{ - if (&other != this) - { - Layer::operator=(std::move(other)); - kernelWidth = std::move(other.kernelWidth); - kernelHeight = std::move(other.kernelHeight); - strideWidth = std::move(other.strideWidth); - strideHeight = std::move(other.strideHeight); - floor = std::move(other.floor); - channels = std::move(other.channels); - offset = std::move(other.offset); - pooling = std::move(other.pooling); - } - - return *this; -} - -template -void MaxPoolingType::Forward(const MatType& input, MatType& output) -{ - arma::Cube inputTemp( - const_cast(input).memptr(), this->inputDimensions[0], - this->inputDimensions[1], input.n_cols * channels, false, false); - - arma::Cube outputTemp(output.memptr(), - this->outputDimensions[0], this->outputDimensions[1], - input.n_cols * channels, false, true); - - if (this->training) - { - // If we are training, we'll do a backwards pass, so we need to ensure that - // we know what indices we used. - poolingIndices.set_size(this->outputDimensions[0], - this->outputDimensions[1], input.n_cols * channels); - - PoolingOperation(inputTemp, outputTemp, poolingIndices); - } - else - { - PoolingOperation(inputTemp, outputTemp); - } -} - -template -void MaxPoolingType::Backward( - const MatType& input, const MatType& gy, MatType& g) -{ - arma::Cube mappedError = - arma::Cube(((MatType&) gy).memptr(), - this->outputDimensions[0], this->outputDimensions[1], - channels * input.n_cols, false, false); - - arma::Cube gTemp(g.memptr(), - this->inputDimensions[0], this->inputDimensions[1], - channels * input.n_cols, false, true); - - // There's no version of UnpoolingOperation without pooling indices, because - // if we call `Backward()`, we know for sure we are training. - UnpoolingOperation(mappedError, gTemp, poolingIndices); -} - -template -void MaxPoolingType::ComputeOutputDimensions() -{ - this->outputDimensions = this->inputDimensions; - - // Compute the size of the output. if (floor) { - this->outputDimensions[0] = std::floor((this->inputDimensions[0] - + outputWidth = std::floor((inputWidth - (double) kernelWidth) / (double) strideWidth + 1); - this->outputDimensions[1] = std::floor((this->inputDimensions[1] - + outputHeight = std::floor((inputHeight - (double) kernelHeight) / (double) strideHeight + 1); - offset = 0; } else { - this->outputDimensions[0] = std::ceil((this->inputDimensions[0] - + outputWidth = std::ceil((inputWidth - (double) kernelWidth) / (double) strideWidth + 1); - this->outputDimensions[1] = std::ceil((this->inputDimensions[1] - + outputHeight = std::ceil((inputHeight - (double) kernelHeight) / (double) strideHeight + 1); - offset = 1; } - // Higher dimensions are not modified. + outputTemp = arma::zeros >(outputWidth, outputHeight, + batchSize * inSize); - // Cache input size and output size. - channels = 1; - for (size_t i = 2; i < this->inputDimensions.size(); ++i) - channels *= this->inputDimensions[i]; + if (!deterministic) + { + poolingIndices.push_back(outputTemp); + } + + if (!reset) + { + size_t elements = inputWidth * inputHeight; + indicesCol = arma::linspace >(0, (elements - 1), + elements); + + indices = arma::Mat(indicesCol.memptr(), inputWidth, inputHeight); + + reset = true; + } + + for (size_t s = 0; s < inputTemp.n_slices; s++) + { + if (!deterministic) + { + PoolingOperation(inputTemp.slice(s), outputTemp.slice(s), + poolingIndices.back().slice(s)); + } + else + { + PoolingOperation(inputTemp.slice(s), outputTemp.slice(s), + inputTemp.slice(s)); + } + } + + output = arma::Mat(outputTemp.memptr(), outputTemp.n_elem / batchSize, + batchSize); + + outputWidth = outputTemp.n_rows; + outputHeight = outputTemp.n_cols; + outSize = batchSize * inSize; } -template +template +template +void MaxPooling::Backward( + const arma::Mat& /* input */, const arma::Mat& gy, arma::Mat& g) +{ + arma::cube mappedError = arma::cube(((arma::Mat&) gy).memptr(), + outputWidth, outputHeight, outSize, false, false); + + gTemp = arma::zeros(inputTemp.n_rows, + inputTemp.n_cols, inputTemp.n_slices); + + for (size_t s = 0; s < mappedError.n_slices; s++) + { + Unpooling(mappedError.slice(s), gTemp.slice(s), + poolingIndices.back().slice(s)); + } + + poolingIndices.pop_back(); + + g = arma::mat(gTemp.memptr(), gTemp.n_elem / batchSize, batchSize); +} + +template template -void MaxPoolingType::serialize( +void MaxPooling::serialize( Archive& ar, const uint32_t /* version */) - { - ar(cereal::base_class>(this)); - ar(CEREAL_NVP(kernelWidth)); ar(CEREAL_NVP(kernelHeight)); ar(CEREAL_NVP(strideWidth)); ar(CEREAL_NVP(strideHeight)); - ar(CEREAL_NVP(channels)); + ar(CEREAL_NVP(batchSize)); ar(CEREAL_NVP(floor)); - ar(CEREAL_NVP(offset)); - - if (Archive::is_loading::value) - { - // Clear any memory used by `poolingIndices`. - poolingIndices.clear(); - } + ar(CEREAL_NVP(inputWidth)); + ar(CEREAL_NVP(inputHeight)); + ar(CEREAL_NVP(outputWidth)); + ar(CEREAL_NVP(outputHeight)); } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/not_adapted/mean_pooling.hpp b/src/mlpack/methods/ann/layer/mean_pooling.hpp similarity index 73% rename from src/mlpack/methods/ann/layer/not_adapted/mean_pooling.hpp rename to src/mlpack/methods/ann/layer/mean_pooling.hpp index 7973fc1aa5..4156667beb 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/mean_pooling.hpp +++ b/src/mlpack/methods/ann/layer/mean_pooling.hpp @@ -21,20 +21,20 @@ namespace ann /** Artificial Neural Network. */ { /** * Implementation of the MeanPooling. * - * @tparam InputType Type of the input data (arma::colvec, arma::mat, + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). - * @tparam OutputType Type of the output data (arma::colvec, arma::mat, + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). */ template < - typename InputType = arma::mat, - typename OutputType = arma::mat + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat > -class MeanPoolingType : public Layer +class MeanPooling { public: - //! Create the MeanPoolingType object. - MeanPoolingType(); + //! Create the MeanPooling object. + MeanPooling(); /** * Create the MeanPooling object using the specified number of units. @@ -45,14 +45,11 @@ class MeanPoolingType : public Layer * @param strideHeight Width of the stride operation. * @param floor Set to true to use floor method. */ - MeanPoolingType(const size_t kernelWidth, - const size_t kernelHeight, - const size_t strideWidth = 1, - const size_t strideHeight = 1, - const bool floor = true); - - // TODO: copy constructor / move constructor - MeanPoolingType* Clone() const { return new MeanPoolingType(*this); } + MeanPooling(const size_t kernelWidth, + const size_t kernelHeight, + const size_t strideWidth = 1, + const size_t strideHeight = 1, + const bool floor = true); /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -61,7 +58,8 @@ class MeanPoolingType : public Layer * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - void Forward(const InputType& input, OutputType& output); + template + void Forward(const arma::Mat& input, arma::Mat& output); /** * Ordinary feed backward pass of a neural network, using 3rd-order tensors as @@ -72,27 +70,64 @@ class MeanPoolingType : public Layer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const InputType& /* input */, - const OutputType& gy, - OutputType& g); + template + void Backward(const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g); + + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } + + //! Get the intput width. + size_t const& InputWidth() const { return inputWidth; } + //! Modify the input width. + size_t& InputWidth() { return inputWidth; } + + //! Get the input height. + size_t const& InputHeight() const { return inputHeight; } + //! Modify the input height. + size_t& InputHeight() { return inputHeight; } + + //! Get the output width. + size_t const& OutputWidth() const { return outputWidth; } + //! Modify the output width. + size_t& OutputWidth() { return outputWidth; } + + //! Get the output height. + size_t const& OutputHeight() const { return outputHeight; } + //! Modify the output height. + size_t& OutputHeight() { return outputHeight; } + + //! Get the input size. + size_t InputSize() const { return inSize; } + + //! Get the output size. + size_t OutputSize() const { return outSize; } //! Get the kernel width. - size_t const& KernelWidth() const { return kernelWidth; } + size_t KernelWidth() const { return kernelWidth; } //! Modify the kernel width. size_t& KernelWidth() { return kernelWidth; } //! Get the kernel height. - size_t const& KernelHeight() const { return kernelHeight; } + size_t KernelHeight() const { return kernelHeight; } //! Modify the kernel height. size_t& KernelHeight() { return kernelHeight; } //! Get the stride width. - size_t const& StrideWidth() const { return strideWidth; } + size_t StrideWidth() const { return strideWidth; } //! Modify the stride width. size_t& StrideWidth() { return strideWidth; } //! Get the stride height. - size_t const& StrideHeight() const { return strideHeight; } + size_t StrideHeight() const { return strideHeight; } //! Modify the stride height. size_t& StrideHeight() { return strideHeight; } @@ -101,40 +136,13 @@ class MeanPoolingType : public Layer //! Modify the value of the rounding operation bool& Floor() { return floor; } - //! Get the size of the output. - const std::vector OutputDimensions() const - { - outputDimensions = this->inputDimensions; + //! Get the value of the deterministic parameter. + bool Deterministic() const { return deterministic; } + //! Modify the value of the deterministic parameter. + bool& Deterministic() { return deterministic; } - // Compute the size of the output. - if (floor) - { - outputDimensions[0] = std::floor((this->inputDimensions[0] - - (double) kernelWidth) / (double) strideWidth + 1); - outputDimensions[1] = std::floor((this->inputDimensions[1] - - (double) kernelHeight) / (double) strideHeight + 1); - offset = 0; - } - else - { - outputDimensions[0] = std::ceil((this->inputDimensions[0] - - (double) kernelWidth) / (double) strideWidth + 1); - outputDimensions[1] = std::ceil((this->inputDimensions[1] - - (double) kernelHeight) / (double) strideHeight + 1); - offset = 1; - } - - // Higher dimensions are not modified. - for (size_t i = 2; i < this->inputDimensions.size(); ++i) - outputDimensions[i] = this->inputDimensions[i]; - - // Cache input size and output size. - channels = 1; - for (size_t i = 2; i < this->inputDimensions.size(); ++i) - channels *= this->inputDimensions[i]; - - return outputDimensions; - } + //! Get the size of the weights. + size_t WeightSize() const { return 0; } /** * Serialize the layer. @@ -149,7 +157,8 @@ class MeanPoolingType : public Layer * @param input The input to be apply the pooling rule. * @param output The pooled result. */ - void Pooling(const InputType& input, OutputType& output) + template + void Pooling(const arma::Mat& input, arma::Mat& output) { arma::Mat inputPre = input; @@ -165,9 +174,9 @@ class MeanPoolingType : public Layer for (size_t i = 0, rowidx = 0; i < output.n_rows; ++i, rowidx += strideWidth) { - InputType subInput = input( - arma::span(rowidx, rowidx + kernelWidth - 1 - offset), - arma::span(colidx, colidx + kernelHeight - 1 - offset)); + double val = 0.0; + size_t rowEnd = rowidx + kernelWidth - 1; + size_t colEnd = colidx + kernelHeight - 1; if (rowEnd > input.n_rows - 1) rowEnd = input.n_rows - 1; @@ -196,9 +205,10 @@ class MeanPoolingType : public Layer * @param input The input to be apply the unpooling rule. * @param output The pooled result. */ - void Unpooling(const InputType& input, - const OutputType& error, - OutputType& output) + template + void Unpooling(const arma::Mat& input, + const arma::Mat& error, + arma::Mat& output) { // This condition comes by comparing the number of operations involved in the brute // force method and the prefix method. Let the area of error be errorArea and area @@ -211,8 +221,7 @@ class MeanPoolingType : public Layer const bool condition = (error.n_elem * kernelHeight * kernelWidth) > (4 * error.n_elem + 2 * input.n_elem); - OutputType unpooledError; - for (size_t j = 0; j < input.n_cols - cStep; j += cStep) + if (condition) { // If this condition is true then theoritically the prefix sum method of // unpooling is faster. The aim of unpooling is to add @@ -251,8 +260,7 @@ class MeanPoolingType : public Layer { for (size_t i = 0, rowidx = 0; i < input.n_rows; i += strideWidth, ++rowidx) { - // We have to add error(i, j) to output(span(rowidx, rowEnd), - // span(colidx, colEnd)). + // We have to add error(i, j) to output(span(rowidx, rowEnd), span(colidx, colEnd)). // The steps of prefix sum method: // // 1. For each (i, j) perform: @@ -354,21 +362,52 @@ class MeanPoolingType : public Layer //! Rounding operation used. bool floor; - //! Locally-stored number channels. - size_t channels; + //! Locally-stored number of input channels. + size_t inSize; - //! Locally-stored cached output dimensions. - std::vector outputDimensions; + //! Locally-stored number of output channels. + size_t outSize; + + //! Locally-stored input width. + size_t inputWidth; + + //! Locally-stored input height. + size_t inputHeight; + + //! Locally-stored output width. + size_t outputWidth; + + //! Locally-stored output height. + size_t outputHeight; + + //! Locally-stored reset parameter used to initialize the module once. + bool reset; + + //! If true use maximum a posteriori during the forward pass. + bool deterministic; //! Locally-stored number of input units. size_t batchSize; - //! Cached last-seen input. - arma::Cube inputTemp; -}; // class MeanPoolingType + //! Locally-stored output parameter. + arma::cube outputTemp; + + //! Locally-stored transformed input parameter. + arma::cube inputTemp; + + //! Locally-stored transformed output parameter. + arma::cube gTemp; + + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored gradient object. + OutputDataType gradient; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; +}; // class MeanPooling -// Standard MeanPooling layer. -typedef MeanPoolingType MeanPooling; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/mean_pooling_impl.hpp b/src/mlpack/methods/ann/layer/mean_pooling_impl.hpp new file mode 100644 index 0000000000..ad4a8e6943 --- /dev/null +++ b/src/mlpack/methods/ann/layer/mean_pooling_impl.hpp @@ -0,0 +1,134 @@ +/** + * @file methods/ann/layer/mean_pooling_impl.hpp + * @author Marcus Edel + * @author Nilay Jain + * + * Implementation of the MeanPooling layer class. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_LAYER_MEAN_POOLING_IMPL_HPP +#define MLPACK_METHODS_ANN_LAYER_MEAN_POOLING_IMPL_HPP + +// In case it hasn't yet been included. +#include "mean_pooling.hpp" + +namespace mlpack { +namespace ann /** Artificial Neural Network. */ { + +template +MeanPooling::MeanPooling() +{ + // Nothing to do here. +} + +template +MeanPooling::MeanPooling( + const size_t kernelWidth, + const size_t kernelHeight, + const size_t strideWidth, + const size_t strideHeight, + const bool floor) : + kernelWidth(kernelWidth), + kernelHeight(kernelHeight), + strideWidth(strideWidth), + strideHeight(strideHeight), + floor(floor), + inSize(0), + outSize(0), + inputWidth(0), + inputHeight(0), + outputWidth(0), + outputHeight(0), + reset(false), + deterministic(false), + batchSize(0) +{ + // Nothing to do here. +} + +template +template +void MeanPooling::Forward( + const arma::Mat& input, arma::Mat& output) +{ + batchSize = input.n_cols; + inSize = input.n_elem / (inputWidth * inputHeight * batchSize); + inputTemp = arma::cube(const_cast&>(input).memptr(), + inputWidth, inputHeight, batchSize * inSize, false, false); + + if (floor) + { + outputWidth = std::floor((inputWidth - + (double) kernelWidth) / (double) strideWidth + 1); + outputHeight = std::floor((inputHeight - + (double) kernelHeight) / (double) strideHeight + 1); + } + else + { + outputWidth = std::ceil((inputWidth - + (double) kernelWidth) / (double) strideWidth + 1); + outputHeight = std::ceil((inputHeight - + (double) kernelHeight) / (double) strideHeight + 1); + } + + outputTemp = arma::zeros >(outputWidth, outputHeight, + batchSize * inSize); + + for (size_t s = 0; s < inputTemp.n_slices; s++) + Pooling(inputTemp.slice(s), outputTemp.slice(s)); + + output = arma::Mat(outputTemp.memptr(), outputTemp.n_elem / batchSize, + batchSize); + + outputWidth = outputTemp.n_rows; + outputHeight = outputTemp.n_cols; + outSize = batchSize * inSize; +} + +template +template +void MeanPooling::Backward( + const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g) +{ + arma::cube mappedError = arma::cube(((arma::Mat&) gy).memptr(), + outputWidth, outputHeight, outSize, false, false); + + gTemp = arma::zeros(inputTemp.n_rows, + inputTemp.n_cols, inputTemp.n_slices); + + for (size_t s = 0; s < mappedError.n_slices; s++) + { + Unpooling(inputTemp.slice(s), mappedError.slice(s), gTemp.slice(s)); + } + + g = arma::mat(gTemp.memptr(), gTemp.n_elem / batchSize, batchSize); +} + +template +template +void MeanPooling::serialize( + Archive& ar, + const uint32_t /* version */) +{ + ar(CEREAL_NVP(kernelWidth)); + ar(CEREAL_NVP(kernelHeight)); + ar(CEREAL_NVP(strideWidth)); + ar(CEREAL_NVP(strideHeight)); + ar(CEREAL_NVP(batchSize)); + ar(CEREAL_NVP(floor)); + ar(CEREAL_NVP(inputWidth)); + ar(CEREAL_NVP(inputHeight)); + ar(CEREAL_NVP(outputWidth)); + ar(CEREAL_NVP(outputHeight)); +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/layer/not_adapted/minibatch_discrimination.hpp b/src/mlpack/methods/ann/layer/minibatch_discrimination.hpp similarity index 61% rename from src/mlpack/methods/ann/layer/not_adapted/minibatch_discrimination.hpp rename to src/mlpack/methods/ann/layer/minibatch_discrimination.hpp index 513021095f..3448f36b5d 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/minibatch_discrimination.hpp +++ b/src/mlpack/methods/ann/layer/minibatch_discrimination.hpp @@ -41,16 +41,16 @@ namespace ann /** Artificial Neural Network. */ { * } * @endcode * - * @tparam InputType Type of the input data (arma::colvec, arma::mat, + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). - * @tparam OutputType Type of the output data (arma::colvec, arma::mat, + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). */ template < - typename InputType = arma::mat, - typename OutputType = arma::mat + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat > -class MiniBatchDiscrimination : public Layer +class MiniBatchDiscrimination { public: //! Create the MiniBatchDiscrimination object. @@ -60,16 +60,18 @@ class MiniBatchDiscrimination : public Layer * Create the MiniBatchDiscrimination layer object using the specified * number of units. * + * @param inSize The number of input units. * @param outSize The number of output units. * @param features The number of features to compute for each dimension. */ - MiniBatchDiscrimination(const size_t outSize, + MiniBatchDiscrimination(const size_t inSize, + const size_t outSize, const size_t features); /** * Reset the layer parameter. */ - void SetWeights(typename OutputType::elem_type* weightsPtr); + void Reset(); /** * Ordinary feed-forward pass of a neural network, evaluating the function @@ -78,7 +80,8 @@ class MiniBatchDiscrimination : public Layer * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - void Forward(const InputType& input, OutputType& output); + template + void Forward(const arma::Mat& input, arma::Mat& output); /** * Ordinary feed-backward pass of a neural network, calculating the function @@ -89,9 +92,10 @@ class MiniBatchDiscrimination : public Layer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const InputType& /* input */, - const OutputType& gy, - OutputType& g); + template + void Backward(const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g); /** * Calculate the gradient using the output delta and the input activation. @@ -100,27 +104,35 @@ class MiniBatchDiscrimination : public Layer * @param * (error) The calculated error. * @param gradient The calculated gradient. */ - void Gradient(const InputType& input, - const OutputType& /* error */, - OutputType& gradient); + template + void Gradient(const arma::Mat& input, + const arma::Mat& /* error */, + arma::Mat& gradient); //! Get the parameters. - OutputType const& Parameters() const { return weights; } + OutputDataType const& Parameters() const { return weights; } //! Modify the parameters. - OutputType& Parameters() { return weights; } + OutputDataType& Parameters() { return weights; } - const size_t WeightSize() const { return a * b * c; } + //! Get the input parameter. + InputDataType const& InputParameter() const { return inputParameter; } + //! Modify the input parameter. + InputDataType& InputParameter() { return inputParameter; } - const std::vector OutputDimensions() const - { - a = std::accumulate(inputDimensions.begin(), inputDimensions.end(), 0); - std::vector outputDimensions(inputDimensions.size(), 1); - // TODO: not sure if this is right... we just interpret it all as - // one-dimensional. - outputDimensions[0] = a + b; + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } - return outputDimensions; - } + //! Get the delta. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } + + //! Get the gradient. + OutputDataType const& Gradient() const { return gradient; } + //! Modify the gradient. + OutputDataType& Gradient() { return gradient; } //! Get the shape of the input. size_t InputShape() const @@ -136,22 +148,43 @@ class MiniBatchDiscrimination : public Layer private: //! Locally-stored dimensions of weight. - size_t a, b, c; + size_t A, B, C; //! Locally-stored input batch size. size_t batchSize; - //! Locally-stored weight object. - OutputType weights; + //! Locally-stored temporary features object. + arma::mat tempM; - //! Locally-stored features of input. Cached to avoid recomputation. - InputType M; + //! Locally-stored weight object. + OutputDataType weights; + + //! Locally-stored weight parameters. + OutputDataType weight; + + //! Locally-stored features of input. + arma::cube M; //! Locally-stored delta for features object. - arma::Cube deltaM; + arma::cube deltaM; //! Locally-stored L1 distances between features. - arma::Cube distances; + arma::cube distances; + + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored temporary delta object. + OutputDataType deltaTemp; + + //! Locally-stored gradient object. + OutputDataType gradient; + + //! Locally-stored input parameter object. + InputDataType inputParameter; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; }; // class MiniBatchDiscrimination } // namespace ann diff --git a/src/mlpack/methods/ann/layer/minibatch_discrimination_impl.hpp b/src/mlpack/methods/ann/layer/minibatch_discrimination_impl.hpp new file mode 100644 index 0000000000..e600b807d4 --- /dev/null +++ b/src/mlpack/methods/ann/layer/minibatch_discrimination_impl.hpp @@ -0,0 +1,147 @@ +/** + * @file methods/ann/layer/minibatch_discrimination_impl.hpp + * @author Saksham Bansal + * + * Implementation of the MiniBatchDiscrimination layer class. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_LAYER_MINIBATCH_DISCRIMINATION_IMPL_HPP +#define MLPACK_METHODS_ANN_LAYER_MINIBATCH_DISCRIMINATION_IMPL_HPP + +// In case it hasn't yet been included. +#include "minibatch_discrimination.hpp" + +namespace mlpack { +namespace ann /** Artificial Neural Network. */ { + +template +MiniBatchDiscrimination::MiniBatchDiscrimination() : + A(0), + B(0), + C(0), + batchSize(0) +{ + // Nothing to do here. +} + +template +MiniBatchDiscrimination::MiniBatchDiscrimination( + const size_t inSize, + const size_t outSize, + const size_t features) : + A(inSize), + B(outSize - inSize), + C(features), + batchSize(0) +{ + weights.set_size(A * B * C, 1); +} + +template +void MiniBatchDiscrimination::Reset() +{ + weight = arma::mat(weights.memptr(), B * C, A, false, false); +} + +template +template +void MiniBatchDiscrimination::Forward( + const arma::Mat& input, arma::Mat& output) +{ + batchSize = input.n_cols; + tempM = weight * input; + M = arma::cube(tempM.memptr(), B, C, batchSize, false, false); + distances.set_size(B, batchSize, batchSize); + output.set_size(B, batchSize); + + for (size_t i = 0; i < M.n_slices; ++i) + { + output.col(i).ones(); + for (size_t j = 0; j < M.n_slices; ++j) + { + if (j < i) + { + output.col(i) += distances.slice(j).col(i); + } + else if (i == j) + { + continue; + } + else + { + distances.slice(i).col(j) = + arma::exp(-arma::sum(abs(M.slice(i) - M.slice(j)), 1)); + output.col(i) += distances.slice(i).col(j); + } + } + } + + output = join_cols(input, output); // (A + B) x batchSize +} + +template +template +void MiniBatchDiscrimination::Backward( + const arma::Mat& /* input */, const arma::Mat& gy, arma::Mat& g) +{ + g = gy.head_rows(A); + arma::Mat gM = gy.tail_rows(B); + deltaM.zeros(B, C, batchSize); + + for (size_t i = 0; i < M.n_slices; ++i) + { + for (size_t j = 0; j < M.n_slices; ++j) + { + if (i == j) + { + continue; + } + arma::mat t = arma::sign(M.slice(i) - M.slice(j)); + t.each_col() %= + distances.slice(std::min(i, j)).col(std::max(i, j)) % gM.col(i); + deltaM.slice(i) -= t; + deltaM.slice(j) += t; + } + } + + deltaTemp = arma::mat(deltaM.memptr(), B * C, batchSize, false, false); + g += weight.t() * deltaTemp; +} + +template +template +void MiniBatchDiscrimination::Gradient( + const arma::Mat& input, + const arma::Mat& /* error */, + arma::Mat& gradient) +{ + gradient = arma::vectorise(deltaTemp * input.t()); +} + +template +template +void MiniBatchDiscrimination::serialize( + Archive& ar, const uint32_t /* version */) +{ + ar(CEREAL_NVP(A)); + ar(CEREAL_NVP(B)); + ar(CEREAL_NVP(C)); + + // This is inefficient, but we have to allocate this memory so that + // WeightSetVisitor gets the right size. + if (cereal::is_loading()) + { + weights.set_size(A * B * C, 1); + } +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/layer/multi_layer.hpp b/src/mlpack/methods/ann/layer/multi_layer.hpp deleted file mode 100644 index 3b9564949b..0000000000 --- a/src/mlpack/methods/ann/layer/multi_layer.hpp +++ /dev/null @@ -1,252 +0,0 @@ -/** - * @file methods/ann/layer/multi_layer.hpp - * @author Ryan Curtin - * - * Base class for neural network layers that are wrappers around other layers. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_LAYER_MULTI_LAYER_HPP -#define MLPACK_METHODS_ANN_LAYER_MULTI_LAYER_HPP - -#include "../make_alias.hpp" -#include "layer.hpp" - -namespace mlpack { -namespace ann { - -/** - * A "multi-layer" is a layer that is a wrapper around other layers. It passes - * the input through all of its child layers sequentially, returning the output - * from the last layer. - * - * It's likely not very useful to use this layer directly; instead, this layer - * is meant as a base class for use by other layers that must store and use - * multiple layers. - * - * @tparam MatType Matrix representation to accept as input and use for - * computation. - */ -template -class MultiLayer : public Layer -{ - public: - /** - * Create an empty MultiLayer that holds no layers of its own. Be sure to add - * layers with Add() before using! - */ - MultiLayer(); - - //! Copy the given MultiLayer. - MultiLayer(const MultiLayer& other); - //! Take ownership of the layers of the given MultiLayer. - MultiLayer(MultiLayer&& other); - //! Copy the given MultiLayer. - MultiLayer& operator=(const MultiLayer& other); - //! Take ownership of the given MultiLayer. - MultiLayer& operator=(MultiLayer&& other); - - //! Virtual destructor: delete all held layers. - virtual ~MultiLayer() - { - for (size_t i = 0; i < network.size(); ++i) - delete network[i]; - } - - //! Create a copy of the MultiLayer (this is safe for polymorphic use). - virtual MultiLayer* Clone() const { return new MultiLayer(*this); } - - /** - * Perform a forward pass with the given input data. `output` is expected to - * have the correct size (e.g. number of rows equal to `OutputSize()` of the - * last held layer; number of columns equal to `input.n_cols`). - * - * @param input Input data to pass through the MultiLayer. - * @param output Matrix to store output in. - */ - virtual void Forward(const MatType& input, MatType& output); - - /** - * Perform a forward pass with the given input data, but only on a subset of - * the layers in the MultiLayer. `output` is expected to have the correct - * size (e.g. number of rows equal to `OutputSize()` of the last layer to be - * computed; number of columns equal to `input.n_cols`). - * - * @param input Input data to pass through the MultiLayer. - * @param output Matrix to store output in. - * @param start Index of first layer to pass data through. - * @param end Index of last layer to pass data through. - */ - void Forward(const MatType& input, - MatType& output, - const size_t start, - const size_t end); - - /** - * Perform a backward pass with the given data. `gy` is expected to be the - * propagated error from the subsequent layer (or output), `input` is expected - * to be the output from this layer when `Forward()` was called, and `g` will - * store the propagated error from this layer (to be passed to the previous - * layer as `gy`). - * - * It is expected that `g` has the correct size already (e.g., number of rows - * equal to `OutputSize()` of the previous layer, and number of columns equal - * to `input.n_cols`). - * - * This function is expected to be called for the same input data as - * `Forward()` was just called for. - * - * @param input Output of Forward(). - * @param gy Propagated error from next layer. - * @param g Matrix to store propagated error in for previous layer. - */ - virtual void Backward(const MatType& input, - const MatType& gy, - MatType& g); - - /** - * Compute the gradients of each layer. - * - * This function is expected to be called for the same input data as - * `Forward()` and `Backward()` were just called for. That is, `input` here - * should be the same data as `Forward()` was called with. - * - * `gradient` is expected to have the correct size already (e.g., number of - * rows equal to 1, and number of columns equal to `WeightSize()`). - * - * @param input Original input data provided to Forward(). - * @param error Error as computed by `Backward()`. - * @param gradient Matrix to store the gradients in. - */ - virtual void Gradient(const MatType& input, - const MatType& error, - MatType& gradient); - - /** - * Set the weights of the layer to use the memory given as `weightsPtr`. - */ - virtual void SetWeights(typename MatType::elem_type* weightsPtr); - - /** - * Return the number of weights in the MultiLayer. This is the sum of the - * number of weights in each layer. - */ - virtual size_t WeightSize() const; - - /** - * Compute the output dimensions of the MultiLayer using `InputDimensions()`. - * This computes the dimensions of each layer held by the MultiLayer, and the - * output dimensions are set to the output dimensions of the last layer. - */ - virtual void ComputeOutputDimensions(); - - /** - * Compute the loss that should be added to the objective. - */ - virtual double Loss() const; - - /* - * Add a new module to the model. - * - * @param args The layer parameter. - */ - template - void Add(Args... args) - { - network.push_back(new LayerType(args...)); - layerOutputs.push_back(MatType()); - layerDeltas.push_back(MatType()); - layerGradients.push_back(MatType()); - } - - /* - * Add a new module to the model. - * - * @param layer The Layer to be added to the model. - */ - void Add(Layer* layer) - { - network.push_back(layer); - layerOutputs.push_back(MatType()); - layerDeltas.push_back(MatType()); - layerGradients.push_back(MatType()); - } - - //! Get the network (series of layers) held by this MultiLayer. - const std::vector*> Network() const - { - return network; - } - //! Modify the network (series of layers) held by this MultiLayer. Be - //! careful! - std::vector*>& Network() { return network; } - - //! Serialize the MultiLayer. - template - void serialize(Archive& ar, const uint32_t /* version */); - - protected: - /** - * Initialize memory that will be used by each layer for the forward pass, - * assuming that the input will have the given `batchSize`. When `Forward()` - * is called, each internally-held layer will output its results into the - * memory allocated by this function (this is the internal member - * `layerOutputMatrix` and its aliases `layerOutputs`). - */ - void InitializeForwardPassMemory(const size_t batchSize); - - /** - * Initialize memory that will be used by each layer for the backwards pass, - * assuming that the input will have the given `batchSize`. When `Backward()` - * is called, each internally-held layer will output the results of its - * backwards pass into the memory allocated by this function (this is the - * internal member `layerDeltaMatrix` and its aliases `layerDeltas`). - */ - void InitializeBackwardPassMemory(const size_t batchSize); - - /** - * Initialize memory for the gradient pass. This sets the internal aliases - * `layerGradients` appropriately using the memory from the given `gradient`, - * such that each layer will output its gradient (via its `Gradient()` method) - * into the appropriate member of `layerGradients`. - */ - void InitializeGradientPassMemory(MatType& gradient); - - //! The internally-held network. - std::vector*> network; - - // Total number of elements in the input, cached for convenience. - size_t inSize; - // Total number of input elements for *every* layer. - size_t totalInputSize; - // Total number of output elements for *every* layer. - size_t totalOutputSize; - - //! This matrix stores all of the outputs of each layer when Forward() is - //! called. See `InitializeForwardPassMemory()`. - MatType layerOutputMatrix; - //! These are aliases of `layerOutputMatrix` for each layer. - std::vector layerOutputs; - - //! This matrix stores all of the backwards pass results of each layer when - //! Backward() is called. See `InitializeBackwardPassMemory()`. - MatType layerDeltaMatrix; - //! These are aliases of `layerDeltaMatrix` for each layer. - std::vector layerDeltas; - - //! Gradient aliases for each layer. Note that this is *only* valid in the - //! context of `Gradient()`! We have it as a class member to avoid - //! reallocating the `MatType`s each call to `Gradient()`. - std::vector layerGradients; -}; - -} // namespace ann -} // namespace mlpack - -// Include implementation. -#include "multi_layer_impl.hpp" - -#endif diff --git a/src/mlpack/methods/ann/layer/multi_layer_impl.hpp b/src/mlpack/methods/ann/layer/multi_layer_impl.hpp deleted file mode 100644 index 55ab7719a1..0000000000 --- a/src/mlpack/methods/ann/layer/multi_layer_impl.hpp +++ /dev/null @@ -1,414 +0,0 @@ -/** - * @file methods/ann/layer/multi_layer_impl.hpp - * @author Ryan Curtin - * - * Implementation of the base class for neural network layers that are wrappers - * around other layers. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_LAYER_MULTI_LAYER_IMPL_HPP -#define MLPACK_METHODS_ANN_LAYER_MULTI_LAYER_IMPL_HPP - -#include "multi_layer.hpp" - -namespace mlpack { -namespace ann { - -template -MultiLayer::MultiLayer() : - inSize(0), - totalInputSize(0), - totalOutputSize(0) -{ - // Nothing to do. -} - -template -MultiLayer::MultiLayer(const MultiLayer& other) : - Layer(other), - inSize(other.inSize), - totalInputSize(other.totalInputSize), - totalOutputSize(other.totalOutputSize), - layerOutputMatrix(other.layerOutputMatrix), - layerDeltaMatrix(other.layerDeltaMatrix) -{ - // Copy each layer. - for (size_t i = 0; i < other.network.size(); ++i) - network.push_back(other.network[i]->Clone()); - - // Ensure that the aliases for layers during passes have the right size. - layerOutputs.resize(network.size(), MatType()); - layerDeltas.resize(network.size(), MatType()); - layerGradients.resize(network.size(), MatType()); - - // layerOutputs, layerDeltas, and layerGradients will be reset the next time - // Forward(), Backward(), or Gradient() is called. -} - -template -MultiLayer::MultiLayer(MultiLayer&& other) : - Layer(other), - network(std::move(other.network)), - inSize(std::move(other.inSize)), - totalInputSize(std::move(other.totalInputSize)), - totalOutputSize(std::move(other.totalOutputSize)), - layerOutputMatrix(std::move(other.layerOutputMatrix)), - layerDeltaMatrix(std::move(other.layerDeltaMatrix)) -{ - // Ensure that the aliases for layers during passes have the right size. - layerOutputs.resize(network.size(), MatType()); - layerDeltas.resize(network.size(), MatType()); - layerGradients.resize(network.size(), MatType()); - - // layerOutputs, layerDeltas, and layerGradients will be reset the next time - // Forward(), Backward(), or Gradient() is called. - - other.layerOutputs.clear(); - other.layerDeltas.clear(); - other.layerGradients.clear(); -} - -template -MultiLayer& MultiLayer::operator=(const MultiLayer& other) -{ - if (this != &other) - { - Layer::operator=(other); - - network.clear(); - layerOutputs.clear(); - layerDeltas.clear(); - layerGradients.clear(); - - inSize = other.inSize; - totalInputSize = other.totalInputSize; - totalOutputSize = other.totalOutputSize; - - layerOutputMatrix = other.layerOutputMatrix; - layerDeltaMatrix = other.layerDeltaMatrix; - - for (size_t i = 0; i < other.network.size(); ++i) - network.push_back(other.network[i]->Clone()); - - // Ensure that the aliases for layers during passes have the right size. - layerOutputs.resize(network.size(), MatType()); - layerDeltas.resize(network.size(), MatType()); - layerGradients.resize(network.size(), MatType()); - } - - return *this; -} - -template -MultiLayer& MultiLayer::operator=(MultiLayer&& other) -{ - if (this != &other) - { - Layer::operator=(other); - - layerOutputs.clear(); - layerDeltas.clear(); - layerGradients.clear(); - - inSize = std::move(other.inSize); - totalInputSize = std::move(other.totalInputSize); - totalOutputSize = std::move(other.totalOutputSize); - - network = std::move(other.network); - - layerOutputs.resize(network.size(), MatType()); - layerDeltas.resize(network.size(), MatType()); - layerGradients.resize(network.size(), MatType()); - - other.layerOutputs.clear(); - other.layerDeltas.clear(); - other.layerGradients.clear(); - } - - return *this; -} - -template -void MultiLayer::Forward( - const MatType& input, MatType& output) -{ - Forward(input, output, 0, network.size() - 1); -} - -template -void MultiLayer::Forward( - const MatType& input, - MatType& output, - const size_t start, - const size_t end) -{ - // Make sure training/testing mode is set right in each layer. - for (size_t i = 0; i < network.size(); ++i) - network[i]->Training() = this->training; - - // Note that we use `output` for the last layer; layerOutputs is only used for - // intermediate values between layers. - if ((end - start) > 0) - { - // Initialize memory for the forward pass (if needed). - InitializeForwardPassMemory(input.n_cols); - - network[start]->Forward(input, layerOutputs[start]); - for (size_t i = start + 1; i < end; ++i) - network[i]->Forward(layerOutputs[i - 1], layerOutputs[i]); - network[end]->Forward(layerOutputs[end - 1], output); - } - else if ((end - start) == 0 && network.size() > 0) - { - network[start]->Forward(input, output); - } - else - { - // Empty network? - output = input; - } -} - -template -void MultiLayer::Backward( - const MatType& input, const MatType& gy, MatType& g) -{ - if (network.size() > 1) - { - // Initialize memory for the backward pass (if needed). - InitializeBackwardPassMemory(input.n_cols); - - network.back()->Backward(input, gy, layerDeltas.back()); - for (size_t i = network.size() - 2; i > 0; --i) - network[i]->Backward(layerOutputs[i], layerDeltas[i + 1], layerDeltas[i]); - network[0]->Backward(layerOutputs[0], layerDeltas[1], g); - } - else if (network.size() == 1) - { - network[0]->Backward(input, gy, g); - } - else - { - // Empty network? - g = input; - } -} - -template -void MultiLayer::Gradient( - const MatType& input, const MatType& error, MatType& gradient) -{ - // We assume gradient has the right size already. - - // Pass gradients through each layer. - if (network.size() > 1) - { - // Initialize memory for the gradient pass (if needed). - InitializeGradientPassMemory(gradient); - - network.front()->Gradient(input, layerDeltas[1], layerGradients.front()); - for (size_t i = 1; i < network.size() - 1; ++i) - { - network[i]->Gradient(layerOutputs[i - 1], layerDeltas[i + 1], - layerGradients[i]); - } - network.back()->Gradient(layerOutputs[network.size() - 2], error, - layerGradients.back()); - } - else if (network.size() == 1) - { - network[0]->Gradient(input, error, gradient); - } - else - { - // Nothing to do if the network is empty... there is no gradient. - } -} - -template -void MultiLayer::SetWeights(typename MatType::elem_type* weightsPtr) -{ - size_t start = 0; - const size_t totalWeightSize = WeightSize(); - for (size_t i = 0; i < network.size(); ++i) - { - const size_t weightSize = network[i]->WeightSize(); - - // Sanity check: ensure we aren't passing memory past the end of the - // parameters. - Log::Assert(start + weightSize <= totalWeightSize, - "FNN::SetLayerMemory(): parameter size does not match total layer " - "weight size!"); - - network[i]->SetWeights(weightsPtr + start); - start += weightSize; - } - - // Technically this check should be unnecessary, but there's nothing wrong - // with a little paranoia... - Log::Assert(start == totalWeightSize, - "FNN::SetLayerMemory(): total layer weight size does not match parameter " - "size!"); -} - -template -size_t MultiLayer::WeightSize() const -{ - // Sum the weights in each layer. - size_t total = 0; - for (size_t i = 0; i < network.size(); ++i) - total += network[i]->WeightSize(); - return total; -} - -template -void MultiLayer::ComputeOutputDimensions() -{ - inSize = 0; - totalInputSize = 0; - totalOutputSize = 0; - - // Propagate the input dimensions forward to the output. - network.front()->InputDimensions() = this->inputDimensions; - inSize = this->inputDimensions[0]; - for (size_t i = 1; i < this->inputDimensions.size(); ++i) - inSize *= this->inputDimensions[i]; - totalInputSize += inSize; - - for (size_t i = 1; i < network.size(); ++i) - { - network[i]->InputDimensions() = network[i - 1]->OutputDimensions(); - size_t layerInputSize = network[i]->InputDimensions()[0]; - for (size_t j = 1; j < network[i]->InputDimensions().size(); ++j) - layerInputSize *= network[i]->InputDimensions()[j]; - - totalInputSize += layerInputSize; - totalOutputSize += layerInputSize; - } - - size_t lastLayerSize = network.back()->OutputDimensions()[0]; - for (size_t i = 1; i < network.back()->OutputDimensions().size(); ++i) - lastLayerSize *= network.back()->OutputDimensions()[i]; - - totalOutputSize += lastLayerSize; - this->outputDimensions = network.back()->OutputDimensions(); -} - -template -double MultiLayer::Loss() const -{ - double loss = 0.0; - for (size_t i = 0; i < network.size(); ++i) - loss += network[i]->Loss(); - - return loss; -} - -template -template -void MultiLayer::serialize( - Archive& ar, const uint32_t /* version */) -{ - ar(cereal::base_class>(this)); - - ar(CEREAL_VECTOR_POINTER(network)); - ar(CEREAL_NVP(inSize)); - ar(CEREAL_NVP(totalInputSize)); - ar(CEREAL_NVP(totalOutputSize)); - - if (Archive::is_loading::value) - { - layerOutputMatrix.clear(); - layerDeltaMatrix.clear(); - layerGradients.clear(); - layerOutputs.resize(network.size(), MatType()); - layerDeltas.resize(network.size(), MatType()); - layerGradients.resize(network.size(), MatType()); - } -} - -template -void MultiLayer::InitializeForwardPassMemory(const size_t batchSize) -{ - // We need to initialize memory to store the output of each layer's Forward() - // call. We'll do this all in one matrix, but, the size of this matrix - // depends on the batch size we are using for computation. We avoid resizing - // layerOutputMatrix down, unless we only need 10% or less of it. - if (batchSize * totalOutputSize > layerOutputMatrix.n_elem || - batchSize * totalOutputSize < - std::floor(0.1 * layerOutputMatrix.n_elem)) - { - // All outputs will be represented by one big block of memory. - layerOutputMatrix = MatType(1, batchSize * totalOutputSize); - } - - // Now, create an alias to the right place for each layer. We assume that - // layerOutputs is already sized correctly (this should be done by Add()). - size_t start = 0; - for (size_t i = 0; i < layerOutputs.size(); ++i) - { - const size_t layerOutputSize = network[i]->OutputSize(); - MakeAlias(layerOutputs[i], layerOutputMatrix.colptr(start), - layerOutputSize, batchSize); - start += batchSize * layerOutputSize; - } -} - -template -void MultiLayer::InitializeBackwardPassMemory( - const size_t batchSize) -{ - // We need to initialize memory to store the output of each layer's Backward() - // call. We do this similarly to InitializeForwardPassMemory(), but we must - // store a matrix to use as the delta for each layer. - if (batchSize * totalInputSize > layerDeltaMatrix.n_elem || - batchSize * totalInputSize < std::floor(0.1 * layerDeltaMatrix.n_elem)) - { - // All deltas will be represented by one big block of memory. - layerDeltaMatrix = MatType(1, batchSize * totalInputSize); - } - - // Now, create an alias to the right place for each layer. We assume that - // layerDeltas is already sized correctly (this should be done by Add()). - size_t start = 0; - for (size_t i = 0; i < layerDeltas.size(); ++i) - { - size_t layerInputSize = 1; - if (i == 0) - { - for (size_t j = 0; j < this->inputDimensions.size(); ++j) - layerInputSize *= this->inputDimensions[j]; - } - else - { - layerInputSize = network[i - 1]->OutputSize(); - } - MakeAlias(layerDeltas[i], layerDeltaMatrix.colptr(start), layerInputSize, - batchSize); - start += batchSize * layerInputSize; - } -} - -template -void MultiLayer::InitializeGradientPassMemory(MatType& gradient) -{ - // We need to initialize memory to store the gradients of each layer. To do - // this, we need to know the weight size of each layer. - size_t gradientStart = 0; - for (size_t i = 0; i < network.size(); ++i) - { - const size_t weightSize = network[i]->WeightSize(); - MakeAlias(layerGradients[i], gradient.memptr() + gradientStart, - weightSize, 1); - gradientStart += weightSize; - } -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/layer/not_adapted/multihead_attention.hpp b/src/mlpack/methods/ann/layer/multihead_attention.hpp similarity index 70% rename from src/mlpack/methods/ann/layer/not_adapted/multihead_attention.hpp rename to src/mlpack/methods/ann/layer/multihead_attention.hpp index e17cb67263..0d7506ea51 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/multihead_attention.hpp +++ b/src/mlpack/methods/ann/layer/multihead_attention.hpp @@ -1,4 +1,3 @@ -// Temporarily drop. /** * @file methods/ann/layer/multihead_attention.hpp * @author Mrityunjay Tripathi @@ -49,26 +48,25 @@ namespace ann /** Artificial Neural Network. */ { * of shape `(embedDim * tgtSeqLen, batchSize)`. The embeddings are stored * consequently. * - * @tparam InputType Type of the input data (arma::colvec, arma::mat, + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). - * @tparam OutputType Type of the output data (arma::colvec, arma::mat, + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). * @tparam RegularizerType Type of the regularizer to be used. */ template < - typename InputType = arma::mat, - typename OutputType = arma::mat, + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat, typename RegularizerType = NoRegularizer > -class MultiheadAttentionType : public Layer +class MultiheadAttention { public: /** * Default constructor. */ - MultiheadAttentionType(); + MultiheadAttention(); - // TODO: does srcSeqLen need to be given? /** * Create the MultiheadAttention object using the specified modules. * @@ -76,27 +74,16 @@ class MultiheadAttentionType : public Layer * @param srcSeqLen Source sequence length. * @param embedDim Total dimension of the model. * @param numHeads Number of parallel attention heads. - * @param attnMask Two dimensional Attention Mask. - * @param keyPaddingMask Key Padding Mask. */ - MultiheadAttentionType(const size_t tgtSeqLen, - const size_t srcSeqLen, - const size_t embedDim, - const size_t numHeads, - const InputType& attnmask = InputType(), - const InputType& keyPaddingMask = InputType()); - - //! Clone the MultiheadAttentionType object. This handles polymorphism - //! correctly. - MultiheadAttentionType* Clone() const - { - return new MultiheadAttentionType(*this); - } + MultiheadAttention(const size_t tgtSeqLen, + const size_t srcSeqLen, + const size_t embedDim, + const size_t numHeads); /** * Reset the layer parameters. */ - void SetWeights(typename OutputType::elem_type* weightsPtr); + void Reset(); /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -105,7 +92,8 @@ class MultiheadAttentionType : public Layer * @param input The query matrix. * @param output Resulting output activation. */ - void Forward(const InputType& input, OutputType& output); + template + void Forward(const arma::Mat& input, arma::Mat& output); /** * Ordinary feed backward pass of a neural network, calculating the function @@ -115,9 +103,10 @@ class MultiheadAttentionType : public Layer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const InputType& /* input */, - const OutputType& gy, - OutputType& g); + template + void Backward(const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g); /** * Calculate the gradient using the output delta and the input activation. @@ -126,9 +115,10 @@ class MultiheadAttentionType : public Layer * @param error The calculated error. * @param gradient The calculated gradient. */ - void Gradient(const InputType& input, - const OutputType& error, - OutputType& gradient); + template + void Gradient(const arma::Mat& input, + const arma::Mat& error, + arma::Mat& gradient); //! Get the size of the weights. size_t WeightSize() const { return 4 * (embedDim + 1) * embedDim; } @@ -160,27 +150,34 @@ class MultiheadAttentionType : public Layer size_t& NumHeads() { return numHeads; } //! Get the two dimensional Attention Mask. - OutputType const& AttentionMask() const { return attnMask; } + OutputDataType const& AttentionMask() const { return attnMask; } //! Modify the two dimensional Attention Mask. - OutputType& AttentionMask() { return attnMask; } + OutputDataType& AttentionMask() { return attnMask; } //! Get Key Padding Mask. - OutputType const& KeyPaddingMask() const { return keyPaddingMask; } + OutputDataType const& KeyPaddingMask() const { return keyPaddingMask; } //! Modify the Key Padding Mask. - OutputType& KeyPaddingMask() { return keyPaddingMask; } + OutputDataType& KeyPaddingMask() { return keyPaddingMask; } - const size_t WeightSize() const { return (4 * embedDim + 4) * embedDim; } + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } - const std::vector OutputDimensions() const - { - // This returns the output as a 2-dimensional (embedDim * tgtSeqLen) - // matrix. - std::vector outputDimensions(inputDimensions.size(), 1); - outputDimensions[0] = embedDim; - outputDimensions[1] = tgtSeqLen; + //! Get the delta. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } - return outputDimensions; - } + //! Get the gradient. + OutputDataType const& Gradient() const { return grad; } + //! Modify the gradient. + OutputDataType& Gradient() { return grad; } + + //! Get the parameters. + OutputDataType const& Parameters() const { return weights; } + //! Modify the parameters. + OutputDataType& Parameters() { return weights; } size_t InputShape() const { @@ -188,16 +185,16 @@ class MultiheadAttentionType : public Layer } private: - //! Element Type of the output. - typedef typename OutputType::elem_type ElemType; + //! Element Type of the input. + typedef typename OutputDataType::elem_type ElemType; //! Target sequence length. size_t tgtSeqLen; - //! Source sequence length. + //! Source sequence lenght. size_t srcSeqLen; - //! Locally-stored dimensionality of each embedding vector. + //! Locally-stored module output size. size_t embedDim; //! Locally-stored number of parallel attention heads. @@ -207,37 +204,37 @@ class MultiheadAttentionType : public Layer size_t headDim; //! Two dimensional Attention Mask of shape (tgtSeqLen, srcSeqLen). - OutputType attnMask; + OutputDataType attnMask; //! Key Padding Mask. - OutputType keyPaddingMask; + OutputDataType keyPaddingMask; //! Locally-stored weight matrix associated with query. - OutputType queryWt; + OutputDataType queryWt; //! Locally-stored weight matrix associated with key. - OutputType keyWt; + OutputDataType keyWt; //! Locally-stored weight matrix associated with value. - OutputType valueWt; + OutputDataType valueWt; //! Locally-stored weight matrix associated with attnWt. - OutputType outWt; + OutputDataType outWt; //! Locally-stored bias associated with query. - OutputType qBias; + OutputDataType qBias; //! Locally-stored bias associated with key. - OutputType kBias; + OutputDataType kBias; //! Locall-stored bias associated with value. - OutputType vBias; + OutputDataType vBias; //! Locally-stored bias associated with attnWt. - OutputType outBias; + OutputDataType outBias; //! Locally-stored weights parameter. - OutputType weights; + OutputDataType weights; //! Locally-stored projected query matrix over linear layer. arma::Cube qProj; @@ -255,16 +252,20 @@ class MultiheadAttentionType : public Layer arma::Cube attnOut; //! Softmax layer to represent the probabilities of next sequence. - Softmax softmax; + Softmax softmax; + + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored gradient. + OutputDataType grad; + + //! Locally-stored output parameter. + OutputDataType outputParameter; //! Locally-stored regularizer object. RegularizerType regularizer; }; // class MultiheadAttention - -// Standard MultiheadAttention layer using no regularization. -typedef MultiheadAttentionType - MultiheadAttention; - } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/not_adapted/multihead_attention_impl.hpp b/src/mlpack/methods/ann/layer/multihead_attention_impl.hpp similarity index 79% rename from src/mlpack/methods/ann/layer/not_adapted/multihead_attention_impl.hpp rename to src/mlpack/methods/ann/layer/multihead_attention_impl.hpp index f61d485ab1..3d687d93af 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/multihead_attention_impl.hpp +++ b/src/mlpack/methods/ann/layer/multihead_attention_impl.hpp @@ -21,35 +21,31 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -MultiheadAttentionType:: -MultiheadAttentionType() : +template +MultiheadAttention:: +MultiheadAttention() : tgtSeqLen(0), srcSeqLen(0), embedDim(0), numHeads(0), - headDim(0), - attnMask(InputType()), - keyPaddingMask(InputType()) + headDim(0) { // Nothing to do here. } -template -MultiheadAttentionType:: -MultiheadAttentionType( +template +MultiheadAttention:: +MultiheadAttention( const size_t tgtSeqLen, const size_t srcSeqLen, const size_t embedDim, - const size_t numHeads, - const InputType& attnMask, - const InputType& keyPaddingMask) : + const size_t numHeads) : tgtSeqLen(tgtSeqLen), srcSeqLen(srcSeqLen), embedDim(embedDim), - numHeads(numHeads), - attnMask(attnMask), - keyPaddingMask(keyPaddingMask) + numHeads(numHeads) { if (embedDim % numHeads != 0) { @@ -58,38 +54,41 @@ MultiheadAttentionType( } headDim = embedDim / numHeads; + weights.set_size(WeightSize(), 1); } -template -void MultiheadAttentionType::SetWeights( - typename OutputType::elem_type* weightsPtr) +template +void MultiheadAttention:: +Reset() { - weights = OutputType(weightsPtr, 1, (4 * embedDim + 4) * embedDim, false, - true); + typedef typename arma::Mat MatType; - queryWt = OutputType(weightsPtr, embedDim, embedDim, false, true); - keyWt = OutputType(weightsPtr + embedDim * embedDim, embedDim, embedDim, - false, true); - valueWt = OutputType(weightsPtr + 2 * embedDim * embedDim, embedDim, embedDim, - false, true); - outWt = OutputType(weightsPtr + 3 * embedDim * embedDim, embedDim, embedDim, - false, true); + queryWt = MatType(weights.memptr(), embedDim, embedDim, false, false); + keyWt = MatType(weights.memptr() + embedDim * embedDim, + embedDim, embedDim, false, false); + valueWt = MatType(weights.memptr() + 2 * embedDim * embedDim, + embedDim, embedDim, false, false); + outWt = MatType(weights.memptr() + 3 * embedDim * embedDim, + embedDim, embedDim, false, false); - qBias = OutputType(weightsPtr + 4 * embedDim * embedDim, embedDim, 1, false, - true); - kBias = OutputType(weightsPtr + (4 * embedDim + 1) * embedDim, embedDim, 1, - false, true); - vBias = OutputType(weightsPtr + (4 * embedDim + 2) * embedDim, embedDim, 1, - false, true); - outBias = OutputType(weightsPtr + (4 * embedDim + 3) * embedDim, 1, embedDim, - false, true); + qBias = MatType(weights.memptr() + + 4 * embedDim * embedDim, embedDim, 1, false, false); + kBias = MatType(weights.memptr() + + (4 * embedDim + 1) * embedDim, embedDim, 1, false, false); + vBias = MatType(weights.memptr() + + (4 * embedDim + 2) * embedDim, embedDim, 1, false, false); + outBias = MatType(weights.memptr() + + (4 * embedDim + 3) * embedDim, 1, embedDim, false, false); } -template -void MultiheadAttentionType:: -Forward(const InputType& input, OutputType& output) +template +template +void MultiheadAttention:: +Forward(const arma::Mat& input, arma::Mat& output) { - typedef typename arma::Cube CubeType; + typedef typename arma::Cube CubeType; if (input.n_rows != embedDim * (tgtSeqLen + 2 * srcSeqLen)) { @@ -105,12 +104,12 @@ Forward(const InputType& input, OutputType& output) // The shape of q : (embedDim, tgtSeqLen, batchSize). // The shape of k : (embedDim, srcSeqLen, batchSize). // The shape of v : (embedDim, srcSeqLen, batchSize). - const CubeType q(const_cast(input).memptr(), + const CubeType q(const_cast&>(input).memptr(), embedDim, tgtSeqLen, batchSize, false, false); - const CubeType k(const_cast(input).memptr() + + const CubeType k(const_cast&>(input).memptr() + embedDim * tgtSeqLen * batchSize, embedDim, srcSeqLen, batchSize, false, false); - const CubeType v(const_cast(input).memptr() + + const CubeType v(const_cast&>(input).memptr() + embedDim * (tgtSeqLen + srcSeqLen) * batchSize, embedDim, srcSeqLen, batchSize, false, false); @@ -189,13 +188,15 @@ Forward(const InputType& input, OutputType& output) } } -template -void MultiheadAttentionType:: -Backward(const InputType& /* input */, - const OutputType& gy, - OutputType& g) +template +template +void MultiheadAttention:: +Backward(const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g) { - typedef typename arma::Cube CubeType; + typedef typename arma::Cube CubeType; if (gy.n_rows != tgtSeqLen * embedDim) { @@ -209,7 +210,7 @@ Backward(const InputType& /* input */, // The shape of gyTemp : (tgtSeqLen, embedDim, batchSize). // We need not split it into n heads now because this is the part when // output were concatenated from n heads. - CubeType gyTemp(const_cast(gy).memptr(), embedDim, + CubeType gyTemp(const_cast&>(gy).memptr(), embedDim, tgtSeqLen, batchSize, true, false); // The shape of gyTemp : (embedDim, tgtSeqLen, batchSize). @@ -279,13 +280,16 @@ Backward(const InputType& /* input */, } } -template -void MultiheadAttentionType:: -Gradient(const InputType& input, - const OutputType& error, - OutputType& gradient) +template +template +void MultiheadAttention:: +Gradient(const arma::Mat& input, + const arma::Mat& error, + arma::Mat& gradient) { - typedef typename arma::Cube CubeType; + typedef typename arma::Cube CubeType; + typedef typename arma::Mat MatType; if (input.n_rows != embedDim * (tgtSeqLen + 2 * srcSeqLen)) { @@ -303,16 +307,16 @@ Gradient(const InputType& input, // The shape of gradient : (4 * embedDim * embedDim + 4 * embedDim, 1). gradient.set_size(arma::size(weights)); - const CubeType q(const_cast(input).memptr(), + const CubeType q(const_cast(input).memptr(), embedDim, tgtSeqLen, batchSize, false, false); - const CubeType k(const_cast(input).memptr() + q.n_elem, + const CubeType k(const_cast(input).memptr() + q.n_elem, embedDim, srcSeqLen, batchSize, false, false); - const CubeType v(const_cast(input).memptr() + q.n_elem + k.n_elem, + const CubeType v(const_cast(input).memptr() + q.n_elem + k.n_elem, embedDim, srcSeqLen, batchSize, false, false); // Reshape the propagated error into a cube. // The shape of errorTemp : (embedDim, tgtSeqLen, batchSize). - CubeType errorTemp(const_cast(error).memptr(), embedDim, + CubeType errorTemp(const_cast&>(error).memptr(), embedDim, tgtSeqLen, batchSize, true, false); // Gradient wrt. outBias, i.e. dL/d(outBias). @@ -426,40 +430,22 @@ Gradient(const InputType& input, regularizer.Evaluate(weights, gradient); } -template +template template -void MultiheadAttentionType:: +void MultiheadAttention:: serialize(Archive& ar, const uint32_t /* version */) { - ar(cereal::base_class>(this)); - ar(CEREAL_NVP(tgtSeqLen)); ar(CEREAL_NVP(srcSeqLen)); ar(CEREAL_NVP(embedDim)); ar(CEREAL_NVP(numHeads)); ar(CEREAL_NVP(headDim)); - ar(CEREAL_NVP(softmax)); - ar(CEREAL_NVP(regularizer)); - if (Archive::is_loading::value) - { - attnMask.clear(); - keyPaddingMask.clear(); - queryWt.clear(); - keyWt.clear(); - valueWt.clear(); - outWt.clear(); - qBias.clear(); - kBias.clear(); - vBias.clear(); - outBias.clear(); - weights.clear(); - qProj.clear(); - kProj.clear(); - vProj.clear(); - scores.clear(); - attnOut.clear(); - } + // This is inefficient, but we have to allocate this memory so that + // WeightSetVisitor gets the right size. + if (cereal::is_loading()) + weights.set_size(4 * embedDim * (embedDim + 1), 1); } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/not_adapted/multiply_constant.hpp b/src/mlpack/methods/ann/layer/multiply_constant.hpp similarity index 64% rename from src/mlpack/methods/ann/layer/not_adapted/multiply_constant.hpp rename to src/mlpack/methods/ann/layer/multiply_constant.hpp index 60e0c72d1a..baa4744c23 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/multiply_constant.hpp +++ b/src/mlpack/methods/ann/layer/multiply_constant.hpp @@ -15,8 +15,6 @@ #include -#include "layer.hpp" - namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -24,25 +22,22 @@ namespace ann /** Artificial Neural Network. */ { * Implementation of the multiply constant layer. The multiply constant layer * multiplies the input by a (non-learnable) constant. * - * @tparam InputType The type of the layer's inputs. The layer automatically - * cast inputs to this type (Default: arma::mat). - * @tparam OutputType The type of the computation which also causes the output - * to also be in this type. The type also allows the computation and weight - * type to differ from the input type (Default: arma::mat). + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class MultiplyConstantType : public Layer +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class MultiplyConstant { public: - //! Create the MultiplyConstant object. - MultiplyConstantType(const double scalar = 1.0); - - //! Clone the MultiplyConstantType object. This handles polymorphism - //! correctly. - MultiplyConstantType* Clone() const - { - return new MultiplyConstantType(*this); - } + /** + * Create the MultiplyConstant object. + */ + MultiplyConstant(const double scalar = 1.0); //! Copy Constructor. MultiplyConstant(const MultiplyConstant& layer); @@ -63,6 +58,7 @@ class MultiplyConstantType : public Layer * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ + template void Forward(const InputType& input, OutputType& output); /** @@ -73,28 +69,43 @@ class MultiplyConstantType : public Layer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const InputType& /* input */, - const OutputType& gy, - OutputType& g); + template + void Backward(const DataType& /* input */, const DataType& gy, DataType& g); + + //! Get the output parameter. + OutputDataType& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } //! Get the scalar multiplier. double Scalar() const { return scalar; } //! Modify the scalar multiplier. double& Scalar() { return scalar; } - //! Serialize the layer. + //! Get the size of the weights. + size_t WeightSize() const { return 0; } + + /** + * Serialize the layer. + */ template void serialize(Archive& ar, const uint32_t /* version */); private: //! Locally-stored constant scalar value. double scalar; -}; // class MultiplyConstantType -// Convenience typedefs. + //! Locally-stored delta object. + OutputDataType delta; -// Standard MultiplyConstant layer. -typedef MultiplyConstantType MultiplyConstant; + //! Locally-stored output parameter object. + OutputDataType outputParameter; +}; // class MultiplyConstant } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/multiply_constant_impl.hpp b/src/mlpack/methods/ann/layer/multiply_constant_impl.hpp new file mode 100644 index 0000000000..4c02fbd1fa --- /dev/null +++ b/src/mlpack/methods/ann/layer/multiply_constant_impl.hpp @@ -0,0 +1,96 @@ +/** + * @file methods/ann/layer/multiply_constant_impl.hpp + * @author Marcus Edel + * + * Implementation of the MultiplyConstantLayer class, which multiplies the + * input by a (non-learnable) constant. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_LAYER_MULTIPLY_CONSTANT_IMPL_HPP +#define MLPACK_METHODS_ANN_LAYER_MULTIPLY_CONSTANT_IMPL_HPP + +// In case it hasn't yet been included. +#include "multiply_constant.hpp" + +namespace mlpack { +namespace ann /** Artificial Neural Network. */ { + +template +MultiplyConstant::MultiplyConstant( + const double scalar) : scalar(scalar) +{ + // Nothing to do here. +} + +template +MultiplyConstant::MultiplyConstant( + const MultiplyConstant& layer) : + scalar(layer.scalar) +{ + // Nothing to do here. +} + +template +MultiplyConstant::MultiplyConstant( + MultiplyConstant&& layer) : + scalar(std::move(layer.scalar)) +{ + // Nothing to do here. +} + +template +MultiplyConstant& +MultiplyConstant::operator=( + const MultiplyConstant& layer) +{ + if (this != &layer) + { + scalar = layer.scalar; + } + return *this; +} + +template +MultiplyConstant& +MultiplyConstant::operator=( + MultiplyConstant&& layer) +{ + if (this != &layer) + { + scalar = std::move(layer.scalar); + } + return *this; +} + +template +template +void MultiplyConstant::Forward( + const InputType& input, OutputType& output) +{ + output = input * scalar; +} + +template +template +void MultiplyConstant::Backward( + const DataType& /* input */, const DataType& gy, DataType& g) +{ + g = gy * scalar; +} + +template +template +void MultiplyConstant::serialize( + Archive& ar, const uint32_t /* version */) +{ + ar(CEREAL_NVP(scalar)); +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/layer/not_adapted/multiply_merge.hpp b/src/mlpack/methods/ann/layer/multiply_merge.hpp similarity index 51% rename from src/mlpack/methods/ann/layer/not_adapted/multiply_merge.hpp rename to src/mlpack/methods/ann/layer/multiply_merge.hpp index b690871063..5c3d9ba6c0 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/multiply_merge.hpp +++ b/src/mlpack/methods/ann/layer/multiply_merge.hpp @@ -15,9 +15,9 @@ #include -// #include "../visitor/delete_visitor.hpp" -// #include "../visitor/delta_visitor.hpp" -// #include "../visitor/output_parameter_visitor.hpp" +#include "../visitor/delete_visitor.hpp" +#include "../visitor/delta_visitor.hpp" +#include "../visitor/output_parameter_visitor.hpp" #include "layer_types.hpp" @@ -28,16 +28,18 @@ namespace ann /** Artificial Neural Network. */ { * Implementation of the MultiplyMerge module class. The MultiplyMerge class * multiplies the output of various modules element-wise. * - * @tparam InputType Type of the input data (arma::colvec, arma::mat, + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). - * @tparam OutputType Type of the output data (arma::colvec, arma::mat, + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). + * @tparam CustomLayers Additional custom layers that can be added. */ template< - typename InputType = arma::mat, - typename OutputType = arma::mat + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat, + typename... CustomLayers > -class MultiplyMergeType : public MultiLayer +class MultiplyMerge { public: /** @@ -46,7 +48,7 @@ class MultiplyMergeType : public MultiLayer * @param model Expose all the network modules. * @param run Call the Forward/Backward method before the output is merged. */ - MultiplyMergeType(const bool model = false, const bool run = true); + MultiplyMerge(const bool model = false, const bool run = true); //! Copy Constructor. MultiplyMerge(const MultiplyMerge& layer); @@ -61,10 +63,7 @@ class MultiplyMergeType : public MultiLayer MultiplyMerge& operator=(MultiplyMerge&& layer); //! Destructor to release allocated memory. - ~MultiplyMergeType(); - - //! Clone the MultiplyMergeType object. This handles polymorphism correctly. - MultiplyMergeType* Clone() const { return new MultiplyMergeType(*this); } + ~MultiplyMerge(); /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -73,6 +72,7 @@ class MultiplyMergeType : public MultiLayer * @param * (input) Input data used for evaluating the specified function. * @param output Resulting output activation. */ + template void Forward(const InputType& /* input */, OutputType& output); /** @@ -84,9 +84,10 @@ class MultiplyMergeType : public MultiLayer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const InputType& /* input */, - const OutputType& gy, - OutputType& g); + template + void Backward(const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g); /* * Calculate the gradient using the output delta and the input activation. @@ -95,14 +96,56 @@ class MultiplyMergeType : public MultiLayer * @param error The calculated error. * @param gradient The calculated gradient. */ - void Gradient(const InputType& input, - const OutputType& error, - OutputType& gradient); + template + void Gradient(const arma::Mat& input, + const arma::Mat& error, + arma::Mat& gradient); + + /* + * Add a new module to the model. + * + * @param args The layer parameter. + */ + template + void Add(Args... args) { network.push_back(new LayerType(args...)); } + + /* + * Add a new module to the model. + * + * @param layer The Layer to be added to the model. + */ + void Add(LayerTypes layer) { network.push_back(layer); } + + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } + + //! Get the gradient. + OutputDataType const& Gradient() const { return gradient; } + //! Modify the gradient. + OutputDataType& Gradient() { return gradient; } + + //! Return the model modules. + std::vector >& Model() + { + if (model) + { + return network; + } + + return empty; + } //! Get the parameters. - OutputType const& Parameters() const { return weights; } + OutputDataType const& Parameters() const { return weights; } //! Modify the parameters. - OutputType& Parameters() { return weights; } + OutputDataType& Parameters() { return weights; } //! Get the size of the weights. size_t WeightSize() const { return 0; } @@ -114,6 +157,9 @@ class MultiplyMergeType : public MultiLayer void serialize(Archive& ar, const uint32_t /* version */); private: + //! Parameter which indicates if the modules should be exposed. + bool model; + //! Parameter which indicates if the Forward/Backward method should be called //! before merging the output. bool run; @@ -121,12 +167,33 @@ class MultiplyMergeType : public MultiLayer //! We need this to know whether we should delete the layer in the destructor. bool ownsLayer; - //! Locally-stored weight object. - OutputType weights; -}; // class MultiplyMergeType + //! Locally-stored network modules. + std::vector > network; -// Standard MultiplyMerge layer. -typedef MultiplyMergeType MultiplyMerge; + //! Locally-stored empty list of modules. + std::vector > empty; + + //! Locally-stored delete visitor module object. + DeleteVisitor deleteVisitor; + + //! Locally-stored output parameter visitor module object. + OutputParameterVisitor outputParameterVisitor; + + //! Locally-stored delta visitor module object. + DeltaVisitor deltaVisitor; + + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored gradient object. + OutputDataType gradient; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; + + //! Locally-stored weight object. + OutputDataType weights; +}; // class MultiplyMerge } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/multiply_merge_impl.hpp b/src/mlpack/methods/ann/layer/multiply_merge_impl.hpp new file mode 100644 index 0000000000..29cd111482 --- /dev/null +++ b/src/mlpack/methods/ann/layer/multiply_merge_impl.hpp @@ -0,0 +1,190 @@ +/** + * @file methods/ann/layer/multiply_merge_impl.hpp + * @author Haritha Nair + * + * Definition of the MultiplyMerge module which multiplies the output of the + * given modules element-wise. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_LAYER_MULTIPLY_MERGE_IMPL_HPP +#define MLPACK_METHODS_ANN_LAYER_MULTIPLY_MERGE_IMPL_HPP + +// In case it hasn't yet been included. +#include "multiply_merge.hpp" + +#include "../visitor/forward_visitor.hpp" +#include "../visitor/backward_visitor.hpp" +#include "../visitor/gradient_visitor.hpp" + +namespace mlpack { +namespace ann /** Artificial Neural Network. */ { + +template +MultiplyMerge::MultiplyMerge( + const bool model, const bool run) : + model(model), run(run), ownsLayer(!model) +{ + // Nothing to do here. +} + +template +MultiplyMerge::MultiplyMerge( + const MultiplyMerge& layer) : + model(layer.model), + run(layer.run), + ownsLayer(layer.ownsLayer), + network(layer.network), + weights(layer.weights) +{ + // Nothing to do here. +} + +template +MultiplyMerge::MultiplyMerge( + MultiplyMerge&& layer) : + model(std::move(layer.model)), + run(std::move(layer.run)), + ownsLayer(std::move(layer.ownsLayer)), + network(std::move(layer.network)), + weights(std::move(layer.weights)) +{ + // Nothing to do here. +} + +template +MultiplyMerge& +MultiplyMerge::operator=( + const MultiplyMerge& layer) +{ + if (this != &layer) + { + model = layer.model; + run = layer.run; + ownsLayer = layer.ownsLayer; + network = layer.network; + weights = layer.weights; + } + return *this; +} + +template +MultiplyMerge& +MultiplyMerge::operator=( + MultiplyMerge&& layer) +{ + if (this != &layer) + { + model = std::move(layer.model); + run = std::move(layer.run); + ownsLayer = std::move(layer.ownsLayer); + network = std::move(layer.network); + weights = std::move(layer.weights); + } + return *this; +} + +template +MultiplyMerge::~MultiplyMerge() +{ + if (ownsLayer) + { + std::for_each(network.begin(), network.end(), + boost::apply_visitor(deleteVisitor)); + } +} + +template +template +void MultiplyMerge::Forward( + const InputType& input, OutputType& output) +{ + if (run) + { + for (size_t i = 0; i < network.size(); ++i) + { + boost::apply_visitor(ForwardVisitor(input, + boost::apply_visitor(outputParameterVisitor, network[i])), + network[i]); + } + } + + output = boost::apply_visitor(outputParameterVisitor, network.front()); + for (size_t i = 1; i < network.size(); ++i) + { + output %= boost::apply_visitor(outputParameterVisitor, network[i]); + } +} + +template +template +void MultiplyMerge::Backward( + const arma::Mat& /* input */, const arma::Mat& gy, arma::Mat& g) +{ + if (run) + { + for (size_t i = 0; i < network.size(); ++i) + { + boost::apply_visitor(BackwardVisitor(boost::apply_visitor( + outputParameterVisitor, network[i]), gy, + boost::apply_visitor(deltaVisitor, network[i])), network[i]); + } + + g = boost::apply_visitor(deltaVisitor, network[0]); + for (size_t i = 1; i < network.size(); ++i) + { + g += boost::apply_visitor(deltaVisitor, network[i]); + } + } + else + g = gy; +} + +template +template +void MultiplyMerge::Gradient( + const arma::Mat& input, + const arma::Mat& error, + arma::Mat& /* gradient */ ) +{ + if (run) + { + for (size_t i = 0; i < network.size(); ++i) + { + boost::apply_visitor(GradientVisitor(input, error), network[i]); + } + } +} + +template +template +void MultiplyMerge::serialize( + Archive& ar, const uint32_t /* version */) +{ + // Be sure to clear other layers before loading. + if (cereal::is_loading()) + network.clear(); + + ar(CEREAL_VECTOR_VARIANT_POINTER(network)); + ar(CEREAL_NVP(model)); + ar(CEREAL_NVP(run)); + ar(CEREAL_NVP(ownsLayer)); +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/layer/not_adapted/nearest_interpolation.hpp b/src/mlpack/methods/ann/layer/nearest_interpolation.hpp similarity index 100% rename from src/mlpack/methods/ann/layer/not_adapted/nearest_interpolation.hpp rename to src/mlpack/methods/ann/layer/nearest_interpolation.hpp diff --git a/src/mlpack/methods/ann/layer/not_adapted/nearest_interpolation_impl.hpp b/src/mlpack/methods/ann/layer/nearest_interpolation_impl.hpp similarity index 100% rename from src/mlpack/methods/ann/layer/not_adapted/nearest_interpolation_impl.hpp rename to src/mlpack/methods/ann/layer/nearest_interpolation_impl.hpp diff --git a/src/mlpack/methods/ann/layer/noisylinear.hpp b/src/mlpack/methods/ann/layer/noisylinear.hpp index feac41131f..993f80725f 100644 --- a/src/mlpack/methods/ann/layer/noisylinear.hpp +++ b/src/mlpack/methods/ann/layer/noisylinear.hpp @@ -14,8 +14,6 @@ #include -#include "layer.hpp" - namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -23,42 +21,55 @@ namespace ann /** Artificial Neural Network. */ { * Implementation of the NoisyLinear layer class. It represents a single * layer of a neural network, with parametric noise added to its weights. * - * @tparam MatType Matrix representation to accept as input and use for - * computation. + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class NoisyLinearType : public Layer +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class NoisyLinear { public: + //! Create the NoisyLinear object. + NoisyLinear(); + /** * Create the NoisyLinear layer object using the specified number of units. * + * @param inSize The number of input units. * @param outSize The number of output units. */ - NoisyLinearType(const size_t outSize = 0); + NoisyLinear(const size_t inSize, + const size_t outSize); - //! Clone the NoisyLinearType object. This handles polymorphism correctly. - NoisyLinearType* Clone() const { return new NoisyLinearType(*this); } + //! Copy constructor. + NoisyLinear(const NoisyLinear&); - // Virtual destructor. - virtual ~NoisyLinearType() { } + //! Move constructor. + NoisyLinear(NoisyLinear&&); - //! Copy the given NoisyLinear layer (but not weights). - NoisyLinearType(const NoisyLinearType& other); - //! Take ownership of the given NoisyLinear layer (but not weights). - NoisyLinearType(NoisyLinearType&& other); - //! Copy the given NoisyLinear layer (but not weights). - NoisyLinearType& operator=(const NoisyLinearType& other); - //! Take ownership of the given NoisyLinear layer (but not weights). - NoisyLinearType& operator=(NoisyLinearType&& other); + //! Operator= copy constructor. + NoisyLinear& operator=(const NoisyLinear& layer); - //! Reset the layer parameter. - void SetWeights(typename MatType::elem_type* weightsPtr); + //! Operator= move constructor. + NoisyLinear& operator=(NoisyLinear&& layer); - //! Reset the noise parameters (epsilons). + /* + * Reset the layer parameter. + */ + void Reset(); + + /* + * Reset the noise parameters(epsilons). + */ void ResetNoise(); - //! Reset the values of layer parameters (factorized gaussian noise). + /* + * Reset the values of layer parameters (factorized gaussian noise). + */ void ResetParameters(); /** @@ -68,7 +79,8 @@ class NoisyLinearType : public Layer * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - void Forward(const MatType& input, MatType& output); + template + void Forward(const arma::Mat& input, arma::Mat& output); /** * Ordinary feed backward pass of a neural network, calculating the function @@ -79,80 +91,117 @@ class NoisyLinearType : public Layer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const MatType& /* input */, - const MatType& gy, - MatType& g); + template + void Backward(const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g); - /** + /* * Calculate the gradient using the output delta and the input activation. * * @param input The input parameter used for calculating the gradient. * @param error The calculated error. * @param gradient The calculated gradient. */ - void Gradient(const MatType& input, - const MatType& error, - MatType& gradient); + template + void Gradient(const arma::Mat& input, + const arma::Mat& error, + arma::Mat& gradient); //! Get the parameters. - MatType const& Parameters() const { return weights; } + OutputDataType const& Parameters() const { return weights; } //! Modify the parameters. - MatType& Parameters() { return weights; } + OutputDataType& Parameters() { return weights; } + + //! Get the input parameter. + InputDataType const& InputParameter() const { return inputParameter; } + //! Modify the input parameter. + InputDataType& InputParameter() { return inputParameter; } + + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } + + //! Get the input size. + size_t InputSize() const { return inSize; } + + //! Get the output size. + size_t OutputSize() const { return outSize; } + + //! Get the gradient. + OutputDataType const& Gradient() const { return gradient; } + //! Modify the gradient. + OutputDataType& Gradient() { return gradient; } //! Get the shape of the input. + size_t InputShape() const + { + return inSize; + } + //! Modify the bias weights of the layer. - MatType& Bias() { return bias; } + arma::mat& Bias() { return bias; } - //! Compute the number of parameters in the layer. + //! Get size of weights. size_t WeightSize() const { return (outSize * inSize + outSize) * 2; } - - //! Compute the output dimensions of the layer given `InputDimensions()`. - void ComputeOutputDimensions(); - - //! Serialize the layer. + /** + * Serialize the layer + */ template void serialize(Archive& ar, const uint32_t /* version */); private: + //! Locally-stored number of input units. + size_t inSize; + //! Locally-stored number of output units. size_t outSize; - //! Locally stored number of input units. - size_t inSize; - //! Locally-stored weight object. - MatType weights; + OutputDataType weights; //! Locally-stored weight parameters. - MatType weight; + OutputDataType weight; //! Locally-stored weight-mean parameters. - MatType weightMu; + OutputDataType weightMu; //! Locally-stored weight-standard-deviation parameters. - MatType weightSigma; + OutputDataType weightSigma; //! Locally-stored weight-epsilon parameters. - MatType weightEpsilon; + OutputDataType weightEpsilon; //! Locally-stored bias parameters. - MatType bias; + OutputDataType bias; //! Locally-stored bias-mean parameters. - MatType biasMu; + OutputDataType biasMu; //! Locally-stored bias-standard-deviation parameters. - MatType biasSigma; + OutputDataType biasSigma; //! Locally-stored bias-epsilon parameters. - MatType biasEpsilon; + OutputDataType biasEpsilon; -}; // class NoisyLinearType + //! Locally-stored delta object. + OutputDataType delta; -// Convenience typedefs. + //! Locally-stored gradient object. + OutputDataType gradient; -// Standard noisy linear layer. -typedef NoisyLinearType NoisyLinear; + //! Locally-stored input parameter object. + InputDataType inputParameter; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; +}; // class NoisyLinear } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/noisylinear_impl.hpp b/src/mlpack/methods/ann/layer/noisylinear_impl.hpp index 4df8685283..c7a37b5863 100644 --- a/src/mlpack/methods/ann/layer/noisylinear_impl.hpp +++ b/src/mlpack/methods/ann/layer/noisylinear_impl.hpp @@ -18,92 +18,107 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -NoisyLinearType::NoisyLinearType(const size_t outSize) : - Layer(), - outSize(outSize), - inSize(0) +template +NoisyLinear::NoisyLinear() : + inSize(0), + outSize(0) { // Nothing to do here. } -template -NoisyLinearType::NoisyLinearType(const NoisyLinearType& other) : - Layer(other), - outSize(other.outSize), - inSize(other.inSize) +template +NoisyLinear::NoisyLinear( + const NoisyLinear& layer) : + inSize(layer.inSize), + outSize(layer.outSize), + weights(layer.weights) { - // Nothing to do. + Reset(); } -template -NoisyLinearType::NoisyLinearType(NoisyLinearType&& other) : - Layer(std::move(other)), - outSize(std::move(other.outSize)), - inSize(std::move(other.inSize)) +template +NoisyLinear::NoisyLinear( + const size_t inSize, + const size_t outSize) : + inSize(inSize), + outSize(outSize) { - // Nothing to do. + weights.set_size(WeightSize(), 1); + weightEpsilon.set_size(outSize, inSize); + biasEpsilon.set_size(outSize, 1); } -template -NoisyLinearType& -NoisyLinearType::operator=(const NoisyLinearType& other) +template +NoisyLinear::NoisyLinear( + NoisyLinear&& layer) : + inSize(std::move(layer.inSize)), + outSize(std::move(layer.outSize)), + weights(std::move(layer.weights)) { - if (&other != this) + layer.inSize = 0; + layer.outSize = 0; + layer.weights = nullptr; + Reset(); +} + +template +NoisyLinear& +NoisyLinear::operator=(const NoisyLinear& layer) +{ + if (this != &layer) { - Layer::operator=(other); - outSize = other.outSize; - inSize = other.inSize; + inSize = layer.inSize; + outSize = layer.outSize; + weights = layer.weights; + Reset(); } - return *this; } -template -NoisyLinearType& -NoisyLinearType::operator=(NoisyLinearType&& other) +template +NoisyLinear& +NoisyLinear::operator=(NoisyLinear&& layer) { - if (&other != this) + if (this != &layer) { - Layer::operator=(std::move(other)); - outSize = std::move(other.outSize); - inSize = std::move(other.inSize); + inSize = std::move(layer.inSize); + layer.inSize = 0; + outSize = std::move(layer.outSize); + layer.outSize = 0; + weights = std::move(layer.weights); + layer.weights = nullptr; + Reset(); } - return *this; } -template -void NoisyLinearType::SetWeights( - typename MatType::elem_type* weightsPtr) +template +void NoisyLinear::Reset() { - MakeAlias(weights, weightsPtr, 1, (outSize * inSize + outSize) * 2); - - MakeAlias(weightMu, weightsPtr, outSize, inSize); - MakeAlias(biasMu, weightsPtr + weightMu.n_elem, outSize, 1); - MakeAlias(weightSigma, weightsPtr + weightMu.n_elem + biasMu.n_elem, outSize, - inSize); - MakeAlias(biasSigma, weightsPtr + weightMu.n_elem * 2 + biasMu.n_elem, - outSize, 1); - + weightMu = arma::mat(weights.memptr(), + outSize, inSize, false, false); + biasMu = arma::mat(weights.memptr() + weightMu.n_elem, + outSize, 1, false, false); + weightSigma = arma::mat(weights.memptr() + weightMu.n_elem + biasMu.n_elem, + outSize, inSize, false, false); + biasSigma = arma::mat(weights.memptr() + weightMu.n_elem * 2 + biasMu.n_elem, + outSize, 1, false, false); this->ResetNoise(); } -template -void NoisyLinearType::ResetNoise() +template +void NoisyLinear::ResetNoise() { - MatType epsilonIn = arma::randn(inSize, 1); + arma::mat epsilonIn = arma::randn(inSize, 1); epsilonIn = arma::sign(epsilonIn) % arma::sqrt(arma::abs(epsilonIn)); - - MatType epsilonOut = arma::randn(outSize, 1); + arma::mat epsilonOut = arma::randn(outSize, 1); epsilonOut = arma::sign(epsilonOut) % arma::sqrt(arma::abs(epsilonOut)); - weightEpsilon = epsilonOut * epsilonIn.t(); biasEpsilon = epsilonOut; } -template -void NoisyLinearType::ResetParameters() +template +void NoisyLinear::ResetParameters() { const double muRange = 1 / std::sqrt(inSize); weightMu.randu(); @@ -114,8 +129,10 @@ void NoisyLinearType::ResetParameters() biasSigma.fill(0.5 / std::sqrt(outSize)); } -template -void NoisyLinearType::Forward(const MatType& input, MatType& output) +template +template +void NoisyLinear::Forward( + const arma::Mat& input, arma::Mat& output) { weight = weightMu + weightSigma % weightEpsilon; bias = biasMu + biasSigma % biasEpsilon; @@ -123,19 +140,23 @@ void NoisyLinearType::Forward(const MatType& input, MatType& output) output.each_col() += bias; } -template -void NoisyLinearType::Backward( - const MatType& /* input */, const MatType& gy, MatType& g) +template +template +void NoisyLinear::Backward( + const arma::Mat& /* input */, const arma::Mat& gy, arma::Mat& g) { g = weight.t() * gy; } -template -void NoisyLinearType::Gradient( - const MatType& input, const MatType& error, MatType& gradient) +template +template +void NoisyLinear::Gradient( + const arma::Mat& input, + const arma::Mat& error, + arma::Mat& gradient) { // Locally stored to prevent multiplication twice. - MatType weightGrad = error * input.t(); + arma::mat weightGrad = error * input.t(); // Gradients for mu values. gradient.rows(0, weight.n_elem - 1) = arma::vectorise(weightGrad); @@ -149,34 +170,18 @@ void NoisyLinearType::Gradient( = arma::sum(error, 1) % biasEpsilon; } -template -void NoisyLinearType::ComputeOutputDimensions() -{ - inSize = this->inputDimensions[0]; - for (size_t i = 1; i < this->inputDimensions.size(); ++i) - inSize *= this->inputDimensions[i]; - - this->outputDimensions = std::vector(this->inputDimensions.size(), - 1); - - // The NoisyLinear layer flattens its output. - this->outputDimensions[0] = outSize; -} - -template +template template -void NoisyLinearType::serialize( +void NoisyLinear::serialize( Archive& ar, const uint32_t /* version */) { - ar(cereal::base_class>(this)); - - ar(CEREAL_NVP(outSize)); ar(CEREAL_NVP(inSize)); + ar(CEREAL_NVP(outSize)); + // This is inefficient, but we have to allocate this memory so that + // WeightSetVisitor gets the right size. if (cereal::is_loading()) - { - ResetNoise(); - } + weights.set_size((outSize * inSize + outSize) * 2, 1); } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/not_adapted/README.md b/src/mlpack/methods/ann/layer/not_adapted/README.md deleted file mode 100644 index ffe867395b..0000000000 --- a/src/mlpack/methods/ann/layer/not_adapted/README.md +++ /dev/null @@ -1,9 +0,0 @@ -Layers in this directory were written with the old boost::visitor interface. In -[#2777](https://github.com/mlpack/mlpack/pull/2777), we adapted each layer to -use inheritance instead. However, time did not permit the adaptation of all -layers, and so remaining layers that have not yet been adapted are in this -directory. - -The intention is that we will work our way through layers in this directory, -updating them to the new interface and re-enabling tests for them in separate, -follow-up PRs. If you'd like to help out, you are more than welcome to! diff --git a/src/mlpack/methods/ann/layer/not_adapted/add_merge_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/add_merge_impl.hpp deleted file mode 100644 index f1c71ae3ad..0000000000 --- a/src/mlpack/methods/ann/layer/not_adapted/add_merge_impl.hpp +++ /dev/null @@ -1,145 +0,0 @@ -/** - * @file methods/ann/layer/add_merge_impl.hpp - * @author Marcus Edel - * - * Definition of the AddMerge module which accumulates the output of the given - * modules. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_LAYER_ADD_MERGE_IMPL_HPP -#define MLPACK_METHODS_ANN_LAYER_ADD_MERGE_IMPL_HPP - -// In case it hasn't yet been included. -#include "add_merge.hpp" - -namespace mlpack { -namespace ann /** Artificial Neural Network. */ { - -template -AddMerge::AddMerge( - const bool run) : - run(run), ownsLayers(true) -{ - // Nothing to do here. -} - -template -AddMerge::AddMerge( - const bool run, const bool ownsLayers) : - run(run), ownsLayers(ownsLayers) -{ - // Nothing to do here. -} - -template -AddMerge::~AddMerge() -{ - -} - -template -void AddMerge::Forward( - const InputType& input, OutputType& output) -{ - this->InitializeForwardPassMemory(); - - if (run) - { - for (size_t i = 0; i < this->network.size(); ++i) - { - this->network[i]->Forward(input, this->layerOutputs[i]); - } - } - - output = this->layerOutputs.front(); - for (size_t i = 1; i < this->network.size(); ++i) - { - output += this->layerOutputs[i]; - } -} - -template -void AddMerge::Backward( - const InputType& /* input */, - const OutputType& gy, - OutputType& g) -{ - this->InitializeBackwardPassMemory(); - - if (run) - { - for (size_t i = 0; i < this->network.size(); ++i) - { - this->network[i]->Backward(this->layerOutputs[i], gy, - this->layerDeltas[i]); - } - - g = this->layerDeltas[0]; - for (size_t i = 1; i < this->network.size(); ++i) - { - g += this->layerDeltas[i]; - } - } - else - { - g = gy; - } -} - -template -void AddMerge::Backward( - const InputType& /* input */, - const OutputType& gy, - OutputType& g, - const size_t index) -{ - this->network[index]->Backward(this->layerOutputs[index], gy, g); -} - -template -void AddMerge::Gradient( - const InputType& input, - const OutputType& error, - OutputType& gradient) -{ - if (run) - { - size_t start = 0; - for (size_t i = 0; i < this->network.size(); ++i) - { - this->network[i]->Gradient(input, error, OutputType(gradient.colptr(start), - 1, this->network[i]->WeightSize(), false, true)); - start += this->network[i]->WeightSize(); - } - } -} - -template -void AddMerge::Gradient( - const InputType& input, - const OutputType& error, - OutputType& gradient, - const size_t index) -{ - this->network[index]->Gradient(input, error, gradient); -} - -template -template -void AddMerge::serialize( - Archive& ar, const uint32_t /* version */) -{ - ar(cereal::base_class>(this)); - - ar(CEREAL_NVP(run)); - ar(CEREAL_NVP(ownsLayers)); -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/layer/not_adapted/concat.hpp b/src/mlpack/methods/ann/layer/not_adapted/concat.hpp deleted file mode 100644 index 1d99ff60c9..0000000000 --- a/src/mlpack/methods/ann/layer/not_adapted/concat.hpp +++ /dev/null @@ -1,222 +0,0 @@ -/** - * @file methods/ann/layer/concat.hpp - * @author Marcus Edel - * @author Mehul Kumar Nirala - * - * Definition of the Concat class, which acts as a concatenation container. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_LAYER_CONCAT_HPP -#define MLPACK_METHODS_ANN_LAYER_CONCAT_HPP - -#include - -#include "layer.hpp" - -namespace mlpack { -namespace ann /** Artificial Neural Network. */ { - -/** - * Implementation of the Concat class. The Concat class works as a - * feed-forward fully connected network container which plugs various layers - * together. - * - * @tparam InputType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - */ -template < - typename InputType = arma::mat, - typename OutputType = arma::mat -> -class ConcatType : public MultiLayer -{ - public: - /** - * Create the Concat object using the specified parameters. - * - * @param run Call the Forward/Backward method before the output is merged. - */ - ConcatType(const bool run = true); - - /** - * Create the Concat object, specifying a particular axis on which the layer - * outputs should be concatenated. - * - * @param axis Concat axis. - * @param run Call the Forward/Backward method before the output is merged. - */ - ConcatType(const size_t axis, const bool run = true); - - /** - * Destroy the layers held by the model. - */ - ~ConcatType(); - - //! Clone the ConcatType object. This handles polymorphism correctly. - ConcatType* Clone() const { return new ConcatType(*this); } - - /** - * Ordinary feed forward pass of a neural network, evaluating the function - * f(x) by propagating the activity forward through f. - * - * @param input Input data used for evaluating the specified function. - * @param output Resulting output activation. - */ - void Forward(const InputType& input, OutputType& output); - - /** - * Ordinary feed backward pass of a neural network, using 3rd-order tensors as - * input, calculating the function f(x) by propagating x backwards through f. - * Using the results from the feed forward pass. - * - * @param * (input) The propagated input activation. - * @param gy The backpropagated error. - * @param g The calculated gradient. - */ - void Backward(const InputType& /* input */, - const OutputType& gy, - OutputType& g); - - /** - * This is the overload of Backward() that runs only a specific layer with - * the given input. - * - * @param * (input) The propagated input activation. - * @param gy The backpropagated error. - * @param g The calculated gradient. - * @param index The index of the layer to run. - */ - void Backward(const InputType& /* input */, - const OutputType& gy, - OutputType& g, - const size_t index); - - /** - * Calculate the gradient using the output delta and the input activation. - * - * @param input The input parameter used for calculating the gradient. - * @param error The calculated error. - * @param gradient The calculated gradient. - */ - void Gradient(const InputType& /* input */, - const OutputType& error, - OutputType& /* gradient */); - - /** - * This is the overload of Gradient() that runs a specific layer with the - * given input. - * - * @param input The input parameter used for calculating the gradient. - * @param error The calculated error. - * @param gradient The calculated gradient. - * @param The index of the layer to run. - */ - void Gradient(const InputType& input, - const OutputType& error, - OutputType& gradient, - const size_t index); - - //! Get the value of run parameter. - bool Run() const { return run; } - //! Modify the value of run parameter. - bool& Run() { return run; } - - //! Get the axis of concatenation. - const size_t& ConcatAxis() const { return axis; } - - //! Get the size of the weight matrix. - size_t WeightSize() const { return 0; } - - void ComputeOutputDimensions() - { - // The input is sent to every layer. - for (size_t i = 0; i < network.size(); ++i) - { - network[i]->InputDimensions() = this->inputDimensions; - network[i]->ComputeOutputDimensions(); - } - - // If the user did not specify an axis, we will use the last one. - // Otherwise, we must sanity check to ensure that the axis we are - // concatenating along is valid. - if (!useAxis) - { - axis = this->inputDimensions.size() - 1; - } - else if (axis >= this->inputDimensions.size()) - { - std::ostringstream oss; - oss << "Concat::ComputeOutputDimensions(): cannot concatenate outputs " - << "along axis " << axis << " when input only has " - << this->inputDimensions.size() << " axes!"; - throw std::invalid_argument(oss.str()); - } - - // Now, we concatenate the output along a specific axis. - this->outputDimensions = std::vector(this->inputDimensions.size(), - 0); - for (size_t i = 0; i < this->inputDimensions.size(); ++i) - { - if (i == axis) - { - // Accumulate output size along this axis for each layer output. - for (size_t n = 0; n < this->network.size(); ++n) - { - this->outputDimensions[i] += this->network[n]->OutputDimensions()[i]; - } - } - else - { - // Ensure that the output size is the same along this axis. - const size_t axisDim = this->network[0]->OutputDimensions()[i]; - for (size_t n = 1; n < this->network.size(); ++n) - { - const size_t axisDim2 = this->network[n]->OutputDimensions()[i]; - if (axisDim != axisDim2) - { - std::ostringstream oss; - oss << "Concat::ComputeOutputDimensions(): cannot concatenate " - << "outputs along axis " << axis << "; held layer " << n - << " has output size " << axisDim2 << " along axis " << i - << ", but the first held layer has output size " << axisDim - << "! All layers must have identical output size in any " - << "axis other than the concatenated axis."; - throw std::invalid_argument(oss.str()); - } - } - - this->outputDimensions[i] = axisDim; - } - } - } - - /** - * Serialize the layer - */ - template - void serialize(Archive& ar, const uint32_t /* version */); - - private: - //! Parameter which indicates the axis of concatenation. - size_t axis; - - //! Parameter which indicates whether to use the axis of concatenation. - bool useAxis; -}; // class ConcatType. - -// Standard Concat layer. -typedef ConcatType Concat; - -} // namespace ann -} // namespace mlpack - -// Include implementation. -#include "concat_impl.hpp" - -#endif diff --git a/src/mlpack/methods/ann/layer/not_adapted/concat_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/concat_impl.hpp deleted file mode 100644 index 7e231b5f81..0000000000 --- a/src/mlpack/methods/ann/layer/not_adapted/concat_impl.hpp +++ /dev/null @@ -1,267 +0,0 @@ -/** - * @file methods/ann/layer/concat_impl.hpp - * @author Marcus Edel - * @author Mehul Kumar Nirala - * - * Implementation of the Concat class, which acts as a concatenation contain. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_LAYER_CONCAT_IMPL_HPP -#define MLPACK_METHODS_ANN_LAYER_CONCAT_IMPL_HPP - -// In case it hasn't yet been included. -#include "concat.hpp" - -namespace mlpack { -namespace ann /** Artificial Neural Network. */ { - -template -ConcatType::ConcatType( - const bool run) : - axis(0), - useAxis(false) -{ - // Nothing to do. -} - -template -ConcatType::ConcatType( - const size_t axis, - const bool run) : - axis(axis), - useAxis(true) -{ - // Nothing to do. -} - -template -ConcatType::~ConcatType() -{ - // Clear memory. - for (size_t i = 0; i < this->network.size(); ++i) - delete this->network[i]; -} - -template -void ConcatType::Forward( - const InputType& input, OutputType& output) -{ - this->InitializeForwardPassMemory(); - - // Pass the input through all the layers in the network. - for (size_t i = 0; i < this->network.size(); ++i) - { - this->network[i]->Forward(input, this->layerOutputs[i]); - } - - // Now concatenate the outputs along the correct axis. - // We can actually use Armadillo to do this for us---we will treat the axis of - // interest as "columns", any axes that come before the axis of interest as - // 'flattened slices', and any axes that come after the axis of interest as - // 'flattened rows'. As a result, we will only have to do join_cols() to - // produce the right result. - // - // Note that we will have one "extra" axis in addition to - // this->outputDimensions.size(); that is the batch size (represented as the - // number of columns in `input`). - - size_t slices = (axis == 0) ? input.n_cols : - std::accumulate(this->outputDimensions.begin(), - this->outputDimensions.begin() + axis, 0) + input.n_cols; - size_t rows = (axis == this->outputDimensions.size() - 1) ? 1 : - std::accumulate(this->outputDimensions.begin() + axis + 1, - this->outputDimensions.end(), 0); - - std::vector> layerOutputAliases; - for (size_t i = 0; i < this->layerOutputs.size(); ++i) - { - layerOutputAliases.emplace_back(arma::Cube( - this->layerOutputs[i].memptr(), rows, - this->network[i]->OutputDimensions()[axis], slices, false, true); - } - - arma::Cube output(output.memptr(), rows, - this->outputDimensions[axis], slices, false, true); - - // Now get the columns from each output. - size_t startCol = 0; - for (size_t i = 0; i < layerOutputAliases.size(); ++i) - { - const size_t cols = layerOutputAliases[i].n_cols; - output.cols(startCol, startCol + cols - 1) = layerOutputAliases[i]; - startCol += cols; - } -} - -template -void ConcatType::Backward( - const InputType& /* input */, const OutputType& gy, OutputType& g) -{ - this->InitializeBackwardPassMemory(); - - // Just like the forward pass, we can treat our inputs as a cube, but here we - // have to distribute the correct parts of `gy` to the layers. - - size_t slices = (axis == 0) ? gy.n_cols : - std::accumulate(this->outputDimensions.begin(), - this->outputDimensions.begin() + axis, 0) + gy.n_cols; - size_t rows = (axis == this->outputDimensions.size() - 1) ? 1 : - std::accumulate(this->outputDimensions.begin() + axis + 1, - this->outputDimensions.end(), 0); - - arma::Cube gyTmp(gy.memptr(), rows, - this->outputDimensions[axis], slices, false, true); - - size_t startCol = 0; - for (size_t i = 0; i < this->network.size(); ++i) - { - const size_t cols = this->network[i]->OutputDimensions()[axis]; - // TODO: is delta size correct? - // TODO: no copy! - OutputType delta = gyTmp.cols(startCol, startCol + cols - 1); - // TODO: consider batch size correctly - delta.reshape( ... ); - this->network[i]->Backward(this->layerOutputs[i], delta, - this->layerDeltas[i]); - - startCol += cols; - } - - g = this->layerDeltas[0]; - for (size_t i = 1; i < this->network.size(); ++i) - { - g += this->layerDeltas[i]; - } -} - -template -void ConcatType::Backward( - const InputType& /* input */, - const OutputType& gy, - OutputType& g, - const size_t index) -{ - // We only intend to perform a backward pass on one layer. - // Thus, we need to extract the parts of gy that correspond to the desired - // layer (specified by `index`). - - size_t slices = (axis == 0) ? gy.n_cols : - std::accumulate(this->outputDimensions.begin(), - this->outputDimensions.begin() + axis, 0) + gy.n_cols; - size_t rows = (axis == this->outputDimensions.size() - 1) ? 1 : - std::accumulate(this->outputDimensions.begin() + axis + 1, - this->outputDimensions.end(), 0); - - arma::Cube gyTmp(gy.memptr(), rows, - this->outputDimensions[axis], slices, false, true); - - size_t startCol = 0; - for (size_t i = 0; i < index; ++i) - { - startCol += this->network[i]->OutputDimensions()[axis]; - } - - // TODO: no copy! - const size_t cols = this->network[index]->OutputDimensions()[axis]; - OutputType delta = gyTmp.cols(startCol, startCol + cols - 1); - delta.reshape( ... ); - - this->network[index]->Backward(this->layerOutputs[index], delta, g); -} - -template -void ConcatType::Gradient( - const InputType& input, - const OutputType& error, - OutputType& gradient) -{ - // Just like the forward pass, we can treat our inputs as a cube, but here we - // have to distribute the correct parts of `gy` to the layers. - - size_t slices = (axis == 0) ? input.n_cols : - std::accumulate(this->outputDimensions.begin(), - this->outputDimensions.begin() + axis, 0) + input.n_cols; - size_t rows = (axis == this->outputDimensions.size() - 1) ? 1 : - std::accumulate(this->outputDimensions.begin() + axis + 1, - this->outputDimensions.end(), 0); - - arma::Cube errorTmp(error.memptr(), rows, - this->outputDimensions[axis], slices, false, true); - - size_t startCol = 0; - size_t startParam = 0; - for (size_t i = 0; i < this->network.size(); ++i) - { - const size_t cols = this->network[i]->OutputDimensions()[axis]; - const size_t params = this->network[i]->WeightSize(); - - OutputType err = errorTmp.cols(startCol, startCol + cols - 1); - err.reshape(input.n_cols, err.n_elem / input.n_cols); - // TODO: what about layerGradients? - OutputType gradientAlias(gradient.colptr(startParam, 1, params, false, - true); - this->network[i]->Gradient(input, err, gradientAlias); - - startCol += cols; - startParam += params; - } -} - -// TODO: adapt -template -void ConcatType::Gradient( - const InputType& input, - const OutputType& error, - OutputType& gradient, - const size_t index) -{ - // Just like the forward pass, we can treat our inputs as a cube, but here we - // have to distribute the correct parts of `gy` to the layers. - - size_t slices = (axis == 0) ? input.n_cols : - std::accumulate(this->outputDimensions.begin(), - this->outputDimensions.begin() + axis, 0) + input.n_cols; - size_t rows = (axis == this->outputDimensions.size() - 1) ? 1 : - std::accumulate(this->outputDimensions.begin() + axis + 1, - this->outputDimensions.end(), 0); - - arma::Cube errorTmp(error.memptr(), rows, - this->outputDimensions[axis], slices, false, true); - - size_t startCol = 0; - size_t startParam = 0; - for (size_t i = 0; i < index; ++i) - { - startCol += this->network[i]->OutputDimensions()[axis]; - startParam += this->network[i]->WeightSize(); - } - - const size_t cols = this->network[index]->OutputDimensions()[axis]; - const size_t params = this->network[index]->WeightSize(); - - // TODO: no copy! - OutputType err = errorTmp.cols(startCol, startCol + cols - 1); - err.reshape(input.n_cols, err.n_elem / input.n_cols); - OutputType gradientAlias(gradient.memptr(), 1, params, false, true); - this->network[index]->Gradient(input, err, gradientAlias); -} - -template -template -void ConcatType::serialize( - Archive& ar, const uint32_t /* version */) -{ - ar(cereal::base_class>(this)); - - ar(CEREAL_NVP(axis)); - ar(CEREAL_NVP(useAxis)); -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/layer/not_adapted/constant_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/constant_impl.hpp deleted file mode 100644 index 601b5c2e76..0000000000 --- a/src/mlpack/methods/ann/layer/not_adapted/constant_impl.hpp +++ /dev/null @@ -1,118 +0,0 @@ -/** - * @file methods/ann/layer/constant_impl.hpp - * @author Marcus Edel - * - * Implementation of the Constant class, which outputs a constant value given - * any input. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_LAYER_CONSTANT_IMPL_HPP -#define MLPACK_METHODS_ANN_LAYER_CONSTANT_IMPL_HPP - -// In case it hasn't yet been included. -#include "constant.hpp" - -namespace mlpack { -namespace ann /** Artificial Neural Network. */ { - -template -ConstantType::ConstantType() : - outSize(0) -{ - // Nothing to do. -} - -template -ConstantType::ConstantType( - const size_t outSize, - const double scalar) : - outSize(outSize) -{ - constantOutput = OutputType(outSize, 1); - constantOutput.fill(scalar); -} - -template -ConstantType::ConstantType( - const ConstantType& other) : - outSize(other.outSize), - constantOutput(other.constantOutput) -{ - // Nothing else to do. -} - -template -ConstantType::ConstantType( - ConstantType&& other) : - outSize(other.outSize), - constantOutput(std::move(other.constantOutput)) -{ - other.outSize = 1; - other.constantOutput = OutputType(other.outSize, 1); -} - -template -ConstantType& -ConstantType::operator=( - const ConstantType& other) -{ - if (this != &other) - { - outSize = other.outSize; - constantOutput = other.constantOutput; - } - - return *this; -} - -template -ConstantType& -ConstantType::operator=( - ConstantType&& other) -{ - if (this != *other) - { - outSize = other.outSize; - constantOutput = std::move(other.constantOutput); - - other.outSize = 1; - other.constantOutput = OutputType(other.outSize, 1); - } - - return *this; -} - -template -void ConstantType::Forward( - const InputType& input, OutputType& output) -{ - output = constantOutput; -} - -template -void ConstantType::Backward( - const InputType& /* input */, const OutputType& /* gy */, OutputType& g) -{ - g.zeros(); -} - -template -template -void ConstantType::serialize( - Archive& ar, const uint32_t /* version */) -{ - ar(cereal::base_class>(this)); - - ar(CEREAL_NVP(constantOutput)); - if (Archive::is_loading::value) - outSize = constantOutput.n_elem; -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/layer/not_adapted/gru_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/gru_impl.hpp deleted file mode 100644 index 2e7fb208c2..0000000000 --- a/src/mlpack/methods/ann/layer/not_adapted/gru_impl.hpp +++ /dev/null @@ -1,369 +0,0 @@ -/** - * @file methods/ann/layer/gru_impl.hpp - * @author Sumedh Ghaisas - * - * Implementation of the GRU class, which implements a gru network - * layer. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_LAYER_GRU_IMPL_HPP -#define MLPACK_METHODS_ANN_LAYER_GRU_IMPL_HPP - -// In case it hasn't yet been included. -#include "gru.hpp" - -namespace mlpack { -namespace ann /** Artificial Neural Network. */ { - -template -GRU::GRU() -{ - // Nothing to do here. -} - -template -GRU::GRU( - const size_t inSize, - const size_t outSize, - const size_t rho) : - inSize(inSize), - outSize(outSize), - rho(rho), - batchSize(1), - forwardStep(0), - backwardStep(0), - gradientStep(0) -{ - // Input specific linear layers(for zt, rt, ot). - input2GateModule = new LinearType(inSize, 3 * outSize); - - // Previous output gates (for zt and rt). - output2GateModule = new LinearNoBiasType(outSize, - 2 * outSize); - - // Previous output gate for ot. - outputHidden2GateModule = new LinearNoBiasType(outSize, - outSize); - - network.push_back(input2GateModule); - network.push_back(output2GateModule); - network.push_back(outputHidden2GateModule); - - inputGateModule = new SigmoidLayer(); - forgetGateModule = new SigmoidLayer(); - hiddenStateModule = new TanHLayer(); - - network.push_back(inputGateModule); - network.push_back(hiddenStateModule); - network.push_back(forgetGateModule); - - prevError = arma::zeros(3 * outSize, batchSize); - - allZeros = arma::zeros(outSize, batchSize); - - outParameter.emplace_back(allZeros.memptr(), - allZeros.n_rows, allZeros.n_cols, false, true); - - prevOutput = outParameter.begin(); - backIterator = outParameter.end(); - gradIterator = outParameter.end(); -} - -template -void GRU::Forward( - const InputType& input, OutputType& output) -{ - if (input.n_cols != batchSize) - { - batchSize = input.n_cols; - prevError.resize(3 * outSize, batchSize); - allZeros.zeros(outSize, batchSize); - // Batch size better not change during an iteration... - if (outParameter.size() > 1) - { - Log::Fatal << "GRU<>::Forward(): batch size cannot change during a " - << "forward pass!" << std::endl; - } - - outParameter.clear(); - outParameter.emplace_back(allZeros.memptr(), - allZeros.n_rows, allZeros.n_cols, false, true); - - prevOutput = outParameter.begin(); - backIterator = outParameter.end(); - gradIterator = outParameter.end(); - } - - // Process the input linearly(zt, rt, ot). - input2GateModule->Forward(input, input2GateModule->OutputParameter()); - - // Process the output(zt, rt) linearly. - output2GateModule->Forward(*prevOutput, output2GateModule->OutputParameter()); - - // Merge the outputs(zt and rt). - output = input2GateModule->OutputParameter().submat(0, 0, 2 * outSize - 1, - batchSize - 1) + output2GateModule->OutputParameter(); - - // Pass the first outSize through inputGate(it). - inputGateModule->Forward(output.submat( 0, 0, 1 * outSize - 1, batchSize - 1), - inputGateModule->OutputParameter()); - - // Pass the second through forgetGate. - forgetGateModule->Forward(output.submat( 1 * outSize, 0, 2 * outSize - 1, - batchSize - 1), forgetGateModule->OutputParameter()); - - OutputType modInput = forgetGateModule->OutputParameter() % *prevOutput; - - // Pass that through the outputHidden2GateModule. - outputHidden2GateModule->Forward(modInput, - outputHidden2GateModule->OutputParameter()); - - // Merge for ot. - OutputType outputH = input2GateModule->OutputParameter().submat(2 * outSize, - 0, 3 * outSize - 1, batchSize - 1) + - outputHidden2GateModule->OutputParameter(); - - // Pass it through hiddenGate. - hiddenStateModule->ForwardVisitor(outputH, - hiddenStateModule->OutputParameter()); - - // Update the output (nextOutput): cmul1 + cmul2 - // Where cmul1 is input gate * prevOutput and - // cmul2 is (1 - input gate) * hidden gate. - output = (inputGateModule->OutputParameter() - % (*prevOutput - hiddenStateModule->OutputParameter())) + - hiddenStateModule->OutputParameter(); - - forwardStep++; - if (forwardStep == rho) - { - forwardStep = 0; - if (this->training) - { - outParameter.emplace_back(allZeros.memptr(), - allZeros.n_rows, allZeros.n_cols, false, true); - prevOutput = --outParameter.end(); - } - else - { - *prevOutput = arma::mat(allZeros.memptr(), - allZeros.n_rows, allZeros.n_cols, false, true); - } - } - else if (this->training) - { - outParameter.push_back(output); - prevOutput = --outParameter.end(); - } - else - { - if (forwardStep == 1) - { - outParameter.clear(); - outParameter.push_back(output); - - prevOutput = outParameter.begin(); - } - else - { - *prevOutput = output; - } - } -} - -template -void GRU::Backward( - const InputType& input, const OutputType& gy, OutputType& g) -{ - if (input.n_cols != batchSize) - { - batchSize = input.n_cols; - prevError.resize(3 * outSize, batchSize); - allZeros.zeros(outSize, batchSize); - // Batch size better not change during an iteration... - if (outParameter.size() > 1) - { - Log::Fatal << "GRU<>::Forward(): batch size cannot change during a " - << "forward pass!" << std::endl; - } - - outParameter.clear(); - outParameter.emplace_back(allZeros.memptr(), - allZeros.n_rows, allZeros.n_cols, false, true); - - prevOutput = outParameter.begin(); - backIterator = outParameter.end(); - gradIterator = outParameter.end(); - } - - OutputType gyLocal; - if ((outParameter.size() - backwardStep - 1) % rho != 0 && backwardStep != 0) - { - gyLocal = gy + output2GateModule->Delta(); - } - else - { - gyLocal = OutputType(((OutputType&) gy).memptr(), gy.n_rows, gy.n_cols, - false, false); - } - - if (backIterator == outParameter.end()) - { - backIterator = --(--outParameter.end()); - } - - // Delta zt. - OutputType dZt = gyLocal % (*backIterator - - hiddenStateModule->OutputParameter()); - - // Delta ot. - OutputType dOt = gyLocal % (arma::ones(outSize, batchSize) - - inputGateModule->OutputParameter()); - - // Delta of input gate. - inputGateModule->Backward(inputGateModule->OutputParameter(), dZt, - inputGateModule->Delta()); - - // Delta of hidden gate. - hiddenStateModule->Backward(hiddenStateModule->OutputParameter(), dOt, - hiddenStateModule->Delta()); - - // Delta of outputHidden2GateModule. - outputHidden2GateModule->Backward(outputHidden2GateModule->OutputParameter(), - hiddenStateModule->Delta(), outputHidden2GateModule->Delta()); - - // Delta rt. - OutputType dRt = outputHidden2GateModule->Delta() % *backIterator; - - // Delta of forget gate. - forgetGateModule->Backward(forgetGateModule->OutputParameter(), dRt, - forgetGateModule->Delta()); - - // Put delta zt. - prevError.submat(0, 0, 1 * outSize - 1, batchSize - 1) = - inputGateModule->Delta(); - - // Put delta rt. - prevError.submat(1 * outSize, 0, 2 * outSize - 1, batchSize - 1) = - forgetGateModule->Delta(); - - // Put delta ot. - prevError.submat(2 * outSize, 0, 3 * outSize - 1, batchSize - 1) = - hiddenStateModule->Delta(); - - // Get delta ht - 1 for input gate and forget gate. - OutputType prevErrorSubview = prevError.submat(0, 0, 2 * outSize - 1, - batchSize - 1); - output2GateModule->Backward(input2GateModule->OutputParameter(), - prevErrorSubview, output2GateModule->Delta()); - - // Add delta ht - 1 from hidden state. - output2GateModule->Delta() += outputHidden2GateModule->Delta() % - forgetGateModule->OutputParameter(); - - // Add delta ht - 1 from ht. - output2GateModule->Delta() += gyLocal % inputGateModule->OutputParameter(); - - // Get delta input. - input2GateModule->Backward(input2GateModule->OutputParameter(), prevError, - input2GateModule->Delta()); - - backwardStep++; - backIterator--; - - g = input2GateModule->Delta(); -} - -template -void GRU::Gradient( - const InputType& input, - const OutputType& /* error */, - OutputType& /* gradient */) -{ - if (input.n_cols != batchSize) - { - batchSize = input.n_cols; - prevError.resize(3 * outSize, batchSize); - allZeros.zeros(outSize, batchSize); - // Batch size better not change during an iteration... - if (outParameter.size() > 1) - { - Log::Fatal << "GRU<>::Forward(): batch size cannot change during a " - << "forward pass!" << std::endl; - } - - outParameter.clear(); - outParameter.emplace_back(allZeros.memptr(), - allZeros.n_rows, allZeros.n_cols, false, true); - - prevOutput = outParameter.begin(); - backIterator = outParameter.end(); - gradIterator = outParameter.end(); - } - - if (gradIterator == outParameter.end()) - { - gradIterator = --(--outParameter.end()); - } - - input2GateModule->Gradient(input, prevError, input2GateModule->Gradient()); - - output2GateModule->Gradient(*gradIterator, - prevError.submat(0, 0, 2 * outSize - 1, batchSize - 1), - output2GateModule->Gradient()); - - outputHidden2GateModule->Gradient( - *gradIterator % forgetGateModule->OutputParameter(), - prevError.submat(2 * outSize, 0, 3 * outSize - 1, batchSize - 1), - outputHidden2GateModule->Gradient()); - - gradIterator--; -} - -template -void GRU::ResetCell(const size_t /* size */) -{ - outParameter.clear(); - outParameter.emplace_back(allZeros.memptr(), - allZeros.n_rows, allZeros.n_cols, false, true); - - prevOutput = outParameter.begin(); - backIterator = outParameter.end(); - gradIterator = outParameter.end(); - - forwardStep = 0; - backwardStep = 0; -} - -template -template -void GRU::serialize( - Archive& ar, const uint32_t /* version */) -{ - ar(cereal::base_class>(this)); - - // If necessary, clean memory from the old model. - // TODO: CEREAL_POINTER() should clean memory automatically... - - ar(CEREAL_NVP(inSize)); - ar(CEREAL_NVP(outSize)); - ar(CEREAL_NVP(rho)); - - ar(CEREAL_NVP(weights)); - - ar(CEREAL_POINTER(input2GateModule)); - ar(CEREAL_POINTER(output2GateModule)); - ar(CEREAL_POINTER(outputHidden2GateModule)); - ar(CEREAL_POINTER(inputGateModule)); - ar(CEREAL_POINTER(forgetGateModule)); - ar(CEREAL_POINTER(hiddenStateModule)); -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/layer/not_adapted/highway.hpp b/src/mlpack/methods/ann/layer/not_adapted/highway.hpp deleted file mode 100644 index 749149e1c3..0000000000 --- a/src/mlpack/methods/ann/layer/not_adapted/highway.hpp +++ /dev/null @@ -1,157 +0,0 @@ -// Temporarily drop. -/** - * @file methods/ann/layer/highway.hpp - * @author Konstantin Sidorov - * @author Saksham Bansal - * - * Definition of the Highway layer. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_LAYER_HIGHWAY_HPP -#define MLPACK_METHODS_ANN_LAYER_HIGHWAY_HPP - -#include - -#include "layer.hpp" - -namespace mlpack { -namespace ann /** Artificial Neural Network. */ { - -/** - * Implementation of the Highway layer. The Highway class can vary its behavior - * between that of feed-forward fully connected network container and that - * of a layer which simply passes its inputs through depending on the transform - * gate. Note that the size of the input and output matrices of this class - * should be equal. - * - * For more information, refer the following paper. - * - * @code - * @article{Srivastava2015, - * author = {Rupesh Kumar Srivastava, Klaus Greff, Jurgen Schmidhuber}, - * title = {Training Very Deep Networks}, - * journal = {Advances in Neural Information Processing Systems}, - * year = {2015}, - * url = {https://arxiv.org/abs/1507.06228}, - * } - * @endcode - * - * @tparam InputType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - */ -template < - typename InputType = arma::mat, - typename OutputType = arma::mat -> -class HighwayType : public MultiLayer -{ - public: - //! Create the HighwayType object. - HighwayType(); - - //! Destroy the HighwayType object. - virtual ~HighwayType(); - - //! Clone the HighwayType object. This handles polymorphism correctly. - HighwayType* Clone() const { return new HighwayType(*this); } - - //! Copy the given HighwayType (but not weights). - HighwayType(const HighwayType& other); - //! Take ownership of the given HighwayType (but not weights). - HighwayType(HighwayType&& other); - //! Copy the given HighwayType (but not weights). - HighwayType& operator=(const HighwayType& other); - //! Take ownership of the given HighwayType (but not weights). - HighwayType& operator=(HighwayType&& other); - - void SetWeights(typename OutputType::elem_type* weightsPtr); - - /** - * Ordinary feed-forward pass of a neural network, evaluating the function - * f(x) by propagating the activity forward through f. - * - * @param input Input data used for evaluating the specified function. - * @param output Resulting output activation. - */ - void Forward(const InputType& input, OutputType& output); - - /** - * Ordinary feed-backward pass of a neural network, calculating the function - * f(x) by propagating x backwards through f. Using the results from the - * feed-forward pass. - * - * @param * (input) The propagated input activation. - * @param gy The backpropagated error. - * @param g The calculated gradient. - */ - void Backward(const InputType& /* input */, - const OutputType& gy, - OutputType& g); - - /** - * Calculate the gradient using the output delta and the input activation. - * - * @param input The input parameter used for calculating the gradient. - * @param error The calculated error. - * @param gradient The calculated gradient. - */ - void Gradient(const InputType& input, - const OutputType& error, - OutputType& gradient); - - //! Get the parameters. - OutputType const& Parameters() const { return weights; } - //! Modify the parameters. - OutputType& Parameters() { return weights; } - - //! Get the number of trainable weights. - size_t WeightSize() const - { - size_t result = this->totalInputSize * (this->totalInputSize + 1); - for (size_t i = 0; i < this->network.size(); ++i) - result += this->network[i]->WeightSize(); - return result; - } - - /** - * Serialize the layer. - */ - template - void serialize(Archive& ar, const uint32_t /* version */); - - private: - //! Locally-stored weight object. - OutputType weights; - - //! Weights for transformation of output. - OutputType transformWeight; - - //! Bias for transformation of output. - OutputType transformBias; - - //! Locally-stored transform gate parameters. - OutputType transformGate; - - //! Locally-stored transform gate activation. - OutputType transformGateActivation; - - //! Locally-stored transform gate error. - OutputType transformGateError; -}; // class HighwayType - -// Standard Highway layer. -typedef HighwayType Highway; - -} // namespace ann -} // namespace mlpack - -// Include implementation. -#include "highway_impl.hpp" - -#endif diff --git a/src/mlpack/methods/ann/layer/not_adapted/highway_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/highway_impl.hpp deleted file mode 100644 index 0b019a8486..0000000000 --- a/src/mlpack/methods/ann/layer/not_adapted/highway_impl.hpp +++ /dev/null @@ -1,194 +0,0 @@ -/** - * @file methods/ann/layer/highway_impl.hpp - * @author Konstantin Sidorov - * @author Saksham Bansal - * - * Implementation of Highway layer. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_LAYER_HIGHWAY_IMPL_HPP -#define MLPACK_METHODS_ANN_LAYER_HIGHWAY_IMPL_HPP - -// In case it hasn't yet been included. -#include "highway.hpp" - -namespace mlpack { -namespace ann /** Artificial Neural Network. */ { - -template -HighwayType::HighwayType() -{ - // Nothing to do here. - // TODO: how do we add the child layers ?? (read paper ...) -} - -template -HighwayType::~HighwayType() -{ - // Nothing to do. -} - -template -HighwayType::HighwayType(const HighwayType& other) : - MultiLayer(other) -{ - // Nothing to do. -} - -template -HighwayType::HighwayType(HighwayType&& other) : - MultiLayer(std::move(other)) -{ - // Nothing to do. -} - -template -HighwayType& -HighwayType::operator=(const HighwayType& other) -{ - if (&other == this) - { - MultiLayer::operator=(other); - } - - return *this; -} - -template -HighwayType& -HighwayType::operator=(HighwayType&& other) -{ - if (&other == this) - { - MultiLayer::operator=(std::move(other)); - } - - return *this; -} - -template -void HighwayType::SetWeights( - typename OutputType::elem_type* weightsPtr) -{ - transformWeight = OutputType(weightsPtr, this->inSize, - this->inSize, false, false); - transformBias = OutputType(weightsPtr + transformWeight.n_elem, - this->inSize, 1, false, false); - - size_t start = transformWeight.n_elem + transformBias.n_elem; - for (size_t i = 0; i < this->network.size(); ++i) - { - this->network[i]->SetWeights(weightsPtr + start); - start += this->network[i]->WeightSize(); - } -} - -template -void HighwayType::Forward( - const InputType& input, OutputType& output) -{ - this->InitializeForwardPassMemory(input.n_cols); - - this->network.front()->Forward(input, this->layerOutputs.front()); - - for (size_t i = 1; i < this->network.size(); ++i) - { - this->network[i]->Forward(this->layerOutputs[i - 1], this->layerOutputs[i]); - } - - output = this->layerOutputs.back(); // TODO: can this be cleaned up? - - // TODO: move to ComputeOutputDimensions() - if (arma::size(output) != arma::size(input)) - { - Log::Fatal << "The sizes of the output and input matrices of the Highway" - << " network should be equal. Please examine the network layers."; - } - - transformGate = transformWeight * input; - transformGate.each_col() += transformBias; - transformGateActivation = 1.0 /(1 + arma::exp(-transformGate)); - output = (this->layerOutputs.back() % transformGateActivation) + - (input % (1 - transformGateActivation)); -} - -template -void HighwayType::Backward( - const InputType& input, - const OutputType& gy, - OutputType& g) -{ - this->InitializeBackwardPassMemory(input.n_cols); - - OutputType gyTransform = gy % transformGateActivation; - this->network.back()->Backward(this->layerOutputs.back(), gyTransform, - this->layerDeltas.back()); - - for (size_t i = 2; i < this->network.size() + 1; ++i) - { - this->network[this->network.size() - i]->Backward( - this->layerOutputs[this->network.size() - i], - this->layerDeltas[this->network.size() - i + 1], - this->layerDeltas[this->network.size() - i]); - } - - transformGateError = gy % (gy - input) % - transformGateActivation % (1.0 - transformGateActivation); - g = this->layerDeltas.front() + (transformWeight.t() * transformGateError) + - (gy % (1 - transformGateActivation)); -} - -template -void HighwayType::Gradient( - const InputType& input, - const OutputType& error, - OutputType& gradient) -{ - // Create an alias for the gradient that only refers to the elements in the - // network itself. - OutputType layerGradient(gradient.memptr() + (this->inSize * - (this->inSize + 1)), 1, gradient.n_elem - (this->inSize * - (this->inSize + 1)), false, true); - this->InitializeGradientPassMemory(layerGradient); - - OutputType errorTransform = error % transformGateActivation; - this->network.back()->Gradient( - this->layerOutputs[this->network.size() - 2], - errorTransform, - this->layerGradients[this->network.size() - 1]); - - for (size_t i = 2; i < this->network.size(); ++i) - { - this->network[this->network.size() - i]->Gradient( - this->layerOutputs[this->network.size() - i - 1], - this->layerDeltas[this->network.size() - i], - this->layerGradients[this->network.size() - i]); - } - - this->network.front()->Gradient( - input, - this->layerDeltas[1], - this->layerGradients.front()); - - gradient.submat(0, 0, transformWeight.n_elem - 1, 0) = arma::vectorise( - transformGateError * input.t()); - gradient.submat(transformWeight.n_elem, 0, transformWeight.n_elem + - transformBias.n_elem - 1, 0) = arma::sum(transformGateError, 1); -} - -template -template -void HighwayType::serialize( - Archive& ar, const uint32_t /* version */) -{ - ar(cereal::base_class>(this)); -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/layer/not_adapted/mean_pooling_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/mean_pooling_impl.hpp deleted file mode 100644 index 845f264c72..0000000000 --- a/src/mlpack/methods/ann/layer/not_adapted/mean_pooling_impl.hpp +++ /dev/null @@ -1,108 +0,0 @@ -/** - * @file methods/ann/layer/mean_pooling_impl.hpp - * @author Marcus Edel - * @author Nilay Jain - * - * Implementation of the MeanPooling layer class. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_LAYER_MEAN_POOLING_IMPL_HPP -#define MLPACK_METHODS_ANN_LAYER_MEAN_POOLING_IMPL_HPP - -// In case it hasn't yet been included. -#include "mean_pooling.hpp" - -namespace mlpack { -namespace ann /** Artificial Neural Network. */ { - -template -MeanPoolingType::MeanPoolingType() -{ - // Nothing to do here. -} - -template -MeanPoolingType::MeanPoolingType( - const size_t kernelWidth, - const size_t kernelHeight, - const size_t strideWidth, - const size_t strideHeight, - const bool floor) : - kernelWidth(kernelWidth), - kernelHeight(kernelHeight), - strideWidth(strideWidth), - strideHeight(strideHeight), - floor(floor), - channels(0), - offset(0), - batchSize(0) -{ - // Nothing to do here. -} - -template -void MeanPoolingType::Forward( - const InputType& input, OutputType& output) -{ - batchSize = input.n_cols; - inputTemp = arma::Cube( - const_cast(input).memptr(), this->inputDimensions[0], - this->inputDimensions[1], batchSize * channels, false, false); - - arma::Cube outputTemp(output.memptr(), - outputDimensions[0], outputDimensions[1], batchSize * channels, false, - true); - - for (size_t s = 0; s < inputTemp.n_slices; s++) - Pooling(inputTemp.slice(s), outputTemp.slice(s)); -} - -template -void MeanPoolingType::Backward( - const InputType& /* input */, - const OutputType& gy, - OutputType& g) -{ - arma::Cube mappedError( - ((OutputType&) gy).memptr(), outputDimensions[0], outputDimensions[1], - batchSize * channels, false, true); - - arma::Cube gTemp(g.memptr(), - this->inputDimensions[0], this->inputDimensions[1], channels * batchSize, - false, true); - - for (size_t s = 0; s < mappedError.n_slices; s++) - { - Unpooling(inputTemp.slice(s), mappedError.slice(s), gTemp.slice(s)); - } -} - -template -template -void MeanPoolingType::serialize( - Archive& ar, - const uint32_t /* version */) -{ - ar(cereal::base_class>(this)); - - ar(CEREAL_NVP(kernelWidth)); - ar(CEREAL_NVP(kernelHeight)); - ar(CEREAL_NVP(strideWidth)); - ar(CEREAL_NVP(strideHeight)); - ar(CEREAL_NVP(batchSize)); - ar(CEREAL_NVP(floor)); - ar(CEREAL_NVP(outputDimensions)); - ar(CEREAL_NVP(offset)); - - if (Archive::is_loading::value) - inputTemp.clear(); -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/layer/not_adapted/minibatch_discrimination_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/minibatch_discrimination_impl.hpp deleted file mode 100644 index ea098a4c25..0000000000 --- a/src/mlpack/methods/ann/layer/not_adapted/minibatch_discrimination_impl.hpp +++ /dev/null @@ -1,138 +0,0 @@ -/** - * @file methods/ann/layer/minibatch_discrimination_impl.hpp - * @author Saksham Bansal - * - * Implementation of the MiniBatchDiscrimination layer class. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_LAYER_MINIBATCH_DISCRIMINATION_IMPL_HPP -#define MLPACK_METHODS_ANN_LAYER_MINIBATCH_DISCRIMINATION_IMPL_HPP - -// In case it hasn't yet been included. -#include "minibatch_discrimination.hpp" - -namespace mlpack { -namespace ann /** Artificial Neural Network. */ { - -template -MiniBatchDiscrimination::MiniBatchDiscrimination() : - A(0), - B(0), - C(0), - batchSize(0) -{ - // Nothing to do here. -} - -template -MiniBatchDiscrimination::MiniBatchDiscrimination( - const size_t outSize, - const size_t features) : - a(0), // This will be set when OutputDimensions() is called. - b(outSize - inSize), - c(features), - batchSize(0) -{ - // Nothing to do. -} - -template -void MiniBatchDiscrimination::SetWeights( - typename OutputType::elem_type* weightsPtr) -{ - weights = OutputType(weightsPtr, b * c, a, false, false); -} - -template -void MiniBatchDiscrimination::Forward( - const InputType& input, OutputType& output) -{ - batchSize = input.n_cols; - M = weight * input; - arma::Cube cubeM(M.memptr(), b, c, batchSize, - false, false); - distances.set_size(b, batchSize, batchSize); - - for (size_t i = 0; i < cubeM.n_slices; ++i) - { - output.col(i).subvec(0, a - 1) = input.col(i); - output.col(i).subvec(a, output.n_rows - 1).ones(); - for (size_t j = 0; j < cubeM.n_slices; ++j) - { - if (j < i) - { - output.col(i).subvec(a, output.n_rows - 1) += distances.slice(j).col(i); - } - else if (i == j) - { - continue; - } - else - { - distances.slice(i).col(j) = - arma::exp(-arma::sum(abs(cubeM.slice(i) - cubeM.slice(j)), 1)); - output.col(i) += distances.slice(i).col(j); - } - } - } -} - -template -void MiniBatchDiscrimination::Backward( - const InputType& /* input */, const OutputType& gy, OutputType& g) -{ - g = gy.head_rows(a); - OutputType gM = gy.tail_rows(B); - deltaM.zeros(b, c, batchSize); - - for (size_t i = 0; i < M.n_slices; ++i) - { - for (size_t j = 0; j < M.n_slices; ++j) - { - if (i == j) - { - continue; - } - InputType t = arma::sign(M.slice(i) - M.slice(j)); - t.each_col() %= - distances.slice(std::min(i, j)).col(std::max(i, j)) % gM.col(i); - deltaM.slice(i) -= t; - deltaM.slice(j) += t; - } - } - - OutputType deltaTemp(deltaM.memptr(), b * c, batchSize, false, true); - g += weight.t() * deltaTemp; -} - -template -void MiniBatchDiscrimination::Gradient( - const InputType& input, - const OutputType& /* error */, - OutputType& gradient) -{ - gradient = arma::vectorise(deltaTemp * input.t()); -} - -template -template -void MiniBatchDiscrimination::serialize( - Archive& ar, const uint32_t /* version */) -{ - ar(cereal::base_class>(this)); - - ar(CEREAL_NVP(a)); - ar(CEREAL_NVP(b)); - ar(CEREAL_NVP(c)); -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/layer/not_adapted/multiply_constant_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/multiply_constant_impl.hpp deleted file mode 100644 index 61ae392dea..0000000000 --- a/src/mlpack/methods/ann/layer/not_adapted/multiply_constant_impl.hpp +++ /dev/null @@ -1,56 +0,0 @@ -/** - * @file methods/ann/layer/multiply_constant_impl.hpp - * @author Marcus Edel - * - * Implementation of the MultiplyConstantLayer class, which multiplies the - * input by a (non-learnable) constant. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_LAYER_MULTIPLY_CONSTANT_IMPL_HPP -#define MLPACK_METHODS_ANN_LAYER_MULTIPLY_CONSTANT_IMPL_HPP - -// In case it hasn't yet been included. -#include "multiply_constant.hpp" - -namespace mlpack { -namespace ann /** Artificial Neural Network. */ { - -template -MultiplyConstantType::MultiplyConstantType( - const double scalar) : scalar(scalar) -{ - // Nothing to do here. -} - -template -void MultiplyConstantType::Forward( - const InputType& input, OutputType& output) -{ - output = input * scalar; -} - -template -void MultiplyConstantType::Backward( - const InputType& /* input */, const OutputType& gy, OutputType& g) -{ - g = gy * scalar; -} - -template -template -void MultiplyConstantType::serialize( - Archive& ar, const uint32_t /* version */) -{ - ar(cereal::base_class>(this)); - - ar(CEREAL_NVP(scalar)); -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/layer/not_adapted/multiply_merge_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/multiply_merge_impl.hpp deleted file mode 100644 index 596f61b445..0000000000 --- a/src/mlpack/methods/ann/layer/not_adapted/multiply_merge_impl.hpp +++ /dev/null @@ -1,116 +0,0 @@ -/** - * @file methods/ann/layer/multiply_merge_impl.hpp - * @author Haritha Nair - * - * Definition of the MultiplyMerge module which multiplies the output of the - * given modules element-wise. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_LAYER_MULTIPLY_MERGE_IMPL_HPP -#define MLPACK_METHODS_ANN_LAYER_MULTIPLY_MERGE_IMPL_HPP - -// In case it hasn't yet been included. -#include "multiply_merge.hpp" - -// #include "../visitor/forward_visitor.hpp" -// #include "../visitor/backward_visitor.hpp" -// #include "../visitor/gradient_visitor.hpp" - -namespace mlpack { -namespace ann /** Artificial Neural Network. */ { - -template -MultiplyMergeType::MultiplyMergeType( - const bool run) : - run(run) -{ - // Nothing to do here. -} - -// TODO: is this destructor needed? -template -MultiplyMergeType::~MultiplyMergeType() -{ - for (size_t i = 0; i < network.size(); ++i) - delete network[i]; -} - -template -void MultiplyMergeType::Forward( - const InputType& input, OutputType& output) -{ - InitializeForwardPassMemory(); - - if (run) - { - for (size_t i = 0; i < network.size(); ++i) - { - network[i]->Forward(input, layerOutputs[i]); - } - } - - output = layerOutputs.front(); - for (size_t i = 1; i < network.size(); ++i) - { - output %= layerOutputs[i]; - } -} - -template -void MultiplyMergeType::Backward( - const InputType& /* input */, const OutputType& gy, OutputType& g) -{ - InitializeBackwardPassMemory(); - - if (run) - { - for (size_t i = 0; i < network.size(); ++i) - { - network[i]->Backward(layerOutputs[i], gy, layerDeltas[i]); - } - - g = layerDeltas.front(); - for (size_t i = 1; i < network.size(); ++i) - { - g += layerDeltas[i]; - } - } - else - { - g = gy; - } -} - -template -void MultiplyMergeType::Gradient( - const InputType& input, - const OutputType& error, - OutputType& /* gradient */ ) -{ - if (run) - { - for (size_t i = 0; i < network.size(); ++i) - { - network[i]->Gradient(input, error, layerGradients[i]); - } - } -} - -template -template -void MultiplyMergeType::serialize( - Archive& ar, const uint32_t /* version */) -{ - ar(cereal::base_class>(this)); - - ar(CEREAL_NVP(run)); -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/layer/not_adapted/recurrent_attention_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/recurrent_attention_impl.hpp deleted file mode 100644 index 62d19bec87..0000000000 --- a/src/mlpack/methods/ann/layer/not_adapted/recurrent_attention_impl.hpp +++ /dev/null @@ -1,237 +0,0 @@ -/** - * @file methods/ann/layer/recurrent_attention_impl.hpp - * @author Marcus Edel - * - * Implementation of the RecurrentAttention class. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_LAYER_RECURRENT_ATTENTION_IMPL_HPP -#define MLPACK_METHODS_ANN_LAYER_RECURRENT_ATTENTION_IMPL_HPP - -// In case it hasn't yet been included. -#include "recurrent_attention.hpp" - -namespace mlpack { -namespace ann /** Artificial Neural Network. */ { - -template -RecurrentAttention::RecurrentAttention() : - outSize(0), - rho(0), - forwardStep(0), - backwardStep(0), - deterministic(false) -{ - // Nothing to do. -} - -template -template -RecurrentAttention::RecurrentAttention( - const size_t outSize, - const RNNModuleType& rnn, - const ActionModuleType& action, - const size_t rho) : - outSize(outSize), - rnnModule(new RNNModuleType(rnn)), - actionModule(new ActionModuleType(action)), - rho(rho), - forwardStep(0), - backwardStep(0), - deterministic(false) -{ - network.push_back(rnnModule); - network.push_back(actionModule); -} - -template -void RecurrentAttention::Forward( - const InputType& input, OutputType& output) -{ - InitializeForwardPassMemory(); - - // Convenience naming. - OutputType& rnnOutput = layerOutputs.front(); - OutputType& actionOutput = layerOutputs.back(); - - // Initialize the action input. - if (initialInput.is_empty()) - { - initialInput = arma::zeros(outSize, input.n_cols); - } - - // Propagate through the action and recurrent module. - for (forwardStep = 0; forwardStep < rho; ++forwardStep) - { - if (forwardStep == 0) - { - actionModule->Forward(initialInput, actionOutput); - } - else - { - actionModule->Forward(rnnOutput, actionOutput); - } - - // Initialize the glimpse input. - InputType glimpseInput = arma::zeros(input.n_elem, 2); - glimpseInput.col(0) = input; - glimpseInput.submat(0, 1, actionOutput.n_elem - 1, 1) = - actionOutput; - - rnnModule->Forward(glimpseInput, rnnOutput); - - // Save the output parameter when training the module. - if (!deterministic) - { - for (size_t l = 0; l < network.size(); ++l) - { - // TODO: what if network[i] has a Model()? - // TODO: what does this actually do? do we need it? - moduleOutputParameter.push_back(network[l]->OutputParameter()); - } - } - } - - output = rnnOutput; - - forwardStep = 0; - backwardStep = 0; -} - -template -void RecurrentAttention::Backward( - const InputType& /* input */, - const OutputType& gy, - OutputType& g) -{ - InitializeBackwardPassMemory(); - - // Convenience names. - OutputType& rnnOutput = layerOutputs.front(); - OutputType& actionOutput = layerOutputs.back(); - OutputType& rnnGradient = layerGradients.front(); - OutputType& actionGradient = layerGradients.back(); - - if (intermediateGradient.is_empty() && backwardStep == 0) - { - // Initialize the attention gradients. - // TODO: do rnnModule or actionModule have a Model()? We may need to - // account for those weights too. - size_t weights = rnnModule->Parameters().n_elem + - actionModule->Parameters().n_elem; - - intermediateGradient = arma::zeros(weights, 1); - attentionGradient = arma::zeros(weights, 1); - - // Initialize the action error. - actionError = arma::zeros(actionOutput.n_rows, actionOutput.n_cols); - } - - // Propagate the attention gradients. - if (backwardStep == 0) - { - size_t offset = 0; - // TODO: what if rnnModule has a Model()? - rnnGradient = OutputType(intermediateGradient.memptr() + offset, - rnnModule->Parameters().n_rows, rnnModule->Parameters().n_cols, false, - false); - offset += rnnModule->Parameters().n_elem; - actionGradient = OutputType(intermediateGradient.memptr() + offset, - actionModule->Parameters().n_rows, actionModule->Parameters().n_cols, - false, false); - - attentionGradient.zeros(); - } - - // Back-propagate through time. - for (; backwardStep < rho; backwardStep++) - { - if (backwardStep == 0) - { - recurrentError = gy; - } - else - { - recurrentError = actionDelta; - } - - for (size_t l = 0; l < network.size(); ++l) - { - // TODO: handle case where HasModelCheck is true - network[network.size() - 1 - l] = moduleOutputParameter.back(); - moduleOutputParameter.pop_back(); - } - - if (backwardStep == (rho - 1)) - { - actionModule->Backward(actionOutput, actionError, actionDelta); - } - else - { - actionModule->Backward(initialInput, actionError, actionDelta); - } - - rnnModule->Backward(rnnOutput, recurrentError, rnnDelta); - - if (backwardStep == 0) - { - g = rnnDelta.col(1); - } - else - { - g += rnnDelta.col(1); - } - - IntermediateGradient(); - } -} - -template -void RecurrentAttention::Gradient( - const InputType& /* input */, - const OutputType& /* error */, - OutputType& /* gradient */) -{ - // Convenience naming. - OutputType& rnnGradient = layerGradients.front(); - OutputType& actionGradient = layerGradients.back(); - - size_t offset = 0; - // TODO: handle case where rnnModule or actionModule have a model - if (rnnModule->Parameters().n_elem != 0) - { - rnnGradient = attentionGradient.submat(offset, 0, offset + - rnnModule->Parameters().n_elem - 1, 0); - offset += rnnModule->Parameters().n_elem; - } - - if (actionModule->Parameters().n_elem != 0) - { - actionGradient = attentionGradient.submat(offset, 0, offset + - actionModule->Parameters().n_elem - 1, 0); - } -} - -template -template -void RecurrentAttention::serialize( - Archive& ar, const uint32_t /* version */) -{ - ar(cereal::base_class>(this)); - - ar(CEREAL_NVP(rho)); - ar(CEREAL_NVP(outSize)); - ar(CEREAL_NVP(forwardStep)); - ar(CEREAL_NVP(backwardStep)); - - // TODO: lots of clearing? -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/layer/not_adapted/recurrent_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/recurrent_impl.hpp deleted file mode 100644 index b99972c9b8..0000000000 --- a/src/mlpack/methods/ann/layer/not_adapted/recurrent_impl.hpp +++ /dev/null @@ -1,284 +0,0 @@ -/** - * @file methods/ann/layer/recurrent_impl.hpp - * @author Marcus Edel - * - * Implementation of the Recurrent class. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_LAYER_RECURRENT_IMPL_HPP -#define MLPACK_METHODS_ANN_LAYER_RECURRENT_IMPL_HPP - -// In case it hasn't yet been included. -#include "recurrent.hpp" - -namespace mlpack { -namespace ann /** Artificial Neural Network. */ { - -template -Recurrent::Recurrent() : - rho(0), - forwardStep(0), - backwardStep(0), - gradientStep(0) -{ - // Nothing to do. -} - -template -template< - typename StartModuleType, - typename InputModuleType, - typename FeedbackModuleType, - typename TransferModuleType -> -Recurrent::Recurrent( - const StartModuleType& start, - const InputModuleType& input, - const FeedbackModuleType& feedback, - const TransferModuleType& transfer, - const size_t rho) : - startModule(new StartModuleType(start)), - inputModule(new InputModuleType(input)), - feedbackModule(new FeedbackModuleType(feedback)), - transferModule(new TransferModuleType(transfer)), - rho(rho), - forwardStep(0), - backwardStep(0), - gradientStep(0) -{ - initialModule = new SequentialType(); - mergeModule = new AddMerge(false, false, false); - recurrentModule = new SequentialType(false, false); - - initialModule->Add(inputModule); - initialModule->Add(startModule); - initialModule->Add(transferModule); - - mergeModule->Add(inputModule); - mergeModule->Add(feedbackModule); - - recurrentModule->Add(mergeModule); - recurrentModule->Add(transferModule); - - network.push_back(initialModule); - network.push_back(mergeModule); - network.push_back(feedbackModule); - network.push_back(recurrentModule); -} - -template -Recurrent::Recurrent( - const Recurrent& network) : - rho(network.rho), - forwardStep(network.forwardStep), - backwardStep(network.backwardStep), - gradientStep(network.gradientStep) -{ - startModule = network.startModule->Clone(); - inputModule = network.inputModule->Clone(); - feedbackModule = network.feedbackModule->Clone(); - transferModule = network.transferModule->Clone(); - - initialModule = new SequentialType(); - mergeModule = new AddMerge(false, false, false); - recurrentModule = new SequentialType(false, false); - - initialModule->Add(inputModule); - initialModule->Add(startModule); - initialModule->Add(transferModule); - - mergeModule->Add(inputModule); - mergeModule->Add(feedbackModule); - - recurrentModule->Add(mergeModule); - recurrentModule->Add(transferModule); - - this->network.push_back(initialModule); - this->network.push_back(mergeModule); - this->network.push_back(feedbackModule); - this->network.push_back(recurrentModule); -} - -template -void Recurrent::Forward( - const InputType& input, OutputType& output) -{ - InitializeForwardPassMemory(); - - // Convenience names. - OutputType& inputOutput = layerOutputs[0]; - OutputType& mergeOutput = layerOutputs[1]; - OutputType& feedbackOutput = layerOutputs[2]; - OutputType& recurrentOutput = layerOutputs[3]; - - if (forwardStep == 0) - { - initialModule->Forward(input, output); - } - else - { - inputModule->Forward(input, inputOutput); - // TODO: how to get transferModule output? - feedbackModule->Forward(transferModule->OutputParameter(), - feedbackOutput); - recurrentModule->Forward(input, output); - } - - // TODO: how to get transferModule output? - output = transferModule->OutputParameter(); - - // Save the feedback output parameter when training the module. - if (this->training) - { - feedbackOutputParameter.push_back(output); - } - - forwardStep++; - if (forwardStep == rho) - { - forwardStep = 0; - backwardStep = 0; - - if (!recurrentError.is_empty()) - { - recurrentError.zeros(); - } - } -} - -template -void Recurrent::Backward( - const InputType& /* input */, const OutputType& gy, OutputType& g) -{ - InitializeBackwardPassMemory(); - - // Convenience names. - OutputType& inputOutput = layerOutputs[0]; - OutputType& mergeOutput = layerOutputs[1]; - OutputType& feedbackOutput = layerOutputs[2]; - OutputType& recurrentOutput = layerOutputs[3]; - OutputType& inputDelta = layerDeltas[0]; - OutputType& mergeDelta = layerDeltas[1]; - OutputType& feedbackDelta = layerDeltas[2]; - OutputType& recurrentDelta = layerDeltas[3]; - - if (!recurrentError.is_empty()) - { - recurrentError += gy; - } - else - { - recurrentError = gy; - } - - if (backwardStep < (rho - 1)) - { - recurrentModule->Backward(recurrentOutput, recurrentError, recurrentDelta); - inputModule->Backward(inputOutput, recurrentDelta, g); - feedbackModule->Backward(feedbackOutput, recurrentDelta, feedbackDelta); - } - else - { - // TODO: how to get these parameters? - initialModule->Backward(initialModule->OutputParameter(), recurrentError, - g); - } - - recurrentError = feedbackDelta; - backwardStep++; -} - -template -void Recurrent::Gradient( - const InputType& input, - const OutputType& error, - OutputType& /* gradient */) -{ - // Convenience names. - OutputType& inputOutput = layerOutputs[0]; - OutputType& mergeOutput = layerOutputs[1]; - OutputType& feedbackOutput = layerOutputs[2]; - OutputType& recurrentOutput = layerOutputs[3]; - OutputType& inputDelta = layerDeltas[0]; - OutputType& mergeDelta = layerDeltas[1]; - OutputType& feedbackDelta = layerDeltas[2]; - OutputType& recurrentDelta = layerDeltas[3]; - OutputType& inputGradient = layerGradients[0]; - OutputType& mergeGradient = layerGradients[1]; - OutputType& feedbackGradient = layerGradients[2]; - OutputType& recurrentGradient = layerGradients[3]; - - if (gradientStep < (rho - 1)) - { - recurrentModule->Gradient(input, error, recurrentGradient); - inputModule->Gradient(input, mergeDelta, inputGradient); - feedbackModule->Gradient( - feedbackOutputParameter[feedbackOutputParameter.size() - 2 - - gradientStep], mergeDelta, feedbackGradient); - } - else - { - recurrentGradient.zeros(); - inputGradient.zeros(); - feedbackGradient.zeros(); - - // TODO: how to do this? - initialModule->Gradient(input, startModule->Delta(), - initialModule->Gradient()); - } - - gradientStep++; - if (gradientStep == rho) - { - gradientStep = 0; - feedbackOutputParameter.clear(); - } -} - -template -template -void Recurrent::serialize( - Archive& ar, const uint32_t /* version */) -{ - ar(cereal::base_class>(this)); - - // TODO: overhaul this - - ar(CEREAL_POINTER(startModule)); - ar(CEREAL_POINTER(inputModule)); - ar(CEREAL_POINTER(feedbackModule)); - ar(CEREAL_POINTER(transferModule)); - ar(CEREAL_NVP(rho)); - - // Set up the network. - if (cereal::is_loading()) - { - initialModule = new SequentialType(); - mergeModule = new AddMerge(false, false, false); - recurrentModule = new SequentialType(false, false); - - initialModule->Add(inputModule); - initialModule->Add(startModule); - initialModule->Add(transferModule); - - mergeModule->Add(inputModule); - mergeModule->Add(feedbackModule); - - recurrentModule->Add(mergeModule); - recurrentModule->Add(transferModule); - - network.push_back(initialModule); - network.push_back(mergeModule); - network.push_back(feedbackModule); - network.push_back(recurrentModule); - } -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/layer/not_adapted/reparametrization_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/reparametrization_impl.hpp deleted file mode 100644 index 72e9093242..0000000000 --- a/src/mlpack/methods/ann/layer/not_adapted/reparametrization_impl.hpp +++ /dev/null @@ -1,152 +0,0 @@ -/** - * @file methods/ann/layer/reparametrization_impl.hpp - * @author Atharva Khandait - * - * Implementation of the Reparametrization layer class which samples from a - * gaussian distribution. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_LAYER_REPARAMETRIZATION_IMPL_HPP -#define MLPACK_METHODS_ANN_LAYER_REPARAMETRIZATION_IMPL_HPP - -// In case it hasn't yet been included. -#include "reparametrization.hpp" - -namespace mlpack { -namespace ann /** Artificial Neural Network. */ { - -template -ReparametrizationType::ReparametrizationType( - const bool stochastic, - const bool includeKl, - const double beta) : - stochastic(stochastic), - includeKl(includeKl), - beta(beta) -{ - if (includeKl == false && beta != 1) - { - Log::Info << "The beta parameter will be ignored as KL divergence is not " - << "included." << std::endl; - } -} - -template -ReparametrizationType::ReparametrizationType( - const ReparametrizationType& layer) : - Layer(layer), - stochastic(layer.stochastic), - includeKl(layer.includeKl), - beta(layer.beta) -{ - // Nothing to do here. -} - -template -ReparametrizationType::ReparametrizationType( - ReparametrizationType&& layer) : - Layer(std::move(layer)), - stochastic(std::move(layer.stochastic)), - includeKl(std::move(layer.includeKl)), - beta(std::move(layer.beta)) -{ - // Nothing to do here. -} - -template -ReparametrizationType& -ReparametrizationType:: -operator=(const ReparametrizationType& layer) -{ - if (this != &layer) - { - Layer::operator=(layer); - stochastic = layer.stochastic; - includeKl = layer.includeKl; - beta = layer.beta; - } - - return *this; -} - -template -ReparametrizationType& -ReparametrizationType:: -operator=(ReparametrizationType&& layer) -{ - if (this != &layer) - { - Layer::operator=(std::move(layer)); - stochastic = std::move(layer.stochastic); - includeKl = std::move(layer.includeKl); - beta = std::move(layer.beta); - } - - return *this; -} - -template -void ReparametrizationType::Forward( - const InputType& input, OutputType& output) -{ - const size_t latentSize = this->outputDimensions[0]; - mean = input.submat(latentSize, 0, 2 * latentSize - 1, input.n_cols - 1); - preStdDev = input.submat(0, 0, latentSize - 1, input.n_cols - 1); - - if (stochastic) - gaussianSample = arma::randn(latentSize, input.n_cols); - else - gaussianSample = arma::ones(latentSize, input.n_cols) * 0.7; - - SoftplusFunction::Fn(preStdDev, stdDev); - output = mean + stdDev % gaussianSample; -} - -template -void ReparametrizationType::Backward( - const InputType& /* input */, const OutputType& gy, OutputType& g) -{ - OutputType tmp; - SoftplusFunction::Deriv(preStdDev, tmp); - - if (includeKl) - { - g = join_cols(gy % std::move(gaussianSample) % tmp + (-1 / stdDev + stdDev) - % tmp * beta, gy + mean * beta / mean.n_cols); - } - else - { - g = join_cols(gy % std::move(gaussianSample) % tmp, gy); - } -} - -template -double ReparametrizationType::Loss() -{ - if (!includeKl) - return 0; - - return -0.5 * beta * arma::accu(2 * arma::log(stdDev) - arma::pow(stdDev, 2) - - arma::pow(mean, 2) + 1) / mean.n_cols; -} - -template -template -void ReparametrizationType::serialize( - Archive& ar, const uint32_t /* version */) -{ - ar(cereal::base_class>(this)); - - ar(CEREAL_NVP(stochastic)); - ar(CEREAL_NVP(includeKl)); - ar(CEREAL_NVP(beta)); -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/layer/not_adapted/sequential.hpp b/src/mlpack/methods/ann/layer/not_adapted/sequential.hpp deleted file mode 100644 index 471d5a1f35..0000000000 --- a/src/mlpack/methods/ann/layer/not_adapted/sequential.hpp +++ /dev/null @@ -1,150 +0,0 @@ -/** - * @file methods/ann/layer/sequential.hpp - * @author Marcus Edel - * - * Definition of the Sequential class, which acts as a feed-forward fully - * connected network container. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_LAYER_SEQUENTIAL_HPP -#define MLPACK_METHODS_ANN_LAYER_SEQUENTIAL_HPP - -#include - -#include "layer.hpp" - -namespace mlpack { -namespace ann /** Artificial Neural Network. */ { - -/** - * Implementation of the Sequential class. The sequential class works as a - * feed-forward fully connected network container which plugs various layers - * together. - * - * This class can also be used as a container for a residual block. In that - * case, the sizes of the input and output matrices of this class should be - * equal. A typedef has been added for use as a Residual<> class. - * - * For more information, refer the following paper. - * - * @code - * @article{He15, - * author = {Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun}, - * title = {Deep Residual Learning for Image Recognition}, - * year = {2015}, - * url = {https://arxiv.org/abs/1512.03385}, - * eprint = {1512.03385}, - * } - * @endcode - * - * Note: If this class is used as the first layer of a network, it should be - * preceded by IdentityLayer<>. - * - * Note: This class should at least have two layers for a call to its Gradient() - * function. - * - * @tparam InputType Type of the input data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam OutputType Type of the output data (arma::colvec, arma::mat, - * arma::sp_mat or arma::cube). - * @tparam Residual If true, use the object as a Residual block. - */ -template < - typename InputType = arma::mat, - typename OutputType = arma::mat, - bool Residual = false -> -class SequentialType : public MultiLayer -{ - public: - /** - * Create the Sequential object. - */ - SequentialType(); - - /** - * Create the Sequential object using the specified parameters. - * - * @param ownsLayers If true, then this module will delete its layers when - * deallocated. - */ - SequentialType(const bool ownsLayers); - - //! Copy constructor. - SequentialType(const SequentialType& layer); - - //! Copy assignment operator. - SequentialType& operator=(const SequentialType& layer); - - //! Destroy the Sequential object. - ~SequentialType(); - - //! Clone the SequentialType object. This handles polymorphism correctly. - SequentialType* Clone() const { return new SequentialType(*this); } - - /** - * Ordinary feed forward pass of a neural network, evaluating the function - * f(x) by propagating the activity forward through f. - * - * @param input Input data used for evaluating the specified function. - * @param output Resulting output activation. - */ - void Forward(const InputType& input, OutputType& output); - - /** - * Ordinary feed backward pass of a neural network, using 3rd-order tensors as - * input, calculating the function f(x) by propagating x backwards through f. - * Using the results from the feed forward pass. - * - * @param * (input) The propagated input activation. - * @param gy The backpropagated error. - * @param g The calculated gradient. - */ - void Backward(const InputType& /* input */, - const OutputType& gy, - OutputType& g); - - /** - * Calculate the gradient using the output delta and the input activation. - * - * @param input The input parameter used for calculating the gradient. - * @param error The calculated error. - * @param gradient The calculated gradient. - */ - void Gradient(const InputType& input, - const OutputType& error, - OutputType& /* gradient */); - - size_t InputShape() const; - - /** - * Serialize the layer - */ - template - void serialize(Archive& ar, const uint32_t /* version */); - - private: - //! Indicator if we already initialized the model. - bool reset; - - //! Whether we are responsible for deleting the layers held in this module. - bool ownsLayers; -}; // class SequentialType - -// Standard Sequential layer. -typedef SequentialType Sequential; - -// Standard Residual layer. -typedef SequentialType Residual; - -} // namespace ann -} // namespace mlpack - -// Include implementation. -#include "sequential_impl.hpp" - -#endif diff --git a/src/mlpack/methods/ann/layer/not_adapted/sequential_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/sequential_impl.hpp deleted file mode 100644 index 6cac9d869f..0000000000 --- a/src/mlpack/methods/ann/layer/not_adapted/sequential_impl.hpp +++ /dev/null @@ -1,163 +0,0 @@ -/** - * @file methods/ann/layer/sequential_impl.hpp - * @author Marcus Edel - * - * Implementation of the Sequential class, which acts as a feed-forward fully - * connected network container. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_LAYER_SEQUENTIAL_IMPL_HPP -#define MLPACK_METHODS_ANN_LAYER_SEQUENTIAL_IMPL_HPP - -// In case it hasn't yet been included. -#include "sequential.hpp" - -// TODO: can this be merged with MultiLayer more closely? - -namespace mlpack { -namespace ann /** Artificial Neural Network. */ { - -template -SequentialType:: -SequentialType() : - reset(false), ownsLayers(true) -{ - // Nothing to do here. -} - -template -SequentialType:: -SequentialType(const bool ownsLayers) : - reset(false), ownsLayers(ownsLayers) -{ - // Nothing to do here. -} - -template -SequentialType:: -SequentialType(const SequentialType& layer) : - reset(layer.reset), - ownsLayers(layer.ownsLayers) -{ - // Nothing to do here. -} - -template -SequentialType& -SequentialType:: -operator = (const SequentialType& layer) -{ - if (this != &layer) - { - reset = layer.reset; - ownsLayers = layer.ownsLayers; - network = layer.network; - // Call copy constructor of parent... - } - return *this; -} - - -template -SequentialType::~SequentialType() -{ - if (ownsLayers) - { - for (size_t i = 0; i < network.size(); ++i) - delete network[i]; - } -} - -template -void SequentialType:: -Forward(const InputType& input, OutputType& output) -{ - InitializeForwardPassMemory(); - - network.front()->Forward(input, layerOutputs.front()); - - for (size_t i = 1; i < network.size(); ++i) - { - network[i]->Forward(layerOutputs[i - 1], layerOutputs[i]); - } - - // TODO: optimization is possible here - output = layerOutputs.back(); - - if (Residual) - { - if (arma::size(output) != arma::size(input)) - { - Log::Fatal << "The sizes of the output and input matrices of the Residual" - << " block should be equal. Please examine the network architecture." - << std::endl; - } - output += input; - } -} - -template -void SequentialType::Backward( - const InputType& /* input */, - const OutputType& gy, - OutputType& g) -{ - InitializeBackwardPassMemory(); - - network.back()->Backward(layerOutputs.back(), gy, layerDeltas.back()); - - for (size_t i = 2; i < network.size() + 1; ++i) - { - network[network.size() - i]->Backward(layerOutputs[network.size() - i], - layerDeltas[network.size() - i + 1], layerDeltas[network.size() - i]); - } - - g = layerDeltas.front(); - - if (Residual) - { - g += gy; - } -} - -template -void SequentialType:: -Gradient(const InputType& input, - const OutputType& error, - OutputType& /* gradient */) -{ - InitializeGradientPassMemory(); - - network.back()->Gradient(layerOutputs[network.size() - 2], error, - layerGradients.back()); - - for (size_t i = 2; i < network.size(); ++i) - { - network[network.size() - i]->Gradient( - layerOutputs[network.size() - i - 1], - layerDeltas[network.size() - i + 1], - layerGradients[network.size() - i] - ); - } - - network.front()->Gradient(input, layerDeltas[1], layerGradients.front()); -} - -template -template -void SequentialType::serialize( - Archive& ar, const uint32_t /* version */) -{ - ar(cereal::base_class>(this)); - - ar(CEREAL_NVP(ownsLayers)); -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/layer/not_adapted/weight_norm_impl.hpp b/src/mlpack/methods/ann/layer/not_adapted/weight_norm_impl.hpp deleted file mode 100644 index 16bfbd8ce5..0000000000 --- a/src/mlpack/methods/ann/layer/not_adapted/weight_norm_impl.hpp +++ /dev/null @@ -1,202 +0,0 @@ -/** - * @file methods/ann/layer/weight_norm_impl.hpp - * @author Toshal Agrawal - * - * Implementation of the WeightNorm Layer. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ - -#ifndef MLPACK_METHODS_ANN_LAYER_WEIGHTNORM_IMPL_HPP -#define MLPACK_METHODS_ANN_LAYER_WEIGHTNORM_IMPL_HPP - -// In case it is not included. -#include "weight_norm.hpp" - -namespace mlpack { -namespace ann { /** Artificial Neural Network. */ - -template -WeightNormType:: -WeightNormType() : wrappedLayer(new LinearType()) -{ - layerWeightSize = wrappedLayer->WeightSize(); - weights.set_size(layerWeightSize + 1, 1); - - layerWeights.set_size(layerWeightSize, 1); - layerGradients.set_size(layerWeightSize, 1); -} - -template -WeightNormType:: -WeightNormType(Layer* layer) : wrappedLayer(layer) -{ - layerWeightSize = wrappedLayer->WeightSize(); - weights.set_size(layerWeightSize + 1, 1); - - layerWeights.set_size(layerWeightSize, 1); - layerGradients.set_size(layerWeightSize, 1); -} - -template -WeightNormType::WeightNormType( - const WeightNormType& other) : - wrappedLayer(other.wrappedLayer->Clone()), - layerWeightSize(other.layerWeightSize), - weights(other.weights), - layerGradients(other.layerGradients), - layerWeights(other.layerWeights) -{ - // Nothing else to do. -} - -template -WeightNormType::WeightNormType( - WeightNormType&& other) : - wrappedLayer(std::move(other.wrappedLayer)), - layerWeightSize(other.layerWeightSize), - weights(std::move(other.weights)), - layerGradients(std::move(other.layerGradients)), - layerWeights(std::move(other.layerWeights)) -{ - // Reset the other layer. - other = WeightNormType(); -} - -template -WeightNormType& -WeightNormType::operator=( - const WeightNormType& other) -{ - if (this != &other) - { - wrappedLayer = other.wrappedLayer->Clone(); - layerWeightSize = other.layerWeightSize; - weights = other.weights; - layerWeights = other.layerWeights; - layerGradients = other.layerGradients; - } - - return *this; -} - -template -WeightNormType& -WeightNormType::operator=( - WeightNormType&& other) -{ - if (this != &other) - { - wrappedLayer = std::move(other.wrappedLayer); - layerWeightSize = other.layerWeightSize; - weights = std::move(other.weights); - layerWeights = std::move(other.layerWeights); - layerGradients = std::move(other.layerGradients); - - // Reset the other layer. - other = WeightNormType(); - } - - return *this; -} - -template -WeightNormType::~WeightNormType() -{ - delete wrappedLayer; -} - -template -void WeightNormType::SetWeights( - typename OutputType::elem_type* weightsPtr) -{ - // Set the weights of the inside layer to layerWeights. - // This is done to set the non-bias terms correctly. - /* boost::apply_visitor(WeightSetVisitor(layerWeights, 0), wrappedLayer); */ - wrappedLayer->SetWeights(weightsPtr); - wrappedLayer->Parameters() = OutputType(layerWeights.memptr(), - wrappedLayer->Parameters().n_rows, wrappedLayer->Parameters().n_cols, - false, false); - - /* biasWeightSize = boost::apply_visitor(BiasSetVisitor(weights, 0), */ - /* wrappedLayer); */ - biasWeightSize = 0; - - vectorParameter = OutputType(weights.memptr() + biasWeightSize, - layerWeightSize - biasWeightSize, 1, false, false); - - scalarParameter = OutputType(weights.memptr() + layerWeightSize, 1, 1, false, - false); -} - -template -void WeightNormType::Forward( - const InputType& input, OutputType& output) -{ - // Initialize the non-bias weights of wrapped layer. - const double normVectorParameter = arma::norm(vectorParameter, 2); - layerWeights.rows(0, layerWeightSize - biasWeightSize - 1) = - scalarParameter(0) * vectorParameter / normVectorParameter; - - wrappedLayer->Forward(input, output); -} - -template -void WeightNormType::Backward( - const InputType& input, const OutputType& gy, OutputType& g) -{ - wrappedLayer->Backward(input, gy, g); -} - -// TODO: this part is not trivial... -template -void WeightNormType::Gradient( - const InputType& input, - const OutputType& error, - OutputType& gradient) -{ - ResetGradients(layerGradients); - - // Calculate the gradients of the wrapped layer. - wrappedLayer->Gradient(input, error, gradient); - - // Store the norm of vector parameter temporarily. - const double normVectorParameter = arma::norm(vectorParameter, 2); - - // Set the gradients of the bias terms. - if (biasWeightSize != 0) - { - gradient.rows(0, biasWeightSize - 1) = OutputType(layerGradients.memptr() + - layerWeightSize - biasWeightSize, biasWeightSize, 1, false, false); - } - - // Calculate the gradients of the scalar parameter. - gradient[gradient.n_rows - 1] = arma::accu(layerGradients.rows(0, - layerWeightSize - biasWeightSize - 1) % vectorParameter) / - normVectorParameter; - - // Calculate the gradients of the vector parameter. - gradient.rows(biasWeightSize, layerWeightSize - 1) = - scalarParameter(0) / normVectorParameter * (layerGradients.rows(0, - layerWeightSize - biasWeightSize - 1) - gradient[gradient.n_rows - 1] / - normVectorParameter * vectorParameter); -} - -template -template -void WeightNormType::serialize( - Archive& ar, const uint32_t /* version */) -{ - ar(cereal::base_class*>(this)); - - ar(CEREAL_POINTER(wrappedLayer)); - ar(CEREAL_NVP(layerWeightSize)); -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/layer/padding.hpp b/src/mlpack/methods/ann/layer/padding.hpp index 018a28479b..b7bfcab976 100644 --- a/src/mlpack/methods/ann/layer/padding.hpp +++ b/src/mlpack/methods/ann/layer/padding.hpp @@ -13,20 +13,25 @@ #define MLPACK_METHODS_ANN_LAYER_PADDING_HPP #include -#include "layer.hpp" +#include namespace mlpack { namespace ann /** Artificial Neural Network. */ { /** - * Implementation of the Padding module class. The Padding module applies - * (zero-valued) padding on the input data. + * Implementation of the Padding module class. The Padding module applies a bias term + * to the incoming data. * - * @tparam MatType Matrix representation to accept as input and use for - * computation. + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class PaddingType : public Layer +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class Padding { public: /** @@ -39,13 +44,12 @@ class PaddingType : public Layer * @param inputWidth Width of the input. * @param inputHeight Height of the input. */ - PaddingType(const size_t padWLeft = 0, - const size_t padWRight = 0, - const size_t padHTop = 0, - const size_t padHBottom = 0); - - //! Clone the PaddingType object. This handles polymorphism correctly. - PaddingType* Clone() const { return new PaddingType(*this); } + Padding(const size_t padWLeft = 0, + const size_t padWRight = 0, + const size_t padHTop = 0, + const size_t padHBottom = 0, + const size_t inputWidth = 0, + const size_t inputHeight = 0); /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -54,7 +58,8 @@ class PaddingType : public Layer * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - void Forward(const MatType& input, MatType& output); + template + void Forward(const arma::Mat& input, arma::Mat& output); /** * Ordinary feed backward pass of a neural network, calculating the function @@ -65,9 +70,20 @@ class PaddingType : public Layer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const MatType& /* input */, - const MatType& gy, - MatType& g); + template + void Backward(const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g); + + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } //! Get the left padding width. size_t PadWLeft() const { return padWLeft; } @@ -89,8 +105,25 @@ class PaddingType : public Layer //! Modify the bottom padding width. size_t& PadHBottom() { return padHBottom; } - //! Compute the output dimensions of the layer using `InputDimensions()`. - void ComputeOutputDimensions(); + //! Get the input width. + size_t InputWidth() const { return inputWidth; } + //! Modify the input width. + size_t& InputWidth() { return inputWidth; } + + //! Get the input height. + size_t InputHeight() const { return inputHeight; } + //! Modify the input height. + size_t& InputHeight() { return inputHeight; } + + //! Get the output width. + size_t OutputWidth() const { return outputWidth; } + //! Modify the output width. + size_t& OutputWidth() { return outputWidth; } + + //! Get the output height. + size_t OutputHeight() const { return outputHeight; } + //! Modify the output height. + size_t& OutputHeight() { return outputHeight; } /** * Serialize the layer. @@ -111,12 +144,36 @@ class PaddingType : public Layer //! Locally-stored bottom padding height. size_t padHBottom; - //! Cached number of input maps. - size_t totalInMaps; -}; // class PaddingType + //! Locally-stored number of rows and columns of input. + size_t nRows, nCols; -// Standard Padding layer. -typedef PaddingType Padding; + //! Locally-stored input height. + size_t inputHeight; + + //! Locally-stored input width. + size_t inputWidth; + + //! Locally-stored output height. + size_t outputHeight; + + //! Locally-stored output width. + size_t outputWidth; + + //! Locally-stored number of input channels. + size_t inSize; + + //! Locally-stored cube input parameter. + arma::cube inputTemp; + + //! Locally-stored output parameter. + arma::cube outputTemp; + + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; +}; // class Padding } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/padding_impl.hpp b/src/mlpack/methods/ann/layer/padding_impl.hpp index 2c2811209b..9a57f69458 100644 --- a/src/mlpack/methods/ann/layer/padding_impl.hpp +++ b/src/mlpack/methods/ann/layer/padding_impl.hpp @@ -19,121 +19,85 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -PaddingType::PaddingType( +template +Padding::Padding( const size_t padWLeft, const size_t padWRight, const size_t padHTop, - const size_t padHBottom) : + const size_t padHBottom, + const size_t inputWidth, + const size_t inputHeight) : padWLeft(padWLeft), padWRight(padWRight), padHTop(padHTop), padHBottom(padHBottom), - totalInMaps(0) + nRows(0), + nCols(0), + inputHeight(inputWidth), + inputWidth(inputHeight), + inSize(0) { // Nothing to do here. } -template -void PaddingType::Forward(const MatType& input, MatType& output) +template +template +void Padding::Forward( + const arma::Mat& input, arma::Mat& output) { - // Make an alias of the input and output so that we can deal with the first - // two dimensions directly. - arma::Cube reshapedInput( - (typename MatType::elem_type*) input.memptr(), - this->inputDimensions[0], this->inputDimensions[1], totalInMaps * - input.n_cols, false, true); - arma::Cube reshapedOutput(output.memptr(), - this->outputDimensions[0], this->outputDimensions[1], totalInMaps * - output.n_cols, false, true); + nRows = input.n_rows; + nCols = input.n_cols; - // Set the padding parts to 0. - if (padWLeft > 0) + if (inputWidth == 0 || inputHeight == 0) { - reshapedOutput.tube(0, - 0, - reshapedOutput.n_rows - 1, - padWLeft - 1).zeros(); + output = arma::zeros(nRows + padWLeft + padWRight, + nCols + padHTop + padHBottom); + output.submat(padWLeft, padHTop, padWLeft + nRows - 1, + padHTop + nCols - 1) = input; + } + else + { + inSize = input.n_elem / (inputWidth * inputHeight * nCols); + inputTemp = arma::Cube(const_cast&>(input).memptr(), + inputWidth, inputHeight, inSize * nCols, false, false); + outputTemp = arma::zeros>(inputWidth + padWLeft + padWRight, + inputHeight + padHTop + padHBottom, inSize * nCols); + for (size_t i = 0; i < inputTemp.n_slices; ++i) + { + outputTemp.slice(i).submat(padWLeft, padHTop, padWLeft + inputWidth - 1, + padHTop + inputHeight - 1) = inputTemp.slice(i); + } + + output = arma::Mat(outputTemp.memptr(), outputTemp.n_elem / nCols, + nCols); } - if (padHTop > 0) - { - reshapedOutput.tube(0, - padWLeft, - padHTop - 1, - padWLeft + this->inputDimensions[1] - 1).zeros(); - } - - if (padWRight > 0) - { - reshapedOutput.tube(0, - padWLeft + this->inputDimensions[1], - reshapedOutput.n_rows - 1, - reshapedOutput.n_cols - 1).zeros(); - } - - if (padHBottom > 0) - { - reshapedOutput.tube(padHTop + this->inputDimensions[0], - padWLeft, - reshapedOutput.n_rows - 1, - padWLeft + this->inputDimensions[1] - 1).zeros(); - } - - // Copy the input matrix. - reshapedOutput.tube(padHTop, - padWLeft, - padHTop + this->inputDimensions[0] - 1, - padWLeft + this->inputDimensions[1] - 1) = reshapedInput; + outputWidth = inputWidth + padWLeft + padWRight; + outputHeight = inputHeight + padHTop + padHBottom; } -template -void PaddingType::Backward( - const MatType& /* input */, - const MatType& gy, - MatType& g) +template +template +void Padding::Backward( + const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g) { - // Reshape g and gy so that extracting the un-padded input is easier to - // understand. - arma::Cube reshapedGy( - (typename MatType::elem_type*) gy.memptr(), this->outputDimensions[0], - this->outputDimensions[1], totalInMaps * gy.n_cols, false, true); - arma::Cube reshapedG(g.memptr(), - this->inputDimensions[0], this->inputDimensions[1], totalInMaps * - g.n_cols, false, true); - - reshapedG = reshapedGy.tube(padHTop, - padWLeft, - padHTop + this->inputDimensions[0] - 1, - padWLeft + this->inputDimensions[1] - 1); + g = gy.submat(padWLeft, padHTop, padWLeft + nRows - 1, + padHTop + nCols - 1); } -template -void PaddingType::ComputeOutputDimensions() -{ - this->outputDimensions = this->inputDimensions; - - this->outputDimensions[0] += padHTop + padHBottom; - this->outputDimensions[1] += padWLeft + padWRight; - - // Higher dimensions remain unchanged. But, we will cache the product of - // these higher dimensions. - totalInMaps = 1; - for (size_t i = 2; i < this->inputDimensions.size(); ++i) - totalInMaps *= this->inputDimensions[i]; -} - -template +template template -void PaddingType::serialize(Archive& ar, const uint32_t /* version */) +void Padding::serialize( + Archive& ar, const uint32_t /* version */) { - ar(cereal::base_class>(this)); - ar(CEREAL_NVP(padWLeft)); ar(CEREAL_NVP(padWRight)); ar(CEREAL_NVP(padHTop)); ar(CEREAL_NVP(padHBottom)); - ar(CEREAL_NVP(totalInMaps)); + ar(CEREAL_NVP(inputWidth)); + ar(CEREAL_NVP(inputHeight)); } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/not_adapted/parametric_relu.hpp b/src/mlpack/methods/ann/layer/parametric_relu.hpp similarity index 64% rename from src/mlpack/methods/ann/layer/not_adapted/parametric_relu.hpp rename to src/mlpack/methods/ann/layer/parametric_relu.hpp index 5fc3ff83a3..f40be33b2a 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/parametric_relu.hpp +++ b/src/mlpack/methods/ann/layer/parametric_relu.hpp @@ -17,8 +17,6 @@ #include -#include "layer.hpp" - namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -35,14 +33,16 @@ namespace ann /** Artificial Neural Network. */ { * \right. * @f} * - * @tparam InputType The type of the layer's inputs. The layer automatically - * cast inputs to this type (Default: arma::mat). - * @tparam OutputType The type of the computation which also causes the output - * to also be in this type. The type also allows the computation and weight - * type to differ from the input type (Default: arma::mat). + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class PReLUType : public Layer +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class PReLU { public: /** @@ -53,13 +53,12 @@ class PReLUType : public Layer * * @param userAlpha Non zero gradient */ - PReLUType(const double userAlpha = 0.03); + PReLU(const double userAlpha = 0.03); - //! Clone the PReLUType object. This handles polymorphism correctly. - PReLUType* Clone() const { return new PReLUType(*this); } - - //! Reset the layer parameter. - void SetWeights(typename OutputType::elem_type* weightsPtr); + /* + * Reset the layer parameter. + */ + void Reset(); /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -68,6 +67,7 @@ class PReLUType : public Layer * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ + template void Forward(const InputType& input, OutputType& output); /** @@ -79,7 +79,8 @@ class PReLUType : public Layer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const InputType& input, const OutputType& gy, OutputType& g); + template + void Backward(const DataType& input, const DataType& gy, DataType& g); /** * Calculate the gradient using the output delta and the input activation. @@ -88,14 +89,30 @@ class PReLUType : public Layer * @param error The calculated error. * @param gradient The calculated gradient. */ - void Gradient(const InputType& input, - const OutputType& error, - OutputType& gradient); + template + void Gradient(const arma::Mat& input, + const arma::Mat& error, + arma::Mat& gradient); //! Get the parameters. - OutputType const& Parameters() const { return alpha; } + OutputDataType const& Parameters() const { return alpha; } //! Modify the parameters. - OutputType& Parameters() { return alpha; } + OutputDataType& Parameters() { return alpha; } + + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } + + //! Get the gradient. + OutputDataType const& Gradient() const { return gradient; } + //! Modify the gradient. + OutputDataType& Gradient() { return gradient; } //! Get the non zero gradient. double const& Alpha() const { return alpha(0); } @@ -112,18 +129,22 @@ class PReLUType : public Layer void serialize(Archive& ar, const uint32_t /* version */); private: + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; + //! Leakyness Parameter object. - OutputType alpha; + OutputDataType alpha; + + //! Locally-stored gradient object. + OutputDataType gradient; //! Leakyness Parameter given by user in the range 0 < alpha < 1. double userAlpha; }; // class PReLU -// Convenience typedefs. - -// Standard PReLU layer. -typedef PReLUType PReLU; - } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/not_adapted/parametric_relu_impl.hpp b/src/mlpack/methods/ann/layer/parametric_relu_impl.hpp similarity index 51% rename from src/mlpack/methods/ann/layer/not_adapted/parametric_relu_impl.hpp rename to src/mlpack/methods/ann/layer/parametric_relu_impl.hpp index 97ce441c09..a584603f5e 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/parametric_relu_impl.hpp +++ b/src/mlpack/methods/ann/layer/parametric_relu_impl.hpp @@ -12,8 +12,8 @@ * 3-clause BSD license along with mlpack. If not, see * http://www.opensource.org/licenses/BSD-3-Clause for more information. */ -#ifndef MLPACK_METHODS_ANN_LAYER_PRELU_IMPL_HPP -#define MLPACK_METHODS_ANN_LAYER_PRELU_IMPL_HPP +#ifndef MLPACK_METHODS_ANN_LAYER_PReLU_IMPL_HPP +#define MLPACK_METHODS_ANN_LAYER_PReLU_IMPL_HPP // In case it hasn't yet been included. #include "parametric_relu.hpp" @@ -21,66 +21,66 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -PReLUType::PReLUType( +template +PReLU::PReLU( const double userAlpha) : userAlpha(userAlpha) { alpha.set_size(WeightSize(), 1); alpha(0) = userAlpha; } -template -void PReLUType::SetWeights( - typename OutputType::elem_type* weightsPtr) +template +void PReLU::Reset() { - alpha = arma::mat(weightsPtr, 1, 1, false, false); - //! Set value of alpha to the one given by user. - // TODO: this doesn't even make any sense. is it trainable or not? - // why is there userAlpha? is that for initialization only? - alpha(0) = userAlpha; + alpha = arma::mat(alpha.memptr(), 1, 1, false, false); } +template template -void PReLUType::Forward( +void PReLU::Forward( const InputType& input, OutputType& output) { - // TODO: use transform()? - output = input; - arma::uvec negative = arma::find(input < 0); - output(negative) = input(negative) * alpha(0); + output = arma::max(input, alpha(0) * input); } -template -void PReLUType::Backward( - const InputType& input, const OutputType& gy, OutputType& g) +template +template +void PReLU::Backward( + const DataType& input, const DataType& gy, DataType& g) { - OutputType derivative; + DataType derivative; derivative.set_size(arma::size(input)); for (size_t i = 0; i < input.n_elem; ++i) + { derivative(i) = (input(i) >= 0) ? 1 : alpha(0); + } g = gy % derivative; } -template -void PReLUType::Gradient( - const InputType& input, - const OutputType& error, - OutputType& gradient) +template +template +void PReLU::Gradient( + const arma::Mat& input, + const arma::Mat& error, + arma::Mat& gradient) { - OutputType zeros = arma::zeros(input.n_rows, input.n_cols); + if (gradient.n_elem == 0) + { + gradient = arma::zeros(1, 1); + } + + arma::mat zeros = arma::zeros(input.n_rows, input.n_cols); gradient(0) = arma::accu(error % arma::min(zeros, input)) / input.n_cols; } -template +template template -void PReLUType::serialize( +void PReLU::serialize( Archive& ar, const uint32_t /* version */) { - ar(cereal::base_class>(this)); - ar(CEREAL_NVP(alpha)); } diff --git a/src/mlpack/methods/ann/layer/not_adapted/pixel_shuffle.hpp b/src/mlpack/methods/ann/layer/pixel_shuffle.hpp similarity index 100% rename from src/mlpack/methods/ann/layer/not_adapted/pixel_shuffle.hpp rename to src/mlpack/methods/ann/layer/pixel_shuffle.hpp diff --git a/src/mlpack/methods/ann/layer/not_adapted/pixel_shuffle_impl.hpp b/src/mlpack/methods/ann/layer/pixel_shuffle_impl.hpp similarity index 100% rename from src/mlpack/methods/ann/layer/not_adapted/pixel_shuffle_impl.hpp rename to src/mlpack/methods/ann/layer/pixel_shuffle_impl.hpp diff --git a/src/mlpack/methods/ann/layer/not_adapted/positional_encoding.hpp b/src/mlpack/methods/ann/layer/positional_encoding.hpp similarity index 63% rename from src/mlpack/methods/ann/layer/not_adapted/positional_encoding.hpp rename to src/mlpack/methods/ann/layer/positional_encoding.hpp index a1317b3e52..8678426414 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/positional_encoding.hpp +++ b/src/mlpack/methods/ann/layer/positional_encoding.hpp @@ -1,4 +1,3 @@ -// Temporarily drop. /** * @file methods/ann/layer/positional_encoding.hpp * @author Mrityunjay Tripathi @@ -26,22 +25,22 @@ namespace ann /** Artificial Neural Network. */ { * `(embedDim * maxSequenceLength, batchSize)`. The embeddings are stored * consequently. * - * @tparam InputType Type of the input data (arma::colvec, arma::mat, + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). */ template < - typename InputType = arma::mat, - typename OutputType = arma::mat + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat > -class PositionalEncodingType : public Layer +class PositionalEncoding { public: /** - * Create PositionalEncodingType object. + * Create PositionalEncoding object. */ - PositionalEncodingType(); + PositionalEncoding(); /** * Create the PositionalEncoding layer object using the specified parameters. @@ -49,11 +48,8 @@ class PositionalEncodingType : public Layer * @param embedDim The length of the embedding vector. * @param maxSequenceLength Number of tokens in each sequence. */ - PositionalEncodingType(const size_t embedDim, - const size_t maxSequenceLength); - - //! Clone the PositionalEncodingType object. This handles polymorphism correctly. - PositionalEncodingType* Clone() const { return new PositionalEncodingType(*this); } + PositionalEncoding(const size_t embedDim, + const size_t maxSequenceLength); /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -62,7 +58,8 @@ class PositionalEncodingType : public Layer * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - void Forward(const InputType& input, OutputType& output); + template + void Forward(const arma::Mat& input, arma::Mat& output); /** * Ordinary feed backward pass of a neural network, calculating the function @@ -73,12 +70,28 @@ class PositionalEncodingType : public Layer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const InputType& /* input */, - const OutputType& gy, - OutputType& g); + template + void Backward(const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g); + + //! Get the input parameter. + InputDataType const& InputParameter() const { return inputParameter; } + //! Modify the input parameter. + InputDataType& InputParameter() { return inputParameter; } + + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } //! Get the positional encoding vector. - InputType const& Encoding() const { return positionalEncoding; } + InputDataType const& Encoding() const { return positionalEncoding; } size_t InputShape() const { @@ -104,11 +117,17 @@ class PositionalEncodingType : public Layer size_t maxSequenceLength; //! Locally-stored positional encodings. - InputType positionalEncoding; -}; // class PositionalEncodingTest + InputDataType positionalEncoding; -// Standard PositionalEncoding layer. -typedef PositionalEncodingType PositionalEncoding; + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored input parameter object. + InputDataType inputParameter; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; +}; // class PositionalEncoding } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/not_adapted/positional_encoding_impl.hpp b/src/mlpack/methods/ann/layer/positional_encoding_impl.hpp similarity index 59% rename from src/mlpack/methods/ann/layer/not_adapted/positional_encoding_impl.hpp rename to src/mlpack/methods/ann/layer/positional_encoding_impl.hpp index 30ad88ae2f..8dc63a5894 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/positional_encoding_impl.hpp +++ b/src/mlpack/methods/ann/layer/positional_encoding_impl.hpp @@ -19,16 +19,16 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -PositionalEncodingType::PositionalEncodingType() : +template +PositionalEncoding::PositionalEncoding() : embedDim(0), maxSequenceLength(0) { // Nothing to do here. } -template -PositionalEncodingType::PositionalEncodingType( +template +PositionalEncoding::PositionalEncoding( const size_t embedDim, const size_t maxSequenceLength) : embedDim(embedDim), @@ -37,14 +37,14 @@ PositionalEncodingType::PositionalEncodingType( InitPositionalEncoding(); } -template -void PositionalEncodingType::InitPositionalEncoding() +template +void PositionalEncoding::InitPositionalEncoding() { positionalEncoding.set_size(maxSequenceLength, embedDim); - const InputType position = arma::regspace(0, 1, maxSequenceLength - 1); - const InputType divTerm = arma::exp(arma::regspace(0, 2, embedDim - 1) + const InputDataType position = arma::regspace(0, 1, maxSequenceLength - 1); + const InputDataType divTerm = arma::exp(arma::regspace(0, 2, embedDim - 1) * (- std::log(10000.0) / embedDim)); - const InputType theta = position * divTerm.t(); + const InputDataType theta = position * divTerm.t(); for (size_t i = 0; i < theta.n_cols; ++i) { positionalEncoding.col(2 * i) = arma::sin(theta.col(i)); @@ -53,9 +53,10 @@ void PositionalEncodingType::InitPositionalEncoding() positionalEncoding = arma::vectorise(positionalEncoding.t()); } -template -void PositionalEncodingType::Forward( - const InputType& input, OutputType& output) +template +template +void PositionalEncoding::Forward( + const arma::Mat& input, arma::Mat& output) { if (input.n_rows != embedDim * maxSequenceLength) Log::Fatal << "Incorrect input dimensions!" << std::endl; @@ -63,20 +64,19 @@ void PositionalEncodingType::Forward( output = input.each_col() + positionalEncoding; } -template -void PositionalEncodingType::Backward( - const InputType& /* input */, const OutputType& gy, OutputType& g) +template +template +void PositionalEncoding::Backward( + const arma::Mat& /* input */, const arma::Mat& gy, arma::Mat& g) { g = gy; } -template +template template -void PositionalEncodingType::serialize( +void PositionalEncoding::serialize( Archive& ar, const uint32_t /* version */) { - ar(cereal::base_class>(this)); - ar(CEREAL_NVP(embedDim)); ar(CEREAL_NVP(maxSequenceLength)); diff --git a/src/mlpack/methods/ann/layer/radial_basis_function.hpp b/src/mlpack/methods/ann/layer/radial_basis_function.hpp index 9865ede4d4..6f84303360 100644 --- a/src/mlpack/methods/ann/layer/radial_basis_function.hpp +++ b/src/mlpack/methods/ann/layer/radial_basis_function.hpp @@ -10,21 +10,22 @@ * 3-clause BSD license along with mlpack. If not, see * http://www.opensource.org/licenses/BSD-3-Clause for more information. */ -#ifndef MLPACK_METHODS_ANN_LAYER_RADIAL_BASIS_FUNCTION_HPP -#define MLPACK_METHODS_ANN_LAYER_RADIAL_BASIS_FUNCTION_HPP +#ifndef MLPACK_METHODS_ANN_LAYER_RBF_HPP +#define MLPACK_METHODS_ANN_LAYER_RBF_HPP #include #include -#include "layer.hpp" +#include "layer_types.hpp" namespace mlpack { namespace ann /** Artificial Neural Network. */ { + /** - * Implementation of the Radial Basis Function layer. The RBFType class, when - * used with a non-linear activation function, acts as a Radial Basis Function - * which can be used with a feed-forward neural network. + * Implementation of the Radial Basis Function layer. The RBF class when use with a + * non-linear activation function acts as a Radial Basis Function which can be used + * with Feed-Forward neural network. * * For more information, refer to the following paper, * @@ -37,47 +38,37 @@ namespace ann /** Artificial Neural Network. */ { * } * @endcode * - * @tparam MatType Matrix representation to accept as input and use for - * computation. + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). * @tparam Activation Type of the activation function (mlpack::ann::Gaussian). */ template < - typename MatType = arma::mat, + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat, typename Activation = GaussianFunction > -class RBFType : public Layer +class RBF { public: - //! Create the RBFType object. - RBFType(); + //! Create the RBF object. + RBF(); /** * Create the Radial Basis Function layer object using the specified * parameters. * + * @param inSize The number of input units. * @param outSize The number of output units. * @param centres The centres calculated using k-means of data. * @param betas The beta value to be used with centres. */ - RBFType(const size_t outSize, - MatType& centres, - double betas = 0); - - //! Clone the LinearType object. This handles polymorphism correctly. - RBFType* Clone() const { return new RBFType(*this); } - - // Virtual destructor. - virtual ~RBFType() { } - - //! Copy the given RBFType layer. - RBFType(const RBFType& other); - //! Take ownership of the given RBFType layer. - RBFType(RBFType&& other); - //! Copy the given RBFType layer. - RBFType& operator=(const RBFType& other); - //! Take ownership of the given RBFType layer. - RBFType& operator=(RBFType&& other); + RBF(const size_t inSize, + const size_t outSize, + arma::mat& centres, + double betas = 0); /** * Ordinary feed forward pass of the radial basis function. @@ -85,21 +76,51 @@ class RBFType : public Layer * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - void Forward(const MatType& input, MatType& output); + template + void Forward(const arma::Mat& input, arma::Mat& output); /** * Ordinary feed backward pass of the radial basis function. + * */ - void Backward(const MatType& /* input */, - const MatType& /* gy */, - MatType& /* g */); + template + void Backward(const arma::Mat& /* input */, + const arma::Mat& /* gy */, + arma::Mat& /* g */); - //! Compute the output dimensions of the layer given `InputDimensions()`. The - //! RBFType layer flattens the input. - void ComputeOutputDimensions(); + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + //! Get the parameters. + + //! Get the input parameter. + InputDataType const& InputParameter() const { return inputParameter; } + //! Modify the input parameter. + InputDataType& InputParameter() { return inputParameter; } + + //! Get the input size. + size_t InputSize() const { return inSize; } + + //! Get the output size. + size_t OutputSize() const { return outSize; } + + //! Get the detla. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } //! Get the size of the weights. - size_t WeightSize() const { return 0; } + size_t WeightSize() const + { + return 0; + } + + //! Get the shape of the input. + size_t InputShape() const + { + return inSize; + } /** * Serialize the layer. @@ -108,20 +129,33 @@ class RBFType : public Layer void serialize(Archive& ar, const uint32_t /* version */); private: + //! Locally-stored number of input units. + size_t inSize; + //! Locally-stored number of output units. size_t outSize; + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; + + //! Locally-stored the sigmas values. + double sigmas; + //! Locally-stored the betas values. double betas; //! Locally-stored the learnable centre of the shape. - MatType centres; + InputDataType centres; + + //! Locally-stored input parameter object. + InputDataType inputParameter; //! Locally-stored the output distances of the shape. - MatType distances; -}; // class RBFType - -typedef RBFType RBF; + OutputDataType distances; +}; // class RBF } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/radial_basis_function_impl.hpp b/src/mlpack/methods/ann/layer/radial_basis_function_impl.hpp index 55887792cd..8ba09b0ab6 100644 --- a/src/mlpack/methods/ann/layer/radial_basis_function_impl.hpp +++ b/src/mlpack/methods/ann/layer/radial_basis_function_impl.hpp @@ -2,13 +2,14 @@ * @file radial_basis_function_impl.hpp * @author Himanshu Pathak * + * * mlpack is free software; you may redistribute it and/or modify it under the * terms of the 3-clause BSD license. You should have received a copy of the * 3-clause BSD license along with mlpack. If not, see * http://www.opensource.org/licenses/BSD-3-Clause for more information. */ -#ifndef MLPACK_METHODS_ANN_LAYER_RADIAL_BASIS_FUNCTION_IMPL_HPP -#define MLPACK_METHODS_ANN_LAYER_RADIAL_BASIS_FUNCTION_IMPL_HPP +#ifndef MLPACK_METHODS_ANN_LAYER_RBF_IMPL_HPP +#define MLPACK_METHODS_ANN_LAYER_RBF_IMPL_HPP // In case it hasn't yet been included. #include "radial_basis_function.hpp" @@ -16,146 +17,85 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -RBFType::RBFType() : - Layer(), +template +RBF::RBF() : + inSize(0), outSize(0), + sigmas(0), betas(0) { // Nothing to do here. } -template -RBFType::RBFType( +template +RBF::RBF( + const size_t inSize, const size_t outSize, - MatType& centres, + arma::mat& centres, double betas) : + inSize(inSize), outSize(outSize), betas(betas), centres(centres) { - double sigmas = 0; + sigmas = 0; if (betas == 0) { for (size_t i = 0; i < centres.n_cols; i++) { - double maxDis = 0; - MatType temp = centres.each_col() - centres.col(i); - maxDis = arma::accu(arma::max(arma::pow(arma::sum( + double max_dis = 0; + arma::mat temp = centres.each_col() - centres.col(i); + max_dis = arma::accu(arma::max(arma::pow(arma::sum( arma::pow((temp), 2), 0), 0.5).t())); - if (maxDis > sigmas) - sigmas = maxDis; + if (max_dis > sigmas) + sigmas = max_dis; } this->betas = std::pow(2 * outSize, 0.5) / sigmas; } } -template -RBFType::RBFType(const RBFType& other) : - Layer(other), - outSize(other.outSize), - betas(other.betas), - centres(other.centres) +template +void RBF::Forward( + const arma::Mat& input, + arma::Mat& output) { - // Nothing to do. -} - -template -RBFType::RBFType(RBFType&& other) : - Layer(other), - outSize(other.outSize), - betas(other.betas), - centres(std::move(other.centres)) -{ - // Nothing to do. -} - -template -RBFType& -RBFType::operator=(const RBFType& other) -{ - if (&other != this) - { - Layer::operator=(other); - outSize = other.outSize; - betas = other.betas; - centres = other.centres; - } - - return *this; -} - -template -RBFType& -RBFType::operator=(RBFType&& other) -{ - if (&other != this) - { - Layer::operator=(std::move(other)); - outSize = std::move(other.outSize); - betas = std::move(other.betas); - centres = std::move(other.centres); - } - - return *this; -} - -template -void RBFType::Forward( - const MatType& input, - MatType& output) -{ - // Sanity check: make sure the dimensions are right. - if (input.n_rows != centres.n_rows) - { - Log::Fatal << "RBFType::Forward(): input size (" << input.n_rows << ") does" - << " not match given center size (" << centres.n_rows << ")!" - << std::endl; - } - - distances = MatType(outSize, input.n_cols); + distances = arma::mat(outSize, input.n_cols); for (size_t i = 0; i < input.n_cols; i++) { - MatType temp = centres.each_col() - input.col(i); + arma::mat temp = centres.each_col() - input.col(i); distances.col(i) = arma::pow(arma::sum( - arma::pow((temp), 2), 0), 0.5).t(); + arma::pow((temp), 2), 0), 0.5).t(); } - Activation::Fn(distances * std::pow(betas, 0.5), output); + Activation::Fn(distances * std::pow(betas, 0.5), + output); } -template -void RBFType::Backward( - const MatType& /* input */, - const MatType& /* gy */, - MatType& /* g */) +template +template +void RBF::Backward( + const arma::Mat& /* input */, + const arma::Mat& /* gy */, + arma::Mat& /* g */) { // Nothing to do here. } -template -void RBFType::ComputeOutputDimensions() -{ - this->outputDimensions = std::vector(this->inputDimensions.size(), 1); - - // This flattens the input. - this->outputDimensions[0] = outSize; -} - -template +template template -void RBFType::serialize( +void RBF::serialize( Archive& ar, const uint32_t /* version */) { - ar(cereal::base_class>(this)); - ar(CEREAL_NVP(distances)); ar(CEREAL_NVP(centres)); - ar(CEREAL_NVP(betas)); } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/not_adapted/recurrent.hpp b/src/mlpack/methods/ann/layer/recurrent.hpp similarity index 54% rename from src/mlpack/methods/ann/layer/not_adapted/recurrent.hpp rename to src/mlpack/methods/ann/layer/recurrent.hpp index 836d1aa4b8..b82fcb175b 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/recurrent.hpp +++ b/src/mlpack/methods/ann/layer/recurrent.hpp @@ -14,6 +14,12 @@ #include +#include "../visitor/delete_visitor.hpp" +#include "../visitor/delta_visitor.hpp" +#include "../visitor/copy_visitor.hpp" +#include "../visitor/output_parameter_visitor.hpp" +#include "../visitor/input_shape_visitor.hpp" + #include "layer_types.hpp" #include "add_merge.hpp" #include "sequential.hpp" @@ -25,16 +31,17 @@ namespace ann /** Artificial Neural Network. */ { * Implementation of the RecurrentLayer class. Recurrent layers can be used * similarly to feed-forward layers. * - * @tparam InputType Type of the input data (arma::colvec, arma::mat, + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). - * @tparam OutputType Type of the output data (arma::colvec, arma::mat, + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). */ template < - typename InputType = arma::mat, - typename OutputType = arma::mat + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat, + typename... CustomLayers > -class Recurrent : public MultiLayer +class Recurrent { public: /** @@ -72,7 +79,8 @@ class Recurrent : public MultiLayer * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - void Forward(const InputType& input, OutputType& output); + template + void Forward(const arma::Mat& input, arma::Mat& output); /** * Ordinary feed backward pass of a neural network, calculating the function @@ -83,9 +91,10 @@ class Recurrent : public MultiLayer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const InputType& /* input */, - const OutputType& gy, - OutputType& g); + template + void Backward(const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g); /* * Calculate the gradient using the output delta and the input activation. @@ -94,9 +103,38 @@ class Recurrent : public MultiLayer * @param error The calculated error. * @param gradient The calculated gradient. */ - void Gradient(const InputType& input, - const OutputType& error, - OutputType& /* gradient */); + template + void Gradient(const arma::Mat& input, + const arma::Mat& error, + arma::Mat& /* gradient */); + + //! Get the model modules. + std::vector >& Model() { return network; } + + //! The value of the deterministic parameter. + bool Deterministic() const { return deterministic; } + //! Modify the value of the deterministic parameter. + bool& Deterministic() { return deterministic; } + + //! Get the parameters. + OutputDataType const& Parameters() const { return parameters; } + //! Modify the parameters. + OutputDataType& Parameters() { return parameters; } + + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } + + //! Get the gradient. + OutputDataType const& Gradient() const { return gradient; } + //! Modify the gradient. + OutputDataType& Gradient() { return gradient; } //! Get the number of steps to backpropagate through time. size_t const& Rho() const { return rho; } @@ -111,17 +149,23 @@ class Recurrent : public MultiLayer void serialize(Archive& ar, const uint32_t /* version */); private: + //! Locally-stored delete visitor module object. + DeleteVisitor deleteVisitor; + + //! Locally-stored copy visitor + CopyVisitor copyVisitor; + //! Locally-stored start module. - Layer* startModule; + LayerTypes startModule; //! Locally-stored input module. - Layer* inputModule; + LayerTypes inputModule; //! Locally-stored feedback module. - Layer* feedbackModule; + LayerTypes feedbackModule; //! Locally-stored transfer module. - Layer* transferModule; + LayerTypes transferModule; //! Number of steps to backpropagate through time (BPTT). size_t rho; @@ -135,26 +179,48 @@ class Recurrent : public MultiLayer //! Locally-stored number of gradient steps. size_t gradientStep; + //! If true dropout and scaling is disabled, see notes above. + bool deterministic; + + //! To know whether this object allocated memory. We need this to know + //! whether we should delete the metric member variable in the destructor. + bool ownsLayer; + //! Locally-stored weight object. - OutputType parameters; + OutputDataType parameters; //! Locally-stored initial module. - SequentialType* initialModule; + LayerTypes initialModule; //! Locally-stored recurrent module. - SequentialType* recurrentModule; + LayerTypes recurrentModule; //! Locally-stored model modules. - std::vector*> network; + std::vector > network; //! Locally-stored merge module. - AddMerge* mergeModule; + LayerTypes mergeModule; + + //! Locally-stored delta visitor. + DeltaVisitor deltaVisitor; + + //! Locally-stored output parameter visitor. + OutputParameterVisitor outputParameterVisitor; //! Locally-stored feedback output parameters. - std::vector feedbackOutputParameter; + std::vector feedbackOutputParameter; + + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored gradient object. + OutputDataType gradient; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; //! Locally-stored recurrent error parameter. - OutputType recurrentError; + arma::mat recurrentError; }; // class Recurrent } // namespace ann diff --git a/src/mlpack/methods/ann/layer/not_adapted/recurrent_attention.hpp b/src/mlpack/methods/ann/layer/recurrent_attention.hpp similarity index 57% rename from src/mlpack/methods/ann/layer/not_adapted/recurrent_attention.hpp rename to src/mlpack/methods/ann/layer/recurrent_attention.hpp index 7783a3f4c9..63838dc479 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/recurrent_attention.hpp +++ b/src/mlpack/methods/ann/layer/recurrent_attention.hpp @@ -14,6 +14,11 @@ #include +#include "../visitor/delta_visitor.hpp" +#include "../visitor/output_parameter_visitor.hpp" +#include "../visitor/reset_visitor.hpp" +#include "../visitor/weight_size_visitor.hpp" + #include "layer_types.hpp" #include "add_merge.hpp" #include "sequential.hpp" @@ -21,7 +26,6 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -// TODO: refactor recurrent layer /** * This class implements the Recurrent Model for Visual Attention, using a * variety of possible layer implementations. @@ -39,16 +43,16 @@ namespace ann /** Artificial Neural Network. */ { * } * @endcode * - * @tparam InputType Type of the input data (arma::colvec, arma::mat, + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). - * @tparam OutputType Type of the output data (arma::colvec, arma::mat, + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). */ template < - typename InputType = arma::mat, - typename OutputType = arma::mat + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat > -class RecurrentAttention : public MultiLayer +class RecurrentAttention { public: /** @@ -78,7 +82,8 @@ class RecurrentAttention : public MultiLayer * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - void Forward(const InputType& input, OutputType& output); + template + void Forward(const arma::Mat& input, arma::Mat& output); /** * Ordinary feed backward pass of a neural network, calculating the function @@ -89,9 +94,10 @@ class RecurrentAttention : public MultiLayer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const InputType& /* input */, - const OutputType& gy, - OutputType& g); + template + void Backward(const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g); /* * Calculate the gradient using the output delta and the input activation. @@ -100,9 +106,41 @@ class RecurrentAttention : public MultiLayer * @param * (error) The calculated error. * @param * (gradient) The calculated gradient. */ - void Gradient(const InputType& /* input */, - const OutputType& /* error */, - OutputType& /* gradient */); + template + void Gradient(const arma::Mat& /* input */, + const arma::Mat& /* error */, + arma::Mat& /* gradient */); + + //! Get the model modules. + std::vector>& Model() { return network; } + + //! The value of the deterministic parameter. + bool Deterministic() const { return deterministic; } + //! Modify the value of the deterministic parameter. + bool& Deterministic() { return deterministic; } + + //! Get the parameters. + OutputDataType const& Parameters() const { return parameters; } + //! Modify the parameters. + OutputDataType& Parameters() { return parameters; } + + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } + + //! Get the gradient. + OutputDataType const& Gradient() const { return gradient; } + //! Modify the gradient. + OutputDataType& Gradient() { return gradient; } + + //! Get the module output size. + size_t OutSize() const { return outSize; } //! Get the number of steps to backpropagate through time. size_t const& Rho() const { return rho; } @@ -122,18 +160,20 @@ class RecurrentAttention : public MultiLayer // Gradient of the action module. if (backwardStep == (rho - 1)) { - actionModule->Gradient(initialInput, actionError, - actionModule->Gradient()); + boost::apply_visitor(GradientVisitor(initialInput, actionError), + actionModule); } else { - actionModule->Gradient(actionModule->OutputParameter(), actionError, - actionModule->Gradient()); + boost::apply_visitor(GradientVisitor(boost::apply_visitor( + outputParameterVisitor, actionModule), actionError), + actionModule); } // Gradient of the recurrent module. - rnnModule->Gradient(rnnModule->OutputParameter(), recurrentError, - rnnModule->Gradient()); + boost::apply_visitor(GradientVisitor(boost::apply_visitor( + outputParameterVisitor, rnnModule), recurrentError), + rnnModule); attentionGradient += intermediateGradient; } @@ -142,10 +182,10 @@ class RecurrentAttention : public MultiLayer size_t outSize; //! Locally-stored start module. - Layer* rnnModule; + LayerTypes<> rnnModule; //! Locally-stored input module. - Layer* actionModule; + LayerTypes<> actionModule; //! Number of steps to backpropagate through time (BPTT). size_t rho; @@ -156,38 +196,62 @@ class RecurrentAttention : public MultiLayer //! Locally-stored number of backward steps. size_t backwardStep; + //! If true dropout and scaling is disabled, see notes above. + bool deterministic; + //! Locally-stored weight object. - OutputType parameters; + OutputDataType parameters; //! Locally-stored model modules. - std::vector*> network; + std::vector> network; + + //! Locally-stored weight size visitor. + WeightSizeVisitor weightSizeVisitor; + + //! Locally-stored delta visitor. + DeltaVisitor deltaVisitor; + + //! Locally-stored output parameter visitor. + OutputParameterVisitor outputParameterVisitor; //! Locally-stored feedback output parameters. - std::vector feedbackOutputParameter; + std::vector feedbackOutputParameter; //! List of all module parameters for the backward pass (BBTT). - std::vector moduleOutputParameter; + std::vector moduleOutputParameter; + + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored gradient object. + OutputDataType gradient; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; //! Locally-stored recurrent error parameter. - OutputType recurrentError; + arma::mat recurrentError; //! Locally-stored action error parameter. - OutputType actionError; + arma::mat actionError; //! Locally-stored action delta. - OutputType actionDelta; + arma::mat actionDelta; //! Locally-stored recurrent delta. - OutputType rnnDelta; + arma::mat rnnDelta; //! Locally-stored initial action input. - InputType initialInput; + arma::mat initialInput; + + //! Locally-stored reset visitor. + ResetVisitor resetVisitor; //! Locally-stored attention gradient. - OutputType attentionGradient; + arma::mat attentionGradient; //! Locally-stored intermediate gradient for the attention module. - OutputType intermediateGradient; + arma::mat intermediateGradient; }; // class RecurrentAttention } // namespace ann diff --git a/src/mlpack/methods/ann/layer/recurrent_attention_impl.hpp b/src/mlpack/methods/ann/layer/recurrent_attention_impl.hpp new file mode 100644 index 0000000000..abc3da7727 --- /dev/null +++ b/src/mlpack/methods/ann/layer/recurrent_attention_impl.hpp @@ -0,0 +1,226 @@ +/** + * @file methods/ann/layer/recurrent_attention_impl.hpp + * @author Marcus Edel + * + * Implementation of the RecurrentAttention class. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_LAYER_RECURRENT_ATTENTION_IMPL_HPP +#define MLPACK_METHODS_ANN_LAYER_RECURRENT_ATTENTION_IMPL_HPP + +// In case it hasn't yet been included. +#include "recurrent_attention.hpp" + +#include "../visitor/load_output_parameter_visitor.hpp" +#include "../visitor/save_output_parameter_visitor.hpp" +#include "../visitor/backward_visitor.hpp" +#include "../visitor/forward_visitor.hpp" +#include "../visitor/gradient_set_visitor.hpp" +#include "../visitor/gradient_update_visitor.hpp" +#include "../visitor/gradient_visitor.hpp" + +namespace mlpack { +namespace ann /** Artificial Neural Network. */ { + +template +RecurrentAttention::RecurrentAttention() : + outSize(0), + rho(0), + forwardStep(0), + backwardStep(0), + deterministic(false) +{ + // Nothing to do. +} + +template +template +RecurrentAttention::RecurrentAttention( + const size_t outSize, + const RNNModuleType& rnn, + const ActionModuleType& action, + const size_t rho) : + outSize(outSize), + rnnModule(new RNNModuleType(rnn)), + actionModule(new ActionModuleType(action)), + rho(rho), + forwardStep(0), + backwardStep(0), + deterministic(false) +{ + network.push_back(rnnModule); + network.push_back(actionModule); +} + +template +template +void RecurrentAttention::Forward( + const arma::Mat& input, arma::Mat& output) +{ + // Initialize the action input. + if (initialInput.is_empty()) + { + initialInput = arma::zeros(outSize, input.n_cols); + } + + // Propagate through the action and recurrent module. + for (forwardStep = 0; forwardStep < rho; ++forwardStep) + { + if (forwardStep == 0) + { + boost::apply_visitor(ForwardVisitor(initialInput, + boost::apply_visitor(outputParameterVisitor, actionModule)), + actionModule); + } + else + { + boost::apply_visitor(ForwardVisitor(boost::apply_visitor( + outputParameterVisitor, rnnModule), boost::apply_visitor( + outputParameterVisitor, actionModule)), actionModule); + } + + // Initialize the glimpse input. + arma::mat glimpseInput = arma::zeros(input.n_elem, 2); + glimpseInput.col(0) = input; + glimpseInput.submat(0, 1, boost::apply_visitor(outputParameterVisitor, + actionModule).n_elem - 1, 1) = boost::apply_visitor( + outputParameterVisitor, actionModule); + + boost::apply_visitor(ForwardVisitor(glimpseInput, + boost::apply_visitor(outputParameterVisitor, rnnModule)), + rnnModule); + + // Save the output parameter when training the module. + if (!deterministic) + { + for (size_t l = 0; l < network.size(); ++l) + { + boost::apply_visitor(SaveOutputParameterVisitor( + moduleOutputParameter), network[l]); + } + } + } + + output = boost::apply_visitor(outputParameterVisitor, rnnModule); + + forwardStep = 0; + backwardStep = 0; +} + +template +template +void RecurrentAttention::Backward( + const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g) +{ + if (intermediateGradient.is_empty() && backwardStep == 0) + { + // Initialize the attention gradients. + size_t weights = boost::apply_visitor(weightSizeVisitor, rnnModule) + + boost::apply_visitor(weightSizeVisitor, actionModule); + + intermediateGradient = arma::zeros(weights, 1); + attentionGradient = arma::zeros(weights, 1); + + // Initialize the action error. + actionError = arma::zeros( + boost::apply_visitor(outputParameterVisitor, actionModule).n_rows, + boost::apply_visitor(outputParameterVisitor, actionModule).n_cols); + } + + // Propagate the attention gradients. + if (backwardStep == 0) + { + size_t offset = 0; + offset += boost::apply_visitor(GradientSetVisitor( + intermediateGradient, offset), rnnModule); + boost::apply_visitor(GradientSetVisitor( + intermediateGradient, offset), actionModule); + + attentionGradient.zeros(); + } + + // Back-propagate through time. + for (; backwardStep < rho; backwardStep++) + { + if (backwardStep == 0) + { + recurrentError = gy; + } + else + { + recurrentError = actionDelta; + } + + for (size_t l = 0; l < network.size(); ++l) + { + boost::apply_visitor(LoadOutputParameterVisitor( + moduleOutputParameter), network[network.size() - 1 - l]); + } + + if (backwardStep == (rho - 1)) + { + boost::apply_visitor(BackwardVisitor(boost::apply_visitor( + outputParameterVisitor, actionModule), actionError, + actionDelta), actionModule); + } + else + { + boost::apply_visitor(BackwardVisitor(initialInput, actionError, + actionDelta), actionModule); + } + + boost::apply_visitor(BackwardVisitor(boost::apply_visitor( + outputParameterVisitor, rnnModule), recurrentError, rnnDelta), + rnnModule); + + if (backwardStep == 0) + { + g = rnnDelta.col(1); + } + else + { + g += rnnDelta.col(1); + } + + IntermediateGradient(); + } +} + +template +template +void RecurrentAttention::Gradient( + const arma::Mat& /* input */, + const arma::Mat& /* error */, + arma::Mat& /* gradient */) +{ + size_t offset = 0; + offset += boost::apply_visitor(GradientUpdateVisitor( + attentionGradient, offset), rnnModule); + boost::apply_visitor(GradientUpdateVisitor( + attentionGradient, offset), actionModule); +} + +template +template +void RecurrentAttention::serialize( + Archive& ar, const uint32_t /* version */) +{ + ar(CEREAL_NVP(rho)); + ar(CEREAL_NVP(outSize)); + ar(CEREAL_NVP(forwardStep)); + ar(CEREAL_NVP(backwardStep)); + + ar(CEREAL_VARIANT_POINTER(rnnModule)); + ar(CEREAL_VARIANT_POINTER(actionModule)); +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/layer/recurrent_impl.hpp b/src/mlpack/methods/ann/layer/recurrent_impl.hpp new file mode 100644 index 0000000000..046c48fa0f --- /dev/null +++ b/src/mlpack/methods/ann/layer/recurrent_impl.hpp @@ -0,0 +1,361 @@ +/** + * @file methods/ann/layer/recurrent_impl.hpp + * @author Marcus Edel + * + * Implementation of the Recurrent class. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_LAYER_RECURRENT_IMPL_HPP +#define MLPACK_METHODS_ANN_LAYER_RECURRENT_IMPL_HPP + +// In case it hasn't yet been included. +#include "recurrent.hpp" + +#include "../visitor/add_visitor.hpp" +#include "../visitor/backward_visitor.hpp" +#include "../visitor/gradient_visitor.hpp" +#include "../visitor/gradient_zero_visitor.hpp" +#include "../visitor/input_shape_visitor.hpp" + +namespace mlpack { +namespace ann /** Artificial Neural Network. */ { + +template +Recurrent::Recurrent() : + rho(0), + forwardStep(0), + backwardStep(0), + gradientStep(0), + deterministic(false), + ownsLayer(false) +{ + // Nothing to do. +} + +template +template< + typename StartModuleType, + typename InputModuleType, + typename FeedbackModuleType, + typename TransferModuleType +> +Recurrent::Recurrent( + const StartModuleType& start, + const InputModuleType& input, + const FeedbackModuleType& feedback, + const TransferModuleType& transfer, + const size_t rho) : + startModule(new StartModuleType(start)), + inputModule(new InputModuleType(input)), + feedbackModule(new FeedbackModuleType(feedback)), + transferModule(new TransferModuleType(transfer)), + rho(rho), + forwardStep(0), + backwardStep(0), + gradientStep(0), + deterministic(false), + ownsLayer(true) +{ + initialModule = new Sequential<>(); + mergeModule = new AddMerge<>(false, false, false); + recurrentModule = new Sequential<>(false, false); + + boost::apply_visitor(AddVisitor(inputModule), + initialModule); + boost::apply_visitor(AddVisitor(startModule), + initialModule); + boost::apply_visitor(AddVisitor(transferModule), + initialModule); + + boost::apply_visitor(AddVisitor(inputModule), mergeModule); + boost::apply_visitor(AddVisitor(feedbackModule), + mergeModule); + boost::apply_visitor(AddVisitor(mergeModule), + recurrentModule); + boost::apply_visitor(AddVisitor(transferModule), + recurrentModule); + + network.push_back(initialModule); + network.push_back(mergeModule); + network.push_back(feedbackModule); + network.push_back(recurrentModule); +} + +template +Recurrent::Recurrent( + const Recurrent& network) : + rho(network.rho), + forwardStep(network.forwardStep), + backwardStep(network.backwardStep), + gradientStep(network.gradientStep), + deterministic(network.deterministic), + ownsLayer(network.ownsLayer) +{ + startModule = boost::apply_visitor(copyVisitor, network.startModule); + inputModule = boost::apply_visitor(copyVisitor, network.inputModule); + feedbackModule = boost::apply_visitor(copyVisitor, network.feedbackModule); + transferModule = boost::apply_visitor(copyVisitor, network.transferModule); + initialModule = new Sequential<>(); + mergeModule = new AddMerge<>(false, false, false); + recurrentModule = new Sequential<>(false, false); + + boost::apply_visitor(AddVisitor(inputModule), + initialModule); + boost::apply_visitor(AddVisitor(startModule), + initialModule); + boost::apply_visitor(AddVisitor(transferModule), + initialModule); + + boost::apply_visitor(AddVisitor(inputModule), mergeModule); + boost::apply_visitor(AddVisitor(feedbackModule), + mergeModule); + boost::apply_visitor(AddVisitor(mergeModule), + recurrentModule); + boost::apply_visitor(AddVisitor(transferModule), + recurrentModule); + this->network.push_back(initialModule); + this->network.push_back(mergeModule); + this->network.push_back(feedbackModule); + this->network.push_back(recurrentModule); +} + +template +size_t +Recurrent::InputShape() const +{ + const size_t inputShapeStartModule = boost::apply_visitor(InShapeVisitor(), + startModule); + + // Return the input shape of the first module that we have. + if (inputShapeStartModule != 0) + { + return inputShapeStartModule; + } + // If input shape of first module is 0. + else + { + // Return input shape of the second module that we have. + const size_t inputShapeInputModule = boost::apply_visitor(InShapeVisitor(), + inputModule); + if (inputShapeInputModule != 0) + { + return inputShapeInputModule; + } + else // If the input shape of second module is 0. + { + // Return input shape of the third module that we have. + const size_t inputShapeFeedbackModule = boost::apply_visitor( + InShapeVisitor(), feedbackModule); + if (inputShapeFeedbackModule != 0) + { + return inputShapeFeedbackModule; + } + else // If the input shape of the third module is 0. + { + // Return the shape of the fourth module that we have. + const size_t inputShapeTransferModule = boost::apply_visitor( + InShapeVisitor(), transferModule); + if (inputShapeTransferModule != 0) + { + return inputShapeTransferModule; + } + else // If the input shape of the fourth module is 0. + { + return 0; + } + } + } + } +} + +template +template +void Recurrent::Forward( + const arma::Mat& input, arma::Mat& output) +{ + if (forwardStep == 0) + { + boost::apply_visitor(ForwardVisitor(input, output), initialModule); + } + else + { + boost::apply_visitor(ForwardVisitor(input, + boost::apply_visitor(outputParameterVisitor, inputModule)), + inputModule); + + boost::apply_visitor(ForwardVisitor(boost::apply_visitor( + outputParameterVisitor, transferModule), + boost::apply_visitor(outputParameterVisitor, feedbackModule)), + feedbackModule); + + boost::apply_visitor(ForwardVisitor(input, output), recurrentModule); + } + + output = boost::apply_visitor(outputParameterVisitor, transferModule); + + // Save the feedback output parameter when training the module. + if (!deterministic) + { + feedbackOutputParameter.push_back(output); + } + + forwardStep++; + if (forwardStep == rho) + { + forwardStep = 0; + backwardStep = 0; + + if (!recurrentError.is_empty()) + { + recurrentError.zeros(); + } + } +} + +template +template +void Recurrent::Backward( + const arma::Mat& /* input */, const arma::Mat& gy, arma::Mat& g) +{ + if (!recurrentError.is_empty()) + { + recurrentError += gy; + } + else + { + recurrentError = gy; + } + + if (backwardStep < (rho - 1)) + { + boost::apply_visitor(BackwardVisitor(boost::apply_visitor( + outputParameterVisitor, recurrentModule), recurrentError, + boost::apply_visitor(deltaVisitor, recurrentModule)), + recurrentModule); + + boost::apply_visitor(BackwardVisitor(boost::apply_visitor( + outputParameterVisitor, inputModule), + boost::apply_visitor(deltaVisitor, recurrentModule), g), + inputModule); + + boost::apply_visitor(BackwardVisitor(boost::apply_visitor( + outputParameterVisitor, feedbackModule), + boost::apply_visitor(deltaVisitor, recurrentModule), + boost::apply_visitor(deltaVisitor, feedbackModule)), feedbackModule); + } + else + { + boost::apply_visitor(BackwardVisitor(boost::apply_visitor( + outputParameterVisitor, initialModule), recurrentError, g), + initialModule); + } + + recurrentError = boost::apply_visitor(deltaVisitor, feedbackModule); + backwardStep++; +} + +template +template +void Recurrent::Gradient( + const arma::Mat& input, + const arma::Mat& error, + arma::Mat& /* gradient */) +{ + if (gradientStep < (rho - 1)) + { + boost::apply_visitor(GradientVisitor(input, error), recurrentModule); + + boost::apply_visitor(GradientVisitor(input, + boost::apply_visitor(deltaVisitor, mergeModule)), inputModule); + + boost::apply_visitor(GradientVisitor( + feedbackOutputParameter[feedbackOutputParameter.size() - 2 - + gradientStep], boost::apply_visitor(deltaVisitor, + mergeModule)), feedbackModule); + } + else + { + boost::apply_visitor(GradientZeroVisitor(), recurrentModule); + boost::apply_visitor(GradientZeroVisitor(), inputModule); + boost::apply_visitor(GradientZeroVisitor(), feedbackModule); + + boost::apply_visitor(GradientVisitor(input, + boost::apply_visitor(deltaVisitor, startModule)), initialModule); + } + + gradientStep++; + if (gradientStep == rho) + { + gradientStep = 0; + feedbackOutputParameter.clear(); + } +} + +template +template +void Recurrent::serialize( + Archive& ar, const uint32_t /* version */) +{ + // Clean up memory, if we are loading. + if (cereal::is_loading()) + { + // Clear old things, if needed. + boost::apply_visitor(DeleteVisitor(), recurrentModule); + boost::apply_visitor(DeleteVisitor(), initialModule); + boost::apply_visitor(DeleteVisitor(), startModule); + network.clear(); + } + + ar(CEREAL_VARIANT_POINTER(startModule)); + ar(CEREAL_VARIANT_POINTER(inputModule)); + ar(CEREAL_VARIANT_POINTER(feedbackModule)); + ar(CEREAL_VARIANT_POINTER(transferModule)); + ar(CEREAL_NVP(rho)); + ar(CEREAL_NVP(ownsLayer)); + + // Set up the network. + if (cereal::is_loading()) + { + initialModule = new Sequential<>(); + mergeModule = new AddMerge<>(false, false, false); + recurrentModule = new Sequential<>(false, false); + + boost::apply_visitor(AddVisitor(inputModule), + initialModule); + boost::apply_visitor(AddVisitor(startModule), + initialModule); + boost::apply_visitor(AddVisitor(transferModule), + initialModule); + + boost::apply_visitor(AddVisitor(inputModule), + mergeModule); + boost::apply_visitor(AddVisitor(feedbackModule), + mergeModule); + boost::apply_visitor(AddVisitor(mergeModule), + recurrentModule); + boost::apply_visitor(AddVisitor(transferModule), + recurrentModule); + + network.push_back(initialModule); + network.push_back(mergeModule); + network.push_back(feedbackModule); + network.push_back(recurrentModule); + } +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/layer/recurrent_layer.hpp b/src/mlpack/methods/ann/layer/recurrent_layer.hpp deleted file mode 100644 index 118bb2385e..0000000000 --- a/src/mlpack/methods/ann/layer/recurrent_layer.hpp +++ /dev/null @@ -1,107 +0,0 @@ -/** - * @file methods/ann/layer/recurrent_layer.hpp - * @author Ryan Curtin - * - * Base layer for recurrent neural network layers. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with the mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_LAYER_RECURRENT_LAYER_HPP -#define MLPACK_METHODS_ANN_LAYER_RECURRENT_LAYER_HPP - -#include -#include "layer.hpp" - -namespace mlpack { -namespace ann { - -/** - * The `RecurrentLayer` provides a base layer for all layers that have recurrent - * functionality and store state between steps in a recurrent network. Any - * RecurrentLayer should only be used with a network type such as `RNN` that - * supports recurrent layers. - * - * Any recurrent layer that inherits from `RecurrentLayer` must implement the - * `ClearRecurrentState(bpttSteps, batchSize)` function; this function should - * allocate space to store previous states with the given batch size. See the - * documentation for that function for more details. - * - * @tparam MatType Matrix representation to accept as input and use for - * computation. - */ -template -class RecurrentLayer : public Layer -{ - public: - /** - * Create the RecurrentLayer. - */ - RecurrentLayer(); - - // Virtual destructor is required for classes using inheritance. - virtual ~RecurrentLayer() { } - - //! Copy the given RecurrentLayer. - RecurrentLayer(const RecurrentLayer& other); - //! Take ownership of the given RecurrentLayer. - RecurrentLayer(RecurrentLayer&& other); - //! Copy the given RecurrentLayer. - RecurrentLayer& operator=(const RecurrentLayer& other); - //! Take ownership of the given RecurrentLayer. - RecurrentLayer& operator=(RecurrentLayer&& other); - - /** - * ClearRecurrentState() is called before any forward pass of a recurrent - * network. This function is responsible for allocating any memory necessary - * to store `bpttSteps` steps of previous forward and backward passes, with a - * batch size of `batchSize`. - * - * Any internal state of the recurrent layer should be set to 0. - */ - virtual void ClearRecurrentState( - const size_t bpttSteps, - const size_t batchSize) = 0; - - //! Get the current step index to use in a forward or backward pass. - size_t CurrentStep() const { return currentStep; } - //! Modify the current step index to use in a forward or backward pass. - //! (Don't do this inside of your recurrent layer's implementation! This is - //! meant to be done by the enclosing network.) - size_t& CurrentStep() { return currentStep; } - - //! Get the previous step index, representing the value of CurrentStep() in - //! the previous call to Forward() or Backward(). - size_t PreviousStep() const { return previousStep; } - //! Modify the previous step index, representing the value of CurrentStep() in - //! the previous call to Forward() or Backward(). (Don't modify this inside - //! of your recurrent layer's implementation! This is meant to be done by the - //! enclosing network.) - size_t& PreviousStep() { return previousStep; } - - //! If Forward() or Backward() has been called since ClearRecurrentState(), - //! this will return true. This should be used to determine if recurrent - //! state should be considered in computations. - bool HasPreviousStep() const { return previousStep != size_t(-1); } - - //! Serialize the recurrent layer. - template - void serialize(Archive& ar, const uint32_t /* version */); - - private: - //! The current index of the step. This is set by the enclosing network - //! during forward and backward passes. - size_t currentStep; - //! The previous index of the step. This is set by the enclosing network - //! during forward and backward passes. - size_t previousStep; -}; - -} // namespace ann -} // namespace mlpack - -#include "recurrent_layer_impl.hpp" - -#endif diff --git a/src/mlpack/methods/ann/layer/recurrent_layer_impl.hpp b/src/mlpack/methods/ann/layer/recurrent_layer_impl.hpp deleted file mode 100644 index 92eeb3da11..0000000000 --- a/src/mlpack/methods/ann/layer/recurrent_layer_impl.hpp +++ /dev/null @@ -1,84 +0,0 @@ -/** - * @file methods/ann/layer/recurrent_layer_impl.hpp - * @author Ryan Curtin - * - * Base layer for recurrent neural network layers. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with the mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_LAYER_RECURRENT_LAYER_IMPL_HPP -#define MLPACK_METHODS_ANN_LAYER_RECURRENT_LAYER_IMPL_HPP - -// In case it hasn't been included yet. -#include "recurrent_layer.hpp" - -namespace mlpack { -namespace ann { - -template -RecurrentLayer::RecurrentLayer() : - Layer(), - currentStep(0), - previousStep(0) -{ /* Nothing to do. */ } - -template -RecurrentLayer::RecurrentLayer(const RecurrentLayer& other) : - Layer(other), - currentStep(other.currentStep), - previousStep(other.previousStep) -{ /* Nothing else to do. */ } - -template -RecurrentLayer::RecurrentLayer(RecurrentLayer&& other) : - Layer(std::move(other)), - currentStep(std::move(other.currentStep)), - previousStep(std::move(other.previousStep)) -{ /* Nothing else to do. */ } - -template -RecurrentLayer& -RecurrentLayer::operator=(const RecurrentLayer& other) -{ - if (this != &other) - { - Layer::operator=(other); - currentStep = other.currentStep; - previousStep = other.previousStep; - } - - return *this; -} - -template -RecurrentLayer& -RecurrentLayer::operator=(RecurrentLayer&& other) -{ - if (this != &other) - { - Layer::operator=(std::move(other)); - currentStep = std::move(other.currentStep); - previousStep = std::move(other.previousStep); - } - - return *this; -} - -template -template -void RecurrentLayer::serialize( - Archive& ar, const uint32_t /* version */) -{ - ar(cereal::base_class>(this)); - - ar(CEREAL_NVP(currentStep)); - ar(CEREAL_NVP(previousStep)); -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/layer/not_adapted/reinforce_normal.hpp b/src/mlpack/methods/ann/layer/reinforce_normal.hpp similarity index 64% rename from src/mlpack/methods/ann/layer/not_adapted/reinforce_normal.hpp rename to src/mlpack/methods/ann/layer/reinforce_normal.hpp index 894c2b1c9f..5f5c8a00e8 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/reinforce_normal.hpp +++ b/src/mlpack/methods/ann/layer/reinforce_normal.hpp @@ -14,7 +14,6 @@ #define MLPACK_METHODS_ANN_LAYER_REINFORCE_NORMAL_HPP #include -#include "layer.hpp" namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -23,16 +22,16 @@ namespace ann /** Artificial Neural Network. */ { * Implementation of the reinforce normal layer. The reinforce normal layer * implements the REINFORCE algorithm for the normal distribution. * - * @tparam InputType Type of the input data (arma::colvec, arma::mat, + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). - * @tparam OutputType Type of the output data (arma::colvec, arma::mat, + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). */ template < - typename InputType = arma::mat, - typename OutputType = arma::mat + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat > -class ReinforceNormalType : public Layer +class ReinforceNormal { public: /** @@ -40,10 +39,7 @@ class ReinforceNormalType : public Layer * * @param stdev Standard deviation used during the forward and backward pass. */ - ReinforceNormalType(const double stdev = 1.0); - - //! Clone the ReinforceNormalType object. This handles polymorphism correctly. - ReinforceNormalType* Clone() const { return new ReinforceNormalType(*this); } + ReinforceNormal(const double stdev = 1.0); /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -52,7 +48,8 @@ class ReinforceNormalType : public Layer * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - void Forward(const InputType& input, OutputType& output); + template + void Forward(const arma::Mat& input, arma::Mat& output); /** * Ordinary feed backward pass of a neural network, calculating the function @@ -63,17 +60,31 @@ class ReinforceNormalType : public Layer * @param * (gy) The backpropagated error. * @param g The calculated gradient. */ - void Backward(const InputType& input, - const OutputType& /* gy */, - OutputType& g); + template + void Backward(const DataType& input, const DataType& /* gy */, DataType& g); + + //! Get the output parameter. + OutputDataType& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } + + //! Get the value of the deterministic parameter. + bool Deterministic() const { return deterministic; } + //! Modify the value of the deterministic parameter. + bool& Deterministic() { return deterministic; } //! Get the value of the reward parameter. - double const& Reward() const { return reward; } + double Reward() const { return reward; } //! Modify the value of the deterministic parameter. double& Reward() { return reward; } //! Get the standard deviation used during forward and backward pass. - double const& StandardDeviation() const { return stdev; } + double StandardDeviation() const { return stdev; } /** * Serialize the layer @@ -88,15 +99,18 @@ class ReinforceNormalType : public Layer //! Locally-stored reward parameter. double reward; + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; + //! Locally-stored output module parameter parameters. - std::vector moduleInputParameter; + std::vector moduleInputParameter; //! If true use maximum a posteriori during the forward pass. bool deterministic; -}; // class ReinforceNormalType. - -// Standard ReinforceNormal layer. -typedef ReinforceNormalType ReinforceNormal; +}; // class ReinforceNormal } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/not_adapted/reinforce_normal_impl.hpp b/src/mlpack/methods/ann/layer/reinforce_normal_impl.hpp similarity index 68% rename from src/mlpack/methods/ann/layer/not_adapted/reinforce_normal_impl.hpp rename to src/mlpack/methods/ann/layer/reinforce_normal_impl.hpp index d98820e5db..c2f92df476 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/reinforce_normal_impl.hpp +++ b/src/mlpack/methods/ann/layer/reinforce_normal_impl.hpp @@ -20,21 +20,21 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { template -ReinforceNormalType::ReinforceNormalType( +ReinforceNormal::ReinforceNormal( const double stdev) : stdev(stdev), reward(0.0), deterministic(false) { // Nothing to do here. } -template -void ReinforceNormalType::Forward( - const InputType& input, OutputType& output) +template +template +void ReinforceNormal::Forward( + const arma::Mat& input, arma::Mat& output) { if (!deterministic) { // Multiply by standard deviations and re-center the means to the mean. - output = arma::randn(input.n_rows, input.n_cols) * - stdev + input; + output = output.randn(input.n_rows, input.n_cols) * stdev + input; moduleInputParameter.push_back(input); } @@ -45,9 +45,10 @@ void ReinforceNormalType::Forward( } } -template -void ReinforceNormalType::Backward( - const InputType& input, const OutputType& /* gy */, OutputType& g) +template +template +void ReinforceNormal::Backward( + const DataType& input, const DataType& /* gy */, DataType& g) { g = (input - moduleInputParameter.back()) / std::pow(stdev, 2.0); @@ -58,13 +59,11 @@ void ReinforceNormalType::Backward( moduleInputParameter.pop_back(); } -template +template template -void ReinforceNormalType::serialize( +void ReinforceNormal::serialize( Archive& ar, const uint32_t /* version */) { - ar(cereal::base_class>(this)); - ar(CEREAL_NVP(stdev)); } diff --git a/src/mlpack/methods/ann/layer/not_adapted/relu6.hpp b/src/mlpack/methods/ann/layer/relu6.hpp similarity index 100% rename from src/mlpack/methods/ann/layer/not_adapted/relu6.hpp rename to src/mlpack/methods/ann/layer/relu6.hpp diff --git a/src/mlpack/methods/ann/layer/not_adapted/relu6_impl.hpp b/src/mlpack/methods/ann/layer/relu6_impl.hpp similarity index 100% rename from src/mlpack/methods/ann/layer/not_adapted/relu6_impl.hpp rename to src/mlpack/methods/ann/layer/relu6_impl.hpp diff --git a/src/mlpack/methods/ann/layer/not_adapted/reparametrization.hpp b/src/mlpack/methods/ann/layer/reparametrization.hpp similarity index 54% rename from src/mlpack/methods/ann/layer/not_adapted/reparametrization.hpp rename to src/mlpack/methods/ann/layer/reparametrization.hpp index 833fa4c1a0..d3a183b9fd 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/reparametrization.hpp +++ b/src/mlpack/methods/ann/layer/reparametrization.hpp @@ -15,15 +15,15 @@ #include -#include "layer.hpp" -// #include "../activation_functions/softplus_function.hpp" +#include "layer_types.hpp" +#include "../activation_functions/softplus_function.hpp" namespace mlpack { namespace ann /** Artificial Neural Network. */ { /** - * Implementation of the Reparametrization layer class. This layer samples from - * the given parameters of a normal distribution. + * Implementation of the Reparametrization layer class. This layer samples from the + * given parameters of a normal distribution. * * This class also supports beta-VAE, a state-of-the-art framework for * automated discovery of interpretable factorised latent representations from @@ -38,77 +38,61 @@ namespace ann /** Artificial Neural Network. */ { * author = {Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, * Xavier Glorot, Matthew Botvinick, Shakir Mohamed and * Alexander Lerchner | Google DeepMind}, - * journal = {2017 International Conference on Learning Representations - * (ICLR)}, + * journal = {2017 International Conference on Learning Representations(ICLR)}, * year = {2017}, * url = {https://deepmind.com/research/publications/beta-VAE-Learning-Basic-Visual-Concepts-with-a-Constrained-Variational-Framework} * } * @endcode * - * @tparam InputType Type of the input data (arma::colvec, arma::mat, + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). - * @tparam OutputType Type of the output data (arma::colvec, arma::mat, + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). */ template < - typename InputType = arma::mat, - typename OutputType = arma::mat + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat > -class ReparametrizationType : public Layer +class Reparametrization { public: + //! Create the Reparametrization object. + Reparametrization(); + /** - * Create the Reparametrization layer object. Note that the inputs are - * expected to be the parameters of the normal distribution; see the - * documentation for Forward(). + * Create the Reparametrization layer object using the specified sample vector size. * + * @param latentSize The number of output latent units. * @param stochastic Whether we want random sample or constant. * @param includeKl Whether we want to include KL loss in backward function. * @param beta The beta (hyper)parameter for beta-VAE mentioned above. */ - ReparametrizationType(const bool stochastic = true, - const bool includeKl = true, - const double beta = 1); - - /** - * Clone the ReparametrizationType object. This handles polymorphism - * correctly. - */ - ReparametrizationType* Clone() const - { - return new ReparametrizationType(*this); - } - - // Virtual destructor. - virtual ~ReparametrizationType() { } + Reparametrization(const size_t latentSize, + const bool stochastic = true, + const bool includeKl = true, + const double beta = 1); //! Copy Constructor. - ReparametrizationType(const ReparametrizationType& layer); + Reparametrization(const Reparametrization& layer); //! Move Constructor. - ReparametrizationType(ReparametrizationType&& layer); + Reparametrization(Reparametrization&& layer); //! Copy assignment operator. - ReparametrizationType& operator=(const ReparametrizationType& layer); + Reparametrization& operator=(const Reparametrization& layer); //! Move assignment operator. - ReparametrizationType& operator=(ReparametrizationType&& layer); + Reparametrization& operator=(Reparametrization&& layer); /** * Ordinary feed forward pass of a neural network, evaluating the function * f(x) by propagating the activity forward through f. * - * Note that `input` is expected to be the parameters of the distribution. - * The first `input.n_rows / 2` elements correspond to the - * pre-standard-deviation values for each output element, and the second - * `input.n_rows / 2` elements correspond to the means for each element. - * Thus, the output size of the layer is the number of input elements divided - * by 2. - * * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - void Forward(const InputType& input, OutputType& output); + template + void Forward(const arma::Mat& input, arma::Mat& output); /** * Ordinary feed backward pass of a neural network, calculating the function @@ -119,54 +103,60 @@ class ReparametrizationType : public Layer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const InputType& input, - const OutputType& gy, - OutputType& g); + template + void Backward(const arma::Mat& input, + const arma::Mat& gy, + arma::Mat& g); + + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } + + //! Get the output size. + size_t const& OutputSize() const { return latentSize; } + //! Modify the output size. + size_t& OutputSize() { return latentSize; } //! Get the KL divergence with standard normal. - double Loss(); + double Loss() + { + if (!includeKl) + return 0; + + return -0.5 * beta * arma::accu(2 * arma::log(stdDev) - arma::pow(stdDev, 2) + - arma::pow(mean, 2) + 1) / mean.n_cols; + } //! Get the value of the stochastic parameter. bool Stochastic() const { return stochastic; } - //! Modify the value of the stochastic parameter. - bool& Stochastic() { return stochastic; } //! Get the value of the includeKl parameter. bool IncludeKL() const { return includeKl; } - //! Modify the value of the includeKl parameter. - bool& IncludeKL() { return includeKl; } //! Get the value of the beta hyperparameter. double Beta() const { return beta; } - //! Modify the value of the beta hyperparameter. - double& Beta() { return beta; } - void ComputeOutputDimensions() + size_t InputShape() const { - const size_t inputElem = std::accumulate(this->inputDimensions.begin(), - this->inputDimensions.end(), 0); - if (inputElem % 2 != 0) - { - std::ostringstream oss; - oss << "Reparametrization layer requires that the total number of input " - << "elements is divisible by 2! (Received input with " << inputElem - << " total elements.)"; - throw std::invalid_argument(oss.str()); - } - - this->outputDimensions = std::vector( - this->inputDimensions.size(), 1); - // This flattens the input, and removes half the elements. - this->outputDimensions[0] = inputElem / 2; + return 2 * latentSize; } /** - * Serialize the layer. + * Serialize the layer */ template void serialize(Archive& ar, const uint32_t /* version */); private: + //! Locally-stored number of output units. + size_t latentSize; + //! If false, sample will be constant. bool stochastic; @@ -176,22 +166,25 @@ class ReparametrizationType : public Layer //! The beta hyperparameter for constrained variational frameworks. double beta; + //! Locally-stored delta object. + OutputDataType delta; + //! Locally-stored current gaussian sample. - OutputType gaussianSample; + OutputDataType gaussianSample; //! Locally-stored current mean. - OutputType mean; + OutputDataType mean; //! Locally-stored pre standard deviation. //! After softplus activation gives standard deviation. - OutputType preStdDev; + OutputDataType preStdDev; //! Locally-stored current standard deviation. - OutputType stdDev; -}; // class ReparametrizationType + OutputDataType stdDev; -// Standard Reparametrization layer. -typedef ReparametrizationType Reparametrization; + //! Locally-stored output parameter object. + OutputDataType outputParameter; +}; // class Reparametrization } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/reparametrization_impl.hpp b/src/mlpack/methods/ann/layer/reparametrization_impl.hpp new file mode 100644 index 0000000000..117e67a620 --- /dev/null +++ b/src/mlpack/methods/ann/layer/reparametrization_impl.hpp @@ -0,0 +1,156 @@ +/** + * @file methods/ann/layer/reparametrization_impl.hpp + * @author Atharva Khandait + * + * Implementation of the Reparametrization layer class which samples from a + * gaussian distribution. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_LAYER_REPARAMETRIZATION_IMPL_HPP +#define MLPACK_METHODS_ANN_LAYER_REPARAMETRIZATION_IMPL_HPP + +// In case it hasn't yet been included. +#include "reparametrization.hpp" + +namespace mlpack { +namespace ann /** Artificial Neural Network. */ { + +template +Reparametrization::Reparametrization() : + latentSize(0), + stochastic(true), + includeKl(true), + beta(1) +{ + // Nothing to do here. +} + +template +Reparametrization::Reparametrization( + const size_t latentSize, + const bool stochastic, + const bool includeKl, + const double beta) : + latentSize(latentSize), + stochastic(stochastic), + includeKl(includeKl), + beta(beta) +{ + if (includeKl == false && beta != 1) + { + Log::Info << "The beta parameter will be ignored as KL divergence is not " + << "included." << std::endl; + } +} + +template +Reparametrization::Reparametrization( + const Reparametrization& layer) : + latentSize(layer.latentSize), + stochastic(layer.stochastic), + includeKl(layer.includeKl), + beta(layer.beta) +{ + // Nothing to do here. +} + +template +Reparametrization::Reparametrization( + Reparametrization&& layer) : + latentSize(std::move(layer.latentSize)), + stochastic(std::move(layer.stochastic)), + includeKl(std::move(layer.includeKl)), + beta(std::move(layer.beta)) +{ + // Nothing to do here. +} + +template +Reparametrization& +Reparametrization:: +operator=(const Reparametrization& layer) +{ + if (this != &layer) + { + latentSize = layer.latentSize; + stochastic = layer.stochastic; + includeKl = layer.includeKl; + beta = layer.beta; + } + return *this; +} + +template +Reparametrization& +Reparametrization:: +operator=(Reparametrization&& layer) +{ + if (this != &layer) + { + latentSize = std::move(layer.latentSize); + stochastic = std::move(layer.stochastic); + includeKl = std::move(layer.includeKl); + beta = std::move(layer.beta); + } + return *this; +} + + +template +template +void Reparametrization::Forward( + const arma::Mat& input, arma::Mat& output) +{ + if (input.n_rows != 2 * latentSize) + { + Log::Fatal << "The output size of layer before the Reparametrization " + << "layer should be 2 * latent size of the Reparametrization layer!" + << std::endl; + } + + mean = input.submat(latentSize, 0, 2 * latentSize - 1, input.n_cols - 1); + preStdDev = input.submat(0, 0, latentSize - 1, input.n_cols - 1); + + if (stochastic) + gaussianSample = arma::randn >(latentSize, input.n_cols); + else + gaussianSample = arma::ones >(latentSize, input.n_cols) * 0.7; + + SoftplusFunction::Fn(preStdDev, stdDev); + output = mean + stdDev % gaussianSample; +} + +template +template +void Reparametrization::Backward( + const arma::Mat& /* input */, const arma::Mat& gy, arma::Mat& g) +{ + SoftplusFunction::Deriv(preStdDev, g); + + if (includeKl) + { + g = join_cols(gy % std::move(gaussianSample) % g + (-1 / stdDev + stdDev) + % g * beta, gy + mean * beta / mean.n_cols); + } + else + g = join_cols(gy % std::move(gaussianSample) % g, gy); +} + +template +template +void Reparametrization::serialize( + Archive& ar, const uint32_t /* version */) +{ + ar(CEREAL_NVP(latentSize)); + ar(CEREAL_NVP(stochastic)); + ar(CEREAL_NVP(includeKl)); +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/layer/not_adapted/select.hpp b/src/mlpack/methods/ann/layer/select.hpp similarity index 53% rename from src/mlpack/methods/ann/layer/not_adapted/select.hpp rename to src/mlpack/methods/ann/layer/select.hpp index 001991658f..f67d57e37f 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/select.hpp +++ b/src/mlpack/methods/ann/layer/select.hpp @@ -14,38 +14,31 @@ #include -#include "layer.hpp" - namespace mlpack { namespace ann /** Artificial Neural Network. */ { /** - * The select module selects the specified dimensions from a given input point. + * The select module selects the specified column from a given input matrix. * - * @tparam InputType Type of the input data (arma::colvec, arma::mat, + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). - * @tparam OutputType Type of the output data (arma::colvec, arma::mat, + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). */ template < - typename InputType = arma::mat, - typename OutputType = arma::mat + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat > -class SelectType : public Layer +class Select { public: /** * Create the Select object. * - * @param index The first dimension to extract from the input. - * @param elements The number of elements that should be used. If 0 is given, - * then all dimensions starting with index up to the number of dimensions - * are used. + * @param index The column which should be extracted from the given input. + * @param elements The number of elements that should be used. */ - SelectType(const size_t index = 0, const size_t elements = 0); - - //! Clone the SelectType object. This handles polymorphism correctly. - SelectType* Clone() const { return new SelectType(*this); } + Select(const size_t index = 0, const size_t elements = 0); /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -54,7 +47,8 @@ class SelectType : public Layer * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - void Forward(const InputType& input, OutputType& output); + template + void Forward(const arma::Mat& input, arma::Mat& output); /** * Ordinary feed backward pass of a neural network, calculating the function @@ -65,15 +59,26 @@ class SelectType : public Layer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const InputType& /* input */, - const OutputType& gy, - OutputType& g); + template + void Backward(const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g); + + //! Get the output parameter. + OutputDataType& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } //! Get the column index. - const size_t& Index() const { return index; } + size_t const& Index() const { return index; } //! Get the number of elements selected. - const size_t& NumElements() const { return elements; } + size_t const& NumElements() const { return elements; } /** * Serialize the layer @@ -81,34 +86,19 @@ class SelectType : public Layer template void serialize(Archive& ar, const uint32_t /* version */); - const std::vector OutputDimensions() const - { - std::vector outputDimensions(inputDimensions.size(), 1); - if (elements > 0) - { - outputDimensions[0] = elements; - } - else - { - // Compute the total number of dimensions. - const size_t totalDims = std::accumulate(inputDimensions.begin(), - inputDimensions.end(), 0); - outputDimensions[0] = (totalDims - index); - } - - return outputDimensions; - } - private: //! Locally-stored column index. size_t index; //! Locally-stored number of elements selected. size_t elements; -}; // class SelectType -// Standard Select layer. -typedef SelectType Select; + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; +}; // class Select } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/not_adapted/select_impl.hpp b/src/mlpack/methods/ann/layer/select_impl.hpp similarity index 58% rename from src/mlpack/methods/ann/layer/not_adapted/select_impl.hpp rename to src/mlpack/methods/ann/layer/select_impl.hpp index 1d36b4d517..55baa59e8c 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/select_impl.hpp +++ b/src/mlpack/methods/ann/layer/select_impl.hpp @@ -18,8 +18,8 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -SelectType::SelectType( +template +Select::Select( const size_t index, const size_t elements) : index(index), @@ -28,44 +28,43 @@ SelectType::SelectType( // Nothing to do here. } -template -void SelectType::Forward( - const InputType& input, OutputType& output) +template +template +void Select::Forward( + const arma::Mat& input, arma::Mat& output) { if (elements == 0) { - output = input.rows(index, input.n_rows - 1); + output = input.col(index); } else { - output = input.rows(index, index + elements - 1); + output = input.submat(0, index, elements - 1, index); } } -template -void SelectType::Backward( - const InputType& /* input */, - const OutputType& gy, - OutputType& g) +template +template +void Select::Backward( + const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g) { - // TODO: not sure if this is right if (elements == 0) { g = gy; } else { - g = gy.rows(0, elements - 1); + g = gy.submat(0, 0, elements - 1, 0); } } -template +template template -void SelectType::serialize( +void Select::serialize( Archive& ar, const uint32_t /* version */) { - ar(cereal::base_class>(this)); - ar(CEREAL_NVP(index)); ar(CEREAL_NVP(elements)); } diff --git a/src/mlpack/methods/ann/layer/sequential.hpp b/src/mlpack/methods/ann/layer/sequential.hpp new file mode 100644 index 0000000000..bd1857b1ba --- /dev/null +++ b/src/mlpack/methods/ann/layer/sequential.hpp @@ -0,0 +1,267 @@ +/** + * @file methods/ann/layer/sequential.hpp + * @author Marcus Edel + * + * Definition of the Sequential class, which acts as a feed-forward fully + * connected network container. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_LAYER_SEQUENTIAL_HPP +#define MLPACK_METHODS_ANN_LAYER_SEQUENTIAL_HPP + +#include + +#include "../visitor/delete_visitor.hpp" +#include "../visitor/copy_visitor.hpp" +#include "../visitor/delta_visitor.hpp" +#include "../visitor/output_height_visitor.hpp" +#include "../visitor/output_parameter_visitor.hpp" +#include "../visitor/output_width_visitor.hpp" +#include "../visitor/input_shape_visitor.hpp" + +#include "layer_types.hpp" +#include "add_merge.hpp" + +namespace mlpack { +namespace ann /** Artificial Neural Network. */ { + +/** + * Implementation of the Sequential class. The sequential class works as a + * feed-forward fully connected network container which plugs various layers + * together. + * + * This class can also be used as a container for a residual block. In that + * case, the sizes of the input and output matrices of this class should be + * equal. A typedef has been added for use as a Residual<> class. + * + * For more information, refer the following paper. + * + * @code + * @article{He15, + * author = {Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun}, + * title = {Deep Residual Learning for Image Recognition}, + * year = {2015}, + * url = {https://arxiv.org/abs/1512.03385}, + * eprint = {1512.03385}, + * } + * @endcode + * + * Note: If this class is used as the first layer of a network, it should be + * preceded by IdentityLayer<>. + * + * Note: This class should at least have two layers for a call to its Gradient() + * function. + * + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam Residual If true, use the object as a Residual block. + */ +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat, + bool Residual = false, + typename... CustomLayers +> +class Sequential +{ + public: + /** + * Create the Sequential object using the specified parameters. + * + * @param model Expose the all network modules. + */ + Sequential(const bool model = true); + + /** + * Create the Sequential object using the specified parameters. + * + * @param model Expose all the network modules. + * @param ownsLayers If true, then this module will delete its layers when + * deallocated. + */ + Sequential(const bool model, const bool ownsLayers); + + //! Copy constructor. + Sequential(const Sequential& layer); + + //! Copy assignment operator. + Sequential& operator = (const Sequential& layer); + + //! Destroy the Sequential object. + ~Sequential(); + + /** + * Ordinary feed forward pass of a neural network, evaluating the function + * f(x) by propagating the activity forward through f. + * + * @param input Input data used for evaluating the specified function. + * @param output Resulting output activation. + */ + template + void Forward(const arma::Mat& input, arma::Mat& output); + + /** + * Ordinary feed backward pass of a neural network, using 3rd-order tensors as + * input, calculating the function f(x) by propagating x backwards through f. + * Using the results from the feed forward pass. + * + * @param * (input) The propagated input activation. + * @param gy The backpropagated error. + * @param g The calculated gradient. + */ + template + void Backward(const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g); + + /* + * Calculate the gradient using the output delta and the input activation. + * + * @param input The input parameter used for calculating the gradient. + * @param error The calculated error. + * @param gradient The calculated gradient. + */ + template + void Gradient(const arma::Mat& input, + const arma::Mat& error, + arma::Mat& /* gradient */); + + /* + * Add a new module to the model. + * + * @param args The layer parameter. + */ + template + void Add(Args... args) { network.push_back(new LayerType(args...)); } + + /* + * Add a new module to the model. + * + * @param layer The Layer to be added to the model. + */ + void Add(LayerTypes layer) { network.push_back(layer); } + + //! Return the model modules. + std::vector >& Model() + { + if (model) + { + return network; + } + + return empty; + } + + //! Return the initial point for the optimization. + const arma::mat& Parameters() const { return parameters; } + //! Modify the initial point for the optimization. + arma::mat& Parameters() { return parameters; } + + //! Get the input parameter. + arma::mat const& InputParameter() const { return inputParameter; } + //! Modify the input parameter. + arma::mat& InputParameter() { return inputParameter; } + + //! Get the output parameter. + arma::mat const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + arma::mat& OutputParameter() { return outputParameter; } + + //! Get the delta. + arma::mat const& Delta() const { return delta; } + //! Modify the delta. + arma::mat& Delta() { return delta; } + + //! Get the gradient. + arma::mat const& Gradient() const { return gradient; } + //! Modify the gradient. + arma::mat& Gradient() { return gradient; } + + size_t InputShape() const; + + /** + * Serialize the layer + */ + template + void serialize(Archive& ar, const uint32_t /* version */); + + private: + //! Parameter which indicates if the modules should be exposed. + bool model; + + //! Indicator if we already initialized the model. + bool reset; + + //! Locally-stored network modules. + std::vector > network; + + //! Locally-stored model parameters. + arma::mat parameters; + + //! Locally-stored delta visitor. + DeltaVisitor deltaVisitor; + + //! Locally-stored output parameter visitor. + OutputParameterVisitor outputParameterVisitor; + + //! Locally-stored delete visitor. + DeleteVisitor deleteVisitor; + + //! Locally-stored empty list of modules. + std::vector > empty; + + //! Locally-stored delta object. + arma::mat delta; + + //! Locally-stored input parameter object. + arma::mat inputParameter; + + //! Locally-stored output parameter object. + arma::mat outputParameter; + + //! Locally-stored gradient object. + arma::mat gradient; + + //! Locally-stored output width visitor. + OutputWidthVisitor outputWidthVisitor; + + //! Locally-stored output height visitor. + OutputHeightVisitor outputHeightVisitor; + + //! Locally-stored copy visitor + CopyVisitor copyVisitor; + + //! The input width. + size_t width; + + //! The input height. + size_t height; + + //! Whether we are responsible for deleting the layers held in this module. + bool ownsLayers; +}; // class Sequential + +/* + * Convenience typedef for use as Residual<> layer. + */ +template< + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat, + typename... CustomLayers +> +using Residual = Sequential< + InputDataType, OutputDataType, true, CustomLayers...>; + +} // namespace ann +} // namespace mlpack + +// Include implementation. +#include "sequential_impl.hpp" + +#endif diff --git a/src/mlpack/methods/ann/layer/sequential_impl.hpp b/src/mlpack/methods/ann/layer/sequential_impl.hpp new file mode 100644 index 0000000000..1290a15ebb --- /dev/null +++ b/src/mlpack/methods/ann/layer/sequential_impl.hpp @@ -0,0 +1,270 @@ +/** + * @file methods/ann/layer/sequential_impl.hpp + * @author Marcus Edel + * + * Implementation of the Sequential class, which acts as a feed-forward fully + * connected network container. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_LAYER_SEQUENTIAL_IMPL_HPP +#define MLPACK_METHODS_ANN_LAYER_SEQUENTIAL_IMPL_HPP + +// In case it hasn't yet been included. +#include "sequential.hpp" + +#include "../visitor/forward_visitor.hpp" +#include "../visitor/backward_visitor.hpp" +#include "../visitor/gradient_visitor.hpp" +#include "../visitor/set_input_height_visitor.hpp" +#include "../visitor/set_input_width_visitor.hpp" +#include "../visitor/input_shape_visitor.hpp" + +namespace mlpack { +namespace ann /** Artificial Neural Network. */ { + +template +Sequential:: +Sequential(const bool model) : + model(model), reset(false), width(0), height(0), ownsLayers(!model) +{ + // Nothing to do here. +} + +template +Sequential:: +Sequential(const bool model, const bool ownsLayers) : + model(model), reset(false), width(0), height(0), ownsLayers(ownsLayers) +{ + // Nothing to do here. +} + +template +Sequential:: +Sequential(const Sequential& layer) : + model(layer.model), + reset(layer.reset), + width(layer.width), + height(layer.height), + ownsLayers(layer.ownsLayers) +{ + // Nothing to do here. +} + +template +Sequential& +Sequential:: +operator = (const Sequential& layer) +{ + if (this != &layer) + { + model = layer.model; + reset = layer.reset; + width = layer.width; + height = layer.height; + ownsLayers = layer.ownsLayers; + parameters = layer.parameters; + network.clear(); + // Build new layers according to source network. + for (size_t i = 0; i < layer.network.size(); ++i) + { + this->network.push_back(boost::apply_visitor(copyVisitor, + layer.network[i])); + } + } + return *this; +} + + +template +Sequential< + InputDataType, OutputDataType, Residual, CustomLayers...>::~Sequential() +{ + if (!model && ownsLayers) + { + for (LayerTypes& layer : network) + boost::apply_visitor(deleteVisitor, layer); + } +} + +template +size_t Sequential:: +InputShape() const +{ + size_t inputShape = 0; + + for (size_t l = 0; l < network.size(); ++l) + { + if (inputShape == 0) + inputShape = boost::apply_visitor(InShapeVisitor(), network[l]); + else + break; + } + + return inputShape; +} + +template +template +void Sequential:: +Forward(const arma::Mat& input, arma::Mat& output) +{ + boost::apply_visitor(ForwardVisitor(input, + boost::apply_visitor(outputParameterVisitor, network.front())), + network.front()); + + if (!reset) + { + if (boost::apply_visitor(outputWidthVisitor, network.front()) != 0) + { + width = boost::apply_visitor(outputWidthVisitor, network.front()); + } + + if (boost::apply_visitor(outputHeightVisitor, network.front()) != 0) + { + height = boost::apply_visitor(outputHeightVisitor, network.front()); + } + } + + for (size_t i = 1; i < network.size(); ++i) + { + if (!reset) + { + // Set the input width. + boost::apply_visitor(SetInputWidthVisitor(width), network[i]); + + // Set the input height. + boost::apply_visitor(SetInputHeightVisitor(height), network[i]); + } + + boost::apply_visitor(ForwardVisitor(boost::apply_visitor( + outputParameterVisitor, network[i - 1]), + boost::apply_visitor(outputParameterVisitor, network[i])), + network[i]); + + if (!reset) + { + // Get the output width. + if (boost::apply_visitor(outputWidthVisitor, network[i]) != 0) + { + width = boost::apply_visitor(outputWidthVisitor, network[i]); + } + + // Get the output height. + if (boost::apply_visitor(outputHeightVisitor, network[i]) != 0) + { + height = boost::apply_visitor(outputHeightVisitor, network[i]); + } + } + } + + if (!reset) + { + reset = true; + } + + output = boost::apply_visitor(outputParameterVisitor, network.back()); + + if (Residual) + { + if (arma::size(output) != arma::size(input)) + { + Log::Fatal << "The sizes of the output and input matrices of the Residual" + << " block should be equal. Please examine the network architecture." + << std::endl; + } + output += input; + } +} + +template +template +void Sequential< + InputDataType, OutputDataType, Residual, CustomLayers...>::Backward( + const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g) +{ + boost::apply_visitor(BackwardVisitor(boost::apply_visitor( + outputParameterVisitor, network.back()), gy, + boost::apply_visitor(deltaVisitor, network.back())), + network.back()); + + for (size_t i = 2; i < network.size() + 1; ++i) + { + boost::apply_visitor(BackwardVisitor(boost::apply_visitor( + outputParameterVisitor, network[network.size() - i]), + boost::apply_visitor(deltaVisitor, network[network.size() - i + 1]), + boost::apply_visitor(deltaVisitor, network[network.size() - i])), + network[network.size() - i]); + } + + g = boost::apply_visitor(deltaVisitor, network.front()); + + if (Residual) + { + g += gy; + } +} + +template +template +void Sequential:: +Gradient(const arma::Mat& input, + const arma::Mat& error, + arma::Mat& /* gradient */) +{ + boost::apply_visitor(GradientVisitor(boost::apply_visitor( + outputParameterVisitor, network[network.size() - 2]), error), + network.back()); + + for (size_t i = 2; i < network.size(); ++i) + { + boost::apply_visitor(GradientVisitor(boost::apply_visitor( + outputParameterVisitor, network[network.size() - i - 1]), + boost::apply_visitor(deltaVisitor, network[network.size() - i + 1])), + network[network.size() - i]); + } + + boost::apply_visitor(GradientVisitor(input, + boost::apply_visitor(deltaVisitor, network[1])), network.front()); +} + +template +template +void Sequential< + InputDataType, OutputDataType, Residual, CustomLayers...>::serialize( + Archive& ar, const uint32_t /* version */) +{ + // If loading, delete the old layers. + if (cereal::is_loading()) + { + for (LayerTypes& layer : network) + { + boost::apply_visitor(deleteVisitor, layer); + } + } + + ar(CEREAL_NVP(model)); + ar(CEREAL_VECTOR_VARIANT_POINTER(network)); + + ar(CEREAL_NVP(ownsLayers)); +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/layer/serialization.hpp b/src/mlpack/methods/ann/layer/serialization.hpp deleted file mode 100644 index 5e13d2a9b4..0000000000 --- a/src/mlpack/methods/ann/layer/serialization.hpp +++ /dev/null @@ -1,53 +0,0 @@ -/** - * @file serialization.hpp - * @author Ryan Curtin - * - * Set up polymorphic serialization correctly for layer types. If you need - * custom serialization for a non-standard type, you will have to use the macros - * in this file. - */ -#ifndef MLPACK_METHODS_ANN_LAYER_SERIALIZATION_HPP -#define MLPACK_METHODS_ANN_LAYER_SERIALIZATION_HPP - -#define CEREAL_REGISTER_MLPACK_LAYERS(...) \ - CEREAL_REGISTER_TYPE(mlpack::ann::Layer<__VA_ARGS__>); \ - CEREAL_REGISTER_TYPE(mlpack::ann::MultiLayer<__VA_ARGS__>); \ - CEREAL_REGISTER_TYPE(mlpack::ann::RecurrentLayer<__VA_ARGS__>); \ - CEREAL_REGISTER_TYPE(mlpack::ann::AddType<__VA_ARGS__>); \ - CEREAL_REGISTER_TYPE(mlpack::ann::AlphaDropoutType<__VA_ARGS__>); \ - /* Base layers from base_layer.hpp. */ \ - CEREAL_REGISTER_TYPE(mlpack::ann::SigmoidType<__VA_ARGS__>); \ - CEREAL_REGISTER_TYPE(mlpack::ann::ReLUType<__VA_ARGS__>); \ - CEREAL_REGISTER_TYPE(mlpack::ann::TanHType<__VA_ARGS__>); \ - CEREAL_REGISTER_TYPE(mlpack::ann::SoftPlusType<__VA_ARGS__>); \ - CEREAL_REGISTER_TYPE(mlpack::ann::HardSigmoidType<__VA_ARGS__>); \ - CEREAL_REGISTER_TYPE(mlpack::ann::SwishType<__VA_ARGS__>); \ - CEREAL_REGISTER_TYPE(mlpack::ann::MishType<__VA_ARGS__>); \ - CEREAL_REGISTER_TYPE(mlpack::ann::LiSHTType<__VA_ARGS__>); \ - CEREAL_REGISTER_TYPE(mlpack::ann::GELUType<__VA_ARGS__>); \ - CEREAL_REGISTER_TYPE(mlpack::ann::ElliotType<__VA_ARGS__>); \ - CEREAL_REGISTER_TYPE(mlpack::ann::ElishType<__VA_ARGS__>); \ - CEREAL_REGISTER_TYPE(mlpack::ann::GaussianType<__VA_ARGS__>); \ - /* (end of base_layer.hpp) */ \ - CEREAL_REGISTER_TYPE(mlpack::ann::ConcatenateType<__VA_ARGS__>); \ - CEREAL_REGISTER_TYPE(mlpack::ann::ConvolutionType< \ - mlpack::ann::NaiveConvolution, \ - mlpack::ann::NaiveConvolution, \ - mlpack::ann::NaiveConvolution, \ - __VA_ARGS__>); \ - CEREAL_REGISTER_TYPE(mlpack::ann::DropConnectType<__VA_ARGS__>); \ - CEREAL_REGISTER_TYPE(mlpack::ann::DropoutType<__VA_ARGS__>); \ - CEREAL_REGISTER_TYPE(mlpack::ann::LeakyReLUType<__VA_ARGS__>); \ - CEREAL_REGISTER_TYPE(mlpack::ann::Linear3DType<__VA_ARGS__>); \ - CEREAL_REGISTER_TYPE(mlpack::ann::LinearType<__VA_ARGS__>); \ - CEREAL_REGISTER_TYPE(mlpack::ann::LinearNoBiasType<__VA_ARGS__>); \ - CEREAL_REGISTER_TYPE(mlpack::ann::LogSoftMaxType<__VA_ARGS__>); \ - CEREAL_REGISTER_TYPE(mlpack::ann::LSTMType<__VA_ARGS__>); \ - CEREAL_REGISTER_TYPE(mlpack::ann::MaxPoolingType<__VA_ARGS__>); \ - CEREAL_REGISTER_TYPE(mlpack::ann::NoisyLinearType<__VA_ARGS__>); \ - CEREAL_REGISTER_TYPE(mlpack::ann::PaddingType<__VA_ARGS__>); \ - CEREAL_REGISTER_TYPE(mlpack::ann::RBFType<__VA_ARGS__>); \ - -CEREAL_REGISTER_MLPACK_LAYERS(arma::mat); - -#endif diff --git a/src/mlpack/methods/ann/layer/not_adapted/softmax.hpp b/src/mlpack/methods/ann/layer/softmax.hpp similarity index 58% rename from src/mlpack/methods/ann/layer/not_adapted/softmax.hpp rename to src/mlpack/methods/ann/layer/softmax.hpp index 18f2cd9db6..43fd0856c9 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/softmax.hpp +++ b/src/mlpack/methods/ann/layer/softmax.hpp @@ -16,8 +16,6 @@ #include -#include "layer.hpp" - namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -28,21 +26,22 @@ namespace ann /** Artificial Neural Network. */ { * numbers. It should be used for inference only and not with NLL loss (use * LogSoftMax instead). * - * @tparam InputType The type of the layer's inputs. The layer automatically - * cast inputs to this type (Default: arma::mat). - * @tparam OutputType The type of the computation which also causes the output - * to also be in this type. The type also allows the computation and weight - * type to differ from the input type (Default: arma::mat). + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class SoftmaxType : public Layer +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class Softmax { public: - //! Create the Softmax object. - SoftmaxType(); - - //! Clone the SoftmaxType object. This handles polymorphism correctly. - SoftmaxType* Clone() const { return new SoftmaxType(*this); } + /** + * Create the Softmax object. + */ + Softmax(); /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -51,6 +50,7 @@ class SoftmaxType : public Layer * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ + template void Forward(const InputType& input, OutputType& output); /** @@ -62,19 +62,37 @@ class SoftmaxType : public Layer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const InputType& input, const OutputType& gy, OutputType& g); + template + void Backward(const arma::Mat& input, + const arma::Mat& gy, + arma::Mat& g); - //! Serialize the layer. + //! Get the output parameter. + OutputDataType& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the size of the weights. + size_t WeightSize() const { return 0; } + + //! Get the delta. + InputDataType& Delta() const { return delta; } + //! Modify the delta. + InputDataType& Delta() { return delta; } + + /** + * Serialize the layer. + */ template - void serialize(Archive& ar, const uint32_t /* version */); + void serialize(Archive& /* ar */, const uint32_t /* version */); private: -}; // class SoftmaxType + //! Locally-stored delta object. + OutputDataType delta; -// Convenience typedefs. - -// Standard Linear layer using no regularization. -typedef SoftmaxType Softmax; + //! Locally-stored output parameter object. + OutputDataType outputParameter; +}; // class Softmax } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/not_adapted/softmax_impl.hpp b/src/mlpack/methods/ann/layer/softmax_impl.hpp similarity index 65% rename from src/mlpack/methods/ann/layer/not_adapted/softmax_impl.hpp rename to src/mlpack/methods/ann/layer/softmax_impl.hpp index 34fbed04e4..500e0e108e 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/softmax_impl.hpp +++ b/src/mlpack/methods/ann/layer/softmax_impl.hpp @@ -19,14 +19,15 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -SoftmaxType::SoftmaxType() +template +Softmax::Softmax() { // Nothing to do here. } +template template -void SoftmaxType::Forward( +void Softmax::Forward( const InputType& input, OutputType& output) { @@ -35,22 +36,23 @@ void SoftmaxType::Forward( output = softmaxInput.each_row() / sum(softmaxInput, 0); } -template -void SoftmaxType::Backward( - const InputType& input, - const OutputType& gy, - OutputType& g) +template +template +void Softmax::Backward( + const arma::Mat& input, + const arma::Mat& gy, + arma::Mat& g) { g = input % (gy - arma::repmat(arma::sum(gy % input), input.n_rows, 1)); } -template +template template -void SoftmaxType::serialize( - Archive& ar, +void Softmax::serialize( + Archive& /* ar */, const uint32_t /* version */) { - ar(cereal::base_class>(this)); + // Nothing to do here. } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/not_adapted/softmin.hpp b/src/mlpack/methods/ann/layer/softmin.hpp similarity index 57% rename from src/mlpack/methods/ann/layer/not_adapted/softmin.hpp rename to src/mlpack/methods/ann/layer/softmin.hpp index ffb8c2e43d..57809992c2 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/softmin.hpp +++ b/src/mlpack/methods/ann/layer/softmin.hpp @@ -15,8 +15,6 @@ #include -#include "layer.hpp" - namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -24,22 +22,23 @@ namespace ann /** Artificial Neural Network. */ { * Implementation of the Softmin layer. The Softmin function takes as a input * a vector of K real numbers, rescaling them so that the elements of the * K-dimensional output vector lie in the range [0, 1] and sum to 1. - * - * @tparam InputType The type of the layer's inputs. The layer automatically - * cast inputs to this type (Default: arma::mat). - * @tparam OutputType The type of the computation which also causes the output - * to also be in this type. The type also allows the computation and weight - * type to differ from the input type (Default: arma::mat). + * + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class SoftminType : public Layer +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class Softmin { public: - //! Create the Softmin object. - SoftminType(); - - //! Clone the SoftminType object. This handles polymorphism correctly. - SoftminType* Clone() const { return new SoftminType(*this); } + /** + * Create the Softmin object. + */ + Softmin(); /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -48,6 +47,7 @@ class SoftminType : public Layer * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ + template void Forward(const InputType& input, OutputType& output); /** @@ -59,18 +59,34 @@ class SoftminType : public Layer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const InputType& input, const OutputType& gy, OutputType& g); + template + void Backward(const arma::Mat& input, + const arma::Mat& gy, + arma::Mat& g); - //! Serialize the layer. + //! Get the output parameter. + OutputDataType& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + InputDataType& Delta() const { return delta; } + //! Modify the delta. + InputDataType& Delta() { return delta; } + + /** + * Serialize the layer. + */ template - void serialize(Archive& ar, const uint32_t /* version */); -}; // class SoftminType + void serialize(Archive& /* ar */, const uint32_t /* version */); -// Convenience typedefs. - -// Standard Softmin layer using no regularization. -typedef SoftminType Softmin; + private: + //! Locally-stored delta object. + OutputDataType delta; + //! Locally stored output parameter object. + OutputDataType outputParameter; +}; // class Softmin } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/not_adapted/softmin_impl.hpp b/src/mlpack/methods/ann/layer/softmin_impl.hpp similarity index 65% rename from src/mlpack/methods/ann/layer/not_adapted/softmin_impl.hpp rename to src/mlpack/methods/ann/layer/softmin_impl.hpp index 4e200f2943..6945256b44 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/softmin_impl.hpp +++ b/src/mlpack/methods/ann/layer/softmin_impl.hpp @@ -18,14 +18,15 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -SoftminType::SoftminType() +template +Softmin::Softmin() { // Nothing to do here. } +template template -void SoftminType::Forward( +void Softmin::Forward( const InputType& input, OutputType& output) { @@ -34,22 +35,23 @@ void SoftminType::Forward( output = softminInput.each_row() / sum(softminInput, 0); } -template -void SoftminType::Backward( - const InputType& input, - const OutputType& gy, - OutputType& g) +template +template +void Softmin::Backward( + const arma::Mat& input, + const arma::Mat& gy, + arma::Mat& g) { g = input % (gy - arma::repmat(arma::sum(gy % input), input.n_rows, 1)); } -template +template template -void SoftminType::serialize( - Archive& ar, +void Softmin::serialize( + Archive& /* ar */, const uint32_t /* version */) { - ar(cereal::base_class>(this)); + // Nothing to do here. } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/not_adapted/softshrink.hpp b/src/mlpack/methods/ann/layer/softshrink.hpp similarity index 66% rename from src/mlpack/methods/ann/layer/not_adapted/softshrink.hpp rename to src/mlpack/methods/ann/layer/softshrink.hpp index ac3f11319c..93db85f55b 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/softshrink.hpp +++ b/src/mlpack/methods/ann/layer/softshrink.hpp @@ -18,8 +18,6 @@ #include -#include "layer.hpp" - namespace mlpack { namespace ann /** Artifical Neural Network. */ { @@ -45,25 +43,26 @@ namespace ann /** Artifical Neural Network. */ { * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). */ -template -class SoftShrinkType : public Layer +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class SoftShrink { public: /** - * Create SoftShrink object using specified hyperparameter lambda. + * Create Soft Shrink object using specified hyperparameter lambda. * * @param lambda The noise level of an image depends on settings of an - * imaging device. The settings can be used to select appropriate - * parameters for denoising methods. It is proportional to the noise - * level entered by the user. And it is calculated by multiplying the - * noise level sigma of the input(noisy image) and a coefficient 'a' - * which is one of the training parameters. Default value of lambda - * is 0.5. + * imaging device. The settings can be used to select appropriate + * parameters for denoising methods. It is proportional to the noise + * level entered by the user. + * And it is calculated by multiplying the + * noise level sigma of the input(noisy image) and a + * coefficient 'a' which is one of the training parameters. + * Default value of lambda is 0.5. */ - SoftShrinkType(const double lambda = 0.5); - - //! Clone the SoftShrinkType object. This handles polymorphism correctly. - SoftShrinkType* Clone() const { return new SoftShrinkType(*this); } + SoftShrink(const double lambda = 0.5); /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -72,6 +71,7 @@ class SoftShrinkType : public Layer * @param input Input data used for evaluating the Soft Shrink function. * @param output Resulting output activation */ + template void Forward(const InputType& input, OutputType& output); /** @@ -83,26 +83,42 @@ class SoftShrinkType : public Layer * @param gy The backpropagated error. * @param g The calculated gradient */ - void Backward(const InputType& input, const OutputType& gy, OutputType& g); + template + void Backward(const DataType& input, + DataType& gy, + DataType& g); + + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } //! Get the hyperparameter lambda. double const& Lambda() const { return lambda; } //! Modify the hyperparameter lambda. double& Lambda() { return lambda; } - //! Serialize the layer. + /** + * Serialize the layer. + */ template void serialize(Archive& ar, const uint32_t /* version */); private: + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; + //! Locally-stored hyperparamater lambda. double lambda; -}; // class SoftShrinkType - -// Convenience typedefs. - -// Standard SoftShrink layer. -typedef SoftShrinkType SoftShrink; +}; // class SoftShrink } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/not_adapted/softshrink_impl.hpp b/src/mlpack/methods/ann/layer/softshrink_impl.hpp similarity index 59% rename from src/mlpack/methods/ann/layer/not_adapted/softshrink_impl.hpp rename to src/mlpack/methods/ann/layer/softshrink_impl.hpp index 80358de911..f4be4b1136 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/softshrink_impl.hpp +++ b/src/mlpack/methods/ann/layer/softshrink_impl.hpp @@ -20,36 +20,38 @@ namespace ann /** Artificial Neural Network. */ { // This constructor is called for Soft Shrink activation function. // lambda is a hyperparameter. -template -SoftShrinkType::SoftShrinkType(const double lambda) : +template +SoftShrink::SoftShrink(const double lambda) : lambda(lambda) { // Nothing to do here. } +template template -void SoftShrinkType::Forward( +void SoftShrink::Forward( const InputType& input, OutputType& output) { - output = (input > lambda) % (input - lambda) + - (input < -lambda) % (input + lambda); + output = (input > lambda) % (input - lambda) + ( + input < -lambda) % (input + lambda); } -template -void SoftShrinkType::Backward( - const InputType& input, const OutputType& gy, OutputType& g) +template +template +void SoftShrink::Backward( + const DataType& input, DataType& gy, DataType& g) { - g = gy % (arma::ones(arma::size(input)) - (input == 0)); + DataType derivative; + derivative = (arma::ones(arma::size(input)) - (input == 0)); + g = gy % derivative; } -template +template template -void SoftShrinkType::serialize( +void SoftShrink::serialize( Archive& ar, const uint32_t /* version */) { - ar(cereal::base_class>(this)); - ar(CEREAL_NVP(lambda)); } diff --git a/src/mlpack/methods/ann/layer/not_adapted/spatial_dropout.hpp b/src/mlpack/methods/ann/layer/spatial_dropout.hpp similarity index 66% rename from src/mlpack/methods/ann/layer/not_adapted/spatial_dropout.hpp rename to src/mlpack/methods/ann/layer/spatial_dropout.hpp index 0fe1c08ef3..e44388b97d 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/spatial_dropout.hpp +++ b/src/mlpack/methods/ann/layer/spatial_dropout.hpp @@ -1,4 +1,3 @@ -// Temporarily drop. /** * @file methods/ann/layer/spatial_dropout.hpp * @author Anjishnu Mukherjee @@ -16,12 +15,9 @@ #include #include -#include "layer.hpp" - namespace mlpack { namespace ann /** Artificial Neural Network. */ { -// TODO: this could likely use inputDimensions to remove the `size` parameter! /** * Implementation of the SpatialDropout layer. * @@ -40,29 +36,27 @@ namespace ann /** Artificial Neural Network. */ { * } * @endcode * - * @tparam InputType The type of the layer's inputs. The layer automatically - * cast inputs to this type (Default: arma::mat). - * @tparam OutputType The type of the computation which also causes the output - * to also be in this type. The type also allows the computation and weight - * type to differ from the input type (Default: arma::mat). + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class SpatialDropoutType : public Layer +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class SpatialDropout { public: //! Create the SpatialDropout object. - SpatialDropoutType(); - + SpatialDropout(); /** * Create the SpatialDropout object using the specified parameters. * * @param size The number of channels of each input image. * @param ratio The probability of each channel getting dropped. */ - SpatialDropoutType(const size_t size, const double ratio = 0.5); - - //! Clone the SpatialDropoutType object. This handles polymorphism correctly. - SpatialDropoutType* Clone() const { return new SpatialDropoutType(*this); } + SpatialDropout(const size_t size, const double ratio = 0.5); /** * Ordinary feed forward pass of the SpatialDropout layer. @@ -70,7 +64,8 @@ class SpatialDropoutType : public Layer * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - void Forward(const InputType& input, OutputType& output); + template + void Forward(const arma::Mat& input, arma::Mat& output); /** * Ordinary feed backward pass of the SpatialDropout layer. @@ -79,7 +74,20 @@ class SpatialDropoutType : public Layer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const InputType& input, const OutputType& gy, OutputType& g); + template + void Backward(const arma::Mat& input, + const arma::Mat& gy, + arma::Mat& g); + + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } //! Get the number of channels. size_t Size() const { return size; } @@ -87,6 +95,11 @@ class SpatialDropoutType : public Layer //! Modify the number of channels. size_t& Size() { return size; } + //! Get the value of the deterministic parameter. + bool Deterministic() const { return deterministic; } + //! Modify the value of the deterministic parameter. + bool& Deterministic() { return deterministic; } + //! Get the probability value. double Ratio() const { return ratio; } @@ -97,13 +110,21 @@ class SpatialDropoutType : public Layer scale = 1.0 / (1.0 - ratio); } - //! Serialize the layer. + /** + * Serialize the layer. + */ template void serialize(Archive& ar, const uint32_t /* version */); private: + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; + //! Locally-stored mast object. - OutputType mask; + OutputDataType mask; //! The number of channels of each input image. size_t size; @@ -122,13 +143,11 @@ class SpatialDropoutType : public Layer //! The number of pixels in each feature map. size_t inputSize; + + //! If true dropout and scaling are disabled. + bool deterministic; }; // class SpatialDropout -// Convenience typedefs. - -// Standard SpatialDropout layer. -typedef SpatialDropoutType SpatialDropout; - } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/not_adapted/spatial_dropout_impl.hpp b/src/mlpack/methods/ann/layer/spatial_dropout_impl.hpp similarity index 57% rename from src/mlpack/methods/ann/layer/not_adapted/spatial_dropout_impl.hpp rename to src/mlpack/methods/ann/layer/spatial_dropout_impl.hpp index d8ae24010b..4bd2cb767b 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/spatial_dropout_impl.hpp +++ b/src/mlpack/methods/ann/layer/spatial_dropout_impl.hpp @@ -18,20 +18,21 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -SpatialDropoutType::SpatialDropoutType() : +template +SpatialDropout::SpatialDropout() : size(0), ratio(0.5), scale(1.0 / (1.0 - ratio)), reset(false), batchSize(0), - inputSize(0) + inputSize(0), + deterministic(false) { // Nothing to do here. } -template -SpatialDropoutType::SpatialDropoutType( +template +SpatialDropout::SpatialDropout( const size_t size, const double ratio) : size(size), @@ -39,14 +40,16 @@ SpatialDropoutType::SpatialDropoutType( scale(1.0 / (1.0 - ratio)), reset(false), batchSize(0), - inputSize(0) + inputSize(0), + deterministic(false) { // Nothing to do here. } -template -void SpatialDropoutType::Forward( - const InputType& input, OutputType& output) +template +template +void SpatialDropout::Forward( + const arma::Mat& input, arma::Mat& output) { Log::Assert(input.n_rows % size == 0, "Input features must be divisible \ by feature maps."); @@ -59,20 +62,16 @@ void SpatialDropoutType::Forward( } if (deterministic) - { output = input; - } else { output.zeros(arma::size(input)); - arma::Cube inputTemp( - const_cast(input).memptr(), inputSize, size, batchSize, - false, true); - arma::Cube outputTemp( - const_cast(output).memptr(), inputSize, size, batchSize, - false, true); - OutputType probabilities(1, size); - OutputType maskRow(1, size); + arma::cube inputTemp(const_cast(input).memptr(), inputSize, + size, batchSize, false, false); + arma::cube outputTemp(const_cast(output).memptr(), inputSize, + size, batchSize, false, false); + arma::mat probabilities(1, size); + arma::mat maskRow(1, size); probabilities.fill(ratio); ann::BernoulliDistribution<> bernoulli_dist(probabilities, false); maskRow = bernoulli_dist.Sample(); @@ -83,39 +82,36 @@ void SpatialDropoutType::Forward( } } -template -void SpatialDropoutType::Backward( - const InputType& input, const OutputType& gy, OutputType& g) +template +template +void SpatialDropout::Backward( + const arma::Mat& input, const arma::Mat& gy, arma::Mat& g) { g.zeros(arma::size(input)); - arma::Cube gyTemp( - const_cast(gy).memptr(), inputSize, size, batchSize, false, - true); - arma::Cube gTemp( - const_cast(g).memptr(), inputSize, size, batchSize, false, - true); + arma::cube gyTemp(const_cast(gy).memptr(), inputSize, size, + batchSize, false, false); + arma::cube gTemp(const_cast(g).memptr(), inputSize, size, + batchSize, false, false); for (size_t n = 0; n < batchSize; n++) gTemp.slice(n) = gyTemp.slice(n) % mask * scale; } -template +template template -void SpatialDropoutType::serialize( +void SpatialDropout::serialize( Archive& ar, const uint32_t /* version */) { - ar(cereal::base_class>(this)); - ar(CEREAL_NVP(size)); ar(CEREAL_NVP(ratio)); ar(CEREAL_NVP(batchSize)); ar(CEREAL_NVP(inputSize)); ar(CEREAL_NVP(reset)); + ar(CEREAL_NVP(deterministic)); // Reset scale. - if (Archive::is_loading::value) - scale = 1.0 / (1.0 - ratio); + scale = 1.0 / (1.0 - ratio); } } // namespace ann diff --git a/src/mlpack/methods/ann/layer/not_adapted/subview.hpp b/src/mlpack/methods/ann/layer/subview.hpp similarity index 69% rename from src/mlpack/methods/ann/layer/not_adapted/subview.hpp rename to src/mlpack/methods/ann/layer/subview.hpp index 77a8a85b3a..bc99b0cc44 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/subview.hpp +++ b/src/mlpack/methods/ann/layer/subview.hpp @@ -13,8 +13,7 @@ #define MLPACK_METHODS_ANN_LAYER_SUBVIEW_HPP #include - -#include "layer.hpp" +#include namespace mlpack { namespace ann { @@ -23,31 +22,34 @@ namespace ann { * Implementation of the subview layer. The subview layer modifies the input to * a submatrix of required size. * - * @tparam InputType Type of the input data (arma::colvec, arma::mat, + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). - * @tparam OutputType Type of the output data (arma::colvec, arma::mat, + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). */ template < - typename InputType = arma::mat, - typename OutputType = arma::mat + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat > -class SubviewType : public Layer +class Subview { public: /** * Create the Subview layer object using the specified range of input to * accept. * + * @param inSize Width of sample. * @param beginRow Starting row index. * @param endRow Ending row index. * @param beginCol Starting column index. * @param endCol Ending column index. */ - SubviewType(const size_t beginRow = 0, - const size_t endRow = 0, - const size_t beginCol = 0, - const size_t endCol = 0) : + Subview(const size_t inSize = 1, + const size_t beginRow = 0, + const size_t endRow = 0, + const size_t beginCol = 0, + const size_t endCol = 0) : + inSize(inSize), beginRow(beginRow), endRow(endRow), beginCol(beginCol), @@ -56,9 +58,6 @@ class SubviewType : public Layer /* Nothing to do here */ } - //! Clone the SubviewType object. This handles polymorphism correctly. - SubviewType* Clone() const { return new SubviewType(*this); } - /** * Ordinary feed forward pass of a neural network, evaluating the function * f(x) by propagating the activity forward through f. @@ -66,18 +65,17 @@ class SubviewType : public Layer * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ + template void Forward(const InputType& input, OutputType& output) { - size_t batchSize = input.n_cols; + size_t batchSize = input.n_cols / inSize; // Check if subview parameters are within the indices of input sample. - // TODO: this seems incorrect - endRow = ((endRow < inputDimensions[0]) && (endRow >= beginRow))? - endRow : (inputDimensions[0] - 1); - endCol = ((endCol < inputDimensions[1]) && (endCol >= beginCol)) ? - endCol : (inputDimensions[1] - 1); + endRow = ((endRow < input.n_rows) && (endRow >= beginRow))? + endRow : (input.n_rows - 1); + endCol = ((endCol < inSize) && (endCol >= beginCol)) ? + endCol : (inSize - 1); - // TODO: this is maybe not right? output.set_size( (endRow - beginRow + 1) * (endCol - beginCol + 1), batchSize); @@ -113,13 +111,27 @@ class SubviewType : public Layer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const InputType& /* input */, - const OutputType& gy, - OutputType& g) + template + void Backward(const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g) { g = gy; } + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } + + //! Get the width of each sample. + size_t InSize() const { return inSize; } + //! Get the starting row index of subview vector or matrix. size_t const& BeginRow() const { return beginRow; } //! Modify the width of each sample. @@ -140,33 +152,13 @@ class SubviewType : public Layer //! Modify the width of each sample. size_t& EndCol() { return endCol; } - const std::vector OutputDimensions() const - { - // TODO: relax this restriction - for (size_t i = 2; i < inputDimensions.size(); ++i) - { - if (inputDimensions[i] > 1) - { - throw std::invalid_argument("Subview(): layer input must be two-" - "dimensional!"); - } - } - - std::vector outputDimensions(inputDimensions); - outputDimensions[0] = (endRow - beginRow + 1); - outputDimensions[1] = (endCol - beginCol + 1); - - return outputDimensions; - } - /** * Serialize the layer. */ template void serialize(Archive& ar, const uint32_t /* version */) { - ar(cereal::base_class>(this)); - + ar(CEREAL_NVP(inSize)); ar(CEREAL_NVP(beginRow)); ar(CEREAL_NVP(endRow)); ar(CEREAL_NVP(beginCol)); @@ -174,6 +166,9 @@ class SubviewType : public Layer } private: + //! Width of each sample. + size_t inSize; + //! Starting row index of subview vector or matrix. size_t beginRow; @@ -185,10 +180,13 @@ class SubviewType : public Layer //! Ending column index of subview vector or matrix. size_t endCol; -}; // class SubviewType -// Standard Subview layer. -typedef SubviewType Subview; + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; +}; // class Subview } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/not_adapted/transposed_convolution.hpp b/src/mlpack/methods/ann/layer/transposed_convolution.hpp similarity index 72% rename from src/mlpack/methods/ann/layer/not_adapted/transposed_convolution.hpp rename to src/mlpack/methods/ann/layer/transposed_convolution.hpp index f3103d492b..f637ca3355 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/transposed_convolution.hpp +++ b/src/mlpack/methods/ann/layer/transposed_convolution.hpp @@ -1,4 +1,3 @@ -// Temporarily drop. /** * @file methods/ann/layer/transposed_convolution.hpp * @author Shikhar Jaiswal @@ -22,7 +21,7 @@ #include #include -#include "layer.hpp" +#include "layer_types.hpp" #include "padding.hpp" namespace mlpack { @@ -35,23 +34,23 @@ namespace ann /** Artificial Neural Network. */ { * @tparam ForwardConvolutionRule Convolution to perform forward process. * @tparam BackwardConvolutionRule Convolution to perform backward process. * @tparam GradientConvolutionRule Convolution to calculate gradient. - * @tparam InputType Type of the input data (arma::colvec, arma::mat, + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). - * @tparam OutputType Type of the output data (arma::colvec, arma::mat, + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). */ template < typename ForwardConvolutionRule = NaiveConvolution, typename BackwardConvolutionRule = NaiveConvolution, typename GradientConvolutionRule = NaiveConvolution, - typename InputType = arma::mat, - typename OutputType = arma::mat + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat > -class TransposedConvolutionType : public Layer +class TransposedConvolution { public: //! Create the Transposed Convolution object. - TransposedConvolutionType(); + TransposedConvolution(); /** * Create the Transposed Convolution object using the specified number of @@ -77,20 +76,19 @@ class TransposedConvolutionType : public Layer * @param outputHeight The height of the output data. * @param paddingType The type of padding (Valid or Same). Defaults to None. */ - // TODO: remove inputWidth and inputHeight? - TransposedConvolutionType(const size_t inSize, - const size_t outSize, - const size_t kernelWidth, - const size_t kernelHeight, - const size_t strideWidth = 1, - const size_t strideHeight = 1, - const size_t padW = 0, - const size_t padH = 0, - const size_t inputWidth = 0, - const size_t inputHeight = 0, - const size_t outputWidth = 0, - const size_t outputHeight = 0, - const std::string& paddingType = "None"); + TransposedConvolution(const size_t inSize, + const size_t outSize, + const size_t kernelWidth, + const size_t kernelHeight, + const size_t strideWidth = 1, + const size_t strideHeight = 1, + const size_t padW = 0, + const size_t padH = 0, + const size_t inputWidth = 0, + const size_t inputHeight = 0, + const size_t outputWidth = 0, + const size_t outputHeight = 0, + const std::string& paddingType = "None"); /** * Create the Transposed Convolution object using the specified number of @@ -120,24 +118,24 @@ class TransposedConvolutionType : public Layer * @param outputHeight The height of the output data. * @param paddingType The type of padding (Valid or Same). Defaults to None. */ - TransposedConvolutionType(const size_t inSize, - const size_t outSize, - const size_t kernelWidth, - const size_t kernelHeight, - const size_t strideWidth, - const size_t strideHeight, - const std::tuple& padW, - const std::tuple& padH, - const size_t inputWidth = 0, - const size_t inputHeight = 0, - const size_t outputWidth = 0, - const size_t outputHeight = 0, - const std::string& paddingType = "None"); + TransposedConvolution(const size_t inSize, + const size_t outSize, + const size_t kernelWidth, + const size_t kernelHeight, + const size_t strideWidth, + const size_t strideHeight, + const std::tuple& padW, + const std::tuple& padH, + const size_t inputWidth = 0, + const size_t inputHeight = 0, + const size_t outputWidth = 0, + const size_t outputHeight = 0, + const std::string& paddingType = "None"); /* * Set the weight and bias term. */ - void SetWeights(const typename OutputType::elem_type* weightsPtr); + void Reset(); /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -146,7 +144,8 @@ class TransposedConvolutionType : public Layer * @param input Input data used for evaluating the specified function. * @param output Resulting output activation. */ - void Forward(const InputType& input, OutputType& output); + template + void Forward(const arma::Mat& input, arma::Mat& output); /** * Ordinary feed backward pass of a neural network, calculating the function @@ -157,122 +156,135 @@ class TransposedConvolutionType : public Layer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const InputType& /* input */, - const OutputType& gy, - OutputType& g); + template + void Backward(const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g); - /** + /* * Calculate the gradient using the output delta and the input activation. * * @param * (input) The input parameter used for calculating the gradient. * @param error The calculated error. * @param gradient The calculated gradient. */ - void Gradient(const InputType& /* input */, - const OutputType& error, - OutputType& gradient); + template + void Gradient(const arma::Mat& /* input */, + const arma::Mat& error, + arma::Mat& gradient); //! Get the parameters. - OutputType const& Parameters() const { return weights; } + OutputDataType const& Parameters() const { return weights; } //! Modify the parameters. - OutputType& Parameters() { return weights; } + OutputDataType& Parameters() { return weights; } //! Get the weight of the layer. - arma::Cube const& Weight() const - { - return weight; - } + arma::cube const& Weight() const { return weight; } //! Modify the weight of the layer. - arma::Cube& Weight() { return weight; } + arma::cube& Weight() { return weight; } //! Get the bias of the layer. - OutputType const& Bias() const { return bias; } + arma::mat const& Bias() const { return bias; } //! Modify the bias of the layer. - OutputType& Bias() { return bias; } + arma::mat& Bias() { return bias; } + + //! Get the input parameter. + InputDataType const& InputParameter() const { return inputParameter; } + //! Modify the input parameter. + InputDataType& InputParameter() { return inputParameter; } + + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } + + //! Get the gradient. + OutputDataType const& Gradient() const { return gradient; } + //! Modify the gradient. + OutputDataType& Gradient() { return gradient; } //! Get the input width. - size_t const& InputWidth() const { return inputWidth; } + size_t InputWidth() const { return inputWidth; } //! Modify input the width. size_t& InputWidth() { return inputWidth; } //! Get the input height. - size_t const& InputHeight() const { return inputHeight; } + size_t InputHeight() const { return inputHeight; } //! Modify the input height. size_t& InputHeight() { return inputHeight; } //! Get the output width. - size_t const& OutputWidth() const { return outputWidth; } + size_t OutputWidth() const { return outputWidth; } //! Modify the output width. size_t& OutputWidth() { return outputWidth; } //! Get the output height. - size_t const& OutputHeight() const { return outputHeight; } + size_t OutputHeight() const { return outputHeight; } //! Modify the output height. size_t& OutputHeight() { return outputHeight; } //! Get the input size. - size_t const& InputSize() const { return inSize; } + size_t InputSize() const { return inSize; } //! Get the output size. - size_t const& OutputSize() const { return outSize; } + size_t OutputSize() const { return outSize; } //! Get the kernel width. - size_t const& KernelWidth() const { return kernelWidth; } + size_t KernelWidth() const { return kernelWidth; } //! Modify the kernel width. size_t& KernelWidth() { return kernelWidth; } //! Get the kernel height. - size_t const& KernelHeight() const { return kernelHeight; } + size_t KernelHeight() const { return kernelHeight; } //! Modify the kernel height. size_t& KernelHeight() { return kernelHeight; } //! Get the stride width. - size_t const& StrideWidth() const { return strideWidth; } + size_t StrideWidth() const { return strideWidth; } //! Modify the stride width. size_t& StrideWidth() { return strideWidth; } //! Get the stride height. - size_t const& StrideHeight() const { return strideHeight; } + size_t StrideHeight() const { return strideHeight; } //! Modify the stride height. size_t& StrideHeight() { return strideHeight; } //! Get the top padding height. - size_t const& PadHTop() const { return padHTop; } + size_t PadHTop() const { return padHTop; } //! Modify the top padding height. size_t& PadHTop() { return padHTop; } //! Get the bottom padding height. - size_t const& PadHBottom() const { return padHBottom; } + size_t PadHBottom() const { return padHBottom; } //! Modify the bottom padding height. size_t& PadHBottom() { return padHBottom; } //! Get the left padding width. - size_t const& PadWLeft() const { return padWLeft; } + size_t PadWLeft() const { return padWLeft; } //! Modify the left padding width. size_t& PadWLeft() { return padWLeft; } //! Get the right padding width. - size_t const& PadWRight() const { return padWRight; } + size_t PadWRight() const { return padWRight; } //! Modify the right padding width. size_t& PadWRight() { return padWRight; } + //! Get the shape of the input. + size_t InputShape() const + { + return inputHeight * inputWidth * inSize; + } + //! Get the size of the weight matrix. size_t WeightSize() const { return (outSize * inSize * kernelWidth * kernelHeight) + outSize; } - - const std::vector& OutputDimensions() const - { - std::vector result(inputDimensions.size(), 0); - result[0] = outputWidth; - result[1] = outputHeight; - // Higher dimensions are unmodified. - for (size_t i = 2; i < inputDimensions.size(); ++i) - result[i] = inputDimensions[i]; - return result; - } - /** * Serialize the layer. */ @@ -413,13 +425,13 @@ class TransposedConvolutionType : public Layer size_t aH; //! Locally-stored weight object. - OutputType weights; + OutputDataType weights; //! Locally-stored weight object. - arma::Cube weight; + arma::cube weight; //! Locally-stored bias term object. - OutputType bias; + arma::mat bias; //! Locally-stored input width. size_t inputWidth; @@ -434,35 +446,38 @@ class TransposedConvolutionType : public Layer size_t outputHeight; //! Locally-stored transformed output parameter. - arma::Cube outputTemp; + arma::cube outputTemp; //! Locally-stored transformed padded input parameter. - arma::Cube inputPaddedTemp; + arma::cube inputPaddedTemp; //! Locally-stored transformed expanded input parameter. - arma::Cube inputExpandedTemp; + arma::cube inputExpandedTemp; //! Locally-stored transformed error parameter. - arma::Cube gTemp; + arma::cube gTemp; //! Locally-stored transformed gradient parameter. - arma::Cube gradientTemp; + arma::cube gradientTemp; //! Locally-stored padding layer for forward propagation. - ann::Padding paddingForward; + ann::Padding<> paddingForward; //! Locally-stored padding layer for back propagation. - ann::Padding paddingBackward; -}; // class TransposedConvolutionType + ann::Padding<> paddingBackward; -// Standard TransposedConvolution -typedef TransposedConvolutionType< - NaiveConvolution, - NaiveConvolution, - NaiveConvolution, - arma::mat, - arma::mat -> TransposedConvolution; + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored gradient object. + OutputDataType gradient; + + //! Locally-stored input parameter object. + InputDataType inputParameter; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; +}; // class TransposedConvolution } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/not_adapted/transposed_convolution_impl.hpp b/src/mlpack/methods/ann/layer/transposed_convolution_impl.hpp similarity index 79% rename from src/mlpack/methods/ann/layer/not_adapted/transposed_convolution_impl.hpp rename to src/mlpack/methods/ann/layer/transposed_convolution_impl.hpp index a660133fe9..d932acb6a7 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/transposed_convolution_impl.hpp +++ b/src/mlpack/methods/ann/layer/transposed_convolution_impl.hpp @@ -23,16 +23,16 @@ template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename InputType, - typename OutputType + typename InputDataType, + typename OutputDataType > -TransposedConvolutionType< +TransposedConvolution< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - InputType, - OutputType ->::TransposedConvolutionType() + InputDataType, + OutputDataType +>::TransposedConvolution() { // Nothing to do here. } @@ -41,16 +41,16 @@ template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename InputType, - typename OutputType + typename InputDataType, + typename OutputDataType > -TransposedConvolutionType< +TransposedConvolution< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - InputType, - OutputType ->::TransposedConvolutionType( + InputDataType, + OutputDataType +>::TransposedConvolution( const size_t inSize, const size_t outSize, const size_t kernelWidth, @@ -64,7 +64,7 @@ TransposedConvolutionType< const size_t outputWidth, const size_t outputHeight, const std::string& paddingType) : - TransposedConvolutionType( + TransposedConvolution( inSize, outSize, kernelWidth, @@ -86,16 +86,16 @@ template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename InputType, - typename OutputType + typename InputDataType, + typename OutputDataType > -TransposedConvolutionType< +TransposedConvolution< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - InputType, - OutputType ->::TransposedConvolutionType( + InputDataType, + OutputDataType +>::TransposedConvolution( const size_t inSize, const size_t outSize, const size_t kernelWidth, @@ -152,10 +152,10 @@ TransposedConvolutionType< const size_t padWidthRightForward = kernelWidth - padWRight - 1; const size_t padHeightBottomtForward = kernelHeight - padHBottom - 1; - paddingForward = ann::Padding(padWidthLeftForward, + paddingForward = ann::Padding<>(padWidthLeftForward, padWidthRightForward + aW, padHeightTopForward, padHeightBottomtForward + aH); - paddingBackward = ann::Padding(padWLeft, padWRight, padHTop, padHBottom); + paddingBackward = ann::Padding<>(padWLeft, padWRight, padHTop, padHBottom); // Check if the output height and width are possible given the other // parameters of the layer. @@ -174,42 +174,42 @@ template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename InputType, - typename OutputType + typename InputDataType, + typename OutputDataType > -void TransposedConvolutionType< +void TransposedConvolution< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - InputType, - OutputType ->::SetWeights(typename OutputType::elem_type* weightPtr) + InputDataType, + OutputDataType +>::Reset() { - weight = arma::Cube(weightPtr, - kernelWidth, kernelHeight, outSize * inSize, false, false); - bias = arma::Mat(weightsPtr + - weight.n_elem, outSize, 1, false, false); + weight = arma::cube(weights.memptr(), kernelWidth, kernelHeight, + outSize * inSize, false, false); + bias = arma::mat(weights.memptr() + weight.n_elem, + outSize, 1, false, false); } template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename InputType, - typename OutputType + typename InputDataType, + typename OutputDataType > -void TransposedConvolutionType< +template +void TransposedConvolution< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - InputType, - OutputType ->::Forward(const InputType& input, OutputType& output) + InputDataType, + OutputDataType +>::Forward(const arma::Mat& input, arma::Mat& output) { batchSize = input.n_cols; - arma::Cube inputTemp( - const_cast(input).memptr(), inputWidth, inputHeight, - inSize * batchSize, false, false); + arma::cube inputTemp(const_cast&>(input).memptr(), + inputWidth, inputHeight, inSize * batchSize, false, false); if (strideWidth > 1 || strideHeight > 1) { @@ -231,9 +231,9 @@ void TransposedConvolutionType< } else { - inputPaddedTemp = arma::Cube( - inputExpandedTemp.memptr(), inputExpandedTemp.n_rows, - inputExpandedTemp.n_cols, inputExpandedTemp.n_slices, false, false);; + inputPaddedTemp = arma::Cube(inputExpandedTemp.memptr(), + inputExpandedTemp.n_rows, inputExpandedTemp.n_cols, + inputExpandedTemp.n_slices, false, false);; } } else if (paddingForward.PadWLeft() != 0 || @@ -253,8 +253,8 @@ void TransposedConvolutionType< } output.set_size(outputWidth * outputHeight * outSize, batchSize); - outputTemp = arma::Cube(output.memptr(), - outputWidth, outputHeight, outSize * batchSize, false, false); + outputTemp = arma::Cube(output.memptr(), outputWidth, outputHeight, + outSize * batchSize, false, false); outputTemp.zeros(); for (size_t outMap = 0, outMapIdx = 0, batchCount = 0; outMap < @@ -268,7 +268,7 @@ void TransposedConvolutionType< for (size_t inMap = 0; inMap < inSize; inMap++, outMapIdx++) { - OutputType convOutput, rotatedFilter; + arma::Mat convOutput, rotatedFilter; Rotate180(weight.slice(outMapIdx), rotatedFilter); if (strideWidth > 1 || @@ -298,22 +298,22 @@ template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename InputType, - typename OutputType + typename InputDataType, + typename OutputDataType > -void TransposedConvolutionType< +template +void TransposedConvolution< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - InputType, - OutputType + InputDataType, + OutputDataType >::Backward( - const InputType& /* input */, const OutputType& gy, OutputType& g) + const arma::Mat& /* input */, const arma::Mat& gy, arma::Mat& g) { - arma::Cube mappedError( - ((OutputType&) gy).memptr(), outputWidth, + arma::Cube mappedError(((arma::Mat&) gy).memptr(), outputWidth, outputHeight, outSize * batchSize, false, false); - arma::Cube mappedErrorPadded; + arma::Cube mappedErrorPadded; if (paddingBackward.PadWLeft() != 0 || paddingBackward.PadWRight() != 0 || paddingBackward.PadHTop() != 0 || paddingBackward.PadHBottom() != 0) { @@ -329,8 +329,8 @@ void TransposedConvolutionType< } } g.set_size(inputWidth * inputHeight * inSize, batchSize); - gTemp = arma::Cube(g.memptr(), inputWidth, - inputHeight, inSize * batchSize, false, false); + gTemp = arma::Cube(g.memptr(), inputWidth, inputHeight, inSize * + batchSize, false, false); gTemp.zeros(); @@ -345,7 +345,7 @@ void TransposedConvolutionType< for (size_t inMap = 0; inMap < inSize; inMap++, outMapIdx++) { - OutputType output; + arma::Mat output; if (paddingBackward.PadWLeft() != 0 || paddingBackward.PadWRight() != 0 || paddingBackward.PadHTop() != 0 || paddingBackward.PadHBottom() != 0) @@ -368,33 +368,32 @@ template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename InputType, - typename OutputType + typename InputDataType, + typename OutputDataType > -void TransposedConvolutionType< +template +void TransposedConvolution< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - InputType, - OutputType + InputDataType, + OutputDataType >::Gradient( - const InputType& input, - const OutputType& error, - OutputType& gradient) + const arma::Mat& input, + const arma::Mat& error, + arma::Mat& gradient) { - arma::Cube mappedError( - ((OutputType&) error).memptr(), outputWidth, outputHeight, - outSize * batchSize, false, false); - arma::Cube inputTemp( - const_cast(input).memptr(), inputWidth, inputHeight, - inSize * batchSize, false, false); + arma::Cube mappedError(((arma::Mat&) error).memptr(), outputWidth, + outputHeight, outSize * batchSize, false, false); + arma::cube inputTemp(const_cast&>(input).memptr(), + inputWidth, inputHeight, inSize * batchSize, false, false); gradient.set_size(weights.n_elem, 1); - gradientTemp = arma::Cube(gradient.memptr(), - weight.n_rows, weight.n_cols, weight.n_slices, false, false); + gradientTemp = arma::Cube(gradient.memptr(), weight.n_rows, + weight.n_cols, weight.n_slices, false, false); gradientTemp.zeros(); - OutputType inputSlice, output, deltaSlice, rotatedOutput; + arma::Mat inputSlice, output, deltaSlice, rotatedOutput; for (size_t outMap = 0, outMapIdx = 0, batchCount = 0; outMap < outSize * batchSize; outMap++) @@ -438,20 +437,18 @@ template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename InputType, - typename OutputType + typename InputDataType, + typename OutputDataType > template -void TransposedConvolutionType< +void TransposedConvolution< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - InputType, - OutputType + InputDataType, + OutputDataType >::serialize(Archive& ar, const uint32_t /* version */) { - ar(cereal::base_class>(this)); - ar(CEREAL_NVP(inSize)); ar(CEREAL_NVP(outSize)); ar(CEREAL_NVP(batchSize)); @@ -472,6 +469,8 @@ void TransposedConvolutionType< if (cereal::is_loading()) { + weights.set_size((outSize * inSize * kernelWidth * kernelHeight) + outSize, + 1); size_t totalPadWidth = padWLeft + padWRight; size_t totalPadHeight = padHTop + padHBottom; aW = (outputWidth + kernelWidth - totalPadWidth - 2) % strideWidth; @@ -483,20 +482,20 @@ template< typename ForwardConvolutionRule, typename BackwardConvolutionRule, typename GradientConvolutionRule, - typename InputType, - typename OutputType + typename InputDataType, + typename OutputDataType > -void TransposedConvolutionType< +void TransposedConvolution< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, - InputType, - OutputType + InputDataType, + OutputDataType >::InitializeSamePadding(){ /** * Using O=s*(I-1) + K -2P + A * where - * s=stride + * s=stride * I=Input Shape * K=Kernel Size * P=Padding diff --git a/src/mlpack/methods/ann/layer/not_adapted/virtual_batch_norm.hpp b/src/mlpack/methods/ann/layer/virtual_batch_norm.hpp similarity index 60% rename from src/mlpack/methods/ann/layer/not_adapted/virtual_batch_norm.hpp rename to src/mlpack/methods/ann/layer/virtual_batch_norm.hpp index 1d47a44e54..43b9a1d3a3 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/virtual_batch_norm.hpp +++ b/src/mlpack/methods/ann/layer/virtual_batch_norm.hpp @@ -13,16 +13,14 @@ #define MLPACK_METHODS_ANN_LAYER_VIRTUALBATCHNORM_HPP #include -#include "layer.hpp" namespace mlpack { namespace ann /** Artificial Neural Network. */ { -// TODO: what about sizes for this layer? /** * Declaration of the VirtualBatchNorm layer class. Instead of using the - * batch statistics for normalizing on a mini-batch, it uses a reference subset - * of the data for calculating the normalization statistics. + * batch statistics for normalizing on a mini-batch, it uses a reference subset of + * the data for calculating the normalization statistics. * * For more information, refer to the following paper, * @@ -36,55 +34,49 @@ namespace ann /** Artificial Neural Network. */ { * } * @endcode * - * @tparam InputType Type of the input data (arma::colvec, arma::mat, + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). - * @tparam OutputType Type of the output data (arma::colvec, arma::mat, + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). */ template < - typename InputType = arma::mat, - typename OutputType = arma::mat + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat > -class VirtualBatchNormType : public Layer +class VirtualBatchNorm { public: //! Create the VirtualBatchNorm object. - VirtualBatchNormType(); + VirtualBatchNorm(); /** - * Create the VirtualBatchNorm layer object for a specified number of input - * units. + * Create the VirtualBatchNorm layer object for a specified number of input units. * * @param referenceBatch The data from which the normalization * statistics are computed. * @param size The number of input units / channels. * @param eps The epsilon added to variance to ensure numerical stability. */ - VirtualBatchNormType(const InputType& referenceBatch, - const size_t size, - const double eps = 1e-8); - - //! Clone the VirtualBatchNormType object. This handles polymorphism - //! correctly. - VirtualBatchNormType* Clone() const - { - return new VirtualBatchNormType(*this); - } + template + VirtualBatchNorm(const arma::Mat& referenceBatch, + const size_t size, + const double eps = 1e-8); /** * Reset the layer parameters. */ - void SetWeights(typename OutputType::elem_type* weightsPtr); + void Reset(); /** - * Forward pass of the Virtual Batch Normalization layer. Transforms the input - * data into zero mean and unit variance, scales the data by a factor gamma - * and shifts it by beta. + * Forward pass of the Virtual Batch Normalization layer. Transforms the input data + * into zero mean and unit variance, scales the data by a factor gamma and + * shifts it by beta. * * @param input Input data for the layer. * @param output Resulting output activations. */ - void Forward(const InputType& input, OutputType& output); + template + void Forward(const arma::Mat& input, arma::Mat& output); /** * Backward pass through the layer. @@ -93,9 +85,10 @@ class VirtualBatchNormType : public Layer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const InputType& /* input */, - const OutputType& gy, - OutputType& g); + template + void Backward(const arma::Mat& /* input */, + const arma::Mat& gy, + arma::Mat& g); /** * Calculate the gradient using the output delta and the input activations. @@ -104,14 +97,30 @@ class VirtualBatchNormType : public Layer * @param error The calculated error. * @param gradient The calculated gradient. */ - void Gradient(const InputType& /* input */, - const OutputType& error, - OutputType& gradient); + template + void Gradient(const arma::Mat& /* input */, + const arma::Mat& error, + arma::Mat& gradient); //! Get the parameters. - OutputType const& Parameters() const { return weights; } + OutputDataType const& Parameters() const { return weights; } //! Modify the parameters. - OutputType& Parameters() { return weights; } + OutputDataType& Parameters() { return weights; } + + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } + + //! Get the gradient. + OutputDataType const& Gradient() const { return gradient; } + //! Modify the gradient. + OutputDataType& Gradient() { return gradient; } //! Get the number of input units. size_t InSize() const { return size; } @@ -119,11 +128,6 @@ class VirtualBatchNormType : public Layer //! Get the epsilon value. double Epsilon() const { return eps; } - const size_t WeightSize() const - { - return 2 * size; - } - /** * Serialize the layer. */ @@ -141,19 +145,19 @@ class VirtualBatchNormType : public Layer bool loading; //! Locally-stored scale parameter. - OutputType gamma; + OutputDataType gamma; //! Locally-stored shift parameter. - OutputType beta; + OutputDataType beta; //! Locally-stored parameters. - OutputType weights; + OutputDataType weights; //! Mean of features in the reference batch. - OutputType referenceBatchMean; + OutputDataType referenceBatchMean; //! Variance of features in the reference batch. - OutputType referenceBatchMeanSquared; + OutputDataType referenceBatchMeanSquared; //! The coefficient for reference batch statistics. double oldCoefficient; @@ -162,20 +166,29 @@ class VirtualBatchNormType : public Layer double newCoefficient; //! Locally-stored mean object. - OutputType mean; + OutputDataType mean; //! Locally-stored variance object. - OutputType variance; + OutputDataType variance; + + //! Locally-stored gradient object. + OutputDataType gradient; + + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; + + //! Locally-stored input parameter object. + OutputDataType inputParameter; //! Locally-stored normalized input. - OutputType normalized; + OutputDataType normalized; //! Locally-stored zero mean input. - OutputType inputSubMean; -}; // class VirtualBatchNormType - -// Standard VirtualBatchNorm layer. -typedef VirtualBatchNormType VirtualBatchNorm; + OutputDataType inputSubMean; +}; // class VirtualBatchNorm } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/not_adapted/virtual_batch_norm_impl.hpp b/src/mlpack/methods/ann/layer/virtual_batch_norm_impl.hpp similarity index 61% rename from src/mlpack/methods/ann/layer/not_adapted/virtual_batch_norm_impl.hpp rename to src/mlpack/methods/ann/layer/virtual_batch_norm_impl.hpp index c87d5e6579..112c625b9b 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/virtual_batch_norm_impl.hpp +++ b/src/mlpack/methods/ann/layer/virtual_batch_norm_impl.hpp @@ -19,8 +19,8 @@ namespace mlpack { namespace ann { /** Artificial Neural Network. */ -template -VirtualBatchNormType::VirtualBatchNormType() : +template +VirtualBatchNorm::VirtualBatchNorm() : size(0), eps(1e-8), loading(false), @@ -29,27 +29,29 @@ VirtualBatchNormType::VirtualBatchNormType() : { // Nothing to do here. } -template -VirtualBatchNormType::VirtualBatchNormType( - const InputType& referenceBatch, +template +template +VirtualBatchNorm::VirtualBatchNorm( + const arma::Mat& referenceBatch, const size_t size, const double eps) : size(size), eps(eps), loading(false) { + weights.set_size(size + size, 1); + referenceBatchMean = arma::mean(referenceBatch, 1); referenceBatchMeanSquared = arma::mean(arma::square(referenceBatch), 1); newCoefficient = 1.0 / (referenceBatch.n_cols + 1); oldCoefficient = 1 - newCoefficient; } -template -void VirtualBatchNormType::SetWeights( - typename OutputType::elem_type* weightsPtr) +template +void VirtualBatchNorm::Reset() { - gamma = OutputType(weightsPtr, size, 1, false, false); - beta = OutputType(weightsPtr + gamma.n_elem, size, 1, false, false); + gamma = arma::mat(weights.memptr(), size, 1, false, false); + beta = arma::mat(weights.memptr() + gamma.n_elem, size, 1, false, false); if (!loading) { @@ -60,19 +62,20 @@ void VirtualBatchNormType::SetWeights( loading = false; } -template -void VirtualBatchNormType::Forward( - const InputType& input, OutputType& output) +template +template +void VirtualBatchNorm::Forward( + const arma::Mat& input, arma::Mat& output) { Log::Assert(input.n_rows % size == 0, "Input features must be divisible \ by feature maps."); inputParameter = input; - InputType inputMean = arma::mean(input, 1); - InputType inputMeanSquared = arma::mean(arma::square(input), 1); + arma::mat inputMean = arma::mean(input, 1); + arma::mat inputMeanSquared = arma::mean(arma::square(input), 1); mean = oldCoefficient * referenceBatchMean + newCoefficient * inputMean; - OutputType meanSquared = oldCoefficient * referenceBatchMeanSquared + + arma::mat meanSquared = oldCoefficient * referenceBatchMeanSquared + newCoefficient * inputMeanSquared; variance = meanSquared - arma::square(mean); // Normalize the input. @@ -87,19 +90,18 @@ void VirtualBatchNormType::Forward( output.each_col() += beta; } -template -void VirtualBatchNormType::Backward( - const InputType& /* input */, - const OutputType& gy, - OutputType& g) +template +template +void VirtualBatchNorm::Backward( + const arma::Mat& /* input */, const arma::Mat& gy, arma::Mat& g) { - const OutputType stdInv = 1.0 / arma::sqrt(variance + eps); + const arma::mat stdInv = 1.0 / arma::sqrt(variance + eps); // dl / dxhat. - const OutputType norm = gy.each_col() % gamma; + const arma::mat norm = gy.each_col() % gamma; // sum dl / dxhat * (x - mu) * -0.5 * stdInv^3. - const OutputType var = arma::sum(norm % inputSubMean, 1) % + const arma::mat var = arma::sum(norm % inputSubMean, 1) % arma::pow(stdInv, 3.0) * -0.5; // dl / dxhat * 1 / stdInv + variance * 2 * (x - mu) / m + @@ -113,11 +115,12 @@ void VirtualBatchNormType::Backward( mean * -2)) * newCoefficient / inputParameter.n_cols; } -template -void VirtualBatchNormType::Gradient( - const InputType& /* input */, - const OutputType& error, - OutputType& gradient) +template +template +void VirtualBatchNorm::Gradient( + const arma::Mat& /* input */, + const arma::Mat& error, + arma::Mat& gradient) { gradient.set_size(size + size, 1); @@ -129,21 +132,22 @@ void VirtualBatchNormType::Gradient( arma::sum(error, 1); } -template +template template -void VirtualBatchNormType::serialize( +void VirtualBatchNorm::serialize( Archive& ar, const uint32_t /* version */) { - ar(cereal::base_class>(this)); - ar(CEREAL_NVP(size)); - ar(CEREAL_NVP(eps)); if (cereal::is_loading()) { weights.set_size(size + size, 1); loading = true; } + + ar(CEREAL_NVP(eps)); + ar(CEREAL_NVP(gamma)); + ar(CEREAL_NVP(beta)); } } // namespace ann diff --git a/src/mlpack/methods/ann/loss_functions/vr_class_reward.hpp b/src/mlpack/methods/ann/layer/vr_class_reward.hpp similarity index 58% rename from src/mlpack/methods/ann/loss_functions/vr_class_reward.hpp rename to src/mlpack/methods/ann/layer/vr_class_reward.hpp index 8e0fe13a37..2d6f626c75 100644 --- a/src/mlpack/methods/ann/loss_functions/vr_class_reward.hpp +++ b/src/mlpack/methods/ann/layer/vr_class_reward.hpp @@ -1,8 +1,8 @@ /** - * @file methods/ann/loss_functions/vr_class_reward.hpp + * @file methods/ann/layer/vr_class_reward.hpp * @author Marcus Edel * - * Definition of the VRClassRewardType class, which implements the variance + * Definition of the VRClassReward class, which implements the variance * reduced classification reinforcement layer. * * mlpack is free software; you may redistribute it and/or modify it under the @@ -10,34 +10,41 @@ * 3-clause BSD license along with mlpack. If not, see * http://www.opensource.org/licenses/BSD-3-Clause for more information. */ -#ifndef MLPACK_METHODS_ANN_LOSS_FUNCTIONS_VR_CLASS_REWARD_HPP -#define MLPACK_METHODS_ANN_LOSS_FUNCTIONS_VR_CLASS_REWARD_HPP +#ifndef MLPACK_METHODS_ANN_LAYER_VR_CLASS_REWARD_HPP +#define MLPACK_METHODS_ANN_LAYER_VR_CLASS_REWARD_HPP #include +#include "layer_types.hpp" + namespace mlpack { namespace ann /** Artificial Neural Network. */ { /** * Implementation of the variance reduced classification reinforcement layer. * This layer is meant to be used in combination with the reinforce normal layer - * (ReinforceNormalLayer), which expects that the reward is 1 for success, and 0 - * otherwise. + * (ReinforceNormalLayer), which expects that an reward: + * (1 for success, 0 otherwise). * - * @tparam MatType Matrix representation to accept as input and use for - * computation. + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class VRClassRewardType +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class VRClassReward { public: /** - * Create the VRClassRewardType object. + * Create the VRClassReward object. * * @param scale Parameter used to scale the reward. * @param sizeAverage Take the average over all batches. */ - VRClassRewardType(const double scale = 1, const bool sizeAverage = true); + VRClassReward(const double scale = 1, const bool sizeAverage = true); /** * Ordinary feed forward pass of a neural network, evaluating the function @@ -47,8 +54,8 @@ class VRClassRewardType * @param target The target vector, that contains the class index in the range * between 1 and the number of classes. */ - typename MatType::elem_type Forward(const MatType& input, - const MatType& target); + template + double Forward(const InputType& input, const TargetType& target); /** * Ordinary feed backward pass of a neural network. The negative log @@ -61,30 +68,38 @@ class VRClassRewardType * between 1 and the number of classes. * @param output The calculated error. */ - void Backward(const MatType& input, const MatType& target, MatType& output); + template + void Backward(const InputType& input, + const TargetType& target, + OutputType& output); - /** + //! Get the output parameter. + OutputDataType& OutputParameter() const {return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType& Delta() const {return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } + + /* * Add a new module to the model. * * @param args The layer parameter. */ - template + template void Add(Args... args) { network.push_back(new LayerType(args...)); } - /** + /* * Add a new module to the model. * * @param layer The Layer to be added to the model. */ - void Add(Layer* layer) - { - network.push_back(layer); - } + void Add(LayerTypes<> layer) { network.push_back(layer); } - //! Get the network. - const std::vector*>& Network() const { return network; } - //! Modify the network. - std::vector*>& Network() { return network; } + //! Get the network modules. + std::vector >& Model() { return network; } //! Get the value of parameter sizeAverage. bool SizeAverage() const { return sizeAverage; } @@ -108,12 +123,15 @@ class VRClassRewardType //! Locally stored reward parameter. double reward; - //! Locally-stored network modules. - std::vector*> network; -}; // class VRClassRewardType + //! Locally-stored delta object. + OutputDataType delta; -// Default typedef for typical `arma::mat` usage. -typedef VRClassRewardType VRClassReward; + //! Locally-stored output parameter object. + OutputDataType outputParameter; + + //! Locally-stored network modules. + std::vector > network; +}; // class VRClassReward } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/vr_class_reward_impl.hpp b/src/mlpack/methods/ann/layer/vr_class_reward_impl.hpp new file mode 100644 index 0000000000..c04da10594 --- /dev/null +++ b/src/mlpack/methods/ann/layer/vr_class_reward_impl.hpp @@ -0,0 +1,107 @@ +/** + * @file methods/ann/layer/vr_class_reward_impl.hpp + * @author Marcus Edel + * + * Implementation of the VRClassReward class, which implements the variance + * reduced classification reinforcement layer. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_LAYER_VR_CLASS_REWARD_IMPL_HPP +#define MLPACK_METHODS_ANN_LAYER_VR_CLASS_REWARD_IMPL_HPP + +// In case it hasn't yet been included. +#include "vr_class_reward.hpp" + +#include "../visitor/reward_set_visitor.hpp" + +namespace mlpack { +namespace ann /** Artificial Neural Network. */ { + +template +VRClassReward::VRClassReward( + const double scale, + const bool sizeAverage) : + scale(scale), + sizeAverage(sizeAverage), + reward(0) +{ + // Nothing to do here. +} + +template +template +double VRClassReward::Forward( + const InputType& input, const TargetType& target) +{ + double output = 0; + for (size_t i = 0; i < input.n_cols - 1; ++i) + { + size_t currentTarget = target(i) - 1; + Log::Assert(currentTarget < input.n_rows, + "Target class out of range."); + + output -= input(currentTarget, i); + } + + reward = 0; + arma::uword index = 0; + + for (size_t i = 0; i < input.n_cols - 1; ++i) + { + input.unsafe_col(i).max(index); + reward = ((index + 1) == target(i)) * scale; + } + + if (sizeAverage) + { + return output - reward / (input.n_cols - 1); + } + + return output - reward; +} + +template +template +void VRClassReward::Backward( + const InputType& input, + const TargetType& target, + OutputType& output) +{ + output = arma::zeros(input.n_rows, input.n_cols); + for (size_t i = 0; i < (input.n_cols - 1); ++i) + { + size_t currentTarget = target(i) - 1; + Log::Assert(currentTarget < input.n_rows, + "Target class out of range."); + + output(currentTarget, i) = -1; + } + + double vrReward = reward - input(0, 1); + if (sizeAverage) + { + vrReward /= input.n_cols - 1; + } + + const double norm = sizeAverage ? 2.0 / (input.n_cols - 1) : 2.0; + + output(0, 1) = norm * (input(0, 1) - reward); + boost::apply_visitor(RewardSetVisitor(vrReward), network.back()); +} + +template +template +void VRClassReward::serialize( + Archive& /* ar */, const uint32_t /* version */) +{ + // Nothing to do here. +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/layer/not_adapted/weight_norm.hpp b/src/mlpack/methods/ann/layer/weight_norm.hpp similarity index 58% rename from src/mlpack/methods/ann/layer/not_adapted/weight_norm.hpp rename to src/mlpack/methods/ann/layer/weight_norm.hpp index b8e39326b0..2bdcd7e955 100644 --- a/src/mlpack/methods/ann/layer/not_adapted/weight_norm.hpp +++ b/src/mlpack/methods/ann/layer/weight_norm.hpp @@ -13,7 +13,14 @@ #define MLPACK_METHODS_ANN_LAYER_WEIGHTNORM_HPP #include -#include "layer.hpp" +#include "layer_types.hpp" + +#include "../visitor/delete_visitor.hpp" +#include "../visitor/delta_visitor.hpp" +#include "../visitor/output_parameter_visitor.hpp" +#include "../visitor/reset_visitor.hpp" +#include "../visitor/weight_size_visitor.hpp" +#include "../visitor/weight_set_visitor.hpp" namespace mlpack { namespace ann /** Artificial Neural Network. */ { @@ -44,45 +51,30 @@ namespace ann /** Artificial Neural Network. */ { * arma::sp_mat or arma::cube). * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, * arma::sp_mat or arma::cube). + * @tparam CustomLayers Additional custom layers that can be added. */ template < - typename InputType = arma::mat, - typename OutputType = arma::mat + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat, + typename... CustomLayers > -class WeightNormType : public Layer +class WeightNorm { public: - /** - * Create an empty WeightNorm layer. - */ - WeightNormType(); - /** * Create the WeightNorm layer object. * * @param layer The layer whose weights are needed to be normalized. */ - WeightNormType(Layer* layer); + WeightNorm(LayerTypes layer = LayerTypes()); //! Destructor to release allocated memory. - ~WeightNormType(); - - //! Create a WeightNorm layer by copying the given layer. - WeightNormType(const WeightNormType& other); - //! Create a WeightNorm layer by taking ownership of the other layer. - WeightNormType(WeightNormType&& other); - //! Copy the given layer. - WeightNormType& operator=(const WeightNormType& other); - //! Take ownership of the data in the given layer. - WeightNormType& operator=(WeightNormType&& other); - - //! Clone the WeightNormType object. This handles polymorphism correctly. - WeightNormType* Clone() const { return new WeightNormType(*this); } + ~WeightNorm(); /** * Reset the layer parameters. */ - void SetWeights(typename OutputType::elem_type* weightsPtr); + void Reset(); /** * Forward pass of the WeightNorm layer. Calculates the weights of the @@ -93,7 +85,8 @@ class WeightNormType : public Layer * @param input Input data for the layer. * @param output Resulting output activations. */ - void Forward(const InputType& input, OutputType& output); + template + void Forward(const arma::Mat& input, arma::Mat& output); /** * Backward pass through the layer. This function calls the Backward() @@ -103,9 +96,10 @@ class WeightNormType : public Layer * @param gy The backpropagated error. * @param g The calculated gradient. */ - void Backward(const InputType& input, - const OutputType& gy, - OutputType& g); + template + void Backward(const arma::Mat& input, + const arma::Mat& gy, + arma::Mat& g); /** * Calculate the gradient using the output delta, input activations and the @@ -115,25 +109,33 @@ class WeightNormType : public Layer * @param error The calculated error. * @param gradient The calculated gradient. */ - void Gradient(const InputType& input, - const OutputType& error, - OutputType& gradient); + template + void Gradient(const arma::Mat& input, + const arma::Mat& error, + arma::Mat& gradient); + + //! Get the delta. + OutputDataType const& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } + + //! Get the gradient. + OutputDataType const& Gradient() const { return gradient; } + //! Modify the gradient. + OutputDataType& Gradient() { return gradient; } + + //! Get the output parameter. + OutputDataType const& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } //! Get the parameters. - OutputType const& Parameters() const { return weights; } + OutputDataType const& Parameters() const { return weights; } //! Modify the parameters. - OutputType& Parameters() { return weights; } + OutputDataType& Parameters() { return weights; } //! Get the wrapped layer. - Layer* const& WrappedLayer() { return wrappedLayer; } - - const size_t WeightSize() const { return wrappedLayer->WeightSize(); } - - const std::vector OutputDimensions() const - { - wrappedLayer->InputDimensions() = inputDimensions; - return wrappedLayer->OutputDimensions(); - } + LayerTypes const& Layer() { return wrappedLayer; } /** * Serialize the layer. @@ -145,33 +147,54 @@ class WeightNormType : public Layer //! Locally-stored number of bias elements in the weights of wrapped layer. size_t biasWeightSize; + //! Locally-stored delete visitor module object. + DeleteVisitor deleteVisitor; + + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored delta visitor module object. + DeltaVisitor deltaVisitor; + + //! Locally-stored gradient object. + OutputDataType gradient; + //! Locally-stored wrapped layer. - Layer* wrappedLayer; + LayerTypes wrappedLayer; //! Locally stored number of elements in the weights of wrapped layer. size_t layerWeightSize; + //! Locally-stored output parameter object. + OutputDataType outputParameter; + + //! Locally-stored output parameter visitor module object. + OutputParameterVisitor outputParameterVisitor; + //! Reset the gradient for all modules that implement the Gradient function. - void ResetGradients(OutputType& gradient); + void ResetGradients(arma::mat& gradient); + + //! Locally-stored reset visitor. + ResetVisitor resetVisitor; //! Locally-stored scalar parameter. - OutputType scalarParameter; + OutputDataType scalarParameter; //! Locally-stored parameter vector. - OutputType vectorParameter; + OutputDataType vectorParameter; //! Locally-stored parameters. - OutputType weights; + OutputDataType weights; + + //! Locally-stored weight size visitor. + WeightSizeVisitor weightSizeVisitor; //! Locally-stored gradients of wrappedLayer. - OutputType layerGradients; + OutputDataType layerGradients; //! Locally-stored weights of wrappedLayer. - OutputType layerWeights; -}; // class WeightNormType. - -// Standard WeightNorm layer. -typedef WeightNormType WeightNorm; + OutputDataType layerWeights; +}; // class WeightNorm } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/layer/weight_norm_impl.hpp b/src/mlpack/methods/ann/layer/weight_norm_impl.hpp new file mode 100644 index 0000000000..c534cc36df --- /dev/null +++ b/src/mlpack/methods/ann/layer/weight_norm_impl.hpp @@ -0,0 +1,165 @@ +/** + * @file methods/ann/layer/weight_norm_impl.hpp + * @author Toshal Agrawal + * + * Implementation of the WeightNorm Layer. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ + +#ifndef MLPACK_METHODS_ANN_LAYER_WEIGHTNORM_IMPL_HPP +#define MLPACK_METHODS_ANN_LAYER_WEIGHTNORM_IMPL_HPP + +// In case it is not included. +#include "weight_norm.hpp" + +#include "../visitor/forward_visitor.hpp" +#include "../visitor/backward_visitor.hpp" +#include "../visitor/gradient_visitor.hpp" +#include "../visitor/bias_set_visitor.hpp" + +namespace mlpack { +namespace ann { /** Artificial Neural Network. */ + +template +WeightNorm::WeightNorm( + LayerTypes layer) : + wrappedLayer(layer) +{ + layerWeightSize = boost::apply_visitor(weightSizeVisitor, wrappedLayer); + weights.set_size(layerWeightSize + 1, 1); + + layerWeights.set_size(layerWeightSize, 1); + layerGradients.set_size(layerWeightSize, 1); +} + +template +WeightNorm::~WeightNorm() +{ + boost::apply_visitor(deleteVisitor, wrappedLayer); +} + +template +void WeightNorm::Reset() +{ + // Set the weights of the inside layer to layerWeights. + // This is done to set the non-bias terms correctly. + boost::apply_visitor(WeightSetVisitor(layerWeights, 0), wrappedLayer); + + boost::apply_visitor(resetVisitor, wrappedLayer); + + biasWeightSize = boost::apply_visitor(BiasSetVisitor(weights, 0), + wrappedLayer); + + vectorParameter = arma::mat(weights.memptr() + biasWeightSize, + layerWeightSize - biasWeightSize, 1, false, false); + + scalarParameter = arma::mat(weights.memptr() + layerWeightSize, 1, 1, false, + false); +} + +template +template +void WeightNorm::Forward( + const arma::Mat& input, arma::Mat& output) +{ + // Initialize the non-bias weights of wrapped layer. + const double normVectorParameter = arma::norm(vectorParameter, 2); + layerWeights.rows(0, layerWeightSize - biasWeightSize - 1) = + scalarParameter(0) * vectorParameter / normVectorParameter; + + boost::apply_visitor(ForwardVisitor(input, + boost::apply_visitor(outputParameterVisitor, wrappedLayer)), + wrappedLayer); + + output = boost::apply_visitor(outputParameterVisitor, wrappedLayer); +} + +template +template +void WeightNorm::Backward( + const arma::Mat& /* input */, const arma::Mat& gy, arma::Mat& g) +{ + boost::apply_visitor(BackwardVisitor(boost::apply_visitor( + outputParameterVisitor, wrappedLayer), gy, + boost::apply_visitor(deltaVisitor, wrappedLayer)), wrappedLayer); + + g = boost::apply_visitor(deltaVisitor, wrappedLayer); +} + +template +template +void WeightNorm::Gradient( + const arma::Mat& input, + const arma::Mat& error, + arma::Mat& gradient) +{ + ResetGradients(layerGradients); + + // Calculate the gradients of the wrapped layer. + boost::apply_visitor(GradientVisitor(input, error), wrappedLayer); + + // Store the norm of vector parameter temporarily. + const double normVectorParameter = arma::norm(vectorParameter, 2); + + // Set the gradients of the bias terms. + if (biasWeightSize != 0) + { + gradient.rows(0, biasWeightSize - 1) = arma::mat(layerGradients.memptr() + + layerWeightSize - biasWeightSize, biasWeightSize, 1, false, false); + } + + // Calculate the gradients of the scalar parameter. + gradient[gradient.n_rows - 1] = arma::accu(layerGradients.rows(0, + layerWeightSize - biasWeightSize - 1) % vectorParameter) / + normVectorParameter; + + // Calculate the gradients of the vector parameter. + gradient.rows(biasWeightSize, layerWeightSize - 1) = + scalarParameter(0) / normVectorParameter * (layerGradients.rows(0, + layerWeightSize - biasWeightSize - 1) - gradient[gradient.n_rows - 1] / + normVectorParameter * vectorParameter); +} + +template +void WeightNorm::ResetGradients( + arma::mat& gradient) +{ + boost::apply_visitor(GradientSetVisitor(gradient, 0), wrappedLayer); +} + +template +template +void WeightNorm::serialize( + Archive& ar, const uint32_t /* version */) +{ + if (cereal::is_loading()) + { + boost::apply_visitor(deleteVisitor, wrappedLayer); + } + + ar(CEREAL_VARIANT_POINTER(wrappedLayer)); + ar(CEREAL_NVP(layerWeightSize)); + + // If we are loading, we need to initialize the weights. + if (cereal::is_loading()) + { + weights.set_size(layerWeightSize + 1, 1); + } +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/loss_functions/CMakeLists.txt b/src/mlpack/methods/ann/loss_functions/CMakeLists.txt index 35116a8831..a3c97ff4f6 100644 --- a/src/mlpack/methods/ann/loss_functions/CMakeLists.txt +++ b/src/mlpack/methods/ann/loss_functions/CMakeLists.txt @@ -47,8 +47,6 @@ set(SOURCES soft_margin_loss_impl.hpp triplet_margin_loss.hpp triplet_margin_loss_impl.hpp - vr_class_reward.hpp - vr_class_reward_impl.hpp ) # Add directory name to sources. diff --git a/src/mlpack/methods/ann/loss_functions/binary_cross_entropy_loss.hpp b/src/mlpack/methods/ann/loss_functions/binary_cross_entropy_loss.hpp index eae005c1d6..8893227bc7 100644 --- a/src/mlpack/methods/ann/loss_functions/binary_cross_entropy_loss.hpp +++ b/src/mlpack/methods/ann/loss_functions/binary_cross_entropy_loss.hpp @@ -18,14 +18,19 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { /** - * The binary-cross-entropy performance function measures the Binary Cross - * Entropy between the target and the output. + * The binary-cross-entropy performance function measures the + * Binary Cross Entropy between the target and the output. * - * @tparam MatType Matrix representation to accept as input and use for - * computation. + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class BCELossType +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class BCELoss { public: /** @@ -40,7 +45,7 @@ class BCELossType * 'sum' reduction is used and the output will be summed. It * is set to true by default. */ - BCELossType(const double eps = 1e-10, const bool reduction = true); + BCELoss(const double eps = 1e-10, const bool reduction = true); /** * Computes the cross-entropy function. @@ -49,8 +54,9 @@ class BCELossType * function. * @param target The target vector. */ - typename MatType::elem_type Forward(const MatType& prediction, - const MatType& target); + template + typename PredictionType::elem_type Forward(const PredictionType& prediction, + const TargetType& target); /** * Ordinary feed backward pass of a neural network. @@ -60,9 +66,15 @@ class BCELossType * @param target The target vector. * @param loss The calculated error. */ - void Backward(const MatType& prediction, - const MatType& target, - MatType& loss); + template + void Backward(const PredictionType& prediction, + const TargetType& target, + LossType& loss); + + //! Get the output parameter. + OutputDataType& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } //! Get the epsilon. double Eps() const { return eps; } @@ -82,23 +94,25 @@ class BCELossType void serialize(Archive& ar, const uint32_t /* version */); private: + //! Locally-stored output parameter object. + OutputDataType outputParameter; + //! The minimum value used for computing logarithms and denominators double eps; //! Boolean value that tells if reduction is 'sum' or 'mean'. bool reduction; -}; // class BCELossType - -// Default typedef for typical `arma::mat` usage. -typedef BCELossType BCELoss; +}; // class BCELoss /** - * Alias of BCELossType. + * Adding alias of BCELoss. */ -typedef BCELossType CrossEntropyError; - -template -using CrossEntropyErrorType = BCELossType; +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +using CrossEntropyError = BCELoss< + InputDataType, OutputDataType>; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/binary_cross_entropy_loss_impl.hpp b/src/mlpack/methods/ann/loss_functions/binary_cross_entropy_loss_impl.hpp index 0855cc580c..a217141564 100644 --- a/src/mlpack/methods/ann/loss_functions/binary_cross_entropy_loss_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/binary_cross_entropy_loss_impl.hpp @@ -18,19 +18,21 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -BCELossType::BCELossType( +template +BCELoss::BCELoss( const double eps, const bool reduction) : eps(eps), reduction(reduction) { // Nothing to do here. } -template -typename MatType::elem_type BCELossType::Forward( - const MatType& prediction, - const MatType& target) +template +template +typename PredictionType::elem_type +BCELoss::Forward( + const PredictionType& prediction, + const TargetType& target) { - typedef typename MatType::elem_type ElemType; + typedef typename PredictionType::elem_type ElemType; ElemType lossSum = -arma::accu(target % arma::log(prediction + eps) + (1. - target) % arma::log(1. - prediction + eps)); @@ -41,11 +43,12 @@ typename MatType::elem_type BCELossType::Forward( return lossSum / target.n_elem; } -template -void BCELossType::Backward( - const MatType& prediction, - const MatType& target, - MatType& loss) +template +template +void BCELoss::Backward( + const PredictionType& prediction, + const TargetType& target, + LossType& loss) { loss = (1. - target) / (1. - prediction + eps) - target / (prediction + eps); @@ -53,9 +56,9 @@ void BCELossType::Backward( loss /= target.n_elem; } -template +template template -void BCELossType::serialize( +void BCELoss::serialize( Archive& ar, const uint32_t /* version */) { diff --git a/src/mlpack/methods/ann/loss_functions/cosine_embedding_loss.hpp b/src/mlpack/methods/ann/loss_functions/cosine_embedding_loss.hpp index 9fc2ee8d35..362d0d4e3b 100644 --- a/src/mlpack/methods/ann/loss_functions/cosine_embedding_loss.hpp +++ b/src/mlpack/methods/ann/loss_functions/cosine_embedding_loss.hpp @@ -26,16 +26,21 @@ namespace ann /** Artificial Neural Network. */ { * f(x) = 1 - cos(x1, x2) , for y = 1 * f(x) = max(0, cos(x1, x2) - margin) , for y = -1 * @f} - * - * @tparam MatType Matrix representation to accept as input and use for - * computation. + * + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class CosineEmbeddingLossType +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class CosineEmbeddingLoss { public: /** - * Create the CosineEmbeddingLossType object. + * Create the CosineEmbeddingLoss object. * * @param margin Increases cosine distance in case of dissimilarity. * Refer definition of cosine-embedding-loss above. @@ -47,7 +52,7 @@ class CosineEmbeddingLossType * 'sum' reduction is used and the output will be summed. It * is set to true by default. */ - CosineEmbeddingLossType(const double margin = 0.0, + CosineEmbeddingLoss(const double margin = 0.0, const bool similarity = true, const bool reduction = true); @@ -58,8 +63,9 @@ class CosineEmbeddingLossType * function. * @param target The target vector. */ - typename MatType::elem_type Forward(const MatType& prediction, - const MatType& target); + template + typename PredictionType::elem_type Forward(const PredictionType& prediction, + const TargetType& target); /** * Ordinary feed backward pass of a neural network. @@ -69,9 +75,25 @@ class CosineEmbeddingLossType * @param target The target vector. * @param loss The calculated error. */ - void Backward(const MatType& prediction, - const MatType& target, - MatType& loss); + template + void Backward(const PredictionType& prediction, + const TargetType& target, + LossType& loss); + + //! Get the input parameter. + InputDataType& InputParameter() const { return inputParameter; } + //! Modify the input parameter. + InputDataType& InputParameter() { return inputParameter; } + + //! Get the output parameter. + OutputDataType& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } //! Get the reduction type, represented as boolean //! (false 'mean' reduction, true 'sum' reduction). @@ -96,6 +118,15 @@ class CosineEmbeddingLossType void serialize(Archive& ar, const uint32_t /* version */); private: + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored input parameter object. + InputDataType inputParameter; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; + //! Locally-stored value of margin hyper-parameter. double margin; @@ -104,10 +135,7 @@ class CosineEmbeddingLossType //! Boolean value that tells if reduction is 'sum' or 'mean'. bool reduction; -}; // class CosineEmbeddingLossType - -// Default typedef for typical `arma::mat` usage. -typedef CosineEmbeddingLossType CosineEmbeddingLoss; +}; // class CosineEmbeddingLoss } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/cosine_embedding_loss_impl.hpp b/src/mlpack/methods/ann/loss_functions/cosine_embedding_loss_impl.hpp index b21c743653..bee9906032 100644 --- a/src/mlpack/methods/ann/loss_functions/cosine_embedding_loss_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/cosine_embedding_loss_impl.hpp @@ -18,28 +18,30 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -CosineEmbeddingLossType::CosineEmbeddingLossType( +template +CosineEmbeddingLoss::CosineEmbeddingLoss( const double margin, const bool similarity, const bool reduction): margin(margin), similarity(similarity), reduction(reduction) { // Nothing to do here. } -template -typename MatType::elem_type CosineEmbeddingLossType::Forward( - const MatType& prediction, - const MatType& target) +template +template +typename PredictionType::elem_type +CosineEmbeddingLoss::Forward( + const PredictionType& prediction, + const TargetType& target) { - typedef typename MatType::elem_type ElemType; + typedef typename PredictionType::elem_type ElemType; const size_t cols = prediction.n_cols; const size_t batchSize = prediction.n_elem / cols; if (arma::size(prediction) != arma::size(target)) Log::Fatal << "Input Tensors must have same dimensions." << std::endl; - arma::Col inputTemp1 = arma::vectorise(prediction); - arma::Col inputTemp2 = arma::vectorise(target); + arma::colvec inputTemp1 = arma::vectorise(prediction); + arma::colvec inputTemp2 = arma::vectorise(target); ElemType lossSum = 0.0; for (size_t i = 0; i < inputTemp1.n_elem; i += cols) @@ -62,24 +64,25 @@ typename MatType::elem_type CosineEmbeddingLossType::Forward( return (ElemType) lossSum / batchSize; } -template -void CosineEmbeddingLossType::Backward( - const MatType& prediction, - const MatType& target, - MatType& loss) +template +template +void CosineEmbeddingLoss::Backward( + const PredictionType& prediction, + const TargetType& target, + LossType& loss) { - typedef typename MatType::elem_type ElemType; + typedef typename PredictionType::elem_type ElemType; const size_t cols = prediction.n_cols; const size_t batchSize = prediction.n_elem / cols; if (arma::size(prediction) != arma::size(target)) Log::Fatal << "Input Tensors must have same dimensions." << std::endl; - arma::Col inputTemp1 = arma::vectorise(prediction); - arma::Col inputTemp2 = arma::vectorise(target); + arma::colvec inputTemp1 = arma::vectorise(prediction); + arma::colvec inputTemp2 = arma::vectorise(target); loss.set_size(arma::size(inputTemp1)); - arma::Col outputTemp(loss.memptr(), inputTemp1.n_elem, + arma::colvec outputTemp(loss.memptr(), inputTemp1.n_elem, false, false); for (size_t i = 0; i < inputTemp1.n_elem; i += cols) { @@ -103,9 +106,9 @@ void CosineEmbeddingLossType::Backward( } } -template +template template -void CosineEmbeddingLossType::serialize( +void CosineEmbeddingLoss::serialize( Archive& ar, const uint32_t /* version */) { ar(CEREAL_NVP(margin)); diff --git a/src/mlpack/methods/ann/loss_functions/dice_loss.hpp b/src/mlpack/methods/ann/loss_functions/dice_loss.hpp index 2e7b89b4be..ce4a80f54d 100644 --- a/src/mlpack/methods/ann/loss_functions/dice_loss.hpp +++ b/src/mlpack/methods/ann/loss_functions/dice_loss.hpp @@ -38,19 +38,24 @@ namespace ann /** Artificial Neural Network. */ { * } * @endcode * - * @tparam MatType Matrix representation to accept as input and use for - * computation. + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class DiceLossType +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class DiceLoss { public: /** - * Create the DiceLossType object. + * Create the DiceLoss object. * * @param smooth The Laplace smoothing parameter. */ - DiceLossType(const double smooth = 1); + DiceLoss(const double smooth = 1); /** * Computes the dice loss function. @@ -59,8 +64,9 @@ class DiceLossType * function. * @param target The target vector. */ - typename MatType::elem_type Forward(const MatType& prediction, - const MatType& target); + template + typename PredictionType::elem_type Forward(const PredictionType& prediction, + const TargetType& target); /** * Ordinary feed backward pass of a neural network. @@ -70,9 +76,15 @@ class DiceLossType * @param target The target vector. * @param loss The calculated error. */ - void Backward(const MatType& prediction, - const MatType& target, - MatType& loss); + template + void Backward(const PredictionType& prediction, + const TargetType& target, + LossType& loss); + + //! Get the output parameter. + OutputDataType& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } //! Get the smooth. double Smooth() const { return smooth; } @@ -86,12 +98,12 @@ class DiceLossType void serialize(Archive& ar, const uint32_t /* version */); private: + //! Locally-stored output parameter object. + OutputDataType outputParameter; + //! The parameter to avoid overfitting. double smooth; -}; // class DiceLossType - -// Default typedef for typical `arma::mat` usage. -typedef DiceLossType DiceLoss; +}; // class DiceLoss } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/dice_loss_impl.hpp b/src/mlpack/methods/ann/loss_functions/dice_loss_impl.hpp index a98534d086..2a1835dc70 100644 --- a/src/mlpack/methods/ann/loss_functions/dice_loss_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/dice_loss_impl.hpp @@ -18,38 +18,41 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -DiceLossType::DiceLossType(const double smooth) : smooth(smooth) +template +DiceLoss::DiceLoss( + const double smooth) : smooth(smooth) { // Nothing to do here. } -template -typename MatType::elem_type DiceLossType::Forward( - const MatType& prediction, - const MatType& target) +template +template +typename PredictionType::elem_type DiceLoss + ::Forward(const PredictionType& prediction, + const TargetType& target) { return 1 - ((2 * arma::accu(target % prediction) + smooth) / - (arma::accu(target % target) + arma::accu( - prediction % prediction) + smooth)); + (arma::accu(target % target) + arma::accu( + prediction % prediction) + smooth)); } -template -void DiceLossType::Backward( - const MatType& prediction, - const MatType& target, - MatType& loss) +template +template +void DiceLoss::Backward( + const PredictionType& prediction, + const TargetType& target, + LossType& loss) { loss = -2 * (target * (arma::accu(prediction % prediction) + - arma::accu(target % target) + smooth) - prediction * - (2 * arma::accu(target % prediction) + smooth)) / std::pow( - arma::accu(target % target) + arma::accu(prediction % prediction) - + smooth, 2.0); + arma::accu(target % target) + smooth) - prediction * + (2 * arma::accu(target % prediction) + smooth)) / std::pow( + arma::accu(target % target) + arma::accu(prediction % prediction) + + smooth, 2.0); } -template +template template -void DiceLossType::serialize( +void DiceLoss::serialize( Archive& ar, const uint32_t /* version */) { diff --git a/src/mlpack/methods/ann/loss_functions/earth_mover_distance.hpp b/src/mlpack/methods/ann/loss_functions/earth_mover_distance.hpp index 4f6da12ea1..e8013aac57 100644 --- a/src/mlpack/methods/ann/loss_functions/earth_mover_distance.hpp +++ b/src/mlpack/methods/ann/loss_functions/earth_mover_distance.hpp @@ -21,15 +21,20 @@ namespace ann /** Artificial Neural Network. */ { * The earth mover distance function measures the network's performance * according to the Kantorovich-Rubinstein duality approximation. * - * @tparam MatType Matrix representation to accept as input and use for - * computation. + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class EarthMoverDistanceType +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class EarthMoverDistance { public: /** - * Create the EarthMoverDistanceType object. + * Create the EarthMoverDistance object. * * @param reduction Specifies the reduction to apply to the output. If false, * 'mean' reduction is used, where sum of the output will be @@ -37,7 +42,7 @@ class EarthMoverDistanceType * 'sum' reduction is used and the output will be summed. It * is set to true by default. */ - EarthMoverDistanceType(const bool reduction = true); + EarthMoverDistance(const bool reduction = true); /** * Ordinary feed forward pass of a neural network. @@ -46,8 +51,9 @@ class EarthMoverDistanceType * function. * @param target The target vector. */ - typename MatType::elem_type Forward(const MatType& prediction, - const MatType& target); + template + typename PredictionType::elem_type Forward(const PredictionType& prediction, + const TargetType& target); /** * Ordinary feed backward pass of a neural network. @@ -57,9 +63,15 @@ class EarthMoverDistanceType * @param target The target vector. * @param loss The calculated error. */ - void Backward(const MatType& prediction, - const MatType& target, - MatType& loss); + template + void Backward(const PredictionType& prediction, + const TargetType& target, + LossType& loss); + + //! Get the output parameter. + OutputDataType& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } //! Get the reduction type, represented as boolean //! (false 'mean' reduction, true 'sum' reduction). @@ -74,12 +86,12 @@ class EarthMoverDistanceType void serialize(Archive& ar, const uint32_t /* version */); private: + //! Locally-stored output parameter object. + OutputDataType outputParameter; + //! Boolean value that tells if reduction is 'sum' or 'mean'. bool reduction; -}; // class EarthMoverDistanceType - -// Default typedef for typical `arma::mat` usage. -typedef EarthMoverDistanceType EarthMoverDistance; +}; // class EarthMoverDistance } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/earth_mover_distance_impl.hpp b/src/mlpack/methods/ann/loss_functions/earth_mover_distance_impl.hpp index a0e0ecaeb1..a049e1dc5a 100644 --- a/src/mlpack/methods/ann/loss_functions/earth_mover_distance_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/earth_mover_distance_impl.hpp @@ -18,19 +18,22 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -EarthMoverDistanceType::EarthMoverDistanceType(const bool reduction) : - reduction(reduction) +template +EarthMoverDistance + ::EarthMoverDistance(const bool reduction) : reduction(reduction) { // Nothing to do here. } -template -typename MatType::elem_type EarthMoverDistanceType::Forward( - const MatType& prediction, - const MatType& target) +template +template +typename PredictionType::elem_type +EarthMoverDistance::Forward( + const PredictionType& prediction, + const TargetType& target) { - typename MatType::elem_type lossSum = -arma::accu(target % prediction); + typename PredictionType::elem_type lossSum = + -arma::accu(target % prediction); if (reduction) return lossSum; @@ -38,11 +41,12 @@ typename MatType::elem_type EarthMoverDistanceType::Forward( return lossSum / target.n_elem; } -template -void EarthMoverDistanceType::Backward( - const MatType& /* prediction */, - const MatType& target, - MatType& loss) +template +template +void EarthMoverDistance::Backward( + const PredictionType& /* prediction */, + const TargetType& target, + LossType& loss) { loss = -target; @@ -50,9 +54,9 @@ void EarthMoverDistanceType::Backward( loss = loss / target.n_elem; } -template +template template -void EarthMoverDistanceType::serialize( +void EarthMoverDistance::serialize( Archive& ar, const uint32_t /* version */) { diff --git a/src/mlpack/methods/ann/loss_functions/empty_loss.hpp b/src/mlpack/methods/ann/loss_functions/empty_loss.hpp index 525823a37f..8cc8caae9d 100644 --- a/src/mlpack/methods/ann/loss_functions/empty_loss.hpp +++ b/src/mlpack/methods/ann/loss_functions/empty_loss.hpp @@ -23,17 +23,22 @@ namespace ann /** Artificial Neural Network. */ { * The empty loss does nothing, letting the user calculate the loss outside * the model. * - * @tparam MatType Matrix representation to accept as input and use for - * computation. + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class EmptyLossType +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class EmptyLoss { public: /** - * Create the EmptyLossType object. + * Create the EmptyLoss object. */ - EmptyLossType(); + EmptyLoss(); /** * Computes the Empty loss function. @@ -42,7 +47,8 @@ class EmptyLossType * function. * @param target The target vector. */ - double Forward(const MatType& input, const MatType& target); + template + double Forward(const PredictionType& input, const TargetType& target); /** * Ordinary feed backward pass of a neural network. @@ -52,17 +58,11 @@ class EmptyLossType * @param target The target vector. * @param loss The calculated error. */ - void Backward(const MatType& prediction, - const MatType& target, - MatType& loss); - - //! Serialize the EmptyLossType. - template - void serialize(Archive& ar, const uint32_t /* version */) { } -}; // class EmptyLossType - -// Default typedef for typical `arma::mat` usage. -typedef EmptyLossType EmptyLoss; + template + void Backward(const PredictionType& prediction, + const TargetType& target, + LossType& loss); +}; // class EmptyLoss } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/empty_loss_impl.hpp b/src/mlpack/methods/ann/loss_functions/empty_loss_impl.hpp index 1083fbcfea..190792030e 100644 --- a/src/mlpack/methods/ann/loss_functions/empty_loss_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/empty_loss_impl.hpp @@ -20,24 +20,26 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -EmptyLossType::EmptyLossType() +template +EmptyLoss::EmptyLoss() { // Nothing to do here. } -template -double EmptyLossType::Forward( - const MatType& /* prediction */, const MatType& /* target */) +template +template +double EmptyLoss::Forward( + const PredictionType& /* prediction */, const TargetType& /* target */) { return 0; } -template -void EmptyLossType::Backward( - const MatType& /* prediction */, - const MatType& target, - MatType& loss) +template +template +void EmptyLoss::Backward( + const PredictionType& /* prediction */, + const TargetType& target, + LossType& loss) { loss = target; } diff --git a/src/mlpack/methods/ann/loss_functions/hinge_embedding_loss.hpp b/src/mlpack/methods/ann/loss_functions/hinge_embedding_loss.hpp index 40fe951650..74def487b8 100644 --- a/src/mlpack/methods/ann/loss_functions/hinge_embedding_loss.hpp +++ b/src/mlpack/methods/ann/loss_functions/hinge_embedding_loss.hpp @@ -24,11 +24,16 @@ namespace ann /** Artificial Neural Network. */ { * The Hinge Embedding loss function is often used to compute the loss * between y_true and y_pred. * - * @tparam MatType Matrix representation to accept as input and use for - * computation. + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class HingeEmbeddingLossType +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class HingeEmbeddingLoss { public: /** @@ -40,7 +45,7 @@ class HingeEmbeddingLossType * 'sum' reduction is used and the output will be summed. It * is set to true by default. */ - HingeEmbeddingLossType(const bool reduction = true); + HingeEmbeddingLoss(const bool reduction = true); /** * Computes the Hinge Embedding loss function. @@ -49,8 +54,9 @@ class HingeEmbeddingLossType * function. * @param target Target data to compare with. */ - typename MatType::elem_type Forward(const MatType& prediction, - const MatType& target); + template + typename PredictionType::elem_type Forward(const PredictionType& prediction, + const TargetType& target); /** * Ordinary feed backward pass of a neural network. @@ -60,9 +66,15 @@ class HingeEmbeddingLossType * @param target The target vector. * @param loss The calculated error. */ - void Backward(const MatType& prediction, - const MatType& target, - MatType& loss); + template + void Backward(const PredictionType& prediction, + const TargetType& target, + LossType& loss); + + //! Get the output parameter. + OutputDataType& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } //! Get the reduction type, represented as boolean //! (false 'mean' reduction, true 'sum' reduction). @@ -77,12 +89,13 @@ class HingeEmbeddingLossType void serialize(Archive& ar, const uint32_t /* version */); private: - //! Boolean values that tells if reduction is 'sum' or 'mean'. - bool reduction; -}; // class HingeEmbeddingLossType + //! Locally-stored output parameter object. + OutputDataType outputParameter; -// Default typedef for typical `arma::mat` usage. -typedef HingeEmbeddingLossType HingeEmbeddingLoss; + //! Boolean values that tells if reduction + // is 'sum' or 'mean'. + bool reduction; +}; // class HingeEmbeddingLoss } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/hinge_embedding_loss_impl.hpp b/src/mlpack/methods/ann/loss_functions/hinge_embedding_loss_impl.hpp index 26773b6523..701891d089 100644 --- a/src/mlpack/methods/ann/loss_functions/hinge_embedding_loss_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/hinge_embedding_loss_impl.hpp @@ -19,20 +19,22 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -HingeEmbeddingLossType::HingeEmbeddingLossType(const bool reduction) : - reduction(reduction) +template +HingeEmbeddingLoss + ::HingeEmbeddingLoss(const bool reduction) : reduction(reduction) { // Nothing to do here. } -template -typename MatType::elem_type HingeEmbeddingLossType::Forward( - const MatType& prediction, - const MatType& target) +template +template +typename PredictionType::elem_type +HingeEmbeddingLoss::Forward( + const PredictionType& prediction, + const TargetType& target) { - MatType loss = (1 - target) / 2 + prediction % (target); - typename MatType::elem_type lossSum = arma::accu(loss); + PredictionType loss = (1 - target) / 2 + prediction % (target); + typename PredictionType::elem_type lossSum = arma::accu(loss); if (reduction) return lossSum; @@ -40,11 +42,12 @@ typename MatType::elem_type HingeEmbeddingLossType::Forward( return lossSum / target.n_elem; } -template -void HingeEmbeddingLossType::Backward( - const MatType& /* prediction */, - const MatType& target, - MatType& loss) +template +template +void HingeEmbeddingLoss::Backward( + const PredictionType& prediction, + const TargetType& target, + LossType& loss) { loss = target; @@ -52,9 +55,9 @@ void HingeEmbeddingLossType::Backward( loss = loss / target.n_elem; } -template +template template -void HingeEmbeddingLossType::serialize( +void HingeEmbeddingLoss::serialize( Archive& ar, const uint32_t /* version */) { diff --git a/src/mlpack/methods/ann/loss_functions/hinge_loss.hpp b/src/mlpack/methods/ann/loss_functions/hinge_loss.hpp index 7b1023078a..7829b20348 100644 --- a/src/mlpack/methods/ann/loss_functions/hinge_loss.hpp +++ b/src/mlpack/methods/ann/loss_functions/hinge_loss.hpp @@ -25,15 +25,20 @@ namespace ann /** Artificial Neural Network. */ { * The hinge loss \f$l(y_true, y_pred)\f$ is defined as * \f$l(y_true, y_pred) = max(0, 1 - y_true*y_pred)\f$. * - * @tparam MatType Matrix representation to accept as input and use for - * computation. + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class HingeLossType +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class HingeLoss { public: /** - * Create HingeLossType object. + * Create HingeLoss object. * * @param reduction Specifies the reduction to apply to the output. If false, * 'mean' reduction is used, where sum of the output will be @@ -41,7 +46,7 @@ class HingeLossType * true, 'sum' reduction is used and the output will be * summed. It is set to true by default. */ - HingeLossType(const bool reduction = true); + HingeLoss(const bool reduction = true); /** * Computes the Hinge loss function. @@ -50,8 +55,9 @@ class HingeLossType * function. * @param target Target data to compare with. */ - typename MatType::elem_type Forward(const MatType& prediction, - const MatType& target); + template + typename PredictionType::elem_type Forward(const PredictionType& prediction, + const TargetType& target); /** * Ordinary feed backward pass of a neural network. @@ -61,9 +67,15 @@ class HingeLossType * @param target The target vector. * @param loss The calculated error. */ - void Backward(const MatType& prediction, - const MatType& target, - MatType& loss); + template + void Backward(const PredictionType& prediction, + const TargetType& target, + LossType& loss); + + //! Get the output parameter. + OutputDataType& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } //! Get the reduction type, represented as boolean //! (false 'mean' reduction, true 'sum' reduction). @@ -78,12 +90,12 @@ class HingeLossType void serialize(Archive& ar, const uint32_t /* version */); private: - //! The boolean value that tells if reduction is sum or mean. - bool reduction; -}; // class HingeLossType + //! Locally-stored output parameter object. + OutputDataType outputParameter; -// Default typedef for typical `arma::mat` usage. -typedef HingeLossType HingeLoss; + //! Boolean value that tells if reduction is 'sum' or 'mean'. + bool reduction; +}; // class HingeLoss } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/hinge_loss_impl.hpp b/src/mlpack/methods/ann/loss_functions/hinge_loss_impl.hpp index 05403e32e8..6de5a553fa 100644 --- a/src/mlpack/methods/ann/loss_functions/hinge_loss_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/hinge_loss_impl.hpp @@ -19,24 +19,26 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -HingeLossType::HingeLossType(const bool reduction): +template +HingeLoss::HingeLoss(const bool reduction): reduction(reduction) { // Nothing to do here. } -template -typename MatType::elem_type HingeLossType::Forward( - const MatType& prediction, - const MatType& target) +template +template +typename PredictionType::elem_type +HingeLoss::Forward( + const PredictionType& prediction, + const TargetType& target) { - MatType temp = target - (target == 0); - MatType temp_zeros(size(target), arma::fill::zeros); + TargetType temp = target - (target == 0); + TargetType temp_zeros(size(target), arma::fill::zeros); - MatType loss = arma::max(temp_zeros, 1 - prediction % temp); + PredictionType loss = arma::max(temp_zeros, 1 - prediction % temp); - typename MatType::elem_type lossSum = arma::accu(loss); + typename PredictionType::elem_type lossSum = arma::accu(loss); if (reduction) return lossSum; @@ -44,22 +46,23 @@ typename MatType::elem_type HingeLossType::Forward( return lossSum / loss.n_elem; } -template -void HingeLossType::Backward( - const MatType& prediction, - const MatType& target, - MatType& loss) +template +template +void HingeLoss::Backward( + const PredictionType& prediction, + const TargetType& target, + LossType& loss) { - MatType temp = target - (target == 0); + TargetType temp = target - (target == 0); loss = (prediction < (1 / temp)) % -temp; if (!reduction) loss /= target.n_elem; } -template +template template -void HingeLossType::serialize( +void HingeLoss::serialize( Archive& ar, const uint32_t /* version */) { diff --git a/src/mlpack/methods/ann/loss_functions/huber_loss.hpp b/src/mlpack/methods/ann/loss_functions/huber_loss.hpp index 8596a260fc..c1533c84f6 100644 --- a/src/mlpack/methods/ann/loss_functions/huber_loss.hpp +++ b/src/mlpack/methods/ann/loss_functions/huber_loss.hpp @@ -24,15 +24,20 @@ namespace ann /** Artificial Neural Network. */ { * and linear for large values, with equal values and slopes of the different * sections at the two points where \f$ |y - f(x)| = delta \f$. * - * @tparam MatType Matrix representation to accept as input and use for - * computation. + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class HuberLossType +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class HuberLoss { public: /** - * Create the HuberLossType object. + * Create the HuberLoss object. * * @param delta The threshold value upto which squared error is followed and * after which absolute error is considered. @@ -42,7 +47,7 @@ class HuberLossType * 'sum' reduction is used and the output will be summed. It * is set to true by default. */ - HuberLossType(const double delta = 1.0, const bool reduction = true); + HuberLoss(const double delta = 1.0, const bool reduction = true); /** * Computes the Huber Loss function. @@ -51,8 +56,9 @@ class HuberLossType * function. * @param target The target vector. */ - typename MatType::elem_type Forward(const MatType& prediction, - const MatType& target); + template + typename PredictionType::elem_type Forward(const PredictionType& prediction, + const TargetType& target); /** * Ordinary feed backward pass of a neural network. @@ -62,9 +68,15 @@ class HuberLossType * @param target The target vector. * @param loss The calculated error. */ - void Backward(const MatType& prediction, - const MatType& target, - MatType& loss); + template + void Backward(const PredictionType& prediction, + const TargetType& target, + LossType& loss); + + //! Get the output parameter. + OutputDataType& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } //! Get the value of delta. double Delta() const { return delta; } @@ -84,15 +96,15 @@ class HuberLossType void serialize(Archive& ar, const uint32_t /* version */); private: + //! Locally-stored output parameter object. + OutputDataType outputParameter; + //! Hyperparameter `delta` defines the point upto which MSE is considered. double delta; //! Boolean value that tells if reduction is 'sum' or 'mean'. bool reduction; -}; // class HuberLossType - -// Default typedef for typical `arma::mat` usage. -typedef HuberLossType HuberLoss; +}; // class HuberLoss } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/huber_loss_impl.hpp b/src/mlpack/methods/ann/loss_functions/huber_loss_impl.hpp index 907a51e552..f2e496aaed 100644 --- a/src/mlpack/methods/ann/loss_functions/huber_loss_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/huber_loss_impl.hpp @@ -18,28 +18,30 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -HuberLossType::HuberLossType( - const double delta, - const bool reduction): - delta(delta), - reduction(reduction) +template +HuberLoss::HuberLoss( + const double delta, + const bool reduction): + delta(delta), + reduction(reduction) { // Nothing to do here. } -template -typename MatType::elem_type HuberLossType::Forward( - const MatType& prediction, - const MatType& target) +template +template +typename PredictionType::elem_type +HuberLoss::Forward( + const PredictionType& prediction, + const TargetType& target) { - typedef typename MatType::elem_type ElemType; + typedef typename PredictionType::elem_type ElemType; ElemType lossSum = 0; for (size_t i = 0; i < prediction.n_elem; ++i) { - const ElemType absError = std::abs(target[i] - prediction[i]); - lossSum += absError > delta ? - delta * (absError - 0.5 * delta) : 0.5 * std::pow(absError, 2); + const ElemType absError = std::abs(target[i] - prediction[i]); + lossSum += absError > delta ? + delta * (absError - 0.5 * delta) : 0.5 * std::pow(absError, 2); } if (reduction) @@ -48,13 +50,14 @@ typename MatType::elem_type HuberLossType::Forward( return lossSum / target.n_elem; } -template -void HuberLossType::Backward( - const MatType& prediction, - const MatType& target, - MatType& loss) +template +template +void HuberLoss::Backward( + const PredictionType& prediction, + const TargetType& target, + LossType& loss) { - typedef typename MatType::elem_type ElemType; + typedef typename PredictionType::elem_type ElemType; loss.set_size(size(prediction)); for (size_t i = 0; i < loss.n_elem; ++i) @@ -69,9 +72,9 @@ void HuberLossType::Backward( loss = loss / target.n_elem; } -template +template template -void HuberLossType::serialize( +void HuberLoss::serialize( Archive& ar, const uint32_t /* version */) { diff --git a/src/mlpack/methods/ann/loss_functions/kl_divergence.hpp b/src/mlpack/methods/ann/loss_functions/kl_divergence.hpp index 16e6471da5..0962c3c4e4 100644 --- a/src/mlpack/methods/ann/loss_functions/kl_divergence.hpp +++ b/src/mlpack/methods/ann/loss_functions/kl_divergence.hpp @@ -33,11 +33,16 @@ namespace ann /** Artificial Neural Network. */ { * } * @endcode * - * @tparam MatType Matrix representation to accept as input and use for - * computation. + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class KLDivergenceType +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class KLDivergence { public: /** @@ -50,7 +55,7 @@ class KLDivergenceType * 'sum' reduction is used and the output will be summed. It * is set to true by default. */ - KLDivergenceType(const bool reduction = true); + KLDivergence(const bool reduction = true); /** * Computes the Kullback–Leibler divergence error function. @@ -59,8 +64,9 @@ class KLDivergenceType * function. * @param target Target data to compare with. */ - typename MatType::elem_type Forward(const MatType& prediction, - const MatType& target); + template + typename PredictionType::elem_type Forward(const PredictionType& prediction, + const TargetType& target); /** * Ordinary feed backward pass of a neural network. @@ -70,29 +76,34 @@ class KLDivergenceType * @param target The target vector. * @param loss The calculated error. */ - void Backward(const MatType& prediction, - const MatType& target, - MatType& loss); + template + void Backward(const PredictionType& prediction, + const TargetType& target, + LossType& loss); + + //! Get the output parameter. + OutputDataType& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } //! Get the reduction type, represented as boolean //! (false 'mean' reduction, true 'sum' reduction). bool Reduction() const { return reduction; } //! Modify the type of reduction used. bool& Reduction() { return reduction; } - /** - * Serialize the loss function. + * Serialize the loss function */ template void serialize(Archive& ar, const uint32_t /* version */); private: + //! Locally-stored output parameter object. + OutputDataType outputParameter; + //! Boolean value that tells if reduction is 'sum' or 'mean'. bool reduction; -}; // class KLDivergenceType - -// Default typedef for typical `arma::mat` usage. -typedef KLDivergenceType KLDivergence; +}; // class KLDivergence } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/kl_divergence_impl.hpp b/src/mlpack/methods/ann/loss_functions/kl_divergence_impl.hpp index 8088972b06..6ff54ac21b 100644 --- a/src/mlpack/methods/ann/loss_functions/kl_divergence_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/kl_divergence_impl.hpp @@ -19,20 +19,22 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -KLDivergenceType::KLDivergenceType(const bool reduction) : +template +KLDivergence::KLDivergence(const bool reduction): reduction(reduction) { // Nothing to do here. } -template -typename MatType::elem_type KLDivergenceType::Forward( - const MatType& prediction, - const MatType& target) +template +template +typename PredictionType::elem_type +KLDivergence::Forward( + const PredictionType& prediction, + const TargetType& target) { - MatType loss = target % (arma::log(target) - prediction); - typename MatType::elem_type lossSum = arma::accu(loss); + PredictionType loss = target % (arma::log(target) - prediction); + typename PredictionType::elem_type lossSum = arma::accu(loss); if (reduction) return lossSum; @@ -40,21 +42,22 @@ typename MatType::elem_type KLDivergenceType::Forward( return lossSum / target.n_elem; } -template -void KLDivergenceType::Backward( - const MatType& /* prediction */, - const MatType& target, - MatType& loss) +template +template +void KLDivergence::Backward( + const PredictionType& prediction, + const TargetType& target, + LossType& loss) { - loss = -target; + loss = - target; if (!reduction) loss = loss / target.n_elem; } -template +template template -void KLDivergenceType::serialize( +void KLDivergence::serialize( Archive& ar, const uint32_t /* version */) { diff --git a/src/mlpack/methods/ann/loss_functions/l1_loss.hpp b/src/mlpack/methods/ann/loss_functions/l1_loss.hpp index cd02255fa9..e8494ca915 100644 --- a/src/mlpack/methods/ann/loss_functions/l1_loss.hpp +++ b/src/mlpack/methods/ann/loss_functions/l1_loss.hpp @@ -21,15 +21,20 @@ namespace ann /** Artificial Neural Network. */ { * The L1 loss is a loss function that measures the mean absolute error (MAE) * between each element in the input x and target y. * - * @tparam MatType Matrix representation to accept as input and use for - * computation. + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class L1LossType +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class L1Loss { public: /** - * Create the L1LossType object. + * Create the L1Loss object. * * @param reduction Specifies the reduction to apply to the output. If false, * 'mean' reduction is used, where sum of the output will be @@ -38,7 +43,7 @@ class L1LossType * is set to true by default. * */ - L1LossType(const bool reduction = true); + L1Loss(const bool reduction = true); /** * Computes the L1 Loss function. @@ -47,8 +52,9 @@ class L1LossType * function. * @param target The target vector. */ - typename MatType::elem_type Forward(const MatType& prediction, - const MatType& target); + template + typename PredictionType::elem_type Forward(const PredictionType& prediction, + const TargetType& target); /** * Ordinary feed backward pass of a neural network. @@ -58,9 +64,15 @@ class L1LossType * @param target The target vector. * @param loss The calculated error. */ - void Backward(const MatType& prediction, - const MatType& target, - MatType& loss); + template + void Backward(const PredictionType& prediction, + const TargetType& target, + LossType& loss); + + //! Get the output parameter. + OutputDataType& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } //! Get the reduction type, represented as boolean //! (false 'mean' reduction, true 'sum' reduction). @@ -75,12 +87,12 @@ class L1LossType void serialize(Archive& ar, const uint32_t /* version */); private: + //! Locally-stored output parameter object. + OutputDataType outputParameter; + //! Boolean value that tells if reduction is 'sum' or 'mean'. bool reduction; -}; // class L1LossType - -// Default typedef for typical `arma::mat` usage. -typedef L1LossType L1Loss; +}; // class L1Loss } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/l1_loss_impl.hpp b/src/mlpack/methods/ann/loss_functions/l1_loss_impl.hpp index 4694f40ba0..dfdfbc573d 100644 --- a/src/mlpack/methods/ann/loss_functions/l1_loss_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/l1_loss_impl.hpp @@ -18,20 +18,22 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -L1LossType::L1LossType(const bool reduction): - reduction(reduction) +template +L1Loss::L1Loss(const bool reduction): + reduction(reduction) { // Nothing to do here. } -template -typename MatType::elem_type L1LossType::Forward( - const MatType& prediction, - const MatType& target) +template +template +typename PredictionType::elem_type +L1Loss::Forward( + const PredictionType& prediction, + const TargetType& target) { - MatType loss = arma::abs(prediction - target); - typename MatType::elem_type lossSum = arma::accu(loss); + PredictionType loss = arma::abs(prediction - target); + typename PredictionType::elem_type lossSum = arma::accu(loss); if (reduction) return lossSum; @@ -39,21 +41,22 @@ typename MatType::elem_type L1LossType::Forward( return lossSum / target.n_elem; } -template -void L1LossType::Backward( - const MatType& prediction, - const MatType& target, - MatType& loss) +template +template +void L1Loss::Backward( + const PredictionType& prediction, + const TargetType& target, + LossType& loss) { loss = arma::sign(prediction - target); - + if (!reduction) loss = loss / target.n_elem; } -template +template template -void L1LossType::serialize( +void L1Loss::serialize( Archive& ar, const uint32_t /* version */) { diff --git a/src/mlpack/methods/ann/loss_functions/log_cosh_loss.hpp b/src/mlpack/methods/ann/loss_functions/log_cosh_loss.hpp index 550f40279a..23aa6baa4e 100644 --- a/src/mlpack/methods/ann/loss_functions/log_cosh_loss.hpp +++ b/src/mlpack/methods/ann/loss_functions/log_cosh_loss.hpp @@ -23,11 +23,16 @@ namespace ann /** Artificial Neural Network. */ { * variational auto encoder. This function is the log of hyperbolic * cosine of difference between true values and predicted values. * - * @tparam MatType Matrix representation to accept as input and use for - * computation. + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class LogCoshLossType +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class LogCoshLoss { public: /** @@ -45,7 +50,7 @@ class LogCoshLossType * 'sum' reduction is used and the output will be summed. It * is set to true by default. */ - LogCoshLossType(const double a = 1.0, const bool reduction = true); + LogCoshLoss(const double a = 1.0, const bool reduction = true); /** * Computes the Log-Hyperbolic-Cosine loss function. @@ -54,8 +59,9 @@ class LogCoshLossType * function. * @param target Target data to compare with. */ - typename MatType::elem_type Forward(const MatType& prediction, - const MatType& target); + template + typename PredictionType::elem_type Forward(const PredictionType& prediction, + const TargetType& target); /** * Ordinary feed backward pass of a neural network. @@ -65,9 +71,15 @@ class LogCoshLossType * @param target The target vector. * @param loss The calculated error. */ - void Backward(const MatType& prediction, - const MatType& target, - MatType& loss); + template + void Backward(const PredictionType& prediction, + const TargetType& target, + LossType& loss); + + //! Get the output parameter. + OutputDataType& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } //! Get the value of hyperparameter a. double A() const { return a; } @@ -87,15 +99,15 @@ class LogCoshLossType void serialize(Archive& ar, const uint32_t /* version */); private: + //! Locally-stored output parameter object. + OutputDataType outputParameter; + //! Hyperparameter a for smoothening function curve. double a; //! Boolean value that tells if reduction is 'sum' or 'mean'. bool reduction; -}; // class LogCoshLossType - -// Default typedef for typical `arma::mat` usage. -typedef LogCoshLossType LogCoshLoss; +}; // class LogCoshLoss } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/log_cosh_loss_impl.hpp b/src/mlpack/methods/ann/loss_functions/log_cosh_loss_impl.hpp index 3ecfe59253..7c4bba7b9b 100644 --- a/src/mlpack/methods/ann/loss_functions/log_cosh_loss_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/log_cosh_loss_impl.hpp @@ -19,22 +19,22 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -LogCoshLossType::LogCoshLossType( - const double a, - const bool reduction) : - a(a), - reduction(reduction) +template +LogCoshLoss::LogCoshLoss( + const double a, const bool reduction) : + a(a) , reduction(reduction) { Log::Assert(a > 0, "Hyper-Parameter \'a\' must be positive"); } -template -typename MatType::elem_type LogCoshLossType::Forward( - const MatType& prediction, - const MatType& target) +template +template +typename PredictionType::elem_type +LogCoshLoss::Forward( + const PredictionType& prediction, + const TargetType& target) { - typename MatType::elem_type lossSum = + typename PredictionType::elem_type lossSum = arma::accu(arma::log(arma::cosh(a * (target - prediction)))) / a; if (reduction) @@ -43,11 +43,12 @@ typename MatType::elem_type LogCoshLossType::Forward( return lossSum / target.n_elem; } -template -void LogCoshLossType::Backward( - const MatType& prediction, - const MatType& target, - MatType& loss) +template +template +void LogCoshLoss::Backward( + const PredictionType& prediction, + const TargetType& target, + LossType& loss) { loss = arma::tanh(a * (target - prediction)); @@ -55,9 +56,9 @@ void LogCoshLossType::Backward( loss = loss / target.n_elem; } -template +template template -void LogCoshLossType::serialize( +void LogCoshLoss::serialize( Archive& ar, const uint32_t /* version */) { diff --git a/src/mlpack/methods/ann/loss_functions/margin_ranking_loss.hpp b/src/mlpack/methods/ann/loss_functions/margin_ranking_loss.hpp index b158c444bc..fabb65d78a 100644 --- a/src/mlpack/methods/ann/loss_functions/margin_ranking_loss.hpp +++ b/src/mlpack/methods/ann/loss_functions/margin_ranking_loss.hpp @@ -22,19 +22,21 @@ namespace ann /** Artificial Neural Network. */ { * values of 1 or -1. If the label is 1 then the first input should be ranked * higher than the second input at a distance larger than a margin, and vice- * versa if the label is -1. - * - * @tparam MatType Matrix representation to accept as input and use for - * computation. + * + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class MarginRankingLossType +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class MarginRankingLoss { public: /** - * Create the MarginRankingLossType object with Hyperparameter margin. - * Hyperparameter margin defines a minimum distance between correctly ranked - * samples. - * + * Create the MarginRankingLoss object with Hyperparameter margin. * @param margin defines a minimum distance between correctly ranked samples. * @param reduction Specifies the reduction to apply to the output. If false, * 'mean' reduction is used, where sum of the output will be @@ -42,7 +44,7 @@ class MarginRankingLossType * 'sum' reduction is used and the output will be summed. It * is set to true by default. */ - MarginRankingLossType(const double margin = 1.0, const bool reduction = true); + MarginRankingLoss(const double margin = 1.0, const bool reduction = true); /** * Computes the Margin Ranking Loss function. @@ -51,8 +53,9 @@ class MarginRankingLossType * function. * @param target The label vector which contains values of -1 or 1. */ - typename MatType::elem_type Forward(const MatType& prediction, - const MatType& target); + template + typename PredictionType::elem_type Forward(const PredictionType& prediction, + const TargetType& target); /** * Ordinary feed backward pass of a neural network. @@ -62,9 +65,19 @@ class MarginRankingLossType * @param target The label vector which contains -1 or 1 values. * @param loss The calculated error. */ - void Backward(const MatType& prediction, - const MatType& target, - MatType& loss); + template < + typename PredictionType, + typename TargetType, + typename LossType + > + void Backward(const PredictionType& prediction, + const TargetType& target, + LossType& loss); + + //! Get the output parameter. + OutputDataType& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } //! Get the margin parameter. double Margin() const { return margin; } @@ -84,15 +97,15 @@ class MarginRankingLossType void serialize(Archive& ar, const uint32_t /* version */); private: + //! Locally-stored output parameter object. + OutputDataType outputParameter; + //! The margin value used in calculating Margin Ranking Loss. double margin; //! Boolean value that tells if reduction is 'sum' or 'mean'. bool reduction; -}; // class MarginRankingLossType - -// Default typedef for typical `arma::mat` usage. -typedef MarginRankingLossType MarginRankingLoss; +}; // class MarginRankingLoss } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/margin_ranking_loss_impl.hpp b/src/mlpack/methods/ann/loss_functions/margin_ranking_loss_impl.hpp index 6551ac4df0..033357d9e8 100644 --- a/src/mlpack/methods/ann/loss_functions/margin_ranking_loss_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/margin_ranking_loss_impl.hpp @@ -18,24 +18,25 @@ namespace mlpack { namespace ann /** Artifical Neural Network. */ { -template -MarginRankingLossType::MarginRankingLossType( - const double margin, const bool reduction) : - margin(margin), - reduction(reduction) +template +MarginRankingLoss::MarginRankingLoss( + const double margin, const bool reduction): + margin(margin), reduction(reduction) { // Nothing to do here. } -template -typename MatType::elem_type MarginRankingLossType::Forward( - const MatType& prediction, - const MatType& target) +template +template +typename PredictionType::elem_type +MarginRankingLoss::Forward( + const PredictionType& prediction, + const TargetType& target) { const int predictionRows = prediction.n_rows; - const MatType& prediction1 = prediction.rows(0, + const PredictionType& prediction1 = prediction.rows(0, predictionRows / 2 - 1); - const MatType& prediction2 = prediction.rows(predictionRows / 2, + const PredictionType& prediction2 = prediction.rows(predictionRows / 2, predictionRows - 1); double lossSum = arma::accu(arma::max(arma::zeros(size(target)), @@ -47,21 +48,26 @@ typename MatType::elem_type MarginRankingLossType::Forward( return lossSum / target.n_elem; } -template -void MarginRankingLossType::Backward( - const MatType& prediction, - const MatType& target, - MatType& loss) +template +template < + typename PredictionType, + typename TargetType, + typename LossType +> +void MarginRankingLoss::Backward( + const PredictionType& prediction, + const TargetType& target, + LossType& loss) { const int predictionRows = prediction.n_rows; - const MatType& prediction1 = prediction.rows(0, + const PredictionType& prediction1 = prediction.rows(0, predictionRows / 2 - 1); - const MatType& prediction2 = prediction.rows(predictionRows / 2, + const PredictionType& prediction2 = prediction.rows(predictionRows / 2, predictionRows - 1); - MatType lossPrediction1 = -target % (prediction1 - prediction2) + margin; + LossType lossPrediction1 = -target % (prediction1 - prediction2) + margin; lossPrediction1.elem(arma::find(lossPrediction1 >= 0)).ones(); lossPrediction1.elem(arma::find(lossPrediction1 < 0)).zeros(); - MatType lossPrediction2 = lossPrediction1; + LossType lossPrediction2 = lossPrediction1; lossPrediction1 = -target % lossPrediction1; lossPrediction2 = target % lossPrediction2; loss = arma::join_cols(lossPrediction1, lossPrediction2); @@ -70,9 +76,9 @@ void MarginRankingLossType::Backward( loss = loss / target.n_elem; } -template +template template -void MarginRankingLossType::serialize( +void MarginRankingLoss::serialize( Archive& ar, const uint32_t /* version */) { diff --git a/src/mlpack/methods/ann/loss_functions/mean_absolute_percentage_error.hpp b/src/mlpack/methods/ann/loss_functions/mean_absolute_percentage_error.hpp index ed3abef2c9..d6eb6e5e89 100644 --- a/src/mlpack/methods/ann/loss_functions/mean_absolute_percentage_error.hpp +++ b/src/mlpack/methods/ann/loss_functions/mean_absolute_percentage_error.hpp @@ -37,17 +37,22 @@ namespace ann /** Artificial Neural Network. */ { * } * @endcode * - * @tparam MatType Matrix representation to accept as input and use for - * computation. + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class MeanAbsolutePercentageErrorType +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class MeanAbsolutePercentageError { public: /** - * Create the MeanAbsolutePercentageErrorType object. + * Create the MeanAbsolutePercentageError object. */ - MeanAbsolutePercentageErrorType(); + MeanAbsolutePercentageError(); /** * Computes the mean absolute percentage error function. @@ -56,8 +61,9 @@ class MeanAbsolutePercentageErrorType * function. * @param target The target vector. */ - typename MatType::elem_type Forward(const MatType& prediction, - const MatType& target); + template + typename PredictionType::elem_type Forward(const PredictionType& prediction, + const TargetType& target); /** * Ordinary feed backward pass of a neural network. @@ -67,19 +73,26 @@ class MeanAbsolutePercentageErrorType * @param target The target vector. * @param loss The calculated error. */ - void Backward(const MatType& prediction, - const MatType& target, - MatType& loss); + template + void Backward(const PredictionType& prediction, + const TargetType& target, + LossType& loss); + + //! Get the output parameter. + OutputDataType& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } /** - * Serialize the layer. - */ + * Serialize the layer. + */ template - void serialize(Archive& ar, const unsigned int /* version */) { } -}; // class MeanAbsolutePercentageErrorType + void serialize(Archive& ar, const unsigned int /* version */); -// Default typedef for typical `arma::mat` usage. -typedef MeanAbsolutePercentageErrorType MeanAbsolutePercentageError; + private: + //! Locally-stored output parameter object. + OutputDataType outputParameter; +}; // class MeanAbsolutePercentageError } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/mean_absolute_percentage_error_impl.hpp b/src/mlpack/methods/ann/loss_functions/mean_absolute_percentage_error_impl.hpp index 8185de852d..b573654e7f 100644 --- a/src/mlpack/methods/ann/loss_functions/mean_absolute_percentage_error_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/mean_absolute_percentage_error_impl.hpp @@ -18,32 +18,45 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -MeanAbsolutePercentageErrorType::MeanAbsolutePercentageErrorType() +template +MeanAbsolutePercentageError:: +MeanAbsolutePercentageError() { // Nothing to do here. } -template -typename MatType::elem_type MeanAbsolutePercentageErrorType::Forward( - const MatType& prediction, - const MatType& target) +template +template +typename PredictionType::elem_type +MeanAbsolutePercentageError::Forward( + const PredictionType& prediction, + const TargetType& target) { - MatType loss = arma::abs((prediction - target) / target); + PredictionType loss = arma::abs((prediction - target) / target); return arma::accu(loss) * (100 / target.n_cols); } -template -void MeanAbsolutePercentageErrorType::Backward( - const MatType& prediction, - const MatType& target, - MatType& loss) +template +template +void MeanAbsolutePercentageError::Backward( + const PredictionType& prediction, + const TargetType& target, + LossType& loss) { loss = (((arma::conv_to::from(prediction < target) * -2) + 1) / target) * (100 / target.n_cols); } +template +template +void MeanAbsolutePercentageError::serialize( + Archive& /* ar */, + const unsigned int /* version */) +{ + // Nothing to do here. +} + } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/mean_bias_error.hpp b/src/mlpack/methods/ann/loss_functions/mean_bias_error.hpp index 6c8324f8b5..fb84b2ba4c 100644 --- a/src/mlpack/methods/ann/loss_functions/mean_bias_error.hpp +++ b/src/mlpack/methods/ann/loss_functions/mean_bias_error.hpp @@ -18,18 +18,23 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { /** - * The mean bias error performance function measures the network's performance - * according to the mean of errors. + * The mean bias error performance function measures the network's + * performance according to the mean of errors. * - * @tparam MatType Matrix representation to accept as input and use for - * computation. + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class MeanBiasErrorType +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class MeanBiasError { public: /** - * Create the MeanBiasErrorType object. + * Create the MeanBiasError object. * * @param reduction Specifies the reduction to apply to * the output. If false, 'mean' reduction @@ -39,7 +44,7 @@ class MeanBiasErrorType * is used and the output will be summed. * It is set to true by default. */ - MeanBiasErrorType(const bool reduction = true); + MeanBiasError(const bool reduction = true); /** * Computes the mean bias error function. @@ -48,8 +53,9 @@ class MeanBiasErrorType * function. * @param target The target vector. */ - typename MatType::elem_type Forward(const MatType& prediction, - const MatType& target); + template + typename PredictionType::elem_type Forward(const PredictionType& prediction, + const TargetType& target); /** * Ordinary feed backward pass of a neural network. @@ -59,9 +65,15 @@ class MeanBiasErrorType * @param target The target vector. * @param loss The calculated error. */ - void Backward(const MatType& prediction, - const MatType& target, - MatType& loss); + template + void Backward(const PredictionType& prediction, + const TargetType& target, + LossType& loss); + + //! Get the output parameter. + OutputDataType& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } //! Get the reduction type, represented as boolean //! (false 'mean' reduction, true 'sum' reduction). @@ -76,12 +88,12 @@ class MeanBiasErrorType void serialize(Archive& ar, const uint32_t /* version */); private: + //! Locally-stored output parameter object. + OutputDataType outputParameter; + //! Boolean value that tells if reduction is 'sum' or 'mean'. bool reduction; -}; // class MeanBiasErrorType - -// Default typedef for typical `arma::mat` usage. -typedef MeanBiasErrorType MeanBiasError; +}; // class MeanBiasError } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/mean_bias_error_impl.hpp b/src/mlpack/methods/ann/loss_functions/mean_bias_error_impl.hpp index 6473eda8e0..fa801ff275 100644 --- a/src/mlpack/methods/ann/loss_functions/mean_bias_error_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/mean_bias_error_impl.hpp @@ -19,20 +19,22 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -MeanBiasErrorType::MeanBiasErrorType(const bool reduction) : - reduction(reduction) +template +MeanBiasError:: + MeanBiasError(const bool reduction) : reduction(reduction) { // Nothing to do here } -template -typename MatType::elem_type MeanBiasErrorType::Forward( - const MatType& prediction, - const MatType& target) +template +template +typename PredictionType::elem_type +MeanBiasError::Forward( + const PredictionType& prediction, + const TargetType& target) { - MatType loss = target - prediction; - typename MatType::elem_type lossSum = arma::accu(loss); + PredictionType loss = target - prediction; + typename PredictionType::elem_type lossSum = arma::accu(loss); if (reduction) return lossSum; @@ -40,11 +42,12 @@ typename MatType::elem_type MeanBiasErrorType::Forward( return lossSum / target.n_elem; } -template -void MeanBiasErrorType::Backward( - const MatType& prediction, - const MatType& /* target */, - MatType& loss) +template +template +void MeanBiasError::Backward( + const PredictionType& prediction, + const TargetType& /* target */, + LossType& loss) { loss.set_size(arma::size(prediction)); loss.fill(-1.0); @@ -53,9 +56,9 @@ void MeanBiasErrorType::Backward( loss = loss / loss.n_elem; } -template +template template -void MeanBiasErrorType::serialize( +void MeanBiasError::serialize( Archive& ar, const uint32_t /* version */) { diff --git a/src/mlpack/methods/ann/loss_functions/mean_squared_error.hpp b/src/mlpack/methods/ann/loss_functions/mean_squared_error.hpp index 6065951f38..6006d76d12 100644 --- a/src/mlpack/methods/ann/loss_functions/mean_squared_error.hpp +++ b/src/mlpack/methods/ann/loss_functions/mean_squared_error.hpp @@ -21,15 +21,21 @@ namespace ann /** Artificial Neural Network. */ { * The mean squared error performance function measures the network's * performance according to the mean of squared errors. * - * @tparam MatType Matrix representation to accept as input and use for - * computation. + * @tparam ActivationFunction Activation function used for the embedding layer. + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class MeanSquaredErrorType +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class MeanSquaredError { public: /** - * Create the MeanSquaredErrorType object. + * Create the MeanSquaredError object. * * @param reduction Specifies the reduction to apply to the output. If false, * 'mean' reduction is used, where sum of the output will be @@ -37,7 +43,7 @@ class MeanSquaredErrorType * 'sum' reduction is used and the output will be summed. It * is set to true by default. */ - MeanSquaredErrorType(const bool reduction = true); + MeanSquaredError(const bool reduction = true); /** * Computes the mean squared error function. @@ -46,8 +52,9 @@ class MeanSquaredErrorType * function. * @param target The target vector. */ - typename MatType::elem_type Forward(const MatType& prediction, - const MatType& target); + template + typename PredictionType::elem_type Forward(const PredictionType& prediction, + const TargetType& target); /** * Ordinary feed backward pass of a neural network. @@ -57,10 +64,16 @@ class MeanSquaredErrorType * @param target The target vector. * @param loss The calculated error. */ - void Backward(const MatType& prediction, - const MatType& target, - MatType& loss); + template + void Backward(const PredictionType& prediction, + const TargetType& target, + LossType& loss); + //! Get the output parameter. + OutputDataType& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + //! Get the reduction type, represented as boolean //! (false 'mean' reduction, true 'sum' reduction). bool Reduction() const { return reduction; } @@ -74,12 +87,12 @@ class MeanSquaredErrorType void serialize(Archive& ar, const uint32_t /* version */); private: + //! Locally-stored output parameter object. + OutputDataType outputParameter; + //! Boolean value that tells if reduction is 'sum' or 'mean'. bool reduction; -}; // class MeanSquaredErrorType - -// Default typedef for typical `arma::mat` usage. -typedef MeanSquaredErrorType MeanSquaredError; +}; // class MeanSquaredError } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/mean_squared_error_impl.hpp b/src/mlpack/methods/ann/loss_functions/mean_squared_error_impl.hpp index c2938a6e3a..3bd64bc638 100644 --- a/src/mlpack/methods/ann/loss_functions/mean_squared_error_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/mean_squared_error_impl.hpp @@ -18,19 +18,21 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -MeanSquaredErrorType::MeanSquaredErrorType(const bool reduction) : - reduction(reduction) +template +MeanSquaredError + ::MeanSquaredError(const bool reduction) : reduction(reduction) { // Nothing to do here. } -template -typename MatType::elem_type MeanSquaredErrorType::Forward( - const MatType& prediction, - const MatType& target) +template +template +typename PredictionType::elem_type +MeanSquaredError::Forward( + const PredictionType& prediction, + const TargetType& target) { - typename MatType::elem_type lossSum = + typename PredictionType::elem_type lossSum = arma::accu(arma::square(prediction - target)); if (reduction) @@ -39,11 +41,12 @@ typename MatType::elem_type MeanSquaredErrorType::Forward( return lossSum / target.n_elem; } -template -void MeanSquaredErrorType::Backward( - const MatType& prediction, - const MatType& target, - MatType& loss) +template +template +void MeanSquaredError::Backward( + const PredictionType& prediction, + const TargetType& target, + LossType& loss) { loss = 2 * (prediction - target); @@ -51,9 +54,9 @@ void MeanSquaredErrorType::Backward( loss = loss / target.n_elem; } -template +template template -void MeanSquaredErrorType::serialize( +void MeanSquaredError::serialize( Archive& ar, const uint32_t /* version */) { diff --git a/src/mlpack/methods/ann/loss_functions/mean_squared_logarithmic_error.hpp b/src/mlpack/methods/ann/loss_functions/mean_squared_logarithmic_error.hpp index 833066f876..bdd702ef54 100644 --- a/src/mlpack/methods/ann/loss_functions/mean_squared_logarithmic_error.hpp +++ b/src/mlpack/methods/ann/loss_functions/mean_squared_logarithmic_error.hpp @@ -18,18 +18,23 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { /** - * The mean squared logarithmic error performance function measures the - * network's performance according to the mean of squared logarithmic errors. + * The mean squared logarithmic error performance function measures the network's + * performance according to the mean of squared logarithmic errors. * - * @tparam MatType Matrix representation to accept as input and use for - * computation. + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class MeanSquaredLogarithmicErrorType +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class MeanSquaredLogarithmicError { public: /** - * Create the MeanSquaredLogarithmicErrorType object. + * Create the MeanSquaredLogarithmicError object. * * @param reduction Specifies the reduction to apply to the output. If false, * 'mean' reduction is used, where sum of the output will be @@ -37,7 +42,7 @@ class MeanSquaredLogarithmicErrorType * 'sum' reduction is used and the output will be summed. It * is set to true by default. */ - MeanSquaredLogarithmicErrorType(const bool reduction = true); + MeanSquaredLogarithmicError(const bool reduction = true); /** * Computes the mean squared logarithmic error function. @@ -46,8 +51,9 @@ class MeanSquaredLogarithmicErrorType * function. * @param target The target vector. */ - typename MatType::elem_type Forward(const MatType& prediction, - const MatType& target); + template + typename PredictionType::elem_type Forward(const PredictionType& prediction, + const TargetType& target); /** * Ordinary feed backward pass of a neural network. @@ -57,9 +63,15 @@ class MeanSquaredLogarithmicErrorType * @param target The target vector. * @param loss The calculated error. */ - void Backward(const MatType& prediction, - const MatType& target, - MatType& loss); + template + void Backward(const PredictionType& prediction, + const TargetType& target, + LossType& loss); + + //! Get the output parameter. + OutputDataType& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } //! Get the reduction type, represented as boolean //! (false 'mean' reduction, true 'sum' reduction). @@ -74,12 +86,12 @@ class MeanSquaredLogarithmicErrorType void serialize(Archive& ar, const uint32_t /* version */); private: + //! Locally-stored output parameter object. + OutputDataType outputParameter; + //! Boolean value that tells if reduction is 'sum' or 'mean'. bool reduction; -}; // class MeanSquaredLogarithmicErrorType - -// Default typedef for typical `arma::mat` usage. -typedef MeanSquaredLogarithmicErrorType MeanSquaredLogarithmicError; +}; // class MeanSquaredLogarithmicError } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/mean_squared_logarithmic_error_impl.hpp b/src/mlpack/methods/ann/loss_functions/mean_squared_logarithmic_error_impl.hpp index a59d71a7bc..4b1d3740c7 100644 --- a/src/mlpack/methods/ann/loss_functions/mean_squared_logarithmic_error_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/mean_squared_logarithmic_error_impl.hpp @@ -18,20 +18,21 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -MeanSquaredLogarithmicErrorType::MeanSquaredLogarithmicErrorType( - const bool reduction) : - reduction(reduction) +template +MeanSquaredLogarithmicError +::MeanSquaredLogarithmicError(const bool reduction) : reduction(reduction) { // Nothing to do here. } -template -typename MatType::elem_type MeanSquaredLogarithmicErrorType::Forward( - const MatType& prediction, - const MatType& target) +template +template +typename PredictionType::elem_type +MeanSquaredLogarithmicError::Forward( + const PredictionType& prediction, + const TargetType& target) { - typename MatType::elem_type lossSum = + typename PredictionType::elem_type lossSum = arma::accu(arma::square(arma::log(1.0 + target) - arma::log(1.0 + prediction))); @@ -41,11 +42,12 @@ typename MatType::elem_type MeanSquaredLogarithmicErrorType::Forward( return lossSum / target.n_elem; } -template -void MeanSquaredLogarithmicErrorType::Backward( - const MatType& prediction, - const MatType& target, - MatType& loss) +template +template +void MeanSquaredLogarithmicError::Backward( + const PredictionType& prediction, + const TargetType& target, + LossType& loss) { loss = 2 * (arma::log(1. + prediction) - arma::log(1. + target)) / (1. + prediction); @@ -54,9 +56,9 @@ void MeanSquaredLogarithmicErrorType::Backward( loss = loss / target.n_elem; } -template +template template -void MeanSquaredLogarithmicErrorType::serialize( +void MeanSquaredLogarithmicError::serialize( Archive& ar, const uint32_t /* version */) { diff --git a/src/mlpack/methods/ann/loss_functions/multilabel_softmargin_loss.hpp b/src/mlpack/methods/ann/loss_functions/multilabel_softmargin_loss.hpp index 008cf09a9c..e707410155 100644 --- a/src/mlpack/methods/ann/loss_functions/multilabel_softmargin_loss.hpp +++ b/src/mlpack/methods/ann/loss_functions/multilabel_softmargin_loss.hpp @@ -22,21 +22,20 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { /** - * The Multi-label Soft Margin Loss function. - * - * It is a criterion that optimizes a multi-label one-versus-all loss based on - * max-entropy, between input x and target y of size (N, C) where N is the - * batch size and C is the number of classes. - * - * @tparam MatType Matrix representation to accept as input and use for - * computation. + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class MultiLabelSoftMarginLossType +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class MultiLabelSoftMarginLoss { public: /** - * Create the MultiLabelSoftMarginLossType object. + * Create the MultiLabelSoftMarginLoss object. * * @param reduction Specifies the reduction to apply to the output. If false, * 'mean' reduction is used, where sum of the output will be @@ -46,10 +45,8 @@ class MultiLabelSoftMarginLossType * @param weights A manual rescaling weight given to each class. It is a * (1, numClasses) row vector. */ - MultiLabelSoftMarginLossType( - const bool reduction = true, - const arma::Row& weights = - arma::Row()); + MultiLabelSoftMarginLoss(const bool reduction = true, + const arma::rowvec& weights = arma::rowvec()); /** * Computes the Multi Label Soft Margin Loss function. @@ -57,8 +54,9 @@ class MultiLabelSoftMarginLossType * @param input Input data used for evaluating the specified function. * @param target The target vector with same shape as input. */ - typename MatType::elem_type Forward(const MatType& input, - const MatType& target); + template + typename InputType::elem_type Forward(const InputType& input, + const TargetType& target); /** * Ordinary feed backward pass of a neural network. @@ -67,20 +65,20 @@ class MultiLabelSoftMarginLossType * @param target The target vector. * @param output The calculated error. */ - void Backward(const MatType& input, - const MatType& target, - MatType& output); + template + void Backward(const InputType& input, + const TargetType& target, + OutputType& output); + + //! Get the output parameter. + OutputDataType& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } //! Get the weights assigned to each class. - const arma::Row& ClassWeights() const - { - return classWeights; - } + const arma::rowvec& ClassWeights() const { return classWeights; } //! Modify the weights assigned to each class. - arma::Row& ClassWeights() - { - return classWeights; - } + arma::rowvec& ClassWeights() { return classWeights; } //! Get the reduction type, represented as boolean //! (false 'mean' reduction, true 'sum' reduction). @@ -95,18 +93,18 @@ class MultiLabelSoftMarginLossType void serialize(Archive& ar, const unsigned int /* version */); private: - //! The boolean value that tells if reduction is sum or mean. + //! Locally-stored output parameter object. + OutputDataType outputParameter; + + //! Boolean value that tells if reduction is 'sum' or 'mean'. bool reduction; //! A (1, numClasses) shaped vector with weights for each class. - arma::Row classWeights; + arma::rowvec classWeights; // An internal parameter used during initialisation of class weights. bool weighted; -}; // class MultiLabelSoftMarginLossType - -// Default typedef for typical `arma::mat` usage. -typedef MultiLabelSoftMarginLossType MultiLabelSoftMarginLoss; +}; // class MultiLabelSoftMarginLoss } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/multilabel_softmargin_loss_impl.hpp b/src/mlpack/methods/ann/loss_functions/multilabel_softmargin_loss_impl.hpp index 453254f100..a497a591d3 100644 --- a/src/mlpack/methods/ann/loss_functions/multilabel_softmargin_loss_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/multilabel_softmargin_loss_impl.hpp @@ -18,10 +18,11 @@ namespace mlpack { namespace ann /** Artifical Neural Network. */ { -template -MultiLabelSoftMarginLossType::MultiLabelSoftMarginLossType( +template +MultiLabelSoftMarginLoss:: +MultiLabelSoftMarginLoss( const bool reduction, - const arma::Row& weights) : + const arma::rowvec& weights) : reduction(reduction), weighted(false) { @@ -32,9 +33,11 @@ MultiLabelSoftMarginLossType::MultiLabelSoftMarginLossType( } } -template -typename MatType::elem_type MultiLabelSoftMarginLossType::Forward( - const MatType& input, const MatType& target) +template +template +typename InputType::elem_type +MultiLabelSoftMarginLoss::Forward( + const InputType& input, const TargetType& target) { if (!weighted) { @@ -42,9 +45,9 @@ typename MatType::elem_type MultiLabelSoftMarginLossType::Forward( weighted = true; } - MatType logSigmoid = arma::log((1 / (1 + arma::exp(-input)))); - MatType logSigmoidNeg = arma::log(1 / (1 + arma::exp(input))); - MatType loss = arma::mean(arma::sum(-(target % logSigmoid + + InputType logSigmoid = arma::log((1 / (1 + arma::exp(-input)))); + InputType logSigmoidNeg = arma::log(1 / (1 + arma::exp(input))); + InputType loss = arma::mean(arma::sum(-(target % logSigmoid + (1 - target) % logSigmoidNeg)) % classWeights, 1); if (reduction) @@ -53,14 +56,15 @@ typename MatType::elem_type MultiLabelSoftMarginLossType::Forward( return arma::as_scalar(loss / input.n_rows); } -template -void MultiLabelSoftMarginLossType::Backward( - const MatType& input, - const MatType& target, - MatType& output) +template +template +void MultiLabelSoftMarginLoss::Backward( + const InputType& input, + const TargetType& target, + OutputType& output) { output.set_size(size(input)); - MatType sigmoid = (1 / (1 + arma::exp(-input))); + InputType sigmoid = (1 / (1 + arma::exp(-input))); output = -(target % (1 - sigmoid) - (1 - target) % sigmoid) % arma::repmat(classWeights, target.n_rows, 1) / output.n_elem; @@ -68,9 +72,9 @@ void MultiLabelSoftMarginLossType::Backward( output = output * input.n_rows; } -template +template template -void MultiLabelSoftMarginLossType::serialize( +void MultiLabelSoftMarginLoss::serialize( Archive& ar, const unsigned int /* version */) { diff --git a/src/mlpack/methods/ann/loss_functions/negative_log_likelihood.hpp b/src/mlpack/methods/ann/loss_functions/negative_log_likelihood.hpp index f78a875268..49e8d06957 100644 --- a/src/mlpack/methods/ann/loss_functions/negative_log_likelihood.hpp +++ b/src/mlpack/methods/ann/loss_functions/negative_log_likelihood.hpp @@ -2,7 +2,7 @@ * @file methods/ann/loss_functions/negative_log_likelihood.hpp * @author Marcus Edel * - * Definition of the NegativeLogLikelihoodType class. + * Definition of the NegativeLogLikelihood class. * * mlpack is free software; you may redistribute it and/or modify it under the * terms of the 3-clause BSD license. You should have received a copy of the @@ -20,18 +20,23 @@ namespace ann /** Artificial Neural Network. */ { /** * Implementation of the negative log likelihood layer. The negative log * likelihood layer expects that the input contains log-probabilities for each - * class. The layer also expects a class index in the range [0, numClasses - 1] - * number of classes, as target when calling the Forward function. + * class. The layer also expects a class index, in the range between 0 and + * number of classes -1, as target when calling the Forward function. * - * @tparam MatType Matrix representation to accept as input and use for - * computation. + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class NegativeLogLikelihoodType +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class NegativeLogLikelihood { public: /** - * Create the NegativeLogLikelihoodTypeLayer object. + * Create the NegativeLogLikelihoodLayer object. * * @param reduction Specifies the reduction to apply to the output. If false, * 'mean' reduction is used, where sum of the output will be @@ -39,7 +44,7 @@ class NegativeLogLikelihoodType * 'sum' reduction is used and the output will be summed. It * is set to true by default. */ - NegativeLogLikelihoodType(const bool reduction = true); + NegativeLogLikelihood(const bool reduction = true); /** * Computes the Negative log likelihood. @@ -49,8 +54,9 @@ class NegativeLogLikelihoodType * @param target The target vector, that contains the class index in the range * between 1 and the number of classes. */ - double Forward(const MatType& prediction, - const MatType& target); + template + typename PredictionType::elem_type Forward(const PredictionType& prediction, + const TargetType& target); /** * Ordinary feed backward pass of a neural network. The negative log @@ -64,9 +70,25 @@ class NegativeLogLikelihoodType * between 1 and the number of classes. * @param loss The calculated error. */ - void Backward(const MatType& prediction, - const MatType& target, - MatType& loss); + template + void Backward(const PredictionType& prediction, + const TargetType& target, + LossType& loss); + + //! Get the input parameter. + InputDataType& InputParameter() const { return inputParameter; } + //! Modify the input parameter. + InputDataType& InputParameter() { return inputParameter; } + + //! Get the output parameter. + OutputDataType& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } + + //! Get the delta. + OutputDataType& Delta() const { return delta; } + //! Modify the delta. + OutputDataType& Delta() { return delta; } //! Get the reduction type, represented as boolean //! (false 'mean' reduction, true 'sum' reduction). @@ -81,12 +103,18 @@ class NegativeLogLikelihoodType void serialize(Archive& /* ar */, const uint32_t /* version */); private: + //! Locally-stored delta object. + OutputDataType delta; + + //! Locally-stored input parameter object. + InputDataType inputParameter; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; + //! Boolean value that tells if reduction is 'sum' or 'mean'. bool reduction; -}; // class NegativeLogLikelihoodType - -// Default typedef for typical `arma::mat` usage. -typedef NegativeLogLikelihoodType NegativeLogLikelihood; +}; // class NegativeLogLikelihood } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/negative_log_likelihood_impl.hpp b/src/mlpack/methods/ann/loss_functions/negative_log_likelihood_impl.hpp index 82c258f838..b33979d130 100644 --- a/src/mlpack/methods/ann/loss_functions/negative_log_likelihood_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/negative_log_likelihood_impl.hpp @@ -2,7 +2,7 @@ * @file methods/ann/loss_functions/negative_log_likelihood_impl.hpp * @author Marcus Edel * - * Implementation of the NegativeLogLikelihoodType class. + * Implementation of the NegativeLogLikelihood class. * * mlpack is free software; you may redistribute it and/or modify it under the * terms of the 3-clause BSD license. You should have received a copy of the @@ -18,19 +18,21 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -NegativeLogLikelihoodType::NegativeLogLikelihoodType( - const bool reduction) : reduction(reduction) +template +NegativeLogLikelihood + ::NegativeLogLikelihood(const bool reduction) : reduction(reduction) { // Nothing to do here. } -template -double NegativeLogLikelihoodType::Forward( - const MatType& prediction, - const MatType& target) +template +template +typename PredictionType::elem_type +NegativeLogLikelihood::Forward( + const PredictionType& prediction, + const TargetType& target) { - typedef typename MatType::elem_type ElemType; + typedef typename PredictionType::elem_type ElemType; ElemType lossSum = 0; for (size_t i = 0; i < prediction.n_cols; ++i) { @@ -46,13 +48,14 @@ double NegativeLogLikelihoodType::Forward( return lossSum / target.n_elem; } -template -void NegativeLogLikelihoodType::Backward( - const MatType& prediction, - const MatType& target, - MatType& loss) +template +template +void NegativeLogLikelihood::Backward( + const PredictionType& prediction, + const TargetType& target, + LossType& loss) { - loss = arma::zeros(prediction.n_rows, prediction.n_cols); + loss = arma::zeros(prediction.n_rows, prediction.n_cols); for (size_t i = 0; i < prediction.n_cols; ++i) { Log::Assert(target(i) >= 0 && target(i) < prediction.n_rows, @@ -65,9 +68,9 @@ void NegativeLogLikelihoodType::Backward( loss = loss / target.n_elem; } -template +template template -void NegativeLogLikelihoodType::serialize( +void NegativeLogLikelihood::serialize( Archive& ar, const uint32_t /* version */) { ar(CEREAL_NVP(reduction)); diff --git a/src/mlpack/methods/ann/loss_functions/poisson_nll_loss.hpp b/src/mlpack/methods/ann/loss_functions/poisson_nll_loss.hpp index b06f366958..e3ba107423 100644 --- a/src/mlpack/methods/ann/loss_functions/poisson_nll_loss.hpp +++ b/src/mlpack/methods/ann/loss_functions/poisson_nll_loss.hpp @@ -2,7 +2,7 @@ * @file methods/ann/loss_functions/poisson_nll_loss.hpp * @author Mrityunjay Tripathi * - * Definition of the PoissonNLLLossType class. It is the negative log likelihood of + * Definition of the PoissonNLLLoss class. It is the negative log likelihood of * the Poisson distribution. * * mlpack is free software; you may redistribute it and/or modify it under the @@ -20,18 +20,24 @@ namespace ann /** Artificial Neural Network. */ { /** * Implementation of the Poisson negative log likelihood loss. This loss - * function expects input for each class. It also expects a class index, in the - * range [0, numClasses - 1], as target when calling the Forward function. + * function expects input for each class. It also expects a class index, + * in the range between 1 and the number of classes, as target when calling + * the Forward function. * - * @tparam MatType Matrix representation to accept as input and use for - * computation. + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class PoissonNLLLossType +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class PoissonNLLLoss { public: /** - * Create the PoissonNLLLossType object. + * Create the PoissonNLLLoss object. * * @param logInput If true the loss is computed as * \f$ \exp(input) - target \cdot input \f$, if false then the loss is @@ -45,9 +51,9 @@ class PoissonNLLLossType * 'sum' reduction is used and the output will be summed. It * is set to true by default. */ - PoissonNLLLossType(const bool logInput = true, + PoissonNLLLoss(const bool logInput = true, const bool full = false, - const typename MatType::elem_type eps = 1e-08, + const typename InputDataType::elem_type eps = 1e-08, const bool reduction = true); /** @@ -58,8 +64,9 @@ class PoissonNLLLossType * @param target The target vector, that contains the class index in the range * between 1 and the number of classes. */ - typename MatType::elem_type Forward(const MatType& prediction, - const MatType& target); + template + typename InputDataType::elem_type Forward(const PredictionType& prediction, + const TargetType& target); /** * Ordinary feed backward pass of a neural network. The Poisson Negative Log @@ -73,9 +80,20 @@ class PoissonNLLLossType * between 1 and the number of classes. * @param loss The calculated error. */ - void Backward(const MatType& prediction, - const MatType& target, - MatType& loss); + template + void Backward(const PredictionType& prediction, + const TargetType& target, + LossType& loss); + + //! Get the input parameter. + InputDataType& InputParameter() const { return inputParameter; } + //! Modify the input parameter. + InputDataType& InputParameter() { return inputParameter; } + + //! Get the output parameter. + OutputDataType& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } //! Get the value of logInput. logInput is a boolean value that tells if //! logits are given as input. @@ -93,17 +111,16 @@ class PoissonNLLLossType //! Get the value of eps. eps is a small value required to prevent 0 in //! logarithms and denominators. - typename MatType::elem_type Eps() const { return eps; } + typename InputDataType::elem_type Eps() const { return eps; } //! Modify the value of eps. eps is a small value required to prevent 0 in //! logarithms and denominators. - typename MatType::elem_type& Eps() { return eps; } + typename InputDataType::elem_type& Eps() { return eps; } //! Get the reduction type, represented as boolean //! (false 'mean' reduction, true 'sum' reduction). bool Reduction() const { return reduction; } //! Modify the type of reduction used. bool& Reduction() { return reduction; } - /** * Serialize the layer. */ @@ -123,6 +140,12 @@ class PoissonNLLLossType } } + //! Locally-stored input parameter object. + InputDataType inputParameter; + + //! Locally-stored output parameter object. + OutputDataType outputParameter; + //! Boolean value that tells if logits are given as input. bool logInput; @@ -131,14 +154,12 @@ class PoissonNLLLossType bool full; //! eps is a small value required to prevent 0 in logarithms and denominators. - typename MatType::elem_type eps; + typename InputDataType::elem_type eps; //! Boolean value that tells if reduction is 'sum' or 'mean'. bool reduction; -}; // class PoissonNLLLossType -// Default typedef for typical `arma::mat` usage. -typedef PoissonNLLLossType PoissonNLLLoss; +}; // class PoissonNLLLoss } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/poisson_nll_loss_impl.hpp b/src/mlpack/methods/ann/loss_functions/poisson_nll_loss_impl.hpp index c23d0d0ad8..3152d73f56 100644 --- a/src/mlpack/methods/ann/loss_functions/poisson_nll_loss_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/poisson_nll_loss_impl.hpp @@ -2,7 +2,7 @@ * @file methods/ann/loss_functions/poisson_nll_loss_impl.hpp * @author Mrityunjay Tripathi * - * Implementation of the PoissonNLLLossType class. + * Implementation of the PoissonNLLLoss class. * * mlpack is free software; you may redistribute it and/or modify it under the * terms of the 3-clause BSD license. You should have received a copy of the @@ -19,12 +19,12 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -PoissonNLLLossType::PoissonNLLLossType( +template +PoissonNLLLoss::PoissonNLLLoss( const bool logInput, const bool full, - const typename MatType::elem_type eps, - const bool reduction) : + const typename InputDataType::elem_type eps, + const bool reduction): logInput(logInput), full(full), eps(eps), @@ -33,12 +33,14 @@ PoissonNLLLossType::PoissonNLLLossType( Log::Assert(eps >= 0, "Epsilon (eps) must be greater than or equal to zero."); } -template -typename MatType::elem_type PoissonNLLLossType::Forward( - const MatType& prediction, - const MatType& target) +template +template +typename InputDataType::elem_type +PoissonNLLLoss::Forward( + const PredictionType& prediction, + const TargetType& target) { - MatType loss(arma::size(prediction)); + PredictionType loss(arma::size(prediction)); if (logInput) loss = arma::exp(prediction) - target % prediction; @@ -51,11 +53,11 @@ typename MatType::elem_type PoissonNLLLossType::Forward( if (full) { const auto mask = target > 1.0; - const MatType approx = target % arma::log(target) - target + const PredictionType approx = target % arma::log(target) - target + 0.5 * arma::log(2 * M_PI * target); loss.elem(arma::find(mask)) += approx.elem(arma::find(mask)); } - typename MatType::elem_type lossSum = arma::accu(loss); + typename PredictionType::elem_type lossSum = arma::accu(loss); if (reduction) return lossSum; @@ -63,11 +65,12 @@ typename MatType::elem_type PoissonNLLLossType::Forward( return lossSum / loss.n_elem; } -template -void PoissonNLLLossType::Backward( - const MatType& prediction, - const MatType& target, - MatType& loss) +template +template +void PoissonNLLLoss::Backward( + const PredictionType& prediction, + const TargetType& target, + LossType& loss) { loss.set_size(size(prediction)); @@ -80,9 +83,9 @@ void PoissonNLLLossType::Backward( loss = loss / loss.n_elem; } -template +template template -void PoissonNLLLossType::serialize( +void PoissonNLLLoss::serialize( Archive& ar, const uint32_t /* version */) { diff --git a/src/mlpack/methods/ann/loss_functions/reconstruction_loss.hpp b/src/mlpack/methods/ann/loss_functions/reconstruction_loss.hpp index 15a290fb8c..454c22cb31 100644 --- a/src/mlpack/methods/ann/loss_functions/reconstruction_loss.hpp +++ b/src/mlpack/methods/ann/loss_functions/reconstruction_loss.hpp @@ -23,19 +23,22 @@ namespace ann /** Artificial Neural Network. */ { * performance equal to the negative log probability of the target with * the input distribution. * - * @tparam MatType Matrix representation to accept as input and use for - * computation. + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). * @tparam DistType The type of distribution parametrized by the input. */ -template< - typename MatType = arma::mat, - typename DistType = BernoulliDistribution +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat, + typename DistType = BernoulliDistribution > -class ReconstructionLossType +class ReconstructionLoss { public: /** - * Create the ReconstructionLossType object. + * Create the ReconstructionLoss object. * * @param reduction Specifies the reduction to apply to the output. If false, * 'mean' reduction is used, where sum of the output will be @@ -43,7 +46,7 @@ class ReconstructionLossType * 'sum' reduction is used and the output will be summed. It * is set to true by default. */ - ReconstructionLossType(const bool reduction = true); + ReconstructionLoss(const bool reduction = true); /** * Computes the reconstruction loss. @@ -52,8 +55,9 @@ class ReconstructionLossType * function. * @param target The target matrix. */ - typename MatType::elem_type Forward(const MatType& prediction, - const MatType& target); + template + typename PredictionType::elem_type Forward(const PredictionType& prediction, + const TargetType& target); /** * Ordinary feed backward pass of a neural network. @@ -63,9 +67,15 @@ class ReconstructionLossType * @param target The target matrix. * @param loss The calculated error. */ - void Backward(const MatType& prediction, - const MatType& target, - MatType& loss); + template + void Backward(const PredictionType& prediction, + const TargetType& target, + LossType& loss); + + //! Get the output parameter. + OutputDataType& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } //! Get the reduction type, represented as boolean //! (false 'mean' reduction, true 'sum' reduction). @@ -83,12 +93,12 @@ class ReconstructionLossType //! Locally-stored distribution object. DistType dist; + //! Locally-stored output parameter object. + OutputDataType outputParameter; + //! Boolean value that tells if reduction is 'sum' or 'mean'. bool reduction; -}; // class ReconstructionLossType - -// Default typedef for typical `arma::mat` usage. -typedef ReconstructionLossType ReconstructionLoss; +}; // class ReconstructionLoss } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/reconstruction_loss_impl.hpp b/src/mlpack/methods/ann/loss_functions/reconstruction_loss_impl.hpp index f1927c1d0c..ccb70b90f0 100644 --- a/src/mlpack/methods/ann/loss_functions/reconstruction_loss_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/reconstruction_loss_impl.hpp @@ -18,20 +18,24 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -ReconstructionLossType::ReconstructionLossType( - const bool reduction) : - reduction(reduction) +template +ReconstructionLoss< + InputDataType, + OutputDataType, + DistType +>::ReconstructionLoss(const bool reduction) : reduction(reduction) { // Nothing to do here. } -template -typename MatType::elem_type ReconstructionLossType::Forward( - const MatType& prediction, const MatType& target) +template +template +typename PredictionType::elem_type +ReconstructionLoss::Forward( + const PredictionType& prediction, const TargetType& target) { dist = DistType(prediction); - typename MatType::elem_type lossSum = -dist.LogProbability(target); + typename PredictionType::elem_type lossSum = -dist.LogProbability(target); if (reduction) return lossSum; @@ -39,11 +43,12 @@ typename MatType::elem_type ReconstructionLossType::Forward( return lossSum / target.n_elem; } -template -void ReconstructionLossType::Backward( - const MatType& /* prediction */, - const MatType& target, - MatType& loss) +template +template +void ReconstructionLoss::Backward( + const PredictionType& /* prediction */, + const TargetType& target, + LossType& loss) { dist.LogProbBackward(target, loss); loss *= -1; @@ -52,13 +57,12 @@ void ReconstructionLossType::Backward( loss = loss / target.n_elem; } -template +template template -void ReconstructionLossType::serialize( - Archive& ar, +void ReconstructionLoss::serialize( + Archive& ar, const uint32_t /* version */) { - ar(CEREAL_NVP(dist)); ar(CEREAL_NVP(reduction)); } diff --git a/src/mlpack/methods/ann/loss_functions/sigmoid_cross_entropy_error.hpp b/src/mlpack/methods/ann/loss_functions/sigmoid_cross_entropy_error.hpp index 6c66efc947..04f9419521 100644 --- a/src/mlpack/methods/ann/loss_functions/sigmoid_cross_entropy_error.hpp +++ b/src/mlpack/methods/ann/loss_functions/sigmoid_cross_entropy_error.hpp @@ -19,7 +19,7 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { /** - * The SigmoidCrossEntropyErrorType performance function measures the network's + * The SigmoidCrossEntropyError performance function measures the network's * performance according to the cross-entropy function between the input and * target distributions. This function calculates the cross entropy * given the real values instead of providing the sigmoid activations. @@ -40,23 +40,28 @@ namespace ann /** Artificial Neural Network. */ { * } * @endcode * - * @tparam MatType Matrix representation to accept as input and use for - * computation. + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class SigmoidCrossEntropyErrorType +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class SigmoidCrossEntropyError { public: /** - * Create the SigmoidCrossEntropyErrorType object. + * Create the SigmoidCrossEntropyError object. * * @param reduction Specifies the reduction to apply to the output. If false, * 'mean' reduction is used, where sum of the output will be * divided by the number of elements in the output. If true, * 'sum' reduction is used and the output will be summed. It * is set to true by default. - */ - SigmoidCrossEntropyErrorType(const bool reduction = true); + */ + SigmoidCrossEntropyError(const bool reduction = true); /** * Computes the Sigmoid CrossEntropy Error functions. @@ -65,9 +70,10 @@ class SigmoidCrossEntropyErrorType * function. * @param target The target vector. */ - inline typename MatType::elem_type Forward( - const MatType& prediction, - const MatType& target); + template + inline typename PredictionType::elem_type Forward( + const PredictionType& prediction, + const TargetType& target); /** * Ordinary feed backward pass of a neural network. @@ -77,9 +83,15 @@ class SigmoidCrossEntropyErrorType * @param target The target vector. * @param loss The calculated error. */ - inline void Backward(const MatType& prediction, - const MatType& target, - MatType& loss); + template + inline void Backward(const PredictionType& prediction, + const TargetType& target, + LossType& loss); + + //! Get the output parameter. + OutputDataType& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } //! Get the reduction type, represented as boolean //! (false 'mean' reduction, true 'sum' reduction). @@ -94,12 +106,12 @@ class SigmoidCrossEntropyErrorType void serialize(Archive& ar, const uint32_t /* version */); private: + //! Locally-stored output parameter object. + OutputDataType outputParameter; + //! Boolean value that tells if reduction is 'sum' or 'mean'. bool reduction; -}; // class SigmoidCrossEntropyErrorType - -// Default typedef for typical `arma::mat` usage. -typedef SigmoidCrossEntropyErrorType SigmoidCrossEntropyError; +}; // class SigmoidCrossEntropy } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/sigmoid_cross_entropy_error_impl.hpp b/src/mlpack/methods/ann/loss_functions/sigmoid_cross_entropy_error_impl.hpp index 7526495579..e29e4a7478 100644 --- a/src/mlpack/methods/ann/loss_functions/sigmoid_cross_entropy_error_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/sigmoid_cross_entropy_error_impl.hpp @@ -20,21 +20,21 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template -SigmoidCrossEntropyErrorType::SigmoidCrossEntropyErrorType( - const bool reduction) : - reduction(reduction) +template +SigmoidCrossEntropyError +::SigmoidCrossEntropyError(const bool reduction): reduction(reduction) { // Nothing to do here. } -template -inline typename MatType::elem_type -SigmoidCrossEntropyErrorType::Forward( - const MatType& prediction, - const MatType& target) +template +template +inline typename PredictionType::elem_type +SigmoidCrossEntropyError::Forward( + const PredictionType& prediction, + const TargetType& target) { - typedef typename MatType::elem_type ElemType; + typedef typename PredictionType::elem_type ElemType; ElemType maximum = 0; for (size_t i = 0; i < prediction.n_elem; ++i) { @@ -50,11 +50,12 @@ SigmoidCrossEntropyErrorType::Forward( return lossSum / target.n_elem; } -template -inline void SigmoidCrossEntropyErrorType::Backward( - const MatType& prediction, - const MatType& target, - MatType& loss) +template +template +inline void SigmoidCrossEntropyError::Backward( + const PredictionType& prediction, + const TargetType& target, + LossType& loss) { loss = 1.0 / (1.0 + arma::exp(-prediction)) - target; @@ -62,10 +63,10 @@ inline void SigmoidCrossEntropyErrorType::Backward( loss = loss / target.n_elem; } -template +template template -void SigmoidCrossEntropyErrorType::serialize( - Archive& ar, +void SigmoidCrossEntropyError::serialize( + Archive& ar , const uint32_t /* version */) { ar(CEREAL_NVP(reduction)); diff --git a/src/mlpack/methods/ann/loss_functions/soft_margin_loss.hpp b/src/mlpack/methods/ann/loss_functions/soft_margin_loss.hpp index 41d6c4d62f..a44e321fcd 100644 --- a/src/mlpack/methods/ann/loss_functions/soft_margin_loss.hpp +++ b/src/mlpack/methods/ann/loss_functions/soft_margin_loss.hpp @@ -22,21 +22,20 @@ namespace mlpack { namespace ann /** Artificial Neural Network. */ { /** - * The Soft Margin Loss function. - * - * It is a criterion that optimizes a two-class classification logistic loss, - * between input x and target y, both having the same shape, with the target - * containing only the values 1 or -1. - * - * @tparam MatType Matrix representation to accept as input and use for - * computation. + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class SoftMarginLossType +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class SoftMarginLoss { public: /** - * Create the SoftMarginLossType object. + * Create the SoftMarginLoss object. * * @param reduction Specifies the reduction to apply to the output. If false, * 'mean' reduction is used, where sum of the output will be @@ -44,7 +43,7 @@ class SoftMarginLossType * true, 'sum' reduction is used and the output will be * summed. It is set to true by default. */ - SoftMarginLossType(const bool reduction = true); + SoftMarginLoss(const bool reduction = true); /** * Computes the Soft Margin Loss function. @@ -53,8 +52,9 @@ class SoftMarginLossType * function. * @param target The target vector with same shape as input. */ - typename MatType::elem_type Forward(const MatType& prediction, - const MatType& target); + template + typename PredictionType::elem_type Forward(const PredictionType& prediction, + const TargetType& target); /** * Ordinary feed backward pass of a neural network. @@ -64,9 +64,15 @@ class SoftMarginLossType * @param target The target vector. * @param loss The calculated error. */ - void Backward(const MatType& prediction, - const MatType& target, - MatType& loss); + template + void Backward(const PredictionType& prediction, + const TargetType& target, + LossType& loss); + + //! Get the output parameter. + OutputDataType& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } //! Get the reduction type, represented as boolean //! (false 'mean' reduction, true 'sum' reduction). @@ -81,12 +87,12 @@ class SoftMarginLossType void serialize(Archive& ar, const uint32_t version); private: + //! Locally-stored output parameter object. + OutputDataType outputParameter; + //! Boolean value that tells if reduction is 'sum' or 'mean'. bool reduction; -}; // class SoftMarginLossType - -// Default typedef for typical `arma::mat` usage. -typedef SoftMarginLossType SoftMarginLoss; +}; // class SoftMarginLoss } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/soft_margin_loss_impl.hpp b/src/mlpack/methods/ann/loss_functions/soft_margin_loss_impl.hpp index 36908ac99e..40453564a3 100644 --- a/src/mlpack/methods/ann/loss_functions/soft_margin_loss_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/soft_margin_loss_impl.hpp @@ -18,19 +18,21 @@ namespace mlpack { namespace ann /** Artifical Neural Network. */ { -template -SoftMarginLossType:: -SoftMarginLossType(const bool reduction) : reduction(reduction) +template +SoftMarginLoss:: +SoftMarginLoss(const bool reduction) : reduction(reduction) { // Nothing to do here. } -template -typename MatType::elem_type SoftMarginLossType::Forward( - const MatType& prediction, const MatType& target) +template +template +typename PredictionType::elem_type +SoftMarginLoss::Forward( + const PredictionType& prediction, const TargetType& target) { - MatType loss = arma::log(1 + arma::exp(-target % prediction)); - typename MatType::elem_type lossSum = arma::accu(loss); + PredictionType loss = arma::log(1 + arma::exp(-target % prediction)); + typename PredictionType::elem_type lossSum = arma::accu(loss); if (reduction) return lossSum; @@ -38,25 +40,26 @@ typename MatType::elem_type SoftMarginLossType::Forward( return lossSum / prediction.n_elem; } -template -void SoftMarginLossType::Backward( - const MatType& prediction, - const MatType& target, - MatType& loss) +template +template +void SoftMarginLoss::Backward( + const PredictionType& prediction, + const TargetType& target, + LossType& loss) { loss.set_size(size(prediction)); - MatType temp = arma::exp(-target % prediction); - MatType numerator = -target % temp; - MatType denominator = 1 + temp; + PredictionType temp = arma::exp(-target % prediction); + PredictionType numerator = -target % temp; + PredictionType denominator = 1 + temp; loss = numerator / denominator; if (!reduction) loss = loss / prediction.n_elem; } -template +template template -void SoftMarginLossType::serialize( +void SoftMarginLoss::serialize( Archive& ar, const uint32_t /* version */) { ar(CEREAL_NVP(reduction)); diff --git a/src/mlpack/methods/ann/loss_functions/triplet_margin_loss.hpp b/src/mlpack/methods/ann/loss_functions/triplet_margin_loss.hpp index 3f3a6dffb9..980d863d17 100644 --- a/src/mlpack/methods/ann/loss_functions/triplet_margin_loss.hpp +++ b/src/mlpack/methods/ann/loss_functions/triplet_margin_loss.hpp @@ -24,34 +24,38 @@ namespace ann /** Artificial Neural Network. */ { * of the positive (truthy) and negative (falsy) inputs. * The distance between two samples A and B is defined as square of L2 norm * of A-B. - * + * * For more information, refer the following paper. * * @code * @article{Schroff2015, * author = {Florian Schroff, Dmitry Kalenichenko, James Philbin}, - * title = {FaceNet: A Unified Embedding for Face Recognition and - * Clustering}, + * title = {FaceNet: A Unified Embedding for Face Recognition and Clustering}, * year = {2015}, * url = {https://arxiv.org/abs/1503.03832}, * } * @endcode * - * @tparam MatType Matrix representation to accept as input and use for - * computation. + * @tparam InputDataType Type of the input data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). + * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat, + * arma::sp_mat or arma::cube). */ -template -class TripletMarginLossType +template < + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat +> +class TripletMarginLoss { public: /** - * Create the TripletMarginLossType object. + * Create the TripletMarginLoss object. * * @param margin The minimum value by which the distance between * Anchor and Negative sample exceeds the distance * between Anchor and Positive sample. */ - TripletMarginLossType(const double margin = 1.0); + TripletMarginLoss(const double margin = 1.0); /** * Computes the Triplet Margin Loss function. @@ -59,9 +63,9 @@ class TripletMarginLossType * @param prediction Concatenated anchor and positive sample. * @param target The negative sample. */ - typename MatType::elem_type Forward(const MatType& prediction, - const MatType& target); - + template + typename PredictionType::elem_type Forward(const PredictionType& prediction, + const TargetType& target); /** * Ordinary feed backward pass of a neural network. * @@ -69,9 +73,15 @@ class TripletMarginLossType * @param target The negative sample. * @param loss The calculated error. */ - void Backward(const MatType& prediction, - const MatType& target, - MatType& loss); + template + void Backward(const PredictionType& prediction, + const TargetType& target, + LossType& loss); + + //! Get the output parameter. + OutputDataType& OutputParameter() const { return outputParameter; } + //! Modify the output parameter. + OutputDataType& OutputParameter() { return outputParameter; } //! Get the value of margin. double Margin() const { return margin; } @@ -85,12 +95,12 @@ class TripletMarginLossType void serialize(Archive& ar, const unsigned int /* version */); private: + //! Locally-stored output parameter object. + OutputDataType outputParameter; + //! The margin value used in calculating Triplet Margin Loss. double margin; -}; // class TripletMarginLoss - -// Default typedef for typical `arma::mat` usage. -typedef TripletMarginLossType TripletMarginLoss; +}; // class TripletLossMargin } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/loss_functions/triplet_margin_loss_impl.hpp b/src/mlpack/methods/ann/loss_functions/triplet_margin_loss_impl.hpp index 3ad7083726..2a43bc4ac4 100644 --- a/src/mlpack/methods/ann/loss_functions/triplet_margin_loss_impl.hpp +++ b/src/mlpack/methods/ann/loss_functions/triplet_margin_loss_impl.hpp @@ -19,42 +19,49 @@ namespace mlpack { namespace ann /** Artifical Neural Network. */ { -template -TripletMarginLossType::TripletMarginLossType(const double margin) : - margin(margin) +template +TripletMarginLoss::TripletMarginLoss( + const double margin) : margin(margin) { // Nothing to do here. } -template -typename MatType::elem_type TripletMarginLossType::Forward( - const MatType& prediction, - const MatType& target) +template +template +typename PredictionType::elem_type +TripletMarginLoss::Forward( + const PredictionType& prediction, + const TargetType& target) { - MatType anchor = + PredictionType anchor = prediction.submat(0, 0, prediction.n_rows / 2 - 1, prediction.n_cols - 1); - MatType positive = + PredictionType positive = prediction.submat(prediction.n_rows / 2, 0, prediction.n_rows - 1, prediction.n_cols - 1); return std::max(0.0, arma::accu(arma::pow(anchor - positive, 2)) - arma::accu(arma::pow(anchor - target, 2)) + margin) / anchor.n_cols; } -template -void TripletMarginLossType::Backward( - const MatType& prediction, - const MatType& target, - MatType& loss) +template +template < + typename PredictionType, + typename TargetType, + typename LossType +> +void TripletMarginLoss::Backward( + const PredictionType& prediction, + const TargetType& target, + LossType& loss) { - MatType positive = + PredictionType positive = prediction.submat(prediction.n_rows / 2, 0, prediction.n_rows - 1, prediction.n_cols - 1); loss = 2 * (target - positive) / target.n_cols; } -template +template template -void TripletMarginLossType::serialize( +void TripletMarginLoss::serialize( Archive& ar, const unsigned int /* version */) { diff --git a/src/mlpack/methods/ann/loss_functions/vr_class_reward_impl.hpp b/src/mlpack/methods/ann/loss_functions/vr_class_reward_impl.hpp deleted file mode 100644 index 761f8529a3..0000000000 --- a/src/mlpack/methods/ann/loss_functions/vr_class_reward_impl.hpp +++ /dev/null @@ -1,104 +0,0 @@ -/** - * @file methods/ann/loss_functions/vr_class_reward_impl.hpp - * @author Marcus Edel - * - * Implementation of the VRClassRewardType class, which implements the variance - * reduced classification reinforcement layer. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_METHODS_ANN_LOSS_FUNCTIONS_VR_CLASS_REWARD_IMPL_HPP -#define MLPACK_METHODS_ANN_LOSS_FUNCTIONS_VR_CLASS_REWARD_IMPL_HPP - -// In case it hasn't yet been included. -#include "vr_class_reward.hpp" - -namespace mlpack { -namespace ann /** Artificial Neural Network. */ { - -template -VRClassRewardType::VRClassRewardType( - const double scale, - const bool sizeAverage) : - scale(scale), - sizeAverage(sizeAverage), - reward(0) -{ - // Nothing to do here. -} - -template -typename MatType::elem_type VRClassRewardType::Forward( - const MatType& input, const MatType& target) -{ - double output = 0; - for (size_t i = 0; i < input.n_cols - 1; ++i) - { - const size_t currentTarget = target(i); - Log::Assert(currentTarget < input.n_rows, "Target class out of range."); - - output -= input(currentTarget, i); - } - - reward = 0; - arma::uword index = 0; - - for (size_t i = 0; i < input.n_cols - 1; ++i) - { - input.unsafe_col(i).max(index); - reward = (index == target(i)) * scale; - } - - if (sizeAverage) - { - return output - reward / (input.n_cols - 1); - } - - return output - reward; -} - -template -void VRClassRewardType::Backward( - const MatType& input, - const MatType& target, - MatType& output) -{ - output = arma::zeros(input.n_rows, input.n_cols); - for (size_t i = 0; i < (input.n_cols - 1); ++i) - { - const size_t currentTarget = target(i); - Log::Assert(currentTarget < input.n_rows, "Target class out of range."); - - output(currentTarget, i) = -1; - } - - double vrReward = reward - input(0, 1); - if (sizeAverage) - { - vrReward /= input.n_cols - 1; - } - - const double norm = sizeAverage ? 2.0 / (input.n_cols - 1) : 2.0; - - output(0, 1) = norm * (input(0, 1) - reward); - network.back()->Reward() = vrReward; -} - -template -template -void VRClassRewardType::serialize( - Archive& ar, const uint32_t /* version */) -{ - ar(CEREAL_NVP(scale)); - ar(CEREAL_NVP(sizeAverage)); - ar(CEREAL_NVP(reward)); - ar(CEREAL_NVP(network)); -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/make_alias.hpp b/src/mlpack/methods/ann/make_alias.hpp deleted file mode 100644 index 65b68565d3..0000000000 --- a/src/mlpack/methods/ann/make_alias.hpp +++ /dev/null @@ -1,55 +0,0 @@ -/** - * @file make_alias.hpp - * @author Ryan Curtin - * - * Implementation of `MakeAlias()`, a utility function. This is meant to be - * used in `SetWeights()` calls in various layers, to wrap internal weight - * objects as aliases around the given memory pointers. - */ -#ifndef MLPACK_METHODS_ANN_MAKE_ALIAS_HPP -#define MLPACK_METHODS_ANN_MAKE_ALIAS_HPP - -#include - -namespace mlpack { -namespace ann { - -/** - * Reconstruct `m` as an alias around the memory `newMem`, with size `numRows` x - * `numCols`. - */ -template -void MakeAlias(MatType& m, - typename MatType::elem_type* newMem, - const size_t numRows, - const size_t numCols) -{ - // We use placement new to reinitialize the object, since the copy and move - // assignment operators in Armadillo will end up copying memory instead of - // making an alias. - m.~MatType(); - new (&m) MatType(newMem, numRows, numCols, false, true); -} - -/** - * Reconstruct `c` as an alias around the memory` newMem`, with size `numRows` x - * `numCols` x `numSlices`. - */ -template -void MakeAlias(CubeType& c, - typename CubeType::elem_type* newMem, - const size_t numRows, - const size_t numCols, - const size_t numSlices) -{ - // We use placement new to reinitialize the object, since the copy and move - // assignment operators in Armadillo will end up copying memory instead of - // making an alias. - c.~CubeType(); - new (&c) CubeType(newMem, numRows, numCols, numSlices, false, true); -} - -} // namespace ann -} // namespace mlpack - -#endif diff --git a/src/mlpack/methods/ann/not_adapted/rbm/CMakeLists.txt b/src/mlpack/methods/ann/rbm/CMakeLists.txt similarity index 100% rename from src/mlpack/methods/ann/not_adapted/rbm/CMakeLists.txt rename to src/mlpack/methods/ann/rbm/CMakeLists.txt diff --git a/src/mlpack/methods/ann/not_adapted/rbm/rbm.hpp b/src/mlpack/methods/ann/rbm/rbm.hpp similarity index 100% rename from src/mlpack/methods/ann/not_adapted/rbm/rbm.hpp rename to src/mlpack/methods/ann/rbm/rbm.hpp diff --git a/src/mlpack/methods/ann/not_adapted/rbm/rbm_impl.hpp b/src/mlpack/methods/ann/rbm/rbm_impl.hpp similarity index 100% rename from src/mlpack/methods/ann/not_adapted/rbm/rbm_impl.hpp rename to src/mlpack/methods/ann/rbm/rbm_impl.hpp diff --git a/src/mlpack/methods/ann/not_adapted/rbm/rbm_policies.hpp b/src/mlpack/methods/ann/rbm/rbm_policies.hpp similarity index 100% rename from src/mlpack/methods/ann/not_adapted/rbm/rbm_policies.hpp rename to src/mlpack/methods/ann/rbm/rbm_policies.hpp diff --git a/src/mlpack/methods/ann/not_adapted/rbm/spike_slab_rbm_impl.hpp b/src/mlpack/methods/ann/rbm/spike_slab_rbm_impl.hpp similarity index 100% rename from src/mlpack/methods/ann/not_adapted/rbm/spike_slab_rbm_impl.hpp rename to src/mlpack/methods/ann/rbm/spike_slab_rbm_impl.hpp diff --git a/src/mlpack/methods/ann/regularizer/lregularizer.hpp b/src/mlpack/methods/ann/regularizer/lregularizer.hpp index 41538ae752..96a88e2959 100644 --- a/src/mlpack/methods/ann/regularizer/lregularizer.hpp +++ b/src/mlpack/methods/ann/regularizer/lregularizer.hpp @@ -44,7 +44,7 @@ class LRegularizer template void Evaluate(const MatType& weight, MatType& gradient); - //! Serialize the regularizer. + //! Serialize the regularizer (nothing to do). template void serialize(Archive& ar, const uint32_t /* version */); diff --git a/src/mlpack/methods/ann/regularizer/no_regularizer.hpp b/src/mlpack/methods/ann/regularizer/no_regularizer.hpp index 3a4f321c1f..920c8d3057 100644 --- a/src/mlpack/methods/ann/regularizer/no_regularizer.hpp +++ b/src/mlpack/methods/ann/regularizer/no_regularizer.hpp @@ -44,12 +44,6 @@ class NoRegularizer { // Nothing to do here. } - - template - void serialize(Archive& /* ar */, const uint32_t /* version */) - { - // Nothing to do. - } }; } // namespace ann diff --git a/src/mlpack/methods/ann/regularizer/orthogonal_regularizer.hpp b/src/mlpack/methods/ann/regularizer/orthogonal_regularizer.hpp index eacc07e20a..3e56521165 100644 --- a/src/mlpack/methods/ann/regularizer/orthogonal_regularizer.hpp +++ b/src/mlpack/methods/ann/regularizer/orthogonal_regularizer.hpp @@ -55,7 +55,7 @@ class OrthogonalRegularizer template void Evaluate(const MatType& weight, MatType& gradient); - //! Serialize the regularizer. + //! Serialize the regularizer (nothing to do). template void serialize(Archive& ar, const uint32_t /* version */); diff --git a/src/mlpack/methods/ann/rnn.hpp b/src/mlpack/methods/ann/rnn.hpp index 9fb3f14203..949aecee9b 100644 --- a/src/mlpack/methods/ann/rnn.hpp +++ b/src/mlpack/methods/ann/rnn.hpp @@ -13,32 +13,43 @@ #define MLPACK_METHODS_ANN_RNN_HPP #include -#include -#include "ffn.hpp" +#include "visitor/delete_visitor.hpp" +#include "visitor/delta_visitor.hpp" +#include "visitor/output_parameter_visitor.hpp" +#include "visitor/reset_visitor.hpp" + +#include "init_rules/network_init.hpp" + +#include +#include +#include +#include + +#include namespace mlpack { namespace ann /** Artificial Neural Network. */ { /** - * Definition of a standard recurrent neural network container. A recurrent - * neural network can handle recurrent layers (i.e. `RecurrentLayer`s), which - * hold internal state and are passed sequences of data as inputs. - * - * As opposed to the standard `FFN`, which takes data in a matrix format where - * each column is a data point, the `RNN` takes a cube format where each column - * is a data point and each slice is a time step. + * Implementation of a standard recurrent neural network container. * * @tparam OutputLayerType The output layer type used to evaluate the network. * @tparam InitializationRuleType Rule used to initialize the weight matrix. */ template< - typename OutputLayerType = NegativeLogLikelihood, - typename InitializationRuleType = RandomInitialization, - typename MatType = arma::mat> + typename OutputLayerType = NegativeLogLikelihood<>, + typename InitializationRuleType = RandomInitialization, + typename... CustomLayers +> class RNN { public: + //! Convenience typedef for the internal model construction. + using NetworkType = RNN; + /** * Create the RNN object. * @@ -48,67 +59,63 @@ class RNN * If you want to pass in a parameter and discard the original parameter * object, be sure to use std::move to avoid unnecessary copy. * - * @param bpttSteps Number of time steps to use for BPTT (backpropagation - * through time) when training. - * @param single If true, then the network will expect only a single timestep - * for responses. (That is, every input sequence only has one single - * output; so, `responses.n_slices` should be 1 when calling `Train()`.) + * @param rho Maximum number of steps to backpropagate through time (BPTT). + * @param single Predict only the last element of the input sequence. * @param outputLayer Output layer used to evaluate the network. * @param initializeRule Optional instantiated InitializationRule object * for initializing the network parameter. */ - RNN(const size_t bpttSteps = 0, + RNN(const size_t rho, const bool single = false, OutputLayerType outputLayer = OutputLayerType(), InitializationRuleType initializeRule = InitializationRuleType()); //! Copy constructor. RNN(const RNN&); + //! Move constructor. RNN(RNN&&); - //! Copy operator. + + //! Copy assignment operator. RNN& operator=(const RNN&); - //! Move assignment operator. + + //! Move assignment operator RNN& operator=(RNN&&); - //! Destroy the RNN and release any memory it is holding. + //! Destructor to release allocated memory. ~RNN(); /** - * Add a new module to the model. + * Check if the optimizer has MaxIterations() parameter, if it does + * then check if it's value is less than the number of datapoints + * in the dataset. * - * @param args The layer parameter. + * @tparam OptimizerType Type of optimizer to use to train the model. + * @param optimizer optimizer used in the training process. + * @param samples Number of datapoints in the dataset. */ - template - void Add(Args... args) { network.template Add(args...); } + template + typename std::enable_if< + HasMaxIterations + ::value, void>::type + WarnMessageMaxIterations(OptimizerType& optimizer, size_t samples) const; /** - * Add a new module to the model. + * Check if the optimizer has MaxIterations() parameter, if it + * doesn't then simply return from the function. * - * @param layer The Layer to be added to the model. + * @tparam OptimizerType Type of optimizer to use to train the model. + * @param optimizer optimizer used in the training process. + * @param samples Number of datapoints in the dataset. */ - void Add(Layer* layer) { network.Add(layer); } - - //! Get the network model. - const std::vector*>& Network() const - { - return network.Network().Network(); - } + template + typename std::enable_if< + !HasMaxIterations + ::value, void>::type + WarnMessageMaxIterations(OptimizerType& optimizer, size_t samples) const; /** - * Modify the network model. Be careful! If you change the structure of the - * network or parameters for layers, its state may become invalid, and the - * next time it is used for any operation the parameters will be reset. - * - * Don't add any layers like this; use `Add()` instead. - */ - std::vector*>& Network() - { - return network.Network().Network(); - } - - /** - * Train the recurrent network on the given input data using the given + * Train the recurrent neural network on the given input data using the given * optimizer. * * This will use the existing model parameters as a starting point for the @@ -118,6 +125,13 @@ class RNN * If you want to pass in a parameter and discard the original parameter * object, be sure to use std::move to avoid unnecessary copy. * + * The format of the data should be as follows: + * - each slice should correspond to a time step + * - each column should correspond to a data point + * - each row should correspond to a dimension + * So, e.g., predictors(i, j, k) is the i'th dimension of the j'th data point + * at time slice k. + * * @tparam OptimizerType Type of optimizer to use to train the model. * @tparam CallbackTypes Types of Callback Functions. * @param predictors Input training variables. @@ -128,16 +142,15 @@ class RNN * @return The final objective of the trained model (NaN or Inf on error). */ template - typename MatType::elem_type Train( - arma::Cube predictors, - arma::Cube responses, - OptimizerType& optimizer, - CallbackTypes&&... callbacks); + double Train(arma::cube predictors, + arma::cube responses, + OptimizerType& optimizer, + CallbackTypes&&... callbacks); /** - * Train the recurrent network on the given input data. By default, the - * RMSProp optimization algorithm is used, but others can be specified - * (such as ens::SGD). + * Train the recurrent neural network on the given input data. By default, the + * SGD optimization algorithm is used, but others can be specified + * (such as ens::RMSprop). * * This will use the existing model parameters as a starting point for the * optimization. If this is not what you want, then you should access the @@ -146,19 +159,25 @@ class RNN * If you want to pass in a parameter and discard the original parameter * object, be sure to use std::move to avoid unnecessary copy. * + * The format of the data should be as follows: + * - each slice should correspond to a time step + * - each column should correspond to a data point + * - each row should correspond to a dimension + * So, e.g., predictors(i, j, k) is the i'th dimension of the j'th data point + * at time slice k. + * * @tparam OptimizerType Type of optimizer to use to train the model. - * @param predictors Input training variables. * @tparam CallbackTypes Types of Callback Functions. + * @param predictors Input training variables. * @param responses Outputs results from input training variables. * @param callbacks Callback function for ensmallen optimizer `OptimizerType`. * See https://www.ensmallen.org/docs.html#callback-documentation. * @return The final objective of the trained model (NaN or Inf on error). */ - template - typename MatType::elem_type Train( - arma::Cube predictors, - arma::Cube responses, - CallbackTypes&&... callbacks); + template + double Train(arma::cube predictors, + arma::cube responses, + CallbackTypes&&... callbacks); /** * Predict the responses to a given set of predictors. The responses will @@ -168,101 +187,42 @@ class RNN * If you want to pass in a parameter and discard the original parameter * object, be sure to use std::move to avoid unnecessary copy. * + * The format of the data should be as follows: + * - each slice should correspond to a time step + * - each column should correspond to a data point + * - each row should correspond to a dimension + * So, e.g., predictors(i, j, k) is the i'th dimension of the j'th data point + * at time slice k. The responses will be in the same format. + * * @param predictors Input predictors. * @param results Matrix to put output predictions of responses into. - * @param batchSize Batch size to use for prediction. + * @param batchSize Number of points to predict at once. */ - void Predict(arma::Cube predictors, - arma::Cube& results, - const size_t batchSize = 128); - - // Return the nujmber of weights in the model. - size_t WeightSize() { return network.WeightSize(); } + void Predict(arma::cube predictors, + arma::cube& results, + const size_t batchSize = 256); /** - * Set the logical dimensions of the input. `Train()` and `Predict()` expect - * data to be passed such that one point corresponds to one column, but this - * data is allowed to be an arbitrary higher-order tensor. - * - * So, if the input is meant to be 28x28x3 images, then the input data to - * `Train()` or `Predict()` should have 28*28*3 = 2352 rows, and - * `InputDImensions()` should be set to `{ 28, 28, 3}`. Then, the layers of - * the network will interpret each input point as a 3-dimensional image - * instead of a 1-dimensional vector. - * - * If `InputDimensions()` is left unset before training, the data will be - * assumed to be a 1-dimensional vector. - */ - std::vector& InputDimensions() { return network.InputDimensions(); } - //! Get the logical dimensions of the input. - const std::vector& InputDimensions() const - { - return network.InputDimensions(); - } - - //! Return the initial point for the optimization. - const MatType& Parameters() const { return network.Parameters(); } - //! Modify the initial point for the optimization. - MatType& Parameters() { return network.Parameters(); } - - //! Return the number of steps allowed for BPTT. - size_t BPTTSteps() const { return bpttSteps; } - //! Modify the number of steps allowed for BPTT. - size_t& BPTTSteps() { return bpttSteps; } - - /** - * Reset the stored data of the network entirely. This reset all weights of - * each layer using `InitializationRuleType`, and prepares the network to - * accept a (flat 1-d) input size of `inputDimensionality` (if passed), or - * whatever input size has been set with `InputDimensions()`. - * - * This also resets the mode of the network to prediction mode (not training - * mode). See `SetNetworkMode()` for more information. - */ - void Reset(const size_t inputDimensionality = 0); - - /** - * Set all the layers in the network to training mode, if `training` is - * `true`, or set all the layers in the network to testing mode, if `training` - * is `false`. - */ - void SetNetworkMode(const bool training) { network.SetNetworkMode(training); } - - /** - * Evaluate the recurrent network with the given predictors and responses. - * This functions is usually used to monitor progress while training. - * - * @param predictors Input variables. - * @param responses Target outputs for input variables. - */ - typename MatType::elem_type Evaluate( - const arma::Cube& predictors, - const arma::Cube& responses); - - //! Serialize the model. - template - void serialize(Archive& ar, const uint32_t /* version */); - - // - // Only ensmallen utility functions for training are found below here. - // They generally aren't useful otherwise. - // - - /** - * Evaluate the recurrent network with the given parameters. This function - * is usually called by the optimizer to train the model. + * Evaluate the recurrent neural network with the given parameters. This + * function is usually called by the optimizer to train the model. * * @param parameters Matrix model parameters. + * @param begin Index of the starting point to use for objective function + * evaluation. + * @param batchSize Number of points to be passed at a time to use for + * objective function evaluation. + * @param deterministic Whether or not to train or test the model. Note some + * layer act differently in training or testing mode. */ - typename MatType::elem_type Evaluate(const MatType& parameters); + double Evaluate(const arma::mat& parameters, + const size_t begin, + const size_t batchSize, + const bool deterministic); - /** - * Evaluate the recurrent network with the given parameters, but using only - * a number of data points. This is useful for optimizers such as SGD, which - * require a separable objective function. - * - * Note that the network may return different results depending on the mode it - * is in (see `SetNetworkMode()`). + /** + * Evaluate the recurrent neural network with the given parameters. This + * function is usually called by the optimizer to train the model. This just + * calls the other overload of Evaluate() with deterministic = true. * * @param parameters Matrix model parameters. * @param begin Index of the starting point to use for objective function @@ -270,26 +230,13 @@ class RNN * @param batchSize Number of points to be passed at a time to use for * objective function evaluation. */ - typename MatType::elem_type Evaluate(const MatType& parameters, - const size_t begin, - const size_t batchSize); + double Evaluate(const arma::mat& parameters, + const size_t begin, + const size_t batchSize); /** - * Evaluate the recurrent network with the given parameters. - * This function is usually called by the optimizer to train the model. - * This just calls the overload of EvaluateWithGradient() with batchSize = 1. - * - * @param parameters Matrix model parameters. - * @param gradient Matrix to output gradient into. - */ - template - typename MatType::elem_type EvaluateWithGradient(const MatType& parameters, - GradType& gradient); - - /** - * Evaluate the recurrent network with the given parameters, but using only - * a number of data points. This is useful for optimizers such as SGD, which - * require a separable objective function. + * Evaluate the recurrent neural network with the given parameters. This + * function is usually called by the optimizer to train the model. * * @param parameters Matrix model parameters. * @param begin Index of the starting point to use for objective function @@ -299,15 +246,16 @@ class RNN * objective function evaluation. */ template - typename MatType::elem_type EvaluateWithGradient(const MatType& parameters, - const size_t begin, - GradType& gradient, - const size_t batchSize); + double EvaluateWithGradient(const arma::mat& parameters, + const size_t begin, + GradType& gradient, + const size_t batchSize); /** - * Evaluate the gradient of the recurrent network with the given parameters, - * and with respect to only a number of points in the dataset. This is useful - * for optimizers such as SGD, which require a separable objective function. + * Evaluate the gradient of the recurrent neural network with the given + * parameters, and with respect to only one point in the dataset. This is + * useful for optimizers such as SGD, which require a separable objective + * function. * * @param parameters Matrix of the model parameters to be optimized. * @param begin Index of the starting point to use for objective function @@ -316,69 +264,191 @@ class RNN * @param batchSize Number of points to be processed as a batch for objective * function gradient evaluation. */ - template - void Gradient(const MatType& parameters, + void Gradient(const arma::mat& parameters, const size_t begin, - GradType& gradient, + arma::mat& gradient, const size_t batchSize); - //! Return the number of separable functions (the number of predictor points). - size_t NumFunctions() const { return predictors.n_cols; } - /** - * Note: this function is implement so that it can be used by ensmallen's - * optimizers. It's not generally meant to be used otherwise. - * - * Shuffle the order of function visitation. (This is equivalent to shuffling - * the dataset during training.) + * Shuffle the order of function visitation. This may be called by the + * optimizer. */ void Shuffle(); - /** - * Prepare the network for the given data. - * This function won't actually trigger training process. + /* + * Add a new module to the model. * - * @param predictors Input data variables. - * @param responses Outputs results from input data variables. + * @param args The layer parameter. */ - void ResetData(arma::Cube predictors, - arma::Cube responses); + template + void Add(Args... args) { network.push_back(new LayerType(args...)); } + + /* + * Add a new module to the model. + * + * @param layer The Layer to be added to the model. + */ + void Add(LayerTypes layer) { network.push_back(layer); } + + //! Return the number of separable functions (the number of predictor points). + size_t NumFunctions() const { return numFunctions; } + + //! Return the initial point for the optimization. + const arma::mat& Parameters() const { return parameter; } + //! Modify the initial point for the optimization. + arma::mat& Parameters() { return parameter; } + + //! Return the maximum length of backpropagation through time. + const size_t& Rho() const { return rho; } + //! Modify the maximum length of backpropagation through time. + size_t& Rho() { return rho; } + + //! Get the matrix of responses to the input data points. + const arma::cube& Responses() const { return responses; } + //! Modify the matrix of responses to the input data points. + arma::cube& Responses() { return responses; } + + //! Get the matrix of data points (predictors). + const arma::cube& Predictors() const { return predictors; } + //! Modify the matrix of data points (predictors). + arma::cube& Predictors() { return predictors; } + + /** + * Reset the state of the network. This ensures that all internally-held + * gradients are set to 0, all memory cells are reset, and the parameters + * matrix is the right size. + */ + void Reset(); + + /** + * Reset the module information (weights/parameters). + */ + void ResetParameters(); + + //! Serialize the model. + template + void serialize(Archive& ar, const uint32_t /* version */); private: // Helper functions. + /** + * The Forward algorithm (part of the Forward-Backward algorithm). Computes + * forward probabilities for each module. + * + * @param input Data sequence to compute probabilities for. + */ + template + void Forward(const InputType& input); /** - * Iterate over all layers and reset the recurrent layers' states. Prepare - * each recurrent layer to store up to `memorySize` previous states, operating - * with a batch size of `batchSize`. + * Reset the state of RNN cells in the network for new input sequence. */ - void ResetMemoryState(const size_t memorySize, const size_t batchSize); + void ResetCells(); - //! Set the previous step index of all recurrent layers to `step`. - void SetPreviousStep(const size_t step); - //! Set the current step index of all recurrent layers to `step`. - void SetCurrentStep(const size_t step); + /** + * The Backward algorithm (part of the Forward-Backward algorithm). Computes + * backward pass for module. + */ + void Backward(); - //! Number of timesteps to consider for backpropagation through time (BPTT). - size_t bpttSteps; - //! Whether the network expects only one single response per sequence, or one - //! response per time step. + /** + * Iterate through all layer modules and update the the gradient using the + * layer defined optimizer. + */ + template + void Gradient(const InputType& input); + + /** + * Reset the module status by setting the current deterministic parameter + * for all modules that implement the Deterministic function. + */ + void ResetDeterministic(); + + /** + * Reset the gradient for all modules that implement the Gradient function. + */ + void ResetGradients(arma::mat& gradient); + + //! Number of steps to backpropagate through time (BPTT). + size_t rho; + + //! Instantiated outputlayer used to evaluate the network. + OutputLayerType outputLayer; + + //! Instantiated InitializationRule object for initializing the network + //! parameter. + InitializationRuleType initializeRule; + + //! The input size. + size_t inputSize; + + //! The output size. + size_t outputSize; + + //! The target size. + size_t targetSize; + + //! Indicator if we already trained the model. + bool reset; + + //! Only predict the last element of the input sequence. bool single; - //! The network itself is stored in this FFN object. Note that this network - //! may contain recursive layers, and thus we will be responsible for - //! occasionally resetting any memory cells. - FFN network; + //! Locally-stored model modules. + std::vector > network; - //! The matrix of data points (predictors). This member is empty, except - //! during training---we must store a local copy of the training data since - //! the ensmallen optimizer will not provide training data. - arma::Cube predictors; + //! The matrix of data points (predictors). + arma::cube predictors; - //! The matrix of responses to the input data points. This member is empty, - //! except during training. - arma::Cube responses; -}; // class RNNType + //! The matrix of responses to the input data points. + arma::cube responses; + + //! Matrix of (trained) parameters. + arma::mat parameter; + + //! The number of separable functions (the number of predictor points). + size_t numFunctions; + + //! The current error for the backward pass. + arma::mat error; + + //! Locally-stored delta visitor. + DeltaVisitor deltaVisitor; + + //! Locally-stored output parameter visitor. + OutputParameterVisitor outputParameterVisitor; + + //! List of all module parameters for the backward pass (BBTT). + std::vector moduleOutputParameter; + + //! Locally-stored weight size visitor. + WeightSizeVisitor weightSizeVisitor; + + //! Locally-stored copy visitor + CopyVisitor copyVisitor; + + //! Locally-stored reset visitor. + ResetVisitor resetVisitor; + + //! Locally-stored delete visitor. + DeleteVisitor deleteVisitor; + + //! The current evaluation mode (training or testing). + bool deterministic; + + //! The current gradient for the gradient pass. + arma::mat currentGradient; + + // The BRN class should have access to internal members. + template< + typename OutputLayerType1, + typename MergeLayerType1, + typename MergeOutputType1, + typename InitializationRuleType1, + typename... CustomLayers1 + > + friend class BRNN; +}; // class RNN } // namespace ann } // namespace mlpack diff --git a/src/mlpack/methods/ann/rnn_impl.hpp b/src/mlpack/methods/ann/rnn_impl.hpp index a276af3e91..a7e7c65f3e 100644 --- a/src/mlpack/methods/ann/rnn_impl.hpp +++ b/src/mlpack/methods/ann/rnn_impl.hpp @@ -14,603 +14,621 @@ // In case it hasn't been included yet. #include "rnn.hpp" -#include "layer/recurrent_layer.hpp" + +#include "visitor/load_output_parameter_visitor.hpp" +#include "visitor/save_output_parameter_visitor.hpp" +#include "visitor/forward_visitor.hpp" +#include "visitor/backward_visitor.hpp" +#include "visitor/reset_cell_visitor.hpp" +#include "visitor/deterministic_set_visitor.hpp" +#include "visitor/gradient_set_visitor.hpp" +#include "visitor/gradient_visitor.hpp" +#include "visitor/weight_set_visitor.hpp" + +#include "util/check_input_shape.hpp" namespace mlpack { namespace ann /** Artificial Neural Network. */ { -template< - typename OutputLayerType, - typename InitializationRuleType, - typename MatType -> -RNN< - OutputLayerType, - InitializationRuleType, - MatType ->::RNN( - const size_t bpttSteps, +template +RNN::RNN( + const size_t rho, const bool single, OutputLayerType outputLayer, InitializationRuleType initializeRule) : - bpttSteps(bpttSteps), + rho(rho), + outputLayer(std::move(outputLayer)), + initializeRule(std::move(initializeRule)), + inputSize(0), + outputSize(0), + targetSize(0), + reset(false), single(single), - network(std::move(outputLayer), std::move(initializeRule)) + numFunctions(0), + deterministic(true) { /* Nothing to do here */ } -template< - typename OutputLayerType, - typename InitializationRuleType, - typename MatType -> -RNN< - OutputLayerType, - InitializationRuleType, - MatType ->::RNN( +template +RNN::RNN( const RNN& network) : - bpttSteps(network.bpttSteps), + rho(network.rho), + outputLayer(network.outputLayer), + initializeRule(network.initializeRule), + inputSize(network.inputSize), + outputSize(network.outputSize), + targetSize(network.targetSize), + reset(network.reset), single(network.single), - network(network.network) + parameter(network.parameter), + numFunctions(network.numFunctions), + deterministic(network.deterministic) { - // Nothing else to do. + for (size_t i = 0; i < network.network.size(); ++i) + { + this->network.push_back(boost::apply_visitor(copyVisitor, + network.network[i])); + boost::apply_visitor(resetVisitor, this->network.back()); + } } -template< - typename OutputLayerType, - typename InitializationRuleType, - typename MatType -> -RNN< - OutputLayerType, - InitializationRuleType, - MatType ->::RNN( +template +RNN::RNN( RNN&& network) : - bpttSteps(std::move(network.bpttSteps)), + rho(std::move(network.rho)), + outputLayer(std::move(network.outputLayer)), + initializeRule(std::move(network.initializeRule)), + inputSize(std::move(network.inputSize)), + outputSize(std::move(network.outputSize)), + targetSize(std::move(network.targetSize)), + reset(std::move(network.reset)), single(std::move(network.single)), - network(std::move(network.network)) + network(std::move(network.network)), + parameter(std::move(network.parameter)), + numFunctions(std::move(network.numFunctions)), + deterministic(std::move(network.deterministic)) { // Nothing to do here. } -template< - typename OutputLayerType, - typename InitializationRuleType, - typename MatType -> -RNN< - OutputLayerType, - InitializationRuleType, - MatType ->& -RNN< - OutputLayerType, - InitializationRuleType, - MatType ->::operator=(const RNN& other) +template +RNN::~RNN() { - if (this != &other) + for (LayerTypes& layer : network) { - bpttSteps = other.bpttSteps; - single = other.single; - network = other.network; - predictors.clear(); - responses.clear(); + boost::apply_visitor(deleteVisitor, layer); } - - return *this; } -template< - typename OutputLayerType, - typename InitializationRuleType, - typename MatType -> -RNN< - OutputLayerType, - InitializationRuleType, - MatType ->& -RNN< - OutputLayerType, - InitializationRuleType, - MatType ->::operator=(RNN&& other) +template +template +typename std::enable_if< + HasMaxIterations + ::value, void>::type +RNN:: +WarnMessageMaxIterations(OptimizerType& optimizer, size_t samples) const { - if (this != &other) + if (optimizer.MaxIterations() < samples && + optimizer.MaxIterations() != 0) { - bpttSteps = std::move(other.bpttSteps); - single = std::move(other.single); - network = std::move(other.network); - predictors.clear(); - responses.clear(); + Log::Warn << "The optimizer's maximum number of iterations " + << "is less than the size of the dataset; the " + << "optimizer will not pass over the entire " + << "dataset. To fix this, modify the maximum " + << "number of iterations to be at least equal " + << "to the number of points of your dataset " + << "(" << samples << ")." << std::endl; } - - return *this; } -template< - typename OutputLayerType, - typename InitializationRuleType, - typename MatType -> -RNN< - OutputLayerType, - InitializationRuleType, - MatType ->::~RNN() +template +template +typename std::enable_if< + !HasMaxIterations + ::value, void>::type +RNN:: +WarnMessageMaxIterations(OptimizerType& /* optimizer */, + size_t /* samples */) const { - // Nothing special to do. + return; } -template< - typename OutputLayerType, - typename InitializationRuleType, - typename MatType -> +template template -typename MatType::elem_type RNN< - OutputLayerType, - InitializationRuleType, - MatType ->::Train( - arma::Cube predictors, - arma::Cube responses, +double RNN::Train( + arma::cube predictors, + arma::cube responses, OptimizerType& optimizer, CallbackTypes&&... callbacks) { - ResetData(std::move(predictors), std::move(responses)); + CheckInputShape > >( + network, predictors.n_rows, "RNN<>::Train()"); - network.WarnMessageMaxIterations(optimizer, this->predictors.n_cols); + numFunctions = responses.n_cols; - // Ensure that the network can be used. - network.CheckNetwork("RNN::Train()", this->predictors.n_rows, true, true); + this->predictors = std::move(predictors); + this->responses = std::move(responses); + + this->deterministic = true; + ResetDeterministic(); + + if (!reset) + { + ResetParameters(); + } + + WarnMessageMaxIterations(optimizer, this->predictors.n_cols); // Train the model. - Timer::Start("rnn_optimization"); - const typename MatType::elem_type out = - optimizer.Optimize(*this, network.Parameters(), callbacks...); - Timer::Stop("rnn_optimization"); + const double out = optimizer.Optimize(*this, parameter, callbacks...); - Log::Info << "RNN::Train(): final objective of trained model is " << out + Log::Info << "RNN::RNN(): final objective of trained model is " << out << "." << std::endl; return out; } -template< - typename OutputLayerType, - typename InitializationRuleType, - typename MatType -> -template -typename MatType::elem_type RNN< - OutputLayerType, - InitializationRuleType, - MatType ->::Train( - arma::Cube predictors, - arma::Cube responses, - CallbackTypes&&... callbacks) +template +void RNN::ResetCells() { - OptimizerType optimizer; - return Train(std::move(predictors), std::move(responses), optimizer, - callbacks...); + for (size_t i = 1; i < network.size(); ++i) + { + boost::apply_visitor(ResetCellVisitor(rho), network[i]); + } } -template< - typename OutputLayerType, - typename InitializationRuleType, - typename MatType -> -void RNN< - OutputLayerType, - InitializationRuleType, - MatType ->::Predict( - arma::Cube predictors, - arma::Cube& results, - const size_t batchSize) +template +template +double RNN::Train( + arma::cube predictors, + arma::cube responses, + CallbackTypes&&... callbacks) { - // Ensure that the network is configured correctly. - network.CheckNetwork("RNN::Predict()", predictors.n_rows, true, false); + CheckInputShape > >( + network, predictors.n_rows, "RNN<>::Train()"); - results.set_size(network.network.OutputSize(), predictors.n_cols, - predictors.n_slices); + numFunctions = responses.n_cols; - MatType inputAlias, outputAlias; - for (size_t i = 0; i < predictors.n_cols; i += batchSize) + this->predictors = std::move(predictors); + this->responses = std::move(responses); + + this->deterministic = true; + ResetDeterministic(); + + if (!reset) + { + ResetParameters(); + } + + OptimizerType optimizer; + + WarnMessageMaxIterations(optimizer, this->predictors.n_cols); + + // Train the model. + const double out = optimizer.Optimize(*this, parameter, callbacks...); + + Log::Info << "RNN::RNN(): final objective of trained model is " << out + << "." << std::endl; + return out; +} + +template +void RNN::Predict( + arma::cube predictors, arma::cube& results, const size_t batchSize) +{ + CheckInputShape > >( + network, predictors.n_rows, "RNN<>::Predict()"); + + ResetCells(); + + if (parameter.is_empty()) + { + ResetParameters(); + } + + if (!deterministic) + { + deterministic = true; + ResetDeterministic(); + } + + const size_t effectiveBatchSize = std::min(batchSize, + size_t(predictors.n_cols)); + + Forward(arma::mat(predictors.slice(0).colptr(0), predictors.n_rows, + effectiveBatchSize, false, true)); + arma::mat resultsTemp = boost::apply_visitor(outputParameterVisitor, + network.back()); + + outputSize = resultsTemp.n_rows; + results = arma::zeros(outputSize, predictors.n_cols, rho); + results.slice(0).submat(0, 0, results.n_rows - 1, + effectiveBatchSize - 1) = resultsTemp; + + // Process in accordance with the given batch size. + for (size_t begin = 0; begin < predictors.n_cols; begin += batchSize) { const size_t effectiveBatchSize = std::min(batchSize, - size_t(predictors.n_cols) - i); - - // Since we aren't doing a backward pass, we don't actually need to store - // the state for each time step---we can fit it all in one buffer. - ResetMemoryState(1, effectiveBatchSize); - SetPreviousStep(size_t(-1)); - SetCurrentStep(size_t(0)); - - // Iterate over all time steps. - for (size_t t = 0; t < predictors.n_slices; ++t) + size_t(predictors.n_cols - begin)); + for (size_t seqNum = !begin; seqNum < rho; ++seqNum) { - // If it is after the first step, we have a previous state. - if (t == 1) - SetPreviousStep(size_t(0)); + Forward(arma::mat(predictors.slice(seqNum).colptr(begin), + predictors.n_rows, effectiveBatchSize, false, true)); - // Create aliases for the input and output. - MakeAlias(inputAlias, - (typename MatType::elem_type*) predictors.slice(t).colptr(i), - predictors.n_rows, effectiveBatchSize); - MakeAlias(outputAlias, results.slice(t).colptr(i), results.n_rows, - effectiveBatchSize); - - network.Forward(inputAlias, outputAlias); + results.slice(seqNum).submat(0, begin, results.n_rows - 1, begin + + effectiveBatchSize - 1) = boost::apply_visitor(outputParameterVisitor, + network.back()); } } } -template< - typename OutputLayerType, - typename InitializationRuleType, - typename MatType -> -void RNN< - OutputLayerType, - InitializationRuleType, - MatType ->::Reset(const size_t inputDimensionality) +template +double RNN::Evaluate( + const arma::mat& /* parameters */, + const size_t begin, + const size_t batchSize, + const bool deterministic) { - // This is a reimplementation of FFN::Reset() that correctly prints - // "RNN::Reset()". - network.Parameters().clear(); - - if (inputDimensionality != 0) + if (parameter.is_empty()) { - network.CheckNetwork("RNN::Reset()", inputDimensionality, true, false); + ResetParameters(); + } + + if (deterministic != this->deterministic) + { + this->deterministic = deterministic; + ResetDeterministic(); + } + + if (!inputSize) + { + inputSize = predictors.n_rows; + targetSize = responses.n_rows; + } + else if (targetSize == 0) + { + targetSize = responses.n_rows; + } + + ResetCells(); + + double performance = 0; + size_t responseSeq = 0; + + for (size_t seqNum = 0; seqNum < rho; ++seqNum) + { + // Wrap a matrix around our data to avoid a copy. + arma::mat stepData(predictors.slice(seqNum).colptr(begin), + predictors.n_rows, batchSize, false, true); + Forward(stepData); + if (!single) + { + responseSeq = seqNum; + } + + performance += outputLayer.Forward(boost::apply_visitor( + outputParameterVisitor, network.back()), + arma::mat(responses.slice(responseSeq).colptr(begin), + responses.n_rows, batchSize, false, true)); + } + + if (outputSize == 0) + { + outputSize = boost::apply_visitor(outputParameterVisitor, + network.back()).n_elem / batchSize; + } + + return performance; +} + +template +double RNN::Evaluate( + const arma::mat& parameters, + const size_t begin, + const size_t batchSize) +{ + return Evaluate(parameters, begin, batchSize, true); +} + +template +template +double RNN:: +EvaluateWithGradient(const arma::mat& /* parameters */, + const size_t begin, + GradType& gradient, + const size_t batchSize) +{ + // Initialize passed gradient. + if (gradient.is_empty()) + { + if (parameter.is_empty()) + { + ResetParameters(); + } + + gradient = arma::zeros(parameter.n_rows, parameter.n_cols); } else { - const size_t inputDims = std::accumulate(network.InputDimensions().begin(), - network.InputDimensions().end(), 0); - network.CheckNetwork("RNN::Reset()", inputDims, true, false); - } -} - -template< - typename OutputLayerType, - typename InitializationRuleType, - typename MatType -> -template -void RNN< - OutputLayerType, - InitializationRuleType, - MatType ->::serialize( - Archive& ar, const uint32_t /* version */) -{ - ar(CEREAL_NVP(bpttSteps)); - ar(CEREAL_NVP(single)); - ar(CEREAL_NVP(network)); - - if (Archive::is_loading::value) - { - // We can clear these members, since it's not possible to serialize in the - // middle of training and resume. - predictors.clear(); - responses.clear(); - } -} - -template< - typename OutputLayerType, - typename InitializationRuleType, - typename MatType -> -typename MatType::elem_type RNN< - OutputLayerType, - InitializationRuleType, - MatType ->::Evaluate( - const MatType& /* parameters */, - const size_t begin, - const size_t batchSize) -{ - // Ensure the network is valid. - network.CheckNetwork("RNN::Evaluate()", predictors.n_rows); - - // The core of the computation here is to pass through each step. Since we - // are not computing the gradient, we can be "clever" and use only one memory - // cell---we don't need to know about the past. - ResetMemoryState(1, batchSize); - SetCurrentStep(0); - SetPreviousStep(size_t(-1)); - MatType output(network.network.OutputSize(), batchSize); - - typename MatType::elem_type loss = 0.0; - MatType stepData, responseData; - for (size_t t = 0; t < predictors.n_slices; ++t) - { - if (t == 1) - SetPreviousStep(0); - - // Manually reset the data of the network to be an alias of the current time - // step. - MakeAlias(network.predictors, predictors.slice(t).colptr(begin), - predictors.n_rows, batchSize); - const size_t responseStep = (single) ? 0 : t; - MakeAlias(network.responses, responses.slice(responseStep).colptr(begin), - responses.n_rows, batchSize); - - loss += network.Evaluate(output, begin, batchSize); + gradient.zeros(); } - return loss; -} - -template< - typename OutputLayerType, - typename InitializationRuleType, - typename MatType -> -template -typename MatType::elem_type RNN< - OutputLayerType, - InitializationRuleType, - MatType ->::EvaluateWithGradient( - const MatType& parameters, - GradType& gradient) -{ - return EvaluateWithGradient(parameters, 0, gradient, predictors.n_cols); -} - -template< - typename OutputLayerType, - typename InitializationRuleType, - typename MatType -> -template -typename MatType::elem_type RNN< - OutputLayerType, - InitializationRuleType, - MatType ->::EvaluateWithGradient( - const MatType& /* parameters */, - const size_t begin, - GradType& gradient, - const size_t batchSize) -{ - network.CheckNetwork("RNN::EvaluateWithGradient()", predictors.n_rows); - - typename MatType::elem_type loss = 0; - - // We must save anywhere between 1 and `bpttSteps` states, but we are limited - // by `predictors.n_slices`. - const size_t effectiveBPTTSteps = std::max(size_t(1), - std::min(bpttSteps, size_t(predictors.n_slices))); - - ResetMemoryState(effectiveBPTTSteps, batchSize); - SetPreviousStep(size_t(-1)); - arma::Cube outputs( - network.network.OutputSize(), batchSize, effectiveBPTTSteps); - - // If `bpttSteps` is less than the number of time steps in the data, then for - // the first few steps, we won't actually need to hold onto any historical - // information, since BPTT will never go back that far. - const size_t extraSteps = (predictors.n_slices - effectiveBPTTSteps + 1); - MatType stepData, outputData, responseData; - for (size_t t = 0; t < std::min(size_t(predictors.n_slices), extraSteps); ++t) + if (this->deterministic) { - SetCurrentStep(0); - - // Make an alias of the step's data. - MakeAlias(stepData, predictors.slice(t).colptr(begin), predictors.n_rows, - batchSize); - MakeAlias(outputData, outputs.slice(t).memptr(), outputs.n_rows, - outputs.n_cols); - network.network.Forward(stepData, outputData); - - const size_t responseStep = (single) ? 0 : t; - MakeAlias(responseData, responses.slice(responseStep).colptr(begin), - responses.n_rows, batchSize); - - loss += network.outputLayer.Forward(outputData, responseData); - - SetPreviousStep(0); + this->deterministic = false; + ResetDeterministic(); } - // Next, we reach the time steps that will be used for BPTT, for which we must - // preserve step data. - for (size_t t = extraSteps; t < predictors.n_slices; ++t) + if (!inputSize) { - SetCurrentStep(t - extraSteps + 1); + inputSize = predictors.n_rows; + targetSize = responses.n_rows; + } + else if (targetSize == 0) + { + targetSize = responses.n_rows; + } + ResetCells(); + + double performance = 0; + size_t responseSeq = 0; + const size_t effectiveRho = std::min(rho, size_t(responses.size())); + + for (size_t seqNum = 0; seqNum < effectiveRho; ++seqNum) + { // Wrap a matrix around our data to avoid a copy. - MakeAlias(stepData, predictors.slice(t).colptr(begin), predictors.n_rows, - batchSize); - MakeAlias(outputData, outputs.slice(t).memptr(), outputs.n_rows, - outputs.n_cols); - network.network.Forward(stepData, outputData); + arma::mat stepData(predictors.slice(seqNum).colptr(begin), + predictors.n_rows, batchSize, false, true); + Forward(stepData); + if (!single) + { + responseSeq = seqNum; + } - const size_t responseStep = (single) ? 0 : t; - MakeAlias(responseData, responses.slice(responseStep).colptr(begin), - responses.n_rows, batchSize); + for (size_t l = 0; l < network.size(); ++l) + { + boost::apply_visitor(SaveOutputParameterVisitor(moduleOutputParameter), + network[l]); + } - loss += network.outputLayer.Forward(outputData, responseData); - - SetPreviousStep(t - extraSteps + 1); + performance += outputLayer.Forward(boost::apply_visitor( + outputParameterVisitor, network.back()), + arma::mat(responses.slice(responseSeq).colptr(begin), + responses.n_rows, batchSize, false, true)); } - // Add loss (this is not dependent on time steps, and should only be added - // once). - loss += network.network.Loss(); + if (outputSize == 0) + { + outputSize = boost::apply_visitor(outputParameterVisitor, + network.back()).n_elem / batchSize; + } // Initialize current/working gradient. - gradient.zeros(network.Parameters().n_rows, network.Parameters().n_cols); - GradType currentGradient; - currentGradient.zeros(network.Parameters().n_rows, - network.Parameters().n_cols); - - SetPreviousStep(size_t(-1)); - const size_t minStep = predictors.n_slices - effectiveBPTTSteps + 1; - for (size_t t = predictors.n_slices; t >= minStep; --t) + if (currentGradient.is_empty()) { - SetCurrentStep(t - 1); + currentGradient = arma::zeros(parameter.n_rows, + parameter.n_cols); + } + ResetGradients(currentGradient); + + for (size_t seqNum = 0; seqNum < effectiveRho; ++seqNum) + { currentGradient.zeros(); - MatType error(outputs.n_rows, outputs.n_cols); + for (size_t l = 0; l < network.size(); ++l) + { + boost::apply_visitor(LoadOutputParameterVisitor(moduleOutputParameter), + network[network.size() - 1 - l]); + } - // Set up the response by backpropagating through the output layer. Note - // that if we are in 'single' mode, we don't care what the network outputs - // until the input sequence is done, so there is no error for any timestep - // other than the first one. - if (single && (t - 1) < responses.n_slices - 1) + if (single && seqNum > 0) { error.zeros(); } + else if (single && seqNum == 0) + { + outputLayer.Backward(boost::apply_visitor( + outputParameterVisitor, network.back()), + arma::mat(responses.slice(0).colptr(begin), + responses.n_rows, batchSize, false, true), error); + } else { - MakeAlias(outputData, outputs.slice(t - 1).colptr(0), outputs.n_rows, - outputs.n_cols); - const size_t respStep = (single) ? 0 : t - 1; - MakeAlias(responseData, responses.slice(respStep).colptr(begin), - responses.n_rows, batchSize); - network.outputLayer.Backward(outputData, responseData, error); + outputLayer.Backward(boost::apply_visitor( + outputParameterVisitor, network.back()), + arma::mat(responses.slice(effectiveRho - seqNum - 1).colptr(begin), + responses.n_rows, batchSize, false, true), error); } - // Now pass that error backwards through the network. - MakeAlias(outputData, outputs.slice(t - 1).colptr(0), outputs.n_rows, - outputs.n_cols); - MatType networkDelta; - network.network.Backward(outputData, error, networkDelta); - - MakeAlias(stepData, predictors.slice(t - 1).colptr(begin), - predictors.n_rows, batchSize); - network.network.Gradient(stepData, error, currentGradient); + Backward(); + Gradient( + arma::mat(predictors.slice(effectiveRho - seqNum - 1).colptr(begin), + predictors.n_rows, batchSize, false, true)); gradient += currentGradient; - - SetPreviousStep(t - 1); } - return loss; + return performance; } -template< - typename OutputLayerType, - typename InitializationRuleType, - typename MatType -> -template -void RNN< - OutputLayerType, - InitializationRuleType, - MatType ->::Gradient( - const MatType& parameters, +template +void RNN::Gradient( + const arma::mat& parameters, const size_t begin, - GradType& gradient, + arma::mat& gradient, const size_t batchSize) { this->EvaluateWithGradient(parameters, begin, gradient, batchSize); } -template< - typename OutputLayerType, - typename InitializationRuleType, - typename MatType -> -void RNN< - OutputLayerType, - InitializationRuleType, - MatType ->::Shuffle() +template +void RNN::Shuffle() { - math::ShuffleData(predictors, responses, predictors, responses); + arma::cube newPredictors, newResponses; + math::ShuffleData(predictors, responses, newPredictors, newResponses); + + predictors = std::move(newPredictors); + responses = std::move(newResponses); } -template< - typename OutputLayerType, - typename InitializationRuleType, - typename MatType -> -void RNN< - OutputLayerType, - InitializationRuleType, - MatType ->::ResetData( - arma::Cube predictors, - arma::Cube responses) +template +void RNN::ResetParameters() { - this->predictors = std::move(predictors); - this->responses = std::move(responses); + ResetDeterministic(); + + // Reset the network parameter with the given initialization rule. + NetworkInitialization networkInit(initializeRule); + networkInit.Initialize(network, parameter); + + reset = true; } -template< - typename OutputLayerType, - typename InitializationRuleType, - typename MatType -> -void RNN< - OutputLayerType, - InitializationRuleType, - MatType ->::ResetMemoryState(const size_t memorySize, const size_t batchSize) +template +void RNN::Reset() { - // Iterate over all layers and set the memory size. - for (Layer* l : network.Network()) + ResetParameters(); + ResetCells(); + currentGradient.zeros(); + ResetGradients(currentGradient); +} + +template +void RNN::ResetDeterministic() +{ + DeterministicSetVisitor deterministicSetVisitor(deterministic); + std::for_each(network.begin(), network.end(), + boost::apply_visitor(deterministicSetVisitor)); +} + +template +void RNN::ResetGradients( + arma::mat& gradient) +{ + size_t offset = 0; + for (LayerTypes& layer : network) { - // We can only call ClearRecurrentState() on RecurrentLayers. - RecurrentLayer* r = - dynamic_cast*>(l); - if (r != nullptr) - r->ClearRecurrentState(memorySize, batchSize); + offset += boost::apply_visitor(GradientSetVisitor(gradient, offset), layer); } } -template< - typename OutputLayerType, - typename InitializationRuleType, - typename MatType -> -void RNN< - OutputLayerType, - InitializationRuleType, - MatType ->::SetPreviousStep(const size_t step) +template +template +void RNN::Forward(const InputType& input) { - // Iterate over all layers and set the memory size. - for (Layer* l : network.Network()) + boost::apply_visitor(ForwardVisitor(input, + boost::apply_visitor(outputParameterVisitor, network.front())), + network.front()); + + for (size_t i = 1; i < network.size(); ++i) { - // We can only call SetPreviousStep() on RecurrentLayers. - RecurrentLayer* r = - dynamic_cast*>(l); - if (r != nullptr) - r->PreviousStep() = step; + boost::apply_visitor(ForwardVisitor( + boost::apply_visitor(outputParameterVisitor, network[i - 1]), + boost::apply_visitor(outputParameterVisitor, network[i])), + network[i]); } } -template< - typename OutputLayerType, - typename InitializationRuleType, - typename MatType -> -void RNN< - OutputLayerType, - InitializationRuleType, - MatType ->::SetCurrentStep(const size_t step) +template +void RNN::Backward() { - // Iterate over all layers and set the memory size. - for (Layer* l : network.Network()) + boost::apply_visitor(BackwardVisitor( + boost::apply_visitor(outputParameterVisitor, network.back()), + error, boost::apply_visitor(deltaVisitor, + network.back())), network.back()); + + for (size_t i = 2; i < network.size(); ++i) { - // We can only call SetPreviousStep() on RecurrentLayers. - RecurrentLayer* r = - dynamic_cast*>(l); - if (r != nullptr) - r->CurrentStep() = step; + boost::apply_visitor(BackwardVisitor( + boost::apply_visitor(outputParameterVisitor, + network[network.size() - i]), boost::apply_visitor( + deltaVisitor, network[network.size() - i + 1]), + boost::apply_visitor(deltaVisitor, network[network.size() - i])), + network[network.size() - i]); + } +} + +template +template +void RNN::Gradient(const InputType& input) +{ + boost::apply_visitor(GradientVisitor(input, + boost::apply_visitor(deltaVisitor, network[1])), network.front()); + + for (size_t i = 1; i < network.size() - 1; ++i) + { + boost::apply_visitor(GradientVisitor( + boost::apply_visitor(outputParameterVisitor, network[i - 1]), + boost::apply_visitor(deltaVisitor, network[i + 1])), + network[i]); + } +} + +template +template +void RNN::serialize( + Archive& ar, const uint32_t /* version */) +{ + ar(CEREAL_NVP(parameter)); + ar(CEREAL_NVP(rho)); + ar(CEREAL_NVP(single)); + ar(CEREAL_NVP(inputSize)); + ar(CEREAL_NVP(outputSize)); + ar(CEREAL_NVP(targetSize)); + ar(CEREAL_NVP(reset)); + + if (cereal::is_loading()) + { + std::for_each(network.begin(), network.end(), + boost::apply_visitor(deleteVisitor)); + network.clear(); + } + + ar(CEREAL_VECTOR_VARIANT_POINTER(network)); + + // If we are loading, we need to initialize the weights. + if (cereal::is_loading()) + { + size_t offset = 0; + for (LayerTypes& layer : network) + { + offset += boost::apply_visitor(WeightSetVisitor(parameter, offset), + layer); + + boost::apply_visitor(resetVisitor, layer); + } + + deterministic = true; + ResetDeterministic(); } } diff --git a/src/mlpack/methods/ann/util/CMakeLists.txt b/src/mlpack/methods/ann/util/CMakeLists.txt new file mode 100644 index 0000000000..dffec0c265 --- /dev/null +++ b/src/mlpack/methods/ann/util/CMakeLists.txt @@ -0,0 +1,14 @@ +# Define the files we need to compile +# Anything not in this list will not be compiled into mlpack. +set(SOURCES + check_input_shape.hpp +) + +# Add directory name to sources. +set(DIR_SRCS) +foreach(file ${SOURCES}) + set(DIR_SRCS ${DIR_SRCS} ${CMAKE_CURRENT_SOURCE_DIR}/${file}) +endforeach() +# Append sources (with directory name) to list of all mlpack sources (used at +# the parent scope). +set(MLPACK_SRCS ${MLPACK_SRCS} ${DIR_SRCS} PARENT_SCOPE) \ No newline at end of file diff --git a/src/mlpack/methods/ann/util/check_input_shape.hpp b/src/mlpack/methods/ann/util/check_input_shape.hpp new file mode 100644 index 0000000000..59f2c72da2 --- /dev/null +++ b/src/mlpack/methods/ann/util/check_input_shape.hpp @@ -0,0 +1,53 @@ +/** + * @file methods/ann/util/check_input_shape.hpp + * @author Khizir Siddiqui + * @author Nippun Sharma + * + * Definition of the CheckInputShape() function that checks + * whether the shape of input is consistent with the first layer + * of the neural network. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ + +#ifndef MLPACK_METHODS_ANN_UTIL_CHECK_INPUT_SHAPE_HPP +#define MLPACK_METHODS_ANN_UTIL_CHECK_INPUT_SHAPE_HPP + +#include + +namespace mlpack { +namespace ann /** Artificial Neural Network. */{ + +template +void CheckInputShape(const T& network, + const size_t inputShape, + const std::string& functionName) +{ + for (size_t l = 0; l < network.size(); ++l) + { + size_t layerInShape = boost::apply_visitor(InShapeVisitor(), network[l]); + if (layerInShape == 0) + { + continue; + } + else if (layerInShape == inputShape) + { + break; + } + else + { + std::string estr = functionName + ": the first layer of the network " + + "expects " + std::to_string(layerInShape) + " elements, but the " + + "input has " + std::to_string(inputShape) + " dimensions!"; + throw std::logic_error(estr); + } + } +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/visitor/CMakeLists.txt b/src/mlpack/methods/ann/visitor/CMakeLists.txt new file mode 100644 index 0000000000..fa207d6092 --- /dev/null +++ b/src/mlpack/methods/ann/visitor/CMakeLists.txt @@ -0,0 +1,71 @@ +# Define the files we need to compile +# Anything not in this list will not be compiled into mlpack. +set(SOURCES + add_visitor.hpp + add_visitor_impl.hpp + backward_visitor.hpp + backward_visitor_impl.hpp + bias_set_visitor.hpp + bias_set_visitor_impl.hpp + copy_visitor.hpp + copy_visitor_impl.hpp + delete_visitor.hpp + delete_visitor_impl.hpp + delta_visitor.hpp + delta_visitor_impl.hpp + deterministic_set_visitor.hpp + deterministic_set_visitor_impl.hpp + forward_visitor.hpp + forward_visitor_impl.hpp + gradient_set_visitor.hpp + gradient_set_visitor_impl.hpp + gradient_update_visitor.hpp + gradient_update_visitor_impl.hpp + gradient_visitor.hpp + gradient_visitor_impl.hpp + gradient_zero_visitor.hpp + gradient_zero_visitor_impl.hpp + load_output_parameter_visitor.hpp + load_output_parameter_visitor_impl.hpp + loss_visitor.hpp + loss_visitor_impl.hpp + output_height_visitor.hpp + output_height_visitor_impl.hpp + output_parameter_visitor.hpp + output_parameter_visitor_impl.hpp + output_width_visitor.hpp + output_width_visitor_impl.hpp + parameters_set_visitor.hpp + parameters_set_visitor_impl.hpp + parameters_visitor.hpp + parameters_visitor_impl.hpp + reset_cell_visitor.hpp + reset_cell_visitor_impl.hpp + reset_visitor.hpp + reset_visitor_impl.hpp + reward_set_visitor.hpp + reward_set_visitor_impl.hpp + run_set_visitor.hpp + run_set_visitor_impl.hpp + save_output_parameter_visitor.hpp + save_output_parameter_visitor_impl.hpp + set_input_height_visitor.hpp + set_input_height_visitor_impl.hpp + set_input_width_visitor.hpp + set_input_width_visitor_impl.hpp + weight_set_visitor.hpp + weight_set_visitor_impl.hpp + weight_size_visitor.hpp + weight_size_visitor_impl.hpp + input_shape_visitor.hpp + input_shape_visitor_impl.hpp +) + +# Add directory name to sources. +set(DIR_SRCS) +foreach(file ${SOURCES}) + set(DIR_SRCS ${DIR_SRCS} ${CMAKE_CURRENT_SOURCE_DIR}/${file}) +endforeach() +# Append sources (with directory name) to list of all mlpack sources (used at +# the parent scope). +set(MLPACK_SRCS ${MLPACK_SRCS} ${DIR_SRCS} PARENT_SCOPE) diff --git a/src/mlpack/methods/ann/visitor/add_visitor.hpp b/src/mlpack/methods/ann/visitor/add_visitor.hpp new file mode 100644 index 0000000000..fe8096a0ba --- /dev/null +++ b/src/mlpack/methods/ann/visitor/add_visitor.hpp @@ -0,0 +1,64 @@ +/** + * @file methods/ann/visitor/add_visitor.hpp + * @author Marcus Edel + * + * This file provides an abstraction for the Add() function for different + * layers and automatically directs any parameter to the right layer type. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_ADD_VISITOR_HPP +#define MLPACK_METHODS_ANN_VISITOR_ADD_VISITOR_HPP + +#include +#include + +#include + +namespace mlpack { +namespace ann { + +/** + * AddVisitor exposes the Add() method of the given module. + */ +template +class AddVisitor : public boost::static_visitor +{ + public: + //! Exposes the Add() method of the given module. + template + AddVisitor(T newLayer); + + //! Exposes the Add() method. + template + void operator()(LayerType* layer) const; + + void operator()(MoreTypes layer) const; + + private: + //! The layer that should be added. + LayerTypes newLayer; + + //! Only add the layer if the module implements the Add() function. + template + typename std::enable_if< + HasAddCheck)>::value, void>::type + LayerAdd(T* layer) const; + + //! Do not add the layer if the module doesn't implement the Add() function. + template + typename std::enable_if< + !HasAddCheck)>::value, void>::type + LayerAdd(T* layer) const; +}; + +} // namespace ann +} // namespace mlpack + +// Include implementation. +#include "add_visitor_impl.hpp" + +#endif diff --git a/src/mlpack/methods/ann/visitor/add_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/add_visitor_impl.hpp new file mode 100644 index 0000000000..62725e95ed --- /dev/null +++ b/src/mlpack/methods/ann/visitor/add_visitor_impl.hpp @@ -0,0 +1,64 @@ +/** + * @file methods/ann/visitor/add_visitor_impl.hpp + * @author Marcus Edel + * + * Implementation of the Add() function layer abstraction. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_ADD_VISITOR_IMPL_HPP +#define MLPACK_METHODS_ANN_VISITOR_ADD_VISITOR_IMPL_HPP + +// In case it hasn't been included yet. +#include "add_visitor.hpp" + +namespace mlpack { +namespace ann { + +//! AddVisitor visitor class. +template +template +inline AddVisitor::AddVisitor(T newLayer) : + newLayer(std::move(newLayer)) +{ + /* Nothing to do here. */ +} + +template +template +inline void AddVisitor::operator()(LayerType* layer) const +{ + LayerAdd(layer); +} + +template +inline void AddVisitor::operator()(MoreTypes layer) const +{ + layer.apply_visitor(*this); +} + +template +template +inline typename std::enable_if< + HasAddCheck)>::value, void>::type +AddVisitor::LayerAdd(T* layer) const +{ + layer->Add(newLayer); +} + +template +template +inline typename std::enable_if< + !HasAddCheck)>::value, void>::type +AddVisitor::LayerAdd(T* /* layer */) const +{ + /* Nothing to do here. */ +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/visitor/backward_visitor.hpp b/src/mlpack/methods/ann/visitor/backward_visitor.hpp new file mode 100644 index 0000000000..e59ff53eef --- /dev/null +++ b/src/mlpack/methods/ann/visitor/backward_visitor.hpp @@ -0,0 +1,85 @@ +/** + * @file methods/ann/visitor/backward_visitor.hpp + * @author Marcus Edel + * + * This file provides an abstraction for the Backward() function for different + * layers and automatically directs any parameter to the right layer type. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_BACKWARD_VISITOR_HPP +#define MLPACK_METHODS_ANN_VISITOR_BACKWARD_VISITOR_HPP + +#include +#include + +#include + +namespace mlpack { +namespace ann { + +/** + * BackwardVisitor executes the Backward() function given the input, error and + * delta parameter. + */ +class BackwardVisitor : public boost::static_visitor +{ + public: + //! Execute the Backward() function given the input, error and delta + //! parameter. + BackwardVisitor(const arma::mat& input, + const arma::mat& error, + arma::mat& delta); + + //! Execute the Backward() function for the layer with the specified index. + BackwardVisitor(const arma::mat& input, + const arma::mat& error, + arma::mat& delta, + const size_t index); + + //! Execute the Backward() function. + template + void operator()(LayerType* layer) const; + + void operator()(MoreTypes layer) const; + + private: + //! The input parameter set. + const arma::mat& input; + + //! The error parameter. + const arma::mat& error; + + //! The delta parameter. + arma::mat& delta; + + //! The index of the layer to run. + size_t index; + + //! Indicates whether to use index or not + bool hasIndex; + + //! Execute the Backward() function if the module does not have Run() + //! check. + template + typename std::enable_if< + !HasRunCheck::value, void>::type + LayerBackward(T* layer, arma::mat& input) const; + + //! Execute the Backward() function if the module is has Run() function. + template + typename std::enable_if< + HasRunCheck::value, void>::type + LayerBackward(T* layer, arma::mat& input) const; +}; + +} // namespace ann +} // namespace mlpack + +// Include implementation. +#include "backward_visitor_impl.hpp" + +#endif diff --git a/src/mlpack/methods/ann/visitor/backward_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/backward_visitor_impl.hpp new file mode 100644 index 0000000000..24f6c180d9 --- /dev/null +++ b/src/mlpack/methods/ann/visitor/backward_visitor_impl.hpp @@ -0,0 +1,84 @@ +/** + * @file methods/ann/visitor/backward_visitor_impl.hpp + * @author Marcus Edel + * + * Implementation of the Backward() function layer abstraction. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_BACKWARD_VISITOR_IMPL_HPP +#define MLPACK_METHODS_ANN_VISITOR_BACKWARD_VISITOR_IMPL_HPP + +// In case it hasn't been included yet. +#include "backward_visitor.hpp" + +namespace mlpack { +namespace ann { + +//! BackwardVisitor visitor class. +inline BackwardVisitor::BackwardVisitor(const arma::mat& input, + const arma::mat& error, + arma::mat& delta) : + input(input), + error(error), + delta(delta), + index(0), + hasIndex(false) +{ + /* Nothing to do here. */ +} + +inline BackwardVisitor::BackwardVisitor(const arma::mat& input, + const arma::mat& error, + arma::mat& delta, + const size_t index) : + input(input), + error(error), + delta(delta), + index(index), + hasIndex(true) +{ + /* Nothing to do here. */ +} + +template +inline void BackwardVisitor::operator()(LayerType* layer) const +{ + LayerBackward(layer, layer->OutputParameter()); +} + +inline void BackwardVisitor::operator()(MoreTypes layer) const +{ + layer.apply_visitor(*this); +} + +template +inline typename std::enable_if< + !HasRunCheck::value, void>::type +BackwardVisitor::LayerBackward(T* layer, arma::mat& /* input */) const +{ + layer->Backward(input, error, delta); +} + +template +inline typename std::enable_if< + HasRunCheck::value, void>::type +BackwardVisitor::LayerBackward(T* layer, arma::mat& /* input */) const +{ + if (!hasIndex) + { + layer->Backward(input, error, delta); + } + else + { + layer->Backward(input, error, delta, index); + } +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/visitor/bias_set_visitor.hpp b/src/mlpack/methods/ann/visitor/bias_set_visitor.hpp new file mode 100644 index 0000000000..c081c73a2a --- /dev/null +++ b/src/mlpack/methods/ann/visitor/bias_set_visitor.hpp @@ -0,0 +1,82 @@ +/** + * @file methods/ann/visitor/bias_set_visitor.hpp + * @author Toshal Agrawal + * + * This file provides an abstraction for the Bias() function for different + * layers and automatically directs any parameter to the right layer type. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_BIAS_SET_VISITOR_HPP +#define MLPACK_METHODS_ANN_VISITOR_BIAS_SET_VISITOR_HPP + +#include + +#include + +namespace mlpack { +namespace ann { + +/** + * BiasSetVisitor updates the module bias parameters given the parameters set. + */ +class BiasSetVisitor : public boost::static_visitor +{ + public: + //! Update the bias parameters given the parameters' set and offset. + BiasSetVisitor(arma::mat& weight, const size_t offset = 0); + + //! Update the parameters' set. + template + size_t operator()(LayerType* layer) const; + + size_t operator()(MoreTypes layer) const; + + private: + //! The parameters' set. + arma::mat& weight; + + //! The parameters' offset. + const size_t offset; + + //! Do not update the bias parameters if the module doesn't implement the + //! Bias() or Model() function. + template + typename std::enable_if< + !HasBiasCheck::value && + !HasModelCheck::value, size_t>::type + LayerSize(T* layer) const; + + //! Update the bias parameters if the module implements the Model() function. + template + typename std::enable_if< + !HasBiasCheck::value && + HasModelCheck::value, size_t>::type + LayerSize(T* layer) const; + + //! Update the bias parameters if the module implements the Bias() function. + template + typename std::enable_if< + HasBiasCheck::value && + !HasModelCheck::value, size_t>::type + LayerSize(T* layer) const; + + //! Update the bias parameters if the module implements the Model() and + //! Bias() function. + template + typename std::enable_if< + HasBiasCheck::value && + HasModelCheck::value, size_t>::type + LayerSize(T* layer) const; +}; + +} // namespace ann +} // namespace mlpack + +// Include implementation. +#include "bias_set_visitor_impl.hpp" + +#endif diff --git a/src/mlpack/methods/ann/visitor/bias_set_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/bias_set_visitor_impl.hpp new file mode 100644 index 0000000000..2eacbb61d6 --- /dev/null +++ b/src/mlpack/methods/ann/visitor/bias_set_visitor_impl.hpp @@ -0,0 +1,101 @@ +/** + * @file methods/ann/visitor/bias_set_visitor_impl.hpp + * @author Toshal Agrawal + * + * Implementation of the Bias() function layer abstraction. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_BIAS_SET_VISITOR_IMPL_HPP +#define MLPACK_METHODS_ANN_VISITOR_BIAS_SET_VISITOR_IMPL_HPP + +// In case it hasn't been included yet. +#include "bias_set_visitor.hpp" + +namespace mlpack { +namespace ann { + +//! BiasSetVisitor visitor class. +inline BiasSetVisitor::BiasSetVisitor(arma::mat& weight, const size_t offset) : + weight(weight), + offset(offset) +{ + /* Nothing to do here. */ +} + +template +inline size_t BiasSetVisitor::operator()(LayerType* layer) const +{ + return LayerSize(layer); +} + +inline size_t BiasSetVisitor::operator()(MoreTypes layer) const +{ + return layer.apply_visitor(*this); +} + +template +inline typename std::enable_if< + !HasBiasCheck::value && + !HasModelCheck::value, size_t>::type +BiasSetVisitor::LayerSize(T* /* layer */) const +{ + return 0; +} + +template +inline typename std::enable_if< + !HasBiasCheck::value && + HasModelCheck::value, size_t>::type +BiasSetVisitor::LayerSize(T* layer) const +{ + size_t modelOffset = 0; + + for (size_t i = 0; i < layer->Model().size(); ++i) + { + modelOffset += boost::apply_visitor(BiasSetVisitor( + weight, modelOffset + offset), layer->Model()[i]); + } + + return modelOffset; +} + +template +inline typename std::enable_if< + HasBiasCheck::value && + !HasModelCheck::value, size_t>::type +BiasSetVisitor::LayerSize(T* layer) const +{ + layer->Bias() = arma::mat(weight.memptr() + offset, + layer->Bias().n_rows, layer->Bias().n_cols, false, false); + + return layer->Bias().n_elem; +} + +template +inline typename std::enable_if< + HasBiasCheck::value && + HasModelCheck::value, size_t>::type +BiasSetVisitor::LayerSize(T* layer) const +{ + layer->Bias() = arma::mat(weight.memptr() + offset, + layer->Bias().n_rows, layer->Bias().n_cols, false, false); + + size_t modelOffset = layer->Bias().n_elem; + + for (size_t i = 0; i < layer->Model().size(); ++i) + { + modelOffset += boost::apply_visitor(BiasSetVisitor( + weight, modelOffset + offset), layer->Model()[i]); + } + + return modelOffset; +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/visitor/copy_visitor.hpp b/src/mlpack/methods/ann/visitor/copy_visitor.hpp new file mode 100644 index 0000000000..14bf5f91ad --- /dev/null +++ b/src/mlpack/methods/ann/visitor/copy_visitor.hpp @@ -0,0 +1,41 @@ +/** + * @file methods/ann/visitor/copy_visitor.hpp + * @author Shangtong Zhang + * + * This file provides an abstraction for copy between layers. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_COPY_VISITOR_HPP +#define MLPACK_METHODS_ANN_VISITOR_COPY_VISITOR_HPP + +#include +#include + +namespace mlpack { +namespace ann { + +/** + * This visitor is to support copy constructor for neural network module. + * We want a layer-wise copy rather than simple duplicate the pointer. + */ +template +class CopyVisitor : public boost::static_visitor > +{ + public: + template + LayerTypes operator()(LayerType*) const; + + LayerTypes operator()(MoreTypes) const; +}; + +} // namespace ann +} // namespace mlpack + +// Include implementation +#include "copy_visitor_impl.hpp" +#endif + diff --git a/src/mlpack/methods/ann/visitor/copy_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/copy_visitor_impl.hpp new file mode 100644 index 0000000000..d143e18799 --- /dev/null +++ b/src/mlpack/methods/ann/visitor/copy_visitor_impl.hpp @@ -0,0 +1,39 @@ +/** + * @file methods/ann/visitor/copy_visitor_impl.hpp + * @author Shangtong Zhang + * + * This file provides an implementation for copy between layers + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_COPY_VISITOR_IMPL_HPP +#define MLPACK_METHODS_ANN_VISITOR_COPY_VISITOR_IMPL_HPP + +#include +#include + +namespace mlpack { +namespace ann { + +template +template +inline LayerTypes +CopyVisitor::operator()(LayerType* layer) const +{ + return new LayerType(*layer); +} + +template +inline LayerTypes +CopyVisitor::operator()(MoreTypes layer) const +{ + return layer.apply_visitor(*this); +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/visitor/delete_visitor.hpp b/src/mlpack/methods/ann/visitor/delete_visitor.hpp new file mode 100644 index 0000000000..d65f4e3e4d --- /dev/null +++ b/src/mlpack/methods/ann/visitor/delete_visitor.hpp @@ -0,0 +1,51 @@ +/** + * @file methods/ann/visitor/delete_visitor.hpp + * @author Marcus Edel + * + * This file provides an abstraction for the Delete() function for different + * layers and automatically directs any parameter to the right layer type. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_DELETE_VISITOR_HPP +#define MLPACK_METHODS_ANN_VISITOR_DELETE_VISITOR_HPP + +#include +#include + +#include + +namespace mlpack { +namespace ann { + +/** + * DeleteVisitor executes the destructor of the instantiated object. + */ +class DeleteVisitor : public boost::static_visitor +{ + public: + //! Execute the destructor if the layer does not hold layers internally. + template + typename std::enable_if< + !HasModelCheck::value, void>::type + operator()(LayerType* layer) const; + + //! Execute the destructor if the layer does hold layers internally. + template + typename std::enable_if< + HasModelCheck::value, void>::type + operator()(LayerType* layer) const; + + void operator()(MoreTypes layer) const; +}; + +} // namespace ann +} // namespace mlpack + +// Include implementation. +#include "delete_visitor_impl.hpp" + +#endif diff --git a/src/mlpack/methods/ann/visitor/delete_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/delete_visitor_impl.hpp new file mode 100644 index 0000000000..f9f43936e3 --- /dev/null +++ b/src/mlpack/methods/ann/visitor/delete_visitor_impl.hpp @@ -0,0 +1,53 @@ +/** + * @file methods/ann/visitor/delete_visitor_impl.hpp + * @author Marcus Edel + * + * Implementation of the Delete() function layer abstraction. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_DELETE_VISITOR_IMPL_HPP +#define MLPACK_METHODS_ANN_VISITOR_DELETE_VISITOR_IMPL_HPP + +// In case it hasn't been included yet. +#include "delete_visitor.hpp" + +namespace mlpack { +namespace ann { + +//! DeleteVisitor visitor class. +template +inline typename std::enable_if< + !HasModelCheck::value, void>::type +DeleteVisitor::operator()(LayerType* layer) const +{ + if (layer) + delete layer; +} + +template +inline typename std::enable_if< + HasModelCheck::value, void>::type +DeleteVisitor::operator()(LayerType* layer) const +{ + if (layer) + { + for (size_t i = 0; i < layer->Model().size(); ++i) + boost::apply_visitor(DeleteVisitor(), layer->Model()[i]); + + delete layer; + } +} + +inline void DeleteVisitor::operator()(MoreTypes layer) const +{ + layer.apply_visitor(*this); +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/visitor/delta_visitor.hpp b/src/mlpack/methods/ann/visitor/delta_visitor.hpp new file mode 100644 index 0000000000..12ecfd2017 --- /dev/null +++ b/src/mlpack/methods/ann/visitor/delta_visitor.hpp @@ -0,0 +1,43 @@ +/** + * @file methods/ann/visitor/delta_visitor.hpp + * @author Marcus Edel + * + * This file provides an abstraction for the Delta() function for different + * layers and automatically directs any parameter to the right layer type. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_DELTA_VISITOR_HPP +#define MLPACK_METHODS_ANN_VISITOR_DELTA_VISITOR_HPP + +#include +#include + +#include + +namespace mlpack { +namespace ann { + +/** + * DeltaVisitor exposes the delta parameter of the given module. + */ +class DeltaVisitor : public boost::static_visitor +{ + public: + //! Return the delta parameter. + template + arma::mat& operator()(LayerType* layer) const; + + arma::mat& operator()(MoreTypes layer) const; +}; + +} // namespace ann +} // namespace mlpack + +// Include implementation. +#include "delta_visitor_impl.hpp" + +#endif diff --git a/src/mlpack/methods/ann/visitor/delta_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/delta_visitor_impl.hpp new file mode 100644 index 0000000000..3a8e2b6b92 --- /dev/null +++ b/src/mlpack/methods/ann/visitor/delta_visitor_impl.hpp @@ -0,0 +1,36 @@ +/** + * @file methods/ann/visitor/delta_visitor_impl.hpp + * @author Marcus Edel + * + * Implementation of the Delta() function layer abstraction. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_DELTA_VISITOR_IMPL_HPP +#define MLPACK_METHODS_ANN_VISITOR_DELTA_VISITOR_IMPL_HPP + +// In case it hasn't been included yet. +#include "delta_visitor.hpp" + +namespace mlpack { +namespace ann { + +//! DeltaVisitor visitor class. +template +inline arma::mat& DeltaVisitor::operator()(LayerType *layer) const +{ + return layer->Delta(); +} + +inline arma::mat& DeltaVisitor::operator()(MoreTypes layer) const +{ + return layer.apply_visitor(*this); +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/visitor/deterministic_set_visitor.hpp b/src/mlpack/methods/ann/visitor/deterministic_set_visitor.hpp new file mode 100644 index 0000000000..f70d740e80 --- /dev/null +++ b/src/mlpack/methods/ann/visitor/deterministic_set_visitor.hpp @@ -0,0 +1,83 @@ +/** + * @file methods/ann/visitor/deterministic_set_visitor.hpp + * @author Marcus Edel + * + * This file provides an abstraction for the Deterministic() function for + * different layers and automatically directs any parameter to the right layer + * type. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_DETERMINISTIC_SET_VISITOR_HPP +#define MLPACK_METHODS_ANN_VISITOR_DETERMINISTIC_SET_VISITOR_HPP + +#include + +#include + +namespace mlpack { +namespace ann { + +/** + * DeterministicSetVisitor set the deterministic parameter given the + * deterministic value. + */ +class DeterministicSetVisitor : public boost::static_visitor +{ + public: + //! Set the deterministic parameter given the current deterministic value. + DeterministicSetVisitor(const bool deterministic = true); + + //! Set the deterministic parameter. + template + void operator()(LayerType* layer) const; + + void operator()(MoreTypes layer) const; + + private: + //! The deterministic parameter. + const bool deterministic; + + //! Set the deterministic parameter if the module implements the + //! Deterministic() and Model() function. + template + typename std::enable_if< + HasDeterministicCheck::value && + HasModelCheck::value, void>::type + LayerDeterministic(T* layer) const; + + //! Set the deterministic parameter if the module implements the + //! Model() function. + template + typename std::enable_if< + !HasDeterministicCheck::value && + HasModelCheck::value, void>::type + LayerDeterministic(T* layer) const; + + //! Set the deterministic parameter if the module implements the + //! Deterministic() function. + template + typename std::enable_if< + HasDeterministicCheck::value && + !HasModelCheck::value, void>::type + LayerDeterministic(T* layer) const; + + //! Do not set the deterministic parameter if the module doesn't implement the + //! Deterministic() or Model() function. + template + typename std::enable_if< + !HasDeterministicCheck::value && + !HasModelCheck::value, void>::type + LayerDeterministic(T* layer) const; +}; + +} // namespace ann +} // namespace mlpack + +// Include implementation. +#include "deterministic_set_visitor_impl.hpp" + +#endif diff --git a/src/mlpack/methods/ann/visitor/deterministic_set_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/deterministic_set_visitor_impl.hpp new file mode 100644 index 0000000000..06d8bafb03 --- /dev/null +++ b/src/mlpack/methods/ann/visitor/deterministic_set_visitor_impl.hpp @@ -0,0 +1,88 @@ +/** + * @file methods/ann/visitor/deterministic_set_visitor_impl.hpp + * @author Marcus Edel + * + * Implementation of the Deterministic() function layer abstraction. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_DETERMINISTIC_SET_VISITOR_IMPL_HPP +#define MLPACK_METHODS_ANN_VISITOR_DETERMINISTIC_SET_VISITOR_IMPL_HPP + +// In case it hasn't been included yet. +#include "deterministic_set_visitor.hpp" + +namespace mlpack { +namespace ann { + +//! DeterministicSetVisitor visitor class. +inline DeterministicSetVisitor::DeterministicSetVisitor( + const bool deterministic) : deterministic(deterministic) +{ + /* Nothing to do here. */ +} + +template +inline void DeterministicSetVisitor::operator()(LayerType* layer) const +{ + LayerDeterministic(layer); +} + +inline void DeterministicSetVisitor::operator()(MoreTypes layer) const +{ + layer.apply_visitor(*this); +} + +template +inline typename std::enable_if< + HasDeterministicCheck::value && + HasModelCheck::value, void>::type +DeterministicSetVisitor::LayerDeterministic(T* layer) const +{ + layer->Deterministic() = deterministic; + + for (size_t i = 0; i < layer->Model().size(); ++i) + { + boost::apply_visitor(DeterministicSetVisitor(deterministic), + layer->Model()[i]); + } +} + +template +inline typename std::enable_if< + !HasDeterministicCheck::value && + HasModelCheck::value, void>::type +DeterministicSetVisitor::LayerDeterministic(T* layer) const +{ + for (size_t i = 0; i < layer->Model().size(); ++i) + { + boost::apply_visitor(DeterministicSetVisitor(deterministic), + layer->Model()[i]); + } +} + +template +inline typename std::enable_if< + HasDeterministicCheck::value && + !HasModelCheck::value, void>::type +DeterministicSetVisitor::LayerDeterministic(T* layer) const +{ + layer->Deterministic() = deterministic; +} + +template +inline typename std::enable_if< + !HasDeterministicCheck::value && + !HasModelCheck::value, void>::type +DeterministicSetVisitor::LayerDeterministic(T* /* input */) const +{ + /* Nothing to do here. */ +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/visitor/forward_visitor.hpp b/src/mlpack/methods/ann/visitor/forward_visitor.hpp new file mode 100644 index 0000000000..ac825f11ee --- /dev/null +++ b/src/mlpack/methods/ann/visitor/forward_visitor.hpp @@ -0,0 +1,54 @@ +/** + * @file methods/ann/visitor/forward_visitor.hpp + * @author Marcus Edel + * + * This file provides an abstraction for the Forward() function for different + * layers and automatically directs any parameter to the right layer type. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_FORWARD_VISITOR_HPP +#define MLPACK_METHODS_ANN_VISITOR_FORWARD_VISITOR_HPP + +#include +#include + +#include + +namespace mlpack { +namespace ann { + +/** + * ForwardVisitor executes the Forward() function given the input and output + * parameter. + */ +class ForwardVisitor : public boost::static_visitor +{ + public: + //! Execute the Forward() function given the input and output parameter. + ForwardVisitor(const arma::mat& input, arma::mat& output); + + //! Execute the Forward() function. + template + void operator()(LayerType* layer) const; + + void operator()(MoreTypes layer) const; + + private: + //! The input parameter set. + const arma::mat& input; + + //! The output parameter set. + arma::mat& output; +}; + +} // namespace ann +} // namespace mlpack + +// Include implementation. +#include "forward_visitor_impl.hpp" + +#endif diff --git a/src/mlpack/methods/ann/visitor/forward_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/forward_visitor_impl.hpp new file mode 100644 index 0000000000..248d852c53 --- /dev/null +++ b/src/mlpack/methods/ann/visitor/forward_visitor_impl.hpp @@ -0,0 +1,43 @@ +/** + * @file methods/ann/visitor/forward_visitor_impl.hpp + * @author Marcus Edel + * + * Implementation of the Forward() function layer abstraction. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_FORWARD_VISITOR_IMPL_HPP +#define MLPACK_METHODS_ANN_VISITOR_FORWARD_VISITOR_IMPL_HPP + +// In case it hasn't been included yet. +#include "forward_visitor.hpp" + +namespace mlpack { +namespace ann { + +//! ForwardVisitor visitor class. +inline ForwardVisitor::ForwardVisitor(const arma::mat& input, arma::mat& output) : + input(input), + output(output) +{ + /* Nothing to do here. */ +} + +template +inline void ForwardVisitor::operator()(LayerType* layer) const +{ + layer->Forward(input, output); +} + +inline void ForwardVisitor::operator()(MoreTypes layer) const +{ + layer.apply_visitor(*this); +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/visitor/gradient_set_visitor.hpp b/src/mlpack/methods/ann/visitor/gradient_set_visitor.hpp new file mode 100644 index 0000000000..4c492b3f0e --- /dev/null +++ b/src/mlpack/methods/ann/visitor/gradient_set_visitor.hpp @@ -0,0 +1,82 @@ +/** + * @file methods/ann/visitor/gradient_set_visitor.hpp + * @author Marcus Edel + * + * This file provides an abstraction for the Gradient() function for different + * layers and automatically directs any parameter to the right layer type. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_GRADIENT_SET_VISITOR_HPP +#define MLPACK_METHODS_ANN_VISITOR_GRADIENT_SET_VISITOR_HPP + +#include + +#include + +namespace mlpack { +namespace ann { + +/** + * GradientSetVisitor update the gradient parameter given the gradient set. + */ +class GradientSetVisitor : public boost::static_visitor +{ + public: + //! Update the gradient parameter given the gradient set. + GradientSetVisitor(arma::mat& gradient, size_t offset = 0); + + //! Update the gradient parameter. + template + size_t operator()(LayerType* layer) const; + + size_t operator()(MoreTypes layer) const; + + private: + //! The gradient set. + arma::mat& gradient; + + //! The gradient offset. + size_t offset; + + //! Update the gradient if the module implements the Gradient() function. + template + typename std::enable_if< + HasGradientCheck::value && + !HasModelCheck::value, size_t>::type + LayerGradients(T* layer, arma::mat& input) const; + + //! Update the gradient if the module implements the Model() function. + template + typename std::enable_if< + !HasGradientCheck::value && + HasModelCheck::value, size_t>::type + LayerGradients(T* layer, arma::mat& input) const; + + //! Update the gradient if the module implements the Gradient() and Model() + //! function. + template + typename std::enable_if< + HasGradientCheck::value && + HasModelCheck::value, size_t>::type + LayerGradients(T* layer, arma::mat& input) const; + + //! Do not update the gradient parameter if the module doesn't implement the + //! Gradient() or Model() function. + template + typename std::enable_if< + !HasGradientCheck::value && + !HasModelCheck::value, size_t>::type + LayerGradients(T* layer, P& input) const; +}; + +} // namespace ann +} // namespace mlpack + +// Include implementation. +#include "gradient_set_visitor_impl.hpp" + +#endif diff --git a/src/mlpack/methods/ann/visitor/gradient_set_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/gradient_set_visitor_impl.hpp new file mode 100644 index 0000000000..85e148da25 --- /dev/null +++ b/src/mlpack/methods/ann/visitor/gradient_set_visitor_impl.hpp @@ -0,0 +1,100 @@ +/** + * @file methods/ann/visitor/gradient_set_visitor_impl.hpp + * @author Marcus Edel + * + * Implementation of the Gradient() function layer abstraction. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_GRADIENT_SET_VISITOR_IMPL_HPP +#define MLPACK_METHODS_ANN_VISITOR_GRADIENT_SET_VISITOR_IMPL_HPP + +// In case it hasn't been included yet. +#include "gradient_set_visitor.hpp" + +namespace mlpack { +namespace ann { + +//! GradientSetVisitor visitor class. +inline GradientSetVisitor::GradientSetVisitor(arma::mat& gradient, + size_t offset) : + gradient(gradient), + offset(offset) +{ + /* Nothing to do here. */ +} + +template +inline size_t GradientSetVisitor::operator()(LayerType* layer) const +{ + return LayerGradients(layer, layer->OutputParameter()); +} + +inline size_t GradientSetVisitor::operator()(MoreTypes layer) const +{ + return layer.apply_visitor(*this); +} + +template +inline typename std::enable_if< + HasGradientCheck::value && + !HasModelCheck::value, size_t>::type +GradientSetVisitor::LayerGradients(T* layer, arma::mat& /* input */) const +{ + layer->Gradient() = arma::mat(gradient.memptr() + offset, + layer->Parameters().n_rows, layer->Parameters().n_cols, false, false); + + return layer->Parameters().n_elem; +} + +template +inline typename std::enable_if< + !HasGradientCheck::value && + HasModelCheck::value, size_t>::type +GradientSetVisitor::LayerGradients(T* layer, arma::mat& /* input */) const +{ + size_t modelOffset = 0; + for (size_t i = 0; i < layer->Model().size(); ++i) + { + modelOffset += boost::apply_visitor(GradientSetVisitor( + gradient, modelOffset + offset), layer->Model()[i]); + } + + return modelOffset; +} + +template +inline typename std::enable_if< + HasGradientCheck::value && + HasModelCheck::value, size_t>::type +GradientSetVisitor::LayerGradients(T* layer, arma::mat& /* input */) const +{ + layer->Gradient() = arma::mat(gradient.memptr() + offset, + layer->Parameters().n_rows, layer->Parameters().n_cols, false, false); + + size_t modelOffset = layer->Parameters().n_elem; + for (size_t i = 0; i < layer->Model().size(); ++i) + { + modelOffset += boost::apply_visitor(GradientSetVisitor( + gradient, modelOffset + offset), layer->Model()[i]); + } + + return modelOffset; +} + +template +inline typename std::enable_if< + !HasGradientCheck::value && + !HasModelCheck::value, size_t>::type +GradientSetVisitor::LayerGradients(T* /* layer */, P& /* input */) const +{ + return 0; +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/visitor/gradient_update_visitor.hpp b/src/mlpack/methods/ann/visitor/gradient_update_visitor.hpp new file mode 100644 index 0000000000..feedf0d299 --- /dev/null +++ b/src/mlpack/methods/ann/visitor/gradient_update_visitor.hpp @@ -0,0 +1,82 @@ +/** + * @file methods/ann/visitor/gradient_update_visitor.hpp + * @author Marcus Edel + * + * This file provides an abstraction for the Gradient() function for different + * layers and automatically directs any parameter to the right layer type. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_GRADIENT_UPDATE_VISITOR_HPP +#define MLPACK_METHODS_ANN_VISITOR_GRADIENT_UPDATE_VISITOR_HPP + +#include + +#include + +namespace mlpack { +namespace ann { + +/** + * GradientUpdateVisitor update the gradient parameter given the gradient set. + */ +class GradientUpdateVisitor : public boost::static_visitor +{ + public: + //! Update the gradient parameter given the gradient set. + GradientUpdateVisitor(arma::mat& gradient, size_t offset = 0); + + //! Update the gradient parameter. + template + size_t operator()(LayerType* layer) const; + + size_t operator()(MoreTypes layer) const; + + private: + //! The gradient set. + arma::mat& gradient; + + //! The gradient offset. + size_t offset; + + //! Update the gradient if the module implements the Gradient() function. + template + typename std::enable_if< + HasGradientCheck::value && + !HasModelCheck::value, size_t>::type + LayerGradients(T* layer, arma::mat& input) const; + + //! Update the gradient if the module implements the Model() function. + template + typename std::enable_if< + !HasGradientCheck::value && + HasModelCheck::value, size_t>::type + LayerGradients(T* layer, arma::mat& input) const; + + //! Update the gradient if the module implements the Gradient() and Model() + //! function. + template + typename std::enable_if< + HasGradientCheck::value && + HasModelCheck::value, size_t>::type + LayerGradients(T* layer, arma::mat& input) const; + + //! Do not update the gradient parameter if the module doesn't implement the + //! Gradient() or Model() function. + template + typename std::enable_if< + !HasGradientCheck::value && + !HasModelCheck::value, size_t>::type + LayerGradients(T* layer, P& input) const; +}; + +} // namespace ann +} // namespace mlpack + +// Include implementation. +#include "gradient_update_visitor_impl.hpp" + +#endif diff --git a/src/mlpack/methods/ann/visitor/gradient_update_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/gradient_update_visitor_impl.hpp new file mode 100644 index 0000000000..f233eaf65d --- /dev/null +++ b/src/mlpack/methods/ann/visitor/gradient_update_visitor_impl.hpp @@ -0,0 +1,106 @@ +/** + * @file methods/ann/visitor/gradient_update_visitor_impl.hpp + * @author Marcus Edel + * + * Implementation of the Gradient() function layer abstraction. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_GRADIENT_UPDATE_VISITOR_IMPL_HPP +#define MLPACK_METHODS_ANN_VISITOR_GRADIENT_UPDATE_VISITOR_IMPL_HPP + +// In case it hasn't been included yet. +#include "gradient_update_visitor.hpp" + +namespace mlpack { +namespace ann { + +//! GradientUpdateVisitor visitor class. +inline GradientUpdateVisitor::GradientUpdateVisitor(arma::mat& gradient, + size_t offset) : + gradient(gradient), + offset(offset) +{ + /* Nothing to do here. */ +} + +template +inline size_t GradientUpdateVisitor::operator()(LayerType* layer) const +{ + return LayerGradients(layer, layer->OutputParameter()); +} + +inline size_t GradientUpdateVisitor::operator()(MoreTypes layer) const +{ + return layer.apply_visitor(*this); +} + +template +inline typename std::enable_if< + HasGradientCheck::value && + !HasModelCheck::value, size_t>::type +GradientUpdateVisitor::LayerGradients(T* layer, arma::mat& /* input */) const +{ + if (layer->Parameters().n_elem != 0) + { + layer->Gradient() = gradient.submat(offset, 0, + offset + layer->Parameters().n_elem - 1, 0);; + } + + return layer->Parameters().n_elem; +} + +template +inline typename std::enable_if< + !HasGradientCheck::value && + HasModelCheck::value, size_t>::type +GradientUpdateVisitor::LayerGradients(T* layer, arma::mat& /* input */) const +{ + size_t modelOffset = 0; + for (size_t i = 0; i < layer->Model().size(); ++i) + { + modelOffset += boost::apply_visitor(GradientUpdateVisitor( + gradient, modelOffset + offset), layer->Model()[i]); + } + + return modelOffset; +} + +template +inline typename std::enable_if< + HasGradientCheck::value && + HasModelCheck::value, size_t>::type +GradientUpdateVisitor::LayerGradients(T* layer, arma::mat& /* input */) const +{ + if (layer->Parameters().n_elem != 0) + { + layer->Gradient() = gradient.submat(offset, 0, + offset + layer->Parameters().n_elem - 1, 0);; + } + + size_t modelOffset = layer->Parameters().n_elem; + for (size_t i = 0; i < layer->Model().size(); ++i) + { + modelOffset += boost::apply_visitor(GradientUpdateVisitor( + gradient, modelOffset + offset), layer->Model()[i]); + } + + return modelOffset; +} + +template +inline typename std::enable_if< + !HasGradientCheck::value && + !HasModelCheck::value, size_t>::type +GradientUpdateVisitor::LayerGradients(T* /* layer */, P& /* input */) const +{ + return 0; +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/visitor/gradient_visitor.hpp b/src/mlpack/methods/ann/visitor/gradient_visitor.hpp new file mode 100644 index 0000000000..fc04c96161 --- /dev/null +++ b/src/mlpack/methods/ann/visitor/gradient_visitor.hpp @@ -0,0 +1,89 @@ +/** + * @file methods/ann/visitor/gradient_visitor.hpp + * @author Marcus Edel + * + * This file provides an abstraction for the Gradient() function for different + * layers and automatically directs any parameter to the right layer type. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_GRADIENT_VISITOR_HPP +#define MLPACK_METHODS_ANN_VISITOR_GRADIENT_VISITOR_HPP + +#include +#include + +#include + +namespace mlpack { +namespace ann { + +/** + * SearchModeVisitor executes the Gradient() method of the given module using + * the input and delta parameter. + */ +class GradientVisitor : public boost::static_visitor +{ + public: + //! Executes the Gradient() method of the given module using the input and + //! delta parameter. + GradientVisitor(const arma::mat& input, const arma::mat& delta); + + //! Executes the Gradient() method for the layer with the specified index. + GradientVisitor(const arma::mat& input, + const arma::mat& delta, + const size_t index); + + //! Executes the Gradient() method. + template + void operator()(LayerType* layer) const; + + void operator()(MoreTypes layer) const; + + private: + //! The input set. + const arma::mat& input; + + //! The delta parameter. + const arma::mat& delta; + + //! Index of the layer to run. + size_t index; + + //! Indicates whether to use index or not + bool hasIndex; + + //! Execute the Gradient() function if the module implements the Gradient() + //! function. + template + typename std::enable_if< + HasGradientCheck::value && + !HasRunCheck::value, void>::type + LayerGradients(T* layer, arma::mat& input) const; + + //! Execute the Gradient() function if the module implements the Gradient() + //! and has a Run() function. + template + typename std::enable_if< + HasGradientCheck::value && + HasRunCheck::value, void>::type + LayerGradients(T* layer, arma::mat& input) const; + + //! Do not execute the Gradient() function if the module doesn't implement + //! the Gradient() function. + template + typename std::enable_if< + !HasGradientCheck::value, void>::type + LayerGradients(T* layer, P& input) const; +}; + +} // namespace ann +} // namespace mlpack + +// Include implementation. +#include "gradient_visitor_impl.hpp" + +#endif diff --git a/src/mlpack/methods/ann/visitor/gradient_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/gradient_visitor_impl.hpp new file mode 100644 index 0000000000..3537aa9959 --- /dev/null +++ b/src/mlpack/methods/ann/visitor/gradient_visitor_impl.hpp @@ -0,0 +1,90 @@ +/** + * @file methods/ann/visitor/gradient_visitor_impl.hpp + * @author Marcus Edel + * + * Implementation of the Gradient() function layer abstraction. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_GRADIENT_VISITOR_IMPL_HPP +#define MLPACK_METHODS_ANN_VISITOR_GRADIENT_VISITOR_IMPL_HPP + +// In case it hasn't been included yet. +#include "gradient_visitor.hpp" + +namespace mlpack { +namespace ann { + +//! GradientVisitor visitor class. +inline GradientVisitor::GradientVisitor(const arma::mat& input, + const arma::mat& delta) : + input(input), + delta(delta), + index(0), + hasIndex(false) +{ + /* Nothing to do here. */ +} + +inline GradientVisitor::GradientVisitor(const arma::mat& input, + const arma::mat& delta, + const size_t index) : + input(input), + delta(delta), + index(index), + hasIndex(true) +{ + /* Nothing to do here. */ +} + +template +inline void GradientVisitor::operator()(LayerType* layer) const +{ + LayerGradients(layer, layer->OutputParameter()); +} + +inline void GradientVisitor::operator()(MoreTypes layer) const +{ + layer.apply_visitor(*this); +} + +template +inline typename std::enable_if< + HasGradientCheck::value && + !HasRunCheck::value, void>::type +GradientVisitor::LayerGradients(T* layer, arma::mat& /* input */) const +{ + layer->Gradient(input, delta, layer->Gradient()); +} + +template +inline typename std::enable_if< + HasGradientCheck::value && + HasRunCheck::value, void>::type +GradientVisitor::LayerGradients(T* layer, arma::mat& /* input */) const +{ + if (!hasIndex) + { + layer->Gradient(input, delta, layer->Gradient()); + } + else + { + layer->Gradient(input, delta, layer->Gradient(), index); + } +} + +template +inline typename std::enable_if< + !HasGradientCheck::value, void>::type +GradientVisitor::LayerGradients(T* /* layer */, P& /* input */) const +{ + /* Nothing to do here. */ +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/visitor/gradient_zero_visitor.hpp b/src/mlpack/methods/ann/visitor/gradient_zero_visitor.hpp new file mode 100644 index 0000000000..3480796089 --- /dev/null +++ b/src/mlpack/methods/ann/visitor/gradient_zero_visitor.hpp @@ -0,0 +1,60 @@ +/** + * @file methods/ann/visitor/gradient_zero_visitor.hpp + * @author Marcus Edel + * + * This file provides an abstraction for the Gradient() function for different + * layers and automatically directs any parameter to the right layer type. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_GRADIENT_ZERO_VISITOR_HPP +#define MLPACK_METHODS_ANN_VISITOR_GRADIENT_ZERO_VISITOR_HPP + +#include +#include + +#include + +namespace mlpack { +namespace ann { + +/* + * GradientZeroVisitor set the gradient to zero for the given module. + */ +class GradientZeroVisitor : public boost::static_visitor +{ + public: + //! Set the gradient to zero for the given module. + GradientZeroVisitor(); + + //! Set the gradient to zero. + template + void operator()(LayerType* layer) const; + + void operator()(MoreTypes layer) const; + + private: + //! Set the gradient to zero if the module implements the Gradient() function. + template + typename std::enable_if< + HasGradientCheck::value, void>::type + LayerGradients(T* layer, arma::mat& input) const; + + //! Do not set the gradient to zero if the module doesn't implement the + //! Gradient() function. + template + typename std::enable_if< + !HasGradientCheck::value, void>::type + LayerGradients(T* layer, P& input) const; +}; + +} // namespace ann +} // namespace mlpack + +// Include implementation. +#include "gradient_zero_visitor_impl.hpp" + +#endif diff --git a/src/mlpack/methods/ann/visitor/gradient_zero_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/gradient_zero_visitor_impl.hpp new file mode 100644 index 0000000000..de39a692a6 --- /dev/null +++ b/src/mlpack/methods/ann/visitor/gradient_zero_visitor_impl.hpp @@ -0,0 +1,57 @@ +/** + * @file methods/ann/visitor/gradient_zero_visitor_impl.hpp + * @author Marcus Edel + * + * Implementation of the Gradient() function layer abstraction. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_GRADIENT_ZERO_VISITOR_IMPL_HPP +#define MLPACK_METHODS_ANN_VISITOR_GRADIENT_ZERO_VISITOR_IMPL_HPP + +// In case it hasn't been included yet. +#include "gradient_zero_visitor.hpp" + +namespace mlpack { +namespace ann { + +//! GradientZeroVisitor visitor class. +inline GradientZeroVisitor::GradientZeroVisitor() +{ + /* Nothing to do here. */ +} + +template +inline void GradientZeroVisitor::operator()(LayerType* layer) const +{ + LayerGradients(layer, layer->OutputParameter()); +} + +inline void GradientZeroVisitor::operator()(MoreTypes layer) const +{ + layer.apply_visitor(*this); +} + +template +inline typename std::enable_if< + HasGradientCheck::value, void>::type +GradientZeroVisitor::LayerGradients(T* layer, arma::mat& /* input */) const +{ + layer->Gradient().zeros(); +} + +template +inline typename std::enable_if< + !HasGradientCheck::value, void>::type +GradientZeroVisitor::LayerGradients(T* /* layer */, P& /* input */) const +{ + /* Nothing to do here. */ +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/visitor/input_shape_visitor.hpp b/src/mlpack/methods/ann/visitor/input_shape_visitor.hpp new file mode 100644 index 0000000000..c27135aae3 --- /dev/null +++ b/src/mlpack/methods/ann/visitor/input_shape_visitor.hpp @@ -0,0 +1,58 @@ +/** + * @file methods/ann/visitor/input_shape_visitor.hpp + * @author Khizir Siddiqui + * @author Nippun Sharma + * + * This file provides an abstraction for the InputShape() function for + * different layers and automatically directs any parameter to the right layer + * type. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_INPUT_SHAPE_VISITOR_HPP +#define MLPACK_METHODS_ANN_VISITOR_INPUT_SHAPE_VISITOR_HPP + +#include +#include + +#include + +namespace mlpack { +namespace ann { + +/** + * InShapeVisitor returns the input shape a Layer expects. + */ +class InShapeVisitor : public boost::static_visitor +{ + public: + //! Return the input shape of layer. + template + size_t operator()(LayerType* layer) const; + + size_t operator()(MoreTypes layer) const; + + private: + //! If the module doesn't implement the InputShape() function return 0. + template + typename std::enable_if< + !HasInputShapeCheck::value, size_t>::type + LayerInputShape(T* layer) const; + + //! If the module implements the InputShape() function returns the input shape. + template + typename std::enable_if< + HasInputShapeCheck::value, size_t>::type + LayerInputShape(T* layer) const; +}; + +} // namespace ann +} // namespace mlpack + +// Include implementation. +#include "input_shape_visitor_impl.hpp" + +#endif diff --git a/src/mlpack/methods/ann/visitor/input_shape_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/input_shape_visitor_impl.hpp new file mode 100644 index 0000000000..bda5f7b604 --- /dev/null +++ b/src/mlpack/methods/ann/visitor/input_shape_visitor_impl.hpp @@ -0,0 +1,53 @@ +/** + * @file methods/ann/visitor/input_shape_visitor_impl.hpp + * @author Khizir Siddiqui + * @author Nippun Sharma + * + * Implementation of the InputShape() function layer abstraction. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_INPUT_SHAPE_VISITOR_IMPL_HPP +#define MLPACK_METHODS_ANN_VISITOR_INPUT_SHAPE_VISITOR_IMPL_HPP + +// In case it hasn't been included yet. +#include "input_shape_visitor.hpp" + +namespace mlpack { +namespace ann { + +//! InShapeVisitor visitor class. +template +inline std::size_t InShapeVisitor::operator()(LayerType* layer) const +{ + return LayerInputShape(layer); +} + +inline std::size_t InShapeVisitor::operator()(MoreTypes layer) const +{ + return layer.apply_visitor(*this); +} + +template +inline typename std::enable_if< + !HasInputShapeCheck::value, std::size_t>::type +InShapeVisitor::LayerInputShape(T* /* layer */) const +{ + return 0; +} + +template +inline typename std::enable_if< + HasInputShapeCheck::value, std::size_t>::type +InShapeVisitor::LayerInputShape(T* layer) const +{ + return layer->InputShape(); +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/visitor/load_output_parameter_visitor.hpp b/src/mlpack/methods/ann/visitor/load_output_parameter_visitor.hpp new file mode 100644 index 0000000000..2546d52f90 --- /dev/null +++ b/src/mlpack/methods/ann/visitor/load_output_parameter_visitor.hpp @@ -0,0 +1,65 @@ +/** + * @file methods/ann/visitor/load_output_parameter_visitor.hpp + * @author Marcus Edel + * + * This file provides an abstraction for the OutputParameter() function for + * different layers and automatically directs any parameter to the right layer + * type. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_LOAD_OUTPUT_PARAMETER_VISITOR_HPP +#define MLPACK_METHODS_ANN_VISITOR_LOAD_OUTPUT_PARAMETER_VISITOR_HPP + +#include + +#include + +namespace mlpack { +namespace ann { + +/** + * LoadOutputParameterVisitor restores the output parameter using the given + * parameter set. + */ +class LoadOutputParameterVisitor : public boost::static_visitor +{ + public: + //! Restore the output parameter given a parameter set. + LoadOutputParameterVisitor(std::vector& parameter); + + //! Restore the output parameter. + template + void operator()(LayerType* layer) const; + + void operator()(MoreTypes layer) const; + + private: + //! The parameter set. + std::vector& parameter; + + //! Restore the output parameter for a module which doesn't implement the + //! Model() function. + template + typename std::enable_if< + !HasModelCheck::value, void>::type + OutputParameter(T* layer) const; + + //! Restore the output parameter for a module which implements the Model() + //! function. + template + typename std::enable_if< + HasModelCheck::value, void>::type + OutputParameter(T* layer) const; +}; + +} // namespace ann +} // namespace mlpack + +// Include implementation. +#include "load_output_parameter_visitor_impl.hpp" + +#endif diff --git a/src/mlpack/methods/ann/visitor/load_output_parameter_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/load_output_parameter_visitor_impl.hpp new file mode 100644 index 0000000000..5c384643ee --- /dev/null +++ b/src/mlpack/methods/ann/visitor/load_output_parameter_visitor_impl.hpp @@ -0,0 +1,66 @@ +/** + * @file methods/ann/visitor/load_output_parameter_visitor_impl.hpp + * @author Marcus Edel + * + * Implementation of the OutputParameter() function layer abstraction. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_LOAD_OUTPUT_PARAMETER_VISITOR_IMPL_HPP +#define MLPACK_METHODS_ANN_VISITOR_LOAD_OUTPUT_PARAMETER_VISITOR_IMPL_HPP + +// In case it hasn't been included yet. +#include "load_output_parameter_visitor.hpp" + +namespace mlpack { +namespace ann { + +//! LoadOutputParameterVisitor visitor class. +inline LoadOutputParameterVisitor::LoadOutputParameterVisitor( + std::vector& parameter) : parameter(parameter) +{ + /* Nothing to do here. */ +} + +template +inline void LoadOutputParameterVisitor::operator()(LayerType* layer) const +{ + OutputParameter(layer); +} + +inline void LoadOutputParameterVisitor::operator()(MoreTypes layer) const +{ + layer.apply_visitor(*this); +} + +template +inline typename std::enable_if< + !HasModelCheck::value, void>::type +LoadOutputParameterVisitor::OutputParameter(T* layer) const +{ + layer->OutputParameter() = parameter.back(); + parameter.pop_back(); +} + +template +inline typename std::enable_if< + HasModelCheck::value, void>::type +LoadOutputParameterVisitor::OutputParameter(T* layer) const +{ + for (size_t i = 0; i < layer->Model().size(); ++i) + { + boost::apply_visitor(LoadOutputParameterVisitor(parameter), + layer->Model()[layer->Model().size() - i - 1]); + } + + layer->OutputParameter() = parameter.back(); + parameter.pop_back(); +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/visitor/loss_visitor.hpp b/src/mlpack/methods/ann/visitor/loss_visitor.hpp new file mode 100644 index 0000000000..d9ca99763f --- /dev/null +++ b/src/mlpack/methods/ann/visitor/loss_visitor.hpp @@ -0,0 +1,71 @@ +/** + * @file methods/ann/visitor/loss_visitor.hpp + * @author Atharva Khandait + * + * This file provides an abstraction for the Loss() function for different + * layers and automatically directs any parameter to the right layer type. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_LOSS_VISITOR_HPP +#define MLPACK_METHODS_ANN_VISITOR_LOSS_VISITOR_HPP + +#include + +#include + +namespace mlpack { +namespace ann { + +/** + * LossVisitor exposes the Loss() method of the given module. + */ +class LossVisitor : public boost::static_visitor +{ + public: + //! Return the Loss. + template + double operator()(LayerType* layer) const; + + double operator()(MoreTypes layer) const; + + private: + //! Return 0 if the module doesn't implement the Loss() or Model() function. + template + typename std::enable_if< + !HasLoss::value && + !HasModelCheck::value, double>::type + LayerLoss(T* layer) const; + + //! Return the output height if the module implements the Loss() function. + template + typename std::enable_if< + HasLoss::value && + !HasModelCheck::value, double>::type + LayerLoss(T* layer) const; + + //! Return the loss if the module implements the Model() function. + template + typename std::enable_if< + !HasLoss::value && + HasModelCheck::value, double>::type + LayerLoss(T* layer) const; + + //! Return the loss if the module implements the Model() or loss() function. + template + typename std::enable_if< + HasLoss::value && + HasModelCheck::value, double>::type + LayerLoss(T* layer) const; +}; + +} // namespace ann +} // namespace mlpack + +// Include implementation. +#include "loss_visitor_impl.hpp" + +#endif diff --git a/src/mlpack/methods/ann/visitor/loss_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/loss_visitor_impl.hpp new file mode 100644 index 0000000000..ee8d6cb402 --- /dev/null +++ b/src/mlpack/methods/ann/visitor/loss_visitor_impl.hpp @@ -0,0 +1,99 @@ +/** + * @file methods/ann/visitor/loss_visitor_impl.hpp + * @author Atharva Khandait + * + * Implementation of the Loss() function layer abstraction. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_LOSS_VISITOR_IMPL_HPP +#define MLPACK_METHODS_ANN_VISITOR_LOSS_VISITOR_IMPL_HPP + +// In case it hasn't been included yet. +#include "loss_visitor.hpp" + +namespace mlpack { +namespace ann { + +//! LossVisitor visitor class. +template +inline double LossVisitor::operator()(LayerType* layer) const +{ + return LayerLoss(layer); +} + +inline double LossVisitor::operator()(MoreTypes layer) const +{ + return layer.apply_visitor(*this); +} + +template +inline typename std::enable_if< + !HasLoss::value && + !HasModelCheck::value, double>::type +LossVisitor::LayerLoss(T* /* layer */) const +{ + return 0; +} + +template +inline typename std::enable_if< + HasLoss::value && + !HasModelCheck::value, double>::type +LossVisitor::LayerLoss(T* layer) const +{ + return layer->Loss(); +} + +template +inline typename std::enable_if< + !HasLoss::value && + HasModelCheck::value, double>::type +LossVisitor::LayerLoss(T* layer) const +{ + for (size_t i = 0; i < layer->Model().size(); ++i) + { + double loss = boost::apply_visitor(LossVisitor(), + layer->Model()[layer->Model().size() - 1 - i]); + + if (loss != 0) + { + return loss; + } + } + + return 0; +} + +template +inline typename std::enable_if< + HasLoss::value && + HasModelCheck::value, double>::type +LossVisitor::LayerLoss(T* layer) const +{ + double loss = layer->Loss(); + + if (loss == 0) + { + for (size_t i = 0; i < layer->Model().size(); ++i) + { + loss = boost::apply_visitor(LossVisitor(), + layer->Model()[layer->Model().size() - 1 - i]); + + if (loss != 0) + { + return loss; + } + } + } + + return loss; +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/visitor/output_height_visitor.hpp b/src/mlpack/methods/ann/visitor/output_height_visitor.hpp new file mode 100644 index 0000000000..f14350b9ef --- /dev/null +++ b/src/mlpack/methods/ann/visitor/output_height_visitor.hpp @@ -0,0 +1,75 @@ +/** + * @file methods/ann/visitor/output_height_visitor.hpp + * @author Marcus Edel + * + * This file provides an abstraction for the OutputHeight() function for + * different layers and automatically directs any parameter to the right layer + * type. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_OUTPUT_HEIGHT_VISITOR_HPP +#define MLPACK_METHODS_ANN_VISITOR_OUTPUT_HEIGHT_VISITOR_HPP + +#include + +#include + +namespace mlpack { +namespace ann { + +/** + * OutputHeightVisitor exposes the OutputHeight() method of the given module. + */ +class OutputHeightVisitor : public boost::static_visitor +{ + public: + //! Return the output height. + template + size_t operator()(LayerType* layer) const; + + size_t operator()(MoreTypes layer) const; + + private: + //! Return 0 if the module doesn't implement the InputHeight() or Model() + //! function. + template + typename std::enable_if< + !HasInputHeight::value && + !HasModelCheck::value, size_t>::type + LayerOutputHeight(T* layer) const; + + //! Return the output height if the module implements the InputHeight() + //! function. + template + typename std::enable_if< + HasInputHeight::value && + !HasModelCheck::value, size_t>::type + LayerOutputHeight(T* layer) const; + + //! Return the output height if the module implements the Model() function. + template + typename std::enable_if< + !HasInputHeight::value && + HasModelCheck::value, size_t>::type + LayerOutputHeight(T* layer) const; + + //! Return the output height if the module implements the Model() or + //! InputHeight() function. + template + typename std::enable_if< + HasInputHeight::value && + HasModelCheck::value, size_t>::type + LayerOutputHeight(T* layer) const; +}; + +} // namespace ann +} // namespace mlpack + +// Include implementation. +#include "output_height_visitor_impl.hpp" + +#endif diff --git a/src/mlpack/methods/ann/visitor/output_height_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/output_height_visitor_impl.hpp new file mode 100644 index 0000000000..ae219da220 --- /dev/null +++ b/src/mlpack/methods/ann/visitor/output_height_visitor_impl.hpp @@ -0,0 +1,99 @@ +/** + * @file methods/ann/visitor/output_height_visitor_impl.hpp + * @author Marcus Edel + * + * Implementation of the OutputHeight() function layer abstraction. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_OUTPUT_HEIGHT_VISITOR_IMPL_HPP +#define MLPACK_METHODS_ANN_VISITOR_OUTPUT_HEIGHT_VISITOR_IMPL_HPP + +// In case it hasn't been included yet. +#include "output_height_visitor.hpp" + +namespace mlpack { +namespace ann { + +//! OutputHeightVisitor visitor class. +template +inline size_t OutputHeightVisitor::operator()(LayerType* layer) const +{ + return LayerOutputHeight(layer); +} + +inline size_t OutputHeightVisitor::operator()(MoreTypes layer) const +{ + return layer.apply_visitor(*this); +} + +template +inline typename std::enable_if< + !HasInputHeight::value && + !HasModelCheck::value, size_t>::type +OutputHeightVisitor::LayerOutputHeight(T* /* layer */) const +{ + return 0; +} + +template +inline typename std::enable_if< + HasInputHeight::value && + !HasModelCheck::value, size_t>::type +OutputHeightVisitor::LayerOutputHeight(T* layer) const +{ + return layer->OutputHeight(); +} + +template +inline typename std::enable_if< + !HasInputHeight::value && + HasModelCheck::value, size_t>::type +OutputHeightVisitor::LayerOutputHeight(T* layer) const +{ + for (size_t i = 0; i < layer->Model().size(); ++i) + { + size_t outputHeight = boost::apply_visitor(OutputHeightVisitor(), + layer->Model()[layer->Model().size() - 1 - i]); + + if (outputHeight != 0) + { + return outputHeight; + } + } + + return 0; +} + +template +inline typename std::enable_if< + HasInputHeight::value && + HasModelCheck::value, size_t>::type +OutputHeightVisitor::LayerOutputHeight(T* layer) const +{ + size_t outputHeight = layer->OutputHeight(); + + if (outputHeight == 0) + { + for (size_t i = 0; i < layer->Model().size(); ++i) + { + outputHeight = boost::apply_visitor(OutputHeightVisitor(), + layer->Model()[layer->Model().size() - 1 - i]); + + if (outputHeight != 0) + { + return outputHeight; + } + } + } + + return outputHeight; +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/visitor/output_parameter_visitor.hpp b/src/mlpack/methods/ann/visitor/output_parameter_visitor.hpp new file mode 100644 index 0000000000..6a12226464 --- /dev/null +++ b/src/mlpack/methods/ann/visitor/output_parameter_visitor.hpp @@ -0,0 +1,43 @@ +/** + * @file methods/ann/visitor/output_parameter_visitor.hpp + * @author Marcus Edel + * + * This file provides an abstraction for the OutputParameter() function for + * different layers and automatically directs any parameter to the right layer type. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_OUTPUT_PARAMETER_VISITOR_HPP +#define MLPACK_METHODS_ANN_VISITOR_OUTPUT_PARAMETER_VISITOR_HPP + +#include +#include + +#include + +namespace mlpack { +namespace ann { + +/** + * OutputParameterVisitor exposes the output parameter of the given module. + */ +class OutputParameterVisitor : public boost::static_visitor +{ + public: + //! Return the output parameter set. + template + arma::mat& operator()(LayerType* layer) const; + + arma::mat& operator()(MoreTypes layer) const; +}; + +} // namespace ann +} // namespace mlpack + +// Include implementation. +#include "output_parameter_visitor_impl.hpp" + +#endif diff --git a/src/mlpack/methods/ann/visitor/output_parameter_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/output_parameter_visitor_impl.hpp new file mode 100644 index 0000000000..5086669548 --- /dev/null +++ b/src/mlpack/methods/ann/visitor/output_parameter_visitor_impl.hpp @@ -0,0 +1,36 @@ +/** + * @file methods/ann/visitor/output_parameter_visitor_impl.hpp + * @author Marcus Edel + * + * Implementation of the OutputParameter() function layer abstraction. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_OUTPUT_PARAMETER_VISITOR_IMPL_HPP +#define MLPACK_METHODS_ANN_VISITOR_OUTPUT_PARAMETER_VISITOR_IMPL_HPP + +// In case it hasn't been included yet. +#include "output_parameter_visitor.hpp" + +namespace mlpack { +namespace ann { + +//! OutputParameterVisitor visitor class. +template +inline arma::mat& OutputParameterVisitor::operator()(LayerType *layer) const +{ + return layer->OutputParameter(); +} + +inline arma::mat& OutputParameterVisitor::operator()(MoreTypes layer) const +{ + return layer.apply_visitor(*this); +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/visitor/output_width_visitor.hpp b/src/mlpack/methods/ann/visitor/output_width_visitor.hpp new file mode 100644 index 0000000000..d6a0fa83ed --- /dev/null +++ b/src/mlpack/methods/ann/visitor/output_width_visitor.hpp @@ -0,0 +1,75 @@ +/** + * @file methods/ann/visitor/output_width_visitor.hpp + * @author Marcus Edel + * + * This file provides an abstraction for the OutputWidth() function for + * different layers and automatically directs any parameter to the right layer + * type. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_OUTPUT_WIDTH_VISITOR_HPP +#define MLPACK_METHODS_ANN_VISITOR_OUTPUT_WIDTH_VISITOR_HPP + +#include + +#include + +namespace mlpack { +namespace ann { + +/** + * OutputWidthVisitor exposes the OutputWidth() method of the given module. + */ +class OutputWidthVisitor : public boost::static_visitor +{ + public: + //! Return the output width. + template + size_t operator()(LayerType* layer) const; + + size_t operator()(MoreTypes layer) const; + + private: + //! Return 0 if the module doesn't implement the InputWidth() or Model() + //! function. + template + typename std::enable_if< + !HasInputWidth::value && + !HasModelCheck::value, size_t>::type + LayerOutputWidth(T* layer) const; + + //! Return the output width if the module implements the InputWidth() + //! function. + template + typename std::enable_if< + HasInputWidth::value && + !HasModelCheck::value, size_t>::type + LayerOutputWidth(T* layer) const; + + //! Return the output width if the module implements the Model() function. + template + typename std::enable_if< + !HasInputWidth::value && + HasModelCheck::value, size_t>::type + LayerOutputWidth(T* layer) const; + + //! Return the output width if the module implements the Model() or + //! InputWidth() function. + template + typename std::enable_if< + HasInputWidth::value && + HasModelCheck::value, size_t>::type + LayerOutputWidth(T* layer) const; +}; + +} // namespace ann +} // namespace mlpack + +// Include implementation. +#include "output_width_visitor_impl.hpp" + +#endif diff --git a/src/mlpack/methods/ann/visitor/output_width_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/output_width_visitor_impl.hpp new file mode 100644 index 0000000000..3d68087d83 --- /dev/null +++ b/src/mlpack/methods/ann/visitor/output_width_visitor_impl.hpp @@ -0,0 +1,99 @@ +/** + * @file methods/ann/visitor/output_width_visitor_impl.hpp + * @author Marcus Edel + * + * Implementation of the OutputWidth() function layer abstraction. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_OUTPUT_WIDTH_VISITOR_IMPL_HPP +#define MLPACK_METHODS_ANN_VISITOR_OUTPUT_WIDTH_VISITOR_IMPL_HPP + +// In case it hasn't been included yet. +#include "output_width_visitor.hpp" + +namespace mlpack { +namespace ann { + +//! OutputWidthVisitor visitor class. +template +inline size_t OutputWidthVisitor::operator()(LayerType* layer) const +{ + return LayerOutputWidth(layer); +} + +inline size_t OutputWidthVisitor::operator()(MoreTypes layer) const +{ + return layer.apply_visitor(*this); +} + +template +inline typename std::enable_if< + !HasInputWidth::value && + !HasModelCheck::value, size_t>::type +OutputWidthVisitor::LayerOutputWidth(T* /* layer */) const +{ + return 0; +} + +template +inline typename std::enable_if< + HasInputWidth::value && + !HasModelCheck::value, size_t>::type +OutputWidthVisitor::LayerOutputWidth(T* layer) const +{ + return layer->OutputWidth(); +} + +template +inline typename std::enable_if< + !HasInputWidth::value && + HasModelCheck::value, size_t>::type +OutputWidthVisitor::LayerOutputWidth(T* layer) const +{ + for (size_t i = 0; i < layer->Model().size(); ++i) + { + size_t outputWidth = boost::apply_visitor(OutputWidthVisitor(), + layer->Model()[layer->Model().size() - 1 - i]); + + if (outputWidth != 0) + { + return outputWidth; + } + } + + return 0; +} + +template +inline typename std::enable_if< + HasInputWidth::value && + HasModelCheck::value, size_t>::type +OutputWidthVisitor::LayerOutputWidth(T* layer) const +{ + size_t outputWidth = layer->OutputWidth(); + + if (outputWidth == 0) + { + for (size_t i = 0; i < layer->Model().size(); ++i) + { + outputWidth = boost::apply_visitor(OutputWidthVisitor(), + layer->Model()[layer->Model().size() - 1 - i]); + + if (outputWidth != 0) + { + return outputWidth; + } + } + } + + return outputWidth; +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/visitor/parameters_set_visitor.hpp b/src/mlpack/methods/ann/visitor/parameters_set_visitor.hpp new file mode 100644 index 0000000000..c8cba59bb2 --- /dev/null +++ b/src/mlpack/methods/ann/visitor/parameters_set_visitor.hpp @@ -0,0 +1,64 @@ +/** + * @file methods/ann/visitor/parameters_set_visitor.hpp + * @author Marcus Edel + * + * This file provides an abstraction for the Parameters() function for different + * layers and automatically directs any parameter to the right layer type. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_PARAMETERS_SET_VISITOR_HPP +#define MLPACK_METHODS_ANN_VISITOR_PARAMETERS_SET_VISITOR_HPP + +#include +#include + +#include + +namespace mlpack { +namespace ann { + +/** + * ParametersSetVisitor update the parameters set using the given matrix. + */ +class ParametersSetVisitor : public boost::static_visitor +{ + public: + //! Update the parameters set given the parameters matrix. + ParametersSetVisitor(arma::mat& parameters); + + //! Update the parameters set. + template + void operator()(LayerType *layer) const; + + void operator()(MoreTypes layer) const; + + private: + //! The parameters set. + arma::mat& parameters; + + //! Do not update the parameters set if the module doesn't implement the + //! Parameters() function. + template + typename std::enable_if< + !HasParametersCheck::value, void>::type + LayerParameters(T* layer, P& output) const; + + //! Update the parameters set if the module implements the Parameters() + //! function. + template + typename std::enable_if< + HasParametersCheck::value, void>::type + LayerParameters(T* layer, P& output) const; +}; + +} // namespace ann +} // namespace mlpack + +// Include implementation. +#include "parameters_set_visitor_impl.hpp" + +#endif diff --git a/src/mlpack/methods/ann/visitor/parameters_set_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/parameters_set_visitor_impl.hpp new file mode 100644 index 0000000000..820a5ab464 --- /dev/null +++ b/src/mlpack/methods/ann/visitor/parameters_set_visitor_impl.hpp @@ -0,0 +1,58 @@ +/** + * @file methods/ann/visitor/parameters_set_visitor_impl.hpp + * @author Marcus Edel + * + * Implementation of the Parameters() function layer abstraction. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_PARAMETERS_SET_VISITOR_IMPL_HPP +#define MLPACK_METHODS_ANN_VISITOR_PARAMETERS_SET_VISITOR_IMPL_HPP + +// In case it hasn't been included yet. +#include "parameters_set_visitor.hpp" + +namespace mlpack { +namespace ann { + +//! ParametersSetVisitor visitor class. +inline ParametersSetVisitor::ParametersSetVisitor(arma::mat& parameters) : + parameters(parameters) +{ + /* Nothing to do here. */ +} + +template +inline void ParametersSetVisitor::operator()(LayerType *layer) const +{ + LayerParameters(layer, layer->OutputParameter()); +} + +inline void ParametersSetVisitor::operator()(MoreTypes layer) const +{ + layer.apply_visitor(*this); +} + +template +inline typename std::enable_if< + !HasParametersCheck::value, void>::type +ParametersSetVisitor::LayerParameters(T* /* layer */, P& /* output */) const +{ + /* Nothing to do here. */ +} + +template +inline typename std::enable_if< + HasParametersCheck::value, void>::type +ParametersSetVisitor::LayerParameters(T* layer, P& /* output */) const +{ + layer->Parameters() = parameters; +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/visitor/parameters_visitor.hpp b/src/mlpack/methods/ann/visitor/parameters_visitor.hpp new file mode 100644 index 0000000000..36a0b30bf5 --- /dev/null +++ b/src/mlpack/methods/ann/visitor/parameters_visitor.hpp @@ -0,0 +1,64 @@ +/** + * @file methods/ann/visitor/parameters_visitor.hpp + * @author Marcus Edel + * + * This file provides an abstraction for the Parameters() function for different + * layers and automatically directs any parameter to the right layer type. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_PARAMETERS_VISITOR_HPP +#define MLPACK_METHODS_ANN_VISITOR_PARAMETERS_VISITOR_HPP + +#include +#include + +#include + +namespace mlpack { +namespace ann { + +/** + * ParametersVisitor exposes the parameters set of the given module and stores + * the parameters set into the given matrix. + */ +class ParametersVisitor : public boost::static_visitor +{ + public: + //! Store the parameters set into the given parameters matrix. + ParametersVisitor(arma::mat& parameters); + + //! Set the parameters set. + template + void operator()(LayerType* layer) const; + + void operator()(MoreTypes layer) const; + + private: + //! The parameters set. + arma::mat& parameters; + + //! Do not set the parameters set if the module doesn't implement the + //! Parameters() function. + template + typename std::enable_if< + !HasParametersCheck::value, void>::type + LayerParameters(T* layer, P& output) const; + + //! Set the parameters set if the module implements the Parameters() function. + template + typename std::enable_if< + HasParametersCheck::value, void>::type + LayerParameters(T* layer, P& output) const; +}; + +} // namespace ann +} // namespace mlpack + +// Include implementation. +#include "parameters_visitor_impl.hpp" + +#endif diff --git a/src/mlpack/methods/ann/visitor/parameters_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/parameters_visitor_impl.hpp new file mode 100644 index 0000000000..c58604c995 --- /dev/null +++ b/src/mlpack/methods/ann/visitor/parameters_visitor_impl.hpp @@ -0,0 +1,58 @@ +/** + * @file methods/ann/visitor/parameters_visitor_impl.hpp + * @author Marcus Edel + * + * Implementation of the Parameters() function layer abstraction. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_PARAMETERS_VISITOR_IMPL_HPP +#define MLPACK_METHODS_ANN_VISITOR_PARAMETERS_VISITOR_IMPL_HPP + +// In case it hasn't been included yet. +#include "parameters_visitor.hpp" + +namespace mlpack { +namespace ann { + +//! ParametersVisitor visitor class. +inline ParametersVisitor::ParametersVisitor(arma::mat& parameters) : + parameters(parameters) +{ + /* Nothing to do here. */ +} + +template +inline void ParametersVisitor::operator()(LayerType *layer) const +{ + LayerParameters(layer, layer->OutputParameter()); +} + +inline void ParametersVisitor::operator()(MoreTypes layer) const +{ + layer.apply_visitor(*this); +} + +template +inline typename std::enable_if< + !HasParametersCheck::value, void>::type +ParametersVisitor::LayerParameters(T* /* layer */, P& /* output */) const +{ + /* Nothing to do here. */ +} + +template +inline typename std::enable_if< + HasParametersCheck::value, void>::type +ParametersVisitor::LayerParameters(T* layer, P& /* output */) const +{ + parameters = layer->Parameters(); +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/visitor/reset_cell_visitor.hpp b/src/mlpack/methods/ann/visitor/reset_cell_visitor.hpp new file mode 100644 index 0000000000..ba88ce161b --- /dev/null +++ b/src/mlpack/methods/ann/visitor/reset_cell_visitor.hpp @@ -0,0 +1,62 @@ +/** + * @file methods/ann/visitor/reset_cell_visitor.hpp + * @author Sumedh Ghaisas + * + * Boost static visitor abstraction for calling ResetCell function on RNN cells. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_RESET_CELL_VISITOR_HPP +#define MLPACK_METHODS_ANN_VISITOR_RESET_CELL_VISITOR_HPP + +#include +#include + +#include + +namespace mlpack { +namespace ann { + +/** + * ResetCellVisitor executes the ResetCell() function. + */ +class ResetCellVisitor : public boost::static_visitor +{ + public: + //! Reset the cell using the given size. + ResetCellVisitor(const size_t size); + + //! Execute the ResetCell() function. + template + void operator()(LayerType* layer) const; + + void operator()(MoreTypes layer) const; + + private: + size_t size; + + //! Execute the ResetCell() function for a module which implements + //! the ResetCell() function. + template + typename std::enable_if< + HasResetCellCheck::value, void>::type + ResetCell(T* layer) const; + + //! Do not execute the Reset() function for a module which doesn't implement + // the Reset() or Model() function. + template + typename std::enable_if< + !HasResetCellCheck::value, void>::type + ResetCell(T* layer) const; +}; + +} // namespace ann +} // namespace mlpack + +// Include implementation. +#include "reset_cell_visitor_impl.hpp" + +#endif diff --git a/src/mlpack/methods/ann/visitor/reset_cell_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/reset_cell_visitor_impl.hpp new file mode 100644 index 0000000000..c687a553c0 --- /dev/null +++ b/src/mlpack/methods/ann/visitor/reset_cell_visitor_impl.hpp @@ -0,0 +1,58 @@ +/** + * @file methods/ann/visitor/reset_cell_visitor_impl.hpp + * @author Sumedh Ghaisas + * + * Implementation of the ResetCell() function layer abstraction. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_RESET_CELL_VISITOR_IMPL_HPP +#define MLPACK_METHODS_ANN_VISITOR_RESET_CELL_VISITOR_IMPL_HPP + +// In case it hasn't been included yet. +#include "reset_cell_visitor.hpp" + +namespace mlpack { +namespace ann { + +//! ResetVisitor visitor class. +inline ResetCellVisitor::ResetCellVisitor(const size_t size) : size(size) +{ + /* Nothing to do here. */ +} + +//! ResetVisitor visitor class. +template +inline void ResetCellVisitor::operator()(LayerType* layer) const +{ + ResetCell(layer); +} + +inline void ResetCellVisitor::operator()(MoreTypes layer) const +{ + layer.apply_visitor(*this); +} + +template +inline typename std::enable_if< + HasResetCellCheck::value, void>::type +ResetCellVisitor::ResetCell(T* layer) const +{ + layer->ResetCell(size); +} + +template +inline typename std::enable_if< + !HasResetCellCheck::value, void>::type +ResetCellVisitor::ResetCell(T* /* layer */) const +{ + /* Nothing to do here. */ +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/visitor/reset_visitor.hpp b/src/mlpack/methods/ann/visitor/reset_visitor.hpp new file mode 100644 index 0000000000..72545cf53d --- /dev/null +++ b/src/mlpack/methods/ann/visitor/reset_visitor.hpp @@ -0,0 +1,75 @@ +/** + * @file methods/ann/visitor/reset_visitor.hpp + * @author Marcus Edel + * + * This file provides an abstraction for the Reset() function for different + * layers and automatically directs any parameter to the right layer type. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_RESET_VISITOR_HPP +#define MLPACK_METHODS_ANN_VISITOR_RESET_VISITOR_HPP + +#include + +#include + +namespace mlpack { +namespace ann { + +/** + * ResetVisitor executes the Reset() function. + */ +class ResetVisitor : public boost::static_visitor +{ + public: + //! Execute the Reset() function. + template + void operator()(LayerType* layer) const; + + void operator()(MoreTypes layer) const; + + private: + //! Execute the Reset() function for a module which implements the Reset() + //! function. + template + typename std::enable_if< + HasResetCheck::value && + !HasModelCheck::value, void>::type + ResetParameter(T* layer) const; + + //! Execute the Reset() function for a module which implements the Model() + //! function. + template + typename std::enable_if< + !HasResetCheck::value && + HasModelCheck::value, void>::type + ResetParameter(T* layer) const; + + //! Execute the Reset() function for a module which implements the Reset() + //! and Model() function. + template + typename std::enable_if< + HasResetCheck::value && + HasModelCheck::value, void>::type + ResetParameter(T* layer) const; + + //! Do not execute the Reset() function for a module which doesn't implement + // the Reset() or Model() function. + template + typename std::enable_if< + !HasResetCheck::value && + !HasModelCheck::value, void>::type + ResetParameter(T* layer) const; +}; + +} // namespace ann +} // namespace mlpack + +// Include implementation. +#include "reset_visitor_impl.hpp" + +#endif diff --git a/src/mlpack/methods/ann/visitor/reset_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/reset_visitor_impl.hpp new file mode 100644 index 0000000000..9754c9baa5 --- /dev/null +++ b/src/mlpack/methods/ann/visitor/reset_visitor_impl.hpp @@ -0,0 +1,80 @@ +/** + * @file methods/ann/visitor/reset_visitor_impl.hpp + * @author Marcus Edel + * + * Implementation of the Reset() function layer abstraction. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_RESET_VISITOR_IMPL_HPP +#define MLPACK_METHODS_ANN_VISITOR_RESET_VISITOR_IMPL_HPP + +// In case it hasn't been included yet. +#include "reset_visitor.hpp" + +namespace mlpack { +namespace ann { + +//! ResetVisitor visitor class. +template +inline void ResetVisitor::operator()(LayerType* layer) const +{ + ResetParameter(layer); +} + +inline void ResetVisitor::operator()(MoreTypes layer) const +{ + layer.apply_visitor(*this); +} + +template +inline typename std::enable_if< + HasResetCheck::value && + !HasModelCheck::value, void>::type +ResetVisitor::ResetParameter(T* layer) const +{ + layer->Reset(); +} + +template +inline typename std::enable_if< + !HasResetCheck::value && + HasModelCheck::value, void>::type +ResetVisitor::ResetParameter(T* layer) const +{ + for (size_t i = 0; i < layer->Model().size(); ++i) + { + boost::apply_visitor(ResetVisitor(), layer->Model()[i]); + } +} + +template +inline typename std::enable_if< + HasResetCheck::value && + HasModelCheck::value, void>::type +ResetVisitor::ResetParameter(T* layer) const +{ + for (size_t i = 0; i < layer->Model().size(); ++i) + { + boost::apply_visitor(ResetVisitor(), layer->Model()[i]); + } + + layer->Reset(); +} + +template +inline typename std::enable_if< + !HasResetCheck::value && + !HasModelCheck::value, void>::type +ResetVisitor::ResetParameter(T* /* layer */) const +{ + /* Nothing to do here. */ +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/visitor/reward_set_visitor.hpp b/src/mlpack/methods/ann/visitor/reward_set_visitor.hpp new file mode 100644 index 0000000000..a4c6301d00 --- /dev/null +++ b/src/mlpack/methods/ann/visitor/reward_set_visitor.hpp @@ -0,0 +1,81 @@ +/** + * @file methods/ann/visitor/reward_set_visitor.hpp + * @author Marcus Edel + * + * This file provides an abstraction for the Reward() function for different + * layers and automatically directs any parameter to the right layer type. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_REWARD_SET_VISITOR_HPP +#define MLPACK_METHODS_ANN_VISITOR_REWARD_SET_VISITOR_HPP + +#include + +#include + +namespace mlpack { +namespace ann { + +/** + * RewardSetVisitor set the reward parameter given the reward value. + */ +class RewardSetVisitor : public boost::static_visitor +{ + public: + //! Set the reward parameter given the reward value. + RewardSetVisitor(const double reward); + + //! Set the reward parameter. + template + void operator()(LayerType* layer) const; + + void operator()(MoreTypes layer) const; + + private: + //! The reward value. + const double reward; + + //! Set the deterministic parameter if the module implements the + //! Deterministic() and Model() function. + template + typename std::enable_if< + HasRewardCheck::value && + HasModelCheck::value, void>::type + LayerReward(T* layer) const; + + //! Set the deterministic parameter if the module implements the + //! Model() function. + template + typename std::enable_if< + !HasRewardCheck::value && + HasModelCheck::value, void>::type + LayerReward(T* layer) const; + + //! Set the deterministic parameter if the module implements the + //! Deterministic() function. + template + typename std::enable_if< + HasRewardCheck::value && + !HasModelCheck::value, void>::type + LayerReward(T* layer) const; + + //! Do not set the deterministic parameter if the module doesn't implement the + //! Deterministic() or Model() function. + template + typename std::enable_if< + !HasRewardCheck::value && + !HasModelCheck::value, void>::type + LayerReward(T* layer) const; +}; + +} // namespace ann +} // namespace mlpack + +// Include implementation. +#include "reward_set_visitor_impl.hpp" + +#endif diff --git a/src/mlpack/methods/ann/visitor/reward_set_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/reward_set_visitor_impl.hpp new file mode 100644 index 0000000000..8bc0eb5a21 --- /dev/null +++ b/src/mlpack/methods/ann/visitor/reward_set_visitor_impl.hpp @@ -0,0 +1,87 @@ +/** + * @file methods/ann/visitor/reward_set_visitor_impl.hpp + * @author Marcus Edel + * + * Implementation of the Reward() function layer abstraction. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_REWARD_SET_VISITOR_IMPL_HPP +#define MLPACK_METHODS_ANN_VISITOR_REWARD_SET_VISITOR_IMPL_HPP + +// In case it hasn't been included yet. +#include "reward_set_visitor.hpp" + +namespace mlpack { +namespace ann { + +//! RewardSetVisitor visitor class. +inline RewardSetVisitor::RewardSetVisitor(const double reward) : reward(reward) +{ + /* Nothing to do here. */ +} + +template +inline void RewardSetVisitor::operator()(LayerType* layer) const +{ + LayerReward(layer); +} + +inline void RewardSetVisitor::operator()(MoreTypes layer) const +{ + layer.apply_visitor(*this); +} + +template +inline typename std::enable_if< + HasRewardCheck::value && + HasModelCheck::value, void>::type +RewardSetVisitor::LayerReward(T* layer) const +{ + layer->Reward() = reward; + + for (size_t i = 0; i < layer->Model().size(); ++i) + { + boost::apply_visitor(RewardSetVisitor(reward), + layer->Model()[i]); + } +} + +template +inline typename std::enable_if< + !HasRewardCheck::value && + HasModelCheck::value, void>::type +RewardSetVisitor::LayerReward(T* layer) const +{ + for (size_t i = 0; i < layer->Model().size(); ++i) + { + boost::apply_visitor(RewardSetVisitor(reward), + layer->Model()[i]); + } +} + +template +inline typename std::enable_if< + HasRewardCheck::value && + !HasModelCheck::value, void>::type +RewardSetVisitor::LayerReward(T* layer) const +{ + layer->Reward() = reward; +} + +template +inline typename std::enable_if< + !HasRewardCheck::value && + !HasModelCheck::value, void>::type +RewardSetVisitor::LayerReward(T* /* input */) const +{ + /* Nothing to do here. */ +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/visitor/run_set_visitor.hpp b/src/mlpack/methods/ann/visitor/run_set_visitor.hpp new file mode 100644 index 0000000000..b993ea07f2 --- /dev/null +++ b/src/mlpack/methods/ann/visitor/run_set_visitor.hpp @@ -0,0 +1,83 @@ +/** + * @file methods/ann/visitor/run_set_visitor.hpp + * @author Saksham Bansal + * + * This file provides an abstraction for the Run() function for + * different layers and automatically directs any parameter to the right layer + * type. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_RUN_SET_VISITOR_HPP +#define MLPACK_METHODS_ANN_VISITOR_RUN_SET_VISITOR_HPP + +#include + +#include + +namespace mlpack { +namespace ann { + +/** + * RunSetVisitor set the run parameter given the + * run value. + */ +class RunSetVisitor : public boost::static_visitor +{ + public: + //! Set the run parameter given the current run value. + RunSetVisitor(const bool run = true); + + //! Set the run parameter. + template + void operator()(LayerType* layer) const; + + void operator()(MoreTypes layer) const; + + private: + //! The run parameter. + const bool run; + + //! Set the run parameter if the module implements the + //! Run() and Model() function. + template + typename std::enable_if< + HasRunCheck::value && + HasModelCheck::value, void>::type + LayerRun(T* layer) const; + + //! Set the run parameter if the module implements the + //! Model() function. + template + typename std::enable_if< + !HasRunCheck::value && + HasModelCheck::value, void>::type + LayerRun(T* layer) const; + + //! Set the run parameter if the module implements the + //! Run() function. + template + typename std::enable_if< + HasRunCheck::value && + !HasModelCheck::value, void>::type + LayerRun(T* layer) const; + + //! Do not set the run parameter if the module doesn't implement the + //! Run() or Model() function. + template + typename std::enable_if< + !HasRunCheck::value && + !HasModelCheck::value, void>::type + LayerRun(T* layer) const; +}; + +} // namespace ann +} // namespace mlpack + +// Include implementation. +#include "run_set_visitor_impl.hpp" + +#endif diff --git a/src/mlpack/methods/ann/visitor/run_set_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/run_set_visitor_impl.hpp new file mode 100644 index 0000000000..5e0ece0217 --- /dev/null +++ b/src/mlpack/methods/ann/visitor/run_set_visitor_impl.hpp @@ -0,0 +1,88 @@ +/** + * @file methods/ann/visitor/run_set_visitor_impl.hpp + * @author Saksham Bansal + * + * Implementation of the Run() function layer abstraction. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_RUN_SET_VISITOR_IMPL_HPP +#define MLPACK_METHODS_ANN_VISITOR_RUN_SET_VISITOR_IMPL_HPP + +// In case it hasn't been included yet. +#include "run_set_visitor.hpp" + +namespace mlpack { +namespace ann { + +//! RunSetVisitor visitor class. +inline RunSetVisitor::RunSetVisitor( + const bool run) : run(run) +{ + /* Nothing to do here. */ +} + +template +inline void RunSetVisitor::operator()(LayerType* layer) const +{ + LayerRun(layer); +} + +inline void RunSetVisitor::operator()(MoreTypes layer) const +{ + layer.apply_visitor(*this); +} + +template +inline typename std::enable_if< + HasRunCheck::value && + HasModelCheck::value, void>::type +RunSetVisitor::LayerRun(T* layer) const +{ + layer->Run() = run; + + for (size_t i = 0; i < layer->Model().size(); ++i) + { + boost::apply_visitor(RunSetVisitor(run), + layer->Model()[i]); + } +} + +template +inline typename std::enable_if< + !HasRunCheck::value && + HasModelCheck::value, void>::type +RunSetVisitor::LayerRun(T* layer) const +{ + for (size_t i = 0; i < layer->Model().size(); ++i) + { + boost::apply_visitor(RunSetVisitor(run), + layer->Model()[i]); + } +} + +template +inline typename std::enable_if< + HasRunCheck::value && + !HasModelCheck::value, void>::type +RunSetVisitor::LayerRun(T* layer) const +{ + layer->Run() = run; +} + +template +inline typename std::enable_if< + !HasRunCheck::value && + !HasModelCheck::value, void>::type +RunSetVisitor::LayerRun(T* /* input */) const +{ + /* Nothing to do here. */ +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/visitor/save_output_parameter_visitor.hpp b/src/mlpack/methods/ann/visitor/save_output_parameter_visitor.hpp new file mode 100644 index 0000000000..2ff0c9ea4e --- /dev/null +++ b/src/mlpack/methods/ann/visitor/save_output_parameter_visitor.hpp @@ -0,0 +1,64 @@ +/** + * @file methods/ann/visitor/save_output_parameter_visitor.hpp + * @author Marcus Edel + * + * This file provides an abstraction for the OutputParameter() function for + * different layers and automatically directs any parameter to the right layer type. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_SAVE_OUTPUT_PARAMETER_VISITOR_HPP +#define MLPACK_METHODS_ANN_VISITOR_SAVE_OUTPUT_PARAMETER_VISITOR_HPP + +#include + +#include + +namespace mlpack { +namespace ann { + +/** + * SaveOutputParameterVisitor saves the output parameter into the given + * parameter set. + */ +class SaveOutputParameterVisitor : public boost::static_visitor +{ + public: + //! Save the output parameter into the given parameter set. + SaveOutputParameterVisitor(std::vector& parameter); + + //! Save the output parameter. + template + void operator()(LayerType* layer) const; + + void operator()(MoreTypes layer) const; + + private: + //! The parameter set. + std::vector& parameter; + + //! Save the output parameter for a module which doesn't implement the + //! Model() function. + template + typename std::enable_if< + !HasModelCheck::value, void>::type + OutputParameter(T* layer) const; + + //! Save the output parameter for a module which implements the Model() + //! function. + template + typename std::enable_if< + HasModelCheck::value, void>::type + OutputParameter(T* layer) const; +}; + +} // namespace ann +} // namespace mlpack + +// Include implementation. +#include "save_output_parameter_visitor_impl.hpp" + +#endif diff --git a/src/mlpack/methods/ann/visitor/save_output_parameter_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/save_output_parameter_visitor_impl.hpp new file mode 100644 index 0000000000..cc3559c165 --- /dev/null +++ b/src/mlpack/methods/ann/visitor/save_output_parameter_visitor_impl.hpp @@ -0,0 +1,64 @@ +/** + * @file methods/ann/visitor/save_output_parameter_visitor_impl.hpp + * @author Marcus Edel + * + * Implementation of the OutputParameter() function layer abstraction. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_SAVE_OUTPUT_PARAMETER_VISITOR_IMPL_HPP +#define MLPACK_METHODS_ANN_VISITOR_SAVE_OUTPUT_PARAMETER_VISITOR_IMPL_HPP + +// In case it hasn't been included yet. +#include "load_output_parameter_visitor.hpp" + +namespace mlpack { +namespace ann { + +//! SaveOutputParameterVisitor visitor class. +inline SaveOutputParameterVisitor::SaveOutputParameterVisitor( + std::vector& parameter) : parameter(parameter) +{ + /* Nothing to do here. */ +} + +template +inline void SaveOutputParameterVisitor::operator()(LayerType* layer) const +{ + OutputParameter(layer); +} + +inline void SaveOutputParameterVisitor::operator()(MoreTypes layer) const +{ + layer.apply_visitor(*this); +} + +template +inline typename std::enable_if< + !HasModelCheck::value, void>::type +SaveOutputParameterVisitor::OutputParameter(T* layer) const +{ + parameter.push_back(layer->OutputParameter()); +} + +template +inline typename std::enable_if< + HasModelCheck::value, void>::type +SaveOutputParameterVisitor::OutputParameter(T* layer) const +{ + parameter.push_back(layer->OutputParameter()); + + for (size_t i = 0; i < layer->Model().size(); ++i) + { + boost::apply_visitor(SaveOutputParameterVisitor(parameter), + layer->Model()[i]); + } +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/visitor/set_input_height_visitor.hpp b/src/mlpack/methods/ann/visitor/set_input_height_visitor.hpp new file mode 100644 index 0000000000..7e47e4ca13 --- /dev/null +++ b/src/mlpack/methods/ann/visitor/set_input_height_visitor.hpp @@ -0,0 +1,84 @@ +/** + * @file methods/ann/visitor/set_input_height_visitor.hpp + * @author Marcus Edel + * + * This file provides an abstraction for the InputHeight() function for different + * layers and automatically directs any parameter to the right layer type. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_SET_INPUT_HEIGHT_VISITOR_HPP +#define MLPACK_METHODS_ANN_VISITOR_SET_INPUT_HEIGHT_VISITOR_HPP + +#include + +#include + +namespace mlpack { +namespace ann { + +/** + * SetInputHeightVisitor updates the input height parameter with the given input + * height. + */ +class SetInputHeightVisitor : public boost::static_visitor +{ + public: + //! Update the input height parameter with the given input height. + SetInputHeightVisitor(const size_t inputHeight = 0, const bool reset = false); + + //! Update the input height parameter. + template + bool operator()(LayerType* layer) const; + + bool operator()(MoreTypes layer) const; + + private: + //! The input height parameter. + size_t inputHeight; + + //! If set reset the height parameter if already set. + bool reset; + + //! Do nothing if the module doesn't implement the InputHeight() or Model() + //! function. + template + typename std::enable_if< + !HasInputHeight::value && + !HasModelCheck::value, bool>::type + LayerInputHeight(T* layer) const; + + //! Update the input height if the module implements the InputHeight() + //! function. + template + typename std::enable_if< + HasInputHeight::value && + !HasModelCheck::value, bool>::type + LayerInputHeight(T* layer) const; + + //! Update the input height if the module implements the Model() function. + template + typename std::enable_if< + !HasInputHeight::value && + HasModelCheck::value, bool>::type + LayerInputHeight(T* layer) const; + + //! Update the input height if the module implements the InputHeight() or + //! Model() function. + template + typename std::enable_if< + HasInputHeight::value && + HasModelCheck::value, bool>::type + LayerInputHeight(T* layer) const; +}; + +} // namespace ann +} // namespace mlpack + +// Include implementation. +#include "set_input_height_visitor_impl.hpp" + +#endif diff --git a/src/mlpack/methods/ann/visitor/set_input_height_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/set_input_height_visitor_impl.hpp new file mode 100644 index 0000000000..a5a3f8c0f7 --- /dev/null +++ b/src/mlpack/methods/ann/visitor/set_input_height_visitor_impl.hpp @@ -0,0 +1,102 @@ +/** + * @file methods/ann/visitor/set_input_height_visitor_impl.hpp + * @author Marcus Edel + * + * Implementation of the InputHeight() function layer abstraction. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_SET_INPUT_HEIGHT_VISITOR_IMPL_HPP +#define MLPACK_METHODS_ANN_VISITOR_SET_INPUT_HEIGHT_VISITOR_IMPL_HPP + +// In case it hasn't been included yet. +#include "set_input_height_visitor.hpp" + +namespace mlpack { +namespace ann { + +//! SetInputHeightVisitor visitor class. +inline SetInputHeightVisitor::SetInputHeightVisitor(const size_t inputHeight, + const bool reset) : + inputHeight(inputHeight), + reset(reset) +{ + /* Nothing to do here. */ +} + +template +inline bool SetInputHeightVisitor::operator()(LayerType* layer) const +{ + return LayerInputHeight(layer); +} + +inline bool SetInputHeightVisitor::operator()(MoreTypes layer) const +{ + return layer.apply_visitor(*this); +} + +template +inline typename std::enable_if< + !HasInputHeight::value && + !HasModelCheck::value, bool>::type +SetInputHeightVisitor::LayerInputHeight(T* /* layer */) const +{ + return false; +} + +template +inline typename std::enable_if< + HasInputHeight::value && + !HasModelCheck::value, bool>::type +SetInputHeightVisitor::LayerInputHeight(T* layer) const +{ + if (layer->InputHeight() == 0 || reset) + { + layer->InputHeight() = inputHeight; + } + + return true; +} + +template +inline typename std::enable_if< + !HasInputHeight::value && + HasModelCheck::value, bool>::type +SetInputHeightVisitor::LayerInputHeight(T* layer) const +{ + for (size_t i = 0; i < layer->Model().size(); ++i) + { + boost::apply_visitor(SetInputHeightVisitor(inputHeight, reset), + layer->Model()[i]); + } + + return true; +} + +template +inline typename std::enable_if< + HasInputHeight::value && + HasModelCheck::value, bool>::type +SetInputHeightVisitor::LayerInputHeight(T* layer) const +{ + if (layer->InputHeight() == 0 || reset) + { + layer->InputHeight() = inputHeight; + } + + for (size_t i = 0; i < layer->Model().size(); ++i) + { + boost::apply_visitor(SetInputHeightVisitor(inputHeight, reset), + layer->Model()[i]); + } + + return true; +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/visitor/set_input_width_visitor.hpp b/src/mlpack/methods/ann/visitor/set_input_width_visitor.hpp new file mode 100644 index 0000000000..f7fbd6f4e2 --- /dev/null +++ b/src/mlpack/methods/ann/visitor/set_input_width_visitor.hpp @@ -0,0 +1,83 @@ +/** + * @file methods/ann/visitor/set_input_width_visitor.hpp + * @author Marcus Edel + * + * This file provides an abstraction for the InputWidth() function for different + * layers and automatically directs any parameter to the right layer type. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_SET_INPUT_WIDTH_VISITOR_HPP +#define MLPACK_METHODS_ANN_VISITOR_SET_INPUT_WIDTH_VISITOR_HPP + +#include + +#include + +namespace mlpack { +namespace ann { + +/** + * SetInputWidthVisitor updates the input width parameter with the given input + * width. + */ +class SetInputWidthVisitor : public boost::static_visitor +{ + public: + //! Update the input width parameter with the given input width. + SetInputWidthVisitor(const size_t inputWidth = 0, const bool reset = false); + + //! Update the input width parameter. + template + bool operator()(LayerType* layer) const; + + bool operator()(MoreTypes layer) const; + + private: + //! The input width parameter. + size_t inputWidth; + + //! If set reset the height parameter if already set. + bool reset; + + //! Do nothing if the module doesn't implement the InputWidth() or Model() + //! function. + template + typename std::enable_if< + !HasInputWidth::value && + !HasModelCheck::value, bool>::type + LayerInputWidth(T* layer) const; + + //! Update the input width if the module implements the InputWidth() function. + template + typename std::enable_if< + HasInputWidth::value && + !HasModelCheck::value, bool>::type + LayerInputWidth(T* layer) const; + + //! Update the input width if the module implements the Model() function. + template + typename std::enable_if< + !HasInputWidth::value && + HasModelCheck::value, bool>::type + LayerInputWidth(T* layer) const; + + //! Update the input width if the module implements the InputWidth() or + //! Model() function. + template + typename std::enable_if< + HasInputWidth::value && + HasModelCheck::value, bool>::type + LayerInputWidth(T* layer) const; +}; + +} // namespace ann +} // namespace mlpack + +// Include implementation. +#include "set_input_width_visitor_impl.hpp" + +#endif diff --git a/src/mlpack/methods/ann/visitor/set_input_width_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/set_input_width_visitor_impl.hpp new file mode 100644 index 0000000000..56224ed009 --- /dev/null +++ b/src/mlpack/methods/ann/visitor/set_input_width_visitor_impl.hpp @@ -0,0 +1,102 @@ +/** + * @file methods/ann/visitor/set_input_width_visitor_impl.hpp + * @author Marcus Edel + * + * Implementation of the InputWidth() function layer abstraction. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_SET_INPUT_WIDTH_VISITOR_IMPL_HPP +#define MLPACK_METHODS_ANN_VISITOR_SET_INPUT_WIDTH_VISITOR_IMPL_HPP + +// In case it hasn't been included yet. +#include "set_input_width_visitor.hpp" + +namespace mlpack { +namespace ann { + +//! SetInputWidthVisitor visitor class. +inline SetInputWidthVisitor::SetInputWidthVisitor(const size_t inputWidth, + const bool reset) : + inputWidth(inputWidth), + reset(reset) +{ + /* Nothing to do here. */ +} + +template +inline bool SetInputWidthVisitor::operator()(LayerType* layer) const +{ + return LayerInputWidth(layer); +} + +inline bool SetInputWidthVisitor::operator()(MoreTypes layer) const +{ + return layer.apply_visitor(*this); +} + +template +inline typename std::enable_if< + !HasInputWidth::value && + !HasModelCheck::value, bool>::type +SetInputWidthVisitor::LayerInputWidth(T* /* layer */) const +{ + return false; +} + +template +inline typename std::enable_if< + HasInputWidth::value && + !HasModelCheck::value, bool>::type +SetInputWidthVisitor::LayerInputWidth(T* layer) const +{ + if (layer->InputWidth() == 0 || reset) + { + layer->InputWidth() = inputWidth; + } + + return true; +} + +template +inline typename std::enable_if< + !HasInputWidth::value && + HasModelCheck::value, bool>::type +SetInputWidthVisitor::LayerInputWidth(T* layer) const +{ + for (size_t i = 0; i < layer->Model().size(); ++i) + { + boost::apply_visitor(SetInputWidthVisitor(inputWidth, reset), + layer->Model()[i]); + } + + return true; +} + +template +inline typename std::enable_if< + HasInputWidth::value && + HasModelCheck::value, bool>::type +SetInputWidthVisitor::LayerInputWidth(T* layer) const +{ + if (layer->InputWidth() == 0 || reset) + { + layer->InputWidth() = inputWidth; + } + + for (size_t i = 0; i < layer->Model().size(); ++i) + { + boost::apply_visitor(SetInputWidthVisitor(inputWidth, reset), + layer->Model()[i]); + } + + return true; +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/visitor/weight_set_visitor.hpp b/src/mlpack/methods/ann/visitor/weight_set_visitor.hpp new file mode 100644 index 0000000000..81c6c110df --- /dev/null +++ b/src/mlpack/methods/ann/visitor/weight_set_visitor.hpp @@ -0,0 +1,82 @@ +/** + * @file methods/ann/visitor/weight_set_visitor.hpp + * @author Marcus Edel + * + * This file provides an abstraction for the Weight() function for different + * layers and automatically directs any parameter to the right layer type. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_WEIGHT_SET_VISITOR_HPP +#define MLPACK_METHODS_ANN_VISITOR_WEIGHT_SET_VISITOR_HPP + +#include + +#include + +namespace mlpack { +namespace ann { + +/** + * WeightSetVisitor update the module parameters given the parameters set. + */ +class WeightSetVisitor : public boost::static_visitor +{ + public: + //! Update the parameters given the parameters set and offset. + WeightSetVisitor(arma::mat& weight, const size_t offset = 0); + + //! Update the parameters set. + template + size_t operator()(LayerType* layer) const; + + size_t operator()(MoreTypes layer) const; + + private: + //! The parameters set. + arma::mat& weight; + + //! The parameters offset. + const size_t offset; + + //! Do not update the parameters if the module doesn't implement the + //! Parameters() or Model() function. + template + typename std::enable_if< + !HasParametersCheck::value && + !HasModelCheck::value, size_t>::type + LayerSize(T* layer, P&& input) const; + + //! Update the parameters if the module implements the Model() function. + template + typename std::enable_if< + !HasParametersCheck::value && + HasModelCheck::value, size_t>::type + LayerSize(T* layer, P&& input) const; + + //! Update the parameters if the module implements the Parameters() function. + template + typename std::enable_if< + HasParametersCheck::value && + !HasModelCheck::value, size_t>::type + LayerSize(T* layer, P&& input) const; + + //! Update the parameters if the module implements the Model() and + //! Parameters() function. + template + typename std::enable_if< + HasParametersCheck::value && + HasModelCheck::value, size_t>::type + LayerSize(T* layer, P&& input) const; +}; + +} // namespace ann +} // namespace mlpack + +// Include implementation. +#include "weight_set_visitor_impl.hpp" + +#endif diff --git a/src/mlpack/methods/ann/visitor/weight_set_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/weight_set_visitor_impl.hpp new file mode 100644 index 0000000000..fa52a469c7 --- /dev/null +++ b/src/mlpack/methods/ann/visitor/weight_set_visitor_impl.hpp @@ -0,0 +1,100 @@ +/** + * @file methods/ann/visitor/weight_set_visitor_impl.hpp + * @author Marcus Edel + * + * Implementation of the Weight() function layer abstraction. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_WEIGHT_SET_VISITOR_IMPL_HPP +#define MLPACK_METHODS_ANN_VISITOR_WEIGHT_SET_VISITOR_IMPL_HPP + +// In case it hasn't been included yet. +#include "weight_set_visitor.hpp" + +namespace mlpack { +namespace ann { + +//! WeightSetVisitor visitor class. +inline WeightSetVisitor::WeightSetVisitor(arma::mat& weight, + const size_t offset) : + weight(weight), + offset(offset) +{ + /* Nothing to do here. */ +} + +template +inline size_t WeightSetVisitor::operator()(LayerType* layer) const +{ + return LayerSize(layer, layer->OutputParameter()); +} + +inline size_t WeightSetVisitor::operator()(MoreTypes layer) const +{ + return layer.apply_visitor(*this); +} + +template +inline typename std::enable_if< + !HasParametersCheck::value && + !HasModelCheck::value, size_t>::type +WeightSetVisitor::LayerSize(T* /* layer */, P&& /*output */) const +{ + return 0; +} + +template +inline typename std::enable_if< + !HasParametersCheck::value && + HasModelCheck::value, size_t>::type +WeightSetVisitor::LayerSize(T* layer, P&& /*output */) const +{ + size_t modelOffset = 0; + for (size_t i = 0; i < layer->Model().size(); ++i) + { + modelOffset += boost::apply_visitor(WeightSetVisitor( + weight, modelOffset + offset), layer->Model()[i]); + } + + return modelOffset; +} + +template +inline typename std::enable_if< + HasParametersCheck::value && + !HasModelCheck::value, size_t>::type +WeightSetVisitor::LayerSize(T* layer, P&& /* output */) const +{ + layer->Parameters() = arma::mat(weight.memptr() + offset, + layer->Parameters().n_rows, layer->Parameters().n_cols, false, false); + + return layer->Parameters().n_elem; +} + +template +inline typename std::enable_if< + HasParametersCheck::value && + HasModelCheck::value, size_t>::type +WeightSetVisitor::LayerSize(T* layer, P&& /* output */) const +{ + layer->Parameters() = arma::mat(weight.memptr() + offset, + layer->Parameters().n_rows, layer->Parameters().n_cols, false, false); + + size_t modelOffset = layer->Parameters().n_elem; + for (size_t i = 0; i < layer->Model().size(); ++i) + { + modelOffset += boost::apply_visitor(WeightSetVisitor( + weight, modelOffset + offset), layer->Model()[i]); + } + + return modelOffset; +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/ann/visitor/weight_size_visitor.hpp b/src/mlpack/methods/ann/visitor/weight_size_visitor.hpp new file mode 100644 index 0000000000..074ca56614 --- /dev/null +++ b/src/mlpack/methods/ann/visitor/weight_size_visitor.hpp @@ -0,0 +1,76 @@ +/** + * @file methods/ann/visitor/weight_size_visitor.hpp + * @author Marcus Edel + * + * This file provides an abstraction for the WeightSize() function for + * different layers and automatically directs any parameter to the right layer + * type. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_WEIGHT_SIZE_VISITOR_HPP +#define MLPACK_METHODS_ANN_VISITOR_WEIGHT_SIZE_VISITOR_HPP + +#include + +#include + +namespace mlpack { +namespace ann { + +/** + * WeightSizeVisitor returns the number of weights of the given module. + */ +class WeightSizeVisitor : public boost::static_visitor +{ + public: + //! Return the number of weights. + template + size_t operator()(LayerType* layer) const; + + size_t operator()(MoreTypes layer) const; + + private: + //! If the module doesn't implement the Parameters() or Model() function + //! return 0. + template + typename std::enable_if< + !HasParametersCheck::value && + !HasModelCheck::value, size_t>::type + LayerSize(T* layer, P& output) const; + + //! Return the number of parameters if the module implements the Model() + //! function. + template + typename std::enable_if< + !HasParametersCheck::value && + HasModelCheck::value, size_t>::type + LayerSize(T* layer, P& output) const; + + //! Return the number of parameters if the module implements the Parameters() + //! function. + template + typename std::enable_if< + HasParametersCheck::value && + !HasModelCheck::value, size_t>::type + LayerSize(T* layer, P& output) const; + + //! Return the accumulated number of parameters if the module implements the + //! Parameters() and Model() function. + template + typename std::enable_if< + HasParametersCheck::value && + HasModelCheck::value, size_t>::type + LayerSize(T* layer, P& output) const; +}; + +} // namespace ann +} // namespace mlpack + +// Include implementation. +#include "weight_size_visitor_impl.hpp" + +#endif diff --git a/src/mlpack/methods/ann/visitor/weight_size_visitor_impl.hpp b/src/mlpack/methods/ann/visitor/weight_size_visitor_impl.hpp new file mode 100644 index 0000000000..50ef266a63 --- /dev/null +++ b/src/mlpack/methods/ann/visitor/weight_size_visitor_impl.hpp @@ -0,0 +1,84 @@ +/** + * @file methods/ann/visitor/weight_size_visitor_impl.hpp + * @author Marcus Edel + * + * Implementation of the WeightSize() function layer abstraction. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_METHODS_ANN_VISITOR_WEIGHT_SIZE_VISITOR_IMPL_HPP +#define MLPACK_METHODS_ANN_VISITOR_WEIGHT_SIZE_VISITOR_IMPL_HPP + +// In case it hasn't been included yet. +#include "weight_size_visitor.hpp" + +namespace mlpack { +namespace ann { + +//! WeightSizeVisitor visitor class. +template +inline size_t WeightSizeVisitor::operator()(LayerType* layer) const +{ + return LayerSize(layer, layer->OutputParameter()); +} + +inline size_t WeightSizeVisitor::operator()(MoreTypes layer) const +{ + return layer.apply_visitor(*this); +} + +template +inline typename std::enable_if< + !HasParametersCheck::value && + !HasModelCheck::value, size_t>::type +WeightSizeVisitor::LayerSize(T* /* layer */, P& /* output */) const +{ + return 0; +} + +template +inline typename std::enable_if< + !HasParametersCheck::value && + HasModelCheck::value, size_t>::type +WeightSizeVisitor::LayerSize(T* layer, P& /* output */) const +{ + size_t weights = 0; + for (size_t i = 0; i < layer->Model().size(); ++i) + { + weights += boost::apply_visitor(WeightSizeVisitor(), layer->Model()[i]); + } + + return weights; +} + +template +inline typename std::enable_if< + HasParametersCheck::value && + !HasModelCheck::value, size_t>::type +WeightSizeVisitor::LayerSize(T* layer, P& /* output */) const +{ + return layer->Parameters().n_elem; +} + +template +inline typename std::enable_if< + HasParametersCheck::value && + HasModelCheck::value, size_t>::type +WeightSizeVisitor::LayerSize(T* layer, P& /* output */) const +{ + size_t weights = layer->Parameters().n_elem; + for (size_t i = 0; i < layer->Model().size(); ++i) + { + weights += boost::apply_visitor(WeightSizeVisitor(), layer->Model()[i]); + } + + return weights; +} + +} // namespace ann +} // namespace mlpack + +#endif diff --git a/src/mlpack/methods/kmeans/kmeans_impl.hpp b/src/mlpack/methods/kmeans/kmeans_impl.hpp index e8f782e916..4195da81ac 100644 --- a/src/mlpack/methods/kmeans/kmeans_impl.hpp +++ b/src/mlpack/methods/kmeans/kmeans_impl.hpp @@ -14,7 +14,6 @@ #include #include -#include namespace mlpack { namespace kmeans { @@ -162,8 +161,15 @@ Cluster(const MatType& data, // Check validity of initial guess. if (initialGuess) { - util::CheckSameSizes(centroids, clusters, "KMeans::Cluster()", "clusters"); - util::CheckSameDimensionality(data, centroids, "KMeans::Cluster()"); + if (centroids.n_cols != clusters) + Log::Fatal << "KMeans::Cluster(): wrong number of initial cluster " + << "centroids (" << centroids.n_cols << ", should be " << clusters + << ")!" << std::endl; + + if (centroids.n_rows != data.n_rows) + Log::Fatal << "KMeans::Cluster(): initial cluster centroids have wrong " + << " dimensionality (" << centroids.n_rows << ", should be " + << data.n_rows << ")!" << std::endl; } // Use the partitioner to come up with the partition assignments and calculate @@ -282,7 +288,10 @@ Cluster(const MatType& data, // Now, the initial assignments. First determine if they are necessary. if (initialAssignmentGuess) { - util::CheckSameSizes(data, assignments, "KMeans::Cluster()", "assignments"); + if (assignments.n_elem != data.n_cols) + Log::Fatal << "KMeans::Cluster(): initial cluster assignments (length " + << assignments.n_elem << ") not the same size as the dataset (size " + << data.n_cols << ")!" << std::endl; // Calculate initial centroids. arma::Row counts; diff --git a/src/mlpack/methods/linear_regression/linear_regression.cpp b/src/mlpack/methods/linear_regression/linear_regression.cpp index 774425ebc7..d3d4a2cd3f 100644 --- a/src/mlpack/methods/linear_regression/linear_regression.cpp +++ b/src/mlpack/methods/linear_regression/linear_regression.cpp @@ -12,7 +12,6 @@ */ #include "linear_regression.hpp" #include -#include using namespace mlpack; using namespace mlpack::regression; @@ -58,10 +57,6 @@ double LinearRegression::Train(const arma::mat& predictors, // We store the number of rows and columns of the predictors. // Reminder: Armadillo stores the data transposed from how we think of it, // that is, columns are actually rows (see: column major order). - - // Sanity check on data. - util::CheckSameSizes(predictors, responses, "LinearRegression::Train()"); - const size_t nCols = predictors.n_cols; arma::mat p = predictors; @@ -100,11 +95,7 @@ void LinearRegression::Predict(const arma::mat& points, { // We want to be sure we have the correct number of dimensions in the // dataset. - // Prevent underflow. - const size_t labels = (parameters.n_rows == 0) ? size_t(0) : - size_t(parameters.n_rows - 1); - util::CheckSameDimensionality(points, labels, "LinearRegression::Predict()", - "points"); + Log::Assert(points.n_rows == parameters.n_rows - 1); // Get the predictions, but this ignores the intercept value // (parameters[0]). predictions = arma::trans(parameters.subvec(1, parameters.n_elem - 1)) @@ -116,8 +107,7 @@ void LinearRegression::Predict(const arma::mat& points, { // We want to be sure we have the correct number of dimensions in // the dataset. - util::CheckSameDimensionality(points, parameters, - "LinearRegression::Predict()", "points"); + Log::Assert(points.n_rows == parameters.n_rows); predictions = arma::trans(parameters) * points; } } @@ -125,9 +115,6 @@ void LinearRegression::Predict(const arma::mat& points, double LinearRegression::ComputeError(const arma::mat& predictors, const arma::rowvec& responses) const { - // Sanity check on data. - util::CheckSameSizes(predictors, responses, "LinearRegression::Train()"); - // Get the number of columns and rows of the dataset. const size_t nCols = predictors.n_cols; const size_t nRows = predictors.n_rows; diff --git a/src/mlpack/methods/perceptron/perceptron.hpp b/src/mlpack/methods/perceptron/perceptron.hpp index 3fdfce6e14..4994588c40 100644 --- a/src/mlpack/methods/perceptron/perceptron.hpp +++ b/src/mlpack/methods/perceptron/perceptron.hpp @@ -68,29 +68,6 @@ class Perceptron const size_t numClasses, const size_t maxIterations = 1000); - /** - * Constructor: construct the perceptron by building the weights matrix, which - * is later used in classification. The number of classes should be specified - * separately, and the labels vector should contain values in the range [0, - * numClasses - 1]. The data::NormalizeLabels() function can be used if the - * labels vector does not contain values in the required range. - * - * This constructor supports weights for each data point. - * - * @param data Input, training data. - * @param labels Labels of dataset. - * @param numClasses Number of classes in the dataset. - * @param instanceWeights Weight vector to use for each training point while - * training. - * @param maxIterations Maximum number of iterations for the perceptron - * learning algorithm. - */ - Perceptron(const MatType& data, - const arma::Row& labels, - const size_t numClasses, - const arma::rowvec& instanceWeights, - const size_t maxIterations = 1000); - /** * Alternate constructor which copies parameters from an already initiated * perceptron. diff --git a/src/mlpack/methods/perceptron/perceptron_impl.hpp b/src/mlpack/methods/perceptron/perceptron_impl.hpp index 7373a3843b..46d1ff804a 100644 --- a/src/mlpack/methods/perceptron/perceptron_impl.hpp +++ b/src/mlpack/methods/perceptron/perceptron_impl.hpp @@ -62,32 +62,6 @@ Perceptron::Perceptron( Train(data, labels, numClasses); } -/** - * Constructor: construct the perceptron by building the weights matrix, which - * is later used in classification. The number of classes should be specified - * separately, and the labels vector should contain values in the range [0, - * numClasses - 1]. The data::NormalizeLabels() function can be used if the - * labels vector does not contain values in the required range. - * - * This constructor supports weights for each data point. - */ -template< - typename LearnPolicy, - typename WeightInitializationPolicy, - typename MatType -> -Perceptron::Perceptron( - const MatType& data, - const arma::Row& labels, - const size_t numClasses, - const arma::rowvec& instanceWeights, - const size_t maxIterations) : - maxIterations(maxIterations) -{ - // Start training. - Train(data, labels, numClasses, instanceWeights); -} - /** * Alternate constructor which copies parameters from an already initiated * perceptron. @@ -134,14 +108,13 @@ void Perceptron::Classify( { arma::vec tempLabelMat; arma::uword maxIndex = 0; - predictedLabels.set_size(test.n_cols); // Could probably be faster if done in batch. for (size_t i = 0; i < test.n_cols; ++i) { tempLabelMat = weights.t() * test.col(i) + biases; tempLabelMat.max(maxIndex); - predictedLabels(i) = maxIndex; + predictedLabels(0, i) = maxIndex; } } diff --git a/src/mlpack/methods/reinforcement_learning/q_learning_impl.hpp b/src/mlpack/methods/reinforcement_learning/q_learning_impl.hpp index 7ff52cde88..de28d05e74 100644 --- a/src/mlpack/methods/reinforcement_learning/q_learning_impl.hpp +++ b/src/mlpack/methods/reinforcement_learning/q_learning_impl.hpp @@ -53,9 +53,9 @@ QLearning< // Set up q-learning network. if (learningNetwork.Parameters().is_empty()) - learningNetwork.Reset(); + learningNetwork.ResetParameters(); - targetNetwork.Reset(); + targetNetwork.ResetParameters(); #if ENS_VERSION_MAJOR == 1 this->updater.Initialize(learningNetwork.Parameters().n_rows, diff --git a/src/mlpack/methods/reinforcement_learning/q_networks/dueling_dqn.hpp b/src/mlpack/methods/reinforcement_learning/q_networks/dueling_dqn.hpp index a55e6c34ba..c4b26a353f 100644 --- a/src/mlpack/methods/reinforcement_learning/q_networks/dueling_dqn.hpp +++ b/src/mlpack/methods/reinforcement_learning/q_networks/dueling_dqn.hpp @@ -15,7 +15,7 @@ #include #include #include -#include +#include #include #include diff --git a/src/mlpack/methods/reinforcement_learning/q_networks/simple_dqn.hpp b/src/mlpack/methods/reinforcement_learning/q_networks/simple_dqn.hpp index d4ceb5c22c..54ad55f01e 100644 --- a/src/mlpack/methods/reinforcement_learning/q_networks/simple_dqn.hpp +++ b/src/mlpack/methods/reinforcement_learning/q_networks/simple_dqn.hpp @@ -15,7 +15,7 @@ #include #include #include -#include +#include #include namespace mlpack { @@ -43,6 +43,7 @@ class SimpleDQN /** * Construct an instance of SimpleDQN class. * + * @param inputDim Number of inputs. * @param h1 Number of neurons in hiddenlayer-1. * @param h2 Number of neurons in hiddenlayer-2. * @param outputDim Number of neurons in output layer. @@ -50,7 +51,8 @@ class SimpleDQN * @param init Specifies the initialization rule for the network. * @param outputLayer Specifies the output layer type for network. */ - SimpleDQN(const int h1, + SimpleDQN(const int inputDim, + const int h1, const int h2, const int outputDim, const bool isNoisy = false, @@ -59,21 +61,21 @@ class SimpleDQN network(outputLayer, init), isNoisy(isNoisy) { - network.Add(new ann::Linear(h1)); - network.Add(new ann::ReLU()); + network.Add(new ann::Linear<>(inputDim, h1)); + network.Add(new ann::ReLULayer<>()); if (isNoisy) { - noisyLayerIndex.push_back(network.Network().size()); - network.Add(new ann::NoisyLinear(h2)); - network.Add(new ann::ReLU()); - noisyLayerIndex.push_back(network.Network().size()); - network.Add(new ann::NoisyLinear(outputDim)); + noisyLayerIndex.push_back(network.Model().size()); + network.Add(new ann::NoisyLinear<>(h1, h2)); + network.Add(new ann::ReLULayer<>()); + noisyLayerIndex.push_back(network.Model().size()); + network.Add(new ann::NoisyLinear<>(h2, outputDim)); } else { - network.Add(new ann::Linear(h2)); - network.Add(new ann::ReLU()); - network.Add(new ann::Linear(outputDim)); + network.Add(new ann::Linear<>(h1, h2)); + network.Add(new ann::ReLULayer<>()); + network.Add(new ann::Linear<>(h2, outputDim)); } } @@ -118,9 +120,9 @@ class SimpleDQN /** * Resets the parameters of the network. */ - void Reset() + void ResetParameters() { - network.Reset(); + network.ResetParameters(); } /** @@ -130,8 +132,8 @@ class SimpleDQN { for (size_t i = 0; i < noisyLayerIndex.size(); i++) { - dynamic_cast( - network.Network()[noisyLayerIndex[i]])->ResetNoise(); + boost::get*> + (network.Model()[noisyLayerIndex[i]])->ResetNoise(); } } diff --git a/src/mlpack/methods/reinforcement_learning/sac.hpp b/src/mlpack/methods/reinforcement_learning/sac.hpp index 55ce00aa9a..27ddb5cba0 100644 --- a/src/mlpack/methods/reinforcement_learning/sac.hpp +++ b/src/mlpack/methods/reinforcement_learning/sac.hpp @@ -19,6 +19,7 @@ #include "replay/random_replay.hpp" #include #include +#include #include "training_config.hpp" namespace mlpack { diff --git a/src/mlpack/prereqs.hpp b/src/mlpack/prereqs.hpp index 6310364f55..cc865820e1 100644 --- a/src/mlpack/prereqs.hpp +++ b/src/mlpack/prereqs.hpp @@ -33,7 +33,6 @@ #include #include #include -#include #include #include @@ -108,6 +107,7 @@ using enable_if_t = typename enable_if::type; #include #include #include +#include #include #include #include @@ -118,10 +118,20 @@ using enable_if_t = typename enable_if::type; #include #include #include +#include +#include #include #include #include +// If we have Boost 1.58 or older and are using C++14, the compilation is likely +// to fail due to boost::visitor issues. We will pre-emptively fail. +#if __cplusplus > 201103L && BOOST_VERSION < 105900 +#error Use of C++14 mode with Boost < 1.59 is known to cause compilation \ +problems. Instead specify the C++11 standard (-std=c++11 with gcc or clang), \ +or upgrade Boost to 1.59 or newer. +#endif + // On Visual Studio, disable C4519 (default arguments for function templates) // since it's by default an error, which doesn't even make any sense because // it's part of the C++11 standard. diff --git a/src/mlpack/tests/CMakeLists.txt b/src/mlpack/tests/CMakeLists.txt index 6e6b0840b5..b27b15619d 100644 --- a/src/mlpack/tests/CMakeLists.txt +++ b/src/mlpack/tests/CMakeLists.txt @@ -11,9 +11,10 @@ add_executable(mlpack_test ann_layer_test.cpp ann_regularizer_test.cpp ann_test_tools.hpp + ann_visitor_test.cpp armadillo_svd_test.cpp arma_extend_test.cpp -# async_learning_test.cpp + async_learning_test.cpp augmented_rnns_tasks_test.cpp bayesian_linear_regression_test.cpp bias_svd_test.cpp @@ -27,7 +28,7 @@ add_executable(mlpack_test cosine_tree_test.cpp cv_test.cpp dbscan_test.cpp -# dcgan_test.cpp + dcgan_test.cpp decision_tree_regressor_test.cpp decision_tree_test.cpp det_test.cpp @@ -39,7 +40,7 @@ add_executable(mlpack_test fastmks_test.cpp feedforward_network_test.cpp feedforward_network_2_test.cpp -# gan_test.cpp + gan_test.cpp gmm_test.cpp hmm_test.cpp hpt_test.cpp @@ -59,6 +60,7 @@ add_executable(mlpack_test krann_search_test.cpp ksinit_test.cpp lars_test.cpp + layer_names_test.cpp lin_alg_test.cpp linear_regression_test.cpp lmnn_test.cpp @@ -88,18 +90,18 @@ add_executable(mlpack_test python_binding_test.cpp qdafn_test.cpp quic_svd_test.cpp -# q_learning_test.cpp + q_learning_test.cpp radical_test.cpp random_forest_test.cpp random_test.cpp randomized_svd_test.cpp range_search_test.cpp -# rbm_network_test.cpp + rbm_network_test.cpp rectangle_tree_test.cpp recurrent_network_test.cpp -# rnn_reber_test.cpp + rnn_reber_test.cpp regularized_svd_test.cpp -# reward_clipping_test.cpp + reward_clipping_test.cpp rl_components_test.cpp scaling_test.cpp size_checks_test.cpp @@ -128,7 +130,7 @@ add_executable(mlpack_test ub_tree_test.cpp union_find_test.cpp vantage_point_tree_test.cpp -# wgan_test.cpp + wgan_test.cpp xgboost_test.cpp main_tests/adaboost_test.cpp main_tests/adaboost_train_test.cpp diff --git a/src/mlpack/tests/activation_functions_test.cpp b/src/mlpack/tests/activation_functions_test.cpp index 2cadc4375c..1c4458c4f9 100644 --- a/src/mlpack/tests/activation_functions_test.cpp +++ b/src/mlpack/tests/activation_functions_test.cpp @@ -12,7 +12,7 @@ */ #include -#include +#include #include #include #include @@ -135,7 +135,7 @@ void CheckInverseCorrect(const arma::colvec input) * * @param input Input data used for evaluating the HardTanH activation function. * @param target Target data used to evaluate the HardTanH activation. - * + */ void CheckHardTanHActivationCorrect(const arma::colvec input, const arma::colvec target) { @@ -148,7 +148,7 @@ void CheckHardTanHActivationCorrect(const arma::colvec input, { REQUIRE(activations.at(i) == Approx(target.at(i)).epsilon(1e-5)); } -}*/ +} /** * Implementation of the HardTanH activation function derivative test. The @@ -157,7 +157,7 @@ void CheckHardTanHActivationCorrect(const arma::colvec input, * @param input Input data used for evaluating the HardTanH activation * function. * @param target Target data used to evaluate the HardTanH activation. - * + */ void CheckHardTanHDerivativeCorrect(const arma::colvec input, const arma::colvec target) { @@ -174,7 +174,7 @@ void CheckHardTanHDerivativeCorrect(const arma::colvec input, { REQUIRE(derivatives.at(i) == Approx(target.at(i)).epsilon(1e-5)); } -}*/ +} /** * Implementation of the LeakyReLU activation function test. The function is @@ -187,7 +187,7 @@ void CheckHardTanHDerivativeCorrect(const arma::colvec input, void CheckLeakyReLUActivationCorrect(const arma::colvec input, const arma::colvec target) { - LeakyReLU lrf; + LeakyReLU<> lrf; // Test the activation function using the entire vector as input. arma::colvec activations; @@ -210,7 +210,7 @@ void CheckLeakyReLUActivationCorrect(const arma::colvec input, void CheckLeakyReLUDerivativeCorrect(const arma::colvec input, const arma::colvec target) { - LeakyReLU lrf; + LeakyReLU<> lrf; // Test the calculation of the derivatives using the entire vector as input. arma::colvec derivatives; @@ -230,7 +230,7 @@ void CheckLeakyReLUDerivativeCorrect(const arma::colvec input, * * @param input Input data used for evaluating the ELU activation function. * @param target Target data used to evaluate the ELU activation. - * + */ void CheckELUActivationCorrect(const arma::colvec input, const arma::colvec target) { @@ -244,7 +244,7 @@ void CheckELUActivationCorrect(const arma::colvec input, { REQUIRE(activations.at(i) == Approx(target.at(i)).epsilon(1e-5)); } -}*/ +} /** * Implementation of the ELU activation function derivative test. The function @@ -252,7 +252,7 @@ void CheckELUActivationCorrect(const arma::colvec input, * * @param input Input data used for evaluating the ELU activation function. * @param target Target data used to evaluate the ELU activation. - * + */ void CheckELUDerivativeCorrect(const arma::colvec input, const arma::colvec target) { @@ -270,7 +270,7 @@ void CheckELUDerivativeCorrect(const arma::colvec input, { REQUIRE(derivatives.at(i) == Approx(target.at(i)).epsilon(1e-5)); } -}*/ +} /** * Implementation of the PReLU activation function test. The function @@ -279,7 +279,7 @@ void CheckELUDerivativeCorrect(const arma::colvec input, * @param input Input data used for evaluating the PReLU activation * function. * @param target Target data used to evaluate the PReLU activation. - * + */ void CheckPReLUActivationCorrect(const arma::colvec input, const arma::colvec target) { @@ -292,7 +292,7 @@ void CheckPReLUActivationCorrect(const arma::colvec input, { REQUIRE(activations.at(i) == Approx(target.at(i)).epsilon(1e-5)); } -}*/ +} /** * Implementation of the PReLU activation function derivative test. @@ -302,7 +302,7 @@ void CheckPReLUActivationCorrect(const arma::colvec input, * @param input Input data used for evaluating the PReLU activation * function. * @param target Target data used to evaluate the PReLU activation. - * + */ void CheckPReLUDerivativeCorrect(const arma::colvec input, const arma::colvec target) { @@ -318,7 +318,7 @@ void CheckPReLUDerivativeCorrect(const arma::colvec input, { REQUIRE(derivatives.at(i) == Approx(target.at(i)).epsilon(1e-5)); } -}*/ +} /** * Implementation of the PReLU activation function gradient test. @@ -328,7 +328,7 @@ void CheckPReLUDerivativeCorrect(const arma::colvec input, * @param input Input data used for evaluating the PReLU activation * function. * @param target Target data used to evaluate the PReLU gradient. - * + */ void CheckPReLUGradientCorrect(const arma::colvec input, const arma::colvec target) { @@ -343,7 +343,7 @@ void CheckPReLUGradientCorrect(const arma::colvec input, REQUIRE(gradient.n_rows == 1); REQUIRE(gradient.n_cols == 1); REQUIRE(gradient(0) == Approx(target(0)).epsilon(1e-5)); -}*/ +} /** * Implementation of the Hard Shrink activation function test. The function is @@ -351,7 +351,7 @@ void CheckPReLUGradientCorrect(const arma::colvec input, * * @param input Input data used for evaluating the Hard Shrink activation function. * @param target Target data used to evaluate the Hard Shrink activation. - * + */ void CheckHardShrinkActivationCorrect(const arma::colvec input, const arma::colvec target) { @@ -364,7 +364,7 @@ void CheckHardShrinkActivationCorrect(const arma::colvec input, { REQUIRE(activations.at(i) == Approx(target.at(i)).epsilon(1e-5)); } -}*/ +} /** * Implementation of the HardShrink activation function derivative test. @@ -374,7 +374,7 @@ void CheckHardShrinkActivationCorrect(const arma::colvec input, * @param input Input data used for evaluating the HardShrink activation * function. * @param target Target data used to evaluate the HardShrink activation. - * + */ void CheckHardShrinkDerivativeCorrect(const arma::colvec input, const arma::colvec target) { @@ -390,7 +390,7 @@ void CheckHardShrinkDerivativeCorrect(const arma::colvec input, { REQUIRE(derivatives.at(i) == Approx(target.at(i)).epsilon(1e-5)); } -}*/ +} /** * Implementation of the Soft Shrink activation function test. The function is @@ -399,7 +399,7 @@ void CheckHardShrinkDerivativeCorrect(const arma::colvec input, * @param input Input data used for evaluating the Soft Shrink activation * function. * @param target Target data used to evaluate the Soft Shrink activation. - * + */ void CheckSoftShrinkActivationCorrect(const arma::colvec input, const arma::colvec target) { @@ -412,7 +412,7 @@ void CheckSoftShrinkActivationCorrect(const arma::colvec input, { REQUIRE(activations.at(i) == Approx(target.at(i)).epsilon(1e-5)); } -}*/ +} /** * Implementation of the Soft Shrink activation function derivative test. @@ -422,7 +422,7 @@ void CheckSoftShrinkActivationCorrect(const arma::colvec input, * @param input Input data used for evaluating the Soft Shrink activation * function. * @param target Target data used to evaluate the Soft Shrink activation. - * + */ void CheckSoftShrinkDerivativeCorrect(const arma::colvec input, const arma::colvec target) { @@ -438,12 +438,12 @@ void CheckSoftShrinkDerivativeCorrect(const arma::colvec input, { REQUIRE(derivatives.at(i) == Approx(target.at(i)).epsilon(1e-5)); } -}*/ +} /** * Simple SELU activation test to check whether the mean and variance remain * invariant after passing normalized inputs through the function. - * + */ TEST_CASE("SELUFunctionNormalizedTest", "[ActivationFunctionsTest]") { arma::mat input = arma::randn(1000, 1); @@ -459,12 +459,12 @@ TEST_CASE("SELUFunctionNormalizedTest", "[ActivationFunctionsTest]") REQUIRE(arma::as_scalar(arma::abs(arma::var(input) - arma::var(output))) <= 0.1); -}*/ +} /** * Simple SELU activation test to check whether the mean and variance * vary significantly after passing unnormalized inputs through the function. - * + */ TEST_CASE("SELUFunctionUnnormalizedTest", "[ActivationFunctionsTest]") { const arma::colvec input("5.96402758 0.9966824 0.99975321 1 \ @@ -481,13 +481,13 @@ TEST_CASE("SELUFunctionUnnormalizedTest", "[ActivationFunctionsTest]") REQUIRE(arma::as_scalar(arma::abs(arma::var(input) - arma::var(output))) >= 0.1); -}*/ +} /** * Simple SELU derivative test to check whether the derivatives * produced by the activation function are correct. * - * + */ TEST_CASE("SELUFunctionDerivativeTest", "[ActivationFunctionsTest]") { arma::mat input = arma::ones(1000, 1); @@ -511,7 +511,7 @@ TEST_CASE("SELUFunctionDerivativeTest", "[ActivationFunctionsTest]") REQUIRE(arma::as_scalar(arma::abs(arma::mean(derivatives) - selu.Lambda() * selu.Alpha() - arma::mean(activations))) <= 10e-4); -}*/ +} /** * Implementation of the CELU activation function test. The function is @@ -519,7 +519,7 @@ TEST_CASE("SELUFunctionDerivativeTest", "[ActivationFunctionsTest]") * * @param input Input data used for evaluating the CELU activation function. * @param target Target data used to evaluate the CELU activation. - * + */ void CheckCELUActivationCorrect(const arma::colvec input, const arma::colvec target) { @@ -533,7 +533,7 @@ void CheckCELUActivationCorrect(const arma::colvec input, { REQUIRE(activations.at(i) == Approx(target.at(i)).epsilon(1e-5)); } -}*/ +} /** * Implementation of the CELU activation function derivative test. The function @@ -541,7 +541,7 @@ void CheckCELUActivationCorrect(const arma::colvec input, * * @param input Input data used for evaluating the CELU activation function. * @param target Target data used to evaluate the CELU activation. - * + */ void CheckCELUDerivativeCorrect(const arma::colvec input, const arma::colvec target) { @@ -559,7 +559,7 @@ void CheckCELUDerivativeCorrect(const arma::colvec input, { REQUIRE(derivatives.at(i) == Approx(target.at(i)).epsilon(1e-5)); } -}*/ +} /** * Implementation of the ISRLU activation function test. The function is @@ -567,7 +567,7 @@ void CheckCELUDerivativeCorrect(const arma::colvec input, * * @param input Input data used for evaluating the ISRLU activation function. * @param target Target data used to evaluate the ISRLU activation. - * + */ void CheckISRLUActivationCorrect(const arma::colvec input, const arma::colvec target) { @@ -581,7 +581,7 @@ void CheckISRLUActivationCorrect(const arma::colvec input, { REQUIRE(activations.at(i) == Approx(target.at(i)).epsilon(1e-5)); } -}*/ +} /** * Implementation of the ISRLU activation function derivative test. The function @@ -589,7 +589,7 @@ void CheckISRLUActivationCorrect(const arma::colvec input, * * @param input Input data used for evaluating the ISRLU activation function. * @param target Target data used to evaluate the ISRLU activation. - * + */ void CheckISRLUDerivativeCorrect(const arma::colvec input, const arma::colvec target) { @@ -607,7 +607,7 @@ void CheckISRLUDerivativeCorrect(const arma::colvec input, { REQUIRE(derivatives.at(i) == Approx(target.at(i)).epsilon(1e-5)); } -}*/ +} /** * Implementation of the Softmin activation function test. The function is @@ -615,7 +615,7 @@ void CheckISRLUDerivativeCorrect(const arma::colvec input, * * @param input Input data used for evaluating the Softmin activation function. * @param target Target data used to evaluate the Softmin activation. - * + */ void CheckSoftminActivationCorrect(const arma::colvec input, const arma::colvec target) { @@ -629,7 +629,7 @@ void CheckSoftminActivationCorrect(const arma::colvec input, { REQUIRE(activations.at(i) == Approx(target.at(i)).epsilon(1e-5)); } -}*/ +} /** * Implementation of the Softmin activation function derivative test. @@ -637,7 +637,7 @@ void CheckSoftminActivationCorrect(const arma::colvec input, * * @param input Input data used for evaluating the Softmin activation function. * @param target Target data used to evaluate the Softmin activation. - * + */ void CheckSoftminDerivativeCorrect(const arma::colvec input, const arma::colvec target) { @@ -658,7 +658,7 @@ void CheckSoftminDerivativeCorrect(const arma::colvec input, { REQUIRE(derivatives.at(i) == Approx(target.at(i)).epsilon(1e-5)); } -}*/ +} /** * Implementation of the Flatten T Swish activation function test. The function is @@ -667,7 +667,7 @@ void CheckSoftminDerivativeCorrect(const arma::colvec input, * @param input Input data used for evaluating the Flatten T Swish activation * function. * @param target Target data used to evaluate the Flatten T Swish activation. - * + */ void CheckFlattenTSwishActivationCorrect(const arma::colvec input, const arma::colvec target) { @@ -679,7 +679,7 @@ void CheckFlattenTSwishActivationCorrect(const arma::colvec input, { REQUIRE(activations.at(i) == Approx(target.at(i)).epsilon(1e-5)); } -}*/ +} /** * Implementation of the Softmin activation function derivative test. @@ -687,7 +687,7 @@ void CheckFlattenTSwishActivationCorrect(const arma::colvec input, * * @param input Input data used for evaluating the Softmin activation function. * @param target Target data used to evaluate the Softmin activation. - * + */ void CheckFlattenTSwishDerivateCorrect(const arma::colvec input, const arma::colvec target) { @@ -702,7 +702,7 @@ void CheckFlattenTSwishDerivateCorrect(const arma::colvec input, { REQUIRE(derivate.at(i) == Approx(target.at(i)).epsilon(1e-5)); } -}*/ +} /** * Implementation of the ReLU6 activation function derivative test. The function @@ -710,7 +710,7 @@ void CheckFlattenTSwishDerivateCorrect(const arma::colvec input, * * @param input Input data used for evaluating the ReLU6 activation function. * @param target Target data used to evaluate the ReLU6 activation. - * + */ void CheckReLU6Correct(const arma::colvec input, const arma::colvec ActivationTarget, const arma::colvec DerivativeTarget) @@ -733,11 +733,11 @@ void CheckReLU6Correct(const arma::colvec input, { REQUIRE(derivatives.at(i) == Approx(DerivativeTarget.at(i)).epsilon(1e-5)); } -}*/ +} /** * Basic test of the ReLU6 function. - * + */ TEST_CASE("ReLU6FunctionTest", "[ActivationFunctionsTest]") { const arma::colvec activationData("-2.0 3.0 0.0 6.0 24.0"); @@ -749,7 +749,7 @@ TEST_CASE("ReLU6FunctionTest", "[ActivationFunctionsTest]") const arma::colvec desiredDerivatives("0.0 1.0 0.0 0.0 0.0"); CheckReLU6Correct(activationData, desiredActivations, desiredDerivatives); -}*/ +} /** * Basic test of the tanh function. @@ -847,7 +847,7 @@ TEST_CASE("LeakyReLUFunctionTest", "[ActivationFunctionsTest]") /** * Basic test of the HardTanH function. - * + */ TEST_CASE("HardTanHFunctionTest", "[ActivationFunctionsTest]") { const arma::colvec desiredActivations("-1 1 1 -1 \ @@ -858,11 +858,11 @@ TEST_CASE("HardTanHFunctionTest", "[ActivationFunctionsTest]") CheckHardTanHActivationCorrect(activationData, desiredActivations); CheckHardTanHDerivativeCorrect(activationData, desiredDerivatives); -}*/ +} /** * Basic test of the ELU function. - * + */ TEST_CASE("ELUFunctionTest", "[ActivationFunctionsTest]") { const arma::colvec desiredActivations("-0.86466471 3.2 4.5 -1.0 \ @@ -873,7 +873,7 @@ TEST_CASE("ELUFunctionTest", "[ActivationFunctionsTest]") CheckELUActivationCorrect(activationData, desiredActivations); CheckELUDerivativeCorrect(activationData, desiredDerivatives); -}*/ +} /** * Basic test of the softplus function. @@ -898,7 +898,7 @@ TEST_CASE("SoftplusFunctionTest", "[ActivationFunctionsTest]") /** * Basic test of the PReLU function. - * + */ TEST_CASE("PReLUFunctionTest", "[ActivationFunctionsTest]") { const arma::colvec desiredActivations("-0.06 3.2 4.5 -3.006 \ @@ -911,11 +911,11 @@ TEST_CASE("PReLUFunctionTest", "[ActivationFunctionsTest]") CheckPReLUActivationCorrect(activationData, desiredActivations); CheckPReLUDerivativeCorrect(desiredActivations, desiredDerivatives); CheckPReLUGradientCorrect(activationData, desiredGradient); -}*/ +} /** * Basic test of the CReLU function. - * + */ TEST_CASE("CReLUFunctionTest", "[ActivationFunctionsTest]") { const arma::colvec desiredActivations("0 3.2 4.5 0 \ @@ -942,11 +942,11 @@ TEST_CASE("CReLUFunctionTest", "[ActivationFunctionsTest]") REQUIRE(derivatives.at(i) == Approx(desiredDerivatives.at(i)).epsilon(1e-5)); } -}*/ +} /** * Basic test of the swish function. - * + */ TEST_CASE("SwishFunctionTest", "[ActivationFunctionsTest]") { // Hand-calculated values using Python interpreter. @@ -961,7 +961,7 @@ TEST_CASE("SwishFunctionTest", "[ActivationFunctionsTest]") CheckActivationCorrect(activationData, desiredActivations); CheckDerivativeCorrect(desiredActivations, desiredDerivatives); -}*/ +} /** * Basic test of the hard sigmoid function. @@ -1049,7 +1049,7 @@ TEST_CASE("GELUFunctionTest", "[ActivationFunctionsTest]") /** * Basic test of the Hard Shrink function. - * + */ TEST_CASE("HardShrinkFunctionTest", "[ActivationFunctionsTest]") { const arma::colvec desiredActivations("-2 3.2 4.5 -100.2 1 -1 2 0"); @@ -1060,7 +1060,7 @@ TEST_CASE("HardShrinkFunctionTest", "[ActivationFunctionsTest]") desiredActivations); CheckHardShrinkDerivativeCorrect(desiredActivations, desiredDerivatives); -}*/ +} /** * Basic test of the Elliot function. @@ -1104,7 +1104,7 @@ TEST_CASE("ElishFunctionTest", "[ActivationFunctionsTest]") /** * Basic test of the Soft Shrink function. - * + */ TEST_CASE("SoftShrinkFunctionTest", "[ActivationFunctionsTest]") { const arma::colvec desiredActivations("-1.5 2.7 4 -99.7 0.5 -0.5 1.5 0"); @@ -1115,11 +1115,11 @@ TEST_CASE("SoftShrinkFunctionTest", "[ActivationFunctionsTest]") desiredActivations); CheckSoftShrinkDerivativeCorrect(desiredActivations, desiredDerivatives); -}*/ +} /** * Basic test of the CELU activation function. - * + */ TEST_CASE("CELUFunctionTest", "[ActivationFunctionsTest]") { const arma::colvec desiredActivations("-0.86466472 3.2 4.5 \ @@ -1131,11 +1131,11 @@ TEST_CASE("CELUFunctionTest", "[ActivationFunctionsTest]") CheckCELUActivationCorrect(activationData, desiredActivations); CheckCELUDerivativeCorrect(desiredActivations, desiredDerivatives); -}*/ +} /** * Basic test of the ISRLU activation function. - * + */ TEST_CASE("ISRLUFunctionTest", "[ActivationFunctionsTest]") { const arma::colvec desiredActivations("-0.89442719 3.2 4.5 \ @@ -1147,7 +1147,7 @@ TEST_CASE("ISRLUFunctionTest", "[ActivationFunctionsTest]") CheckISRLUActivationCorrect(activationData, desiredActivations); CheckISRLUDerivativeCorrect(activationData, desiredDerivatives); -}*/ +} /** * Basic test of the inverse quadratic function. @@ -1275,7 +1275,7 @@ TEST_CASE("GaussianFunctionTest", "[ActivationFunctionsTest]") /** * Basic test of the Softmin function. - * + */ TEST_CASE("SoftminFunctionTest", "[ActivationFunctionsTest]") { const arma::colvec activationData("4.2 2.4 7.0 6.4"); @@ -1291,7 +1291,7 @@ TEST_CASE("SoftminFunctionTest", "[ActivationFunctionsTest]") desiredActivations); CheckSoftminDerivativeCorrect(activationData, desiredDerivatives); -}*/ +} /** * Basic test of the Hard Swish function. @@ -1360,7 +1360,7 @@ TEST_CASE("SILUFunctionTest", "[ActivationFunctionsTest]") /** * Basic test of Flatten T Swish function. - * + */ TEST_CASE("FlattenTSwishFunctionTest", "[ActivationFunctionsTest]") { // Random Value. @@ -1378,4 +1378,4 @@ TEST_CASE("FlattenTSwishFunctionTest", "[ActivationFunctionsTest]") CheckFlattenTSwishActivationCorrect(input, desiredActivation); CheckFlattenTSwishDerivateCorrect(desiredActivation, desiredDerivation); -}*/ +} diff --git a/src/mlpack/tests/ann_layer_test.cpp b/src/mlpack/tests/ann_layer_test.cpp index cb1c5d766f..b9d5452c41 100644 --- a/src/mlpack/tests/ann_layer_test.cpp +++ b/src/mlpack/tests/ann_layer_test.cpp @@ -21,6 +21,7 @@ #include #include #include +#include #include "test_catch_tools.hpp" #include "catch.hpp" @@ -30,59 +31,59 @@ using namespace mlpack; using namespace mlpack::ann; -// // network1 should be allocated with `new`, and trained on some data. -// template -// void CheckRNNCopyFunction(ModelType* network1, -// MatType& trainData, -// MatType& trainLabels, -// const size_t maxEpochs) -// { -// arma::cube predictions1; -// arma::cube predictions2; -// ens::StandardSGD opt(0.1, 1, maxEpochs * trainData.n_slices, -100, false); +// network1 should be allocated with `new`, and trained on some data. +template +void CheckRNNCopyFunction(ModelType* network1, + MatType& trainData, + MatType& trainLabels, + const size_t maxEpochs) +{ + arma::cube predictions1; + arma::cube predictions2; + ens::StandardSGD opt(0.1, 1, maxEpochs * trainData.n_slices, -100, false); -// network1->Train(trainData, trainLabels, opt); -// network1->Predict(trainData, predictions1); + network1->Train(trainData, trainLabels, opt); + network1->Predict(trainData, predictions1); -// RNN<> network2 = *network1; -// delete network1; + RNN<> network2 = *network1; + delete network1; -// // Deallocating all of network1's memory, so that network2 does not use any -// // of that memory. -// network2.Predict(trainData, predictions2); -// CheckMatrices(predictions1, predictions2); -// } + // Deallocating all of network1's memory, so that network2 does not use any + // of that memory. + network2.Predict(trainData, predictions2); + CheckMatrices(predictions1, predictions2); +} -// // network1 should be allocated with `new`, and trained on some data. -// template -// void CheckRNNMoveFunction(ModelType* network1, -// MatType& trainData, -// MatType& trainLabels, -// const size_t maxEpochs) -// { -// arma::cube predictions1; -// arma::cube predictions2; -// ens::StandardSGD opt(0.1, 1, maxEpochs * trainData.n_slices, -100, false); +// network1 should be allocated with `new`, and trained on some data. +template +void CheckRNNMoveFunction(ModelType* network1, + MatType& trainData, + MatType& trainLabels, + const size_t maxEpochs) +{ + arma::cube predictions1; + arma::cube predictions2; + ens::StandardSGD opt(0.1, 1, maxEpochs * trainData.n_slices, -100, false); -// network1->Train(trainData, trainLabels, opt); -// network1->Predict(trainData, predictions1); + network1->Train(trainData, trainLabels, opt); + network1->Predict(trainData, predictions1); -// RNN<> network2(std::move(*network1)); -// delete network1; + RNN<> network2(std::move(*network1)); + delete network1; -// // Deallocating all of network1's memory, so that network2 does not use any -// // of that memory. -// network2.Predict(trainData, predictions2); -// CheckMatrices(predictions1, predictions2); -// } + // Deallocating all of network1's memory, so that network2 does not use any + // of that memory. + network2.Predict(trainData, predictions2); + CheckMatrices(predictions1, predictions2); +} /** * Simple add module test. - * + */ TEST_CASE("SimpleAddLayerTest", "[ANNLayerTest]") { arma::mat output, input, delta; - Add module(10); + Add<> module(10); module.Parameters().randu(); // Test the Forward function. @@ -104,11 +105,10 @@ TEST_CASE("SimpleAddLayerTest", "[ANNLayerTest]") module.Backward(input, output, delta); REQUIRE(arma::accu(output) == Approx(arma::accu(delta)).epsilon(1e-5)); } -*/ /** * Jacobian add module test. - * + */ TEST_CASE("JacobianAddLayerTest", "[ANNLayerTest]") { for (size_t i = 0; i < 5; ++i) @@ -117,18 +117,17 @@ TEST_CASE("JacobianAddLayerTest", "[ANNLayerTest]") arma::mat input; input.set_size(elements, 1); - Add module(elements); + Add<> module(elements); module.Parameters().randu(); double error = JacobianTest(module, input); REQUIRE(error <= 1e-5); } } -*/ /** * Add layer numerical gradient test. - * + */ TEST_CASE("GradientAddLayerTest", "[ANNLayerTest]") { // Add function gradient instantiation. @@ -138,12 +137,13 @@ TEST_CASE("GradientAddLayerTest", "[ANNLayerTest]") input(arma::randu(10, 1)), target(arma::mat("0")) { - model = new FFN(); - model->ResetData(input, target); - model->Add(); - model->Add(10, 10); - model->Add(10); - model->Add(); + model = new FFN, NguyenWidrowInitialization>(); + model->Predictors() = input; + model->Responses() = target; + model->Add >(); + model->Add >(10, 10); + model->Add >(10); + model->Add >(); } ~GradientFunction() @@ -160,33 +160,33 @@ TEST_CASE("GradientAddLayerTest", "[ANNLayerTest]") arma::mat& Parameters() { return model->Parameters(); } - FFN* model; + FFN, NguyenWidrowInitialization>* model; arma::mat input, target; } function; REQUIRE(CheckGradient(function) <= 1e-4); -}*/ +} /** * Test that the function that can access the outSize parameter of * the Add layer works. - * + */ TEST_CASE("AddLayerParametersTest", "[ANNLayerTest]") { // Parameter : outSize. - Add layer(7); + Add<> layer(7); // Make sure we can get the parameter successfully. REQUIRE(layer.OutputSize() == 7); -}*/ +} /** * Simple constant module test. - * + */ TEST_CASE("SimpleConstantLayerTest", "[ANNLayerTest]") { arma::mat output, input, delta; - Constant module(10, 3.0); + Constant<> module(10, 3.0); // Test the Forward function. input = arma::zeros(10, 1); @@ -205,11 +205,11 @@ TEST_CASE("SimpleConstantLayerTest", "[ANNLayerTest]") // Test the backward function. module.Backward(input, output, delta); REQUIRE(arma::accu(delta) == 0); -}*/ +} /** * Jacobian constant module test. - * + */ TEST_CASE("JacobianConstantLayerTest", "[ANNLayerTest]") { for (size_t i = 0; i < 5; ++i) @@ -218,25 +218,25 @@ TEST_CASE("JacobianConstantLayerTest", "[ANNLayerTest]") arma::mat input; input.set_size(elements, 1); - Constant module(elements, 1.0); + Constant<> module(elements, 1.0); double error = JacobianTest(module, input); REQUIRE(error <= 1e-5); } -}*/ +} /** * Test that the function that can access the outSize parameter of the * Constant layer works. - * + */ TEST_CASE("ConstantLayerParametersTest", "[ANNLayerTest]") { // Parameter : outSize. - Constant layer(7); + Constant<> layer(7); // Make sure we can get the parameter successfully. REQUIRE(layer.OutSize() == 7); -}*/ +} /** * Simple dropout module test. @@ -250,8 +250,8 @@ TEST_CASE("SimpleDropoutLayerTest", "[ANNLayerTest]") arma::mat input(1000, 1); input.fill(1 - p); - Dropout module(p); - module.Training() = true; + Dropout<> module(p); + module.Deterministic() = false; // Test the Forward function. arma::mat output; @@ -264,7 +264,7 @@ TEST_CASE("SimpleDropoutLayerTest", "[ANNLayerTest]") REQUIRE(arma::as_scalar(arma::abs(arma::mean(delta) - (1 - p))) <= 0.05); // Test the Forward function. - module.Training() = false; + module.Deterministic() = true; module.Forward(input, output); REQUIRE(arma::accu(input) == arma::accu(output)); } @@ -285,8 +285,8 @@ TEST_CASE("DropoutProbabilityTest", "[ANNLayerTest]") double nonzeroCount = 0; for (size_t i = 0; i < iterations; ++i) { - Dropout module(probability[trial]); - module.Training() = true; + Dropout<> module(probability[trial]); + module.Deterministic() = false; arma::mat output; module.Forward(input, output); @@ -310,8 +310,8 @@ TEST_CASE("DropoutProbabilityTest", "[ANNLayerTest]") TEST_CASE("NoDropoutTest", "[ANNLayerTest]") { arma::mat input = arma::ones(1500, 1); - Dropout module(0); - module.Training() = true; + Dropout<> module(0); + module.Deterministic() = false; arma::mat output; module.Forward(input, output); @@ -332,11 +332,11 @@ TEST_CASE("SimpleAlphaDropoutLayerTest", "[ANNLayerTest]") // and variance nearabout 1. arma::mat input = arma::randn(1000, 1); - AlphaDropout module(p); - module.Training() = true; + AlphaDropout<> module(p); + module.Deterministic() = false; // Test the Forward function when training phase. - arma::mat output(arma::size(input)); + arma::mat output; module.Forward(input, output); // Check whether mean remains nearly same. REQUIRE(arma::as_scalar(arma::abs(arma::mean(input) - arma::mean(output))) <= @@ -352,7 +352,7 @@ TEST_CASE("SimpleAlphaDropoutLayerTest", "[ANNLayerTest]") REQUIRE(arma::as_scalar(arma::abs(arma::mean(delta) - 0)) <= 0.05); // Test the Forward function when testing phase. - module.Training() = false; + module.Deterministic() = true; module.Forward(input, output); REQUIRE(arma::accu(input) == arma::accu(output)); } @@ -373,10 +373,10 @@ TEST_CASE("AlphaDropoutProbabilityTest", "[ANNLayerTest]") double nonzeroCount = 0; for (size_t i = 0; i < iterations; ++i) { - AlphaDropout module(probability[trial]); - module.Training() = true; + AlphaDropout<> module(probability[trial]); + module.Deterministic() = false; - arma::mat output(arma::size(input)); + arma::mat output; module.Forward(input, output); // Return a column vector containing the indices of elements of X @@ -401,8 +401,8 @@ TEST_CASE("AlphaDropoutProbabilityTest", "[ANNLayerTest]") TEST_CASE("NoAlphaDropoutTest", "[ANNLayerTest]") { arma::mat input = arma::ones(1500, 1); - AlphaDropout module(0); - module.Training() = false; + AlphaDropout<> module(0); + module.Deterministic() = false; arma::mat output; module.Forward(input, output); @@ -410,89 +410,90 @@ TEST_CASE("NoAlphaDropoutTest", "[ANNLayerTest]") REQUIRE(arma::accu(output) == arma::accu(input)); } -// /** -// * Simple linear module test. -// */ -// TEST_CASE("SimpleLinearLayerTest", "[ANNLayerTest]") -// { -// arma::mat output, input, delta; -// Linear<> module(10, 10); -// module.Parameters().randu(); -// module.Reset(); +/** + * Simple linear module test. + */ +TEST_CASE("SimpleLinearLayerTest", "[ANNLayerTest]") +{ + arma::mat output, input, delta; + Linear<> module(10, 10); + module.Parameters().randu(); + module.Reset(); -// // Test the Forward function. -// input = arma::zeros(10, 1); -// module.Forward(input, output); -// REQUIRE(arma::accu(module.Parameters().submat(100, -// 0, module.Parameters().n_elem - 1, 0)) == -// Approx(arma::accu(output)).epsilon(1e-5)); + // Test the Forward function. + input = arma::zeros(10, 1); + module.Forward(input, output); + REQUIRE(arma::accu(module.Parameters().submat(100, + 0, module.Parameters().n_elem - 1, 0)) == + Approx(arma::accu(output)).epsilon(1e-5)); -// // Test the Backward function. -// module.Backward(input, input, delta); -// REQUIRE(arma::accu(delta) == 0); -// } + // Test the Backward function. + module.Backward(input, input, delta); + REQUIRE(arma::accu(delta) == 0); +} -// /** -// * Jacobian linear module test. -// */ -// TEST_CASE("JacobianLinearLayerTest", "[ANNLayerTest]") -// { -// for (size_t i = 0; i < 5; ++i) -// { -// const size_t inputElements = math::RandInt(2, 1000); -// const size_t outputElements = math::RandInt(2, 1000); +/** + * Jacobian linear module test. + */ +TEST_CASE("JacobianLinearLayerTest", "[ANNLayerTest]") +{ + for (size_t i = 0; i < 5; ++i) + { + const size_t inputElements = math::RandInt(2, 1000); + const size_t outputElements = math::RandInt(2, 1000); -// arma::mat input; -// input.set_size(inputElements, 1); + arma::mat input; + input.set_size(inputElements, 1); -// Linear<> module(inputElements, outputElements); -// module.Parameters().randu(); + Linear<> module(inputElements, outputElements); + module.Parameters().randu(); -// double error = JacobianTest(module, input); -// REQUIRE(error <= 1e-5); -// } -// } + double error = JacobianTest(module, input); + REQUIRE(error <= 1e-5); + } +} -// /** -// * Linear layer numerical gradient test. -// */ -// TEST_CASE("GradientLinearLayerTest", "[ANNLayerTest]") -// { -// // Linear function gradient instantiation. -// struct GradientFunction -// { -// GradientFunction() : -// input(arma::randu(10, 1)), -// target(arma::mat("1")) -// { -// model = new FFN(); -// model->ResetData(input, target); -// model->Add >(); -// model->Add >(10, 10); -// model->Add >(10, 2); -// model->Add >(); -// } +/** + * Linear layer numerical gradient test. + */ +TEST_CASE("GradientLinearLayerTest", "[ANNLayerTest]") +{ + // Linear function gradient instantiation. + struct GradientFunction + { + GradientFunction() : + input(arma::randu(10, 1)), + target(arma::mat("0")) + { + model = new FFN, NguyenWidrowInitialization>(); + model->Predictors() = input; + model->Responses() = target; + model->Add >(); + model->Add >(10, 10); + model->Add >(10, 2); + model->Add >(); + } -// ~GradientFunction() -// { -// delete model; -// } + ~GradientFunction() + { + delete model; + } -// double Gradient(arma::mat& gradient) const -// { -// double error = model->Evaluate(model->Parameters(), 0, 1); -// model->Gradient(model->Parameters(), 0, gradient, 1); -// return error; -// } + double Gradient(arma::mat& gradient) const + { + double error = model->Evaluate(model->Parameters(), 0, 1); + model->Gradient(model->Parameters(), 0, gradient, 1); + return error; + } -// arma::mat& Parameters() { return model->Parameters(); } + arma::mat& Parameters() { return model->Parameters(); } -// FFN* model; -// arma::mat input, target; -// } function; + FFN, NguyenWidrowInitialization>* model; + arma::mat input, target; + } function; -// REQUIRE(CheckGradient(function) <= 1e-4); -// } + REQUIRE(CheckGradient(function) <= 1e-4); +} /** * Simple Linear3D layer test. @@ -505,26 +506,18 @@ TEST_CASE("SimpleLinear3DLayerTest", "[ANNLayerTest]") const size_t batchSize = 1; arma::mat input, output, delta; - // Create a Linear3D layer outside of a network, and then set its memory. - Linear3D module(outSize); - module.InputDimensions() = std::vector({ 4, 2 }); - module.ComputeOutputDimensions(); - arma::mat weights(module.WeightSize(), 1); - module.SetWeights(weights.memptr()); - + Linear3D<> module(inSize, outSize); + module.Reset(); module.Parameters().randu(); // Test the Forward function. input = arma::zeros(inSize * nPoints, batchSize); - output.set_size(outSize * nPoints, batchSize); module.Forward(input, output); REQUIRE(arma::accu(module.Bias()) == Approx(arma::accu(output) / (nPoints * batchSize)).epsilon(1e-3)); // Test the Backward function. - delta.set_size(input.n_rows, input.n_cols); - output.zeros(); - module.Backward(input, output, delta); + module.Backward(input, input, delta); REQUIRE(arma::accu(delta) == 0); } @@ -543,13 +536,7 @@ TEST_CASE("JacobianLinear3DLayerTest", "[ANNLayerTest]") arma::mat input; input.set_size(inSize * nPoints, batchSize); - // Create a Linear3D layer outside a network and initialize its memory. - Linear3D module(outSize); - module.InputDimensions() = std::vector({ inSize, nPoints }); - module.ComputeOutputDimensions(); - arma::mat weights(module.WeightSize(), 1); - module.SetWeights(weights.memptr()); - + Linear3D<> module(inSize, outSize); module.Parameters().randu(); double error = JacobianTest(module, input); @@ -578,10 +565,11 @@ TEST_CASE("GradientLinear3DLayerTest", "[ANNLayerTest]") target(1, 1) = 1; target(1, 2) = 1; - model = new FFN(); - model->ResetData(input, target); - model->Add(outSize); - model->InputDimensions() = std::vector{ 4, 2 }; + model = new FFN, RandomInitialization>(); + model->Predictors() = input; + model->Responses() = target; + model->Add>(); + model->Add>(inSize, outSize); } ~GradientFunction() @@ -598,7 +586,7 @@ TEST_CASE("GradientLinear3DLayerTest", "[ANNLayerTest]") arma::mat& Parameters() { return model->Parameters(); } - FFN* model; + FFN, RandomInitialization>* model; arma::mat input, target; const size_t inSize; const size_t outSize; @@ -609,79 +597,80 @@ TEST_CASE("GradientLinear3DLayerTest", "[ANNLayerTest]") REQUIRE(CheckGradient(function) <= 1e-7); } -// /** -// * Simple noisy linear module test. -// */ -// TEST_CASE("SimpleNoisyLinearLayerTest", "[ANNLayerTest]") -// { -// arma::mat output, input, delta; -// NoisyLinear<> module(10, 10); -// module.Parameters().randu(); -// module.Reset(); +/** + * Simple noisy linear module test. + */ +TEST_CASE("SimpleNoisyLinearLayerTest", "[ANNLayerTest]") +{ + arma::mat output, input, delta; + NoisyLinear<> module(10, 10); + module.Parameters().randu(); + module.Reset(); -// // Test the Backward function. -// module.Backward(input, input, delta); -// REQUIRE(arma::accu(delta) == 0); -// } + // Test the Backward function. + module.Backward(input, input, delta); + REQUIRE(arma::accu(delta) == 0); +} -// /** -// * Jacobian noisy linear module test. -// */ -// TEST_CASE("JacobianNoisyLinearLayerTest", "[ANNLayerTest]") -// { -// const size_t inputElements = math::RandInt(2, 1000); -// const size_t outputElements = math::RandInt(2, 1000); +/** + * Jacobian noisy linear module test. + */ +TEST_CASE("JacobianNoisyLinearLayerTest", "[ANNLayerTest]") +{ + const size_t inputElements = math::RandInt(2, 1000); + const size_t outputElements = math::RandInt(2, 1000); -// arma::mat input; -// input.set_size(inputElements, 1); + arma::mat input; + input.set_size(inputElements, 1); -// NoisyLinear<> module(inputElements, outputElements); -// module.Parameters().randu(); + NoisyLinear<> module(inputElements, outputElements); + module.Parameters().randu(); -// double error = JacobianTest(module, input); -// REQUIRE(error <= 1e-5); -// } + double error = JacobianTest(module, input); + REQUIRE(error <= 1e-5); +} -// /** -// * Noisy Linear layer numerical gradient test. -// */ -// TEST_CASE("GradientNoisyLinearLayerTest", "[ANNLayerTest]") -// { -// // Noisy linear function gradient instantiation. -// struct GradientFunction -// { -// GradientFunction() : -// input(arma::randu(10, 1)), -// target(arma::mat("1")) -// { -// model = new FFN(); -// model->ResetData(input, target); -// model->Add >(); -// model->Add >(10, 10); -// model->Add >(10, 2); -// model->Add >(); -// } +/** + * Noisy Linear layer numerical gradient test. + */ +TEST_CASE("GradientNoisyLinearLayerTest", "[ANNLayerTest]") +{ + // Noisy linear function gradient instantiation. + struct GradientFunction + { + GradientFunction() : + input(arma::randu(10, 1)), + target(arma::mat("0")) + { + model = new FFN, NguyenWidrowInitialization>(); + model->Predictors() = input; + model->Responses() = target; + model->Add >(); + model->Add >(10, 10); + model->Add >(10, 2); + model->Add >(); + } -// ~GradientFunction() -// { -// delete model; -// } + ~GradientFunction() + { + delete model; + } -// double Gradient(arma::mat& gradient) const -// { -// double error = model->Evaluate(model->Parameters(), 0, 1); -// model->Gradient(model->Parameters(), 0, gradient, 1); -// return error; -// } + double Gradient(arma::mat& gradient) const + { + double error = model->Evaluate(model->Parameters(), 0, 1); + model->Gradient(model->Parameters(), 0, gradient, 1); + return error; + } -// arma::mat& Parameters() { return model->Parameters(); } + arma::mat& Parameters() { return model->Parameters(); } -// FFN* model; -// arma::mat input, target; -// } function; + FFN, NguyenWidrowInitialization>* model; + arma::mat input, target; + } function; -// REQUIRE(CheckGradient(function) <= 1e-4); -// } + REQUIRE(CheckGradient(function) <= 1e-4); +} /** * Simple linear no bias module test. @@ -689,13 +678,9 @@ TEST_CASE("GradientLinear3DLayerTest", "[ANNLayerTest]") TEST_CASE("SimpleLinearNoBiasLayerTest", "[ANNLayerTest]") { arma::mat output, input, delta; - LinearNoBias module(10); - arma::mat weights(10 * 10, 1); - module.InputDimensions() = std::vector({ 10 }); - module.ComputeOutputDimensions(); - module.SetWeights(weights.memptr()); - + LinearNoBias<> module(10, 10); module.Parameters().randu(); + module.Reset(); // Test the Forward function. input = arma::zeros(10, 1); @@ -703,7 +688,7 @@ TEST_CASE("SimpleLinearNoBiasLayerTest", "[ANNLayerTest]") REQUIRE(0 == arma::accu(output)); // Test the Backward function. - module.Backward(input, output, delta); + module.Backward(input, input, delta); REQUIRE(arma::accu(delta) == 0); } @@ -712,61 +697,39 @@ TEST_CASE("SimpleLinearNoBiasLayerTest", "[ANNLayerTest]") */ TEST_CASE("SimplePaddingLayerTest", "[ANNLayerTest]") { - arma::mat output, input, delta; - Padding module(1, 2, 3, 4); - module.InputDimensions() = std::vector({ 2, 5 }); - module.ComputeOutputDimensions(); + arma::mat output, input, delta, input1, output1; + Padding<> module(1, 2, 3, 4); // Test the Forward function. input = arma::randu(10, 1); - size_t totalOutputDimensions = module.OutputDimensions()[0]; - for (size_t i = 1; i < module.OutputDimensions().size(); ++i) - totalOutputDimensions *= module.OutputDimensions()[i]; - output.set_size(totalOutputDimensions, input.n_cols); - output.randu(); module.Forward(input, output); - REQUIRE(arma::accu(input) == Approx(arma::accu(output))); - REQUIRE(output.n_rows == (9 * 8)); // 2x5 --> 9x8 + REQUIRE(arma::accu(input) == arma::accu(output)); + REQUIRE(output.n_rows == input.n_rows + 3); + REQUIRE(output.n_cols == input.n_cols + 7); // Test the Backward function. - delta.set_size(input.n_rows, input.n_cols); module.Backward(input, output, delta); CheckMatrices(delta, input); // Test forward function for multiple filters. // Here it's 3 filters with height = 224, width = 224 // the output should be [226 * 226 * 3, 1] with 1 padding. - module = Padding(1, 1, 1, 1); - module.InputDimensions() = std::vector({ 224, 224, 3 }); - module.ComputeOutputDimensions(); - - input = arma::randu(224 * 224 * 3, 1); - totalOutputDimensions = module.OutputDimensions()[0]; - for (size_t i = 1; i < module.OutputDimensions().size(); ++i) - totalOutputDimensions *= module.OutputDimensions()[i]; - output.set_size(totalOutputDimensions, input.n_cols); - output.randu(); - module.Forward(input, output); - REQUIRE(arma::accu(input) == Approx(arma::accu(output))); - REQUIRE(output.n_rows == (226 * 226 * 3)); - REQUIRE(output.n_cols == 1); + Padding<> module1(1, 1, 1, 1, 224, 224); + input1 = arma::randu(224 * 224 * 3, 1); + module1.Forward(input1, output1); + REQUIRE(arma::accu(input1) == arma::accu(output1)); + REQUIRE(output1.n_rows == (226 * 226 * 3)); + REQUIRE(output1.n_cols == 1); // Test forward function for multiple batches with multiple filters. // Here it's 3 filters with height = 244, width = 244 // the output should be [246 * 246 * 3, 3] with 1 padding. - module.InputDimensions() = std::vector({ 244, 244, 3 }); - module.ComputeOutputDimensions(); - totalOutputDimensions = module.OutputDimensions()[0]; - for (size_t i = 1; i < module.OutputDimensions().size(); ++i) - totalOutputDimensions *= module.OutputDimensions()[i]; - - input = arma::randu(244 * 244 * 3, 3); - output.set_size(totalOutputDimensions, input.n_cols); - output.randu(); - module.Forward(input, output); - REQUIRE(output.n_rows == (246 * 246 * 3)); - REQUIRE(output.n_cols == 3); - REQUIRE(arma::accu(input) == Approx(arma::accu(output))); + Padding<> module2(1, 1, 1, 1, 244, 244); + input1 = arma::randu(244 * 244 * 3, 3); + module2.Forward(input1, output1); + REQUIRE(arma::accu(input1) == arma::accu(output1)); + REQUIRE(output1.n_rows == (246 * 246 * 3)); + REQUIRE(output1.n_cols == 3); } /** @@ -782,12 +745,7 @@ TEST_CASE("JacobianLinearNoBiasLayerTest", "[ANNLayerTest]") arma::mat input; input.set_size(inputElements, 1); - LinearNoBias module(outputElements); - arma::mat weights(inputElements * outputElements, 1); - module.InputDimensions() = std::vector({ inputElements }); - module.ComputeOutputDimensions(); - module.SetWeights(weights.memptr()); - + LinearNoBias<> module(inputElements, outputElements); module.Parameters().randu(); double error = JacobianTest(module, input); @@ -807,11 +765,13 @@ TEST_CASE("GradientLinearNoBiasLayerTest", "[ANNLayerTest]") input(arma::randu(10, 1)), target(arma::mat("0")) { - model = new FFN(); - model->ResetData(input, target); - model->Add(10); - model->Add(2); - model->Add(); + model = new FFN, NguyenWidrowInitialization>(); + model->Predictors() = input; + model->Responses() = target; + model->Add >(); + model->Add >(10, 10); + model->Add >(10, 2); + model->Add >(); } ~GradientFunction() @@ -828,37 +788,37 @@ TEST_CASE("GradientLinearNoBiasLayerTest", "[ANNLayerTest]") arma::mat& Parameters() { return model->Parameters(); } - FFN* model; + FFN, NguyenWidrowInitialization>* model; arma::mat input, target; } function; REQUIRE(CheckGradient(function) <= 1e-4); } -// /** -// * Jacobian negative log likelihood module test. -// */ -// TEST_CASE("JacobianNegativeLogLikelihoodLayerTest", "[ANNLayerTest]") -// { -// for (size_t i = 0; i < 5; ++i) -// { -// NegativeLogLikelihood module; -// const size_t inputElements = math::RandInt(5, 100); -// arma::mat input; -// RandomInitialization init(0, 1); -// init.Initialize(input, inputElements, 1); +/** + * Jacobian negative log likelihood module test. + */ +TEST_CASE("JacobianNegativeLogLikelihoodLayerTest", "[ANNLayerTest]") +{ + for (size_t i = 0; i < 5; ++i) + { + NegativeLogLikelihood<> module; + const size_t inputElements = math::RandInt(5, 100); + arma::mat input; + RandomInitialization init(0, 1); + init.Initialize(input, inputElements, 1); -// arma::mat target(1, 1); -// target(0) = math::RandInt(0, inputElements - 2); + arma::mat target(1, 1); + target(0) = math::RandInt(0, inputElements - 2); -// double error = JacobianPerformanceTest(module, input, target); -// REQUIRE(error <= 1e-5); -// } -// } + double error = JacobianPerformanceTest(module, input, target); + REQUIRE(error <= 1e-5); + } +} /** * Jacobian LeakyReLU module test. - * + */ TEST_CASE("JacobianLeakyReLULayerTest", "[ANNLayerTest]") { for (size_t i = 0; i < 5; ++i) @@ -868,17 +828,16 @@ TEST_CASE("JacobianLeakyReLULayerTest", "[ANNLayerTest]") arma::mat input; input.set_size(inputElements, 1); - LeakyReLU module; + LeakyReLU<> module; double error = JacobianTest(module, input); REQUIRE(error <= 1e-5); } } -*/ /** * Jacobian FlexibleReLU module test. - * + */ TEST_CASE("JacobianFlexibleReLULayerTest", "[ANNLayerTest]") { for (size_t i = 0; i < 5; ++i) @@ -888,17 +847,16 @@ TEST_CASE("JacobianFlexibleReLULayerTest", "[ANNLayerTest]") arma::mat input; input.set_size(inputElements, 1); - FlexibleReLU module; + FlexibleReLU<> module; double error = JacobianTest(module, input); REQUIRE(error <= 1e-5); } } -*/ /** * Flexible ReLU layer numerical gradient test. - * + */ TEST_CASE("GradientFlexibleReLULayerTest", "[ANNLayerTest]") { // Add function gradient instantiation. @@ -908,14 +866,15 @@ TEST_CASE("GradientFlexibleReLULayerTest", "[ANNLayerTest]") input(arma::randu(2, 1)), target(arma::mat("0")) { - model = new FFN( - NegativeLogLikelihood(), RandomInitialization(0.1, 0.5)); + model = new FFN, RandomInitialization>( + NegativeLogLikelihood<>(), RandomInitialization(0.1, 0.5)); - model->ResetData(input, target); - model->Add(2, 2); - model->Add(2, 5); - model->Add(0.05); - model->Add(); + model->Predictors() = input; + model->Responses() = target; + model->Add >(2, 2); + model->Add >(2, 5); + model->Add >(0.05); + model->Add >(); } ~GradientFunction() @@ -932,17 +891,16 @@ TEST_CASE("GradientFlexibleReLULayerTest", "[ANNLayerTest]") arma::mat& Parameters() { return model->Parameters(); } - FFN* model; + FFN, RandomInitialization>* model; arma::mat input, target; } function; REQUIRE(CheckGradient(function) <= 1e-4); } -*/ /** * Jacobian MultiplyConstant module test. - * + */ TEST_CASE("JacobianMultiplyConstantLayerTest", "[ANNLayerTest]") { for (size_t i = 0; i < 5; ++i) @@ -952,50 +910,49 @@ TEST_CASE("JacobianMultiplyConstantLayerTest", "[ANNLayerTest]") arma::mat input; input.set_size(inputElements, 1); - MultiplyConstant module(3.0); + MultiplyConstant<> module(3.0); double error = JacobianTest(module, input); REQUIRE(error <= 1e-5); } } -*/ /** * Check whether copying and moving network with MultiplyConstant is working or * not. */ -// TEST_CASE("CheckCopyMoveMultiplyConstantTest", "[ANNLayerTest]") -// { -// arma::mat input(2, 1000); -// input.randu(); -// -// arma::mat output1; -// arma::mat output2; -// arma::mat output3; -// arma::mat output4; -// -// MultiplyConstant<> *module1 = new MultiplyConstant<>(3.0); -// module1->Forward(input, output1); -// -// MultiplyConstant<> module2 = *module1; -// delete module1; -// -// module2.Forward(input, output2); -// CheckMatrices(output1, output2); -// -// MultiplyConstant<> *module3 = new MultiplyConstant<>(3.0); -// module3->Forward(input, output3); -// -// MultiplyConstant<> module4(std::move(*module3)); -// delete module3; -// -// module4.Forward(input, output4); -// CheckMatrices(output3, output4); -// } +TEST_CASE("CheckCopyMoveMultiplyConstantTest", "[ANNLayerTest]") +{ + arma::mat input(2, 1000); + input.randu(); + + arma::mat output1; + arma::mat output2; + arma::mat output3; + arma::mat output4; + + MultiplyConstant<> *module1 = new MultiplyConstant<>(3.0); + module1->Forward(input, output1); + + MultiplyConstant<> module2 = *module1; + delete module1; + + module2.Forward(input, output2); + CheckMatrices(output1, output2); + + MultiplyConstant<> *module3 = new MultiplyConstant<>(3.0); + module3->Forward(input, output3); + + MultiplyConstant<> module4(std::move(*module3)); + delete module3; + + module4.Forward(input, output4); + CheckMatrices(output3, output4); +} /** * Jacobian HardTanH module test. - * + */ TEST_CASE("JacobianHardTanHLayerTest", "[ANNLayerTest]") { for (size_t i = 0; i < 5; ++i) @@ -1005,20 +962,18 @@ TEST_CASE("JacobianHardTanHLayerTest", "[ANNLayerTest]") arma::mat input; input.set_size(inputElements, 1); - HardTanH module; + HardTanH<> module; double error = JacobianTest(module, input); REQUIRE(error <= 1e-5); } } -*/ /** * Simple select module test. - * + */ TEST_CASE("SimpleSelectLayerTest", "[ANNLayerTest]") { - // TODO: this needs to be adapted arma::mat outputA, outputB, input, delta; input = arma::ones(10, 5); @@ -1028,12 +983,12 @@ TEST_CASE("SimpleSelectLayerTest", "[ANNLayerTest]") } // Test the Forward function. - Select moduleA(3); + Select<> moduleA(3); moduleA.Forward(input, outputA); REQUIRE(30 == arma::accu(outputA)); // Test the Forward function. - Select moduleB(3, 5); + Select<> moduleB(3, 5); moduleB.Forward(input, outputB); REQUIRE(15 == arma::accu(outputB)); @@ -1045,33 +1000,31 @@ TEST_CASE("SimpleSelectLayerTest", "[ANNLayerTest]") moduleB.Backward(input, outputA, delta); REQUIRE(15 == arma::accu(delta)); } -*/ /** * Test that the functions that can access the parameters of the * Select layer work. - * + */ TEST_CASE("SelectLayerParametersTest", "[ANNLayerTest]") { // Parameter order : index, elements. - Select layer(3, 5); + Select<> layer(3, 5); // Make sure we can get the parameters successfully. REQUIRE(layer.Index() == 3); REQUIRE(layer.NumElements() == 5); } -*/ /** * Simple join module test. - * + */ TEST_CASE("SimpleJoinLayerTest", "[ANNLayerTest]") { arma::mat output, input, delta; input = arma::ones(10, 5); // Test the Forward function. - Join module; + Join<> module; module.Forward(input, output); REQUIRE(50 == arma::accu(output)); @@ -1085,1234 +1038,635 @@ TEST_CASE("SimpleJoinLayerTest", "[ANNLayerTest]") b = delta.n_rows == input.n_rows && input.n_cols; REQUIRE(b == true); } -*/ - -// /** -// * Simple add merge module test. -// */ -// TEST_CASE("SimpleAddMergeLayerTest", "[ANNLayerTest]") -// { -// arma::mat output, input, delta; -// input = arma::ones(10, 1); - -// for (size_t i = 0; i < 5; ++i) -// { -// AddMerge<> module(false, false); -// const size_t numMergeModules = math::RandInt(2, 10); -// for (size_t m = 0; m < numMergeModules; ++m) -// { -// IdentityLayer<> identityLayer; -// identityLayer.Forward(input, identityLayer.OutputParameter()); - -// module.Add >(identityLayer); -// } - -// // Test the Forward function. -// module.Forward(input, output); -// REQUIRE(10 * numMergeModules == arma::accu(output)); - -// // Test the Backward function. -// module.Backward(input, output, delta); -// REQUIRE(arma::accu(output) == arma::accu(delta)); -// } -// } - -// /** -// * Test the LSTM layer with a user defined rho parameter and without. -// */ -// TEST_CASE("LSTMRrhoTest", "[ANNLayerTest]") -// { -// const size_t rho = 5; -// arma::cube input = arma::randu(1, 1, 5); -// arma::cube target = arma::ones(1, 1, 5); -// RandomInitialization init(0.5, 0.5); - -// // Create model with user defined rho parameter. -// RNN modelA( -// rho, false, NegativeLogLikelihood(), init); -// modelA.Add >(); -// modelA.Add >(1, 10); - -// // Use LSTM layer with rho. -// modelA.Add >(10, 3, rho); -// modelA.Add >(); - -// // Create model without user defined rho parameter. -// RNN modelB( -// rho, false, NegativeLogLikelihood(), init); -// modelB.Add >(); -// modelB.Add >(1, 10); - -// // Use LSTM layer with rho = MAXSIZE. -// modelB.Add >(10, 3); -// modelB.Add >(); - -// ens::StandardSGD opt(0.1, 1, 5, -100, false); -// modelA.Train(input, target, opt); -// modelB.Train(input, target, opt); - -// CheckMatrices(modelB.Parameters(), modelA.Parameters()); -// } - -// /** -// * LSTM layer numerical gradient test. -// */ -// TEST_CASE("GradientLSTMLayerTest", "[ANNLayerTest]") -// { -// // LSTM function gradient instantiation. -// struct GradientFunction -// { -// GradientFunction() : -// input(arma::randu(1, 1, 5)), -// target(arma::ones(1, 1, 5)) -// { -// const size_t rho = 5; - -// model = new RNN(rho); -// model->ResetData(input, target); -// model->Add >(); -// model->Add >(1, 10); -// model->Add >(10, 3, rho); -// model->Add >(); -// } - -// ~GradientFunction() -// { -// delete model; -// } - -// double Gradient(arma::mat& gradient) const -// { -// double error = model->Evaluate(model->Parameters(), 0, 1); -// model->Gradient(model->Parameters(), 0, gradient, 1); -// return error; -// } - -// arma::mat& Parameters() { return model->Parameters(); } - -// RNN* model; -// arma::cube input, target; -// } function; - -// REQUIRE(CheckGradient(function) <= 1e-4); -// } - -// /** -// * Test that the functions that can modify and access the parameters of the -// * LSTM layer work. -// */ -// TEST_CASE("LSTMLayerParametersTest", "[ANNLayerTest]") -// { -// // Parameter order : inSize, outSize, rho. -// LSTM<> layer1(1, 2, 3); -// LSTM<> layer2(1, 2, 4); - -// // Make sure we can get the parameters successfully. -// REQUIRE(layer1.InSize() == 1); -// REQUIRE(layer1.OutSize() == 2); -// REQUIRE(layer1.Rho() == 3); - -// // Now modify the parameters to match the second layer. -// layer1.Rho() = 4; - -// // Now ensure all the results are the same. -// REQUIRE(layer1.InSize() == layer2.InSize()); -// REQUIRE(layer1.OutSize() == layer2.OutSize()); -// REQUIRE(layer1.Rho() == layer2.Rho()); -// } - -// /** -// * Test the FastLSTM layer with a user defined rho parameter and without. -// */ -// TEST_CASE("FastLSTMRrhoTest", "[ANNLayerTest]") -// { -// const size_t rho = 5; -// arma::cube input = arma::randu(1, 1, 5); -// arma::cube target = arma::ones(1, 1, 5); -// RandomInitialization init(0.5, 0.5); - -// // Create model with user defined rho parameter. -// RNN modelA( -// rho, false, NegativeLogLikelihood(), init); -// modelA.Add >(); -// modelA.Add >(1, 10); - -// // Use FastLSTM layer with rho. -// modelA.Add >(10, 3, rho); -// modelA.Add >(); - -// // Create model without user defined rho parameter. -// RNN modelB( -// rho, false, NegativeLogLikelihood(), init); -// modelB.Add >(); -// modelB.Add >(1, 10); - -// // Use FastLSTM layer with rho = MAXSIZE. -// modelB.Add >(10, 3); -// modelB.Add >(); - -// ens::StandardSGD opt(0.1, 1, 5, -100, false); -// modelA.Train(input, target, opt); -// modelB.Train(input, target, opt); - -// CheckMatrices(modelB.Parameters(), modelA.Parameters()); -// } - -// /** -// * FastLSTM layer numerical gradient test. -// */ -// TEST_CASE("GradientFastLSTMLayerTest", "[ANNLayerTest]") -// { -// // Fast LSTM function gradient instantiation. -// struct GradientFunction -// { -// GradientFunction() : -// input(arma::randu(1, 1, 5)), -// target(arma::ones(1, 1, 5)) -// { -// const size_t rho = 5; - -// model = new RNN(rho); -// model->ResetData(input, target); -// model->Add >(); -// model->Add >(1, 10); -// model->Add >(10, 3, rho); -// model->Add >(); -// } - -// ~GradientFunction() -// { -// delete model; -// } - -// double Gradient(arma::mat& gradient) const -// { -// double error = model->Evaluate(model->Parameters(), 0, 1); -// model->Gradient(model->Parameters(), 0, gradient, 1); -// return error; -// } - -// arma::mat& Parameters() { return model->Parameters(); } - -// RNN* model; -// arma::cube input, target; -// } function; - -// // The threshold should be << 0.1 but since the Fast LSTM layer uses an -// // approximation of the sigmoid function the estimated gradient is not -// // correct. -// REQUIRE(CheckGradient(function) <= 0.2); -// } - -// /** -// * Test that the functions that can modify and access the parameters of the -// * Fast LSTM layer work. -// */ -// TEST_CASE("FastLSTMLayerParametersTest", "[ANNLayerTest]") -// { -// // Parameter order : inSize, outSize, rho. -// FastLSTM<> layer1(1, 2, 3); -// FastLSTM<> layer2(1, 2, 4); - -// // Make sure we can get the parameters successfully. -// REQUIRE(layer1.InSize() == 1); -// REQUIRE(layer1.OutSize() == 2); -// REQUIRE(layer1.Rho() == 3); - -// // Now modify the parameters to match the second layer. -// layer1.Rho() = 4; - -// // Now ensure all the results are the same. -// REQUIRE(layer1.InSize() == layer2.InSize()); -// REQUIRE(layer1.OutSize() == layer2.OutSize()); -// REQUIRE(layer1.Rho() == layer2.Rho()); -// } - -// /** -// * Check whether copying and moving network with FastLSTM is working or not. -// */ -// TEST_CASE("CheckCopyMoveFastLSTMTest", "[ANNLayerTest]") -// { -// arma::cube input = arma::randu(1, 1, 5); -// arma::cube target = arma::ones(1, 1, 5); -// const size_t rho = 5; - -// RNN *model1 = -// new RNN(rho); -// model1->ResetData(input, target); -// model1->Add >(); -// model1->Add >(1, 10); -// model1->Add >(10, 3, rho); -// model1->Add >(); - -// RNN *model2 = -// new RNN(rho); -// model2->ResetData(input, target); -// model2->Add >(); -// model2->Add >(1, 10); -// model2->Add >(10, 3, rho); -// model2->Add >(); - -// // Check whether copy constructor is working or not. -// CheckRNNCopyFunction<>(model1, input, target, 1); - -// // Check whether move constructor is working or not. -// CheckRNNMoveFunction<>(model2, input, target, 1); -// } - -// /** -// * Check whether copying and moving network with LSTM is working or not. -// */ -// TEST_CASE("CheckCopyMoveLSTMTest", "[ANNLayerTest]") -// { -// arma::cube input = arma::randu(1, 1, 5); -// arma::cube target = arma::ones(1, 1, 5); -// const size_t rho = 5; - -// RNN *model1 = -// new RNN(rho); -// model1->ResetData(input, target); -// model1->Add >(); -// model1->Add >(1, 10); -// model1->Add >(10, 3, rho); -// model1->Add >(); - -// RNN *model2 = -// new RNN(rho); -// model2->ResetData(input, target); -// model2->Add >(); -// model2->Add >(1, 10); -// model2->Add >(10, 3, rho); -// model2->Add >(); - -// // Check whether copy constructor is working or not. -// CheckRNNCopyFunction<>(model1, input, target, 1); - -// // Check whether move constructor is working or not. -// CheckRNNMoveFunction<>(model2, input, target, 1); -// } - -// /** -// * Testing the overloaded Forward() of the LSTM layer, for retrieving the cell -// * state. Besides output, the overloaded function provides read access to cell -// * state of the LSTM layer. -// */ -// TEST_CASE("ReadCellStateParamLSTMLayerTest", "[ANNLayerTest]") -// { -// const size_t rho = 5, inputSize = 3, outputSize = 2; - -// // Provide input of all ones. -// arma::cube input = arma::ones(inputSize, outputSize, rho); - -// arma::mat inputGate, forgetGate, outputGate, hidden; -// arma::mat outLstm, cellLstm; - -// // LSTM layer. -// LSTM<> lstm(inputSize, outputSize, rho); -// lstm.Reset(); -// lstm.ResetCell(rho); - -// // Initialize the weights to all ones. -// lstm.Parameters().ones(); - -// arma::mat inputWeight = arma::ones(outputSize, inputSize); -// arma::mat outputWeight = arma::ones(outputSize, outputSize); -// arma::mat bias = arma::ones(outputSize, input.n_cols); -// arma::mat cellCalc = arma::zeros(outputSize, input.n_cols); -// arma::mat outCalc = arma::zeros(outputSize, input.n_cols); - -// for (size_t seqNum = 0; seqNum < rho; ++seqNum) -// { -// // Wrap a matrix around our data to avoid a copy. -// arma::mat stepData(input.slice(seqNum).memptr(), -// input.n_rows, input.n_cols, false, true); - -// // Apply Forward() on LSTM layer. -// lstm.Forward(stepData, // Input. -// outLstm, // Output. -// cellLstm, // Cell state. -// false); // Don't write into the cell state. - -// // Compute the value of cell state and output. -// // i = sigmoid(W.dot(x) + W.dot(h) + W.dot(c) + b). -// inputGate = 1.0 /(1 + arma::exp(-(inputWeight * stepData + -// outputWeight * outCalc + outputWeight % cellCalc + bias))); - -// // f = sigmoid(W.dot(x) + W.dot(h) + W.dot(c) + b). -// forgetGate = 1.0 /(1 + arma::exp(-(inputWeight * stepData + -// outputWeight * outCalc + outputWeight % cellCalc + bias))); - -// // z = tanh(W.dot(x) + W.dot(h) + b). -// hidden = arma::tanh(inputWeight * stepData + -// outputWeight * outCalc + bias); - -// // c = f * c + i * z. -// cellCalc = forgetGate % cellCalc + inputGate % hidden; - -// // o = sigmoid(W.dot(x) + W.dot(h) + W.dot(c) + b). -// outputGate = 1.0 /(1 + arma::exp(-(inputWeight * stepData + -// outputWeight * outCalc + outputWeight % cellCalc + bias))); - -// // h = o * tanh(c). -// outCalc = outputGate % arma::tanh(cellCalc); - -// CheckMatrices(outLstm, outCalc, 1e-12); -// CheckMatrices(cellLstm, cellCalc, 1e-12); -// } -// } - -// /** -// * Testing the overloaded Forward() of the LSTM layer, for retrieving the cell -// * state. Besides output, the overloaded function provides write access to cell -// * state of the LSTM layer. -// */ -// TEST_CASE("WriteCellStateParamLSTMLayerTest", "[ANNLayerTest]") -// { -// const size_t rho = 5, inputSize = 3, outputSize = 2; - -// // Provide input of all ones. -// arma::cube input = arma::ones(inputSize, outputSize, rho); - -// arma::mat inputGate, forgetGate, outputGate, hidden; -// arma::mat outLstm, cellLstm; -// arma::mat cellCalc; - -// // LSTM layer. -// LSTM<> lstm(inputSize, outputSize, rho); -// lstm.Reset(); -// lstm.ResetCell(rho); - -// // Initialize the weights to all ones. -// lstm.Parameters().ones(); - -// arma::mat inputWeight = arma::ones(outputSize, inputSize); -// arma::mat outputWeight = arma::ones(outputSize, outputSize); -// arma::mat bias = arma::ones(outputSize, input.n_cols); -// arma::mat outCalc = arma::zeros(outputSize, input.n_cols); - -// for (size_t seqNum = 0; seqNum < rho; ++seqNum) -// { -// // Wrap a matrix around our data to avoid a copy. -// arma::mat stepData(input.slice(seqNum).memptr(), -// input.n_rows, input.n_cols, false, true); - -// if (cellLstm.is_empty()) -// { -// // Set the cell state to zeros. -// cellLstm = arma::zeros(outputSize, input.n_cols); -// cellCalc = arma::zeros(outputSize, input.n_cols); -// } -// else -// { -// // Set the cell state to zeros. -// cellLstm = arma::zeros(cellLstm.n_rows, cellLstm.n_cols); -// cellCalc = arma::zeros(cellCalc.n_rows, cellCalc.n_cols); -// } - -// // Apply Forward() on the LSTM layer. -// lstm.Forward(stepData, // Input. -// outLstm, // Output. -// cellLstm, // Cell state. -// true); // Write into cell state. - -// // Compute the value of cell state and output. -// // i = sigmoid(W.dot(x) + W.dot(h) + W.dot(c) + b). -// inputGate = 1.0 /(1 + arma::exp(-(inputWeight * stepData + -// outputWeight * outCalc + outputWeight % cellCalc + bias))); - -// // f = sigmoid(W.dot(x) + W.dot(h) + W.dot(c) + b). -// forgetGate = 1.0 /(1 + arma::exp(-(inputWeight * stepData + -// outputWeight * outCalc + outputWeight % cellCalc + bias))); - -// // z = tanh(W.dot(x) + W.dot(h) + b). -// hidden = arma::tanh(inputWeight * stepData + -// outputWeight * outCalc + bias); - -// // c = f * c + i * z. -// cellCalc = forgetGate % cellCalc + inputGate % hidden; - -// // o = sigmoid(W.dot(x) + W.dot(h) + W.dot(c) + b). -// outputGate = 1.0 /(1 + arma::exp(-(inputWeight * stepData + -// outputWeight * outCalc + outputWeight % cellCalc + bias))); - -// // h = o * tanh(c). -// outCalc = outputGate % arma::tanh(cellCalc); - -// CheckMatrices(outLstm, outCalc, 1e-12); -// CheckMatrices(cellLstm, cellCalc, 1e-12); -// } - -// // Attempting to write empty matrix into cell state. -// lstm.Reset(); -// lstm.ResetCell(rho); -// arma::mat stepData(input.slice(0).memptr(), -// input.n_rows, input.n_cols, false, true); - -// lstm.Forward(stepData, // Input. -// outLstm, // Output. -// cellLstm, // Cell state. -// true); // Write into cell state. - -// for (size_t seqNum = 1; seqNum < rho; ++seqNum) -// { -// arma::mat empty; -// // Should throw error. -// REQUIRE_THROWS_AS(lstm.Forward(stepData, // Input. -// outLstm, // Output. -// empty, // Cell state. -// true), // Write into cell state. -// std::runtime_error); -// } -// } - -// /** -// * Test that the functions that can modify and access the parameters of the -// * GRU layer work. -// */ -// TEST_CASE("GRULayerParametersTest", "[ANNLayerTest]") -// { -// // Parameter order : inSize, outSize, rho. -// GRU<> layer1(1, 2, 3); -// GRU<> layer2(1, 2, 4); - -// // Make sure we can get the parameters successfully. -// REQUIRE(layer1.InSize() == 1); -// REQUIRE(layer1.OutSize() == 2); -// REQUIRE(layer1.Rho() == 3); - -// // Now modify the parameters to match the second layer. -// layer1.Rho() = 4; - -// // Now ensure all the results are the same. -// REQUIRE(layer1.InSize() == layer2.InSize()); -// REQUIRE(layer1.OutSize() == layer2.OutSize()); -// REQUIRE(layer1.Rho() == layer2.Rho()); -// } - -// /** -// * Check if the gradients computed by GRU cell are close enough to the -// * approximation of the gradients. -// */ -// TEST_CASE("GradientGRULayerTest", "[ANNLayerTest]") -// { -// // GRU function gradient instantiation. -// struct GradientFunction -// { -// GradientFunction() : -// input(arma::randu(1, 1, 5)), -// target(arma::ones(1, 1, 5)) -// { -// const size_t rho = 5; - -// model = new RNN(rho); -// model->ResetData(input, target); -// model->Add >(); -// model->Add >(1, 10); -// model->Add >(10, 3, rho); -// model->Add >(); -// } - -// ~GradientFunction() -// { -// delete model; -// } - -// double Gradient(arma::mat& gradient) const -// { -// arma::mat output; -// double error = model->Evaluate(model->Parameters(), 0, 1); -// model->Gradient(model->Parameters(), 0, gradient, 1); -// return error; -// } - -// arma::mat& Parameters() { return model->Parameters(); } - -// RNN* model; -// arma::cube input, target; -// } function; - -// REQUIRE(CheckGradient(function) <= 1e-4); -// } - -// /** -// * GRU layer manual forward test. -// */ -// TEST_CASE("ForwardGRULayerTest", "[ANNLayerTest]") -// { -// // This will make it easier to clean memory later. -// GRU<>* gruAlloc = new GRU<>(3, 3, 5); -// GRU<>& gru = *gruAlloc; - -// // Initialize the weights to all ones. -// NetworkInitialization -// networkInit(ConstInitialization(1)); -// networkInit.Initialize(gru.Model(), gru.Parameters()); - -// // Provide input of all ones. -// arma::mat input = arma::ones(3, 1); -// arma::mat output; - -// gru.Forward(input, output); - -// // Compute the z_t gate output. -// arma::mat expectedOutput = arma::ones(3, 1); -// expectedOutput *= -4; -// expectedOutput = arma::exp(expectedOutput); -// expectedOutput = arma::ones(3, 1) / (arma::ones(3, 1) + expectedOutput); -// expectedOutput = (arma::ones(3, 1) - expectedOutput) % expectedOutput; - -// // For the first input the output should be equal to the output of -// // gate z_t as the previous output fed to the cell is all zeros. -// REQUIRE(arma::as_scalar(arma::trans(output) * expectedOutput) <= 1e-2); - -// expectedOutput = output; - -// gru.Forward(input, output); - -// double s = arma::as_scalar(arma::sum(expectedOutput)); - -// // Compute the value of z_t gate for the second input. -// arma::mat z_t = arma::ones(3, 1); -// z_t *= -(s + 4); -// z_t = arma::exp(z_t); -// z_t = arma::ones(3, 1) / (arma::ones(3, 1) + z_t); - -// // Compute the value of o_t gate for the second input. -// arma::mat o_t = arma::ones(3, 1); -// o_t *= -(arma::as_scalar(arma::sum(expectedOutput % z_t)) + 4); -// o_t = arma::exp(o_t); -// o_t = arma::ones(3, 1) / (arma::ones(3, 1) + o_t); - -// // Expected output for the second input. -// expectedOutput = z_t % expectedOutput + (arma::ones(3, 1) - z_t) % o_t; - -// REQUIRE(arma::as_scalar(arma::trans(output) * expectedOutput) <= 1e-2); - -// LayerTypes<> layer(gruAlloc); -// boost::apply_visitor(DeleteVisitor(), layer); -// } /** * Simple add merge module test. */ -// TEST_CASE("SimpleAddMergeLayerTest", "[ANNLayerTest]") -// { -// arma::mat output, input, delta; -// input = arma::ones(10, 1); -// -// for (size_t i = 0; i < 5; ++i) -// { -// AddMerge<> module(false, false); -// const size_t numMergeModules = math::RandInt(2, 10); -// for (size_t m = 0; m < numMergeModules; ++m) -// { -// IdentityLayer<> identityLayer; -// identityLayer.Forward(input, identityLayer.OutputParameter()); -// -// module.Add >(identityLayer); -// } -// -// // Test the Forward function. -// module.Forward(input, output); -// REQUIRE(10 * numMergeModules == arma::accu(output)); -// -// // Test the Backward function. -// module.Backward(input, output, delta); -// REQUIRE(arma::accu(output) == arma::accu(delta)); -// } -// } +TEST_CASE("SimpleAddMergeLayerTest", "[ANNLayerTest]") +{ + arma::mat output, input, delta; + input = arma::ones(10, 1); + + for (size_t i = 0; i < 5; ++i) + { + AddMerge<> module(false, false); + const size_t numMergeModules = math::RandInt(2, 10); + for (size_t m = 0; m < numMergeModules; ++m) + { + IdentityLayer<> identityLayer; + identityLayer.Forward(input, identityLayer.OutputParameter()); + + module.Add >(identityLayer); + } + + // Test the Forward function. + module.Forward(input, output); + REQUIRE(10 * numMergeModules == arma::accu(output)); + + // Test the Backward function. + module.Backward(input, output, delta); + REQUIRE(arma::accu(output) == arma::accu(delta)); + } +} /** * Test the LSTM layer with a user defined rho parameter and without. */ -// TEST_CASE("LSTMRrhoTest", "[ANNLayerTest]") -// { -// const size_t rho = 5; -// arma::cube input = arma::randu(1, 1, 5); -// arma::cube target = arma::zeros(1, 1, 5); -// RandomInitialization init(0.5, 0.5); -// -// // Create model with user defined rho parameter. -// RNN modelA( -// rho, false, NegativeLogLikelihood(), init); -// modelA.Add >(); -// modelA.Add >(1, 10); -// -// // Use LSTM layer with rho. -// modelA.Add >(10, 3, rho); -// modelA.Add >(); -// -// // Create model without user defined rho parameter. -// RNN modelB( -// rho, false, NegativeLogLikelihood(), init); -// modelB.Add >(); -// modelB.Add >(1, 10); -// -// // Use LSTM layer with rho = MAXSIZE. -// modelB.Add >(10, 3); -// modelB.Add >(); -// -// ens::StandardSGD opt(0.1, 1, 5, -100, false); -// modelA.Train(input, target, opt); -// modelB.Train(input, target, opt); -// -// CheckMatrices(modelB.Parameters(), modelA.Parameters()); -// } +TEST_CASE("LSTMRrhoTest", "[ANNLayerTest]") +{ + const size_t rho = 5; + arma::cube input = arma::randu(1, 1, 5); + arma::cube target = arma::zeros(1, 1, 5); + RandomInitialization init(0.5, 0.5); + + // Create model with user defined rho parameter. + RNN, RandomInitialization> modelA( + rho, false, NegativeLogLikelihood<>(), init); + modelA.Add >(); + modelA.Add >(1, 10); + + // Use LSTM layer with rho. + modelA.Add >(10, 3, rho); + modelA.Add >(); + + // Create model without user defined rho parameter. + RNN > modelB( + rho, false, NegativeLogLikelihood<>(), init); + modelB.Add >(); + modelB.Add >(1, 10); + + // Use LSTM layer with rho = MAXSIZE. + modelB.Add >(10, 3); + modelB.Add >(); + + ens::StandardSGD opt(0.1, 1, 5, -100, false); + modelA.Train(input, target, opt); + modelB.Train(input, target, opt); + + CheckMatrices(modelB.Parameters(), modelA.Parameters()); +} /** * LSTM layer numerical gradient test. */ -// TEST_CASE("GradientLSTMLayerTest", "[ANNLayerTest]") -// { -// // LSTM function gradient instantiation. -// struct GradientFunction -// { -// GradientFunction() : -// input(arma::randu(1, 1, 5)), -// target(arma::zeros(1, 1, 5)) -// { -// const size_t rho = 5; -// -// model = new RNN(rho); -// model->ResetData(input, target); -// model->Add >(); -// model->Add >(1, 10); -// model->Add >(10, 3, rho); -// model->Add >(); -// } -// -// ~GradientFunction() -// { -// delete model; -// } -// -// double Gradient(arma::mat& gradient) const -// { -// double error = model->Evaluate(model->Parameters(), 0, 1); -// model->Gradient(model->Parameters(), 0, gradient, 1); -// return error; -// } -// -// arma::mat& Parameters() { return model->Parameters(); } -// -// RNN* model; -// arma::cube input, target; -// } function; -// -// REQUIRE(CheckGradient(function) <= 1e-4); -// } +TEST_CASE("GradientLSTMLayerTest", "[ANNLayerTest]") +{ + // LSTM function gradient instantiation. + struct GradientFunction + { + GradientFunction() : + input(arma::randu(1, 1, 5)), + target(arma::zeros(1, 1, 5)) + { + const size_t rho = 5; + + model = new RNN >(rho); + model->Predictors() = input; + model->Responses() = target; + model->Add >(); + model->Add >(1, 10); + model->Add >(10, 3, rho); + model->Add >(); + } + + ~GradientFunction() + { + delete model; + } + + double Gradient(arma::mat& gradient) const + { + double error = model->Evaluate(model->Parameters(), 0, 1); + model->Gradient(model->Parameters(), 0, gradient, 1); + return error; + } + + arma::mat& Parameters() { return model->Parameters(); } + + RNN >* model; + arma::cube input, target; + } function; + + REQUIRE(CheckGradient(function) <= 1e-4); +} /** * Test that the functions that can modify and access the parameters of the * LSTM layer work. */ -// TEST_CASE("LSTMLayerParametersTest", "[ANNLayerTest]") -// { -// // Parameter order : inSize, outSize, rho. -// LSTM<> layer1(1, 2, 3); -// LSTM<> layer2(1, 2, 4); -// -// // Make sure we can get the parameters successfully. -// REQUIRE(layer1.InSize() == 1); -// REQUIRE(layer1.OutSize() == 2); -// REQUIRE(layer1.Rho() == 3); -// -// // Now modify the parameters to match the second layer. -// layer1.Rho() = 4; -// -// // Now ensure all the results are the same. -// REQUIRE(layer1.InSize() == layer2.InSize()); -// REQUIRE(layer1.OutSize() == layer2.OutSize()); -// REQUIRE(layer1.Rho() == layer2.Rho()); -// } +TEST_CASE("LSTMLayerParametersTest", "[ANNLayerTest]") +{ + // Parameter order : inSize, outSize, rho. + LSTM<> layer1(1, 2, 3); + LSTM<> layer2(1, 2, 4); + + // Make sure we can get the parameters successfully. + REQUIRE(layer1.InSize() == 1); + REQUIRE(layer1.OutSize() == 2); + REQUIRE(layer1.Rho() == 3); + + // Now modify the parameters to match the second layer. + layer1.Rho() = 4; + + // Now ensure all the results are the same. + REQUIRE(layer1.InSize() == layer2.InSize()); + REQUIRE(layer1.OutSize() == layer2.OutSize()); + REQUIRE(layer1.Rho() == layer2.Rho()); +} /** * Test the FastLSTM layer with a user defined rho parameter and without. */ -// TEST_CASE("FastLSTMRrhoTest", "[ANNLayerTest]") -// { -// const size_t rho = 5; -// arma::cube input = arma::randu(1, 1, 5); -// arma::cube target = arma::zeros(1, 1, 5); -// RandomInitialization init(0.5, 0.5); -// -// // Create model with user defined rho parameter. -// RNN modelA( -// rho, false, NegativeLogLikelihood(), init); -// modelA.Add >(); -// modelA.Add >(1, 10); -// -// // Use FastLSTM layer with rho. -// modelA.Add >(10, 3, rho); -// modelA.Add >(); -// -// // Create model without user defined rho parameter. -// RNN modelB( -// rho, false, NegativeLogLikelihood(), init); -// modelB.Add >(); -// modelB.Add >(1, 10); -// -// // Use FastLSTM layer with rho = MAXSIZE. -// modelB.Add >(10, 3); -// modelB.Add >(); -// -// ens::StandardSGD opt(0.1, 1, 5, -100, false); -// modelA.Train(input, target, opt); -// modelB.Train(input, target, opt); -// -// CheckMatrices(modelB.Parameters(), modelA.Parameters()); -// } +TEST_CASE("FastLSTMRrhoTest", "[ANNLayerTest]") +{ + const size_t rho = 5; + arma::cube input = arma::randu(1, 1, 5); + arma::cube target = arma::zeros(1, 1, 5); + RandomInitialization init(0.5, 0.5); + + // Create model with user defined rho parameter. + RNN, RandomInitialization> modelA( + rho, false, NegativeLogLikelihood<>(), init); + modelA.Add >(); + modelA.Add >(1, 10); + + // Use FastLSTM layer with rho. + modelA.Add >(10, 3, rho); + modelA.Add >(); + + // Create model without user defined rho parameter. + RNN > modelB( + rho, false, NegativeLogLikelihood<>(), init); + modelB.Add >(); + modelB.Add >(1, 10); + + // Use FastLSTM layer with rho = MAXSIZE. + modelB.Add >(10, 3); + modelB.Add >(); + + ens::StandardSGD opt(0.1, 1, 5, -100, false); + modelA.Train(input, target, opt); + modelB.Train(input, target, opt); + + CheckMatrices(modelB.Parameters(), modelA.Parameters()); +} /** * FastLSTM layer numerical gradient test. */ -// TEST_CASE("GradientFastLSTMLayerTest", "[ANNLayerTest]") -// { -// // Fast LSTM function gradient instantiation. -// struct GradientFunction -// { -// GradientFunction() : -// input(arma::randu(1, 1, 5)), -// target(arma::zeros(1, 1, 5)) -// { -// const size_t rho = 5; -// -// model = new RNN(rho); -// model->ResetData(input, target); -// model->Add >(); -// model->Add >(1, 10); -// model->Add >(10, 3, rho); -// model->Add >(); -// } -// -// ~GradientFunction() -// { -// delete model; -// } -// -// double Gradient(arma::mat& gradient) const -// { -// double error = model->Evaluate(model->Parameters(), 0, 1); -// model->Gradient(model->Parameters(), 0, gradient, 1); -// return error; -// } -// -// arma::mat& Parameters() { return model->Parameters(); } -// -// RNN* model; -// arma::cube input, target; -// } function; -// -// // The threshold should be << 0.1 but since the Fast LSTM layer uses an -// // approximation of the sigmoid function the estimated gradient is not -// // correct. -// REQUIRE(CheckGradient(function) <= 0.2); -// } +TEST_CASE("GradientFastLSTMLayerTest", "[ANNLayerTest]") +{ + // Fast LSTM function gradient instantiation. + struct GradientFunction + { + GradientFunction() : + input(arma::randu(1, 1, 5)), + target(arma::zeros(1, 1, 5)) + { + const size_t rho = 5; + + model = new RNN >(rho); + model->Predictors() = input; + model->Responses() = target; + model->Add >(); + model->Add >(1, 10); + model->Add >(10, 3, rho); + model->Add >(); + } + + ~GradientFunction() + { + delete model; + } + + double Gradient(arma::mat& gradient) const + { + double error = model->Evaluate(model->Parameters(), 0, 1); + model->Gradient(model->Parameters(), 0, gradient, 1); + return error; + } + + arma::mat& Parameters() { return model->Parameters(); } + + RNN >* model; + arma::cube input, target; + } function; + + // The threshold should be << 0.1 but since the Fast LSTM layer uses an + // approximation of the sigmoid function the estimated gradient is not + // correct. + REQUIRE(CheckGradient(function) <= 0.2); +} /** * Test that the functions that can modify and access the parameters of the * Fast LSTM layer work. */ -// TEST_CASE("FastLSTMLayerParametersTest", "[ANNLayerTest]") -// { -// // Parameter order : inSize, outSize, rho. -// FastLSTM<> layer1(1, 2, 3); -// FastLSTM<> layer2(1, 2, 4); -// -// // Make sure we can get the parameters successfully. -// REQUIRE(layer1.InSize() == 1); -// REQUIRE(layer1.OutSize() == 2); -// REQUIRE(layer1.Rho() == 3); -// -// // Now modify the parameters to match the second layer. -// layer1.Rho() = 4; -// -// // Now ensure all the results are the same. -// REQUIRE(layer1.InSize() == layer2.InSize()); -// REQUIRE(layer1.OutSize() == layer2.OutSize()); -// REQUIRE(layer1.Rho() == layer2.Rho()); -// } +TEST_CASE("FastLSTMLayerParametersTest", "[ANNLayerTest]") +{ + // Parameter order : inSize, outSize, rho. + FastLSTM<> layer1(1, 2, 3); + FastLSTM<> layer2(1, 2, 4); + + // Make sure we can get the parameters successfully. + REQUIRE(layer1.InSize() == 1); + REQUIRE(layer1.OutSize() == 2); + REQUIRE(layer1.Rho() == 3); + + // Now modify the parameters to match the second layer. + layer1.Rho() = 4; + + // Now ensure all the results are the same. + REQUIRE(layer1.InSize() == layer2.InSize()); + REQUIRE(layer1.OutSize() == layer2.OutSize()); + REQUIRE(layer1.Rho() == layer2.Rho()); +} /** * Check whether copying and moving network with FastLSTM is working or not. */ -// TEST_CASE("CheckCopyMoveFastLSTMTest", "[ANNLayerTest]") -// { -// arma::cube input = arma::randu(1, 1, 5); -// arma::cube target = arma::ones(1, 1, 5); -// const size_t rho = 5; -// -// RNN *model1 = -// new RNN(rho); -// model1->ResetData(input, target); -// model1->Add >(); -// model1->Add >(1, 10); -// model1->Add >(10, 3, rho); -// model1->Add >(); -// -// RNN *model2 = -// new RNN(rho); -// model2->ResetData(input, target); -// model2->Add >(); -// model2->Add >(1, 10); -// model2->Add >(10, 3, rho); -// model2->Add >(); -// -// // Check whether copy constructor is working or not. -// CheckRNNCopyFunction<>(model1, input, target, 1); -// -// // Check whether move constructor is working or not. -// CheckRNNMoveFunction<>(model2, input, target, 1); -// } +TEST_CASE("CheckCopyMoveFastLSTMTest", "[ANNLayerTest]") +{ + arma::cube input = arma::randu(1, 1, 5); + arma::cube target = arma::ones(1, 1, 5); + const size_t rho = 5; + + RNN > *model1 = + new RNN >(rho); + model1->Predictors() = input; + model1->Responses() = target; + model1->Add >(); + model1->Add >(1, 10); + model1->Add >(10, 3, rho); + model1->Add >(); + + RNN > *model2 = + new RNN >(rho); + model2->Predictors() = input; + model2->Responses() = target; + model2->Add >(); + model2->Add >(1, 10); + model2->Add >(10, 3, rho); + model2->Add >(); + + // Check whether copy constructor is working or not. + CheckRNNCopyFunction<>(model1, input, target, 1); + + // Check whether move constructor is working or not. + CheckRNNMoveFunction<>(model2, input, target, 1); +} /** * Check whether copying and moving network with LSTM is working or not. */ -// TEST_CASE("CheckCopyMoveLSTMTest", "[ANNLayerTest]") -// { -// arma::cube input = arma::randu(1, 1, 5); -// arma::cube target = arma::ones(1, 1, 5); -// const size_t rho = 5; -// -// RNN *model1 = -// new RNN(rho); -// model1->ResetData(input, target); -// model1->Add >(); -// model1->Add >(1, 10); -// model1->Add >(10, 3, rho); -// model1->Add >(); -// -// RNN *model2 = -// new RNN(rho); -// model2->ResetData(input, target); -// model2->Add >(); -// model2->Add >(1, 10); -// model2->Add >(10, 3, rho); -// model2->Add >(); -// -// // Check whether copy constructor is working or not. -// CheckRNNCopyFunction<>(model1, input, target, 1); -// -// // Check whether move constructor is working or not. -// CheckRNNMoveFunction<>(model2, input, target, 1); -// } +TEST_CASE("CheckCopyMoveLSTMTest", "[ANNLayerTest]") +{ + arma::cube input = arma::randu(1, 1, 5); + arma::cube target = arma::ones(1, 1, 5); + const size_t rho = 5; + + RNN > *model1 = + new RNN >(rho); + model1->Predictors() = input; + model1->Responses() = target; + model1->Add >(); + model1->Add >(1, 10); + model1->Add >(10, 3, rho); + model1->Add >(); + + RNN > *model2 = + new RNN >(rho); + model2->Predictors() = input; + model2->Responses() = target; + model2->Add >(); + model2->Add >(1, 10); + model2->Add >(10, 3, rho); + model2->Add >(); + + // Check whether copy constructor is working or not. + CheckRNNCopyFunction<>(model1, input, target, 1); + + // Check whether move constructor is working or not. + CheckRNNMoveFunction<>(model2, input, target, 1); +} /** * Testing the overloaded Forward() of the LSTM layer, for retrieving the cell * state. Besides output, the overloaded function provides read access to cell * state of the LSTM layer. */ -// TEST_CASE("ReadCellStateParamLSTMLayerTest", "[ANNLayerTest]") -// { -// const size_t rho = 5, inputSize = 3, outputSize = 2; -// -// // Provide input of all ones. -// arma::cube input = arma::ones(inputSize, outputSize, rho); -// -// arma::mat inputGate, forgetGate, outputGate, hidden; -// arma::mat outLstm, cellLstm; -// -// // LSTM layer. -// LSTM<> lstm(inputSize, outputSize, rho); -// lstm.Reset(); -// lstm.ResetCell(rho); -// -// // Initialize the weights to all ones. -// lstm.Parameters().ones(); -// -// arma::mat inputWeight = arma::ones(outputSize, inputSize); -// arma::mat outputWeight = arma::ones(outputSize, outputSize); -// arma::mat bias = arma::ones(outputSize, input.n_cols); -// arma::mat cellCalc = arma::zeros(outputSize, input.n_cols); -// arma::mat outCalc = arma::zeros(outputSize, input.n_cols); -// -// for (size_t seqNum = 0; seqNum < rho; ++seqNum) -// { -// // Wrap a matrix around our data to avoid a copy. -// arma::mat stepData(input.slice(seqNum).memptr(), -// input.n_rows, input.n_cols, false, true); -// -// // Apply Forward() on LSTM layer. -// lstm.Forward(stepData, // Input. -// outLstm, // Output. -// cellLstm, // Cell state. -// false); // Don't write into the cell state. -// -// // Compute the value of cell state and output. -// // i = sigmoid(W.dot(x) + W.dot(h) + W.dot(c) + b). -// inputGate = 1.0 /(1 + arma::exp(-(inputWeight * stepData + -// outputWeight * outCalc + outputWeight % cellCalc + bias))); -// -// // f = sigmoid(W.dot(x) + W.dot(h) + W.dot(c) + b). -// forgetGate = 1.0 /(1 + arma::exp(-(inputWeight * stepData + -// outputWeight * outCalc + outputWeight % cellCalc + bias))); -// -// // z = tanh(W.dot(x) + W.dot(h) + b). -// hidden = arma::tanh(inputWeight * stepData + -// outputWeight * outCalc + bias); -// -// // c = f * c + i * z. -// cellCalc = forgetGate % cellCalc + inputGate % hidden; -// -// // o = sigmoid(W.dot(x) + W.dot(h) + W.dot(c) + b). -// outputGate = 1.0 /(1 + arma::exp(-(inputWeight * stepData + -// outputWeight * outCalc + outputWeight % cellCalc + bias))); -// -// // h = o * tanh(c). -// outCalc = outputGate % arma::tanh(cellCalc); -// -// CheckMatrices(outLstm, outCalc, 1e-12); -// CheckMatrices(cellLstm, cellCalc, 1e-12); -// } -// } +TEST_CASE("ReadCellStateParamLSTMLayerTest", "[ANNLayerTest]") +{ + const size_t rho = 5, inputSize = 3, outputSize = 2; + + // Provide input of all ones. + arma::cube input = arma::ones(inputSize, outputSize, rho); + + arma::mat inputGate, forgetGate, outputGate, hidden; + arma::mat outLstm, cellLstm; + + // LSTM layer. + LSTM<> lstm(inputSize, outputSize, rho); + lstm.Reset(); + lstm.ResetCell(rho); + + // Initialize the weights to all ones. + lstm.Parameters().ones(); + + arma::mat inputWeight = arma::ones(outputSize, inputSize); + arma::mat outputWeight = arma::ones(outputSize, outputSize); + arma::mat bias = arma::ones(outputSize, input.n_cols); + arma::mat cellCalc = arma::zeros(outputSize, input.n_cols); + arma::mat outCalc = arma::zeros(outputSize, input.n_cols); + + for (size_t seqNum = 0; seqNum < rho; ++seqNum) + { + // Wrap a matrix around our data to avoid a copy. + arma::mat stepData(input.slice(seqNum).memptr(), + input.n_rows, input.n_cols, false, true); + + // Apply Forward() on LSTM layer. + lstm.Forward(stepData, // Input. + outLstm, // Output. + cellLstm, // Cell state. + false); // Don't write into the cell state. + + // Compute the value of cell state and output. + // i = sigmoid(W.dot(x) + W.dot(h) + W.dot(c) + b). + inputGate = 1.0 /(1 + arma::exp(-(inputWeight * stepData + + outputWeight * outCalc + outputWeight % cellCalc + bias))); + + // f = sigmoid(W.dot(x) + W.dot(h) + W.dot(c) + b). + forgetGate = 1.0 /(1 + arma::exp(-(inputWeight * stepData + + outputWeight * outCalc + outputWeight % cellCalc + bias))); + + // z = tanh(W.dot(x) + W.dot(h) + b). + hidden = arma::tanh(inputWeight * stepData + + outputWeight * outCalc + bias); + + // c = f * c + i * z. + cellCalc = forgetGate % cellCalc + inputGate % hidden; + + // o = sigmoid(W.dot(x) + W.dot(h) + W.dot(c) + b). + outputGate = 1.0 /(1 + arma::exp(-(inputWeight * stepData + + outputWeight * outCalc + outputWeight % cellCalc + bias))); + + // h = o * tanh(c). + outCalc = outputGate % arma::tanh(cellCalc); + + CheckMatrices(outLstm, outCalc, 1e-12); + CheckMatrices(cellLstm, cellCalc, 1e-12); + } +} /** * Testing the overloaded Forward() of the LSTM layer, for retrieving the cell * state. Besides output, the overloaded function provides write access to cell * state of the LSTM layer. */ -// TEST_CASE("WriteCellStateParamLSTMLayerTest", "[ANNLayerTest]") -// { -// const size_t rho = 5, inputSize = 3, outputSize = 2; -// -// // Provide input of all ones. -// arma::cube input = arma::ones(inputSize, outputSize, rho); -// -// arma::mat inputGate, forgetGate, outputGate, hidden; -// arma::mat outLstm, cellLstm; -// arma::mat cellCalc; -// -// // LSTM layer. -// LSTM<> lstm(inputSize, outputSize, rho); -// lstm.Reset(); -// lstm.ResetCell(rho); -// -// // Initialize the weights to all ones. -// lstm.Parameters().ones(); -// -// arma::mat inputWeight = arma::ones(outputSize, inputSize); -// arma::mat outputWeight = arma::ones(outputSize, outputSize); -// arma::mat bias = arma::ones(outputSize, input.n_cols); -// arma::mat outCalc = arma::zeros(outputSize, input.n_cols); -// -// for (size_t seqNum = 0; seqNum < rho; ++seqNum) -// { -// // Wrap a matrix around our data to avoid a copy. -// arma::mat stepData(input.slice(seqNum).memptr(), -// input.n_rows, input.n_cols, false, true); -// -// if (cellLstm.is_empty()) -// { -// // Set the cell state to zeros. -// cellLstm = arma::zeros(outputSize, input.n_cols); -// cellCalc = arma::zeros(outputSize, input.n_cols); -// } -// else -// { -// // Set the cell state to zeros. -// cellLstm = arma::zeros(cellLstm.n_rows, cellLstm.n_cols); -// cellCalc = arma::zeros(cellCalc.n_rows, cellCalc.n_cols); -// } -// -// // Apply Forward() on the LSTM layer. -// lstm.Forward(stepData, // Input. -// outLstm, // Output. -// cellLstm, // Cell state. -// true); // Write into cell state. -// -// // Compute the value of cell state and output. -// // i = sigmoid(W.dot(x) + W.dot(h) + W.dot(c) + b). -// inputGate = 1.0 /(1 + arma::exp(-(inputWeight * stepData + -// outputWeight * outCalc + outputWeight % cellCalc + bias))); -// -// // f = sigmoid(W.dot(x) + W.dot(h) + W.dot(c) + b). -// forgetGate = 1.0 /(1 + arma::exp(-(inputWeight * stepData + -// outputWeight * outCalc + outputWeight % cellCalc + bias))); -// -// // z = tanh(W.dot(x) + W.dot(h) + b). -// hidden = arma::tanh(inputWeight * stepData + -// outputWeight * outCalc + bias); -// -// // c = f * c + i * z. -// cellCalc = forgetGate % cellCalc + inputGate % hidden; -// -// // o = sigmoid(W.dot(x) + W.dot(h) + W.dot(c) + b). -// outputGate = 1.0 /(1 + arma::exp(-(inputWeight * stepData + -// outputWeight * outCalc + outputWeight % cellCalc + bias))); -// -// // h = o * tanh(c). -// outCalc = outputGate % arma::tanh(cellCalc); -// -// CheckMatrices(outLstm, outCalc, 1e-12); -// CheckMatrices(cellLstm, cellCalc, 1e-12); -// } -// -// // Attempting to write empty matrix into cell state. -// lstm.Reset(); -// lstm.ResetCell(rho); -// arma::mat stepData(input.slice(0).memptr(), -// input.n_rows, input.n_cols, false, true); -// -// lstm.Forward(stepData, // Input. -// outLstm, // Output. -// cellLstm, // Cell state. -// true); // Write into cell state. -// -// for (size_t seqNum = 1; seqNum < rho; ++seqNum) -// { -// arma::mat empty; -// // Should throw error. -// REQUIRE_THROWS_AS(lstm.Forward(stepData, // Input. -// outLstm, // Output. -// empty, // Cell state. -// true), // Write into cell state. -// std::runtime_error); -// } -// } +TEST_CASE("WriteCellStateParamLSTMLayerTest", "[ANNLayerTest]") +{ + const size_t rho = 5, inputSize = 3, outputSize = 2; + + // Provide input of all ones. + arma::cube input = arma::ones(inputSize, outputSize, rho); + + arma::mat inputGate, forgetGate, outputGate, hidden; + arma::mat outLstm, cellLstm; + arma::mat cellCalc; + + // LSTM layer. + LSTM<> lstm(inputSize, outputSize, rho); + lstm.Reset(); + lstm.ResetCell(rho); + + // Initialize the weights to all ones. + lstm.Parameters().ones(); + + arma::mat inputWeight = arma::ones(outputSize, inputSize); + arma::mat outputWeight = arma::ones(outputSize, outputSize); + arma::mat bias = arma::ones(outputSize, input.n_cols); + arma::mat outCalc = arma::zeros(outputSize, input.n_cols); + + for (size_t seqNum = 0; seqNum < rho; ++seqNum) + { + // Wrap a matrix around our data to avoid a copy. + arma::mat stepData(input.slice(seqNum).memptr(), + input.n_rows, input.n_cols, false, true); + + if (cellLstm.is_empty()) + { + // Set the cell state to zeros. + cellLstm = arma::zeros(outputSize, input.n_cols); + cellCalc = arma::zeros(outputSize, input.n_cols); + } + else + { + // Set the cell state to zeros. + cellLstm = arma::zeros(cellLstm.n_rows, cellLstm.n_cols); + cellCalc = arma::zeros(cellCalc.n_rows, cellCalc.n_cols); + } + + // Apply Forward() on the LSTM layer. + lstm.Forward(stepData, // Input. + outLstm, // Output. + cellLstm, // Cell state. + true); // Write into cell state. + + // Compute the value of cell state and output. + // i = sigmoid(W.dot(x) + W.dot(h) + W.dot(c) + b). + inputGate = 1.0 /(1 + arma::exp(-(inputWeight * stepData + + outputWeight * outCalc + outputWeight % cellCalc + bias))); + + // f = sigmoid(W.dot(x) + W.dot(h) + W.dot(c) + b). + forgetGate = 1.0 /(1 + arma::exp(-(inputWeight * stepData + + outputWeight * outCalc + outputWeight % cellCalc + bias))); + + // z = tanh(W.dot(x) + W.dot(h) + b). + hidden = arma::tanh(inputWeight * stepData + + outputWeight * outCalc + bias); + + // c = f * c + i * z. + cellCalc = forgetGate % cellCalc + inputGate % hidden; + + // o = sigmoid(W.dot(x) + W.dot(h) + W.dot(c) + b). + outputGate = 1.0 /(1 + arma::exp(-(inputWeight * stepData + + outputWeight * outCalc + outputWeight % cellCalc + bias))); + + // h = o * tanh(c). + outCalc = outputGate % arma::tanh(cellCalc); + + CheckMatrices(outLstm, outCalc, 1e-12); + CheckMatrices(cellLstm, cellCalc, 1e-12); + } + + // Attempting to write empty matrix into cell state. + lstm.Reset(); + lstm.ResetCell(rho); + arma::mat stepData(input.slice(0).memptr(), + input.n_rows, input.n_cols, false, true); + + lstm.Forward(stepData, // Input. + outLstm, // Output. + cellLstm, // Cell state. + true); // Write into cell state. + + for (size_t seqNum = 1; seqNum < rho; ++seqNum) + { + arma::mat empty; + // Should throw error. + REQUIRE_THROWS_AS(lstm.Forward(stepData, // Input. + outLstm, // Output. + empty, // Cell state. + true), // Write into cell state. + std::runtime_error); + } +} /** * Test that the functions that can modify and access the parameters of the * GRU layer work. */ -// TEST_CASE("GRULayerParametersTest", "[ANNLayerTest]") -// { -// // Parameter order : inSize, outSize, rho. -// GRU<> layer1(1, 2, 3); -// GRU<> layer2(1, 2, 4); -// -// // Make sure we can get the parameters successfully. -// REQUIRE(layer1.InSize() == 1); -// REQUIRE(layer1.OutSize() == 2); -// REQUIRE(layer1.Rho() == 3); -// -// // Now modify the parameters to match the second layer. -// layer1.Rho() = 4; -// -// // Now ensure all the results are the same. -// REQUIRE(layer1.InSize() == layer2.InSize()); -// REQUIRE(layer1.OutSize() == layer2.OutSize()); -// REQUIRE(layer1.Rho() == layer2.Rho()); -// } +TEST_CASE("GRULayerParametersTest", "[ANNLayerTest]") +{ + // Parameter order : inSize, outSize, rho. + GRU<> layer1(1, 2, 3); + GRU<> layer2(1, 2, 4); + + // Make sure we can get the parameters successfully. + REQUIRE(layer1.InSize() == 1); + REQUIRE(layer1.OutSize() == 2); + REQUIRE(layer1.Rho() == 3); + + // Now modify the parameters to match the second layer. + layer1.Rho() = 4; + + // Now ensure all the results are the same. + REQUIRE(layer1.InSize() == layer2.InSize()); + REQUIRE(layer1.OutSize() == layer2.OutSize()); + REQUIRE(layer1.Rho() == layer2.Rho()); +} /** * Check if the gradients computed by GRU cell are close enough to the * approximation of the gradients. */ -// TEST_CASE("GradientGRULayerTest", "[ANNLayerTest]") -// { -// // GRU function gradient instantiation. -// struct GradientFunction -// { -// GradientFunction() : -// input(arma::randu(1, 1, 5)), -// target(arma::zeros(1, 1, 5)) -// { -// const size_t rho = 5; -// -// model = new RNN(rho); -// model->ResetData(input, target); -// model->Add >(); -// model->Add >(1, 10); -// model->Add >(10, 3, rho); -// model->Add >(); -// } -// -// ~GradientFunction() -// { -// delete model; -// } -// -// double Gradient(arma::mat& gradient) const -// { -// arma::mat output; -// double error = model->Evaluate(model->Parameters(), 0, 1); -// model->Gradient(model->Parameters(), 0, gradient, 1); -// return error; -// } -// -// arma::mat& Parameters() { return model->Parameters(); } -// -// RNN* model; -// arma::cube input, target; -// } function; -// -// REQUIRE(CheckGradient(function) <= 1e-4); -// } +TEST_CASE("GradientGRULayerTest", "[ANNLayerTest]") +{ + // GRU function gradient instantiation. + struct GradientFunction + { + GradientFunction() : + input(arma::randu(1, 1, 5)), + target(arma::zeros(1, 1, 5)) + { + const size_t rho = 5; + + model = new RNN >(rho); + model->Predictors() = input; + model->Responses() = target; + model->Add >(); + model->Add >(1, 10); + model->Add >(10, 3, rho); + model->Add >(); + } + + ~GradientFunction() + { + delete model; + } + + double Gradient(arma::mat& gradient) const + { + arma::mat output; + double error = model->Evaluate(model->Parameters(), 0, 1); + model->Gradient(model->Parameters(), 0, gradient, 1); + return error; + } + + arma::mat& Parameters() { return model->Parameters(); } + + RNN >* model; + arma::cube input, target; + } function; + + REQUIRE(CheckGradient(function) <= 1e-4); +} /** * GRU layer manual forward test. */ -// TEST_CASE("ForwardGRULayerTest", "[ANNLayerTest]") -// { -// // This will make it easier to clean memory later. -// GRU<>* gruAlloc = new GRU<>(3, 3, 5); -// GRU<>& gru = *gruAlloc; -// -// // Initialize the weights to all ones. -// NetworkInitialization -// networkInit(ConstInitialization(1)); -// networkInit.Initialize(gru.Model(), gru.Parameters()); -// -// // Provide input of all ones. -// arma::mat input = arma::ones(3, 1); -// arma::mat output; -// -// gru.Forward(input, output); -// -// // Compute the z_t gate output. -// arma::mat expectedOutput = arma::ones(3, 1); -// expectedOutput *= -4; -// expectedOutput = arma::exp(expectedOutput); -// expectedOutput = arma::ones(3, 1) / (arma::ones(3, 1) + expectedOutput); -// expectedOutput = (arma::ones(3, 1) - expectedOutput) % expectedOutput; -// -// // For the first input the output should be equal to the output of -// // gate z_t as the previous output fed to the cell is all zeros. -// REQUIRE(arma::as_scalar(arma::trans(output) * expectedOutput) <= 1e-2); -// -// expectedOutput = output; -// -// gru.Forward(input, output); -// -// double s = arma::as_scalar(arma::sum(expectedOutput)); -// -// // Compute the value of z_t gate for the second input. -// arma::mat z_t = arma::ones(3, 1); -// z_t *= -(s + 4); -// z_t = arma::exp(z_t); -// z_t = arma::ones(3, 1) / (arma::ones(3, 1) + z_t); -// -// // Compute the value of o_t gate for the second input. -// arma::mat o_t = arma::ones(3, 1); -// o_t *= -(arma::as_scalar(arma::sum(expectedOutput % z_t)) + 4); -// o_t = arma::exp(o_t); -// o_t = arma::ones(3, 1) / (arma::ones(3, 1) + o_t); -// -// // Expected output for the second input. -// expectedOutput = z_t % expectedOutput + (arma::ones(3, 1) - z_t) % o_t; -// -// REQUIRE(arma::as_scalar(arma::trans(output) * expectedOutput) <= 1e-2); -// -// LayerTypes<> layer(gruAlloc); -// boost::apply_visitor(DeleteVisitor(), layer); -// } +TEST_CASE("ForwardGRULayerTest", "[ANNLayerTest]") +{ + // This will make it easier to clean memory later. + GRU<>* gruAlloc = new GRU<>(3, 3, 5); + GRU<>& gru = *gruAlloc; + + // Initialize the weights to all ones. + NetworkInitialization + networkInit(ConstInitialization(1)); + networkInit.Initialize(gru.Model(), gru.Parameters()); + + // Provide input of all ones. + arma::mat input = arma::ones(3, 1); + arma::mat output; + + gru.Forward(input, output); + + // Compute the z_t gate output. + arma::mat expectedOutput = arma::ones(3, 1); + expectedOutput *= -4; + expectedOutput = arma::exp(expectedOutput); + expectedOutput = arma::ones(3, 1) / (arma::ones(3, 1) + expectedOutput); + expectedOutput = (arma::ones(3, 1) - expectedOutput) % expectedOutput; + + // For the first input the output should be equal to the output of + // gate z_t as the previous output fed to the cell is all zeros. + REQUIRE(arma::as_scalar(arma::trans(output) * expectedOutput) <= 1e-2); + + expectedOutput = output; + + gru.Forward(input, output); + + double s = arma::as_scalar(arma::sum(expectedOutput)); + + // Compute the value of z_t gate for the second input. + arma::mat z_t = arma::ones(3, 1); + z_t *= -(s + 4); + z_t = arma::exp(z_t); + z_t = arma::ones(3, 1) / (arma::ones(3, 1) + z_t); + + // Compute the value of o_t gate for the second input. + arma::mat o_t = arma::ones(3, 1); + o_t *= -(arma::as_scalar(arma::sum(expectedOutput % z_t)) + 4); + o_t = arma::exp(o_t); + o_t = arma::ones(3, 1) / (arma::ones(3, 1) + o_t); + + // Expected output for the second input. + expectedOutput = z_t % expectedOutput + (arma::ones(3, 1) - z_t) % o_t; + + REQUIRE(arma::as_scalar(arma::trans(output) * expectedOutput) <= 1e-2); + + LayerTypes<> layer(gruAlloc); + boost::apply_visitor(DeleteVisitor(), layer); +} /** * Simple concat module test. - * + */ TEST_CASE("SimpleConcatLayerTest", "[ANNLayerTest]") { arma::mat output, input, delta, error; - Linear* moduleA = new Linear(10, 10); + Linear<>* moduleA = new Linear<>(10, 10); moduleA->Parameters().randu(); moduleA->Reset(); - Linear* moduleB = new Linear(10, 10); + Linear<>* moduleB = new Linear<>(10, 10); moduleB->Parameters().randu(); moduleB->Reset(); - Concat module; + Concat<> module; module.Add(moduleA); module.Add(moduleB); @@ -2334,11 +1688,10 @@ TEST_CASE("SimpleConcatLayerTest", "[ANNLayerTest]") module.Backward(input, error, delta); REQUIRE(arma::accu(delta) == 0); } -*/ /** * Test to check Concat layer along different axes. - * + */ TEST_CASE("ConcatAlongAxisTest", "[ANNLayerTest]") { arma::mat output, input, error, outputA, outputB; @@ -2354,9 +1707,9 @@ TEST_CASE("ConcatAlongAxisTest", "[ANNLayerTest]") input = arma::ones(inputWidth * inputHeight * inputChannel, batch); - Convolution* moduleA = new Convolution(inputChannel, outputChannel, + Convolution<>* moduleA = new Convolution<>(inputChannel, outputChannel, kW, kH, 1, 1, 0, 0, inputWidth, inputHeight); - Convolution* moduleB = new Convolution(inputChannel, outputChannel, + Convolution<>* moduleB = new Convolution<>(inputChannel, outputChannel, kW, kH, 1, 1, 0, 0, inputWidth, inputHeight); moduleA->Reset(); @@ -2407,7 +1760,7 @@ TEST_CASE("ConcatAlongAxisTest", "[ANNLayerTest]") // Compute output of Concat<> layer. arma::Row inputSize{outputWidth, outputHeight, outputChannel}; - Concat module(inputSize, axis, true); + Concat<> module(inputSize, axis, true); module.Add(moduleA); module.Add(moduleB); module.Forward(input, output); @@ -2419,116 +1772,45 @@ TEST_CASE("ConcatAlongAxisTest", "[ANNLayerTest]") } delete moduleA; delete moduleB; -}*/ +} /** * Test that the function that can access the axis parameter of the * Concat layer works. - * + */ TEST_CASE("ConcatLayerParametersTest", "[ANNLayerTest]") { // Parameter order : inputSize{width, height, channels}, axis, model, run. arma::Row inputSize{128, 128, 3}; - Concat layer(inputSize, 2, false, true); + Concat<> layer(inputSize, 2, false, true); // Make sure we can get the parameters successfully. REQUIRE(layer.ConcatAxis() == 2); } -*/ /** * Concat layer numerical gradient test. */ -// TEST_CASE("GradientConcatLayerTest", "[ANNLayerTest]") -// { -// // Concat function gradient instantiation. -// struct GradientFunction -// { -// GradientFunction() : -// input(arma::randu(10, 1)), -// target(arma::mat("0")) -// { -// model = new FFN(); -// model->ResetData(input, target); -// model->Add(); -// model->Add(10, 10); - -// concat = new Concat(true); -// concat->Add(10, 2); -// model->Add(concat); - -// model->Add(); -// } - -// ~GradientFunction() -// { -// delete model; -// } - -// double Gradient(arma::mat& gradient) const -// { -// double error = model->Evaluate(model->Parameters(), 0, 1); -// model->Gradient(model->Parameters(), 0, gradient, 1); -// return error; -// } - -// arma::mat& Parameters() { return model->Parameters(); } - -// FFN* model; -// Concat* concat; -// arma::mat input, target; -// } function; - -// REQUIRE(CheckGradient(function) <= 1e-4); -// } - -/** - * Simple concatenate module test. - * -TEST_CASE("SimpleConcatenateLayerTest", "[ANNLayerTest]") +TEST_CASE("GradientConcatLayerTest", "[ANNLayerTest]") { - arma::mat input = arma::ones(5, 1); - arma::mat output, delta; - - Concatenate module; - module.Concat() = arma::ones(5, 1) * 0.5; - - // Test the Forward function. - module.Forward(input, output); - - REQUIRE(arma::accu(output) == 7.5); - - // Test the Backward function. - module.Backward(input, output, delta); - REQUIRE(arma::accu(delta) == 5); -} -*/ - -/** - * Concatenate layer numerical gradient test. - * -TEST_CASE("GradientConcatenateLayerTest", "[ANNLayerTest]") -{ - // Concatenate function gradient instantiation. + // Concat function gradient instantiation. struct GradientFunction { GradientFunction() : input(arma::randu(10, 1)), target(arma::mat("0")) { - model = new FFN(); - model->ResetData(input, target); - model->Add(); - model->Add(10, 5); + model = new FFN, NguyenWidrowInitialization>(); + model->Predictors() = input; + model->Responses() = target; + model->Add >(); + model->Add >(10, 10); - arma::mat concat = arma::ones(5, 1); - // concatenate = new Concatenate(); - // concatenate->Concat() = concat; - // model->Add(concatenate); - model->Add(concat); + concat = new Concat<>(true); + concat->Add >(10, 2); + model->Add(concat); - model->Add(10, 5); - model->Add(); + model->Add >(); } ~GradientFunction() @@ -2545,18 +1827,87 @@ TEST_CASE("GradientConcatenateLayerTest", "[ANNLayerTest]") arma::mat& Parameters() { return model->Parameters(); } - FFN* model; - Concatenate* concatenate; + FFN, NguyenWidrowInitialization>* model; + Concat<>* concat; + arma::mat input, target; + } function; + + REQUIRE(CheckGradient(function) <= 1e-4); +} + +/** + * Simple concatenate module test. + */ +TEST_CASE("SimpleConcatenateLayerTest", "[ANNLayerTest]") +{ + arma::mat input = arma::ones(5, 1); + arma::mat output, delta; + + Concatenate<> module; + module.Concat() = arma::ones(5, 1) * 0.5; + + // Test the Forward function. + module.Forward(input, output); + + REQUIRE(arma::accu(output) == 7.5); + + // Test the Backward function. + module.Backward(input, output, delta); + REQUIRE(arma::accu(delta) == 5); +} + +/** + * Concatenate layer numerical gradient test. + */ +TEST_CASE("GradientConcatenateLayerTest", "[ANNLayerTest]") +{ + // Concatenate function gradient instantiation. + struct GradientFunction + { + GradientFunction() : + input(arma::randu(10, 1)), + target(arma::mat("0")) + { + model = new FFN, NguyenWidrowInitialization>(); + model->Predictors() = input; + model->Responses() = target; + model->Add >(); + model->Add >(10, 5); + + arma::mat concat = arma::ones(5, 1); + concatenate = new Concatenate<>(); + concatenate->Concat() = concat; + model->Add(concatenate); + + model->Add >(10, 5); + model->Add >(); + } + + ~GradientFunction() + { + delete model; + } + + double Gradient(arma::mat& gradient) const + { + double error = model->Evaluate(model->Parameters(), 0, 1); + model->Gradient(model->Parameters(), 0, gradient, 1); + return error; + } + + arma::mat& Parameters() { return model->Parameters(); } + + FFN, NguyenWidrowInitialization>* model; + Concatenate<>* concatenate; arma::mat input, target; } function; REQUIRE(CheckGradient(function) <= 1e-4); } -*/ /** * Simple lookup module test. - * + */ TEST_CASE("SimpleLookupLayerTest", "[ANNLayerTest]") { const size_t vocabSize = 10; @@ -2566,7 +1917,7 @@ TEST_CASE("SimpleLookupLayerTest", "[ANNLayerTest]") arma::mat output, input, gy, g, gradient; - Lookup module(vocabSize, embeddingSize); + Lookup<> module(vocabSize, embeddingSize); module.Parameters().randu(); // Test the Forward function. @@ -2593,11 +1944,10 @@ TEST_CASE("SimpleLookupLayerTest", "[ANNLayerTest]") REQUIRE(std::fabs(arma::accu(error) - arma::accu(gradient)) <= 1e-07); } -*/ /** * Lookup layer numerical gradient test. - * + */ TEST_CASE("GradientLookupLayerTest", "[ANNLayerTest]") { // Lookup function gradient instantiation. @@ -2618,10 +1968,11 @@ TEST_CASE("GradientLookupLayerTest", "[ANNLayerTest]") } model = new FFN, GlorotInitialization>(BCELoss<>(1e-10, false)); - model->ResetData(input, target); - model->Add(vocabSize, embeddingSize); - model->Add(embeddingSize * seqLength, vocabSize); - model->Add(); + model->Predictors() = input; + model->Responses() = target; + model->Add >(vocabSize, embeddingSize); + model->Add >(embeddingSize * seqLength, vocabSize); + model->Add >(); } ~GradientFunction() @@ -2649,22 +2000,20 @@ TEST_CASE("GradientLookupLayerTest", "[ANNLayerTest]") REQUIRE(CheckGradient(function) <= 1e-6); } -*/ /** * Test that the functions that can access the parameters of the * Lookup layer work. - * + */ TEST_CASE("LookupLayerParametersTest", "[ANNLayerTest]") { // Parameter order : vocabSize, embedingSize. - Lookup layer(100, 8); + Lookup<> layer(100, 8); // Make sure we can get the parameters successfully. REQUIRE(layer.VocabSize() == 100); REQUIRE(layer.EmbeddingSize() == 8); } -*/ /** * Simple LogSoftMax module test. @@ -2672,7 +2021,7 @@ TEST_CASE("LookupLayerParametersTest", "[ANNLayerTest]") TEST_CASE("SimpleLogSoftmaxLayerTest", "[ANNLayerTest]") { arma::mat output, input, error, delta; - LogSoftMax module; + LogSoftMax<> module; // Test the Forward function. input = arma::mat("0.5; 0.5"); @@ -2691,11 +2040,11 @@ TEST_CASE("SimpleLogSoftmaxLayerTest", "[ANNLayerTest]") /** * Simple Softmax module test. - * + */ TEST_CASE("SimpleSoftmaxLayerTest", "[ANNLayerTest]") { arma::mat input, output, gy, g; - Softmax module; + Softmax<> module; // Test the forward function. input = arma::mat("1.7; 3.6"); @@ -2710,11 +2059,10 @@ TEST_CASE("SimpleSoftmaxLayerTest", "[ANNLayerTest]") REQUIRE(arma::accu(arma::abs(arma::mat("0.11318; -0.11318") - g)) == Approx(0.0).margin(1e-04)); } -*/ /** * Softmax layer numerical gradient test. - * + */ TEST_CASE("GradientSoftmaxTest", "[ANNLayerTest]") { // Softmax function gradient instantiation. @@ -2724,12 +2072,13 @@ TEST_CASE("GradientSoftmaxTest", "[ANNLayerTest]") input(arma::randu(10, 1)), target(arma::mat("1; 0")) { - model = new FFN; - model->ResetData(input, target); - model->Add(10, 10); - model->Add(); - model->Add(10, 2); - model->Add(); + model = new FFN, RandomInitialization>; + model->Predictors() = input; + model->Responses() = target; + model->Add >(10, 10); + model->Add >(); + model->Add >(10, 2); + model->Add >(); } ~GradientFunction() @@ -2746,17 +2095,16 @@ TEST_CASE("GradientSoftmaxTest", "[ANNLayerTest]") arma::mat& Parameters() { return model->Parameters(); } - FFN* model; + FFN >* model; arma::mat input, target; } function; REQUIRE(CheckGradient(function) <= 1e-4); } -*/ -/** +/* * Simple test for the NearestInterpolation layer - * + */ TEST_CASE("SimpleNearestInterpolationLayerTest", "[ANNLayerTest]") { // Tested output against torch.nn.Upsample(mode="nearest"). @@ -2816,11 +2164,10 @@ TEST_CASE("SimpleNearestInterpolationLayerTest", "[ANNLayerTest]") REQUIRE(arma::accu(unzoomedOutput1) - 1317.00 == Approx(0.0).margin(1e-05)); } -*/ /* * Simple test for the BilinearInterpolation layer - * + */ TEST_CASE("SimpleBilinearInterpolationLayerTest", "[ANNLayerTest]") { // Tested output against tensorflow.image.resize_bilinear() @@ -2834,7 +2181,7 @@ TEST_CASE("SimpleBilinearInterpolationLayerTest", "[ANNLayerTest]") input[0] = 1.0; input[1] = input[2] = 2.0; input[3] = 3.0; - BilinearInterpolation layer(inRowSize, inColSize, outRowSize, outColSize, + BilinearInterpolation<> layer(inRowSize, inColSize, outRowSize, outColSize, depth); expectedOutput = arma::mat("1.0000 1.4000 1.8000 2.0000 2.0000 \ 1.4000 1.8000 2.2000 2.4000 2.4000 \ @@ -2851,17 +2198,16 @@ TEST_CASE("SimpleBilinearInterpolationLayerTest", "[ANNLayerTest]") CheckMatrices(unzoomedOutput - expectedOutput, arma::zeros(input.n_rows), 1e-12); } -*/ /** * Test that the functions that can modify and access the parameters of the * Bilinear Interpolation layer work. - * + */ TEST_CASE("BilinearInterpolationLayerParametersTest", "[ANNLayerTest]") { // Parameter order : inRowSize, inColSize, outRowSize, outColSize, depth. - BilinearInterpolation layer1(1, 2, 3, 4, 5); - BilinearInterpolation layer2(2, 3, 4, 5, 6); + BilinearInterpolation<> layer1(1, 2, 3, 4, 5); + BilinearInterpolation<> layer2(2, 3, 4, 5, 6); // Make sure we can get the parameters successfully. REQUIRE(layer1.InRowSize() == 1); @@ -2884,11 +2230,10 @@ TEST_CASE("BilinearInterpolationLayerParametersTest", "[ANNLayerTest]") REQUIRE(layer1.OutColSize() == layer2.OutColSize()); REQUIRE(layer1.InDepth() == layer2.InDepth()); } -*/ /* * Simple test for the BicubicInterpolation layer. - * + */ TEST_CASE("SimpleBicubicInterpolationLayerTest", "[ANNLayerTest]") { // Tested output against torch.nn.Upsample(mode="bicubic"). @@ -2983,171 +2328,171 @@ TEST_CASE("SimpleBicubicInterpolationLayerTest", "[ANNLayerTest]") layer1.Backward(output1, output1, unzoomedOutput1); CheckMatrices(unzoomedOutput1, expectedUnzoomed, 1e-6); } -*/ -// /** -// * Tests the BatchNorm Layer, compares the layers parameters with -// * the values from another implementation. -// * Link to the implementation - http://cthorey.github.io./backpropagation/ -// */ -// TEST_CASE("BatchNormTest", "[ANNLayerTest]") -// { -// arma::mat input, output; -// input << 5.1 << 3.5 << 1.4 << arma::endr -// << 4.9 << 3.0 << 1.4 << arma::endr -// << 4.7 << 3.2 << 1.3 << arma::endr; +/** + * Tests the BatchNorm Layer, compares the layers parameters with + * the values from another implementation. + * Link to the implementation - http://cthorey.github.io./backpropagation/ + */ +TEST_CASE("BatchNormTest", "[ANNLayerTest]") +{ + arma::mat input, output; + input = { { 5.1, 3.5, 1.4 }, + { 4.9, 3.0, 1.4 }, + { 4.7, 3.2, 1.3 } }; -// // BatchNorm layer with average parameter set to true. -// BatchNorm<> model(input.n_rows); -// model.Reset(); + // BatchNorm layer with average parameter set to true. + BatchNorm<> model(input.n_rows); + model.Reset(); -// // BatchNorm layer with average parameter set to false. -// BatchNorm<> model2(input.n_rows, 1e-5, false); -// model2.Reset(); + // BatchNorm layer with average parameter set to false. + BatchNorm<> model2(input.n_rows, 1e-5, false); + model2.Reset(); -// // Non-Deteministic Forward Pass Test. -// model.Deterministic() = false; -// model.Forward(input, output); + // Non-Deteministic Forward Pass Test. + model.Deterministic() = false; + model.Forward(input, output); -// // Value calculates using torch.nn.BatchNorm2d(momentum = None). -// arma::mat result; -// result = { { 1.1658, 0.1100, -1.2758 }, -// { 1.2579, -0.0699, -1.1880}, -// { 1.1737, 0.0958, -1.2695 } }; + // Value calculates using torch.nn.BatchNorm2d(momentum = None). + arma::mat result; + result = { { 1.1658, 0.1100, -1.2758 }, + { 1.2579, -0.0699, -1.1880}, + { 1.1737, 0.0958, -1.2695 } }; -// CheckMatrices(output, result, 1e-1); + CheckMatrices(output, result, 1e-1); -// model2.Forward(input, output); -// CheckMatrices(output, result, 1e-1); -// result.clear(); - -// // Values calculated using torch.nn.BatchNorm2d(momentum = None). -// output = model.TrainingMean(); -// result = arma::mat({ 3.33333333, 3.1, 3.06666666 }).t(); - -// CheckMatrices(output, result, 1e-1); - -// // Values calculated using torch.nn.BatchNorm2d(). -// output = model2.TrainingMean(); -// result = arma::mat({ 0.3333, 0.3100, 0.3067 }).t(); - -// CheckMatrices(output, result, 1e-1); -// result.clear(); + model2.Forward(input, output); + CheckMatrices(output, result, 1e-1); + result.clear(); // Values calculated using torch.nn.BatchNorm2d(momentum = None). -// output = model.TrainingVariance(); -// result = arma::mat({ 3.4433, 3.0700, 2.9033 }).t(); + output = model.TrainingMean(); + result = arma::mat({ 3.33333333, 3.1, 3.06666666 }).t(); -// CheckMatrices(output, result, 1e-1); -// result.clear(); + CheckMatrices(output, result, 1e-1); // Values calculated using torch.nn.BatchNorm2d(). -// output = model2.TrainingVariance(); -// result = arma::mat({ 1.2443, 1.2070, 1.1903 }).t(); + output = model2.TrainingMean(); + result = arma::mat({ 0.3333, 0.3100, 0.3067 }).t(); -// CheckMatrices(output, result, 1e-1); -// result.clear(); - -// // Deterministic Forward Pass test. -// model.Deterministic() = true; -// model.Forward(input, output); + CheckMatrices(output, result, 1e-1); + result.clear(); // Values calculated using torch.nn.BatchNorm2d(momentum = None). -// result = { { 0.9521, 0.0898, -1.0419 }, -// { 1.0273, -0.0571, -0.9702 }, -// { 0.9586, 0.0783, -1.0368 } }; + output = model.TrainingVariance(); + result = arma::mat({ 3.4433, 3.0700, 2.9033 }).t(); -// CheckMatrices(output, result, 1e-1); + CheckMatrices(output, result, 1e-1); + result.clear(); -// // Values calculated using torch.nn.BatchNorm2d(). -// model2.Deterministic() = true; -// model2.Forward(input, output); + // Values calculated using torch.nn.BatchNorm2d(). + output = model2.TrainingVariance(); + result = arma::mat({ 1.2443, 1.2070, 1.1903 }).t(); -// result = { { 4.2731, 2.8388, 0.9562 }, -// { 4.1779, 2.4485, 0.9921 }, -// { 4.0268, 2.6519, 0.9105 } }; -// -// CheckMatrices(output, result, 1e-1); -// } + CheckMatrices(output, result, 1e-1); + result.clear(); -// /** -// * BatchNorm layer numerical gradient test. -// */ -// TEST_CASE("GradientBatchNormTest", "[ANNLayerTest]") -// { -// bool pass = false; -// for (size_t trial = 0; trial < 10; trial++) -// { -// // Add function gradient instantiation. -// struct GradientFunction -// { -// GradientFunction() : -// input(arma::randn(32, 2048)), -// target(arma::zeros(1, 2048)) -// { -// model = new FFN(); -// model->ResetData(input, target); -// model->Add >(); -// model->Add >(32, 4); -// model->Add >(4); -// model->Add>(4, 2); -// model->Add >(); -// } + // Deterministic Forward Pass test. + model.Deterministic() = true; + model.Forward(input, output); -// ~GradientFunction() -// { -// delete model; -// } + // Values calculated using torch.nn.BatchNorm2d(momentum = None). + result = { { 0.9521, 0.0898, -1.0419 }, + { 1.0273, -0.0571, -0.9702 }, + { 0.9586, 0.0783, -1.0368 } }; -// double Gradient(arma::mat& gradient) const -// { -// double error = model->Evaluate(model->Parameters(), 0, 2048, false); -// model->Gradient(model->Parameters(), 0, gradient, 2048); -// return error; -// } + CheckMatrices(output, result, 1e-1); -// arma::mat& Parameters() { return model->Parameters(); } + // Values calculated using torch.nn.BatchNorm2d(). + model2.Deterministic() = true; + model2.Forward(input, output); -// FFN* model; -// arma::mat input, target; -// } function; + result = { { 4.2731, 2.8388, 0.9562 }, + { 4.1779, 2.4485, 0.9921 }, + { 4.0268, 2.6519, 0.9105 } }; -// double gradient = CheckGradient(function); -// if (gradient < 2e-1) -// { -// pass = true; -// break; -// } -// } + CheckMatrices(output, result, 1e-1); +} -// REQUIRE(pass); -// } +/** + * BatchNorm layer numerical gradient test. + */ +TEST_CASE("GradientBatchNormTest", "[ANNLayerTest]") +{ + bool pass = false; + for (size_t trial = 0; trial < 10; trial++) + { + // Add function gradient instantiation. + struct GradientFunction + { + GradientFunction() : + input(arma::randn(32, 2048)), + target(arma::zeros(1, 2048)) + { + model = new FFN, NguyenWidrowInitialization>(); + model->Predictors() = input; + model->Responses() = target; + model->Add >(); + model->Add >(32, 4); + model->Add >(4); + model->Add>(4, 2); + model->Add >(); + } -// /** -// * Test that the functions that can access the parameters of the -// * Batch Norm layer work. -// */ -// TEST_CASE("BatchNormLayerParametersTest", "[ANNLayerTest]") -// { -// // Parameter order : size, eps. -// BatchNorm<> layer(7, 1e-3); + ~GradientFunction() + { + delete model; + } -// // Make sure we can get the parameters successfully. -// REQUIRE(layer.InputSize() == 7); -// REQUIRE(layer.Epsilon() == 1e-3); + double Gradient(arma::mat& gradient) const + { + double error = model->Evaluate(model->Parameters(), 0, 2048, false); + model->Gradient(model->Parameters(), 0, gradient, 2048); + return error; + } -// arma::mat runningMean(7, 1, arma::fill::randn); -// arma::mat runningVariance(7, 1, arma::fill::randn); + arma::mat& Parameters() { return model->Parameters(); } -// layer.TrainingVariance() = runningVariance; -// layer.TrainingMean() = runningMean; -// CheckMatrices(layer.TrainingVariance(), runningVariance); -// CheckMatrices(layer.TrainingMean(), runningMean); -// } + FFN, NguyenWidrowInitialization>* model; + arma::mat input, target; + } function; + + double gradient = CheckGradient(function); + if (gradient < 2e-1) + { + pass = true; + break; + } + } + + REQUIRE(pass); +} + +/** + * Test that the functions that can access the parameters of the + * Batch Norm layer work. + */ +TEST_CASE("BatchNormLayerParametersTest", "[ANNLayerTest]") +{ + // Parameter order : size, eps. + BatchNorm<> layer(7, 1e-3); + + // Make sure we can get the parameters successfully. + REQUIRE(layer.InputSize() == 7); + REQUIRE(layer.Epsilon() == 1e-3); + + arma::mat runningMean(7, 1, arma::fill::randn); + arma::mat runningVariance(7, 1, arma::fill::randn); + + layer.TrainingVariance() = runningVariance; + layer.TrainingMean() = runningMean; + CheckMatrices(layer.TrainingVariance(), runningVariance); + CheckMatrices(layer.TrainingMean(), runningMean); +} /** * VirtualBatchNorm layer numerical gradient test. - * + */ TEST_CASE("GradientVirtualBatchNormTest", "[ANNLayerTest]") { // Add function gradient instantiation. @@ -3157,15 +2502,16 @@ TEST_CASE("GradientVirtualBatchNormTest", "[ANNLayerTest]") input(arma::randn(5, 256)), target(arma::zeros(1, 256)) { - arma::mat referenceBatch = arma::mat(input.memptr(), input.n_rows, 4); + arma::mat referenceBatch = arma::mat(input.memptr(), input.n_rows, 16); - model = new FFN(); - model->ResetData(input, target); - model->Add(); - model->Add(5, 5); - model->Add(referenceBatch, 5); - model->Add(5, 2); - model->Add(); + model = new FFN, NguyenWidrowInitialization>(); + model->Predictors() = input; + model->Responses() = target; + model->Add >(); + model->Add >(5, 5); + model->Add >(referenceBatch, 5); + model->Add >(5, 2); + model->Add >(); } ~GradientFunction() @@ -3175,87 +2521,86 @@ TEST_CASE("GradientVirtualBatchNormTest", "[ANNLayerTest]") double Gradient(arma::mat& gradient) const { - double error = model->Evaluate(model->Parameters(), 0, 16, false); - model->Gradient(model->Parameters(), 0, gradient, 16); + double error = model->Evaluate(model->Parameters(), 0, 256, false); + model->Gradient(model->Parameters(), 0, gradient, 256); return error; } arma::mat& Parameters() { return model->Parameters(); } - FFN* model; + FFN, NguyenWidrowInitialization>* model; arma::mat input, target; } function; REQUIRE(CheckGradient(function) <= 1e-4); } -*/ /** * Test that the functions that can modify and access the parameters of the * Virtual Batch Norm layer work. - * + */ TEST_CASE("VirtualBatchNormLayerParametersTest", "[ANNLayerTest]") { - arma::mat input = arma::randn(5, 16); - arma::mat referenceBatch = arma::mat(input.memptr(), input.n_rows, 4); + arma::mat input = arma::randn(5, 256); + arma::mat referenceBatch = arma::mat(input.memptr(), input.n_rows, 16); // Parameter order : referenceBatch, size, eps. - VirtualBatchNorm layer(referenceBatch, 5, 1e-3); + VirtualBatchNorm<> layer(referenceBatch, 5, 1e-3); // Make sure we can get the parameters successfully. REQUIRE(layer.InSize() == 5); REQUIRE(layer.Epsilon() == 1e-3); } -*/ -// /** -// * MiniBatchDiscrimination layer numerical gradient test. -// */ -// TEST_CASE("MiniBatchDiscriminationTest", "[ANNLayerTest]") -// { -// // Add function gradient instantiation. -// struct GradientFunction -// { -// GradientFunction() : -// input(arma::randn(5, 4)), -// target(arma::zeros(1, 4)) -// { -// model = new FFN(); -// model->ResetData(input, target); -// model->Add >(); -// model->Add >(5, 5); -// model->Add >(5, 10, 16); -// model->Add >(10, 2); -// model->Add >(); -// } +/** + * MiniBatchDiscrimination layer numerical gradient test. + */ +TEST_CASE("MiniBatchDiscriminationTest", "[ANNLayerTest]") +{ + // Add function gradient instantiation. + struct GradientFunction + { + GradientFunction() : + input(arma::randn(5, 4)), + target(arma::zeros(1, 4)) + { + model = new FFN, NguyenWidrowInitialization>(); + model->Predictors() = input; + model->Responses() = target; + model->Add >(); + model->Add >(5, 5); + model->Add >(5, 10, 16); + model->Add >(10, 2); + model->Add >(); + } -// ~GradientFunction() -// { -// delete model; -// } + ~GradientFunction() + { + delete model; + } -// double Gradient(arma::mat& gradient) const -// { -// return model->EvaluateWithGradient(model->Parameters(), 0, gradient, 4); -// } + double Gradient(arma::mat& gradient) const + { + return model->EvaluateWithGradient(model->Parameters(), 0, gradient, 4); + } -// arma::mat& Parameters() { return model->Parameters(); } + arma::mat& Parameters() { return model->Parameters(); } -// FFN* model; -// arma::mat input, target; -// } function; + FFN, NguyenWidrowInitialization>* model; + arma::mat input, target; + } function; -// REQUIRE(CheckGradient(function) <= 1e-4); -// } + REQUIRE(CheckGradient(function) <= 1e-4); +} /** * Simple Transposed Convolution layer test. - * + */ TEST_CASE("SimpleTransposedConvolutionLayerTest", "[ANNLayerTest]") { arma::mat output, input, delta; - TransposedConvolution module1(1, 1, 3, 3, 1, 1, 0, 0, 4, 4, 6, 6); + TransposedConvolution<> module1(1, 1, 3, 3, 1, 1, 0, 0, 4, 4, 6, 6); // Test the forward function. input = arma::linspace(0, 15, 16); module1.Parameters() = arma::mat(9 + 1, 1, arma::fill::zeros); @@ -3271,7 +2616,7 @@ TEST_CASE("SimpleTransposedConvolutionLayerTest", "[ANNLayerTest]") // Value calculated using tensorflow.nn.conv2d() REQUIRE(arma::accu(delta) == 720.0); - TransposedConvolution module2(1, 1, 4, 4, 1, 1, 1, 1, 5, 5, 6, 6); + TransposedConvolution<> module2(1, 1, 4, 4, 1, 1, 1, 1, 5, 5, 6, 6); // Test the forward function. input = arma::linspace(0, 24, 25); module2.Parameters() = arma::mat(16 + 1, 1, arma::fill::zeros); @@ -3291,7 +2636,7 @@ TEST_CASE("SimpleTransposedConvolutionLayerTest", "[ANNLayerTest]") // Value calculated using torch.nn.functional.conv2d() REQUIRE(arma::accu(delta) == 6504.0); - TransposedConvolution module3(1, 1, 3, 3, 1, 1, 1, 1, 5, 5, 5, 5); + TransposedConvolution<> module3(1, 1, 3, 3, 1, 1, 1, 1, 5, 5, 5, 5); // Test the forward function. input = arma::linspace(0, 24, 25); module3.Parameters() = arma::mat(9 + 1, 1, arma::fill::zeros); @@ -3309,7 +2654,7 @@ TEST_CASE("SimpleTransposedConvolutionLayerTest", "[ANNLayerTest]") // Value calculated using torch.nn.functional.conv2d() REQUIRE(arma::accu(delta) == 19154.0); - TransposedConvolution module4(1, 1, 3, 3, 1, 1, 0, 0, 5, 5, 7, 7); + TransposedConvolution<> module4(1, 1, 3, 3, 1, 1, 0, 0, 5, 5, 7, 7); // Test the forward function. input = arma::linspace(0, 24, 25); module4.Parameters() = arma::mat(9 + 1, 1, arma::fill::zeros); @@ -3327,7 +2672,7 @@ TEST_CASE("SimpleTransposedConvolutionLayerTest", "[ANNLayerTest]") // Value calculated using torch.nn.functional.conv2d() REQUIRE(arma::accu(delta) == 86208.0); - TransposedConvolution module5(1, 1, 3, 3, 2, 2, 0, 0, 2, 2, 5, 5); + TransposedConvolution<> module5(1, 1, 3, 3, 2, 2, 0, 0, 2, 2, 5, 5); // Test the forward function. input = arma::linspace(0, 3, 4); module5.Parameters() = arma::mat(25 + 1, 1, arma::fill::zeros); @@ -3345,7 +2690,7 @@ TEST_CASE("SimpleTransposedConvolutionLayerTest", "[ANNLayerTest]") // Value calculated using torch.nn.functional.conv2d() REQUIRE(arma::accu(delta) == 960.0); - TransposedConvolution module6(1, 1, 3, 3, 2, 2, 1, 1, 3, 3, 5, 5); + TransposedConvolution<> module6(1, 1, 3, 3, 2, 2, 1, 1, 3, 3, 5, 5); // Test the forward function. input = arma::linspace(0, 8, 9); module6.Parameters() = arma::mat(9 + 1, 1, arma::fill::zeros); @@ -3363,7 +2708,7 @@ TEST_CASE("SimpleTransposedConvolutionLayerTest", "[ANNLayerTest]") // Value calculated using torch.nn.functional.conv2d() REQUIRE(arma::accu(delta) == 4444.0); - TransposedConvolution module7(1, 1, 3, 3, 2, 2, 1, 1, 3, 3, 6, 6); + TransposedConvolution<> module7(1, 1, 3, 3, 2, 2, 1, 1, 3, 3, 6, 6); // Test the forward function. input = arma::linspace(0, 8, 9); module7.Parameters() = arma::mat(9 + 1, 1, arma::fill::zeros); @@ -3380,11 +2725,10 @@ TEST_CASE("SimpleTransposedConvolutionLayerTest", "[ANNLayerTest]") // Value calculated using torch.nn.functional.conv2d() REQUIRE(arma::accu(delta) == 7732.0); } -*/ /** * Transposed Convolution layer numerical gradient test. - * + */ TEST_CASE("GradientTransposedConvolutionLayerTest", "[ANNLayerTest]") { // Add function gradient instantiation. @@ -3398,10 +2742,12 @@ TEST_CASE("GradientTransposedConvolutionLayerTest", "[ANNLayerTest]") input(arma::linspace(0, 35, 36)), target(arma::mat("0")) { - model = new FFN(); - model->ResetData(input, target); - model->Add(1, 1, 3, 3, 2, 2, 1, 1, 6, 6, 12, 12); - model->Add(); + model = new FFN, RandomInitialization>(); + model->Predictors() = input; + model->Responses() = target; + model->Add > + (1, 1, 3, 3, 2, 2, 1, 1, 6, 6, 12, 12); + model->Add >(); } ~GradientFunction() @@ -3418,7 +2764,7 @@ TEST_CASE("GradientTransposedConvolutionLayerTest", "[ANNLayerTest]") arma::mat& Parameters() { return model->Parameters(); } - FFN* model; + FFN, RandomInitialization>* model; arma::mat input, target; } function; @@ -3430,11 +2776,10 @@ TEST_CASE("GradientTransposedConvolutionLayerTest", "[ANNLayerTest]") } REQUIRE(pass == true); } -*/ /** * Simple MultiplyMerge module test. - * + */ TEST_CASE("SimpleMultiplyMergeLayerTest", "[ANNLayerTest]") { arma::mat output, input, delta; @@ -3442,14 +2787,14 @@ TEST_CASE("SimpleMultiplyMergeLayerTest", "[ANNLayerTest]") for (size_t i = 0; i < 5; ++i) { - MultiplyMerge module(false, false); + MultiplyMerge<> module(false, false); const size_t numMergeModules = math::RandInt(2, 10); for (size_t m = 0; m < numMergeModules; ++m) { - IdentityLayer* identityLayer = new IdentityLayer(); - identityLayer->Forward(input, identityLayer->OutputParameter()); + IdentityLayer<> identityLayer; + identityLayer.Forward(input, identityLayer.OutputParameter()); - module.Add(identityLayer); + module.Add >(identityLayer); } // Test the Forward function. @@ -3461,324 +2806,325 @@ TEST_CASE("SimpleMultiplyMergeLayerTest", "[ANNLayerTest]") REQUIRE(arma::accu(output) == arma::accu(delta)); } } -*/ /** * Check whether copying and moving network with MultiplyMerge is working or * not. */ -// TEST_CASE("CheckCopyMoveMultiplyMergeTest", "[ANNLayerTest]") -// { -// arma::mat input(10, 1); -// input.randu(); -// -// arma::mat output1; -// arma::mat output2; -// arma::mat output3; -// arma::mat output4; -// -// const size_t numMergeModules = math::RandInt(2, 10); -// -// MultiplyMerge<> *module1 = new MultiplyMerge<>(true, false); -// for (size_t m = 0; m < numMergeModules; ++m) -// { -// IdentityLayer<> identityLayer; -// identityLayer.Forward(input, identityLayer.OutputParameter()); -// -// module1->Add >(identityLayer); -// } -// -// module1->Forward(input, output1); -// -// MultiplyMerge<> module2 = *module1; -// delete module1; -// -// module2.Forward(input, output2); -// CheckMatrices(output1, output2); -// -// MultiplyMerge<> *module3 = new MultiplyMerge<>(true, false); -// for (size_t m = 0; m < numMergeModules; ++m) -// { -// IdentityLayer<> identityLayer; -// identityLayer.Forward(input, identityLayer.OutputParameter()); -// -// module3->Add >(identityLayer); -// } -// module3->Forward(input, output3); -// -// MultiplyMerge<> module4(std::move(*module3)); -// delete module3; -// -// module4.Forward(input, output4); -// CheckMatrices(output3, output4); -// } +TEST_CASE("CheckCopyMoveMultiplyMergeTest", "[ANNLayerTest]") +{ + arma::mat input(10, 1); + input.randu(); -// /** -// * Simple Atrous Convolution layer test. -// */ -// TEST_CASE("SimpleAtrousConvolutionLayerTest", "[ANNLayerTest]") -// { -// arma::mat output, input, delta; + arma::mat output1; + arma::mat output2; + arma::mat output3; + arma::mat output4; -// AtrousConvolution<> module1(1, 1, 3, 3, 1, 1, 0, 0, 7, 7, 2, 2); -// // Test the Forward function. -// input = arma::linspace(0, 48, 49); -// module1.Parameters() = arma::mat(9 + 1, 1, arma::fill::zeros); -// module1.Parameters()(0) = 1.0; -// module1.Parameters()(8) = 2.0; -// module1.Reset(); -// module1.Forward(input, output); -// // Value calculated using tensorflow.nn.atrous_conv2d() -// REQUIRE(arma::accu(output) == 792.0); + const size_t numMergeModules = math::RandInt(2, 10); -// // Test the Backward function. -// module1.Backward(input, output, delta); -// REQUIRE(arma::accu(delta) == 2376); + MultiplyMerge<> *module1 = new MultiplyMerge<>(true, false); + for (size_t m = 0; m < numMergeModules; ++m) + { + IdentityLayer<> identityLayer; + identityLayer.Forward(input, identityLayer.OutputParameter()); -// AtrousConvolution<> module2(1, 1, 3, 3, 2, 2, 0, 0, 7, 7, 2, 2); -// // Test the forward function. -// input = arma::linspace(0, 48, 49); -// module2.Parameters() = arma::mat(9 + 1, 1, arma::fill::zeros); -// module2.Parameters()(0) = 1.0; -// module2.Parameters()(3) = 1.0; -// module2.Parameters()(6) = 1.0; -// module2.Reset(); -// module2.Forward(input, output); -// // Value calculated using tensorflow.nn.conv2d() -// REQUIRE(arma::accu(output) == 264.0); + module1->Add >(identityLayer); + } -// // Test the backward function. -// module2.Backward(input, output, delta); -// REQUIRE(arma::accu(delta) == 792.0); -// } + module1->Forward(input, output1); -// /** -// * Atrous Convolution layer numerical gradient test. -// */ -// TEST_CASE("GradientAtrousConvolutionLayerTest", "[ANNLayerTest]") -// { -// // Add function gradient instantiation. -// struct GradientFunction -// { -// GradientFunction() : -// input(arma::linspace(0, 35, 36)), -// target(arma::mat("0")) -// { -// model = new FFN(); -// model->ResetData(input, target); -// model->Add >(); -// model->Add >(1, 1, 3, 3, 1, 1, 0, 0, 6, 6, 2, 2); -// model->Add >(); -// } + MultiplyMerge<> module2 = *module1; + delete module1; -// ~GradientFunction() -// { -// delete model; -// } + module2.Forward(input, output2); + CheckMatrices(output1, output2); -// double Gradient(arma::mat& gradient) const -// { -// double error = model->Evaluate(model->Parameters(), 0, 1); -// model->Gradient(model->Parameters(), 0, gradient, 1); -// return error; -// } + MultiplyMerge<> *module3 = new MultiplyMerge<>(true, false); + for (size_t m = 0; m < numMergeModules; ++m) + { + IdentityLayer<> identityLayer; + identityLayer.Forward(input, identityLayer.OutputParameter()); -// arma::mat& Parameters() { return model->Parameters(); } + module3->Add >(identityLayer); + } + module3->Forward(input, output3); -// FFN* model; -// arma::mat input, target; -// } function; + MultiplyMerge<> module4(std::move(*module3)); + delete module3; -// // TODO: this tolerance seems far higher than necessary. The implementation -// // should be checked. -// REQUIRE(CheckGradient(function) <= 0.2); -// } + module4.Forward(input, output4); + CheckMatrices(output3, output4); +} -// /** -// * Test the functions to access and modify the parameters of the -// * AtrousConvolution layer. -// */ -// TEST_CASE("AtrousConvolutionLayerParametersTest", "[ANNLayerTest]") -// { -// // Parameter order for the constructor: inSize, outSize, kW, kH, dW, dH, padW, -// // padH, inputWidth, inputHeight, dilationW, dilationH, paddingType ("none"). -// AtrousConvolution<> layer1(1, 2, 3, 4, 5, 6, std::make_tuple(7, 8), -// std::make_tuple(9, 10), 11, 12, 13, 14); -// AtrousConvolution<> layer2(2, 3, 4, 5, 6, 7, std::make_tuple(8, 9), -// std::make_tuple(10, 11), 12, 13, 14, 15); +/** + * Simple Atrous Convolution layer test. + */ +TEST_CASE("SimpleAtrousConvolutionLayerTest", "[ANNLayerTest]") +{ + arma::mat output, input, delta; -// // Make sure we can get the parameters successfully. -// REQUIRE(layer1.InputWidth() == 11); -// REQUIRE(layer1.InputHeight() == 12); -// REQUIRE(layer1.KernelWidth() == 3); -// REQUIRE(layer1.KernelHeight() == 4); -// REQUIRE(layer1.StrideWidth() == 5); -// REQUIRE(layer1.StrideHeight() == 6); -// REQUIRE(layer1.Padding().PadHTop() == 9); -// REQUIRE(layer1.Padding().PadHBottom() == 10); -// REQUIRE(layer1.Padding().PadWLeft() == 7); -// REQUIRE(layer1.Padding().PadWRight() == 8); -// REQUIRE(layer1.DilationWidth() == 13); -// REQUIRE(layer1.DilationHeight() == 14); + AtrousConvolution<> module1(1, 1, 3, 3, 1, 1, 0, 0, 7, 7, 2, 2); + // Test the Forward function. + input = arma::linspace(0, 48, 49); + module1.Parameters() = arma::mat(9 + 1, 1, arma::fill::zeros); + module1.Parameters()(0) = 1.0; + module1.Parameters()(8) = 2.0; + module1.Reset(); + module1.Forward(input, output); + // Value calculated using tensorflow.nn.atrous_conv2d() + REQUIRE(arma::accu(output) == 792.0); -// // Now modify the parameters to match the second layer. -// layer1.InputWidth() = 12; -// layer1.InputHeight() = 13; -// layer1.KernelWidth() = 4; -// layer1.KernelHeight() = 5; -// layer1.StrideWidth() = 6; -// layer1.StrideHeight() = 7; -// layer1.Padding().PadHTop() = 10; -// layer1.Padding().PadHBottom() = 11; -// layer1.Padding().PadWLeft() = 8; -// layer1.Padding().PadWRight() = 9; -// layer1.DilationWidth() = 14; -// layer1.DilationHeight() = 15; + // Test the Backward function. + module1.Backward(input, output, delta); + REQUIRE(arma::accu(delta) == 2376); -// // Now ensure all results are the same. -// REQUIRE(layer1.InputWidth() == layer2.InputWidth()); -// REQUIRE(layer1.InputHeight() == layer2.InputHeight()); -// REQUIRE(layer1.KernelWidth() == layer2.KernelWidth()); -// REQUIRE(layer1.KernelHeight() == layer2.KernelHeight()); -// REQUIRE(layer1.StrideWidth() == layer2.StrideWidth()); -// REQUIRE(layer1.StrideHeight() == layer2.StrideHeight()); -// REQUIRE(layer1.Padding().PadHTop() == layer2.Padding().PadHTop()); -// REQUIRE(layer1.Padding().PadHBottom() == -// layer2.Padding().PadHBottom()); -// REQUIRE(layer1.Padding().PadWLeft() == -// layer2.Padding().PadWLeft()); -// REQUIRE(layer1.Padding().PadWRight() == -// layer2.Padding().PadWRight()); -// REQUIRE(layer1.DilationWidth() == layer2.DilationWidth()); -// REQUIRE(layer1.DilationHeight() == layer2.DilationHeight()); -// } + AtrousConvolution<> module2(1, 1, 3, 3, 2, 2, 0, 0, 7, 7, 2, 2); + // Test the forward function. + input = arma::linspace(0, 48, 49); + module2.Parameters() = arma::mat(9 + 1, 1, arma::fill::zeros); + module2.Parameters()(0) = 1.0; + module2.Parameters()(3) = 1.0; + module2.Parameters()(6) = 1.0; + module2.Reset(); + module2.Forward(input, output); + // Value calculated using tensorflow.nn.conv2d() + REQUIRE(arma::accu(output) == 264.0); -// /** -// * Test that the padding options are working correctly in Atrous Convolution -// * layer. -// */ -// TEST_CASE("AtrousConvolutionLayerPaddingTest", "[ANNLayerTest]") -// { -// arma::mat output, input, delta; + // Test the backward function. + module2.Backward(input, output, delta); + REQUIRE(arma::accu(delta) == 792.0); +} -// // Check valid padding option. -// AtrousConvolution<> module1(1, 1, 3, 3, 1, 1, -// std::tuple(1, 1), std::tuple(1, 1), 7, 7, -// 2, 2, "valid"); +/** + * Atrous Convolution layer numerical gradient test. + */ +TEST_CASE("GradientAtrousConvolutionLayerTest", "[ANNLayerTest]") +{ + // Add function gradient instantiation. + struct GradientFunction + { + GradientFunction() : + input(arma::linspace(0, 35, 36)), + target(arma::mat("0")) + { + model = new FFN, RandomInitialization>(); + model->Predictors() = input; + model->Responses() = target; + model->Add >(); + model->Add >(1, 1, 3, 3, 1, 1, 0, 0, 6, 6, 2, 2); + model->Add >(); + } -// // Test the Forward function. -// input = arma::linspace(0, 48, 49); -// module1.Parameters() = arma::mat(9 + 1, 1, arma::fill::zeros); -// module1.Reset(); -// module1.Forward(input, output); + ~GradientFunction() + { + delete model; + } -// REQUIRE(arma::accu(output) == 0); -// REQUIRE(output.n_rows == 9); -// REQUIRE(output.n_cols == 1); + double Gradient(arma::mat& gradient) const + { + double error = model->Evaluate(model->Parameters(), 0, 1); + model->Gradient(model->Parameters(), 0, gradient, 1); + return error; + } -// // Test the Backward function. -// module1.Backward(input, output, delta); + arma::mat& Parameters() { return model->Parameters(); } -// // Check same padding option. -// AtrousConvolution<> module2(1, 1, 3, 3, 1, 1, -// std::tuple(0, 0), std::tuple(0, 0), 7, 7, -// 2, 2, "same"); + FFN, RandomInitialization>* model; + arma::mat input, target; + } function; -// // Test the forward function. -// input = arma::linspace(0, 48, 49); -// module2.Parameters() = arma::mat(9 + 1, 1, arma::fill::zeros); -// module2.Reset(); -// module2.Forward(input, output); + // TODO: this tolerance seems far higher than necessary. The implementation + // should be checked. + REQUIRE(CheckGradient(function) <= 0.2); +} -// REQUIRE(arma::accu(output) == 0); -// REQUIRE(output.n_rows == 49); -// REQUIRE(output.n_cols == 1); +/** + * Test the functions to access and modify the parameters of the + * AtrousConvolution layer. + */ +TEST_CASE("AtrousConvolutionLayerParametersTest", "[ANNLayerTest]") +{ + // Parameter order for the constructor: inSize, outSize, kW, kH, dW, dH, padW, + // padH, inputWidth, inputHeight, dilationW, dilationH, paddingType ("none"). + AtrousConvolution<> layer1(1, 2, 3, 4, 5, 6, std::make_tuple(7, 8), + std::make_tuple(9, 10), 11, 12, 13, 14); + AtrousConvolution<> layer2(2, 3, 4, 5, 6, 7, std::make_tuple(8, 9), + std::make_tuple(10, 11), 12, 13, 14, 15); -// // Test the backward function. -// module2.Backward(input, output, delta); -// } + // Make sure we can get the parameters successfully. + REQUIRE(layer1.InputWidth() == 11); + REQUIRE(layer1.InputHeight() == 12); + REQUIRE(layer1.KernelWidth() == 3); + REQUIRE(layer1.KernelHeight() == 4); + REQUIRE(layer1.StrideWidth() == 5); + REQUIRE(layer1.StrideHeight() == 6); + REQUIRE(layer1.Padding().PadHTop() == 9); + REQUIRE(layer1.Padding().PadHBottom() == 10); + REQUIRE(layer1.Padding().PadWLeft() == 7); + REQUIRE(layer1.Padding().PadWRight() == 8); + REQUIRE(layer1.DilationWidth() == 13); + REQUIRE(layer1.DilationHeight() == 14); + + // Now modify the parameters to match the second layer. + layer1.InputWidth() = 12; + layer1.InputHeight() = 13; + layer1.KernelWidth() = 4; + layer1.KernelHeight() = 5; + layer1.StrideWidth() = 6; + layer1.StrideHeight() = 7; + layer1.Padding().PadHTop() = 10; + layer1.Padding().PadHBottom() = 11; + layer1.Padding().PadWLeft() = 8; + layer1.Padding().PadWRight() = 9; + layer1.DilationWidth() = 14; + layer1.DilationHeight() = 15; + + // Now ensure all results are the same. + REQUIRE(layer1.InputWidth() == layer2.InputWidth()); + REQUIRE(layer1.InputHeight() == layer2.InputHeight()); + REQUIRE(layer1.KernelWidth() == layer2.KernelWidth()); + REQUIRE(layer1.KernelHeight() == layer2.KernelHeight()); + REQUIRE(layer1.StrideWidth() == layer2.StrideWidth()); + REQUIRE(layer1.StrideHeight() == layer2.StrideHeight()); + REQUIRE(layer1.Padding().PadHTop() == layer2.Padding().PadHTop()); + REQUIRE(layer1.Padding().PadHBottom() == + layer2.Padding().PadHBottom()); + REQUIRE(layer1.Padding().PadWLeft() == + layer2.Padding().PadWLeft()); + REQUIRE(layer1.Padding().PadWRight() == + layer2.Padding().PadWRight()); + REQUIRE(layer1.DilationWidth() == layer2.DilationWidth()); + REQUIRE(layer1.DilationHeight() == layer2.DilationHeight()); +} + +/** + * Test that the padding options are working correctly in Atrous Convolution + * layer. + */ +TEST_CASE("AtrousConvolutionLayerPaddingTest", "[ANNLayerTest]") +{ + arma::mat output, input, delta; + + // Check valid padding option. + AtrousConvolution<> module1(1, 1, 3, 3, 1, 1, + std::tuple(1, 1), std::tuple(1, 1), 7, 7, + 2, 2, "valid"); + + // Test the Forward function. + input = arma::linspace(0, 48, 49); + module1.Parameters() = arma::mat(9 + 1, 1, arma::fill::zeros); + module1.Reset(); + module1.Forward(input, output); + + REQUIRE(arma::accu(output) == 0); + REQUIRE(output.n_rows == 9); + REQUIRE(output.n_cols == 1); + + // Test the Backward function. + module1.Backward(input, output, delta); + + // Check same padding option. + AtrousConvolution<> module2(1, 1, 3, 3, 1, 1, + std::tuple(0, 0), std::tuple(0, 0), 7, 7, + 2, 2, "same"); + + // Test the forward function. + input = arma::linspace(0, 48, 49); + module2.Parameters() = arma::mat(9 + 1, 1, arma::fill::zeros); + module2.Reset(); + module2.Forward(input, output); + + REQUIRE(arma::accu(output) == 0); + REQUIRE(output.n_rows == 49); + REQUIRE(output.n_cols == 1); + + // Test the backward function. + module2.Backward(input, output, delta); +} /** * Tests the GroupNorm layer. */ -// TEST_CASE("GroupNormTest", "[ANNLayerTest]") -// { -// arma::mat input, output, backwardOutput; -// input = { -// { 2, 0, 1 }, -// { 3, 1, 2 }, -// { 5, 1, 3 }, -// { 7, 2, 4 }, -// { 11, 3, 5 }, -// { 13, 5, 6 }, -// { 17, 8, 7 }, -// { 19, 13, 8 } -// }; -// -// GroupNorm<> model(2, 4); -// model.Reset(); -// -// model.Forward(input, output); -// arma::mat result; -// result = { -// { -1.1717001972, -1.4142135482, -1.3416407811 }, -// { -0.6509445540, 0.0000000000 , -0.4472135937 }, -// { 0.3905667324 , 0.0000000000 , 0.4472135937 }, -// { 1.4320780188 , 1.4142135482 , 1.341640781 }, -// { -1.2649110634, -1.1283296293, -1.3416407811 }, -// { -0.6324555317, -0.5973509802, -0.4472135937 }, -// { 0.6324555317 , 0.1991169934 , 0.4472135937 }, -// { 1.2649110634 , 1.5265636161 , 1.3416407811 } -// }; -// -// CheckMatrices(output, result, 1e-5); -// } +TEST_CASE("GroupNormTest", "[ANNLayerTest]") +{ + arma::mat input, output, backwardOutput; + input = { + { 2, 0, 1 }, + { 3, 1, 2 }, + { 5, 1, 3 }, + { 7, 2, 4 }, + { 11, 3, 5 }, + { 13, 5, 6 }, + { 17, 8, 7 }, + { 19, 13, 8 } + }; + + GroupNorm<> model(2, 4); + model.Reset(); + + model.Forward(input, output); + arma::mat result; + result = { + { -1.1717001972, -1.4142135482, -1.3416407811 }, + { -0.6509445540, 0.0000000000 , -0.4472135937 }, + { 0.3905667324 , 0.0000000000 , 0.4472135937 }, + { 1.4320780188 , 1.4142135482 , 1.341640781 }, + { -1.2649110634, -1.1283296293, -1.3416407811 }, + { -0.6324555317, -0.5973509802, -0.4472135937 }, + { 0.6324555317 , 0.1991169934 , 0.4472135937 }, + { 1.2649110634 , 1.5265636161 , 1.3416407811 } + }; + + CheckMatrices(output, result, 1e-5); +} /** * GroupNorm layer numerical gradient test. */ -// TEST_CASE("GradientGroupNormTest", "[ANNLayerTest]") -// { -// // Add function gradient instantiation. -// struct GradientFunction -// { -// GradientFunction() : -// input(arma::randn(10, 256)), -// target(arma::zeros(1, 256)) -// { -// model = new FFN(); -// model->ResetData(input, target); -// model->Add >(); -// model->Add >(10, 10); -// model->Add >(1, 10); -// model->Add >(10, 2); -// model->Add >(); -// } -// -// ~GradientFunction() -// { -// delete model; -// } -// -// double Gradient(arma::mat& gradient) const -// { -// double error = model->Evaluate(model->Parameters(), 0, 256, false); -// model->Gradient(model->Parameters(), 0, gradient, 256); -// return error; -// } -// -// arma::mat& Parameters() { return model->Parameters(); } -// -// FFN* model; -// arma::mat input, target; -// } function; -// -// REQUIRE(CheckGradient(function) <= 1e-4); -// } +TEST_CASE("GradientGroupNormTest", "[ANNLayerTest]") +{ + // Add function gradient instantiation. + struct GradientFunction + { + GradientFunction() : + input(arma::randn(10, 256)), + target(arma::zeros(1, 256)) + { + model = new FFN, NguyenWidrowInitialization>(); + model->Predictors() = input; + model->Responses() = target; + model->Add >(); + model->Add >(10, 10); + model->Add >(1, 10); + model->Add >(10, 2); + model->Add >(); + } + + ~GradientFunction() + { + delete model; + } + + double Gradient(arma::mat& gradient) const + { + double error = model->Evaluate(model->Parameters(), 0, 256, false); + model->Gradient(model->Parameters(), 0, gradient, 256); + return error; + } + + arma::mat& Parameters() { return model->Parameters(); } + + FFN, NguyenWidrowInitialization>* model; + arma::mat input, target; + } function; + + REQUIRE(CheckGradient(function) <= 1e-4); +} /** * Tests the LayerNorm layer. - * + */ TEST_CASE("LayerNormTest", "[ANNLayerTest]") { arma::mat input, output; @@ -3786,7 +3132,7 @@ TEST_CASE("LayerNormTest", "[ANNLayerTest]") { 4.9, 3.0 }, { 4.7, 3.2 } }; - LayerNorm model(input.n_rows); + LayerNorm<> model(input.n_rows); model.Reset(); model.Forward(input, output); @@ -3809,11 +3155,10 @@ TEST_CASE("LayerNormTest", "[ANNLayerTest]") CheckMatrices(output, result, 1e-1); } -*/ /** * LayerNorm layer numerical gradient test. - * + */ TEST_CASE("GradientLayerNormTest", "[ANNLayerTest]") { // Add function gradient instantiation. @@ -3823,13 +3168,14 @@ TEST_CASE("GradientLayerNormTest", "[ANNLayerTest]") input(arma::randn(10, 256)), target(arma::zeros(1, 256)) { - model = new FFN(); - model->ResetData(input, target); - model->Add(); - model->Add(10, 10); - model->Add(10); - model->Add(10, 2); - model->Add(); + model = new FFN, NguyenWidrowInitialization>(); + model->Predictors() = input; + model->Responses() = target; + model->Add >(); + model->Add >(10, 10); + model->Add >(10); + model->Add >(10, 2); + model->Add >(); } ~GradientFunction() @@ -3839,79 +3185,77 @@ TEST_CASE("GradientLayerNormTest", "[ANNLayerTest]") double Gradient(arma::mat& gradient) const { - double error = model->Evaluate(model->Parameters(), 0, 16, false); - model->Gradient(model->Parameters(), 0, gradient, 16); + double error = model->Evaluate(model->Parameters(), 0, 256, false); + model->Gradient(model->Parameters(), 0, gradient, 256); return error; } arma::mat& Parameters() { return model->Parameters(); } - FFN* model; + FFN, NguyenWidrowInitialization>* model; arma::mat input, target; } function; REQUIRE(CheckGradient(function) <= 1e-4); } -*/ /** * Test that the functions that can access the parameters of the * Layer Norm layer work. - * + */ TEST_CASE("LayerNormLayerParametersTest", "[ANNLayerTest]") { // Parameter order : size, eps. - LayerNorm layer(5, 1e-3); + LayerNorm<> layer(5, 1e-3); // Make sure we can get the parameters successfully. REQUIRE(layer.InSize() == 5); REQUIRE(layer.Epsilon() == 1e-3); } -*/ - -// /** -// * Test if the AddMerge layer is able to forward the -// * Forward/Backward/Gradient calls. -// */ -// TEST_CASE("AddMergeRunTest", "[ANNLayerTest]") -// { -// arma::mat output, input, delta, error; - -// AddMerge<> module(true, true); - -// Linear<>* linear = new Linear<>(10, 10); -// module.Add(linear); - -// linear->Parameters().randu(); -// linear->Reset(); - -// input = arma::zeros(10, 1); -// module.Forward(input, output); - -// double parameterSum = arma::accu(linear->Parameters().submat( -// 100, 0, linear->Parameters().n_elem - 1, 0)); - -// // Test the Backward function. -// module.Backward(input, input, delta); - -// // Clean up before we break, -// delete linear; - -// REQUIRE(parameterSum == Approx(arma::accu(output)).epsilon(1e-5)); -// REQUIRE(arma::accu(delta) == 0); -// } /** - * Test if the MultiplyMerge layer is able to forward the + * Test if the AddMerge layer is able to forward the * Forward/Backward/Gradient calls. - * -TEST_CASE("MultiplyMergeRunTest", "[ANNLayerTest]") + */ +TEST_CASE("AddMergeRunTest", "[ANNLayerTest]") { arma::mat output, input, delta, error; - MultiplyMerge module(true, true); + AddMerge<> module(true, true); - Linear* linear = new Linear(10, 10); + Linear<>* linear = new Linear<>(10, 10); + module.Add(linear); + + linear->Parameters().randu(); + linear->Reset(); + + input = arma::zeros(10, 1); + module.Forward(input, output); + + double parameterSum = arma::accu(linear->Parameters().submat( + 100, 0, linear->Parameters().n_elem - 1, 0)); + + // Test the Backward function. + module.Backward(input, input, delta); + + // Clean up before we break, + delete linear; + + REQUIRE(parameterSum == Approx(arma::accu(output)).epsilon(1e-5)); + REQUIRE(arma::accu(delta) == 0); +} + +/** + * Test if the MultiplyMerge layer is able to forward the + * Forward/Backward/Gradient calls. + */ +TEST_CASE("MultiplyMergeRunTest", "[ANNLayerTest]") +{ + arma::mat output, input, delta, error; + + MultiplyMerge<> module(true, true); + + Linear<>* linear = new Linear<>(10, 10); module.Add(linear); linear->Parameters().randu(); @@ -3932,22 +3276,21 @@ TEST_CASE("MultiplyMergeRunTest", "[ANNLayerTest]") REQUIRE(parameterSum == Approx(arma::accu(output)).epsilon(1e-5)); REQUIRE(arma::accu(delta) == 0); } -*/ /** * Simple subview module test. - * + */ TEST_CASE("SimpleSubviewLayerTest", "[ANNLayerTest]") { arma::mat output, input, delta, outputMat; - Subview moduleRow(1, 10, 19); + Subview<> moduleRow(1, 10, 19); // Test the Forward function for a vector. input = arma::ones(20, 1); moduleRow.Forward(input, output); REQUIRE(output.n_rows == 10); - Subview moduleMat(4, 3, 6, 0, 2); + Subview<> moduleMat(4, 3, 6, 0, 2); // Test the Forward function for a matrix. input = arma::ones(20, 8); @@ -3960,48 +3303,46 @@ TEST_CASE("SimpleSubviewLayerTest", "[ANNLayerTest]") REQUIRE(accu(delta) == 160); REQUIRE(delta.n_rows == 20); } -*/ /** * Subview index test. - * + */ TEST_CASE("SubviewIndexTest", "[ANNLayerTest]") { arma::mat outputEnd, outputMid, outputStart, input, delta; input = arma::linspace(1, 20, 20); // Slicing from the initial indices. - Subview moduleStart(1, 0, 9); + Subview<> moduleStart(1, 0, 9); arma::mat subStart = arma::linspace(1, 10, 10); moduleStart.Forward(input, outputStart); CheckMatrices(outputStart, subStart); // Slicing from the mid indices. - Subview moduleMid(1, 6, 15); + Subview<> moduleMid(1, 6, 15); arma::mat subMid = arma::linspace(7, 16, 10); moduleMid.Forward(input, outputMid); CheckMatrices(outputMid, subMid); // Slicing from the end indices. - Subview moduleEnd(1, 10, 19); + Subview<> moduleEnd(1, 10, 19); arma::mat subEnd = arma::linspace(11, 20, 10); moduleEnd.Forward(input, outputEnd); CheckMatrices(outputEnd, subEnd); } -*/ /** * Subview batch test. - * + */ TEST_CASE("SubviewBatchTest", "[ANNLayerTest]") { arma::mat output, input, outputCol, outputMat, outputDef; // All rows selected. - Subview moduleCol(1, 0, 19); + Subview<> moduleCol(1, 0, 19); // Test with inSize 1. input = arma::ones(20, 8); @@ -4009,7 +3350,7 @@ TEST_CASE("SubviewBatchTest", "[ANNLayerTest]") CheckMatrices(outputCol, input); // Few rows and columns selected. - Subview moduleMat(4, 3, 6, 0, 2); + Subview<> moduleMat(4, 3, 6, 0, 2); // Test with inSize greater than 1. moduleMat.Forward(input, outputMat); @@ -4017,24 +3358,23 @@ TEST_CASE("SubviewBatchTest", "[ANNLayerTest]") CheckMatrices(outputMat, output); // endCol changed to 3 by default. - Subview moduleDef(4, 1, 6, 0, 4); + Subview<> moduleDef(4, 1, 6, 0, 4); // Test with inSize greater than 1 and endCol >= inSize. moduleDef.Forward(input, outputDef); output = arma::ones(24, 2); CheckMatrices(outputDef, output); } -*/ /** * Test that the functions that can modify and access the parameters of the * Subview layer work. - * + */ TEST_CASE("SubviewLayerParametersTest", "[ANNLayerTest]") { // Parameter order : inSize, beginRow, endRow, beginCol, endCol. - Subview layer1(1, 2, 3, 4, 5); - Subview layer2(1, 3, 4, 5, 6); + Subview<> layer1(1, 2, 3, 4, 5); + Subview<> layer2(1, 3, 4, 5, 6); // Make sure we can get the parameters correctly. REQUIRE(layer1.InSize() == 1); @@ -4056,15 +3396,14 @@ TEST_CASE("SubviewLayerParametersTest", "[ANNLayerTest]") REQUIRE(layer1.BeginCol() == layer2.BeginCol()); REQUIRE(layer1.EndCol() == layer2.EndCol()); } -*/ /* * Simple Reparametrization module test. - * + */ TEST_CASE("SimpleReparametrizationLayerTest", "[ANNLayerTest]") { arma::mat input, output, delta; - Reparametrization module(5); + Reparametrization<> module(5); // Test the Forward function. As the mean is zero and the standard // deviation is small, after multiplying the gaussian sample, the @@ -4079,15 +3418,14 @@ TEST_CASE("SimpleReparametrizationLayerTest", "[ANNLayerTest]") module.Backward(input, gy, delta); REQUIRE(arma::accu(delta) != 0); // klBackward will be added. } -*/ /** * Reparametrization module stochastic boolean test. - * + */ TEST_CASE("ReparametrizationLayerStochasticTest", "[ANNLayerTest]") { arma::mat input, outputA, outputB; - Reparametrization module(5, false); + Reparametrization<> module(5, false); input = join_cols(arma::ones(5, 1), arma::zeros(5, 1)); @@ -4098,15 +3436,14 @@ TEST_CASE("ReparametrizationLayerStochasticTest", "[ANNLayerTest]") CheckMatrices(outputA, outputB); } -*/ /** * Reparametrization module includeKl boolean test. - * + */ TEST_CASE("ReparametrizationLayerIncludeKlTest", "[ANNLayerTest]") { arma::mat input, output, gy, delta; - Reparametrization module(5, true, false); + Reparametrization<> module(5, true, false); input = join_cols(arma::ones(5, 1), arma::zeros(5, 1)); @@ -4119,31 +3456,29 @@ TEST_CASE("ReparametrizationLayerIncludeKlTest", "[ANNLayerTest]") REQUIRE(arma::accu(delta) == 0); } -*/ /** * Jacobian Reparametrization module test. - * + */ TEST_CASE("JacobianReparametrizationLayerTest", "[ANNLayerTest]") { for (size_t i = 0; i < 5; ++i) { - const size_t inputElementsHalf = math::RandInt(2, 10); + const size_t inputElementsHalf = math::RandInt(2, 1000); arma::mat input; input.set_size(inputElementsHalf * 2, 1); - Reparametrization module(inputElementsHalf, false, false); + Reparametrization<> module(inputElementsHalf, false, false); double error = JacobianTest(module, input); REQUIRE(error <= 1e-5); } } -*/ /** * Reparametrization layer numerical gradient test. - * + */ TEST_CASE("GradientReparametrizationLayerTest", "[ANNLayerTest]") { // Linear function gradient instantiation. @@ -4153,13 +3488,14 @@ TEST_CASE("GradientReparametrizationLayerTest", "[ANNLayerTest]") input(arma::randu(10, 1)), target(arma::mat("0")) { - model = new FFN(); - model->ResetData(input, target); - model->Add(); - model->Add(10, 6); - model->Add(3, false, true, 1); - model->Add(3, 2); - model->Add(); + model = new FFN, NguyenWidrowInitialization>(); + model->Predictors() = input; + model->Responses() = target; + model->Add >(); + model->Add >(10, 6); + model->Add >(3, false, true, 1); + model->Add >(3, 2); + model->Add >(); } ~GradientFunction() @@ -4176,17 +3512,16 @@ TEST_CASE("GradientReparametrizationLayerTest", "[ANNLayerTest]") arma::mat& Parameters() { return model->Parameters(); } - FFN* model; + FFN, NguyenWidrowInitialization>* model; arma::mat input, target; } function; - // REQUIRE(CheckGradient(function) <= 1e-4); + REQUIRE(CheckGradient(function) <= 1e-4); } -*/ /** * Reparametrization layer beta numerical gradient test. - * + */ TEST_CASE("GradientReparametrizationLayerBetaTest", "[ANNLayerTest]") { // Linear function gradient instantiation. @@ -4196,14 +3531,15 @@ TEST_CASE("GradientReparametrizationLayerBetaTest", "[ANNLayerTest]") input(arma::randu(10, 2)), target(arma::mat("0 0")) { - model = new FFN(); - model->ResetData(input, target); - model->Add(); - model->Add(10, 6); + model = new FFN, NguyenWidrowInitialization>(); + model->Predictors() = input; + model->Responses() = target; + model->Add >(); + model->Add >(10, 6); // Use a value of beta not equal to 1. - model->Add(3, false, true, 2); - model->Add(3, 2); - model->Add(); + model->Add >(3, false, true, 2); + model->Add >(3, 2); + model->Add >(); } ~GradientFunction() @@ -4220,22 +3556,21 @@ TEST_CASE("GradientReparametrizationLayerBetaTest", "[ANNLayerTest]") arma::mat& Parameters() { return model->Parameters(); } - FFN* model; + FFN, NguyenWidrowInitialization>* model; arma::mat input, target; } function; - // REQUIRE(CheckGradient(function) <= 1e-4); + REQUIRE(CheckGradient(function) <= 1e-4); } -*/ /** * Test that the functions that can access the parameters of the * Reparametrization layer work. - * + */ TEST_CASE("ReparametrizationLayerParametersTest", "[ANNLayerTest]") { // Parameter order : latentSize, stochastic, includeKL, beta. - Reparametrization layer(5, false, false, 2); + Reparametrization<> layer(5, false, false, 2); // Make sure we can get the parameters successfully. REQUIRE(layer.OutputSize() == 5); @@ -4243,22 +3578,21 @@ TEST_CASE("ReparametrizationLayerParametersTest", "[ANNLayerTest]") REQUIRE(layer.IncludeKL() == false); REQUIRE(layer.Beta() == 2); } -*/ /** * Simple residual module test. - * + */ TEST_CASE("SimpleResidualLayerTest", "[ANNLayerTest]") { arma::mat outputA, outputB, input, deltaA, deltaB; - Sequential* sequential = new Sequential(true); - Residual* residual = new Residual(true); + Sequential<>* sequential = new Sequential<>(true); + Residual<>* residual = new Residual<>(true); - Linear* linearA = new Linear(10, 10); + Linear<>* linearA = new Linear<>(10, 10); linearA->Parameters().randu(); linearA->Reset(); - Linear* linearB = new Linear(10, 10); + Linear<>* linearB = new Linear<>(10, 10); linearB->Parameters().randu(); linearB->Reset(); @@ -4288,23 +3622,22 @@ TEST_CASE("SimpleResidualLayerTest", "[ANNLayerTest]") delete linearA; delete linearB; } -*/ /** * Simple Highway module test. - * + */ TEST_CASE("SimpleHighwayLayerTest", "[ANNLayerTest]") { arma::mat outputA, outputB, input, deltaA, deltaB; - Sequential* sequential = new Sequential(true); - Highway* highway = new Highway(10, true); + Sequential<>* sequential = new Sequential<>(true); + Highway<>* highway = new Highway<>(10, true); highway->Parameters().zeros(); highway->Reset(); - Linear* linearA = new Linear(10, 10); + Linear<>* linearA = new Linear<>(10, 10); linearA->Parameters().randu(); linearA->Reset(); - Linear* linearB = new Linear(10, 10); + Linear<>* linearB = new Linear<>(10, 10); linearB->Parameters().randu(); linearB->Reset(); @@ -4327,247 +3660,249 @@ TEST_CASE("SimpleHighwayLayerTest", "[ANNLayerTest]") delete linearA; delete linearB; } -*/ /** * Test that the function that can access the inSize parameter of the * Highway layer works. - * + */ TEST_CASE("HighwayLayerParametersTest", "[ANNLayerTest]") { // Parameter order : inSize, model. - Highway layer(1, true); + Highway<> layer(1, true); // Make sure we can get the parameter successfully. REQUIRE(layer.InSize() == 1); } -*/ - -// /** -// * Sequential layer numerical gradient test. -// */ -// TEST_CASE("GradientHighwayLayerTest", "[ANNLayerTest]") -// { -// // Linear function gradient instantiation. -// struct GradientFunction -// { -// GradientFunction() : -// input(arma::randu(5, 1)), -// target(arma::mat("0")) -// { -// model = new FFN(); -// model->ResetData(input, target); -// model->Add(); -// model->Add(5, 10); - -// highway = new Highway(10); -// highway->Add(10, 10); -// highway->Add(); -// highway->Add(10, 10); -// highway->Add(); - -// model->Add(highway); -// model->Add(10, 2); -// model->Add(); -// } - -// ~GradientFunction() -// { -// delete model; -// } - -// double Gradient(arma::mat& gradient) const -// { -// double error = model->Evaluate(model->Parameters(), 0, 1); -// model->Gradient(model->Parameters(), 0, gradient, 1); -// return error; -// } - -// arma::mat& Parameters() { return model->Parameters(); } - -// FFN* model; -// Highway* highway; -// arma::mat input, target; -// } function; - -// REQUIRE(CheckGradient(function) <= 1e-4); -// } /** * Sequential layer numerical gradient test. */ -// TEST_CASE("GradientSequentialLayerTest", "[ANNLayerTest]") -// { -// // Linear function gradient instantiation. -// struct GradientFunction -// { -// GradientFunction() : -// input(arma::randu(10, 1)), -// target(arma::mat("0")) -// { -// model = new FFN(); -// model->ResetData(input, target); -// model->Add(); -// model->Add(10, 10); -// sequential = new Sequential(); -// sequential->Add(10, 10); -// sequential->Add(); -// sequential->Add(10, 5); -// sequential->Add(); +TEST_CASE("GradientHighwayLayerTest", "[ANNLayerTest]") +{ + // Linear function gradient instantiation. + struct GradientFunction + { + GradientFunction() : + input(arma::randu(5, 1)), + target(arma::mat("0")) + { + model = new FFN, NguyenWidrowInitialization>(); + model->Predictors() = input; + model->Responses() = target; + model->Add >(); + model->Add >(5, 10); -// model->Add(sequential); -// model->Add(5, 2); -// model->Add(); -// } + highway = new Highway<>(10); + highway->Add >(10, 10); + highway->Add >(); + highway->Add >(10, 10); + highway->Add >(); -// ~GradientFunction() -// { -// delete model; -// } + model->Add(highway); + model->Add >(10, 2); + model->Add >(); + } -// double Gradient(arma::mat& gradient) const -// { -// double error = model->Evaluate(model->Parameters(), 0, 1); -// model->Gradient(model->Parameters(), 0, gradient, 1); -// return error; -// } + ~GradientFunction() + { + delete model; + } -// arma::mat& Parameters() { return model->Parameters(); } + double Gradient(arma::mat& gradient) const + { + double error = model->Evaluate(model->Parameters(), 0, 1); + model->Gradient(model->Parameters(), 0, gradient, 1); + return error; + } -// FFN* model; -// Sequential* sequential; -// arma::mat input, target; -// } function; + arma::mat& Parameters() { return model->Parameters(); } -// REQUIRE(CheckGradient(function) <= 1e-4); -// } + FFN, NguyenWidrowInitialization>* model; + Highway<>* highway; + arma::mat input, target; + } function; -// /** -// * WeightNorm layer numerical gradient test. -// */ -// TEST_CASE("GradientWeightNormLayerTest", "[ANNLayerTest]") -// { -// // Linear function gradient instantiation. -// struct GradientFunction -// { -// GradientFunction() : -// input(arma::randu(10, 1)), -// target(arma::mat("0")) -// { -// model = new FFN(); -// model->ResetData(input, target); -// model->Add(10, 10); + REQUIRE(CheckGradient(function) <= 1e-4); +} -// Linear* linear = new Linear(10, 2); -// weightNorm = new WeightNorm(linear); +/** + * Sequential layer numerical gradient test. + */ +TEST_CASE("GradientSequentialLayerTest", "[ANNLayerTest]") +{ + // Linear function gradient instantiation. + struct GradientFunction + { + GradientFunction() : + input(arma::randu(10, 1)), + target(arma::mat("0")) + { + model = new FFN, NguyenWidrowInitialization>(); + model->Predictors() = input; + model->Responses() = target; + model->Add >(); + model->Add >(10, 10); + sequential = new Sequential<>(); + sequential->Add >(10, 10); + sequential->Add >(); + sequential->Add >(10, 5); + sequential->Add >(); -// model->Add(weightNorm); -// model->Add(); -// } + model->Add(sequential); + model->Add >(5, 2); + model->Add >(); + } -// ~GradientFunction() -// { -// delete model; -// } + ~GradientFunction() + { + delete model; + } -// double Gradient(arma::mat& gradient) const -// { -// double error = model->Evaluate(model->Parameters(), 0, 1); -// model->Gradient(model->Parameters(), 0, gradient, 1); -// return error; -// } + double Gradient(arma::mat& gradient) const + { + double error = model->Evaluate(model->Parameters(), 0, 1); + model->Gradient(model->Parameters(), 0, gradient, 1); + return error; + } -// arma::mat& Parameters() { return model->Parameters(); } + arma::mat& Parameters() { return model->Parameters(); } -// FFN* model; -// WeightNorm* weightNorm; -// arma::mat input, target; -// } function; + FFN, NguyenWidrowInitialization>* model; + Sequential<>* sequential; + arma::mat input, target; + } function; -// REQUIRE(CheckGradient(function) <= 1e-4); -// } + REQUIRE(CheckGradient(function) <= 1e-4); +} -// /** -// * Test if the WeightNorm layer is able to forward the -// * Forward/Backward/Gradient calls. -// */ -// TEST_CASE("WeightNormRunTest", "[ANNLayerTest]") -// { -// arma::mat output, input, delta, error; -// Linear* linear = new Linear(10, 10); +/** + * WeightNorm layer numerical gradient test. + */ +TEST_CASE("GradientWeightNormLayerTest", "[ANNLayerTest]") +{ + // Linear function gradient instantiation. + struct GradientFunction + { + GradientFunction() : + input(arma::randu(10, 1)), + target(arma::mat("0")) + { + model = new FFN, NguyenWidrowInitialization>(); + model->Predictors() = input; + model->Responses() = target; + model->Add >(10, 10); -// WeightNorm module(linear); + Linear<>* linear = new Linear<>(10, 2); + weightNorm = new WeightNorm<>(linear); -// module.Parameters().randu(); -// module.Reset(); + model->Add(weightNorm); + model->Add >(); + } -// linear->Bias().zeros(); + ~GradientFunction() + { + delete model; + } -// input = arma::zeros(10, 1); -// module.Forward(input, output); + double Gradient(arma::mat& gradient) const + { + double error = model->Evaluate(model->Parameters(), 0, 1); + model->Gradient(model->Parameters(), 0, gradient, 1); + return error; + } -// // Test the Backward function. -// module.Backward(input, input, delta); + arma::mat& Parameters() { return model->Parameters(); } -// REQUIRE(0 == arma::accu(output)); -// REQUIRE(arma::accu(delta) == 0); -// } + FFN, NguyenWidrowInitialization>* model; + WeightNorm<>* weightNorm; + arma::mat input, target; + } function; -// // General ANN serialization test. -// template -// void ANNLayerSerializationTest(LayerType& layer) -// { -// arma::mat input(5, 100, arma::fill::randu); -// arma::mat output(5, 100, arma::fill::randu); + REQUIRE(CheckGradient(function) <= 1e-4); +} -// FFN model; -// model.Add>(input.n_rows, 10); -// model.Add(layer); -// model.Add>(); -// model.Add>(10, output.n_rows); -// model.Add>(); +/** + * Test if the WeightNorm layer is able to forward the + * Forward/Backward/Gradient calls. + */ +TEST_CASE("WeightNormRunTest", "[ANNLayerTest]") +{ + arma::mat output, input, delta, error; -// ens::StandardSGD opt(0.1, 1, 5, -100, false); -// model.Train(input, output, opt); + Linear<>* linear = new Linear<>(10, 10); -// arma::mat originalOutput; -// model.Predict(input, originalOutput); + WeightNorm<> module(linear); -// // Now serialize the model. -// FFN xmlModel, jsonModel, -// binaryModel; -// SerializeObjectAll(model, xmlModel, jsonModel, binaryModel); + module.Parameters().randu(); + module.Reset(); -// // Ensure that predictions are the same. -// arma::mat modelOutput, xmlOutput, jsonOutput, binaryOutput; -// model.Predict(input, modelOutput); -// xmlModel.Predict(input, xmlOutput); -// jsonModel.Predict(input, jsonOutput); -// binaryModel.Predict(input, binaryOutput); + linear->Bias().zeros(); -// CheckMatrices(originalOutput, modelOutput, 1e-5); -// CheckMatrices(originalOutput, xmlOutput, 1e-5); -// CheckMatrices(originalOutput, jsonOutput, 1e-5); -// CheckMatrices(originalOutput, binaryOutput, 1e-5); -// } + input = arma::zeros(10, 1); + module.Forward(input, output); -// /** -// * Simple serialization test for batch normalization layer. -// */ -// TEST_CASE("BatchNormSerializationTest", "[ANNLayerTest]") -// { -// BatchNorm<> layer(10); -// ANNLayerSerializationTest(layer); -// } + // Test the Backward function. + module.Backward(input, input, delta); -// /** -// * Simple serialization test for layer normalization layer. -// */ -// TEST_CASE("LayerNormSerializationTest", "[ANNLayerTest]") -// { -// LayerNorm<> layer(10); -// ANNLayerSerializationTest(layer); -// } + REQUIRE(0 == arma::accu(output)); + REQUIRE(arma::accu(delta) == 0); +} + +// General ANN serialization test. +template +void ANNLayerSerializationTest(LayerType& layer) +{ + arma::mat input(5, 100, arma::fill::randu); + arma::mat output(5, 100, arma::fill::randu); + + FFN, ann::RandomInitialization> model; + model.Add>(input.n_rows, 10); + model.Add(layer); + model.Add>(); + model.Add>(10, output.n_rows); + model.Add>(); + + ens::StandardSGD opt(0.1, 1, 5, -100, false); + model.Train(input, output, opt); + + arma::mat originalOutput; + model.Predict(input, originalOutput); + + // Now serialize the model. + FFN, ann::RandomInitialization> xmlModel, jsonModel, + binaryModel; + SerializeObjectAll(model, xmlModel, jsonModel, binaryModel); + + // Ensure that predictions are the same. + arma::mat modelOutput, xmlOutput, jsonOutput, binaryOutput; + model.Predict(input, modelOutput); + xmlModel.Predict(input, xmlOutput); + jsonModel.Predict(input, jsonOutput); + binaryModel.Predict(input, binaryOutput); + + CheckMatrices(originalOutput, modelOutput, 1e-5); + CheckMatrices(originalOutput, xmlOutput, 1e-5); + CheckMatrices(originalOutput, jsonOutput, 1e-5); + CheckMatrices(originalOutput, binaryOutput, 1e-5); +} + +/** + * Simple serialization test for batch normalization layer. + */ +TEST_CASE("BatchNormSerializationTest", "[ANNLayerTest]") +{ + BatchNorm<> layer(10); + ANNLayerSerializationTest(layer); +} + +/** + * Simple serialization test for layer normalization layer. + */ +TEST_CASE("LayerNormSerializationTest", "[ANNLayerTest]") +{ + LayerNorm<> layer(10); + ANNLayerSerializationTest(layer); +} /** * Test that the functions that can modify and access the parameters of the @@ -4575,13 +3910,16 @@ TEST_CASE("HighwayLayerParametersTest", "[ANNLayerTest]") */ TEST_CASE("ConvolutionLayerParametersTest", "[ANNLayerTest]") { - // Parameter order: outSize, kW, kH, dW, dH, padW, padH, paddingType. - Convolution layer1(2, 3, 4, 5, 6, std::tuple(7, 8), - std::tuple(9, 10), "none"); - Convolution layer2(3, 4, 5, 6, 7, std::tuple(8, 9), - std::tuple(10, 11), "none"); + // Parameter order: inSize, outSize, kW, kH, dW, dH, padW, padH, inputWidth, + // inputHeight, paddingType. + Convolution<> layer1(1, 2, 3, 4, 5, 6, std::tuple(7, 8), + std::tuple(9, 10), 11, 12, "none"); + Convolution<> layer2(2, 3, 4, 5, 6, 7, std::tuple(8, 9), + std::tuple(10, 11), 12, 13, "none"); // Make sure we can get the parameters successfully. + REQUIRE(layer1.InputWidth() == 11); + REQUIRE(layer1.InputHeight() == 12); REQUIRE(layer1.KernelWidth() == 3); REQUIRE(layer1.KernelHeight() == 4); REQUIRE(layer1.StrideWidth() == 5); @@ -4592,6 +3930,8 @@ TEST_CASE("ConvolutionLayerParametersTest", "[ANNLayerTest]") REQUIRE(layer1.PadHBottom() == 10); // Now modify the parameters to match the second layer. + layer1.InputWidth() = 12; + layer1.InputHeight() = 13; layer1.KernelWidth() = 4; layer1.KernelHeight() = 5; layer1.StrideWidth() = 6; @@ -4602,6 +3942,8 @@ TEST_CASE("ConvolutionLayerParametersTest", "[ANNLayerTest]") layer1.PadHBottom() = 11; // Now ensure all results are the same. + REQUIRE(layer1.InputWidth() == layer2.InputWidth()); + REQUIRE(layer1.InputHeight() == layer2.InputHeight()); REQUIRE(layer1.KernelWidth() == layer2.KernelWidth()); REQUIRE(layer1.KernelHeight() == layer2.KernelHeight()); REQUIRE(layer1.StrideWidth() == layer2.StrideWidth()); @@ -4620,18 +3962,13 @@ TEST_CASE("ConvolutionLayerPaddingTest", "[ANNLayerTest]") arma::mat output, input, delta; // Check valid padding option. - Convolution module1(1, 3, 3, 1, 1, std::tuple(1, 1), - std::tuple(1, 1), "valid"); - module1.InputDimensions() = std::vector({ 7, 7 }); - module1.ComputeOutputDimensions(); - arma::mat weights1(module1.WeightSize(), 1); - REQUIRE(weights1.n_elem == 10); - module1.SetWeights(weights1.memptr()); + Convolution<> module1(1, 1, 3, 3, 1, 1, std::tuple(1, 1), + std::tuple(1, 1), 7, 7, "valid"); // Test the Forward function. input = arma::linspace(0, 48, 49); - output.set_size(module1.OutputSize(), 1); - module1.Parameters().zeros(); + module1.Parameters() = arma::mat(9 + 1, 1, arma::fill::zeros); + module1.Reset(); module1.Forward(input, output); REQUIRE(arma::accu(output) == 0); @@ -4639,22 +3976,16 @@ TEST_CASE("ConvolutionLayerPaddingTest", "[ANNLayerTest]") REQUIRE(output.n_cols == 1); // Test the Backward function. - delta.set_size(arma::size(input)); module1.Backward(input, output, delta); // Check same padding option. - Convolution module2(1, 3, 3, 1, 1, std::tuple(0, 0), - std::tuple(0, 0), "same"); - module2.InputDimensions() = std::vector({ 7, 7 }); - module2.ComputeOutputDimensions(); - arma::mat weights2(module2.WeightSize(), 1); - REQUIRE(weights2.n_elem == 10); - module2.SetWeights(weights2.memptr()); + Convolution<> module2(1, 1, 3, 3, 1, 1, std::tuple(0, 0), + std::tuple(0, 0), 7, 7, "same"); // Test the forward function. input = arma::linspace(0, 48, 49); - output.set_size(module2.OutputSize(), 1); - module2.Parameters().zeros(); + module2.Parameters() = arma::mat(9 + 1, 1, arma::fill::zeros); + module2.Reset(); module2.Forward(input, output); REQUIRE(arma::accu(output) == 0); @@ -4662,59 +3993,17 @@ TEST_CASE("ConvolutionLayerPaddingTest", "[ANNLayerTest]") REQUIRE(output.n_cols == 1); // Test the backward function. - delta.set_size(arma::size(input)); module2.Backward(input, output, delta); } -/** - * Convolution layer numerical gradient test. - */ -TEST_CASE("GradientConvolutionLayerTest", "[ANNLayerTest]") -{ - struct GradientFunction - { - GradientFunction() : - input(arma::linspace(0, 35, 36)), - target(arma::mat("1")) - { - model = new FFN(); - model->ResetData(input, target); - model->Add(1, 3, 3, 1, 1, std::tuple(0, 0), - std::tuple(0, 0), "same"); - model->Add(); - - model->InputDimensions() = std::vector({ 6, 6 }); - } - - ~GradientFunction() - { - delete model; - } - - double Gradient(arma::mat& gradient) const - { - double error = model->Evaluate(model->Parameters(), 0, 1); - model->Gradient(model->Parameters(), 0, gradient, 1); - return error; - } - - arma::mat& Parameters() { return model->Parameters(); } - - FFN* model; - arma::mat input, target; - } function; - - REQUIRE(CheckGradient(function) < 1e3); -} - /** * Test that the padding options in Transposed Convolution layer. - * + */ TEST_CASE("TransposedConvolutionLayerPaddingTest", "[ANNLayerTest]") { arma::mat output, input, delta; - TransposedConvolution module1(1, 1, 3, 3, 1, 1, 0, 0, 4, 4, 6, 6, "VALID"); + TransposedConvolution<> module1(1, 1, 3, 3, 1, 1, 0, 0, 4, 4, 6, 6, "VALID"); // Test the forward function. // Valid Should give the same result. input = arma::linspace(0, 15, 16); @@ -4729,7 +4018,7 @@ TEST_CASE("TransposedConvolutionLayerPaddingTest", "[ANNLayerTest]") REQUIRE(arma::accu(delta) == 0.0); // Test Valid for non zero padding. - TransposedConvolution module2(1, 1, 3, 3, 2, 2, + TransposedConvolution<> module2(1, 1, 3, 3, 2, 2, std::tuple(0, 0), std::tuple(0, 0), 2, 2, 5, 5, "VALID"); // Test the forward function. @@ -4749,7 +4038,7 @@ TEST_CASE("TransposedConvolutionLayerPaddingTest", "[ANNLayerTest]") REQUIRE(arma::accu(delta) == 960.0); // Test for same padding type. - TransposedConvolution module3(1, 1, 3, 3, 2, 2, 0, 0, 3, 3, 3, 3, "SAME"); + TransposedConvolution<> module3(1, 1, 3, 3, 2, 2, 0, 0, 3, 3, 3, 3, "SAME"); // Test the forward function. input = arma::linspace(0, 8, 9); module3.Parameters() = arma::mat(9 + 1, 1, arma::fill::zeros); @@ -4764,7 +4053,7 @@ TEST_CASE("TransposedConvolutionLayerPaddingTest", "[ANNLayerTest]") REQUIRE(arma::accu(delta) == 0.0); // Output shape should equal input. - TransposedConvolution module4(1, 1, 3, 3, 1, 1, + TransposedConvolution<> module4(1, 1, 3, 3, 1, 1, std::tuple(2, 2), std::tuple(2, 2), 5, 5, 5, 5, "SAME"); // Test the forward function. @@ -4780,7 +4069,7 @@ TEST_CASE("TransposedConvolutionLayerPaddingTest", "[ANNLayerTest]") module4.Backward(input, output, delta); REQUIRE(arma::accu(delta) == 0.0); - TransposedConvolution module5(1, 1, 3, 3, 2, 2, 0, 0, 2, 2, 2, 2, "SAME"); + TransposedConvolution<> module5(1, 1, 3, 3, 2, 2, 0, 0, 2, 2, 2, 2, "SAME"); // Test the forward function. input = arma::linspace(0, 3, 4); module5.Parameters() = arma::mat(25 + 1, 1, arma::fill::zeros); @@ -4794,7 +4083,7 @@ TEST_CASE("TransposedConvolutionLayerPaddingTest", "[ANNLayerTest]") module5.Backward(input, output, delta); REQUIRE(arma::accu(delta) == 0.0); - TransposedConvolution module6(1, 1, 4, 4, 1, 1, 1, 1, 5, 5, 5, 5, "SAME"); + TransposedConvolution<> module6(1, 1, 4, 4, 1, 1, 1, 1, 5, 5, 5, 5, "SAME"); // Test the forward function. input = arma::linspace(0, 24, 25); module6.Parameters() = arma::mat(16 + 1, 1, arma::fill::zeros); @@ -4808,103 +4097,102 @@ TEST_CASE("TransposedConvolutionLayerPaddingTest", "[ANNLayerTest]") module6.Backward(input, output, delta); REQUIRE(arma::accu(delta) == 0.0); } -*/ /** * Simple test for Lp Pooling layer. */ -// TEST_CASE("LpMaxPoolingTestCase", "[ANNLayerTest]") -// { -// // For rectangular input to pooling layers. -// arma::mat input = arma::mat(8, 1); -// arma::mat output; -// input.zeros(); -// input(0) = input(6) = 30; -// input(1) = input(7) = 120; -// input(2) = input(4) = 272; -// input(3) = input(5) = 315; -// // Output-Size should be 1 x 2. -// // Square output. -// LpPooling<> module1(4, 2, 2, 2, 2); -// module1.InputHeight() = 2; -// module1.InputWidth() = 4; -// module1.Forward(input, output); -// // Calculated using torch.nn.LPPool2d(). -// REQUIRE(arma::accu(output) - 706.0 == Approx(0.0).margin(2e-5)); -// REQUIRE(output.n_elem == 2); -// -// // For Square input. -// input = arma::mat(16, 1); -// input.zeros(); -// input(0) = 4; -// input(1) = 3; -// input(3) = 12; -// input(7) = 35; -// input(8) = 6; -// input(11) = 7; -// input(12) = 8; -// input(15) = 24; -// // Output-Size should be 2 x 2. -// // Square output. -// LpPooling<> module3(2, 2, 2, 2, 2); -// module3.InputHeight() = 4; -// module3.InputWidth() = 4; -// module3.Forward(input, output); -// // Calculated using torch.nn.LPPool2d(). -// REQUIRE(arma::accu(output) - 77.0 == Approx(0.0).margin(2e-5)); -// REQUIRE(output.n_elem == 4); -// } +TEST_CASE("LpMaxPoolingTestCase", "[ANNLayerTest]") +{ + // For rectangular input to pooling layers. + arma::mat input = arma::mat(8, 1); + arma::mat output; + input.zeros(); + input(0) = input(6) = 30; + input(1) = input(7) = 120; + input(2) = input(4) = 272; + input(3) = input(5) = 315; + // Output-Size should be 1 x 2. + // Square output. + LpPooling<> module1(4, 2, 2, 2, 2); + module1.InputHeight() = 2; + module1.InputWidth() = 4; + module1.Forward(input, output); + // Calculated using torch.nn.LPPool2d(). + REQUIRE(arma::accu(output) - 706.0 == Approx(0.0).margin(2e-5)); + REQUIRE(output.n_elem == 2); + + // For Square input. + input = arma::mat(16, 1); + input.zeros(); + input(0) = 4; + input(1) = 3; + input(3) = 12; + input(7) = 35; + input(8) = 6; + input(11) = 7; + input(12) = 8; + input(15) = 24; + // Output-Size should be 2 x 2. + // Square output. + LpPooling<> module3(2, 2, 2, 2, 2); + module3.InputHeight() = 4; + module3.InputWidth() = 4; + module3.Forward(input, output); + // Calculated using torch.nn.LPPool2d(). + REQUIRE(arma::accu(output) - 77.0 == Approx(0.0).margin(2e-5)); + REQUIRE(output.n_elem == 4); +} /** * Simple test for Mean Pooling layer. */ -// TEST_CASE("MeanPoolingTestCase", "[ANNLayerTest]") -// { -// // For rectangular input to pooling layers. -// arma::mat input = arma::mat(28, 1); -// input.zeros(); -// input(0) = input(16) = 1; -// input(1) = input(17) = 2; -// input(2) = input(18) = 3; -// input(3) = input(19) = 4; -// input(4) = input(20) = 5; -// input(5) = input(23) = 6; -// input(6) = input(24) = 7; -// input(14) = input(25) = 8; -// input(15) = input(26) = 9; -// -// MeanPooling<> module1(2, 2, 2, 2, false); -// MeanPooling<> module2(2, 2, 2, 2, true); -// module1.InputWidth() = 7; -// module1.InputHeight() = 4; -// module2.InputWidth() = 7; -// module2.InputHeight() = 4; -// -// // Calculated using torch.nn.MeanPool2d(). -// arma::mat result1, result2; -// result1 << 0.7500 << 4.2500 << arma::endr -// << 1.7500 << 4.0000 << arma::endr -// << 2.7500 << 6.0000 << arma::endr -// << 3.5000 << 2.5000 << arma::endr; -// -// result2 << 0.7500 << 4.2500 << arma::endr -// << 1.7500 << 4.0000 << arma::endr -// << 2.7500 << 6.0000 << arma::endr; -// -// arma::mat output1, output2; -// module1.Forward(input, output1); -// module2.Forward(input, output2); -// output1.reshape(4, 2); -// output2.reshape(3, 2); -// CheckMatrices(output1, result1, 1e-1); -// CheckMatrices(output2, result2, 1e-1); -// -// arma::mat delta1, delta2; -// module1.Backward(input, output1, delta1); -// REQUIRE(arma::accu(delta1) == 25.5); -// module2.Backward(input, output2, delta2); -// REQUIRE(arma::accu(delta2) == 19.5); -// } +TEST_CASE("MeanPoolingTestCase", "[ANNLayerTest]") +{ + // For rectangular input to pooling layers. + arma::mat input = arma::mat(28, 1); + input.zeros(); + input(0) = input(16) = 1; + input(1) = input(17) = 2; + input(2) = input(18) = 3; + input(3) = input(19) = 4; + input(4) = input(20) = 5; + input(5) = input(23) = 6; + input(6) = input(24) = 7; + input(14) = input(25) = 8; + input(15) = input(26) = 9; + + MeanPooling<> module1(2, 2, 2, 2, false); + MeanPooling<> module2(2, 2, 2, 2, true); + module1.InputWidth() = 7; + module1.InputHeight() = 4; + module2.InputWidth() = 7; + module2.InputHeight() = 4; + + // Calculated using torch.nn.MeanPool2d(). + arma::mat result1, result2; + result1 << 0.7500 << 4.2500 << arma::endr + << 1.7500 << 4.0000 << arma::endr + << 2.7500 << 6.0000 << arma::endr + << 3.5000 << 2.5000 << arma::endr; + + result2 << 0.7500 << 4.2500 << arma::endr + << 1.7500 << 4.0000 << arma::endr + << 2.7500 << 6.0000 << arma::endr; + + arma::mat output1, output2; + module1.Forward(input, output1); + module2.Forward(input, output2); + output1.reshape(4, 2); + output2.reshape(3, 2); + CheckMatrices(output1, result1, 1e-1); + CheckMatrices(output2, result2, 1e-1); + + arma::mat delta1, delta2; + module1.Backward(input, output1, delta1); + REQUIRE(arma::accu(delta1) == 25.5); + module2.Backward(input, output2, delta2); + REQUIRE(arma::accu(delta2) == 19.5); +} /** * Simple test for Max Pooling layer. @@ -4925,12 +4213,10 @@ TEST_CASE("MaxPoolingTestCase", "[ANNLayerTest]") input(10) = 8; input(11) = 9; // Output-Size should be 2 x 2. - output.set_size(4, 1); - // Square output. - MaxPooling module1(2, 2, 2, 1); - module1.InputDimensions() = std::vector({ 4, 3 }); - module1.ComputeOutputDimensions(); + MaxPooling<> module1(2, 2, 2, 1); + module1.InputHeight() = 3; + module1.InputWidth() = 4; module1.Forward(input, output); // Calculated using torch.nn.MaxPool2d(). REQUIRE(arma::accu(output) == 28); @@ -4946,12 +4232,10 @@ TEST_CASE("MaxPoolingTestCase", "[ANNLayerTest]") input(3) = 3; input(6) = 3; // Output-Size should be 1 x 2. - output.set_size(2, 1); - // Rectangular output. - MaxPooling module2(3, 2, 3, 1); - module2.InputDimensions() = std::vector({ 3, 3 }); - module2.ComputeOutputDimensions(); + MaxPooling<> module2(3, 2, 3, 1); + module2.InputHeight() = 3; + module2.InputWidth() = 3; module2.Forward(input, output); // Calculated using torch.nn.MaxPool2d(). REQUIRE(arma::accu(output) == 12.0); @@ -4967,12 +4251,10 @@ TEST_CASE("MaxPoolingTestCase", "[ANNLayerTest]") input(4) = 3; input(8) = 3; // Output-Size should be 3 x 3. - output.set_size(9, 1); - // Square output. - MaxPooling module3(2, 2, 1, 1); - module3.InputDimensions() = std::vector({ 4, 4 }); - module3.ComputeOutputDimensions(); + MaxPooling<> module3(2, 2, 1, 1); + module3.InputHeight() = 4; + module3.InputWidth() = 4; module3.Forward(input, output); // Calculated using torch.nn.MaxPool2d(). REQUIRE(arma::accu(output) == 30.0); @@ -4986,12 +4268,10 @@ TEST_CASE("MaxPoolingTestCase", "[ANNLayerTest]") input(1) = 1; input(3) = 1; // Output-Size should be 2 x 2. - output.set_size(4, 1); - // Square output. - MaxPooling module4(2, 1, 1, 1); - module4.InputDimensions() = std::vector({ 3, 2 }); - module4.ComputeOutputDimensions(); + MaxPooling<> module4(2, 1, 1, 1); + module4.InputHeight() = 2; + module4.InputWidth() = 3; module4.Forward(input, output); // Calculated using torch.nn.MaxPool2d(). REQUIRE(arma::accu(output) == 3); @@ -5002,12 +4282,12 @@ TEST_CASE("MaxPoolingTestCase", "[ANNLayerTest]") /** * Test that the functions that can modify and access the parameters of the * Glimpse layer work. - * + */ TEST_CASE("GlimpseLayerParametersTest", "[ANNLayerTest]") { // Parameter order : inSize, size, depth, scale, inputWidth, inputHeight. - Glimpse layer1(1, 2, 3, 4, 5, 6); - Glimpse layer2(1, 2, 3, 4, 6, 7); + Glimpse<> layer1(1, 2, 3, 4, 5, 6); + Glimpse<> layer2(1, 2, 3, 4, 6, 7); // Make sure we can get the parameters successfully. REQUIRE(layer1.InputHeight() == 6); @@ -5029,25 +4309,37 @@ TEST_CASE("GlimpseLayerParametersTest", "[ANNLayerTest]") REQUIRE(layer1.GlimpseSize() == layer2.GlimpseSize()); REQUIRE(layer1.InSize() == layer2.InSize()); } -*/ /** * Test that the function that can access the stdev parameter of the * Reinforce Normal layer works. - * + */ TEST_CASE("ReinforceNormalLayerParametersTest", "[ANNLayerTest]") { // Parameter : stdev. - ReinforceNormal layer(4.0); + ReinforceNormal<> layer(4.0); // Make sure we can get the parameter successfully. REQUIRE(layer.StandardDeviation() == 4.0); } -*/ + +/** + * Test that the function that can access the parameters of the + * VR Class Reward layer works. + */ +TEST_CASE("VRClassRewardLayerParametersTest", "[ANNLayerTest]") +{ + // Parameter order : scale, sizeAverage. + VRClassReward<> layer(2, false); + + // Make sure we can get the parameters successfully. + REQUIRE(layer.Scale() == 2); + REQUIRE(layer.SizeAverage() == false); +} /** * Simple test for Adaptive pooling for Max Pooling layer. - * + */ TEST_CASE("AdaptiveMaxPoolingTestCase", "[ANNLayerTest]") { // For rectangular input. @@ -5066,7 +4358,7 @@ TEST_CASE("AdaptiveMaxPoolingTestCase", "[ANNLayerTest]") input(11) = 9; // Output-Size should be 2 x 2. // Square output. - AdaptiveMaxPooling module1(2, 2); + AdaptiveMaxPooling<> module1(2, 2); module1.InputHeight() = 3; module1.InputWidth() = 4; module1.Forward(input, output); @@ -5088,7 +4380,7 @@ TEST_CASE("AdaptiveMaxPoolingTestCase", "[ANNLayerTest]") input(6) = 3; // Output-Size should be 1 x 2. // Rectangular output. - AdaptiveMaxPooling module2(2, 1); + AdaptiveMaxPooling<> module2(2, 1); module2.InputHeight() = 3; module2.InputWidth() = 3; module2.Forward(input, output); @@ -5110,7 +4402,7 @@ TEST_CASE("AdaptiveMaxPoolingTestCase", "[ANNLayerTest]") input(8) = 3; // Output-Size should be 3 x 3. // Square output. - AdaptiveMaxPooling module3(std::tuple(3, 3)); + AdaptiveMaxPooling<> module3(std::tuple(3, 3)); module3.InputHeight() = 4; module3.InputWidth() = 4; module3.Forward(input, output); @@ -5130,7 +4422,7 @@ TEST_CASE("AdaptiveMaxPoolingTestCase", "[ANNLayerTest]") input(3) = 1; // Output-Size should be 2 x 2. // Square output. - AdaptiveMaxPooling module4(std::tuple(2, 2)); + AdaptiveMaxPooling<> module4(std::tuple(2, 2)); module4.InputHeight() = 4; module4.InputWidth() = 5; module4.Forward(input, output); @@ -5142,11 +4434,10 @@ TEST_CASE("AdaptiveMaxPoolingTestCase", "[ANNLayerTest]") module4.Backward(input, output, delta); REQUIRE(arma::accu(delta) == 2.0); } -*/ /** * Simple test for Adaptive pooling for Mean Pooling layer. - * + */ TEST_CASE("AdaptiveMeanPoolingTestCase", "[ANNLayerTest]") { // For rectangular input. @@ -5165,7 +4456,7 @@ TEST_CASE("AdaptiveMeanPoolingTestCase", "[ANNLayerTest]") input(11) = 9; // Output-Size should be 2 x 2. // Square output. - AdaptiveMeanPooling module1(2, 2); + AdaptiveMeanPooling<> module1(2, 2); module1.InputHeight() = 3; module1.InputWidth() = 4; module1.Forward(input, output); @@ -5187,7 +4478,7 @@ TEST_CASE("AdaptiveMeanPoolingTestCase", "[ANNLayerTest]") input(6) = 3; // Output-Size should be 1 x 2. // Rectangular output. - AdaptiveMeanPooling module2(1, 2); + AdaptiveMeanPooling<> module2(1, 2); module2.InputHeight() = 3; module2.InputWidth() = 3; module2.Forward(input, output); @@ -5209,7 +4500,7 @@ TEST_CASE("AdaptiveMeanPoolingTestCase", "[ANNLayerTest]") input(8) = 3; // Output-Size should be 3 x 3. // Square output. - AdaptiveMeanPooling module3(std::tuple(3, 3)); + AdaptiveMeanPooling<> module3(std::tuple(3, 3)); module3.InputHeight() = 4; module3.InputWidth() = 4; module3.Forward(input, output); @@ -5229,7 +4520,7 @@ TEST_CASE("AdaptiveMeanPoolingTestCase", "[ANNLayerTest]") input(4) = 3; // Output-Size should be 3 x 3. // Square output. - AdaptiveMeanPooling module4(std::tuple(3, 3)); + AdaptiveMeanPooling<> module4(std::tuple(3, 3)); module4.InputHeight() = 4; module4.InputWidth() = 6; module4.Forward(input, output); @@ -5241,226 +4532,224 @@ TEST_CASE("AdaptiveMeanPoolingTestCase", "[ANNLayerTest]") module4.Backward(input, output, delta); REQUIRE(arma::accu(delta) == 2.25); } -*/ -/* TEST_CASE("TransposedConvolutionalLayerOptionalParameterTest", "[ANNLayerTest]") { - Sequential* decoder = new Sequential(); + Sequential<>* decoder = new Sequential<>(); // Check if we can create an object without specifying output. - REQUIRE_NOTHROW(decoder->Add(24, 16, + REQUIRE_NOTHROW(decoder->Add>(24, 16, 5, 5, 1, 1, 0, 0, 10, 10)); - REQUIRE_NOTHROW(decoder->Add(16, 1, + REQUIRE_NOTHROW(decoder->Add>(16, 1, 15, 15, 1, 1, 1, 1, 14, 14)); delete decoder; } -*/ -// TEST_CASE("BatchNormWithMinBatchesTest", "[ANNLayerTest]") -// { -// arma::mat input, output, result, runningMean, runningVar, delta; +TEST_CASE("BatchNormWithMinBatchesTest", "[ANNLayerTest]") +{ + arma::mat input, output, result, runningMean, runningVar, delta; -// // The input test matrix is of the form 3 x 2 x 4 x 1 where -// // number of images are 3 and number of feature maps are 2. -// input = { { 1, 446, 42 }, -// { 2, 16, 63 }, -// { 3, 13, 63 }, -// { 4, 21, 21 }, -// { 1, 13, 11 }, -// { 32, 45, 42 }, -// { 22, 16, 63 }, -// { 32, 13, 42 } }; -// -// // Output calculated using torch.nn.BatchNorm2d(). -// result = { { -0.4786, 3.2634, -0.1338 }, -// { -0.4702, -0.3525, 0.0427 }, -// { -0.4618, -0.3777, 0.0427 }, -// { -0.4534, -0.3104, -0.3104 }, -// { -1.5429, -0.8486, -0.9643 }, -// { 0.2507, 1.0029, 0.8293 }, -// { -0.3279, -0.675, 2.0443 }, -// { 0.2507 , -0.8486 , 0.8293 } }; + // The input test matrix is of the form 3 x 2 x 4 x 1 where + // number of images are 3 and number of feature maps are 2. + input = { { 1, 446, 42 }, + { 2, 16, 63 }, + { 3, 13, 63 }, + { 4, 21, 21 }, + { 1, 13, 11 }, + { 32, 45, 42 }, + { 22, 16, 63 }, + { 32, 13, 42 } }; -// // Check correctness of batch normalization. -// BatchNorm<> module1(2, 1e-5, false, 0.1); -// module1.Reset(); -// module1.Forward(input, output); -// CheckMatrices(output, result, 1e-1); + // Output calculated using torch.nn.BatchNorm2d(). + result = { { -0.4786, 3.2634, -0.1338 }, + { -0.4702, -0.3525, 0.0427 }, + { -0.4618, -0.3777, 0.0427 }, + { -0.4534, -0.3104, -0.3104 }, + { -1.5429, -0.8486, -0.9643 }, + { 0.2507, 1.0029, 0.8293 }, + { -0.3279, -0.675, 2.0443 }, + { 0.2507 , -0.8486 , 0.8293 } }; -// // Check backward function. -// module1.Backward(input, output, delta); -// REQUIRE(arma::accu(delta) == Approx(0.0102676).epsilon(1e-5)); + // Check correctness of batch normalization. + BatchNorm<> module1(2, 1e-5, false, 0.1); + module1.Reset(); + module1.Forward(input, output); + CheckMatrices(output, result, 1e-1); -// // Check values for running mean and running variance. -// // Calculated using torch.nn.BatchNorm2d(). -// runningMean = arma::mat(2, 1); -// runningVar = arma::mat(2, 1); -// runningMean(0) = 5.7917; -// runningMean(1) = 2.76667; -// runningVar(0) = 1543.6545; -// runningVar(1) = 33.488; + // Check backward function. + module1.Backward(input, output, delta); + REQUIRE(arma::accu(delta) == Approx(0.0102676).epsilon(1e-5)); -// CheckMatrices(runningMean, module1.TrainingMean(), 1e-3); -// CheckMatrices(runningVar, module1.TrainingVariance(), 1e-2); + // Check values for running mean and running variance. + // Calculated using torch.nn.BatchNorm2d(). + runningMean = arma::mat(2, 1); + runningVar = arma::mat(2, 1); + runningMean(0) = 5.7917; + runningMean(1) = 2.76667; + runningVar(0) = 1543.6545; + runningVar(1) = 33.488; -// // Check correctness of layer when running mean and variance -// // are updated using cumulative average. -// BatchNorm<> module2(2); -// module2.Reset(); -// module2.Forward(input, output); -// CheckMatrices(output, result, 1e-1); + CheckMatrices(runningMean, module1.TrainingMean(), 1e-3); + CheckMatrices(runningVar, module1.TrainingVariance(), 1e-2); -// // Check values for running mean and running variance. -// // Calculated using torch.nn.BatchNorm2d(). -// runningMean(0) = 57.9167; -// runningMean(1) = 27.6667; -// runningVar(0) = 15427.5380; -// runningVar(1) = 325.8787; + // Check correctness of layer when running mean and variance + // are updated using cumulative average. + BatchNorm<> module2(2); + module2.Reset(); + module2.Forward(input, output); + CheckMatrices(output, result, 1e-1); -// CheckMatrices(runningMean, module2.TrainingMean(), 1e-2); -// CheckMatrices(runningVar, module2.TrainingVariance(), 1e-2); + // Check values for running mean and running variance. + // Calculated using torch.nn.BatchNorm2d(). + runningMean(0) = 57.9167; + runningMean(1) = 27.6667; + runningVar(0) = 15427.5380; + runningVar(1) = 325.8787; -// // Check correctness when model is testing. -// arma::mat deterministicOutput; -// module1.Deterministic() = true; -// module1.Forward(input, deterministicOutput); + CheckMatrices(runningMean, module2.TrainingMean(), 1e-2); + CheckMatrices(runningVar, module2.TrainingVariance(), 1e-2); -// result.clear(); -// result = { { -0.12195, 11.20426, 0.92158 }, -// { -0.0965, 0.259824, 1.4560 }, -// { -0.071054, 0.183567, 1.45607 }, -// { -0.045601, 0.3870852, 0.38708 }, -// { -0.305288, 1.7683, 1.4227 }, -// { 5.05166, 7.29812, 6.7797 }, -// { 3.323614, 2.2867, 10.4086 }, -// { 5.05166, 1.7683, 6.7797 } }; + // Check correctness when model is testing. + arma::mat deterministicOutput; + module1.Deterministic() = true; + module1.Forward(input, deterministicOutput); -// CheckMatrices(result, deterministicOutput, 1e-1); + result.clear(); + result = { { -0.12195, 11.20426, 0.92158 }, + { -0.0965, 0.259824, 1.4560 }, + { -0.071054, 0.183567, 1.45607 }, + { -0.045601, 0.3870852, 0.38708 }, + { -0.305288, 1.7683, 1.4227 }, + { 5.05166, 7.29812, 6.7797 }, + { 3.323614, 2.2867, 10.4086 }, + { 5.05166, 1.7683, 6.7797 } }; -// // Check correctness by updating the running mean and variance again. -// module1.Deterministic() = false; + CheckMatrices(result, deterministicOutput, 1e-1); -// // Clean up. -// output.clear(); -// input.clear(); + // Check correctness by updating the running mean and variance again. + module1.Deterministic() = false; -// // The input test matrix is of the form 2 x 2 x 3 x 1 where -// // number of images are 2 and number of feature maps are 2. -// input = { { 12, 443 }, -// { 134, 45 }, -// { 11, 13 }, -// { 14, 55 }, -// { 110, 4 }, -// { 1, 45 } }; -// -// result = { { -0.629337, 2.14791 }, -// { 0.156797, -0.416694 }, -// { -0.63578, -0.622893 }, -// { -0.637481, 0.4440386 }, -// { 1.894857, -0.901267 }, -// { -0.980402, 0.180253 } }; + // Clean up. + output.clear(); + input.clear(); -// module1.Forward(input, output); -// CheckMatrices(result, output, 1e-3); + // The input test matrix is of the form 2 x 2 x 3 x 1 where + // number of images are 2 and number of feature maps are 2. + input = { { 12, 443 }, + { 134, 45 }, + { 11, 13 }, + { 14, 55 }, + { 110, 4 }, + { 1, 45 } }; -// // Check correctness for the second module as well. -// module2.Forward(input, output); -// CheckMatrices(result, output, 1e-3); + result = { { -0.629337, 2.14791 }, + { 0.156797, -0.416694 }, + { -0.63578, -0.622893 }, + { -0.637481, 0.4440386 }, + { 1.894857, -0.901267 }, + { -0.980402, 0.180253 } }; -// // Calculated using torch.nn.BatchNorm2d(). -// runningMean(0) = 16.1792; -// runningMean(1) = 6.30667; -// runningVar(0) = 4276.5849; -// runningVar(1) = 202.595; + module1.Forward(input, output); + CheckMatrices(result, output, 1e-3); -// CheckMatrices(runningMean, module1.TrainingMean(), 1e-3); -// CheckMatrices(runningVar, module1.TrainingVariance(), 1e-1); + // Check correctness for the second module as well. + module2.Forward(input, output); + CheckMatrices(result, output, 1e-3); -// // Check correctness of running mean and variance when their -// // values are updated using cumulative average. -// runningMean(0) = 83.79166; -// runningMean(1) = 32.9166; -// runningVar(0) = 22164.1035; -// runningVar(1) = 1025.2227; + // Calculated using torch.nn.BatchNorm2d(). + runningMean(0) = 16.1792; + runningMean(1) = 6.30667; + runningVar(0) = 4276.5849; + runningVar(1) = 202.595; -// CheckMatrices(runningMean, module2.TrainingMean(), 1e-3); -// CheckMatrices(runningVar, module2.TrainingVariance(), 1e-3); + CheckMatrices(runningMean, module1.TrainingMean(), 1e-3); + CheckMatrices(runningVar, module1.TrainingVariance(), 1e-1); -// // Check backward function. -// module1.Backward(input, output, delta); + // Check correctness of running mean and variance when their + // values are updated using cumulative average. + runningMean(0) = 83.79166; + runningMean(1) = 32.9166; + runningVar(0) = 22164.1035; + runningVar(1) = 1025.2227; -// deterministicOutput.clear(); -// module1.Deterministic() = true; -// module1.Forward(input, deterministicOutput); + CheckMatrices(runningMean, module2.TrainingMean(), 1e-3); + CheckMatrices(runningVar, module2.TrainingVariance(), 1e-3); -// result.clear(); -// result = { { -0.06388436, 6.524754114 }, -// { 1.799655281, 0.44047968 }, -// { -0.07913291, -0.04784981 }, -// { 0.5405045, 3.4210097 }, -// { 7.2851023, -0.1620577 }, -// { -0.37282639, 2.7184474 } }; + // Check backward function. + module1.Backward(input, output, delta); -// // Calculated using torch.nn.BatchNorm2d(). -// CheckMatrices(result, deterministicOutput, 1e-1); -// } + deterministicOutput.clear(); + module1.Deterministic() = true; + module1.Forward(input, deterministicOutput); -// /** -// * Batch Normalization layer numerical gradient test. -// */ -// TEST_CASE("GradientBatchNormWithMiniBatchesTest", "[ANNLayerTest]") -// { -// // Add function gradient instantiation. -// // To make this test robust, check it ten times. -// bool pass = false; -// for (size_t trial = 0; trial < 10; trial++) -// { -// struct GradientFunction -// { -// GradientFunction() : -// input(arma::randn(16, 1024)), -// target(arma::zeros(1, 1024)) -// { -// model = new FFN(); -// model->ResetData(input, target); -// model->Add>(); -// model->Add>(1, 2, 3, 3, 1, 1, 0, 0, 4, 4); -// model->Add>(2); -// model->Add>(2 * 2 * 2, 2); -// model->Add>(); -// } + result.clear(); + result = { { -0.06388436, 6.524754114 }, + { 1.799655281, 0.44047968 }, + { -0.07913291, -0.04784981 }, + { 0.5405045, 3.4210097 }, + { 7.2851023, -0.1620577 }, + { -0.37282639, 2.7184474 } }; -// ~GradientFunction() -// { -// delete model; -// } + // Calculated using torch.nn.BatchNorm2d(). + CheckMatrices(result, deterministicOutput, 1e-1); +} -// double Gradient(arma::mat& gradient) const -// { -// double error = model->Evaluate(model->Parameters(), 0, 1024, false); -// model->Gradient(model->Parameters(), 0, gradient, 1024); -// return error; -// } +/** + * Batch Normalization layer numerical gradient test. + */ +TEST_CASE("GradientBatchNormWithMiniBatchesTest", "[ANNLayerTest]") +{ + // Add function gradient instantiation. + // To make this test robust, check it ten times. + bool pass = false; + for (size_t trial = 0; trial < 10; trial++) + { + struct GradientFunction + { + GradientFunction() : + input(arma::randn(16, 1024)), + target(arma::zeros(1, 1024)) + { + model = new FFN, NguyenWidrowInitialization>(); + model->Predictors() = input; + model->Responses() = target; + model->Add>(); + model->Add>(1, 2, 3, 3, 1, 1, 0, 0, 4, 4); + model->Add>(2); + model->Add>(2 * 2 * 2, 2); + model->Add>(); + } -// arma::mat& Parameters() { return model->Parameters(); } + ~GradientFunction() + { + delete model; + } -// FFN* model; -// arma::mat input, target; -// } function; + double Gradient(arma::mat& gradient) const + { + double error = model->Evaluate(model->Parameters(), 0, 1024, false); + model->Gradient(model->Parameters(), 0, gradient, 1024); + return error; + } -// double gradient = CheckGradient(function); -// if (gradient < 1e-1) -// { -// pass = true; -// break; -// } -// } + arma::mat& Parameters() { return model->Parameters(); } -// REQUIRE(pass); -// } + FFN, NguyenWidrowInitialization>* model; + arma::mat input, target; + } function; + + double gradient = CheckGradient(function); + if (gradient < 1e-1) + { + pass = true; + break; + } + } + + REQUIRE(pass); +} TEST_CASE("ConvolutionLayerTestCase", "[ANNLayerTest]") { @@ -5477,116 +4766,81 @@ TEST_CASE("ConvolutionLayerTestCase", "[ANNLayerTest]") { 22, 16 , 63 }, { 32, 13 , 42 } }; - Convolution layer(4, 1, 1, 1, 1, 0, 0); - layer.InputDimensions() = std::vector({ 4, 1, 2 }); - layer.ComputeOutputDimensions(); - arma::mat layerWeights(layer.WeightSize(), 1); - layer.SetWeights(layerWeights.memptr()); - output.set_size(layer.OutputSize(), 3); + Convolution<> layer(2, 4, 1, 1, 1, 1, 0, 0, 4, 1); + layer.Reset(); // Set weights to 1.0 and bias to 0.0. - layer.Weight().fill(1.0); - layer.Bias().zeros(); + layer.Parameters().zeros(); + arma::mat weight(2 * 4, 1); + weight.fill(1.0); + layer.Parameters().submat(arma::span(0, 2 * 4 - 1), arma::span()) = weight; layer.Forward(input, output); // Value calculated using torch.nn.Conv2d(). REQUIRE(arma::accu(output) == 4108); // Set bias to one. - layer.Bias().fill(1.0); + layer.Parameters().fill(1.0); layer.Forward(input, output); // Value calculated using torch.nn.Conv2d(). REQUIRE(arma::accu(output) == 4156); } -// TEST_CASE("BatchNormDeterministicTest", "[ANNLayerTest]") -// { -// FFN<> module; -// module.Add>(2, 1e-5, false); -// module.Add>(); +TEST_CASE("BatchNormDeterministicTest", "[ANNLayerTest]") +{ + FFN<> module; + module.Add>(2, 1e-5, false); + module.Add>(); -// arma::mat input(4, 3), output; -// module.ResetParameters(); + arma::mat input(4, 3), output; + module.ResetParameters(); -// // The model should switch to Deterministic mode for predicting. -// module.Predict(input, output); -// REQUIRE(boost::get*>(module.Model()[0])->Deterministic() == true); + // The model should switch to Deterministic mode for predicting. + module.Predict(input, output); + REQUIRE(boost::get*>(module.Model()[0])->Deterministic() == true); -// output.ones(); -// module.Train(input, output); -// // The model should switch to training mode for predicting. -// REQUIRE(boost::get*>(module.Model()[0])->Deterministic() == 0); -// } - -// /** -// * Linear module weight initialization test. -// */ -// TEST_CASE("LinearLayerWeightInitializationTest", "[ANNLayerTest]") -// { -// size_t inSize = 10, outSize = 4; -// Linear<> linear = Linear<>(inSize, outSize); -// linear.Reset(); -// RandomInitialization().Initialize(linear.Weight()); -// linear.Bias().ones(); - -// REQUIRE(std::equal(linear.Weight().begin(), -// linear.Weight().end(), linear.Parameters().begin())); - -// REQUIRE(std::equal(linear.Bias().begin(), -// linear.Bias().end(), linear.Parameters().begin() + inSize * outSize)); - -// REQUIRE(linear.Weight().n_rows == outSize); -// REQUIRE(linear.Weight().n_cols == inSize); -// REQUIRE(linear.Bias().n_rows == outSize); -// REQUIRE(linear.Bias().n_cols == 1); -// REQUIRE(linear.Parameters().n_rows == inSize * outSize + outSize); -// } - -// /** -// * Atrous Convolution module weight initialization test. -// */ -// TEST_CASE("AtrousConvolutionLayerWeightInitializationTest", "[ANNLayerTest]") -// { -// size_t inSize = 2, outSize = 3; -// size_t kernelWidth = 4, kernelHeight = 5; -// AtrousConvolution<> module = AtrousConvolution<>(inSize, outSize, -// kernelWidth, kernelHeight, 6, 7, std::make_tuple(8, 9), -// std::make_tuple(10, 11), 12, 13, 14, 15); -// module.Reset(); -// RandomInitialization().Initialize(module.Weight()); -// module.Bias().ones(); - -// REQUIRE(std::equal(module.Weight().begin(), -// module.Weight().end(), module.Parameters().begin())); - -// REQUIRE(std::equal(module.Bias().begin(), -// module.Bias().end(), module.Parameters().end() - outSize)); - -// REQUIRE(module.Weight().n_rows == kernelWidth); -// REQUIRE(module.Weight().n_cols == kernelHeight); -// REQUIRE(module.Weight().n_slices == inSize * outSize); -// REQUIRE(module.Bias().n_rows == outSize); -// REQUIRE(module.Bias().n_cols == 1); -// REQUIRE(module.Parameters().n_rows -// == (outSize * inSize * kernelWidth * kernelHeight) + outSize); -// } + output.ones(); + module.Train(input, output); + // The model should switch to training mode for predicting. + REQUIRE(boost::get*>(module.Model()[0])->Deterministic() == 0); +} /** - * Convolution module weight initialization test. + * Linear module weight initialization test. */ -TEST_CASE("ConvolutionLayerWeightInitializationTest", "[ANNLayerTest]") +TEST_CASE("LinearLayerWeightInitializationTest", "[ANNLayerTest]") +{ + size_t inSize = 10, outSize = 4; + Linear<> linear = Linear<>(inSize, outSize); + linear.Reset(); + RandomInitialization().Initialize(linear.Weight()); + linear.Bias().ones(); + + REQUIRE(std::equal(linear.Weight().begin(), + linear.Weight().end(), linear.Parameters().begin())); + + REQUIRE(std::equal(linear.Bias().begin(), + linear.Bias().end(), linear.Parameters().begin() + inSize * outSize)); + + REQUIRE(linear.Weight().n_rows == outSize); + REQUIRE(linear.Weight().n_cols == inSize); + REQUIRE(linear.Bias().n_rows == outSize); + REQUIRE(linear.Bias().n_cols == 1); + REQUIRE(linear.Parameters().n_rows == inSize * outSize + outSize); +} + +/** + * Atrous Convolution module weight initialization test. + */ +TEST_CASE("AtrousConvolutionLayerWeightInitializationTest", "[ANNLayerTest]") { size_t inSize = 2, outSize = 3; size_t kernelWidth = 4, kernelHeight = 5; - Convolution module = Convolution(outSize, - kernelWidth, kernelHeight, 6, 7, std::tuple(8, 9), - std::tuple(10, 11), "none"); - module.InputDimensions() = std::vector({ 12, 13, 2 }); - module.ComputeOutputDimensions(); - arma::mat weights(module.WeightSize(), 1); - module.SetWeights(weights.memptr()); - + AtrousConvolution<> module = AtrousConvolution<>(inSize, outSize, + kernelWidth, kernelHeight, 6, 7, std::make_tuple(8, 9), + std::make_tuple(10, 11), 12, 13, 14, 15); + module.Reset(); RandomInitialization().Initialize(module.Weight()); module.Bias().ones(); @@ -5598,7 +4852,36 @@ TEST_CASE("ConvolutionLayerWeightInitializationTest", "[ANNLayerTest]") REQUIRE(module.Weight().n_rows == kernelWidth); REQUIRE(module.Weight().n_cols == kernelHeight); - REQUIRE(module.Weight().n_slices == outSize * inSize); + REQUIRE(module.Weight().n_slices == inSize * outSize); + REQUIRE(module.Bias().n_rows == outSize); + REQUIRE(module.Bias().n_cols == 1); + REQUIRE(module.Parameters().n_rows + == (outSize * inSize * kernelWidth * kernelHeight) + outSize); +} + +/** + * Convolution module weight initialization test. + */ +TEST_CASE("ConvolutionLayerWeightInitializationTest", "[ANNLayerTest]") +{ + size_t inSize = 2, outSize = 3; + size_t kernelWidth = 4, kernelHeight = 5; + Convolution<> module = Convolution<>(inSize, outSize, + kernelWidth, kernelHeight, 6, 7, std::tuple(8, 9), + std::tuple(10, 11), 12, 13, "none"); + module.Reset(); + RandomInitialization().Initialize(module.Weight()); + module.Bias().ones(); + + REQUIRE(std::equal(module.Weight().begin(), + module.Weight().end(), module.Parameters().begin())); + + REQUIRE(std::equal(module.Bias().begin(), + module.Bias().end(), module.Parameters().end() - outSize)); + + REQUIRE(module.Weight().n_rows == kernelWidth); + REQUIRE(module.Weight().n_cols == kernelHeight); + REQUIRE(module.Weight().n_slices == inSize * outSize); REQUIRE(module.Bias().n_rows == outSize); REQUIRE(module.Bias().n_cols == 1); REQUIRE(module.Parameters().n_rows @@ -5607,12 +4890,12 @@ TEST_CASE("ConvolutionLayerWeightInitializationTest", "[ANNLayerTest]") /** * Transposed Convolution module weight initialization test. - * + */ TEST_CASE("TransposedConvolutionWeightInitializationTest", "[ANNLayerTest]") { size_t inSize = 3, outSize = 3; size_t kernelWidth = 4, kernelHeight = 4; - TransposedConvolution module = TransposedConvolution(inSize, outSize, + TransposedConvolution<> module = TransposedConvolution<>(inSize, outSize, kernelWidth, kernelHeight, 1, 1, 1, 1, 5, 5, 6, 6); module.Reset(); RandomInitialization().Initialize(module.Weight()); @@ -5632,263 +4915,262 @@ TEST_CASE("TransposedConvolutionWeightInitializationTest", "[ANNLayerTest]") REQUIRE(module.Parameters().n_rows == (outSize * inSize * kernelWidth * kernelHeight) + outSize); } -*/ /** * Simple Test for ChannelShuffle layer. */ -// TEST_CASE("ChannelShuffleLayerTest", "[ANNLayerTest]") -// { -// arma::mat input1, output1, outputExpected1, outputBackward1; -// ChannelShuffle<> module1(2, 2, 6, 2); -// -// input1 << 1 << 13 << arma::endr -// << 2 << 14 << arma::endr -// << 3 << 15 << arma::endr -// << 4 << 16 << arma::endr -// << 5 << 17 << arma::endr -// << 6 << 18 << arma::endr -// << 7 << 19 << arma::endr -// << 8 << 20 << arma::endr -// << 9 << 21 << arma::endr -// << 10 << 22 << arma::endr -// << 11 << 23 << arma::endr -// << 12 << 24 << arma::endr; -// input1.reshape(24, 1); -// // Value calculated using torch.nn.ChannelShuffle(). -// outputExpected1 << 1 << 17 << arma::endr -// << 2 << 18 << arma::endr -// << 3 << 19 << arma::endr -// << 4 << 20 << arma::endr -// << 13 << 9 << arma::endr -// << 14 << 10 << arma::endr -// << 15 << 11 << arma::endr -// << 16 << 12 << arma::endr -// << 5 << 21 << arma::endr -// << 6 << 22 << arma::endr -// << 7 << 23 << arma::endr -// << 8 << 24 << arma::endr; -// outputExpected1.reshape(24, 1); -// // Check the Forward pass of the layer. -// module1.Forward(input1, output1); -// CheckMatrices(output1, outputExpected1); -// -// // Check the Backward pass of the layer. -// module1.Backward(output1, output1, outputBackward1); -// CheckMatrices(input1, outputBackward1); -// -// } +TEST_CASE("ChannelShuffleLayerTest", "[ANNLayerTest]") +{ + arma::mat input1, output1, outputExpected1, outputBackward1; + ChannelShuffle<> module1(2, 2, 6, 2); + + input1 << 1 << 13 << arma::endr + << 2 << 14 << arma::endr + << 3 << 15 << arma::endr + << 4 << 16 << arma::endr + << 5 << 17 << arma::endr + << 6 << 18 << arma::endr + << 7 << 19 << arma::endr + << 8 << 20 << arma::endr + << 9 << 21 << arma::endr + << 10 << 22 << arma::endr + << 11 << 23 << arma::endr + << 12 << 24 << arma::endr; + input1.reshape(24, 1); + // Value calculated using torch.nn.ChannelShuffle(). + outputExpected1 << 1 << 17 << arma::endr + << 2 << 18 << arma::endr + << 3 << 19 << arma::endr + << 4 << 20 << arma::endr + << 13 << 9 << arma::endr + << 14 << 10 << arma::endr + << 15 << 11 << arma::endr + << 16 << 12 << arma::endr + << 5 << 21 << arma::endr + << 6 << 22 << arma::endr + << 7 << 23 << arma::endr + << 8 << 24 << arma::endr; + outputExpected1.reshape(24, 1); + // Check the Forward pass of the layer. + module1.Forward(input1, output1); + CheckMatrices(output1, outputExpected1); + + // Check the Backward pass of the layer. + module1.Backward(output1, output1, outputBackward1); + CheckMatrices(input1, outputBackward1); + +} /** * Simple Test for PixelShuffle layer. */ -// TEST_CASE("PixelShuffleLayerTest", "[ANNLayerTest]") -// { -// arma::mat input1, output1, gy1, g1, outputExpected1, gExpected1; -// arma::mat input2, output2, gy2, g2, outputExpected2, gExpected2; -// PixelShuffle<> module1(2, 2, 2, 4); -// PixelShuffle<> module2(2, 2, 2, 4); -// -// // Input is a single image, of size (2,2) and having 4 channels. -// input1 << 1 << 3 << 2 << 4 << 0 << 0 << 0 << 0 << 0 << 0 << 0 << 0 << 0 << 0 -// << 0 << 0 << arma::endr; -// gy1 << 1 << 5 << 9 << 13 << 2 << 6 << 10 << 14 << 3 << 7 << 11 << 15 << 4 << 8 -// << 12 << 16 << arma::endr; -// -// // Calculated using torch.nn.PixelShuffle(). -// outputExpected1 << 1 << 0 << 3 << 0 << 0 << 0 << 0 << 0 << 2 << 0 << 4 << 0 -// << 0 << 0 << 0 << 0 << arma::endr; -// gExpected1 << 1 << 9 << 3 << 11 << 5 << 13 << 7 << 15 << 2 << 10 << 4 << 12 -// << 6 << 14 << 8 << 16 << arma::endr; -// -// input1 = input1.t(); -// outputExpected1 = outputExpected1.t(); -// gy1 = gy1.t(); -// gExpected1 = gExpected1.t(); -// -// // Check the Forward pass of the layer. -// module1.Forward(input1, output1); -// CheckMatrices(output1, outputExpected1); -// -// // Check the Backward pass of the layer. -// module1.Backward(input1, gy1, g1); -// CheckMatrices(g1, gExpected1); -// -// // Input is a batch of 2 images, each of size (2,2) and having 4 channels. -// input2 << 1 << 3 << 2 << 4 << 0 << 0 << 0 << 0 << 0 << 0 << 0 << 0 << 0 << 0 -// << 0 << 0 << arma::endr << 5 << 7 << 6 << 8 << 0 << 0 << 0 << 0 << 0 << 0 -// << 0 << 0 << 0 << 0 << 0 << 0 << arma::endr; -// gy2 << 1 << 5 << 9 << 13 << 2 << 6 << 10 << 14 << 3 << 7 << 11 << 15 << 4 << 8 -// << 12 << 16 << arma::endr << 17 << 21 << 25 << 29 << 18 << 22 << 26 << 30 -// << 19 << 23 << 27 << 31 << 20 << 24 << 28 << 32 << arma::endr; -// -// // Calculated using torch.nn.PixelShuffle(). -// outputExpected2 << 1 << 0 << 3 << 0 << 0 << 0 << 0 << 0 << 2 << 0 << 4 << 0 -// << 0 << 0 << 0 << 0 << arma::endr << 5 << 0 << 7 << 0 << 0 << 0 << 0 << 0 -// << 6 << 0 << 8 << 0 << 0 << 0 << 0 << 0 << arma::endr; -// gExpected2 << 1 << 9 << 3 << 11 << 5 << 13 << 7 << 15 << 2 << 10 << 4 << 12 -// << 6 << 14 << 8 << 16 << arma::endr << 17 << 25 << 19 << 27 << 21 << 29 -// << 23 << 31 << 18 << 26 << 20 << 28 << 22 << 30 << 24 << 32 << arma::endr; -// -// input2 = input2.t(); -// outputExpected2 = outputExpected2.t(); -// gy2 = gy2.t(); -// gExpected2 = gExpected2.t(); -// -// // Check the Forward pass of the layer. -// module2.Forward(input2, output2); -// CheckMatrices(output2, outputExpected2); -// -// // Check the Backward pass of the layer. -// module2.Backward(input2, gy2, g2); -// CheckMatrices(g2, gExpected2); -// } +TEST_CASE("PixelShuffleLayerTest", "[ANNLayerTest]") +{ + arma::mat input1, output1, gy1, g1, outputExpected1, gExpected1; + arma::mat input2, output2, gy2, g2, outputExpected2, gExpected2; + PixelShuffle<> module1(2, 2, 2, 4); + PixelShuffle<> module2(2, 2, 2, 4); + + // Input is a single image, of size (2,2) and having 4 channels. + input1 << 1 << 3 << 2 << 4 << 0 << 0 << 0 << 0 << 0 << 0 << 0 << 0 << 0 << 0 + << 0 << 0 << arma::endr; + gy1 << 1 << 5 << 9 << 13 << 2 << 6 << 10 << 14 << 3 << 7 << 11 << 15 << 4 << 8 + << 12 << 16 << arma::endr; + + // Calculated using torch.nn.PixelShuffle(). + outputExpected1 << 1 << 0 << 3 << 0 << 0 << 0 << 0 << 0 << 2 << 0 << 4 << 0 + << 0 << 0 << 0 << 0 << arma::endr; + gExpected1 << 1 << 9 << 3 << 11 << 5 << 13 << 7 << 15 << 2 << 10 << 4 << 12 + << 6 << 14 << 8 << 16 << arma::endr; + + input1 = input1.t(); + outputExpected1 = outputExpected1.t(); + gy1 = gy1.t(); + gExpected1 = gExpected1.t(); + + // Check the Forward pass of the layer. + module1.Forward(input1, output1); + CheckMatrices(output1, outputExpected1); + + // Check the Backward pass of the layer. + module1.Backward(input1, gy1, g1); + CheckMatrices(g1, gExpected1); + + // Input is a batch of 2 images, each of size (2,2) and having 4 channels. + input2 << 1 << 3 << 2 << 4 << 0 << 0 << 0 << 0 << 0 << 0 << 0 << 0 << 0 << 0 + << 0 << 0 << arma::endr << 5 << 7 << 6 << 8 << 0 << 0 << 0 << 0 << 0 << 0 + << 0 << 0 << 0 << 0 << 0 << 0 << arma::endr; + gy2 << 1 << 5 << 9 << 13 << 2 << 6 << 10 << 14 << 3 << 7 << 11 << 15 << 4 << 8 + << 12 << 16 << arma::endr << 17 << 21 << 25 << 29 << 18 << 22 << 26 << 30 + << 19 << 23 << 27 << 31 << 20 << 24 << 28 << 32 << arma::endr; + + // Calculated using torch.nn.PixelShuffle(). + outputExpected2 << 1 << 0 << 3 << 0 << 0 << 0 << 0 << 0 << 2 << 0 << 4 << 0 + << 0 << 0 << 0 << 0 << arma::endr << 5 << 0 << 7 << 0 << 0 << 0 << 0 << 0 + << 6 << 0 << 8 << 0 << 0 << 0 << 0 << 0 << arma::endr; + gExpected2 << 1 << 9 << 3 << 11 << 5 << 13 << 7 << 15 << 2 << 10 << 4 << 12 + << 6 << 14 << 8 << 16 << arma::endr << 17 << 25 << 19 << 27 << 21 << 29 + << 23 << 31 << 18 << 26 << 20 << 28 << 22 << 30 << 24 << 32 << arma::endr; + + input2 = input2.t(); + outputExpected2 = outputExpected2.t(); + gy2 = gy2.t(); + gExpected2 = gExpected2.t(); + + // Check the Forward pass of the layer. + module2.Forward(input2, output2); + CheckMatrices(output2, outputExpected2); + + // Check the Backward pass of the layer. + module2.Backward(input2, gy2, g2); + CheckMatrices(g2, gExpected2); +} /** * Test that the function that can access the parameters of the * PixelShuffle layer works. */ -// TEST_CASE("PixelShuffleLayerParametersTest", "[ANNLayerTest]") -// { -// // Create the layer using the empty constructor. -// PixelShuffle<> layer; -// -// // Set the different input parameters of the layer. -// layer.UpscaleFactor() = 2; -// layer.InputHeight() = 2; -// layer.InputWidth() = 2; -// layer.InputChannels() = 4; -// -// // Make sure we can get the parameters successfully. -// REQUIRE(layer.UpscaleFactor() == 2); -// REQUIRE(layer.InputHeight() == 2); -// REQUIRE(layer.InputWidth() == 2); -// REQUIRE(layer.InputChannels() == 4); -// -// arma::mat input, output; -// // Input is a batch of 2 images, each of size (2,2) and having 4 channels. -// input << 1 << 3 << 2 << 4 << 0 << 0 << 0 << 0 << 0 << 0 << 0 << 0 << 0 << 0 -// << 0 << 0 << arma::endr << 5 << 7 << 6 << 8 << 0 << 0 << 0 << 0 << 0 << 0 -// << 0 << 0 << 0 << 0 << 0 << 0 << arma::endr; -// input = input.t(); -// layer.Forward(input, output); -// -// // Check whether output parameters are returned correctly. -// REQUIRE(layer.OutputHeight() == 4); -// REQUIRE(layer.OutputWidth() == 4); -// REQUIRE(layer.OutputChannels() == 1); -// } +TEST_CASE("PixelShuffleLayerParametersTest", "[ANNLayerTest]") +{ + // Create the layer using the empty constructor. + PixelShuffle<> layer; -// /** -// * Simple Test for SpatialDropout layer. -// */ -// TEST_CASE("SpatialDropoutLayerTest", "[ANNLayerTest]") -// { -// arma::mat input, output, gy, g, temp; -// arma::mat outputsExpected = arma::zeros(8, 12); -// arma::mat gsExpected = arma::zeros(8, 12); + // Set the different input parameters of the layer. + layer.UpscaleFactor() = 2; + layer.InputHeight() = 2; + layer.InputWidth() = 2; + layer.InputChannels() = 4; -// // Set the seed to a random value. -// arma::arma_rng::set_seed_random(); -// SpatialDropout<> module(3, 0.2); + // Make sure we can get the parameters successfully. + REQUIRE(layer.UpscaleFactor() == 2); + REQUIRE(layer.InputHeight() == 2); + REQUIRE(layer.InputWidth() == 2); + REQUIRE(layer.InputChannels() == 4); -// // Input is a batch of 2 images, each of size (2,2) and having 4 channels. -// input = { 0.4963, 0.0885, 0.7682, 0.1320, 0.3074, 0.4901, 0.6341, 0.8964, -// 0.4556, 0.3489, 0.6323, 0.4017 }; -// -// gy = { 1, 3, 2, 4, 5, 7, 6, 8, 9, 11, 10, 12 }; -// -// // Following values have been calculated using torch.nn.Dropout2d(p=0.2). -// temp = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }; -// outputsExpected.row(0) = temp; -// temp = { 0, 0, 0, 0, 0.3842, 0.6126, 0.7926, 1.1205, 0.5695, 0.4361, 0.7904, -// 0.5021 }; -// outputsExpected.row(1) = temp; -// temp = { 0.6204, 0.1106, 0.9603, 0.1650, 0, 0, 0, 0, 0.5695, 0.4361, -// 0.7904, 0.5021 }; -// outputsExpected.row(2) = temp; -// temp = { 0.6204, 0.1106, 0.9603, 0.1650, 0.3842, 0.6126, 0.7926, 1.1205, 0, -// 0, 0, 0 }; -// outputsExpected.row(3) = temp; -// temp = { 0, 0, 0, 0, 0, 0, 0, 0, 0.5695, 0.4361, 0.7904, 0.5021 }; -// outputsExpected.row(4) = temp; -// temp = { 0, 0, 0, 0, 0.3842, 0.6126, 0.7926, 1.1205, 0, 0, 0, 0 }; -// outputsExpected.row(5) = temp; -// temp = { 0.6204, 0.1106, 0.9603, 0.1650, 0, 0, 0, 0, 0, 0, 0, 0 }; -// outputsExpected.row(6) = temp; -// temp = { 0.6204, 0.1106, 0.9603, 0.1650, 0.3842, 0.6126, 0.7926, 1.1205, -// 0.5695, 0.4361, 0.7904, 0.5021 }; -// outputsExpected.row(7) = temp; -// temp = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }; -// gsExpected.row(0) = temp; -// temp = { 0, 0, 0, 0, 6.2500, 8.7500, 7.5000, 10.0000, 11.2500, 13.7500, -// 12.5000, 15.0000 }; -// gsExpected.row(1) = temp; -// temp = { 1.2500, 3.7500, 2.5000, 5.0000, 0, 0, 0, 0, 11.2500, 13.7500, -// 12.5000, 15.0000 }; -// gsExpected.row(2) = temp; -// temp = { 1.2500, 3.7500, 2.5000, 5.0000, 6.2500, 8.7500, 7.5000, 10.0000, 0, -// 0, 0, 0 }; -// gsExpected.row(3) = temp; -// temp = { 0, 0, 0, 0, 0, 0, 0, 0, 11.2500, 13.7500, 12.5000, 15.0000 }; -// gsExpected.row(4) = temp; -// temp = { 0, 0, 0, 0, 6.2500, 8.7500, 7.5000, 10.0000, 0, 0, 0, 0 }; -// gsExpected.row(5) = temp; -// temp = { 1.2500, 3.7500, 2.5000, 5.0000, 0, 0, 0, 0, 0, 0, 0, 0 }; -// gsExpected.row(6) = temp; -// temp = { 1.2500, 3.7500, 2.5000, 5.0000, 6.2500, 8.7500, 7.5000, 10.0000, -// 11.2500, 13.7500, 12.5000, 15.0000 }; -// gsExpected.row(7) = temp; + arma::mat input, output; + // Input is a batch of 2 images, each of size (2,2) and having 4 channels. + input << 1 << 3 << 2 << 4 << 0 << 0 << 0 << 0 << 0 << 0 << 0 << 0 << 0 << 0 + << 0 << 0 << arma::endr << 5 << 7 << 6 << 8 << 0 << 0 << 0 << 0 << 0 << 0 + << 0 << 0 << 0 << 0 << 0 << 0 << arma::endr; + input = input.t(); + layer.Forward(input, output); -// input = input.t(); -// gy = gy.t(); -// outputsExpected = outputsExpected.t(); -// gsExpected = gsExpected.t(); + // Check whether output parameters are returned correctly. + REQUIRE(layer.OutputHeight() == 4); + REQUIRE(layer.OutputWidth() == 4); + REQUIRE(layer.OutputChannels() == 1); +} -// // Compute the Forward and Backward passes and store the results. -// module.Forward(input, output); -// module.Backward(input, gy, g); +/* + * Simple Test for SpatialDropout layer. + */ +TEST_CASE("SpatialDropoutLayerTest", "[ANNLayerTest]") +{ + arma::mat input, output, gy, g, temp; + arma::mat outputsExpected = arma::zeros(8, 12); + arma::mat gsExpected = arma::zeros(8, 12); -// // Check through all possible cases, to find a match and then compare results. -// for (size_t i = 0; i < outputsExpected.n_cols; ++i) -// { -// if (arma::approx_equal(outputsExpected.col(i), output, "absdiff", 1e-1)) -// { -// // Check the correctness of the Forward pass of the layer. -// CheckMatrices(output, outputsExpected.col(i), 1e-1); -// // Check the correctness of the Backward pass of the layer. -// CheckMatrices(g, gsExpected.col(i), 1e-1); -// } -// } + // Set the seed to a random value. + arma::arma_rng::set_seed_random(); + SpatialDropout<> module(3, 0.2); -// // Check if the output is same as input when using deterministic mode. -// module.Deterministic() = true; -// output.clear(); -// module.Forward(input, output); -// CheckMatrices(output, input, 1e-1); -// } + // Input is a batch of 2 images, each of size (2,2) and having 4 channels. + input = { 0.4963, 0.0885, 0.7682, 0.1320, 0.3074, 0.4901, 0.6341, 0.8964, + 0.4556, 0.3489, 0.6323, 0.4017 }; -// /** -// * Test that the function that can access the parameters of the -// * SpatialDropout layer works. -// */ -// TEST_CASE("SpatialDropoutLayerParametersTest", "[ANNLayerTest]") -// { -// // Create the layer using the empty constructor. -// SpatialDropout<> layer; + gy = { 1, 3, 2, 4, 5, 7, 6, 8, 9, 11, 10, 12 }; -// // Set the input parameters. -// layer.Size() = 3; -// layer.Ratio(0.2); + // Following values have been calculated using torch.nn.Dropout2d(p=0.2). + temp = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }; + outputsExpected.row(0) = temp; + temp = { 0, 0, 0, 0, 0.3842, 0.6126, 0.7926, 1.1205, 0.5695, 0.4361, 0.7904, + 0.5021 }; + outputsExpected.row(1) = temp; + temp = { 0.6204, 0.1106, 0.9603, 0.1650, 0, 0, 0, 0, 0.5695, 0.4361, + 0.7904, 0.5021 }; + outputsExpected.row(2) = temp; + temp = { 0.6204, 0.1106, 0.9603, 0.1650, 0.3842, 0.6126, 0.7926, 1.1205, 0, + 0, 0, 0 }; + outputsExpected.row(3) = temp; + temp = { 0, 0, 0, 0, 0, 0, 0, 0, 0.5695, 0.4361, 0.7904, 0.5021 }; + outputsExpected.row(4) = temp; + temp = { 0, 0, 0, 0, 0.3842, 0.6126, 0.7926, 1.1205, 0, 0, 0, 0 }; + outputsExpected.row(5) = temp; + temp = { 0.6204, 0.1106, 0.9603, 0.1650, 0, 0, 0, 0, 0, 0, 0, 0 }; + outputsExpected.row(6) = temp; + temp = { 0.6204, 0.1106, 0.9603, 0.1650, 0.3842, 0.6126, 0.7926, 1.1205, + 0.5695, 0.4361, 0.7904, 0.5021 }; + outputsExpected.row(7) = temp; + temp = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }; + gsExpected.row(0) = temp; + temp = { 0, 0, 0, 0, 6.2500, 8.7500, 7.5000, 10.0000, 11.2500, 13.7500, + 12.5000, 15.0000 }; + gsExpected.row(1) = temp; + temp = { 1.2500, 3.7500, 2.5000, 5.0000, 0, 0, 0, 0, 11.2500, 13.7500, + 12.5000, 15.0000 }; + gsExpected.row(2) = temp; + temp = { 1.2500, 3.7500, 2.5000, 5.0000, 6.2500, 8.7500, 7.5000, 10.0000, 0, + 0, 0, 0 }; + gsExpected.row(3) = temp; + temp = { 0, 0, 0, 0, 0, 0, 0, 0, 11.2500, 13.7500, 12.5000, 15.0000 }; + gsExpected.row(4) = temp; + temp = { 0, 0, 0, 0, 6.2500, 8.7500, 7.5000, 10.0000, 0, 0, 0, 0 }; + gsExpected.row(5) = temp; + temp = { 1.2500, 3.7500, 2.5000, 5.0000, 0, 0, 0, 0, 0, 0, 0, 0 }; + gsExpected.row(6) = temp; + temp = { 1.2500, 3.7500, 2.5000, 5.0000, 6.2500, 8.7500, 7.5000, 10.0000, + 11.2500, 13.7500, 12.5000, 15.0000 }; + gsExpected.row(7) = temp; -// // Check whether the input parameters have been set correctly. -// REQUIRE(layer.Size() == 3); -// REQUIRE(layer.Ratio() == 0.2); -// } + input = input.t(); + gy = gy.t(); + outputsExpected = outputsExpected.t(); + gsExpected = gsExpected.t(); + + // Compute the Forward and Backward passes and store the results. + module.Forward(input, output); + module.Backward(input, gy, g); + + // Check through all possible cases, to find a match and then compare results. + for (size_t i = 0; i < outputsExpected.n_cols; ++i) + { + if (arma::approx_equal(outputsExpected.col(i), output, "absdiff", 1e-1)) + { + // Check the correctness of the Forward pass of the layer. + CheckMatrices(output, outputsExpected.col(i), 1e-1); + // Check the correctness of the Backward pass of the layer. + CheckMatrices(g, gsExpected.col(i), 1e-1); + } + } + + // Check if the output is same as input when using deterministic mode. + module.Deterministic() = true; + output.clear(); + module.Forward(input, output); + CheckMatrices(output, input, 1e-1); +} + +/** + * Test that the function that can access the parameters of the + * SpatialDropout layer works. + */ +TEST_CASE("SpatialDropoutLayerParametersTest", "[ANNLayerTest]") +{ + // Create the layer using the empty constructor. + SpatialDropout<> layer; + + // Set the input parameters. + layer.Size() = 3; + layer.Ratio(0.2); + + // Check whether the input parameters have been set correctly. + REQUIRE(layer.Size() == 3); + REQUIRE(layer.Ratio() == 0.2); +} /** * Simple Positional Encoding layer test. - * + */ TEST_CASE("SimplePositionalEncodingTest", "[ANNLayerTest]") { const size_t seqLength = 5; @@ -5899,7 +5181,7 @@ TEST_CASE("SimplePositionalEncodingTest", "[ANNLayerTest]") arma::mat gy = 0.01 * arma::randu(embedDim * seqLength, batchSize); arma::mat output, g; - PositionalEncoding module(embedDim, seqLength); + PositionalEncoding<> module(embedDim, seqLength); // Check Forward function. module.Forward(input, output); @@ -5910,11 +5192,10 @@ TEST_CASE("SimplePositionalEncodingTest", "[ANNLayerTest]") module.Backward(input, gy, g); REQUIRE(std::equal(gy.begin(), gy.end(), g.begin())); } -*/ /** * Jacobian test for Positional Encoding layer. - * + */ TEST_CASE("JacobianPositionalEncodingTest", "[ANNLayerTest]") { for (size_t i = 0; i < 5; ++i) @@ -5924,17 +5205,16 @@ TEST_CASE("JacobianPositionalEncodingTest", "[ANNLayerTest]") arma::mat input; input.set_size(embedDim * seqLength, 1); - PositionalEncoding module(embedDim, seqLength); + PositionalEncoding<> module(embedDim, seqLength); double error = JacobianTest(module, input); REQUIRE(error <= 1e-5); } } -*/ /** * Simple Multihead Attention test. - * + */ TEST_CASE("SimpleMultiheadAttentionTest", "[ANNLayerTest]") { size_t tLen = 5; @@ -5959,7 +5239,7 @@ TEST_CASE("SimpleMultiheadAttentionTest", "[ANNLayerTest]") arma::mat keyPaddingMask = arma::zeros(1, sLen); keyPaddingMask(sLen - 1) = std::numeric_limits::lowest(); - MultiheadAttention module(tLen, sLen, embedDim, numHeads); + MultiheadAttention<> module(tLen, sLen, embedDim, numHeads); module.AttentionMask() = attnMask; module.KeyPaddingMask() = keyPaddingMask; module.Reset(); @@ -5986,11 +5266,10 @@ TEST_CASE("SimpleMultiheadAttentionTest", "[ANNLayerTest]") REQUIRE(gradient.n_rows == module.Parameters().n_rows); REQUIRE(gradient.n_cols == module.Parameters().n_cols); } -*/ /** * Jacobian MultiheadAttention module test. - * + */ TEST_CASE("JacobianMultiheadAttentionTest", "[ANNLayerTest]") { // Check when query = key = value. @@ -6004,7 +5283,7 @@ TEST_CASE("JacobianMultiheadAttentionTest", "[ANNLayerTest]") arma::mat query = arma::randu(embedDim * tgtSeqLen, batchSize); arma::mat input = arma::join_cols(arma::join_cols(query, query), query); - MultiheadAttention module(tgtSeqLen, tgtSeqLen, embedDim, nHeads); + MultiheadAttention<> module(tgtSeqLen, tgtSeqLen, embedDim, nHeads); module.Parameters().randu(); double error = CustomJacobianTest(module, input); @@ -6024,7 +5303,7 @@ TEST_CASE("JacobianMultiheadAttentionTest", "[ANNLayerTest]") arma::mat key = 0.091 * arma::randu(embedDim * srcSeqLen, batchSize); arma::mat input = arma::join_cols(arma::join_cols(query, key), key); - MultiheadAttention module(tgtSeqLen, srcSeqLen, embedDim, nHeads); + MultiheadAttention<> module(tgtSeqLen, srcSeqLen, embedDim, nHeads); module.Parameters().randu(); double error = CustomJacobianTest(module, input); @@ -6045,18 +5324,17 @@ TEST_CASE("JacobianMultiheadAttentionTest", "[ANNLayerTest]") arma::mat value = 0.045 * arma::randu(embedDim * srcSeqLen, batchSize); arma::mat input = arma::join_cols(arma::join_cols(query, key), value); - MultiheadAttention module(tgtSeqLen, srcSeqLen, embedDim, nHeads); + MultiheadAttention<> module(tgtSeqLen, srcSeqLen, embedDim, nHeads); module.Parameters().randu(); double error = JacobianTest(module, input); REQUIRE(error <= 1e-5); } } -*/ /** * Numerical gradient test for MultiheadAttention layer. - * + */ TEST_CASE("GradientMultiheadAttentionTest", "[ANNLayerTest]") { struct GradientFunction @@ -6090,17 +5368,16 @@ TEST_CASE("GradientMultiheadAttentionTest", "[ANNLayerTest]") keyPaddingMask = arma::zeros(1, srcSeqLen); keyPaddingMask(srcSeqLen - 1) = std::numeric_limits::lowest(); - model = new FFN(); - model->ResetData(input, target); - // attnModule = new MultiheadAttention(tgtSeqLen, srcSeqLen, embedDim, - // nHeads); - // attnModule->AttentionMask() = attnMask; - // attnModule->KeyPaddingMask() = keyPaddingMask; - // model->Add(attnModule); - model->Add(tgtSeqLen, srcSeqLen, embedDim, nHeads, - attnMask, keyPaddingMask); - model->Add(embedDim * tgtSeqLen, vocabSize); - model->Add(); + model = new FFN, XavierInitialization>(); + model->Predictors() = input; + model->Responses() = target; + attnModule = new MultiheadAttention<>(tgtSeqLen, srcSeqLen, + embedDim, nHeads); + attnModule->AttentionMask() = attnMask; + attnModule->KeyPaddingMask() = keyPaddingMask; + model->Add(attnModule); + model->Add>(embedDim * tgtSeqLen, vocabSize); + model->Add>(); } ~GradientFunction() @@ -6117,8 +5394,8 @@ TEST_CASE("GradientMultiheadAttentionTest", "[ANNLayerTest]") arma::mat& Parameters() { return model->Parameters(); } - FFN* model; - // MultiheadAttention* attnModule; + FFN, XavierInitialization>* model; + MultiheadAttention<>* attnModule; arma::mat input, target, attnMask, keyPaddingMask; const size_t tgtSeqLen; @@ -6131,11 +5408,10 @@ TEST_CASE("GradientMultiheadAttentionTest", "[ANNLayerTest]") REQUIRE(CheckGradient(function) <= 3e-06); } -*/ /** * Simple tests for instance normalization layer. - * + */ TEST_CASE("InstanceNormLayerTest", "[ANNLayerTest]") { arma::mat input, result, output, delta, deltaExpected; @@ -6250,12 +5526,11 @@ TEST_CASE("InstanceNormLayerTest", "[ANNLayerTest]") CheckMatrices(output, result, 1e-1); } -*/ /** * Test that the functions that can access the parameters of the * Instance Norm layer work. - * + */ TEST_CASE("InstanceNormLayerParametersTest", "[ANNLayerTest]") { // Parameter order : size, eps. @@ -6273,11 +5548,10 @@ TEST_CASE("InstanceNormLayerParametersTest", "[ANNLayerTest]") CheckMatrices(layer.TrainingVariance(), runningVariance); CheckMatrices(layer.TrainingMean(), runningMean); } -*/ /** * Instance Norm layer numerical gradient test. - * + */ TEST_CASE("GradientInstanceNormLayerTest", "[ANNLayerTest]") { // Add function gradient instantiation. @@ -6293,8 +5567,9 @@ TEST_CASE("GradientInstanceNormLayerTest", "[ANNLayerTest]") arma::mat target; target.ones(1, 1024); - model = new FFN(); - model->ResetData(input, target); + model = new FFN, NguyenWidrowInitialization>(); + model->Predictors() = input; + model->Responses() = target; model->Add >(); model->Add >(1, 2, 3, 3, 1, 1, 0, 0, 4, 4); model->Add > (2, 1024); @@ -6316,7 +5591,7 @@ TEST_CASE("GradientInstanceNormLayerTest", "[ANNLayerTest]") arma::mat& Parameters() { return model->Parameters(); } - FFN* model; + FFN, NguyenWidrowInitialization>* model; arma::mat input, target; } function; @@ -6330,4 +5605,3 @@ TEST_CASE("GradientInstanceNormLayerTest", "[ANNLayerTest]") REQUIRE(pass); } -*/ diff --git a/src/mlpack/tests/ann_test_tools.hpp b/src/mlpack/tests/ann_test_tools.hpp index d9f485484e..dff9bbf5a9 100644 --- a/src/mlpack/tests/ann_test_tools.hpp +++ b/src/mlpack/tests/ann_test_tools.hpp @@ -17,24 +17,41 @@ using namespace mlpack; using namespace mlpack::ann; +// Helper function which calls the Reset function of the given module. +template +void ResetFunction( + T& layer, + typename std::enable_if::value>::type* = 0) +{ + layer.Reset(); +} + +template +void ResetFunction( + T& /* layer */, + typename std::enable_if::value>::type* = 0) +{ + /* Nothing to do here */ +} + // Approximate Jacobian and supposedly-true Jacobian, then compare them // similarly to before. template double JacobianTest(ModuleType& module, - arma::mat& input, - const double minValue = -2, - const double maxValue = -1, - const double perturbation = 1e-6) + arma::mat& input, + const double minValue = -2, + const double maxValue = -1, + const double perturbation = 1e-6) { arma::mat output, outputA, outputB, jacobianA, jacobianB; - output.set_size(module.OutputSize(), input.n_cols); - outputA.set_size(module.OutputSize(), input.n_cols); - outputB.set_size(module.OutputSize(), input.n_cols); // Initialize the input matrix. RandomInitialization init(minValue, maxValue); init.Initialize(input, input.n_rows, input.n_cols); + // Initialize the module parameters. + ResetFunction(module); + // Initialize the jacobian matrix. module.Forward(input, output); jacobianA = arma::zeros(input.n_elem, output.n_elem); @@ -72,7 +89,7 @@ double JacobianTest(ModuleType& module, deriv.zeros(); derivTemp(i) = 1; - arma::mat delta(input.n_rows, input.n_cols); + arma::mat delta; module.Backward(input, deriv, delta); jacobianB.col(i) = delta; @@ -90,9 +107,9 @@ double CustomJacobianTest(ModuleType& module, const double perturbation = 1e-6) { arma::mat output, outputA, outputB, jacobianA, jacobianB; - output.set_size(module.OutputSize(), input.n_cols); - outputA.set_size(module.OutputSize(), input.n_cols); - outputB.set_size(module.OutputSize(), input.n_cols); + + // Initialize the module parameters. + ResetFunction(module); // Initialize the jacobian matrix. module.Forward(input, output); @@ -123,7 +140,7 @@ double CustomJacobianTest(ModuleType& module, deriv.zeros(); deriv(i) = 1; - arma::mat delta(input.n_rows, input.n_cols); + arma::mat delta; module.Backward(input, deriv, delta); jacobianB.col(i) = delta; diff --git a/src/mlpack/tests/ann_visitor_test.cpp b/src/mlpack/tests/ann_visitor_test.cpp new file mode 100644 index 0000000000..29f376611e --- /dev/null +++ b/src/mlpack/tests/ann_visitor_test.cpp @@ -0,0 +1,235 @@ +/** + * @file tests/ann_visitor_test.cpp + * + * Tests for testing visitors in ANN's of mlpack. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#include +#include +#include +#include +#include +#include + +#include "catch.hpp" +#include "test_catch_tools.hpp" + +using namespace mlpack; +using namespace mlpack::ann; + +/** + * Test that the BiasSetVisitor works properly. + */ +TEST_CASE("BiasSetVisitorTest", "[ANNVisitorTest]") +{ + LayerTypes<> linear = new Linear<>(10, 10); + + arma::mat layerWeights(110, 1); + layerWeights.zeros(); + + ResetVisitor resetVisitor; + + boost::apply_visitor(WeightSetVisitor(layerWeights, 0), linear); + + boost::apply_visitor(resetVisitor, linear); + + arma::mat weight = {"1 2 3 4 5 6 7 8 9 10"}; + + size_t biasSize = boost::apply_visitor(BiasSetVisitor(weight, 0), linear); + + REQUIRE(biasSize == 10); + + arma::mat input(10, 1), output; + input.randu(); + + boost::apply_visitor(ForwardVisitor(input, output), linear); + + REQUIRE(arma::accu(output) == 55); + + boost::apply_visitor(DeleteVisitor(), linear); +} + +/** + * Check correctness of WeightSize() for a layer. + */ +void CheckCorrectnessOfWeightSize(LayerTypes<>& layer) +{ + size_t weightSize = boost::apply_visitor(WeightSizeVisitor(), + layer); + + arma::mat parameters; + boost::apply_visitor(ParametersVisitor(parameters), layer); + + REQUIRE(weightSize == parameters.n_elem); +} + +/** + * Test that WeightSetVisitor works properly. + */ +TEST_CASE("WeightSetVisitorTest", "[ANNVisitorTest]") +{ + size_t randomSize = arma::randi(arma::distr_param(1, 100)); + + LayerTypes<> linear = new Linear<>(randomSize, randomSize); + + arma::mat layerWeights(randomSize * randomSize + randomSize, 1); + layerWeights.zeros(); + + size_t setWeights = boost::apply_visitor(WeightSetVisitor(layerWeights, 0), + linear); + + REQUIRE(setWeights == randomSize * randomSize + randomSize); +} + +/** + * Test that WeightSizeVisitor works properly for linear layer. + */ +TEST_CASE("WeightSizeVisitorTestForLinearLayer", "[ANNVisitorTest]") +{ + size_t randomInSize = arma::randi(arma::distr_param(1, 100)); + size_t randomOutSize = arma::randi(arma::distr_param(1, 100)); + + LayerTypes<> linearLayer = new Linear<>(randomInSize, randomOutSize); + + CheckCorrectnessOfWeightSize(linearLayer); +} + +/** + * Test that WeightSizeVisitor works properly for concat layer. + */ +TEST_CASE("WeightSizeVisitorTestForConcatLayer", "[ANNVisitorTest]") +{ + LayerTypes<> concatLayer = new Concat<>(); + + CheckCorrectnessOfWeightSize(concatLayer); +} + +/** + * Test that WeightSizeVisitor works properly for fast lstm layer. + */ +TEST_CASE("WeightSizeVisitorTestForFastLSTMLayer", "[ANNVisitorTest]") +{ + size_t randomInSize = arma::randi(arma::distr_param(1, 100)); + size_t randomOutSize = arma::randi(arma::distr_param(1, 100)); + + LayerTypes<> fastLSTMLayer = new FastLSTM<>(randomInSize, randomOutSize); + + CheckCorrectnessOfWeightSize(fastLSTMLayer); +} + +/** + * Test that WeightSizeVisitor works properly for Add layer. + */ +TEST_CASE("WeightSizeVisitorTestForAddLayer", "[ANNVisitorTest]") +{ + size_t randomOutSize = arma::randi(arma::distr_param(1, 100)); + + LayerTypes<> addLayer = new Add<>(randomOutSize); + + CheckCorrectnessOfWeightSize(addLayer); +} + +/** + * Test that WeightSizeVisitor works properly for Atrous Convolution Layer. + */ +TEST_CASE("WeightSizeVisitorTestForAtrousConvolutionLayer", "[ANNVisitorTest]") +{ + size_t randomInSize = arma::randi(arma::distr_param(1, 100)); + size_t randomOutSize = arma::randi(arma::distr_param(1, 100)); + size_t randomKernelWidth = arma::randi(arma::distr_param(1, 100)); + size_t randomKernelHeight = arma::randi(arma::distr_param(1, 100)); + + LayerTypes<> atrousConvLayer = new AtrousConvolution<>(randomInSize, + randomOutSize, randomKernelWidth, randomKernelHeight); + + CheckCorrectnessOfWeightSize(atrousConvLayer); +} + + +/** + * Test that WeightSizeVisitor works properly for Convolution layer. + */ +TEST_CASE("WeightSizeVisitorTestForConvLayer", "[ANNVisitorTest]") +{ + size_t randomInSize = arma::randi(arma::distr_param(1, 100)); + size_t randomOutSize = arma::randi(arma::distr_param(1, 100)); + size_t randomKernelWidth = arma::randi(arma::distr_param(1, 100)); + size_t randomKernelHeight = arma::randi(arma::distr_param(1, 100)); + + LayerTypes<> convLayer = new Convolution<>(randomInSize, randomOutSize, + randomKernelWidth, randomKernelHeight); + CheckCorrectnessOfWeightSize(convLayer); +} + +/** + * Test that WeightSizeVisitor works properly for BatchNorm layer. + */ +TEST_CASE("WeightSizeVisitorTestForBatchNormLayer", "[ANNVisitorTest]") +{ + size_t randomSize = arma::randi(arma::distr_param(1, 100)); + + LayerTypes<> batchNorm = new BatchNorm<>(randomSize); + CheckCorrectnessOfWeightSize(batchNorm); +} + +/** + * Test that WeightSizeVisitor works properly for LSTM layer. + */ +TEST_CASE("WeightSizeVisitorTestForLSTMLayer", "[ANNVisitorTest]") +{ + size_t randomInSize = arma::randi(arma::distr_param(1, 100)); + size_t randomOutSize = arma::randi(arma::distr_param(1, 100)); + + LayerTypes<> lstm = new LSTM<>(randomInSize, randomOutSize); + CheckCorrectnessOfWeightSize(lstm); +} + +/** + * Test that WeightSizeVisitor works properly for Transposed Convolution layer. + */ +TEST_CASE("WeightSizeVisitorTestForTransposedConvLayer", "[ANNVisitorTest]") +{ + size_t randomInSize = arma::randi(arma::distr_param(1, 100)); + size_t randomOutSize = arma::randi(arma::distr_param(1, 100)); + size_t randomKernelWidth = arma::randi(arma::distr_param(1, 100)); + size_t randomKernelHeight = arma::randi(arma::distr_param(1, 100)); + + LayerTypes<> transposedConvLayer = new TransposedConvolution<>(randomInSize, + randomOutSize, randomKernelWidth, randomKernelHeight); + + CheckCorrectnessOfWeightSize(transposedConvLayer); +} + +/** + * Test that WeightSizeVisitor works properly for noisy linear layer. + */ +TEST_CASE("WeightSizeVisitorTestForNoisyLinearLayer", "[ANNVisitorTest]") +{ + size_t randomInSize = arma::randi(arma::distr_param(1, 100)); + size_t randomOutSize = arma::randi(arma::distr_param(1, 100)); + + LayerTypes<> noisyLinearLayer = new NoisyLinear<>(randomInSize, + randomOutSize); + + CheckCorrectnessOfWeightSize(noisyLinearLayer); +} + +/** + * Test that WeightSizeVisitor works properly for Multihead Attention layer. + */ +TEST_CASE("WeightSizeVisitorTestForMultiheadAttentionLayer", "[ANNVisitorTest]") +{ + size_t randomtgtSeqLen = arma::randi(arma::distr_param(1, 100)); + size_t randomsrcSeqLen = arma::randi(arma::distr_param(1, 100)); + size_t randomembedDim = 768; + size_t randomnumHeads = 12; + + LayerTypes<> MultiheadAttentionLayer = new MultiheadAttention<>( + randomtgtSeqLen, randomsrcSeqLen, randomembedDim, randomnumHeads); + + CheckCorrectnessOfWeightSize(MultiheadAttentionLayer); +} diff --git a/src/mlpack/tests/async_learning_test.cpp b/src/mlpack/tests/async_learning_test.cpp index d788386c4f..4e8f03e9e3 100644 --- a/src/mlpack/tests/async_learning_test.cpp +++ b/src/mlpack/tests/async_learning_test.cpp @@ -13,7 +13,7 @@ #include #include -#include +#include #include #include #include @@ -48,11 +48,11 @@ TEST_CASE("OneStepQLearningTest", "[AsyncLearningTest]") // Set up the network. FFN, GaussianInitialization> model(MeanSquaredError<>(), GaussianInitialization(0, 0.001)); - model.Add(20); - model.Add(); - model.Add(20); - model.Add(); - model.Add(2); + model.Add>(4, 20); + model.Add>(); + model.Add>(20, 20); + model.Add>(); + model.Add>(20, 2); // Set up the policy. using Policy = GreedyPolicy; @@ -124,11 +124,11 @@ TEST_CASE("OneStepSarsaTest", "[AsyncLearningTest]") // Set up the network. FFN, GaussianInitialization> model(MeanSquaredError<>(), GaussianInitialization(0, 0.001)); - model.Add(20); - model.Add(); - model.Add(20); - model.Add(); - model.Add(2); + model.Add>(4, 20); + model.Add>(); + model.Add>(20, 20); + model.Add>(); + model.Add>(20, 2); // Set up the policy. using Policy = GreedyPolicy; @@ -199,11 +199,11 @@ TEST_CASE("NStepQLearningTest", "[AsyncLearningTest]") // Set up the network. FFN, GaussianInitialization> model(MeanSquaredError<>(), GaussianInitialization(0, 0.001)); - model.Add(20); - model.Add(); - model.Add(20); - model.Add(); - model.Add(2); + model.Add>(4, 20); + model.Add>(); + model.Add>(20, 20); + model.Add>(); + model.Add>(20, 2); // Set up the policy. using Policy = GreedyPolicy; diff --git a/src/mlpack/tests/callback_test.cpp b/src/mlpack/tests/callback_test.cpp index 203ce3d9a5..aeeed3fe26 100644 --- a/src/mlpack/tests/callback_test.cpp +++ b/src/mlpack/tests/callback_test.cpp @@ -13,8 +13,7 @@ #include #include #include -#include -//#include +#include #include #include #include @@ -48,12 +47,12 @@ TEST_CASE("FFNCallbackTest", "[CallbackTest]") if (!data::Load("lab3.csv", labels)) FAIL("Cannot load test dataset lab3.csv!"); - FFN model; + FFN, RandomInitialization> model; - model.Add(2); - model.Add(); - model.Add(1); - model.Add(); + model.Add>(1, 2); + model.Add>(); + model.Add>(2, 1); + model.Add>(); std::stringstream stream; model.Train(data, labels, ens::PrintLoss(stream)); @@ -74,12 +73,12 @@ TEST_CASE("FFNWithOptimizerCallbackTest", "[CallbackTest]") if (!data::Load("lab3.csv", labels)) FAIL("Cannot load test dataset lab3.csv!"); - FFN model; + FFN, RandomInitialization> model; - model.Add(2); - model.Add(); - model.Add(1); - model.Add(); + model.Add>(1, 2); + model.Add>(); + model.Add>(2, 1); + model.Add>(); std::stringstream stream; ens::StandardSGD opt(0.1, 1, 5); @@ -99,13 +98,14 @@ TEST_CASE("RNNCallbackTest", "[CallbackTest]") RandomInitialization init(0.5, 0.5); // Create model with user defined rho parameter. - RNN model( - rho, false, NegativeLogLikelihood(), init); - model.Add(10); + RNN, RandomInitialization> model( + rho, false, NegativeLogLikelihood<>(), init); + model.Add>(); + model.Add>(1, 10); - // Use LSTM layer with 3 units. - model.Add(3); - model.Add(); + // Use LSTM layer with rho. + model.Add>(10, 3, rho); + model.Add>(); std::stringstream stream; model.Train(input, target, ens::PrintLoss(stream)); @@ -124,13 +124,14 @@ TEST_CASE("RNNWithOptimizerCallbackTest", "[CallbackTest]") RandomInitialization init(0.5, 0.5); // Create model with user defined rho parameter. - RNN model( - rho, false, NegativeLogLikelihood(), init); - model.Add(10); + RNN, RandomInitialization> model( + rho, false, NegativeLogLikelihood<>(), init); + model.Add>(); + model.Add>(1, 10); - // Use LSTM layer with 3 units. - model.Add(3); - model.Add(); + // Use LSTM layer with rho. + model.Add>(10, 3, rho); + model.Add>(); std::stringstream stream; ens::StandardSGD opt(0.1, 1, 5); @@ -233,7 +234,7 @@ TEST_CASE("SRWithOptimizerCallback", "[CallbackTest]") /* * Tests the RBM Implementation with PrintLoss callback. - * + */ TEST_CASE("RBMCallbackTest", "[CallbackTest]") { // Normalised dataset. @@ -258,7 +259,7 @@ TEST_CASE("RBMCallbackTest", "[CallbackTest]") double objVal = model.Train(msgd, ens::ProgressBar(70, stream)); REQUIRE(!std::isnan(objVal)); REQUIRE(stream.str().length() > 0); -}*/ +} /** * Tests the SparseAutoencoder implementation with diff --git a/src/mlpack/tests/catch.hpp b/src/mlpack/tests/catch.hpp index d2a12427b2..db1fed3b98 100644 --- a/src/mlpack/tests/catch.hpp +++ b/src/mlpack/tests/catch.hpp @@ -1,6 +1,6 @@ /* - * Catch v2.13.9 - * Generated: 2022-04-12 22:37:23.260201 + * Catch v2.13.8 + * Generated: 2022-01-03 21:20:09.589503 * ---------------------------------------------------------- * This file has been merged from multiple headers. Please don't edit it directly * Copyright (c) 2022 Two Blue Cubes Ltd. All rights reserved. @@ -15,7 +15,7 @@ #define CATCH_VERSION_MAJOR 2 #define CATCH_VERSION_MINOR 13 -#define CATCH_VERSION_PATCH 9 +#define CATCH_VERSION_PATCH 8 #ifdef __clang__ # pragma clang system_header @@ -13392,10 +13392,6 @@ namespace Catch { filename.erase(0, lastSlash); filename[0] = '#'; } - else - { - filename.insert(0, "#"); - } auto lastDot = filename.find_last_of('.'); if (lastDot != std::string::npos) { @@ -15391,7 +15387,7 @@ namespace Catch { } Version const& libraryVersion() { - static Version version( 2, 13, 9, "", 0 ); + static Version version( 2, 13, 8, "", 0 ); return version; } @@ -17894,7 +17890,7 @@ using Catch::Detail::Approx; #define INFO( msg ) (void)(0) #define UNSCOPED_INFO( msg ) (void)(0) #define WARN( msg ) (void)(0) -#define CAPTURE( ... ) (void)(0) +#define CAPTURE( msg ) (void)(0) #define TEST_CASE( ... ) INTERNAL_CATCH_TESTCASE_NO_REGISTRATION(INTERNAL_CATCH_UNIQUE_NAME( C_A_T_C_H_T_E_S_T_ )) #define TEST_CASE_METHOD( className, ... ) INTERNAL_CATCH_TESTCASE_NO_REGISTRATION(INTERNAL_CATCH_UNIQUE_NAME( C_A_T_C_H_T_E_S_T_ )) diff --git a/src/mlpack/tests/convolution_test.cpp b/src/mlpack/tests/convolution_test.cpp index 1893804aa5..c2798090ce 100644 --- a/src/mlpack/tests/convolution_test.cpp +++ b/src/mlpack/tests/convolution_test.cpp @@ -54,46 +54,6 @@ void Convolution2DMethodTest(const arma::mat input, REQUIRE(*outputPtr == Approx(*convOutputPtr).epsilon(1e-5)); } -/* - * Implementation of the convolution function test with custom stride and - * dilation. This does not work for every convolution type. - * - * @param input Input used to perform the convolution. - * @param filter Filter used to perform the convolution. - * @param output The reference output data that contains the results of the - * convolution. - * @param strideH Height stride parameter. - * @param strideW Width stride parameter. - * @param dilationH Height dilation parameter. - * @param dilationW Width dilation parameter. - * - * @tparam ConvolutionFunction Convolution function used for the check. - */ -template -void Convolution2DMethodTest(const arma::mat input, - const arma::mat filter, - const arma::mat output, - const size_t strideW, - const size_t strideH, - const size_t dilationW, - const size_t dilationH) -{ - arma::mat convOutput; - ConvolutionFunction::Convolution(input, filter, convOutput, strideW, strideH, - dilationW, dilationH); - - // Check the output dimension. - bool b = (convOutput.n_rows == output.n_rows) && - (convOutput.n_cols == output.n_cols); - REQUIRE(b == 1); - - const double* outputPtr = output.memptr(); - const double* convOutputPtr = convOutput.memptr(); - - for (size_t i = 0; i < output.n_elem; ++i, outputPtr++, convOutputPtr++) - REQUIRE(*outputPtr == Approx(*convOutputPtr).epsilon(1e-5)); -} - /* * Implementation of the convolution function test using 3rd order tensors. * @@ -408,163 +368,3 @@ TEST_CASE("FullConvolutionBatchTest", "[ConvolutionTest]") ConvolutionMethodBatchTest >(input, filterCube, outputCube); } - -/** - * Test that non-stride-1 convolution works the same as stride-1 convolution on - * a smaller matrix. - */ -TEST_CASE("Stride2ConvolutionTest", "[ConvolutionTest]") -{ - // Generate dataset. - arma::mat input, filter, output; - input = { { 1, 2, 3, 4 }, - { 4, 1, 2, 3 }, - { 3, 4, 1, 2 }, - { 2, 3, 4, 1 } }; - - filter = { { 1, -1 }, - { -1, 1 } }; - - output = { { 1, 1, -4 }, - { -1, -4, 1 }, - { -2, -1, 1 } }; - - // Perform the naive convolution approach. - Convolution2DMethodTest >(input, filter, - output, 2, 2, 1, 1); -} - -TEST_CASE("Stride3ConvolutionTest", "[ConvolutionTest]") -{ - // Generate dataset. - arma::mat input, filter, output; - input = { { 1, 2, 3, 4 }, - { 4, 1, 2, 3 }, - { 3, 4, 1, 2 }, - { 2, 3, 4, 1 } }; - - filter = { { 1, -1 }, - { -1, 1 } }; - - output = { { 1, 1 }, - { -1, -4 } }; - - // Perform the naive convolution approach. - Convolution2DMethodTest >(input, filter, - output, 3, 3, 1, 1); -} - -TEST_CASE("UnequalStrideConvolutionTest", "[ConvolutionTest]") -{ - // Generate dataset. - arma::mat input, filter, output; - input = { { 1, 2, 3, 4 }, - { 4, 1, 2, 3 }, - { 3, 4, 1, 2 }, - { 2, 3, 4, 1 } }; - - filter = { { 1, -1 }, - { -1, 1 } }; - - output = { { 1, 1 }, - { -1, 0 }, - { -2, 3 } }; - - // Perform the naive convolution approach. - Convolution2DMethodTest >(input, filter, - output, 3, 2, 1, 1); -} - -TEST_CASE("Dilation2ConvolutionTest", "[ConvolutionTest]") -{ - // Generate dataset. - arma::mat input, filter, output; - input = { { 1, 2, 3, 4 }, - { 4, 1, 2, 3 }, - { 3, 4, 1, 2 }, - { 2, 3, 4, 1 } }; - - filter = { { 1, -1 }, - { -1, 1 } }; - - output = { { 1, 2, 2, 2, -3, -4 }, - { 4, 1, -2, 2, -2, -3 }, - { 2, 2, -4, -4, 2, 2 }, - { -2, 2, 4, -4, -2, 2 }, - { -3, -4, 2, 2, 1, 2 }, - { -2, -3, -2, 2, 4, 1 } }; - - // Perform the naive convolution approach. - Convolution2DMethodTest >(input, filter, - output, 1, 1, 2, 2); -} - -TEST_CASE("Dilation3ConvolutionTest", "[ConvolutionTest]") -{ - // Generate dataset. - arma::mat input, filter, output; - input = { { 1, 2, 3, 4 }, - { 4, 1, 2, 3 }, - { 3, 4, 1, 2 }, - { 2, 3, 4, 1 } }; - - filter = { { 1, -1 }, - { -1, 1 } }; - - output = { { 1, 2, 3, 3, -2, -3, -4 }, - { 4, 1, 2, -1, -1, -2, -3 }, - { 3, 4, 1, -1, -4, -1, -2 }, - { 1, 1, 1, -4, -1, -1, 3 }, - { -4, -1, -2, 1, 1, 2, 3 }, - { -3, -4, -1, 1, 4, 1, 2 }, - { -2, -3, -4, 1, 3, 4, 1 } }; - - // Perform the naive convolution approach. - Convolution2DMethodTest >(input, filter, - output, 1, 1, 3, 3); -} - -TEST_CASE("UnequalDilationConvolutionTest", "[ConvolutionTest]") -{ - // Generate dataset. - arma::mat input, filter, output; - input = { { 1, 2, 3, 4 }, - { 4, 1, 2, 3 }, - { 3, 4, 1, 2 }, - { 2, 3, 4, 1 } }; - - filter = { { 1, -1 }, - { -1, 1 } }; - - output = { { 1, 2, 3, 3, -2, -3, -4 }, - { 4, 1, 2, -1, -1, -2, -3 }, - { 2, 2, -2, -4, -2, 2, 2 }, - { -2, 2, 2, 0, -2, -2, 2 }, - { -3, -4, -1, 1, 4, 1, 2 }, - { -2, -3, -4, 1, 3, 4, 1 } }; - - // Perform the naive convolution approach. - Convolution2DMethodTest >(input, filter, - output, 1, 1, 3, 2); -} - -TEST_CASE("DilationAndStrideConvolutionTest", "[ConvolutionTest]") -{ - // Generate dataset. - arma::mat input, filter, output; - input = { { 1, 2, 3, 4 }, - { 4, 1, 2, 3 }, - { 3, 4, 1, 2 }, - { 2, 3, 4, 1 } }; - - filter = { { 1, -1 }, - { -1, 1 } }; - - output = { { 1, 2, -3 }, - { 2, -4, 2 }, - { -3, 2, 1 } }; - - // Perform the naive convolution approach. - Convolution2DMethodTest >(input, filter, - output, 2, 2, 2, 2); -} diff --git a/src/mlpack/tests/convolutional_network_test.cpp b/src/mlpack/tests/convolutional_network_test.cpp index 91994e6b8c..43eb1bb0d2 100644 --- a/src/mlpack/tests/convolutional_network_test.cpp +++ b/src/mlpack/tests/convolutional_network_test.cpp @@ -14,7 +14,6 @@ #include #include -#include #include #include @@ -71,84 +70,6 @@ void CheckMoveFunction(ModelType* network1, CheckMatrices(predictions1, predictions2); } -/** - * Build a trivial network with a single padding layer, and make sure it - * successfully pads the input. - */ -TEST_CASE("PaddingTest", "[ConvolutionalNetworktest]") -{ - arma::mat X; - X.load("mnist_first250_training_4s_and_9s.arm"); - - // Create the network. - FFN model; - - model.Add(1, 2, 3, 4); - - // Now, pass the data through. - arma::mat results; - model.InputDimensions() = std::vector({ 28, 28 }); - model.Forward(X, results); - - // Ensure that things are correctly padded. - arma::cube reshapedResults(results.memptr(), 35, 31, results.n_cols, false, - true); - - for (size_t i = 0; i < reshapedResults.n_slices; ++i) - { - // Check left. - for (size_t j = 0; j < reshapedResults.n_rows; ++j) - REQUIRE(reshapedResults(j, 0, i) == 0.0); - - // Check top. - for (size_t j = 0; j < 3; ++j) - for (size_t k = 0; k < reshapedResults.n_cols; ++k) - REQUIRE(reshapedResults(j, k, i) == 0.0); - - // Check bottom. - for (size_t j = 31; j < reshapedResults.n_rows; ++j) - for (size_t k = 0; k < reshapedResults.n_cols; ++k) - REQUIRE(reshapedResults(j, k, i) == 0.0); - - // Check right. - for (size_t j = 0; j < reshapedResults.n_rows; ++j) - for (size_t k = 29; k < reshapedResults.n_cols; ++k) - REQUIRE(reshapedResults(j, k, i) == 0.0); - } -} - -/** - * Build a trivial network with a MaxPooling layer, and make sure it - * successfully does the max-pool operation. - */ -TEST_CASE("MaxPoolingTest", "[ConvolutionalNetworkTest]") -{ - arma::mat X(8, 3); - X.col(0) = arma::vec("1, 2, 3, 4, 5, 6, 7, 8"); - X.col(1) = arma::vec("5, 7, 6, 8, 4, 3, 1, 2"); - X.col(2) = arma::vec("3, 4, 1, -1, 5, 5, 5, 5"); - - // Create the network. - FFN model; - model.Add(2, 2); - - arma::mat results; - model.InputDimensions() = std::vector({ 2, 4 }); - model.Forward(X, results); - - REQUIRE(results.n_rows == 3); - REQUIRE(results.n_cols == 3); - REQUIRE(results(0, 0) == 4); - REQUIRE(results(1, 0) == 6); - REQUIRE(results(2, 0) == 8); - REQUIRE(results(0, 1) == 8); - REQUIRE(results(1, 1) == 8); - REQUIRE(results(2, 1) == 4); - REQUIRE(results(0, 2) == 4); - REQUIRE(results(1, 2) == 5); - REQUIRE(results(2, 2) == 5); -} - /** * Train the vanilla network on a larger dataset. */ @@ -204,27 +125,25 @@ TEST_CASE("VanillaNetworkTest", "[ConvolutionalNetworkTest]") bool success = false; for (size_t trial = 0; trial < 5; ++trial) { - FFN model; + FFN, RandomInitialization> model; - model.Add(8, 5, 5, 1, 1, 0, 0); - model.Add(); - model.Add(2, 2); - model.Add(12, 2, 2); - model.Add(); - model.Add(2, 2); - model.Add(20); - model.Add(); - model.Add(10); - model.Add(); - model.Add(2); - model.Add(); - - model.InputDimensions() = std::vector({ 28, 28 }); + model.Add >(1, 8, 5, 5, 1, 1, 0, 0, 28, 28); + model.Add >(); + model.Add >(8, 8, 2, 2); + model.Add >(8, 12, 2, 2); + model.Add >(); + model.Add >(2, 2, 2, 2); + model.Add >(192, 20); + model.Add >(); + model.Add >(20, 10); + model.Add >(); + model.Add >(10, 2); + model.Add >(); // Train for only 8 epochs. ens::RMSProp opt(0.001, 1, 0.88, 1e-8, 8 * nPoints, -1); - double objVal = model.Train(X, Y, opt, ens::PrintLoss()); + double objVal = model.Train(X, Y, opt); // Test that objective value returned by FFN::Train() is finite. REQUIRE(std::isfinite(objVal) == true); @@ -251,103 +170,6 @@ TEST_CASE("VanillaNetworkTest", "[ConvolutionalNetworkTest]") REQUIRE(success == true); } -TEST_CASE("VanillaNetworkBatchSizeTest", "[ConvolutionalNetworkTest]") -{ - FFN model; - - model.Add(8, 5, 5, 1, 1, 0, 0); - model.Add(); - model.Add(2, 2); - model.Add(12, 2, 2); - model.Add(); - model.Add(2, 2); - model.Add(20); - model.Add(); - model.Add(10); - model.Add(); - model.Add(2); - model.Add(); - - model.InputDimensions() = std::vector({ 28, 28 }); - - arma::mat X; - X.load("mnist_first250_training_4s_and_9s.arm"); - - // Normalize each point since these are images. - arma::uword nPoints = X.n_cols; - for (arma::uword i = 0; i < nPoints; ++i) - { - X.col(i) /= norm(X.col(i), 2); - } - - // Build the target matrix. - arma::mat Y = arma::zeros(1, nPoints); - for (size_t i = 0; i < nPoints; ++i) - { - if (i < nPoints / 2) - { - // Assign label "1" to all samples with digit = 4 - Y(i) = 1; - } - else - { - // Assign label "0" to all samples with digit = 9 - Y(i) = 0; - } - } - - // Perform one epoch of training to get the weights to somewhere reasonable. - ens::RMSProp opt(0.001, 1, 0.88, 1e-8, nPoints, -1); - model.Train(X, Y, opt); - - size_t trials = 7; - for (size_t trial = 0; trial < trials; ++trial) - { - const size_t batchSize = std::pow(2.0, (double) trial + 1.0); - - // Check the forward pass, and then call EvaluateWithGradient() to compute - // the gradient. - arma::mat results; - arma::mat batchData = X.cols(0, batchSize - 1); - arma::mat batchResponses = Y.cols(0, batchSize - 1); - model.ResetData(std::move(batchData), std::move(batchResponses)); - model.Forward(X.cols(0, batchSize - 1), results); - - arma::mat gradient(1, model.WeightSize()); - const double obj = model.EvaluateWithGradient(model.Parameters(), gradient); - - REQUIRE(results.n_cols == batchSize); - - // Now compute results with a batch size of 1. - arma::mat singleResults(results.n_rows, results.n_cols); - arma::mat singleGradient(gradient.n_rows, gradient.n_cols); - double singleObj = 0.0; - - for (size_t i = 0; i < batchSize; ++i) - { - arma::mat tmpResult; - arma::mat singleData = X.cols(i, i); - arma::mat singleResponses = Y.cols(i, i); - model.ResetData(std::move(singleData), std::move(singleResponses)); - model.Forward(X.cols(i, i), tmpResult); - REQUIRE(tmpResult.n_cols == 1); - singleResults.col(i) = tmpResult; - - arma::mat tmpGradient(1, model.WeightSize()); - singleObj += model.EvaluateWithGradient(model.Parameters(), tmpGradient); - - singleGradient += tmpGradient; - } - - // Check the forward pass results. - CheckMatrices(results, singleResults); - - // Now, check EvaluateWithGradient()'s results. - REQUIRE(obj == Approx(singleObj)); - CheckMatrices(gradient, singleGradient); - } -} - /** * Train the vanilla network on a larger dataset. */ @@ -400,132 +222,39 @@ TEST_CASE("CheckCopyVanillaNetworkTest", "[ConvolutionalNetworkTest]") // of iterations using random weights. If this works 1 of 5 times, I'm fine // with that. All I want to know is that the network is able to escape from // local minima and to solve the task. - FFN *model = - new FFN; + FFN, RandomInitialization> *model = new FFN, RandomInitialization>; - model->Add(8, 5, 5, 1, 1, 0, 0); - model->Add(); - model->Add(2, 2); - model->Add(12, 2, 2); - model->Add(); - model->Add(2, 2); - model->Add(20); - model->Add(); - model->Add(10); - model->Add(); - model->Add(2); - model->Add(); - model->InputDimensions() = std::vector({ 28, 28 }); + model->Add >(1, 8, 5, 5, 1, 1, 0, 0, 28, 28); + model->Add >(); + model->Add >(8, 8, 2, 2); + model->Add >(8, 12, 2, 2); + model->Add >(); + model->Add >(2, 2, 2, 2); + model->Add >(192, 20); + model->Add >(); + model->Add >(20, 10); + model->Add >(); + model->Add >(10, 2); + model->Add >(); - FFN *model1 = - new FFN; - - model1->Add(8, 5, 5, 1, 1, 0, 0); - model1->Add(); - model1->Add(2, 2); - model1->Add(12, 2, 2); - model1->Add(); - model1->Add(2, 2); - model1->Add(20); - model1->Add(); - model1->Add(10); - model1->Add(); - model1->Add(2); - model1->Add(); - model1->InputDimensions() = std::vector({ 28, 28 }); + FFN, RandomInitialization> *model1 = new FFN, RandomInitialization>; + model1->Add >(1, 8, 5, 5, 1, 1, 0, 0, 28, 28); + model1->Add >(); + model1->Add >(8, 8, 2, 2); + model1->Add >(8, 12, 2, 2); + model1->Add >(); + model1->Add >(2, 2, 2, 2); + model1->Add >(192, 20); + model1->Add >(); + model1->Add >(20, 10); + model1->Add >(); + model1->Add >(10, 2); + model1->Add >(); + // Check whether copy constructor is working or not. CheckCopyFunction<>(model, X, Y, 8); // Check whether move constructor is working or not. CheckMoveFunction<>(model1, X, Y, 8); } - -TEST_CASE("Issue2986", "[ConvolutionalNetworkTest]") -{ - // Ensure that the code snippet in issue #2986 succeeds without any issues. - arma::mat input, output, delta; - input.ones(36, 1); - - // Note that the stride here is 2, not 1. - Convolution c(1, 3, 3, 2, 2, 0, 0); - - // Set up the layer without an enclosing FFN. - c.InputDimensions() = std::vector({ 6, 6 }); - c.ComputeOutputDimensions(); - arma::mat weights(c.WeightSize(), 1, arma::fill::randu); - c.SetWeights(weights.memptr()); - - output.set_size(c.OutputSize(), 1); - delta.set_size(input.size()); - - REQUIRE_NOTHROW(c.Forward(input, output)); - REQUIRE_NOTHROW(c.Backward(input, output, delta)); - - // Now test with a stride of 3. - c = Convolution(1, 3, 3, 3, 3, 0, 0); - - // Set up the layer without an enclosing FFN. - c.InputDimensions() = std::vector({ 6, 6 }); - c.ComputeOutputDimensions(); - weights.set_size(c.WeightSize(), 1); - weights.randu(); - c.SetWeights(weights.memptr()); - - output.set_size(c.OutputSize(), 1); - delta.set_size(input.size()); - - REQUIRE_NOTHROW(c.Forward(input, output)); - REQUIRE_NOTHROW(c.Backward(input, output, delta)); - - // Now test with different strides for height and width. - c = Convolution(1, 3, 3, 2, 3, 0, 0); - - // Set up the layer without an enclosing FFN. - c.InputDimensions() = std::vector({ 6, 6 }); - c.ComputeOutputDimensions(); - weights.set_size(c.WeightSize(), 1); - weights.randu(); - c.SetWeights(weights.memptr()); - - output.set_size(c.OutputSize(), 1); - delta.set_size(input.size()); - - REQUIRE_NOTHROW(c.Forward(input, output)); - REQUIRE_NOTHROW(c.Backward(input, output, delta)); -} - -// Test that the Convolution layer gives reasonable output when a non-zero -// padding size is used. -TEST_CASE("CustomPaddingTest", "[ConvolutionalNetworkTest]") -{ - arma::mat input, output, delta, weights; - input.ones(36, 1); - - Convolution c = Convolution(1, 3, 3, 1, 1, { 1, 2 }, { 3, 4 }, "none"); - - c.InputDimensions() = std::vector({ 6, 6 }); - c.ComputeOutputDimensions(); - - // First, check that the output dimensions are reasonable. - REQUIRE(c.OutputDimensions().size() == 3); - REQUIRE(c.OutputDimensions()[0] == 7); - REQUIRE(c.OutputDimensions()[1] == 11); - REQUIRE(c.OutputDimensions()[2] == 1); - - weights.set_size(c.WeightSize(), 1); - weights.ones(); - c.SetWeights(weights.memptr()); - - // Now make sure that the forward pass returns the correct output. - output.set_size(c.OutputSize(), 1); - REQUIRE_NOTHROW(c.Forward(input, output)); - REQUIRE(output.n_rows == c.OutputSize()); - REQUIRE(output.n_cols == 1); - // The lower right corner's convolution entry should only touch one input - // value (and everything else padding). - REQUIRE(output(output.n_rows - 1, 0) == 1.0); - - delta.set_size(input.size()); - REQUIRE_NOTHROW(c.Backward(input, output, delta)); -} diff --git a/src/mlpack/tests/custom_layer.hpp b/src/mlpack/tests/custom_layer.hpp index 3a3eaa78ad..593496bb50 100644 --- a/src/mlpack/tests/custom_layer.hpp +++ b/src/mlpack/tests/custom_layer.hpp @@ -2,7 +2,7 @@ * @file tests/custom_layer.hpp * @author Projyal Dev * - * A simple custom layer mimicking SigmoidLayer for testing if custom + * A simple custom layer mimicing SigmoidLayer for testing if custom * layers work. * * mlpack is free software; you may redistribute it and/or modify it under the @@ -15,20 +15,20 @@ #include #include -#include -#include + namespace mlpack { namespace ann { - -/** - * Standard Sigmoid layer. - */ -template < - class ActivationFunction = LogisticFunction, - typename MatType = arma::mat -> -using CustomLayer = BaseLayer; + /** + * Standard Sigmoid layer. + */ + template < + class ActivationFunction = LogisticFunction, + typename InputDataType = arma::mat, + typename OutputDataType = arma::mat + > + using CustomLayer = BaseLayer< + ActivationFunction, InputDataType, OutputDataType>; } // namespace ann } // namespace mlpack diff --git a/src/mlpack/tests/cv_test.cpp b/src/mlpack/tests/cv_test.cpp index 1ee34b0566..aafee5db11 100644 --- a/src/mlpack/tests/cv_test.cpp +++ b/src/mlpack/tests/cv_test.cpp @@ -24,7 +24,7 @@ #include #include #include -#include +#include #include #include #include @@ -33,7 +33,6 @@ #include #include #include -#include #include #include #include @@ -45,7 +44,6 @@ using namespace mlpack; using namespace mlpack::ann; using namespace mlpack::cv; using namespace mlpack::naive_bayes; -using namespace mlpack::perceptron; using namespace mlpack::regression; using namespace mlpack::tree; using namespace mlpack::data; @@ -303,9 +301,10 @@ TEST_CASE("MSEMatResponsesTest", "[CVTest]") arma::mat data("1 2"); arma::mat trainingResponses("1 2; 3 4"); - FFN ffn(MeanSquaredError(), + FFN, ConstInitialization> ffn(MeanSquaredError<>(), ConstInitialization(0)); - ffn.Add(2); + ffn.Add>(1, 2); + ffn.Add>(); ens::RMSProp opt(0.2); opt.BatchSize() = 1; @@ -613,33 +612,6 @@ TEST_CASE("KFoldCVAccuracyTest", "[CVTest]") REQUIRE_NOTHROW(cv.Model()); } -/** - * Test k-fold cross-validation with the perceptron. - */ -TEST_CASE("KFoldCVPerceptronTest", "[CVTest]") -{ - // The same as the test above (for Naive Bayes), but with the perceptron. - - // Making a 10-points dataset. The last point should be classified wrong when - // it is tested separately. - arma::mat data("0 1 2 3 100 101 102 103 104 5"); - arma::Row labels("0 0 0 0 1 1 1 1 1 1"); - size_t numClasses = 2; - - // 10-fold cross-validation, no shuffling. - KFoldCV, Accuracy> cv(10, data, labels, numClasses); - - // We should succeed in classifying separately the first nine samples, and - // fail with the remaining one. - double expectedAccuracy = (9 * 1.0 + 0.0) / 10; - - REQUIRE(cv.Evaluate() == Approx(expectedAccuracy).epsilon(1e-7)); - - // Assert we can access a trained model without the exception of - // uninitialization. - REQUIRE_NOTHROW(cv.Model()); -} - /** * Test k-fold cross-validation with weighted linear regression. */ diff --git a/src/mlpack/tests/feedforward_network_2_test.cpp b/src/mlpack/tests/feedforward_network_2_test.cpp index a115468305..90ba3c3fbd 100644 --- a/src/mlpack/tests/feedforward_network_2_test.cpp +++ b/src/mlpack/tests/feedforward_network_2_test.cpp @@ -14,7 +14,6 @@ #include #include -#include #include #include #include @@ -102,9 +101,9 @@ TEST_CASE("RBFNetworkTest", "[FeedForwardNetworkTest]") KMeans<> kmeans; kmeans.Cluster(trainData, 8, centroids); - FFN model; - model.Add(8, centroids); - model.Add(3); + FFN > model; + model.Add >(trainData.n_rows, 8, centroids); + model.Add >(8, 3); // RBFN neural net with MeanSquaredError. TestNetwork<>(model, trainData, trainLabels1, testData, testLabels, 10, 0.1); @@ -134,9 +133,9 @@ TEST_CASE("RBFNetworkTest", "[FeedForwardNetworkTest]") KMeans<> kmeans1; kmeans1.Cluster(dataset, 140, centroids1); - FFN model1; - model1.Add(140, centroids1, 4.1); - model1.Add(2); + FFN > model1; + model1.Add >(dataset.n_rows, 140, centroids1, 4.1); + model1.Add >(140, 2); // RBFN neural net with MeanSquaredError. TestNetwork<>(model1, dataset, labels1, dataset, labels, 10, 0.1); diff --git a/src/mlpack/tests/feedforward_network_test.cpp b/src/mlpack/tests/feedforward_network_test.cpp index 613d0e56e8..6fd5af4e30 100644 --- a/src/mlpack/tests/feedforward_network_test.cpp +++ b/src/mlpack/tests/feedforward_network_test.cpp @@ -12,7 +12,7 @@ */ #include -#include +#include #include #include @@ -20,7 +20,7 @@ #include "catch.hpp" #include "serialization.hpp" -//#include "custom_layer.hpp" +#include "custom_layer.hpp" using namespace mlpack; using namespace mlpack::ann; @@ -37,7 +37,7 @@ void TestNetwork(ModelType& model, const size_t maxEpochs, const double classificationErrorThreshold) { - ens::RMSProp opt(0.01, 32, 0.88, 1e-8, trainData.n_cols * maxEpochs, -100); + ens::RMSProp opt(0.01, 32, 0.88, 1e-8, maxEpochs * trainData.n_cols, -1); model.Train(trainData, trainLabels, opt); MatType predictionTemp; @@ -51,7 +51,6 @@ void TestNetwork(ModelType& model, } size_t correct = arma::accu(prediction == testLabels); - double classificationError = 1 - double(correct) / testData.n_cols; REQUIRE(classificationError <= classificationErrorThreshold); } @@ -60,14 +59,14 @@ void TestNetwork(ModelType& model, template void CheckCopyFunction(ModelType* network1, MatType& trainData, - MatType& trainLabels) + MatType& trainLabels, + const size_t maxEpochs) { - ens::RMSProp opt(0.01, 32, 0.88, 1e-8, trainData.n_cols, -1); + ens::RMSProp opt(0.01, 32, 0.88, 1e-8, maxEpochs * trainData.n_cols, -1); network1->Train(trainData, trainLabels, opt); arma::mat predictions1; network1->Predict(trainData, predictions1); - FFN<> network2; network2 = *network1; delete network1; @@ -86,7 +85,7 @@ void CheckMoveFunction(ModelType* network1, MatType& trainLabels, const size_t maxEpochs) { - ens::RMSProp opt(0.01, 32, 0.88, 1e-8, trainData.n_cols, -1); + ens::RMSProp opt(0.01, 32, 0.88, 1e-8, maxEpochs * trainData.n_cols, -1); network1->Train(trainData, trainLabels, opt); arma::mat predictions1; @@ -137,28 +136,28 @@ TEST_CASE("CheckCopyMovingVanillaNetworkTest", "[FeedForwardNetworkTest]") * +-----+ +-----+ */ - FFN *model = new FFN; - model->Add(8); - model->Add(); - model->Add(3); - model->Add(); + FFN > *model = new FFN >; + model->Add >(trainData.n_rows, 8); + model->Add >(); + model->Add >(8, 3); + model->Add >(); - FFN *model1 = new FFN; - model1->Add(8); - model1->Add(); - model1->Add(3); - model1->Add(); + FFN > *model1 = new FFN >; + model1->Add >(trainData.n_rows, 8); + model1->Add >(); + model1->Add >(8, 3); + model1->Add >(); // Check whether copy constructor is working or not. - CheckCopyFunction(model, trainData, trainLabels); + CheckCopyFunction<>(model, trainData, trainLabels, 1); // Check whether move constructor is working or not. - CheckMoveFunction(model1, trainData, trainLabels, 1); + CheckMoveFunction<>(model1, trainData, trainLabels, 1); } /** * Check whether copying and moving network with Reparametrization is working or not. - * + */ TEST_CASE("CheckCopyMovingReparametrizationNetworkTest", "[FeedForwardNetworkTest]") { @@ -166,64 +165,31 @@ TEST_CASE("CheckCopyMovingReparametrizationNetworkTest", arma::mat trainData; data::Load("thyroid_train.csv", trainData, true); - arma::mat trainLabels = trainData.row(trainData.n_rows - 1); + // Normalize labels to [0, 2]. + arma::mat trainLabels = trainData.row(trainData.n_rows - 1) - 1; trainData.shed_row(trainData.n_rows - 1); - // Construct a feed forward network with trainData.n_rows input nodes, - // followed by a linear layer and then a reparametrization layer. - FFN *model = new FFN; - model->Add(8); - model->Add(false, true, 1); - model->Add(); + /* + * Construct a feed forward network with trainData.n_rows input nodes, + * followed by a linear layer and then a reparametrization layer. + */ - FFN *model1 = new FFN; - model1->Add(8); - model1->Add(false, true, 1); - model1->Add(); + FFN > *model = new FFN >; + model->Add >(trainData.n_rows, 8); + model->Add >(4, false, true, 1); + model->Add >(); + + FFN > *model1 = new FFN >; + model1->Add >(trainData.n_rows, 8); + model1->Add >(4, false, true, 1); + model1->Add >(); // Check whether copy constructor is working or not. - CheckCopyFunction(model, trainData, trainLabels); + CheckCopyFunction<>(model, trainData, trainLabels, 1); // Check whether move constructor is working or not. - CheckMoveFunction(model1, trainData, trainLabels, 1); + CheckMoveFunction<>(model1, trainData, trainLabels, 1); } -*/ - -/** - * Check whether copying and moving network with Reparametrization is working or not. - */ -// TEST_CASE("CheckCopyMovingReparametrizationNetworkTest", -// "[FeedForwardNetworkTest]") -// { -// // Load the dataset. -// arma::mat trainData; -// data::Load("thyroid_train.csv", trainData, true); -// -// // Normalize labels to [0, 2]. -// arma::mat trainLabels = trainData.row(trainData.n_rows - 1) - 1; -// trainData.shed_row(trainData.n_rows - 1); -// -// /* -// * Construct a feed forward network with trainData.n_rows input nodes, -// * followed by a linear layer and then a reparametrization layer. -// */ -// -// FFN *model = new FFN; -// model->Add >(trainData.n_rows, 8); -// model->Add >(4, false, true, 1); -// model->Add >(); -// -// FFN *model1 = new FFN; -// model1->Add >(trainData.n_rows, 8); -// model1->Add >(4, false, true, 1); -// model1->Add >(); -// -// // Check whether copy constructor is working or not. -// CheckCopyFunction<>(model, trainData, trainLabels, 1); -// -// // Check whether move constructor is working or not. -// CheckMoveFunction<>(model1, trainData, trainLabels, 1); -// } /** * Check whether copying and moving network with linear3d is working or not. @@ -238,25 +204,45 @@ TEST_CASE("CheckCopyMovingLinear3DNetworkTest", "[FeedForwardNetworkTest]") arma::mat trainLabels = trainData.row(trainData.n_rows - 1) - 1; trainData.shed_row(trainData.n_rows - 1); - // Construct a feed forward network with trainData.n_rows input nodes, - // followed by a linear layer and then a Linear3D layer. - FFN *model = new FFN; - model->Add(8); - model->Add(); - model->Add(3); - model->Add(); + /* + * Construct a feed forward network with trainData.n_rows input nodes, + * hiddenLayerSize hidden nodes and trainLabels.n_rows output nodes. The + * network structure looks like: + * + * Input Hidden Output + * Layer Layer Layer + * +-----+ +-----+ +-----+ + * | | | | | | + * | +------>| +------>| | + * | | +>| | +>| | + * +-----+ | +--+--+ | +-----+ + * | | + * Bias | Bias | + * Layer | Layer | + * +-----+ | +-----+ | + * | | | | | | + * | +-----+ | +-----+ + * | | | | + * +-----+ +-----+ + */ - FFN *model1 = new FFN; - model1->Add(8); - model1->Add(); - model1->Add(3); - model1->Add(); + FFN > *model = new FFN >; + model->Add >(trainData.n_rows, 8); + model->Add >(); + model->Add >(8, 3); + model->Add >(); + + FFN > *model1 = new FFN >; + model1->Add >(trainData.n_rows, 8); + model1->Add >(); + model1->Add >(8, 3); + model1->Add >(); // Check whether copy constructor is working or not. - CheckCopyFunction(model, trainData, trainLabels); + CheckCopyFunction<>(model, trainData, trainLabels, 1); // Check whether move constructor is working or not. - CheckMoveFunction(model1, trainData, trainLabels, 1); + CheckMoveFunction<>(model1, trainData, trainLabels, 1); } /** @@ -270,24 +256,28 @@ TEST_CASE("CheckCopyMovingNoisyLinearTest", "[FeedForwardNetworkTest]") arma::mat output = arma::mat("0"); // Check copying constructor. - FFN *model1 = new FFN(); - model1->ResetData(input, output); - model1->Add(5); - model1->Add(1); - model1->Add(); + FFN> *model1 = new FFN>(); + model1->Predictors() = input; + model1->Responses() = output; + model1->Add>(); + model1->Add>(10, 5); + model1->Add >(5, 1); + model1->Add>(); // Check whether copy constructor is working or not. - CheckCopyFunction(model1, input, output); + CheckCopyFunction<>(model1, input, output, 1); // Check moving constructor. - FFN *model2 = new FFN(); - model2->ResetData(input, output); - model2->Add(5); - model2->Add(1); - model2->Add(); + FFN> *model2 = new FFN>(); + model2->Predictors() = input; + model2->Responses() = output; + model2->Add>(); + model2->Add>(10, 5); + model2->Add >(5, 1); + model2->Add>(); // Check whether move constructor is working or not. - CheckMoveFunction(model2, input, output, 1); + CheckMoveFunction<>(model2, input, output, 1); } /** @@ -301,39 +291,43 @@ TEST_CASE("CheckCopyMovingConcatenateTest", "[FeedForwardNetworkTest]") arma::mat output = arma::mat("1"); // Check copying constructor. - FFN *model1 = new FFN(); - model1->ResetData(input, output); - model1->Add(5); + FFN> *model1 = new FFN>(); + model1->Predictors() = input; + model1->Responses() = output; + model1->Add>(); + model1->Add>(10, 5); // Create concatenate layer. arma::mat concatMatrix = arma::ones(5, 1); - Concatenate* concatLayer = new Concatenate(); + Concatenate<>* concatLayer = new Concatenate<>(); concatLayer->Concat() = concatMatrix; // Add concatenate layer to the current network. model1->Add(concatLayer); - model1->Add(5); - model1->Add(); + model1->Add >(10, 5); + model1->Add>(); // Check whether copy constructor is working or not. - CheckCopyFunction(model1, input, output); + CheckCopyFunction<>(model1, input, output, 1); // Check moving constructor. - FFN *model2 = new FFN(); - model2->ResetData(input, output); - model2->Add(5); + FFN> *model2 = new FFN>(); + model2->Predictors() = input; + model2->Responses() = output; + model2->Add>(); + model2->Add>(10, 5); // Create new concat layer. - Concatenate* concatLayer2 = new Concatenate(); + Concatenate<>* concatLayer2 = new Concatenate<>(); concatLayer2->Concat() = concatMatrix; // Add concatenate layer to the current network. model2->Add(concatLayer2); - model2->Add(5); - model2->Add(); + model2->Add >(10, 5); + model2->Add>(); // Check whether move constructor is working or not. - CheckMoveFunction(model2, input, output, 1); + CheckMoveFunction<>(model2, input, output, 1); } /** @@ -349,25 +343,47 @@ TEST_CASE("CheckCopyMovingDropoutNetworkTest", "[FeedForwardNetworkTest]") arma::mat trainLabels = trainData.row(trainData.n_rows - 1) - 1; trainData.shed_row(trainData.n_rows - 1); - FFN *model = new FFN; - model->Add(8); - model->Add(); - model->Add(0.3); - model->Add(3); - model->Add(); + /* + * Construct a feed forward network with trainData.n_rows input nodes, + * hiddenLayerSize hidden nodes and trainLabels.n_rows output nodes. The + * network structure looks like: + * + * Input Hidden Output + * Layer Layer Layer + * +-----+ +-----+ +-----+ + * | | | | | | + * | +------>| +------>| | + * | | +>| | +>| | + * +-----+ | +--+--+ | +-----+ + * | | + * Bias | Bias | + * Layer | Layer | + * +-----+ | +-----+ | + * | | | | | | + * | +-----+ | +-----+ + * | | | | + * +-----+ +-----+ + */ - FFN *model1 = new FFN; - model1->Add(8); - model1->Add(); - model1->Add(0.3); - model1->Add(3); - model1->Add(); + FFN > *model = new FFN >; + model->Add >(trainData.n_rows, 8); + model->Add >(); + model->Add >(0.3); + model->Add >(8, 3); + model->Add >(); + + FFN > *model1 = new FFN >; + model1->Add >(trainData.n_rows, 8); + model1->Add >(); + model1->Add >(0.3); + model1->Add >(8, 3); + model1->Add >(); // Check whether copy constructor is working or not. - CheckCopyFunction(model, trainData, trainLabels); + CheckCopyFunction<>(model, trainData, trainLabels, 1); // Check whether move constructor is working or not. - CheckMoveFunction(model1, trainData, trainLabels, 1); + CheckMoveFunction<>(model1, trainData, trainLabels, 1); } /** @@ -398,20 +414,20 @@ TEST_CASE("CheckCopyMovingVanillaNetworkTestNoBias", "[FeedForwardNetworkTest]") * +-----+ +--+--+ +-----+ */ - FFN *model = new FFN; - model->Add(8); - model->Add(); - model->Add(3); - model->Add(); + FFN > *model = new FFN >; + model->Add >(trainData.n_rows, 8); + model->Add >(); + model->Add >(8, 3); + model->Add >(); - FFN *model1 = new FFN; - model1->Add(8); - model1->Add(); - model1->Add(3); - model1->Add(); + FFN > *model1 = new FFN >; + model1->Add >(trainData.n_rows, 8); + model1->Add >(); + model1->Add >(8, 3); + model1->Add >(); // Check whether copy constructor is working or not. - CheckCopyFunction<>(model, trainData, trainLabels); + CheckCopyFunction<>(model, trainData, trainLabels, 1); // Check whether move constructor is working or not. CheckMoveFunction<>(model1, trainData, trainLabels, 1); @@ -420,38 +436,38 @@ TEST_CASE("CheckCopyMovingVanillaNetworkTestNoBias", "[FeedForwardNetworkTest]") /** * Check whether copying and moving network with Reparametrization is working or not. */ -// TEST_CASE("CheckCopyMovingReparametrizationNetworkTestNoBias", -// "[FeedForwardNetworkTest]") -// { -// // Load the dataset. -// arma::mat trainData; -// data::Load("thyroid_train.csv", trainData, true); -// -// // Normalize labels to [0, 2]. -// arma::mat trainLabels = trainData.row(trainData.n_rows - 1) - 1; -// trainData.shed_row(trainData.n_rows - 1); -// -// /* -// * Construct a feed forward network with trainData.n_rows input nodes, -// * followed by a linear layer and then a reparametrization layer. -// */ -// -// FFN *model = new FFN; -// model->Add >(trainData.n_rows, 8); -// model->Add >(4, false, true, 1); -// model->Add >(); -// -// FFN *model1 = new FFN; -// model1->Add >(trainData.n_rows, 8); -// model1->Add >(4, false, true, 1); -// model1->Add >(); -// -// // Check whether copy constructor is working or not. -// CheckCopyFunction<>(model, trainData, trainLabels, 1); -// -// // Check whether move constructor is working or not. -// CheckMoveFunction<>(model1, trainData, trainLabels, 1); -// } +TEST_CASE("CheckCopyMovingReparametrizationNetworkTestNoBias", + "[FeedForwardNetworkTest]") +{ + // Load the dataset. + arma::mat trainData; + data::Load("thyroid_train.csv", trainData, true); + + // Normalize labels to [0, 2]. + arma::mat trainLabels = trainData.row(trainData.n_rows - 1) - 1; + trainData.shed_row(trainData.n_rows - 1); + + /* + * Construct a feed forward network with trainData.n_rows input nodes, + * followed by a linear layer and then a reparametrization layer. + */ + + FFN > *model = new FFN >; + model->Add >(trainData.n_rows, 8); + model->Add >(4, false, true, 1); + model->Add >(); + + FFN > *model1 = new FFN >; + model1->Add >(trainData.n_rows, 8); + model1->Add >(4, false, true, 1); + model1->Add >(); + + // Check whether copy constructor is working or not. + CheckCopyFunction<>(model, trainData, trainLabels, 1); + + // Check whether move constructor is working or not. + CheckMoveFunction<>(model1, trainData, trainLabels, 1); +} /** * Train the vanilla network on a larger dataset. @@ -497,11 +513,11 @@ TEST_CASE("FFVanillaNetworkTest", "[FeedForwardNetworkTest]") * +-----+ +-----+ */ - FFN model; - model.Add(8); - model.Add(); - model.Add(3); - model.Add(); + FFN > model; + model.Add >(trainData.n_rows, 8); + model.Add >(); + model.Add >(8, 3); + model.Add >(); // Vanilla neural net with logistic activation function. // Because 92% of the patients are not hyperthyroid the neural @@ -518,13 +534,13 @@ TEST_CASE("FFVanillaNetworkTest", "[FeedForwardNetworkTest]") arma::mat labels = arma::zeros(1, dataset.n_cols); labels.submat(0, labels.n_cols / 2, 0, labels.n_cols - 1).fill(1); - FFN model1; - model1.Add(10); - model1.Add(); - model1.Add(2); - model1.Add(); + FFN > model1; + model1.Add >(dataset.n_rows, 10); + model1.Add >(); + model1.Add >(10, 2); + model1.Add >(); // Vanilla neural net with logistic activation function. - TestNetwork(model1, dataset, labels, dataset, labels, 10, 0.2); + TestNetwork<>(model1, dataset, labels, dataset, labels, 10, 0.2); } TEST_CASE("ForwardBackwardTest", "[FeedForwardNetworkTest]") @@ -539,13 +555,14 @@ TEST_CASE("ForwardBackwardTest", "[FeedForwardNetworkTest]") arma::mat labels = arma::zeros(1, dataset.n_cols); labels.submat(0, labels.n_cols / 2, 0, labels.n_cols - 1).fill(1); - FFN model; - model.Add(50); - model.Add(); - model.Add(10); - model.Add(); + FFN > model; + model.Add >(dataset.n_rows, 50); + model.Add >(); + model.Add >(50, 10); + model.Add >(); ens::VanillaUpdate opt; + model.ResetParameters(); #if ENS_VERSION_MAJOR == 1 opt.Initialize(model.Parameters().n_rows, model.Parameters().n_cols); #else @@ -645,12 +662,12 @@ TEST_CASE("DropoutNetworkTest", "[FeedForwardNetworkTest]") * +-----+ */ - FFN model; - model.Add(8); - model.Add(); - model.Add(); - model.Add(3); - model.Add(); + FFN > model; + model.Add >(trainData.n_rows, 8); + model.Add >(); + model.Add >(); + model.Add >(8, 3); + model.Add >(); // Vanilla neural net with logistic activation function. // Because 92% of the patients are not hyperthyroid the neural @@ -668,19 +685,19 @@ TEST_CASE("DropoutNetworkTest", "[FeedForwardNetworkTest]") arma::mat labels = arma::zeros(1, dataset.n_cols); labels.submat(0, labels.n_cols / 2, 0, labels.n_cols - 1).fill(1); - FFN model1; - model1.Add(10); - model1.Add(); - model.Add(); - model1.Add(2); - model1.Add(); + FFN > model1; + model1.Add >(dataset.n_rows, 10); + model1.Add >(); + model.Add >(); + model1.Add >(10, 2); + model1.Add >(); // Vanilla neural net with logistic activation function. - TestNetwork(model1, dataset, labels, dataset, labels, 10, 0.2); + TestNetwork<>(model1, dataset, labels, dataset, labels, 10, 0.2); } /** * Train the highway network on a larger dataset. - * + */ TEST_CASE("HighwayNetworkTest", "[FeedForwardNetworkTest]") { arma::mat dataset; @@ -693,16 +710,16 @@ TEST_CASE("HighwayNetworkTest", "[FeedForwardNetworkTest]") arma::mat labels = arma::zeros(1, dataset.n_cols); labels.submat(0, labels.n_cols / 2, 0, labels.n_cols - 1).fill(1); - FFN model; - model.Add(10); - Highway* highway = new Highway(); - highway->Add(10); - highway->Add(); + FFN > model; + model.Add >(dataset.n_rows, 10); + Highway<>* highway = new Highway<>(10, true); + highway->Add >(10, 10); + highway->Add >(); model.Add(highway); // This takes ownership of the memory. - model.Add(2); - model.Add(); - TestNetwork(model, dataset, labels, dataset, labels, 10, 0.2); -}*/ + model.Add >(10, 2); + model.Add >(); + TestNetwork<>(model, dataset, labels, dataset, labels, 10, 0.2); +} /** * Train the DropConnect network on a larger dataset. @@ -750,16 +767,16 @@ TEST_CASE("DropConnectNetworkTest", "[FeedForwardNetworkTest]") * */ - FFN model; - model.Add(8); - model.Add(); - model.Add(3); - model.Add(); + FFN > model; + model.Add >(trainData.n_rows, 8); + model.Add >(); + model.Add >(8, 3); + model.Add >(); // Vanilla neural net with logistic activation function. // Because 92% of the patients are not hyperthyroid the neural // network must be significant better than 92%. - TestNetwork(model, trainData, trainLabels, testData, testLabels, 10, 0.1); + TestNetwork<>(model, trainData, trainLabels, testData, testLabels, 10, 0.1); arma::mat dataset; dataset.load("mnist_first250_training_4s_and_9s.arm"); @@ -771,14 +788,13 @@ TEST_CASE("DropConnectNetworkTest", "[FeedForwardNetworkTest]") arma::mat labels = arma::zeros(1, dataset.n_cols); labels.submat(0, labels.n_cols / 2, 0, labels.n_cols - 1).fill(1); - FFN model1; - model1.Add(10); - model1.Add(); - model1.Add(2); - model1.Add(); - + FFN > model1; + model1.Add >(dataset.n_rows, 10); + model1.Add >(); + model1.Add >(10, 2); + model1.Add >(); // Vanilla neural net with logistic activation function. - TestNetwork(model1, dataset, labels, dataset, labels, 10, 0.2); + TestNetwork<>(model1, dataset, labels, dataset, labels, 10, 0.2); } /** @@ -787,9 +803,9 @@ TEST_CASE("DropConnectNetworkTest", "[FeedForwardNetworkTest]") */ TEST_CASE("FFNMiscTest", "[FeedForwardNetworkTest]") { - FFN model; - model.Add(3); - model.Add(); + FFN> model; + model.Add>(2, 3); + model.Add>(); auto copiedModel(model); copiedModel = model; @@ -822,19 +838,19 @@ TEST_CASE("FFSerializationTest", "[FeedForwardNetworkTest]") // Vanilla neural net with logistic activation function. // Because 92% of the patients are not hyperthyroid the neural // network must be significant better than 92%. - FFN model; - model.Add(8); - model.Add(); - model.Add(); - model.Add(3); - model.Add(); + FFN > model; + model.Add >(trainData.n_rows, 8); + model.Add >(); + model.Add >(); + model.Add >(8, 3); + model.Add >(); ens::RMSProp opt(0.01, 32, 0.88, 1e-8, trainData.n_cols /* 1 epoch */, -1); model.Train(trainData, trainLabels, opt); - FFN xmlModel, jsonModel, binaryModel; - xmlModel.Add(10); // Layer that will get removed. + FFN> xmlModel, jsonModel, binaryModel; + xmlModel.Add>(10, 10); // Layer that will get removed. // Serialize into other models. SerializeObjectAll(model, xmlModel, jsonModel, binaryModel); @@ -843,7 +859,7 @@ TEST_CASE("FFSerializationTest", "[FeedForwardNetworkTest]") model.Predict(testData, predictions); xmlModel.Predict(testData, xmlPredictions); jsonModel.Predict(testData, jsonPredictions); - binaryModel.Predict(testData, binaryPredictions); + jsonModel.Predict(testData, binaryPredictions); CheckMatrices(predictions, xmlPredictions, jsonPredictions, binaryPredictions); @@ -852,112 +868,110 @@ TEST_CASE("FFSerializationTest", "[FeedForwardNetworkTest]") /** * Test that serialization works ok for PReLU. */ -// TEST_CASE("PReLUSerializationTest", "[FeedForwardNetworkTest]") -// { -// // Load the dataset. -// arma::mat trainData; -// if (!data::Load("thyroid_train.csv", trainData)) -// FAIL("Cannot open thyroid_train.csv"); -// -// arma::mat trainLabels = trainData.row(trainData.n_rows - 1); -// trainData.shed_row(trainData.n_rows - 1); -// trainLabels -= 1; // The labels should be between 0 and numClasses - 1. -// -// arma::mat testData; -// if (!data::Load("thyroid_test.csv", testData)) -// FAIL("Cannot load dataset thyroid_test.csv"); -// -// arma::mat testLabels = testData.row(testData.n_rows - 1); -// testData.shed_row(testData.n_rows - 1); -// testLabels -= 1; // The labels should be between 0 and numClasses - 1. -// -// // Vanilla neural net with logistic activation function. -// // Because 92% of the patients are not hyperthyroid the neural -// // network must be significant better than 92%. -// FFN model; -// model.Add >(trainData.n_rows, 8); -// model.Add >(); -// model.Add >(); -// model.Add >(8, 3); -// model.Add >(); -// -// ens::RMSProp opt(0.01, 32, 0.88, 1e-8, trainData.n_cols /* 1 epoch */, -1); -// -// model.Train(trainData, trainLabels, opt); -// -// FFN xmlModel, jsonModel, binaryModel; -// xmlModel.Add>(10, 10); // Layer that will get removed. -// -// // Serialize into other models. -// SerializeObjectAll(model, xmlModel, jsonModel, binaryModel); -// -// arma::mat predictions, xmlPredictions, jsonPredictions, binaryPredictions; -// model.Predict(testData, predictions); -// xmlModel.Predict(testData, xmlPredictions); -// jsonModel.Predict(testData, jsonPredictions); -// jsonModel.Predict(testData, binaryPredictions); -// -// CheckMatrices(predictions, xmlPredictions, jsonPredictions, -// binaryPredictions); -// } +TEST_CASE("PReLUSerializationTest", "[FeedForwardNetworkTest]") +{ + // Load the dataset. + arma::mat trainData; + if (!data::Load("thyroid_train.csv", trainData)) + FAIL("Cannot open thyroid_train.csv"); + + arma::mat trainLabels = trainData.row(trainData.n_rows - 1); + trainData.shed_row(trainData.n_rows - 1); + trainLabels -= 1; // The labels should be between 0 and numClasses - 1. + + arma::mat testData; + if (!data::Load("thyroid_test.csv", testData)) + FAIL("Cannot load dataset thyroid_test.csv"); + + arma::mat testLabels = testData.row(testData.n_rows - 1); + testData.shed_row(testData.n_rows - 1); + testLabels -= 1; // The labels should be between 0 and numClasses - 1. + + // Vanilla neural net with logistic activation function. + // Because 92% of the patients are not hyperthyroid the neural + // network must be significant better than 92%. + FFN > model; + model.Add >(trainData.n_rows, 8); + model.Add >(); + model.Add >(); + model.Add >(8, 3); + model.Add >(); + + ens::RMSProp opt(0.01, 32, 0.88, 1e-8, trainData.n_cols /* 1 epoch */, -1); + + model.Train(trainData, trainLabels, opt); + + FFN> xmlModel, jsonModel, binaryModel; + xmlModel.Add>(10, 10); // Layer that will get removed. + + // Serialize into other models. + SerializeObjectAll(model, xmlModel, jsonModel, binaryModel); + + arma::mat predictions, xmlPredictions, jsonPredictions, binaryPredictions; + model.Predict(testData, predictions); + xmlModel.Predict(testData, xmlPredictions); + jsonModel.Predict(testData, jsonPredictions); + jsonModel.Predict(testData, binaryPredictions); + + CheckMatrices(predictions, xmlPredictions, jsonPredictions, + binaryPredictions); +} /** * Test if the custom layers work. The target is to see if the code compiles * when the Train and Prediction are called. */ -// TEST_CASE("CustomLayerTest", "[FeedForwardNetworkTest]") -// { -// // Load the dataset. -// arma::mat trainData; -// if (!data::Load("thyroid_train.csv", trainData)) -// FAIL("Cannot open thyroid_train.csv"); -// -// arma::mat trainLabels = trainData.row(trainData.n_rows - 1); -// trainData.shed_row(trainData.n_rows - 1); -// trainLabels -= 1; // The labels should be between 0 and numClasses - 1. -// -// arma::mat testData; -// if (!data::Load("thyroid_test.csv", testData)) -// FAIL("Cannot load dataset thyroid_test.csv"); -// -// arma::mat testLabels = testData.row(testData.n_rows - 1); -// testData.shed_row(testData.n_rows - 1); -// testLabels -= 1; // The labels should be between 0 and numClasses - 1. -// -// FFN > model; -// model.Add >(trainData.n_rows, 8); -// model.Add >(); -// model.Add >(8, 3); -// model.Add >(); -// -// ens::RMSProp opt(0.01, 32, 0.88, 1e-8, 15, -1); -// model.Train(trainData, trainLabels, opt); -// -// arma::mat predictionTemp; -// model.Predict(testData, predictionTemp); -// arma::mat prediction = arma::zeros(1, predictionTemp.n_cols); -// } +TEST_CASE("CustomLayerTest", "[FeedForwardNetworkTest]") +{ + // Load the dataset. + arma::mat trainData; + if (!data::Load("thyroid_train.csv", trainData)) + FAIL("Cannot open thyroid_train.csv"); + + arma::mat trainLabels = trainData.row(trainData.n_rows - 1); + trainData.shed_row(trainData.n_rows - 1); + trainLabels -= 1; // The labels should be between 0 and numClasses - 1. + + arma::mat testData; + if (!data::Load("thyroid_test.csv", testData)) + FAIL("Cannot load dataset thyroid_test.csv"); + + arma::mat testLabels = testData.row(testData.n_rows - 1); + testData.shed_row(testData.n_rows - 1); + testLabels -= 1; // The labels should be between 0 and numClasses - 1. + + FFN, RandomInitialization, CustomLayer<> > model; + model.Add >(trainData.n_rows, 8); + model.Add >(); + model.Add >(8, 3); + model.Add >(); + + ens::RMSProp opt(0.01, 32, 0.88, 1e-8, 15, -1); + model.Train(trainData, trainLabels, opt); + + arma::mat predictionTemp; + model.Predict(testData, predictionTemp); + arma::mat prediction = arma::zeros(1, predictionTemp.n_cols); +} /** * Test the overload of Forward function which allows partial forward pass. */ TEST_CASE("PartialForwardTest", "[FeedForwardNetworkTest]") { - FFN model; - model.Add(10); + FFN, RandomInitialization> model; + model.Add >(5, 10); - // Add a new Add<> module which adds a (learnable) constant term to the input. - Add* addModule = new Add(); + // Add a new Add<> module which adds a constant term to the input. + Add<>* addModule = new Add<>(10); model.Add(addModule); - LinearNoBias* linearNoBiasModule = new LinearNoBias(10); + LinearNoBias<>* linearNoBiasModule = new LinearNoBias<>(10, 10); model.Add(linearNoBiasModule); - model.Add(10); - - // Set up the network for inputs of dimensionality 10. - model.Reset(10); + model.Add >(10, 10); + model.ResetParameters(); // Set the parameters of the Add<> module to a matrix of ones. addModule->Parameters() = arma::ones(10, 1); // Set the parameters of the LinearNoBias<> module to a matrix of ones. @@ -1012,12 +1026,12 @@ TEST_CASE("FFNTrainReturnObjective", "[FeedForwardNetworkTest]") // Vanilla neural net with logistic activation function. // Because 92% of the patients are not hyperthyroid the neural // network must be significantly better than 92%. - FFN model; - model.Add(8); - model.Add(); - model.Add(); - model.Add(3); - model.Add(); + FFN > model; + model.Add >(trainData.n_rows, 8); + model.Add >(); + model.Add >(); + model.Add >(8, 3); + model.Add >(); ens::RMSProp opt(0.01, 32, 0.88, 1e-8, trainData.n_cols /* 1 epoch */, -1); @@ -1032,14 +1046,14 @@ TEST_CASE("FFNTrainReturnObjective", "[FeedForwardNetworkTest]") TEST_CASE("FFNReturnModel", "[FeedForwardNetworkTest]") { // Create dummy network. - FFN model; - Linear* linearA = new Linear(3); + FFN > model; + Linear<>* linearA = new Linear<>(3, 3); model.Add(linearA); - Linear* linearB = new Linear(4); + Linear<>* linearB = new Linear<>(3, 4); model.Add(linearB); - // Initialize network parameters, with a new input size of 3. - model.Reset(3); + // Initialize network parameter. + model.ResetParameters(); // Set all network parameter to one. model.Parameters().ones(); @@ -1049,8 +1063,9 @@ TEST_CASE("FFNReturnModel", "[FeedForwardNetworkTest]") // Get the layer parameter from layer A and layer B and store them in // parameterA and parameterB. - const arma::mat parameterA = model.Network()[0]->Parameters(); - const arma::mat parameterB = model.Network()[1]->Parameters(); + arma::mat parameterA, parameterB; + boost::apply_visitor(ParametersVisitor(parameterA), model.Model()[0]); + boost::apply_visitor(ParametersVisitor(parameterB), model.Model()[1]); CheckMatrices(parameterA, arma::ones(3 * 3 + 3, 1)); CheckMatrices(parameterB, arma::zeros(3 * 4 + 4, 1)); @@ -1082,10 +1097,11 @@ TEST_CASE("OptimizerTest", "[FeedForwardNetworkTest]") testData.shed_row(testData.n_rows - 1); testLabels -= 1; // The labels should be between 0 and numClasses. - FFN model; - model.Add(8); - model.Add(3); - model.Add(); + FFN, RandomInitialization, CustomLayer<> > model; + model.Add >(trainData.n_rows, 8); + model.Add >(); + model.Add >(8, 3); + model.Add >(); ens::DE opt(200, 1000, 0.6, 0.8, 1e-5); model.Train(trainData, trainLabels, opt); @@ -1109,18 +1125,23 @@ TEST_CASE("FFNCheckInputShapeTest", "[FeedForwardNetworkTest]") arma::mat testData; data::Load("thyroid_test.csv", testData, true); - arma::mat testLabels = testData.row(testData.n_rows - 1); + arma::mat testLabels = testData.row(testData.n_rows - 1) - 1; testData.shed_row(testData.n_rows - 1); - FFN model; - model.Add(8); - model.Add(3); - model.Add(); + FFN, RandomInitialization, CustomLayer<> > model; + // Purposely putting wrong input shape so that error is thrown. + model.Add >(trainData.n_rows - 3, 8); + model.Add >(); + model.Add >(8, 3); + model.Add >(); + + std::string expectedMsg = "FFN<>::Train(): "; + expectedMsg += "the first layer of the network expects "; + expectedMsg += std::to_string(trainData.n_rows - 3) + " elements, "; + expectedMsg += "but the input has " + std::to_string(trainData.n_rows) + + " dimensions! "; ens::DE opt(200, 1000, 0.6, 0.8, 1e-5); - // Now set up the input incorrectly. - model.InputDimensions() = std::vector({ 1, 2, 3 }); - REQUIRE_THROWS_AS(model.Train(trainData, trainLabels, opt), std::logic_error); } diff --git a/src/mlpack/tests/not_adapted/gan_test.cpp b/src/mlpack/tests/gan_test.cpp similarity index 100% rename from src/mlpack/tests/not_adapted/gan_test.cpp rename to src/mlpack/tests/gan_test.cpp diff --git a/src/mlpack/tests/init_rules_test.cpp b/src/mlpack/tests/init_rules_test.cpp index 5d56e904b8..46c76e32f1 100644 --- a/src/mlpack/tests/init_rules_test.cpp +++ b/src/mlpack/tests/init_rules_test.cpp @@ -214,17 +214,18 @@ TEST_CASE("NetworkInitTest", "[InitRulesTest]") { arma::mat input = arma::ones(5, 1); arma::mat response; - NegativeLogLikelihood outputLayer; + NegativeLogLikelihood<> outputLayer; // Create a simple network and use the RandomInitialization rule to // initialize the network parameters. RandomInitialization randomInit(0.5, 0.5); - FFN randomModel( + FFN, RandomInitialization> randomModel( std::move(outputLayer), randomInit); - randomModel.Add(5); - randomModel.Add(2); - randomModel.Add(); + randomModel.Add >(); + randomModel.Add >(5, 5); + randomModel.Add >(5, 2); + randomModel.Add >(); randomModel.Predict(input, response); bool b = arma::all(arma::vectorise(randomModel.Parameters()) == 0.5); @@ -233,21 +234,23 @@ TEST_CASE("NetworkInitTest", "[InitRulesTest]") // Create a simple network and use the OrthogonalInitialization rule to // initialize the network parameters. - FFN orthogonalModel; - orthogonalModel.Add(5); - orthogonalModel.Add(2); - orthogonalModel.Add(); + FFN, OrthogonalInitialization> orthogonalModel; + orthogonalModel.Add >(); + orthogonalModel.Add >(5, 5); + orthogonalModel.Add >(5, 2); + orthogonalModel.Add >(); orthogonalModel.Predict(input, response); REQUIRE(orthogonalModel.Parameters().n_elem == 42); // Create a simple network and use the ZeroInitialization rule to // initialize the network parameters. - FFN - zeroModel(NegativeLogLikelihood(), ConstInitialization(0)); - zeroModel.Add(5); - zeroModel.Add(2); - zeroModel.Add(); + FFN, ConstInitialization> + zeroModel(NegativeLogLikelihood<>(), ConstInitialization(0)); + zeroModel.Add >(); + zeroModel.Add >(5, 5); + zeroModel.Add >(5, 2); + zeroModel.Add >(); zeroModel.Predict(input, response); REQUIRE(arma::accu(zeroModel.Parameters()) == 0); @@ -258,31 +261,34 @@ TEST_CASE("NetworkInitTest", "[InitRulesTest]") // parameters. KathirvalavakumarSubavathiInitialization kathirvalavakumarSubavathiInit( input, 1.5); - FFN + FFN, KathirvalavakumarSubavathiInitialization> ksModel(std::move(outputLayer), kathirvalavakumarSubavathiInit); - ksModel.Add(5); - ksModel.Add(2); - ksModel.Add(); + ksModel.Add >(); + ksModel.Add >(5, 5); + ksModel.Add >(5, 2); + ksModel.Add >(); ksModel.Predict(input, response); REQUIRE(ksModel.Parameters().n_elem == 42); // Create a simple network and use the OivsInitialization rule to // initialize the network parameters. - FFN > oivsModel; - oivsModel.Add(5); - oivsModel.Add(2); - oivsModel.Add(); + FFN, OivsInitialization<> > oivsModel; + oivsModel.Add >(); + oivsModel.Add >(5, 5); + oivsModel.Add >(5, 2); + oivsModel.Add >(); oivsModel.Predict(input, response); REQUIRE(oivsModel.Parameters().n_elem == 42); // Create a simple network and use the GaussianInitialization rule to // initialize the network parameters. - FFN gaussianModel; - gaussianModel.Add(5); - gaussianModel.Add(2); - gaussianModel.Add(); + FFN, GaussianInitialization> gaussianModel; + gaussianModel.Add >(); + gaussianModel.Add >(5, 5); + gaussianModel.Add >(5, 2); + gaussianModel.Add >(); gaussianModel.Predict(input, response); REQUIRE(gaussianModel.Parameters().n_elem == 42); diff --git a/src/mlpack/tests/ksinit_test.cpp b/src/mlpack/tests/ksinit_test.cpp index 15a9dec67c..930df617e4 100644 --- a/src/mlpack/tests/ksinit_test.cpp +++ b/src/mlpack/tests/ksinit_test.cpp @@ -15,7 +15,7 @@ #include #include -#include +#include #include #include #include @@ -75,12 +75,12 @@ void BuildVanillaNetwork(MatType& trainData, // Cauchy’s Inequality Based on Sensitivity Analysis" paper. KathirvalavakumarSubavathiInitialization init(trainData, 4.59); - FFN - model(MeanSquaredError(), init); + FFN, KathirvalavakumarSubavathiInitialization> + model(MeanSquaredError<>(), init); - model.Add(hiddenLayerSize); - model.Add(); - model.Add(outputSize); + model.Add >(trainData.n_rows, hiddenLayerSize); + model.Add >(); + model.Add >(hiddenLayerSize, outputSize); ens::RMSProp opt(0.01, 1, 0.88, 1e-8, maxEpochs * trainData.n_cols, 1e-18); diff --git a/src/mlpack/tests/layer_names_test.cpp b/src/mlpack/tests/layer_names_test.cpp new file mode 100644 index 0000000000..9d94f0ff67 --- /dev/null +++ b/src/mlpack/tests/layer_names_test.cpp @@ -0,0 +1,160 @@ +/** + * @file tests/layer_names_test.cpp + * @author Sreenik Seal + * + * Tests for testing the string representation of + * layers in mlpack's ANN module. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#include +#include +#include +#include + +#include "catch.hpp" + +using namespace mlpack; +using namespace ann; + +/** + * Test if the LayerNameVisitor works properly. + */ +TEST_CASE("LayerNameVisitorTest", "[LayerNamesTest]") +{ + LayerTypes<> atrousConvolution = new AtrousConvolution<>(); + LayerTypes<> alphaDropout = new AlphaDropout<>(); + LayerTypes<> batchNorm = new BatchNorm<>(); + LayerTypes<> constant = new Constant<>(); + LayerTypes<> convolution = new Convolution<>(); + LayerTypes<> dropConnect = new DropConnect<>(); + LayerTypes<> dropout = new Dropout<>(); + LayerTypes<> flexibleReLU = new FlexibleReLU<>(); + LayerTypes<> layerNorm = new LayerNorm<>(); + LayerTypes<> linear = new Linear<>(); + LayerTypes<> linearNoBias = new LinearNoBias<>(); + LayerTypes<> maxPooling = new MaxPooling<>(); + LayerTypes<> meanPooling = new MeanPooling<>(); + LayerTypes<> multiplyConstant = new MultiplyConstant<>(); + LayerTypes<> reLULayer = new ReLULayer<>(); + LayerTypes<> transposedConvolution = new TransposedConvolution<>(); + LayerTypes<> identityLayer = new IdentityLayer<>(); + LayerTypes<> tanHLayer = new TanHLayer<>(); + LayerTypes<> eLU = new ELU<>(); + LayerTypes<> hardTanH = new HardTanH<>(); + LayerTypes<> leakyReLU = new LeakyReLU<>(); + LayerTypes<> pReLU = new PReLU<>(); + LayerTypes<> sigmoidLayer = new SigmoidLayer<>(); + LayerTypes<> logSoftMax = new LogSoftMax<>(); + LayerTypes<> lstmLayer = new LSTM<>(100, 10); + LayerTypes<> creluLayer = new CReLU<>(); + LayerTypes<> highwayLayer = new Highway<>(); + LayerTypes<> gruLayer = new GRU<>(); + LayerTypes<> glimpseLayer = new Glimpse<>(); + LayerTypes<> fastlstmLayer = new FastLSTM<>(); + LayerTypes<> weightnormLayer = new WeightNorm<>(new IdentityLayer<>()); + + // Bilinear interpolation is not yet supported by the string converter. + LayerTypes<> unsupportedLayer = new BilinearInterpolation<>(); + + REQUIRE(boost::apply_visitor(LayerNameVisitor(), + atrousConvolution) == "atrousconvolution"); + REQUIRE(boost::apply_visitor(LayerNameVisitor(), + alphaDropout) == "alphadropout"); + REQUIRE(boost::apply_visitor(LayerNameVisitor(), + batchNorm) == "batchnorm"); + REQUIRE(boost::apply_visitor(LayerNameVisitor(), + constant) == "constant"); + REQUIRE(boost::apply_visitor(LayerNameVisitor(), + convolution) == "convolution"); + REQUIRE(boost::apply_visitor(LayerNameVisitor(), + dropConnect) == "dropconnect"); + REQUIRE(boost::apply_visitor(LayerNameVisitor(), + dropout) == "dropout"); + REQUIRE(boost::apply_visitor(LayerNameVisitor(), + flexibleReLU) == "flexiblerelu"); + REQUIRE(boost::apply_visitor(LayerNameVisitor(), + layerNorm) == "layernorm"); + REQUIRE(boost::apply_visitor(LayerNameVisitor(), + linear) == "linear"); + REQUIRE(boost::apply_visitor(LayerNameVisitor(), + linearNoBias) == "linearnobias"); + REQUIRE(boost::apply_visitor(LayerNameVisitor(), + maxPooling) == "maxpooling"); + REQUIRE(boost::apply_visitor(LayerNameVisitor(), + meanPooling) == "meanpooling"); + REQUIRE(boost::apply_visitor(LayerNameVisitor(), + multiplyConstant) == "multiplyconstant"); + REQUIRE(boost::apply_visitor(LayerNameVisitor(), + reLULayer) == "relu"); + REQUIRE(boost::apply_visitor(LayerNameVisitor(), + transposedConvolution) == "transposedconvolution"); + REQUIRE(boost::apply_visitor(LayerNameVisitor(), + identityLayer) == "identity"); + REQUIRE(boost::apply_visitor(LayerNameVisitor(), + tanHLayer) == "tanh"); + REQUIRE(boost::apply_visitor(LayerNameVisitor(), + eLU) == "elu"); + REQUIRE(boost::apply_visitor(LayerNameVisitor(), + hardTanH) == "hardtanh"); + REQUIRE(boost::apply_visitor(LayerNameVisitor(), + leakyReLU) == "leakyrelu"); + REQUIRE(boost::apply_visitor(LayerNameVisitor(), + pReLU) == "prelu"); + REQUIRE(boost::apply_visitor(LayerNameVisitor(), + sigmoidLayer) == "sigmoid"); + REQUIRE(boost::apply_visitor(LayerNameVisitor(), + logSoftMax) == "logsoftmax"); + REQUIRE(boost::apply_visitor(LayerNameVisitor(), + unsupportedLayer) == "unsupported"); + REQUIRE(boost::apply_visitor(LayerNameVisitor(), + lstmLayer) == "lstm"); + REQUIRE(boost::apply_visitor(LayerNameVisitor(), + creluLayer) == "crelu"); + REQUIRE(boost::apply_visitor(LayerNameVisitor(), + highwayLayer) == "highway"); + REQUIRE(boost::apply_visitor(LayerNameVisitor(), + gruLayer) == "gru"); + REQUIRE(boost::apply_visitor(LayerNameVisitor(), + glimpseLayer) == "glimpse"); + REQUIRE(boost::apply_visitor(LayerNameVisitor(), + fastlstmLayer) == "fastlstm"); + REQUIRE(boost::apply_visitor(LayerNameVisitor(), + weightnormLayer) == "weightnorm"); + // Delete all instances. + boost::apply_visitor(DeleteVisitor(), atrousConvolution); + boost::apply_visitor(DeleteVisitor(), alphaDropout); + boost::apply_visitor(DeleteVisitor(), batchNorm); + boost::apply_visitor(DeleteVisitor(), constant); + boost::apply_visitor(DeleteVisitor(), convolution); + boost::apply_visitor(DeleteVisitor(), dropConnect); + boost::apply_visitor(DeleteVisitor(), dropout); + boost::apply_visitor(DeleteVisitor(), flexibleReLU); + boost::apply_visitor(DeleteVisitor(), layerNorm); + boost::apply_visitor(DeleteVisitor(), linear); + boost::apply_visitor(DeleteVisitor(), linearNoBias); + boost::apply_visitor(DeleteVisitor(), maxPooling); + boost::apply_visitor(DeleteVisitor(), meanPooling); + boost::apply_visitor(DeleteVisitor(), multiplyConstant); + boost::apply_visitor(DeleteVisitor(), reLULayer); + boost::apply_visitor(DeleteVisitor(), transposedConvolution); + boost::apply_visitor(DeleteVisitor(), identityLayer); + boost::apply_visitor(DeleteVisitor(), tanHLayer); + boost::apply_visitor(DeleteVisitor(), eLU); + boost::apply_visitor(DeleteVisitor(), hardTanH); + boost::apply_visitor(DeleteVisitor(), leakyReLU); + boost::apply_visitor(DeleteVisitor(), pReLU); + boost::apply_visitor(DeleteVisitor(), sigmoidLayer); + boost::apply_visitor(DeleteVisitor(), logSoftMax); + boost::apply_visitor(DeleteVisitor(), unsupportedLayer); + boost::apply_visitor(DeleteVisitor(), lstmLayer); + boost::apply_visitor(DeleteVisitor(), creluLayer); + boost::apply_visitor(DeleteVisitor(), highwayLayer); + boost::apply_visitor(DeleteVisitor(), gruLayer); + boost::apply_visitor(DeleteVisitor(), glimpseLayer); + boost::apply_visitor(DeleteVisitor(), fastlstmLayer); + boost::apply_visitor(DeleteVisitor(), weightnormLayer); +} diff --git a/src/mlpack/tests/loss_functions_test.cpp b/src/mlpack/tests/loss_functions_test.cpp index f7699b4fc9..2c800b1e21 100644 --- a/src/mlpack/tests/loss_functions_test.cpp +++ b/src/mlpack/tests/loss_functions_test.cpp @@ -14,7 +14,7 @@ */ #include -#include +#include #include #include #include @@ -34,7 +34,6 @@ #include #include #include -#include #include #include #include @@ -55,7 +54,7 @@ TEST_CASE("HuberLossTest", "[LossFunctionsTest]") arma::mat input, target, output; arma::mat expectedOutput; double loss; - HuberLoss module; + HuberLoss<> module; // Test for sum reduction. input = arma::mat("-0.0494 -1.1958 -1.0486 -0.2121 1.6028 0.0737 -0.7091 " @@ -109,10 +108,10 @@ TEST_CASE("PoissonNLLLossTest", "[LossFunctionsTest]") arma::mat input, target, input4, target4; arma::mat output1, output2, output3, output4; arma::mat expOutput1, expOutput2, expOutput3, expOutput4; - PoissonNLLLoss module1(true, false, 1e-8, false); - PoissonNLLLoss module2(true, true, 1e-08, true); - PoissonNLLLoss module3(true, true, 1e-08, false); - PoissonNLLLoss module4(false, true, 1e-08, false); + PoissonNLLLoss<> module1(true, false, 1e-8, false); + PoissonNLLLoss<> module2(true, true, 1e-08, true); + PoissonNLLLoss<> module3(true, true, 1e-08, false); + PoissonNLLLoss<> module4(false, true, 1e-08, false); // Test the Forward function on a user generated input. input = arma::mat("1.0 1.0 1.9 1.6 -1.9 3.7 -1.0 0.5"); @@ -176,7 +175,7 @@ TEST_CASE("SimpleKLDivergenceTest", "[LossFunctionsTest]") arma::mat input, target, output; arma::mat expectedOutput; double loss; - KLDivergence module; + KLDivergence<> module; // Test for sum reduction. input = arma::mat("-0.7007 -2.0247 -0.7132 -0.4584 -0.2637 -1.1795 -0.1093 " @@ -225,9 +224,9 @@ TEST_CASE("SimpleKLDivergenceTest", "[LossFunctionsTest]") */ TEST_CASE("SimpleMeanSquaredLogarithmicErrorTest", "[LossFunctionsTest]") { - arma::mat input, target, output, expectedOutput; + arma::mat input, target, output, expectedOutput; double loss; - MeanSquaredLogarithmicError module; + MeanSquaredLogarithmicError<> module; // Test for sum reduction. input = arma::mat("-0.0494 1.1958 1.0486 -0.2121 1.6028 0.0737 -0.7091 " @@ -246,22 +245,28 @@ TEST_CASE("SimpleMeanSquaredLogarithmicErrorTest", "[LossFunctionsTest]") // Test the Backward function. module.Backward(input, target, output); - REQUIRE(arma::as_scalar(arma::accu(output)) == - Approx(-10.5619).epsilon(1e-3)); + REQUIRE(arma::as_scalar(arma::accu(output)) == Approx(-10.5619).epsilon(1e-3)); REQUIRE(output.n_rows == input.n_rows); REQUIRE(output.n_cols == input.n_cols); CheckMatrices(output, expectedOutput, 0.1); - // Test the error function on a single input. - input = arma::mat("2"); - target = arma::mat("3"); - loss = module.Forward(input, target); - REQUIRE(loss == Approx(0.082760974810151655).epsilon(1e-3)); + // Test for mean reduction by modifying reduction parameter using accessor. + module.Reduction() = false; + expectedOutput = arma::mat("-0.0718 0.0585 0.1067 -0.2119 0.0348 -0.0907 " + "-0.9612 0.0482 0.0120 0.2070 0.0148 -0.0266"); + expectedOutput.reshape(4, 3); - // Test the Backward function on a single input. + // Test the Forward function. Loss should be 1.10606. + loss = module.Forward(input, target); + REQUIRE(loss == Approx(1.10606).epsilon(1e-3)); + + // Test the Backward function. module.Backward(input, target, output); - REQUIRE(arma::accu(output) == Approx(-0.1917880483011872).epsilon(1e-3)); - REQUIRE(output.n_elem == 1); + REQUIRE(arma::as_scalar(arma::accu(output)) == + Approx(-0.880156).epsilon(1e-3)); + REQUIRE(output.n_rows == input.n_rows); + REQUIRE(output.n_cols == input.n_cols); + CheckMatrices(output, expectedOutput, 0.1); } /* @@ -270,7 +275,7 @@ TEST_CASE("SimpleMeanSquaredLogarithmicErrorTest", "[LossFunctionsTest]") TEST_CASE("SimpleMeanSquaredErrorTest", "[LossFunctionsTest]") { arma::mat input, output, target; - MeanSquaredError module(false); + MeanSquaredError<> module(false); // Test the Forward function on a user generated input and compare it against // the manually calculated result. @@ -320,8 +325,8 @@ TEST_CASE("SimpleMeanSquaredErrorTest", "[LossFunctionsTest]") TEST_CASE("SimpleBinaryCrossEntropyLossTest", "[LossFunctionsTest]") { arma::mat input1, input2, input3, output, target1, target2, target3; - BCELoss module1(1e-6, true); - BCELoss module2(1e-6, false); + BCELoss<> module1(1e-6, true); + BCELoss<> module2(1e-6, false); // Test the Forward function on a user generator input and compare it against // the manually calculated result. input1 = arma::mat("0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5"); @@ -373,7 +378,7 @@ TEST_CASE("SimpleSigmoidCrossEntropyErrorTest", "[LossFunctionsTest]") { arma::mat input1, input2, input3, output, target1, target2, target3, expectedOutput; - SigmoidCrossEntropyError module; + SigmoidCrossEntropyError<> module; // Test the Forward function on a user generator input and compare it against // the calculated result. @@ -434,7 +439,7 @@ TEST_CASE("SimpleEarthMoverDistanceLayerTest", "[LossFunctionsTest]") arma::mat input1, input2, output, target1, target2, expectedOutput; arma::mat input3, target3; double loss; - EarthMoverDistance module; + EarthMoverDistance<> module; // Test the Forward function on a user generator input and compare it against // the manually calculated result. @@ -504,10 +509,12 @@ TEST_CASE("GradientMeanSquaredErrorTest", "[LossFunctionsTest]") input = arma::randu(10, 1); target = arma::randu(2, 1); - model = new FFN(); - model->ResetData(input, target); - model->Add(2); - model->Add(); + model = new FFN, NguyenWidrowInitialization>(); + model->Predictors() = input; + model->Responses() = target; + model->Add >(); + model->Add >(10, 2); + model->Add >(); } ~GradientFunction() @@ -525,7 +532,7 @@ TEST_CASE("GradientMeanSquaredErrorTest", "[LossFunctionsTest]") arma::mat& Parameters() { return model->Parameters(); } - FFN* model; + FFN, NguyenWidrowInitialization>* model; arma::mat input, target; } function; @@ -545,10 +552,12 @@ TEST_CASE("GradientReconstructionLossTest", "[LossFunctionsTest]") input = arma::randu(10, 1); target = arma::randu(2, 1); - model = new FFN(); - model->ResetData(input, target); - model->Add(2); - model->Add(); + model = new FFN, NguyenWidrowInitialization>(); + model->Predictors() = input; + model->Responses() = target; + model->Add >(); + model->Add >(10, 2); + model->Add >(); } ~GradientFunction() @@ -566,7 +575,7 @@ TEST_CASE("GradientReconstructionLossTest", "[LossFunctionsTest]") arma::mat& Parameters() { return model->Parameters(); } - FFN* model; + FFN, NguyenWidrowInitialization>* model; arma::mat input, target; } function; @@ -580,7 +589,7 @@ TEST_CASE("DiceLossTest", "[LossFunctionsTest]") { arma::mat input1, input2, target, output; double loss; - DiceLoss module; + DiceLoss<> module; // Test the Forward function. Loss should be 0 if input = target. input1 = arma::ones(10, 1); @@ -621,7 +630,7 @@ TEST_CASE("SimpleMeanBiasErrorTest", "[LossFunctionsTest]") { arma::mat input, target, output; double loss; - MeanBiasError module; + MeanBiasError<> module; // Test for sum reduction. input = arma::mat("-0.0494 -1.1958 -1.0486 -0.2121 1.6028 0.0737 -0.7091 " @@ -671,7 +680,7 @@ TEST_CASE("LogCoshLossTest", "[LossFunctionsTest]") { arma::mat input, target, output; double loss; - LogCoshLoss module(2); + LogCoshLoss<> module(2); // Test the Forward function. Loss should be 0 if input = target. input = arma::ones(10, 1); @@ -724,7 +733,7 @@ TEST_CASE("HingeEmbeddingLossTest", "[LossFunctionsTest]") { arma::mat input, target, output, expectedOutput; double loss; - HingeEmbeddingLoss module; + HingeEmbeddingLoss<> module; // Test for sum reduction input = arma::mat("0.1778 0.0957 0.1397 0.2256 0.1203 0.2403 0.1925 0.3144 " @@ -776,7 +785,7 @@ TEST_CASE("SimpleL1LossTest", "[LossFunctionsTest]") { arma::mat input, output, target; double loss; - L1Loss module(true); + L1Loss<> module(true); // Test the Forward function on a user generator input and compare it against // the manually calculated result. @@ -802,7 +811,7 @@ TEST_CASE("CosineEmbeddingLossTest", "[LossFunctionsTest]") { arma::mat input1, input2, y, output; double loss; - CosineEmbeddingLoss module; + CosineEmbeddingLoss<> module; // Test the Forward function. Loss should be 0 if input1 = input2 and y = 1. input1 = arma::mat(1, 10); @@ -842,6 +851,44 @@ TEST_CASE("CosineEmbeddingLossTest", "[LossFunctionsTest]") // Test the Backward function. module.Backward(input1, input2, output); REQUIRE(arma::accu(output) == Approx(0.06324556).epsilon(1e-3)); + + // Check for correctness for cube. + CosineEmbeddingLoss<> module2(0.5, true); + + arma::cube input3(3, 2, 2); + arma::cube input4(3, 2, 2); + input3.fill(1); + input4.fill(1); + input3(0) = 2; + input3(1) = 2; + input3(4) = 2; + input3(6) = 2; + input3(8) = 2; + input3(10) = 2; + input4(2) = 2; + input4(9) = 2; + input4(11) = 2; + loss = module2.Forward(input3, input4); + // Calculated using torch.nn.CosineEmbeddingLoss(). + REQUIRE(loss == Approx(0.55395).epsilon(1e-3)); + + // Test the Backward function. + module2.Backward(input3, input4, output); + REQUIRE(arma::accu(output) == Approx(-0.36649111).epsilon(1e-3)); + + // Check Output for mean type of reduction. + CosineEmbeddingLoss<> module3(0.0, true, false); + loss = module3.Forward(input3, input4); + REQUIRE(loss == Approx(0.092325).epsilon(1e-3)); + + // Check correctness for cube. + module3.Similarity() = false; + loss = module3.Forward(input3, input4); + REQUIRE(loss == Approx(0.90767498236).epsilon(1e-3)); + + // Test the Backward function. + module3.Backward(input3, input4, output); + REQUIRE(arma::accu(output) == Approx(0.0236749374).epsilon(1e-4)); } /* @@ -852,7 +899,7 @@ TEST_CASE("MarginRankingLossTest", "[LossFunctionsTest]") arma::mat input, input1, input2, target, output, expectedOutput; double loss; // Test sum reduction - MarginRankingLoss module; + MarginRankingLoss<> module; input1 = arma::mat("0.4287 -1.6208 -1.5006 -0.4473 1.5208 -4.5184 9.3574 " "-4.8090 4.3455 5.2070"); input2 = arma::mat("-4.5288 -9.2766 -0.5882 -5.6643 -6.0175 8.8506 3.4759 " @@ -878,7 +925,7 @@ TEST_CASE("MarginRankingLossTest", "[LossFunctionsTest]") // Test for mean reduction by modifying reduction parameter using accessor. module.Reduction() = false; - + // Test the forward function // loss should be 3.0353 // value calculated using torch.nn.MarginRankingLoss(margin=1.0,reduction='mean') @@ -903,8 +950,8 @@ TEST_CASE("SoftMarginLossTest", "[LossFunctionsTest]") { arma::mat input, target, output, expectedOutput; double loss; - SoftMarginLoss module1; - SoftMarginLoss module2(false); + SoftMarginLoss<> module1; + SoftMarginLoss<> module2(false); input = arma::mat("0.1778 0.0957 0.1397 0.1203 0.2403 0.1925 -0.2264 -0.3400 " "-0.3336"); @@ -958,7 +1005,7 @@ TEST_CASE("SoftMarginLossTest", "[LossFunctionsTest]") TEST_CASE("MeanAbsolutePercentageErrorTest", "[LossFunctionsTest]") { arma::mat input, target, output, expectedOutput; - MeanAbsolutePercentageError module; + MeanAbsolutePercentageError<> module; input = arma::mat("3 -0.5 2 7"); target = arma::mat("2.5 0.2 2 8"); @@ -978,20 +1025,6 @@ TEST_CASE("MeanAbsolutePercentageErrorTest", "[LossFunctionsTest]") CheckMatrices(output, expectedOutput, 0.1); } -/** - * Test that the function that can access the parameters of the - * VR Class Reward layer works. - */ -TEST_CASE("VRClassRewardLayerParametersTest", "[LossFunctionsTest]") -{ - // Parameter order : scale, sizeAverage. - VRClassReward layer(2, false); - - // Make sure we can get the parameters successfully. - REQUIRE(layer.Scale() == 2); - REQUIRE(layer.SizeAverage() == false); -} - /* * Simple test for the Triplet Margin Loss function. */ @@ -999,7 +1032,7 @@ TEST_CASE("TripletMarginLossTest") { arma::mat anchor, positive, negative; arma::mat input, target, output; - TripletMarginLoss module; + TripletMarginLoss<> module; // Test the Forward function on a user generated input and compare it against // the manually calculated result. @@ -1047,8 +1080,8 @@ TEST_CASE("HingeLossTest", "[LossFunctionsTest]") { arma::mat input, target, target_b, output; double loss, loss_b; - HingeLoss module1; - HingeLoss module2(false); + HingeLoss<> module1; + HingeLoss<> module2(false); // Test the Forward function. Loss should be 0 if input = target. input = arma::ones(10, 1); @@ -1124,8 +1157,8 @@ TEST_CASE("MultiLabelSoftMarginLossTest", "[LossFunctionsTest]") { arma::mat input, target, output, expectedOutput; double loss; - MultiLabelSoftMarginLoss module1; - MultiLabelSoftMarginLoss module2(false); + MultiLabelSoftMarginLoss<> module1; + MultiLabelSoftMarginLoss<> module2(false); input = arma::mat("0.1778 0.0957 0.1397 0.1203 0.2403 0.1925 -0.2264 -0.3400 " "-0.3336"); @@ -1183,8 +1216,8 @@ TEST_CASE("MultiLabelSoftMarginLossWeightedTest", "[LossFunctionsTest]") arma::rowvec weights; double loss; weights = arma::mat("1 2 3"); - MultiLabelSoftMarginLoss module1(true, weights); - MultiLabelSoftMarginLoss module2(false, weights); + MultiLabelSoftMarginLoss<> module1(true, weights); + MultiLabelSoftMarginLoss<> module2(false, weights); input = arma::mat("0.1778 0.0957 0.1397 0.2256 0.1203 0.2403 0.1925 0.3144 " "-0.2264 -0.3400 -0.3336 -0.8695"); @@ -1241,7 +1274,7 @@ TEST_CASE("NegativeLogLikelihoodLossTest", "[LossFunctionsTest]") arma::mat input, target, output; arma::mat expectedOutput; double loss; - NegativeLogLikelihood module; + NegativeLogLikelihood<> module; // Test for sum reduction. input = arma::mat("-0.1689 -2.0033 -3.8886 -0.2862 -1.9392 -2.2532" diff --git a/src/mlpack/tests/perceptron_test.cpp b/src/mlpack/tests/perceptron_test.cpp index 57422961fc..d53903c259 100644 --- a/src/mlpack/tests/perceptron_test.cpp +++ b/src/mlpack/tests/perceptron_test.cpp @@ -120,7 +120,7 @@ TEST_CASE("And", "[PerceptronTest]") mat testData; testData = { { 0, 1, 1, 0 }, { 1, 0, 1, 0 } }; - Row predictedLabels; + Row predictedLabels(testData.n_cols); p.Classify(testData, predictedLabels); CHECK(predictedLabels(0, 0) == 0); @@ -146,7 +146,7 @@ TEST_CASE("Or", "[PerceptronTest]") mat testData; testData = { { 0, 1, 1, 0 }, { 1, 0, 1, 0 } }; - Row predictedLabels; + Row predictedLabels(testData.n_cols); p.Classify(testData, predictedLabels); CHECK(predictedLabels(0, 0) == 1); @@ -173,7 +173,7 @@ TEST_CASE("Random3", "[PerceptronTest]") mat testData; testData = { { 0, 1, 1 }, { 1, 0, 1 } }; - Row predictedLabels; + Row predictedLabels(testData.n_cols); p.Classify(testData, predictedLabels); for (size_t i = 0; i < predictedLabels.n_cols; ++i) @@ -198,7 +198,7 @@ TEST_CASE("TwoPoints", "[PerceptronTest]") mat testData; testData = { { 0, 1 }, { 1, 0 } }; - Row predictedLabels; + Row predictedLabels(testData.n_cols); p.Classify(testData, predictedLabels); CHECK(predictedLabels(0, 0) == 0); @@ -223,7 +223,7 @@ TEST_CASE("NonLinearlySeparableDataset", "[PerceptronTest]") mat testData; testData = { { 3, 4, 5, 6 }, { 3, 2.3, 1.7, 1.5 } }; - Row predictedLabels; + Row predictedLabels(testData.n_cols); p.Classify(testData, predictedLabels); CHECK(predictedLabels(0, 0) == 0); @@ -244,28 +244,4 @@ TEST_CASE("SecondaryConstructor", "[PerceptronTest]") Perceptron<> p1(trainData, labels.row(0), 2, 1000); Perceptron<> p2(p1); - - REQUIRE(p1.Weights().n_elem > 0); - REQUIRE(p2.Weights().n_elem > 0); -} - -/** - * This tests that we can build the Perceptron when specifying instance weights. - */ -TEST_CASE("InstanceWeightsConstructor", "[PerceptronTest]") -{ - mat trainData; - trainData = { { 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8 }, - { 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2 } }; - - Mat labels; - labels = { 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1 }; - - rowvec instanceWeights; - instanceWeights = { 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 0.9, - 0.8, 0.7, 0.6, 0.5, 0.4 }; - - Perceptron<> p(trainData, labels.row(0), 2, instanceWeights, 1000); - - REQUIRE(p.Weights().n_elem > 0); } diff --git a/src/mlpack/tests/not_adapted/rbm_network_test.cpp b/src/mlpack/tests/rbm_network_test.cpp similarity index 100% rename from src/mlpack/tests/not_adapted/rbm_network_test.cpp rename to src/mlpack/tests/rbm_network_test.cpp diff --git a/src/mlpack/tests/recurrent_network_test.cpp b/src/mlpack/tests/recurrent_network_test.cpp index a7d3c459c3..4b5aca4afb 100644 --- a/src/mlpack/tests/recurrent_network_test.cpp +++ b/src/mlpack/tests/recurrent_network_test.cpp @@ -11,16 +11,17 @@ */ #include -#include -#include -#include -#include -#include - #include +#include +#include +#include +#include +#include +#include #include "catch.hpp" #include "serialization.hpp" +#include "custom_layer.hpp" using namespace mlpack; using namespace mlpack::ann; @@ -68,32 +69,6 @@ void GenerateNoisySines(arma::cube& data, } } -/** - * Construct dataset for sine wave prediction. - * - * @param data Input data used to store the noisy sines. - * @param labels Labels used to store the target class of the noisy sines. - * @param points Number of points/features in a single sequence. - * @param sequences Number of sequences for each class. - * @param noise The noise factor that influences the sines. - */ -void GenerateSines(arma::cube& data, - arma::cube& labels, - const size_t sequences, - const size_t len) -{ - arma::vec x = arma::sin(arma::linspace(0, - sequences + len, sequences + len)); - data.set_size(1, len, sequences); - labels.set_size(1, 1, sequences); - - for (size_t i = 0; i < sequences; ++i) - { - data.slice(i) = arma::reshape(x.subvec(i, i + len), 1, len); - labels.slice(i) = x(i + len); - } -} - /* * This sample is a simplified version of Derek D. Monner's Distracted Sequence * Recall task, which involves 10 symbols: @@ -149,33 +124,150 @@ void GenerateDistractedSequence(arma::mat& input, arma::mat& output) output.reshape(output.n_elem, 1); } +/** + * Train the specified network and the construct distracted sequence recall + * dataset. + */ +template +void DistractedSequenceRecallTestNetwork( + const size_t cellSize, const size_t hiddenSize) +{ + const size_t trainDistractedSequenceCount = 600; + const size_t testDistractedSequenceCount = 300; + + arma::field trainInput(1, trainDistractedSequenceCount); + arma::field trainLabels(1, trainDistractedSequenceCount); + arma::field testInput(1, testDistractedSequenceCount); + arma::field testLabels(1, testDistractedSequenceCount); + + // Generate the training data. + for (size_t i = 0; i < trainDistractedSequenceCount; ++i) + GenerateDistractedSequence(trainInput(0, i), trainLabels(0, i)); + + // Generate the test data. + for (size_t i = 0; i < testDistractedSequenceCount; ++i) + GenerateDistractedSequence(testInput(0, i), testLabels(0, i)); + + /* + * Construct a network with 10 input units, layerSize hidden units and 3 + * output units. The hidden layer is connected to itself. The network + * structure looks like: + * + * Input Recurrent Hidden Output + * Layer(10) Layer(cellSize) Layer(3) Layer(3) + * +-----+ +-----+ +-----+ +-----+ + * | | | | | | | | + * | +------>| +------>| |------>| | + * | | ..>| | | | | | + * +-----+ . +--+--+ +-----+ +-----+ + * . . + * . . + * ....... + */ + const size_t outputSize = 3; + const size_t inputSize = 10; + const size_t rho = trainInput.at(0, 0).n_elem / inputSize; + + // It isn't guaranteed that the recurrent network will converge in the + // specified number of iterations using random weights. If this works 1 of 5 + // times, I'm fine with that. All I want to know is that the network is able + // to escape from local minima and to solve the task. + size_t successes = 0; + size_t offset = 0; + for (size_t trial = 0; trial < 5; ++trial) + { + RNN > model(rho); + model.Add >(); + model.Add >(inputSize, cellSize); + model.Add(cellSize, hiddenSize); + model.Add >(hiddenSize, outputSize); + model.Add >(); + + StandardSGD opt(0.1, 50, 2, -50000); + + // We increase the number of iterations (training) if the first run didn't + // pass. + arma::cube inputTemp, labelsTemp; + for (size_t iteration = 0; iteration < (9 + offset); iteration++) + { + for (size_t j = 0; j < trainDistractedSequenceCount; ++j) + { + inputTemp = arma::cube(trainInput.at(0, j).memptr(), inputSize, 1, + trainInput.at(0, j).n_elem / inputSize, false, true); + labelsTemp = arma::cube(trainLabels.at(0, j).memptr(), outputSize, 1, + trainLabels.at(0, j).n_elem / outputSize, false, true); + + model.Train(inputTemp, labelsTemp, opt); + } + } + + double error = 0; + + // Ask the network to predict the targets in the given sequence at the + // prompts. + for (size_t i = 0; i < testDistractedSequenceCount; ++i) + { + arma::cube output; + arma::cube input(testInput.at(0, i).memptr(), inputSize, 1, + testInput.at(0, i).n_elem / inputSize, false, true); + + model.Predict(input, output); + for (size_t j = 0; j < output.n_slices; ++j) + { + arma::mat outputSlice = output.slice(j); + data::Binarize(outputSlice, outputSlice, 0.5); + output.slice(j) = outputSlice; + } + + arma::cube label(testLabels.at(0, i).memptr(), outputSize, 1, + testLabels.at(0, i).n_elem / outputSize, false, true); + if (arma::accu(arma::abs(label - output)) != 0) + error += 1; + } + + error /= testDistractedSequenceCount; + // Can we reproduce the results from the paper. They provide an 95% accuracy + // on a test set of 1000 randomly selected sequences. + // Ensure that this is within tolerance, which is at least as good as the + // paper's results (plus a little bit for noise). + if (error <= 0.3) + { + ++successes; + break; + } + + offset += 2; + } + + REQUIRE(successes >= 1); +} /** * Train the specified networks on the Derek D. Monner's distracted sequence * recall task. */ -/* TEST_CASE("LSTMDistractedSequenceRecallTest", "[RecurrentNetworkTest]") */ -/* { */ -/* DistractedSequenceRecallTestNetwork >(4, 8); */ -/* } */ +TEST_CASE("LSTMDistractedSequenceRecallTest", "[RecurrentNetworkTest]") +{ + DistractedSequenceRecallTestNetwork >(4, 8); +} /** * Train the specified networks on the Derek D. Monner's distracted sequence * recall task. */ -/* TEST_CASE("FastLSTMDistractedSequenceRecallTest", "[RecurrentNetworkTest]") */ -/* { */ -/* DistractedSequenceRecallTestNetwork >(4, 8); */ -/* } */ +TEST_CASE("FastLSTMDistractedSequenceRecallTest", "[RecurrentNetworkTest]") +{ + DistractedSequenceRecallTestNetwork >(4, 8); +} /** * Train the specified networks on the Derek D. Monner's distracted sequence * recall task. */ -/* TEST_CASE("GRUDistractedSequenceRecallTest", "[RecurrentNetworkTest]") */ -/* { */ -/* DistractedSequenceRecallTestNetwork >(4, 8); */ -/* } */ +TEST_CASE("GRUDistractedSequenceRecallTest", "[RecurrentNetworkTest]") +{ + DistractedSequenceRecallTestNetwork >(4, 8); +} /** * Create a simple recurrent neural network for the noisy sines task, and @@ -184,16 +276,15 @@ void GenerateDistractedSequence(arma::mat& input, arma::mat& output) template void BatchSizeTest() { - const size_t T = 50; - const size_t bpttTruncate = 10; + const size_t rho = 10; // Generate 12 (2 * 6) noisy sines. A single sine contains rho // points/features. arma::cube input; arma::mat labelsTemp; - GenerateNoisySines(input, labelsTemp, 4, 5); + GenerateNoisySines(input, labelsTemp, rho, 6); - arma::cube labels = arma::zeros(1, labelsTemp.n_cols, T); + arma::cube labels = arma::zeros(1, labelsTemp.n_cols, rho); for (size_t i = 0; i < labelsTemp.n_cols; ++i) { const int value = arma::as_scalar(arma::find( @@ -201,15 +292,15 @@ void BatchSizeTest() labels.tube(0, i).fill(value); } - RNN<> model(bpttTruncate); - model.Add(100); - model.Add(); - model.Add(10); - model.Add(); - model.Add(10); - model.Add(); + RNN<> model(rho); + model.Add>(1, 10); + model.Add>(); + model.Add(10, 10); + model.Add>(); + model.Add>(10, 10); + model.Add>(); - model.Reset(1); + model.Reset(); arma::mat initParams = model.Parameters(); StandardSGD opt(1e-5, 1, 5, -100, false); @@ -218,14 +309,13 @@ void BatchSizeTest() // This is trained with one point. arma::mat outputParams = model.Parameters(); - model.Reset(1); + model.Reset(); model.Parameters() = initParams; opt.BatchSize() = 2; model.Train(input, labels, opt); CheckMatrices(outputParams, model.Parameters(), 1); - model.Reset(1); model.Parameters() = initParams; opt.BatchSize() = 5; model.Train(input, labels, opt); @@ -238,28 +328,28 @@ void BatchSizeTest() */ TEST_CASE("LSTMBatchSizeTest", "[RecurrentNetworkTest]") { - BatchSizeTest(); + BatchSizeTest>(); } /** * Ensure fast LSTMs work with larger batch sizes. */ -//TEST_CASE("FastLSTMBatchSizeTest", "[RecurrentNetworkTest]") -//{ -// BatchSizeTest>(); -//} +TEST_CASE("FastLSTMBatchSizeTest", "[RecurrentNetworkTest]") +{ + BatchSizeTest>(); +} /** * Ensure GRUs work with larger batch sizes. */ -//TEST_CASE("GRUBatchSizeTest", "[RecurrentNetworkTest]") -//{ -// BatchSizeTest>(); -//} +TEST_CASE("GRUBatchSizeTest", "[RecurrentNetworkTest]") +{ + BatchSizeTest>(); +} /** * Make sure the RNN can be properly serialized. - * + */ TEST_CASE("RNNSerializationTest", "[RecurrentNetworkTest]") { const size_t rho = 10; @@ -293,7 +383,7 @@ TEST_CASE("RNNSerializationTest", "[RecurrentNetworkTest]") * . . * . . * ....... - * + */ Add<> add(4); Linear<> lookup(1, 4); SigmoidLayer<> sigmoidLayer; @@ -307,7 +397,7 @@ TEST_CASE("RNNSerializationTest", "[RecurrentNetworkTest]") model.Add >(4, 10); model.Add >(); - StandardSGD opt(0.1, 1, input.n_cols /* 1 epoch *, -100); + StandardSGD opt(0.1, 1, input.n_cols /* 1 epoch */, -100); model.Train(input, labels, opt); // Serialize the network. @@ -323,11 +413,10 @@ TEST_CASE("RNNSerializationTest", "[RecurrentNetworkTest]") CheckMatrices(prediction, xmlPrediction, jsonPrediction, binaryPrediction); } -*/ /** * Train the BRNN on a larger dataset. - * + */ TEST_CASE("SequenceClassificationBRNNTest", "[RecurrentNetworkTest]") { // Using same test for RNN below. @@ -396,11 +485,10 @@ TEST_CASE("SequenceClassificationBRNNTest", "[RecurrentNetworkTest]") REQUIRE(successes >= 1); } -*/ /** * Train the vanilla network on a larger dataset. - * + */ TEST_CASE("SequenceClassificationTest", "[RecurrentNetworkTest]") { // It isn't guaranteed that the recurrent network will converge in the @@ -441,7 +529,7 @@ TEST_CASE("SequenceClassificationTest", "[RecurrentNetworkTest]") * . . * . . * ....... - * + */ Add<> add(4); Linear<> lookup(1, 4); SigmoidLayer<> sigmoidLayer; @@ -487,7 +575,6 @@ TEST_CASE("SequenceClassificationTest", "[RecurrentNetworkTest]") REQUIRE(successes >= 1); } -*/ /** * @brief Generates noisy sine wave and outputs the data and the labels that @@ -571,10 +658,12 @@ void GenerateNoisySinRNN(arma::cube& data, */ double RNNSineTest(size_t hiddenUnits, size_t rho, size_t numEpochs = 100) { - RNN net(rho, true); - net.Add(hiddenUnits); - net.Add(hiddenUnits); - net.Add(1); + RNN > net(rho, true); + net.Add >(1, hiddenUnits); + net.Add >(hiddenUnits, hiddenUnits); + net.Add >(hiddenUnits, 1); + + RMSProp opt(0.005, 100, 0.9, 1e-08, 50000, 1e-5); // Generate data arma::cube data; @@ -589,12 +678,12 @@ double RNNSineTest(size_t hiddenUnits, size_t rho, size_t numEpochs = 100) arma::cube testLabels = labels.subcube(0, labels.n_cols - testCols, 0, labels.n_rows - 1, labels.n_cols - 1, labels.n_slices - 1); - RMSProp opt(0.005, 16, 0.9, 1e-08, trainCols * numEpochs, 1e-5); - - net.Train(data.subcube(0, 0, 0, data.n_rows - 1, trainCols - 1, - data.n_slices - 1), labels.subcube(0, 0, 0, labels.n_rows - 1, - trainCols - 1, labels.n_slices - 1), opt); - + for (size_t i = 0; i < numEpochs; ++i) + { + net.Train(data.subcube(0, 0, 0, data.n_rows - 1, trainCols - 1, + data.n_slices - 1), labels.subcube(0, 0, 0, labels.n_rows - 1, + trainCols - 1, labels.n_slices - 1), opt); + } // Well now it should be trained. Do the test here. arma::cube prediction; net.Predict(testData, prediction); @@ -624,7 +713,7 @@ TEST_CASE("MultiTimestepTest", "[RecurrentNetworkTest]") /** * Test that RNN::Train() returns finite objective value. - * + */ TEST_CASE("RNNTrainReturnObjective", "[RecurrentNetworkTest]") { const size_t rho = 10; @@ -658,7 +747,7 @@ TEST_CASE("RNNTrainReturnObjective", "[RecurrentNetworkTest]") * . . * . . * ....... - * + */ Add<> add(4); Linear<> lookup(1, 4); SigmoidLayer<> sigmoidLayer; @@ -672,16 +761,15 @@ TEST_CASE("RNNTrainReturnObjective", "[RecurrentNetworkTest]") model.Add >(4, 10); model.Add >(); - StandardSGD opt(0.1, 1, input.n_cols /* 1 epoch *, -100); + StandardSGD opt(0.1, 1, input.n_cols /* 1 epoch */, -100); double objVal = model.Train(input, labels, opt); REQUIRE(std::isfinite(objVal) == true); } -*/ /** * Test that BRNN::Train() returns finite objective value. - * + */ TEST_CASE("BRNNTrainReturnObjective", "[RecurrentNetworkTest]") { const size_t rho = 10; @@ -717,7 +805,6 @@ TEST_CASE("BRNNTrainReturnObjective", "[RecurrentNetworkTest]") // Test that BRNN::Train() returns finite objective value. REQUIRE(std::isfinite(objVal) == true); } -*/ /** * Test that RNN::Train() does not give an error for large rho. @@ -735,9 +822,10 @@ TEST_CASE("LargeRhoValueRnnTest", "[RecurrentNetworkTest]") RNN<> model(rho); - model.Add(hiddenSize); - model.Add(0.1); - model.Add(numLetters); + model.Add>(); + model.Add>(numLetters, hiddenSize, rho); + model.Add>(0.1); + model.Add>(hiddenSize, numLetters); const auto makeInput = [numLetters](const char *line) -> MatType { @@ -776,7 +864,7 @@ TEST_CASE("LargeRhoValueRnnTest", "[RecurrentNetworkTest]") inputs[i] = makeInput(trainingData[i].c_str()); targets[i] = makeTarget(trainingData[i].c_str()); } - ens::StandardSGD opt(0.01, 1, 100); + ens::SGD<> opt(0.01, 1, 100); model.Train(inputs[0], targets[0], opt); INFO("Training over"); } @@ -784,7 +872,7 @@ TEST_CASE("LargeRhoValueRnnTest", "[RecurrentNetworkTest]") /** * Test to make sure that an error is thrown when input with * wrong input shape is provided to a RNN. - * + */ TEST_CASE("RNNCheckInputShapeTest", "[RecurrentNetworkTest]") { const size_t rho = 10; @@ -818,7 +906,7 @@ TEST_CASE("RNNCheckInputShapeTest", "[RecurrentNetworkTest]") * . . * . . * ....... - * + */ Add<> add(4); // Purposely providing wrong input shape of 3. // The correct input shape is 1. @@ -839,41 +927,7 @@ TEST_CASE("RNNCheckInputShapeTest", "[RecurrentNetworkTest]") expectedMsg += std::to_string(3) + " elements, "; expectedMsg += "but the input has " + std::to_string(1) + " dimensions! "; - StandardSGD opt(0.1, 1, input.n_cols /* 1 epoch *, -100); + StandardSGD opt(0.1, 1, input.n_cols /* 1 epoch */, -100); REQUIRE_THROWS_AS(model.Train(input, labels, opt), std::logic_error); } -*/ - -/** - * Test that a simple RNN with no recurrent components behaves the same as an - * FFN. - */ -TEST_CASE("RNNFFNTest", "[RecurrentNetworkTest]") -{ - // We'll create an RNN with *no* BPTT, just a simple single-layer linear - // network. - RNN rnn; - FFN ffn; - - rnn.Add(10); - rnn.Add(); - rnn.Add(1); - - ffn.Add(10); - ffn.Add(); - ffn.Add(1); - - // Now create some random data. - arma::cube data(20, 200, 1, arma::fill::randu); - arma::cube responses(1, 200, 1, arma::fill::randu); - - // Train the FFN. - ens::StandardSGD optimizer(1e-5, 1, 200, 1e-8, false); - - ffn.Train(data.slice(0), responses.slice(0), optimizer); - rnn.Train(data, responses, optimizer); - - // Now, the weights should be the same! - CheckMatrices(ffn.Parameters(), rnn.Parameters()); -} diff --git a/src/mlpack/tests/reward_clipping_test.cpp b/src/mlpack/tests/reward_clipping_test.cpp index fe2381d4b9..d1fd873740 100644 --- a/src/mlpack/tests/reward_clipping_test.cpp +++ b/src/mlpack/tests/reward_clipping_test.cpp @@ -22,7 +22,7 @@ #include #include -#include +#include #include #include #include @@ -62,7 +62,7 @@ TEST_CASE("RewardClippedAcrobotWithDQN", "[RewardClippingTest]") for (size_t trial = 0; trial < 3; ++trial) { // Set up the network. - SimpleDQN<> model(64, 32, 3); + SimpleDQN<> model(4, 64, 32, 3); // Set up the policy and replay method. GreedyPolicy> policy(1.0, 1000, 0.1, 0.99); diff --git a/src/mlpack/tests/serialization_test.cpp b/src/mlpack/tests/serialization_test.cpp index 9d4beb8b94..17b51cc865 100644 --- a/src/mlpack/tests/serialization_test.cpp +++ b/src/mlpack/tests/serialization_test.cpp @@ -38,7 +38,7 @@ #include #include #include -//#include +#include #include using namespace mlpack; @@ -1498,7 +1498,7 @@ TEST_CASE("HoeffdingTreeTest", "[SerializationTest]") /** * Build a Binary RBM, then save it and make sure the parameters of the * all the RBM are equal. - * + */ TEST_CASE("BinaryRBMTest", "[SerializationTest]") { arma::mat data; @@ -1531,12 +1531,11 @@ TEST_CASE("BinaryRBMTest", "[SerializationTest]") CheckMatrices(Rbm.Weight(), RbmText.Weight()); CheckMatrices(Rbm.Weight(), RbmBinary.Weight()); } -*/ /** * Build a ssRBM, then save it and make sure the parameters of the * all the RBM are equal. - * + */ TEST_CASE("ssRBMTest", "[SerializationTest]") { arma::mat data; @@ -1586,7 +1585,6 @@ TEST_CASE("ssRBMTest", "[SerializationTest]") CheckMatrices(Rbm.Weight(), RbmText.Weight()); CheckMatrices(Rbm.Weight(), RbmBinary.Weight()); } -*/ // Make sure serialization works for BayesianLinearRegression. TEST_CASE("BayesianLinearRegressionTest", "[SerializationTest]") diff --git a/src/mlpack/tests/not_adapted/wgan_test.cpp b/src/mlpack/tests/wgan_test.cpp similarity index 100% rename from src/mlpack/tests/not_adapted/wgan_test.cpp rename to src/mlpack/tests/wgan_test.cpp From caf37713fc511621b652812a7490ff8108200be0 Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Tue, 10 May 2022 14:36:18 +0530 Subject: [PATCH 36/57] converted lars to .hpp --- src/mlpack/methods/lars/CMakeLists.txt | 1 - src/mlpack/methods/lars/lars.cpp | 652 ------------------------- src/mlpack/methods/lars/lars.hpp | 2 + src/mlpack/methods/lars/lars_impl.hpp | 641 ++++++++++++++++++++++++ 4 files changed, 643 insertions(+), 653 deletions(-) delete mode 100644 src/mlpack/methods/lars/lars.cpp diff --git a/src/mlpack/methods/lars/CMakeLists.txt b/src/mlpack/methods/lars/CMakeLists.txt index 307d695708..3c20f37d56 100644 --- a/src/mlpack/methods/lars/CMakeLists.txt +++ b/src/mlpack/methods/lars/CMakeLists.txt @@ -3,7 +3,6 @@ set(SOURCES lars.hpp lars_impl.hpp - lars.cpp ) # add directory name to sources diff --git a/src/mlpack/methods/lars/lars.cpp b/src/mlpack/methods/lars/lars.cpp deleted file mode 100644 index 21b6ff0687..0000000000 --- a/src/mlpack/methods/lars/lars.cpp +++ /dev/null @@ -1,652 +0,0 @@ -/** - * @file methods/lars/lars.cpp - * @author Nishant Mehta (niche) - * - * Implementation of LARS and LASSO. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#include "lars.hpp" -#include -#include - -using namespace mlpack; -using namespace mlpack::regression; - -LARS::LARS(const bool useCholesky, - const double lambda1, - const double lambda2, - const double tolerance) : - matGram(&matGramInternal), - useCholesky(useCholesky), - lasso((lambda1 != 0)), - lambda1(lambda1), - elasticNet((lambda1 != 0) && (lambda2 != 0)), - lambda2(lambda2), - tolerance(tolerance) -{ /* Nothing left to do. */ } - -LARS::LARS(const bool useCholesky, - const arma::mat& gramMatrix, - const double lambda1, - const double lambda2, - const double tolerance) : - matGram(&gramMatrix), - useCholesky(useCholesky), - lasso((lambda1 != 0)), - lambda1(lambda1), - elasticNet((lambda1 != 0) && (lambda2 != 0)), - lambda2(lambda2), - tolerance(tolerance) -{ /* Nothing left to do */ } - -LARS::LARS(const arma::mat& data, - const arma::rowvec& responses, - const bool transposeData, - const bool useCholesky, - const double lambda1, - const double lambda2, - const double tolerance) : - LARS(useCholesky, lambda1, lambda2, tolerance) -{ - Train(data, responses, transposeData); -} - -LARS::LARS(const arma::mat& data, - const arma::rowvec& responses, - const bool transposeData, - const bool useCholesky, - const arma::mat& gramMatrix, - const double lambda1, - const double lambda2, - const double tolerance) : - LARS(useCholesky, gramMatrix, lambda1, lambda2, tolerance) -{ - Train(data, responses, transposeData); -} - -// Copy Constructor. -LARS::LARS(const LARS& other) : - matGramInternal(other.matGramInternal), - matGram(other.matGram != &other.matGramInternal ? - other.matGram : &matGramInternal), - matUtriCholFactor(other.matUtriCholFactor), - useCholesky(other.useCholesky), - lasso(other.lasso), - lambda1(other.lambda1), - elasticNet(other.elasticNet), - lambda2(other.lambda2), - tolerance(other.tolerance), - betaPath(other.betaPath), - lambdaPath(other.lambdaPath), - activeSet(other.activeSet), - isActive(other.isActive), - ignoreSet(other.ignoreSet), - isIgnored(other.isIgnored) -{ - // Nothing to do here. -} - -// Move constructor. -LARS::LARS(LARS&& other) : - matGramInternal(std::move(other.matGramInternal)), - matGram(other.matGram != &other.matGramInternal ? - other.matGram : &matGramInternal), - matUtriCholFactor(std::move(other.matUtriCholFactor)), - useCholesky(other.useCholesky), - lasso(other.lasso), - lambda1(other.lambda1), - elasticNet(other.elasticNet), - lambda2(other.lambda2), - tolerance(other.tolerance), - betaPath(std::move(other.betaPath)), - lambdaPath(std::move(other.lambdaPath)), - activeSet(std::move(other.activeSet)), - isActive(std::move(other.isActive)), - ignoreSet(std::move(other.ignoreSet)), - isIgnored(std::move(other.isIgnored)) -{ - // Nothing to do here. -} - -// Copy operator. -LARS& LARS::operator=(const LARS& other) -{ - if (&other == this) - return *this; - - matGramInternal = other.matGramInternal; - matGram = other.matGram != &other.matGramInternal ? - other.matGram : &matGramInternal; - matUtriCholFactor = other.matUtriCholFactor; - useCholesky = other.useCholesky; - lasso = other.lasso; - lambda1 = other.lambda1; - elasticNet = other.elasticNet; - lambda2 = other.lambda2; - tolerance = other.tolerance; - betaPath = other.betaPath; - lambdaPath = other.lambdaPath; - activeSet = other.activeSet; - isActive = other.isActive; - ignoreSet = other.ignoreSet; - isIgnored = other.isIgnored; - return *this; -} - -// Move Operator. -LARS& LARS::operator=(LARS&& other) -{ - if (&other == this) - return *this; - - matGramInternal = std::move(other.matGramInternal); - matGram = other.matGram != &other.matGramInternal ? - other.matGram : &matGramInternal; - matUtriCholFactor = std::move(other.matUtriCholFactor); - useCholesky = other.useCholesky; - lasso = other.lasso; - lambda1 = other.lambda1; - elasticNet = other.elasticNet; - lambda2 = other.lambda2; - tolerance = other.tolerance; - betaPath = std::move(other.betaPath); - lambdaPath = std::move(other.lambdaPath); - activeSet = std::move(other.activeSet); - isActive = std::move(other.isActive); - ignoreSet = std::move(other.ignoreSet); - isIgnored = std::move(other.isIgnored); - return *this; -} - -double LARS::Train(const arma::mat& matX, - const arma::rowvec& y, - arma::vec& beta, - const bool transposeData) -{ - // Clear any previous solution information. - betaPath.clear(); - lambdaPath.clear(); - activeSet.clear(); - isActive.clear(); - ignoreSet.clear(); - isIgnored.clear(); - matUtriCholFactor.reset(); - - // Update values in case lambda1 or lambda2 changed. - lasso = (lambda1 != 0); - elasticNet = (lambda1 != 0 && lambda2 != 0); - - // This matrix may end up holding the transpose -- if necessary. - arma::mat dataTrans; - // dataRef is row-major. - const arma::mat& dataRef = (transposeData ? dataTrans : matX); - if (transposeData) - dataTrans = trans(matX); - - // Compute X' * y. - arma::vec vecXTy = trans(y * dataRef); - - // Set up active set variables. In the beginning, the active set has size 0 - // (all dimensions are inactive). - isActive.resize(dataRef.n_cols, false); - - // Set up ignores set variables. Initialized empty. - isIgnored.resize(dataRef.n_cols, false); - - // Initialize yHat and beta. - beta = arma::zeros(dataRef.n_cols); - arma::vec yHat = arma::zeros(dataRef.n_rows); - arma::vec yHatDirection(dataRef.n_rows); - - bool lassocond = false; - - // Compute the initial maximum correlation among all dimensions. - arma::vec corr = vecXTy; - double maxCorr = 0; - size_t changeInd = 0; - for (size_t i = 0; i < vecXTy.n_elem; ++i) - { - if (fabs(corr(i)) > maxCorr) - { - maxCorr = fabs(corr(i)); - changeInd = i; - } - } - - betaPath.push_back(beta); - lambdaPath.push_back(maxCorr); - - // If the maximum correlation is too small, there is no reason to continue. - if (maxCorr < lambda1) - { - lambdaPath[0] = lambda1; - return maxCorr; - } - - // Compute the Gram matrix. If this is the elastic net problem, we will add - // lambda2 * I_n to the matrix. - if (matGram->n_elem != dataRef.n_cols * dataRef.n_cols) - { - // In this case, matGram should reference matGramInternal. - matGramInternal = trans(dataRef) * dataRef; - - if (elasticNet && !useCholesky) - matGramInternal += lambda2 * arma::eye(dataRef.n_cols, dataRef.n_cols); - } - - // Main loop. - while (((activeSet.size() + ignoreSet.size()) < dataRef.n_cols) && - (maxCorr > tolerance)) - { - // Compute the maximum correlation among inactive dimensions. - maxCorr = 0; - for (size_t i = 0; i < dataRef.n_cols; ++i) - { - if ((!isActive[i]) && (!isIgnored[i]) && (fabs(corr(i)) > maxCorr)) - { - maxCorr = fabs(corr(i)); - changeInd = i; - } - } - - if (!lassocond) - { - if (useCholesky) - { - // vec newGramCol = vec(activeSet.size()); - // for (size_t i = 0; i < activeSet.size(); ++i) - // { - // newGramCol[i] = dot(matX.col(activeSet[i]), matX.col(changeInd)); - // } - // This is equivalent to the above 5 lines. - arma::vec newGramCol = matGram->elem(changeInd * dataRef.n_cols + - arma::conv_to::from(activeSet)); - - CholeskyInsert((*matGram)(changeInd, changeInd), newGramCol); - } - - // Add variable to active set. - Activate(changeInd); - } - - // Compute signs of correlations. - arma::vec s = arma::vec(activeSet.size()); - for (size_t i = 0; i < activeSet.size(); ++i) - s(i) = corr(activeSet[i]) / fabs(corr(activeSet[i])); - - // Compute the "equiangular" direction in parameter space (betaDirection). - // We use quotes because in the case of non-unit norm variables, this need - // not be equiangular. - arma::vec unnormalizedBetaDirection; - double normalization; - arma::vec betaDirection; - if (useCholesky) - { - // Check for singularity. - const double lastUtriElement = matUtriCholFactor( - matUtriCholFactor.n_cols - 1, matUtriCholFactor.n_rows - 1); - if (std::abs(lastUtriElement) > tolerance) - { - // Ok, no singularity. - /** - * Note that: - * R^T R % S^T % S = (R % S)^T (R % S) - * Now, for 1 the ones vector: - * inv( (R % S)^T (R % S) ) 1 - * = inv(R % S) inv((R % S)^T) 1 - * = inv(R % S) Solve((R % S)^T, 1) - * = inv(R % S) Solve(R^T, s) - * = Solve(R % S, Solve(R^T, s) - * = s % Solve(R, Solve(R^T, s)) - */ - unnormalizedBetaDirection = solve(trimatu(matUtriCholFactor), - solve(trimatl(trans(matUtriCholFactor)), s)); - - normalization = 1.0 / sqrt(dot(s, unnormalizedBetaDirection)); - betaDirection = normalization * unnormalizedBetaDirection; - } - else - { - // Singularity, so remove variable from active set, add to ignores set, - // and look for new variable to add. - Log::Warn << "Encountered singularity when adding variable " - << changeInd << " to active set; permanently removing." - << std::endl; - Deactivate(activeSet.size() - 1); - Ignore(changeInd); - CholeskyDelete(matUtriCholFactor.n_rows - 1); - continue; - } - } - else - { - arma::mat matGramActive = arma::mat(activeSet.size(), activeSet.size()); - for (size_t i = 0; i < activeSet.size(); ++i) - for (size_t j = 0; j < activeSet.size(); ++j) - matGramActive(i, j) = (*matGram)(activeSet[i], activeSet[j]); - - // Check for singularity. - arma::mat matS = s * arma::ones(1, activeSet.size()); - const bool solvedOk = solve(unnormalizedBetaDirection, - matGramActive % trans(matS) % matS, - arma::ones(activeSet.size(), 1)); - if (solvedOk) - { - // Ok, no singularity. - normalization = 1.0 / sqrt(sum(unnormalizedBetaDirection)); - betaDirection = normalization * unnormalizedBetaDirection % s; - } - else - { - // Singularity, so remove variable from active set, add to ignores set, - // and look for new variable to add. - Deactivate(activeSet.size() - 1); - Ignore(changeInd); - Log::Warn << "Encountered singularity when adding variable " - << changeInd << " to active set; permanently removing." - << std::endl; - continue; - } - } - - // compute "equiangular" direction in output space - ComputeYHatDirection(dataRef, betaDirection, yHatDirection); - - double gamma = maxCorr / normalization; - - // If not all variables are active. - if ((activeSet.size() + ignoreSet.size()) < dataRef.n_cols) - { - // Compute correlations with direction. - for (size_t ind = 0; ind < dataRef.n_cols; ind++) - { - if (isActive[ind] || isIgnored[ind]) - continue; - - const double dirCorr = dot(dataRef.col(ind), yHatDirection); - const double val1 = (maxCorr - corr(ind)) / (normalization - dirCorr); - const double val2 = (maxCorr + corr(ind)) / (normalization + dirCorr); - if ((val1 > 0.0) && (val1 < gamma)) - gamma = val1; - if ((val2 > 0.0) && (val2 < gamma)) - gamma = val2; - // Handle edge case where the largest actually is equal to 0. - if (std::max(val1, val2) == 0.0) - gamma = 0.0; - } - } - - // Bound gamma according to LASSO. - if (lasso) - { - lassocond = false; - double lassoboundOnGamma = DBL_MAX; - size_t activeIndToKickOut = -1; - - for (size_t i = 0; i < activeSet.size(); ++i) - { - double val = -beta(activeSet[i]) / betaDirection(i); - if ((val > 0) && (val < lassoboundOnGamma)) - { - lassoboundOnGamma = val; - activeIndToKickOut = i; - } - } - - if (lassoboundOnGamma < gamma) - { - gamma = lassoboundOnGamma; - lassocond = true; - changeInd = activeIndToKickOut; - } - } - - // Update the prediction. - yHat += gamma * yHatDirection; - - // Update the estimator. - for (size_t i = 0; i < activeSet.size(); ++i) - { - beta(activeSet[i]) += gamma * betaDirection(i); - } - - // Sanity check to make sure the kicked out dimension is actually zero. - if (lassocond) - { - if (beta(activeSet[changeInd]) != 0) - beta(activeSet[changeInd]) = 0; - } - - betaPath.push_back(beta); - - if (lassocond) - { - // Index is in position changeInd in activeSet. - if (useCholesky) - CholeskyDelete(changeInd); - - Deactivate(changeInd); - } - - corr = vecXTy - trans(dataRef) * yHat; - if (elasticNet) - corr -= lambda2 * beta; - - double curLambda = 0; - for (size_t i = 0; i < activeSet.size(); ++i) - curLambda += fabs(corr(activeSet[i])); - - curLambda /= ((double) activeSet.size()); - - lambdaPath.push_back(curLambda); - - // Time to stop for LASSO? - if (lasso) - { - if (curLambda <= lambda1) - { - InterpolateBeta(); - break; - } - } - } - - // Unfortunate copy... - beta = betaPath.back(); - - return ComputeError(matX, y, !transposeData); -} - -double LARS::Train(const arma::mat& data, - const arma::rowvec& responses, - const bool transposeData) -{ - arma::vec beta; - return Train(data, responses, beta, transposeData); -} - -void LARS::Predict(const arma::mat& points, - arma::rowvec& predictions, - const bool rowMajor) const -{ - // We really only need to store beta internally... - if (rowMajor) - predictions = trans(points * betaPath.back()); - else - predictions = betaPath.back().t() * points; -} - -// Private functions. -void LARS::Deactivate(const size_t activeVarInd) -{ - isActive[activeSet[activeVarInd]] = false; - activeSet.erase(activeSet.begin() + activeVarInd); -} - -void LARS::Activate(const size_t varInd) -{ - isActive[varInd] = true; - activeSet.push_back(varInd); -} - -void LARS::Ignore(const size_t varInd) -{ - isIgnored[varInd] = true; - ignoreSet.push_back(varInd); -} - -void LARS::ComputeYHatDirection(const arma::mat& matX, - const arma::vec& betaDirection, - arma::vec& yHatDirection) -{ - yHatDirection.fill(0); - for (size_t i = 0; i < activeSet.size(); ++i) - yHatDirection += betaDirection(i) * matX.col(activeSet[i]); -} - -void LARS::InterpolateBeta() -{ - int pathLength = betaPath.size(); - - // interpolate beta and stop - double ultimateLambda = lambdaPath[pathLength - 1]; - double penultimateLambda = lambdaPath[pathLength - 2]; - double interp = (penultimateLambda - lambda1) - / (penultimateLambda - ultimateLambda); - - betaPath[pathLength - 1] = (1 - interp) * (betaPath[pathLength - 2]) - + interp * betaPath[pathLength - 1]; - - lambdaPath[pathLength - 1] = lambda1; -} - -void LARS::CholeskyInsert(const arma::vec& newX, const arma::mat& X) -{ - if (matUtriCholFactor.n_rows == 0) - { - matUtriCholFactor = arma::mat(1, 1); - - if (elasticNet) - matUtriCholFactor(0, 0) = sqrt(dot(newX, newX) + lambda2); - else - matUtriCholFactor(0, 0) = norm(newX, 2); - } - else - { - arma::vec newGramCol = trans(X) * newX; - CholeskyInsert(dot(newX, newX), newGramCol); - } -} - -void LARS::CholeskyInsert(double sqNormNewX, const arma::vec& newGramCol) -{ - int n = matUtriCholFactor.n_rows; - - if (n == 0) - { - matUtriCholFactor = arma::mat(1, 1); - - if (elasticNet) - matUtriCholFactor(0, 0) = sqrt(sqNormNewX + lambda2); - else - matUtriCholFactor(0, 0) = sqrt(sqNormNewX); - } - else - { - arma::mat matNewR = arma::mat(n + 1, n + 1); - - if (elasticNet) - sqNormNewX += lambda2; - - arma::vec matUtriCholFactork = solve(trimatl(trans(matUtriCholFactor)), - newGramCol); - - matNewR(arma::span(0, n - 1), arma::span(0, n - 1)) = matUtriCholFactor; - matNewR(arma::span(0, n - 1), n) = matUtriCholFactork; - matNewR(n, arma::span(0, n - 1)).fill(0.0); - matNewR(n, n) = sqrt(sqNormNewX - dot(matUtriCholFactork, - matUtriCholFactork)); - - matUtriCholFactor = matNewR; - } -} - -void LARS::GivensRotate(const arma::vec::fixed<2>& x, - arma::vec::fixed<2>& rotatedX, - arma::mat& matG) -{ - if (x(1) == 0) - { - matG = arma::eye(2, 2); - rotatedX = x; - } - else - { - double r = norm(x, 2); - matG = arma::mat(2, 2); - - double scaledX1 = x(0) / r; - double scaledX2 = x(1) / r; - - matG(0, 0) = scaledX1; - matG(1, 0) = -scaledX2; - matG(0, 1) = scaledX2; - matG(1, 1) = scaledX1; - - rotatedX = arma::vec(2); - rotatedX(0) = r; - rotatedX(1) = 0; - } -} - -void LARS::CholeskyDelete(const size_t colToKill) -{ - size_t n = matUtriCholFactor.n_rows; - - if (colToKill == (n - 1)) - { - matUtriCholFactor = matUtriCholFactor(arma::span(0, n - 2), - arma::span(0, n - 2)); - } - else - { - matUtriCholFactor.shed_col(colToKill); // remove column colToKill - n--; - - for (size_t k = colToKill; k < n; ++k) - { - arma::mat matG; - arma::vec::fixed<2> rotatedVec; - GivensRotate(matUtriCholFactor(arma::span(k, k + 1), k), rotatedVec, - matG); - matUtriCholFactor(arma::span(k, k + 1), k) = rotatedVec; - if (k < n - 1) - { - matUtriCholFactor(arma::span(k, k + 1), arma::span(k + 1, n - 1)) = - matG * matUtriCholFactor(arma::span(k, k + 1), - arma::span(k + 1, n - 1)); - } - } - - matUtriCholFactor.shed_row(n); - } -} - -double LARS::ComputeError(const arma::mat& matX, - const arma::rowvec& y, - const bool rowMajor) -{ - if (rowMajor) - { - return arma::accu(arma::pow(y - trans(matX * betaPath.back()), 2.0)); - } - - else - { - return arma::accu(arma::pow(y - betaPath.back().t() * matX, 2.0)); - } -} diff --git a/src/mlpack/methods/lars/lars.hpp b/src/mlpack/methods/lars/lars.hpp index d019fec900..37dd212507 100644 --- a/src/mlpack/methods/lars/lars.hpp +++ b/src/mlpack/methods/lars/lars.hpp @@ -25,6 +25,8 @@ #define MLPACK_METHODS_LARS_LARS_HPP #include +#include +#include namespace mlpack { namespace regression { diff --git a/src/mlpack/methods/lars/lars_impl.hpp b/src/mlpack/methods/lars/lars_impl.hpp index 2b3edaea15..a3ed10a63c 100644 --- a/src/mlpack/methods/lars/lars_impl.hpp +++ b/src/mlpack/methods/lars/lars_impl.hpp @@ -18,6 +18,647 @@ namespace mlpack { namespace regression { +inline LARS::LARS( + const bool useCholesky, + const double lambda1, + const double lambda2, + const double tolerance) : + matGram(&matGramInternal), + useCholesky(useCholesky), + lasso((lambda1 != 0)), + lambda1(lambda1), + elasticNet((lambda1 != 0) && (lambda2 != 0)), + lambda2(lambda2), + tolerance(tolerance) +{ /* Nothing left to do. */ } + +inline LARS::LARS( + const bool useCholesky, + const arma::mat& gramMatrix, + const double lambda1, + const double lambda2, + const double tolerance) : + matGram(&gramMatrix), + useCholesky(useCholesky), + lasso((lambda1 != 0)), + lambda1(lambda1), + elasticNet((lambda1 != 0) && (lambda2 != 0)), + lambda2(lambda2), + tolerance(tolerance) +{ /* Nothing left to do */ } + +inline LARS::LARS( + const arma::mat& data, + const arma::rowvec& responses, + const bool transposeData, + const bool useCholesky, + const double lambda1, + const double lambda2, + const double tolerance) : + LARS(useCholesky, lambda1, lambda2, tolerance) +{ + Train(data, responses, transposeData); +} + +inline LARS::LARS( + const arma::mat& data, + const arma::rowvec& responses, + const bool transposeData, + const bool useCholesky, + const arma::mat& gramMatrix, + const double lambda1, + const double lambda2, + const double tolerance) : + LARS(useCholesky, gramMatrix, lambda1, lambda2, tolerance) +{ + Train(data, responses, transposeData); +} + +// Copy Constructor. +inline LARS::LARS(const LARS& other) : + matGramInternal(other.matGramInternal), + matGram(other.matGram != &other.matGramInternal ? + other.matGram : &matGramInternal), + matUtriCholFactor(other.matUtriCholFactor), + useCholesky(other.useCholesky), + lasso(other.lasso), + lambda1(other.lambda1), + elasticNet(other.elasticNet), + lambda2(other.lambda2), + tolerance(other.tolerance), + betaPath(other.betaPath), + lambdaPath(other.lambdaPath), + activeSet(other.activeSet), + isActive(other.isActive), + ignoreSet(other.ignoreSet), + isIgnored(other.isIgnored) +{ + // Nothing to do here. +} + +// Move constructor. +inline LARS::LARS(LARS&& other) : + matGramInternal(std::move(other.matGramInternal)), + matGram(other.matGram != &other.matGramInternal ? + other.matGram : &matGramInternal), + matUtriCholFactor(std::move(other.matUtriCholFactor)), + useCholesky(other.useCholesky), + lasso(other.lasso), + lambda1(other.lambda1), + elasticNet(other.elasticNet), + lambda2(other.lambda2), + tolerance(other.tolerance), + betaPath(std::move(other.betaPath)), + lambdaPath(std::move(other.lambdaPath)), + activeSet(std::move(other.activeSet)), + isActive(std::move(other.isActive)), + ignoreSet(std::move(other.ignoreSet)), + isIgnored(std::move(other.isIgnored)) +{ + // Nothing to do here. +} + +// Copy operator. +inline LARS& LARS::operator=(const LARS& other) +{ + if (&other == this) + return *this; + + matGramInternal = other.matGramInternal; + matGram = other.matGram != &other.matGramInternal ? + other.matGram : &matGramInternal; + matUtriCholFactor = other.matUtriCholFactor; + useCholesky = other.useCholesky; + lasso = other.lasso; + lambda1 = other.lambda1; + elasticNet = other.elasticNet; + lambda2 = other.lambda2; + tolerance = other.tolerance; + betaPath = other.betaPath; + lambdaPath = other.lambdaPath; + activeSet = other.activeSet; + isActive = other.isActive; + ignoreSet = other.ignoreSet; + isIgnored = other.isIgnored; + return *this; +} + +// Move Operator. +inline LARS& LARS::operator=(LARS&& other) +{ + if (&other == this) + return *this; + + matGramInternal = std::move(other.matGramInternal); + matGram = other.matGram != &other.matGramInternal ? + other.matGram : &matGramInternal; + matUtriCholFactor = std::move(other.matUtriCholFactor); + useCholesky = other.useCholesky; + lasso = other.lasso; + lambda1 = other.lambda1; + elasticNet = other.elasticNet; + lambda2 = other.lambda2; + tolerance = other.tolerance; + betaPath = std::move(other.betaPath); + lambdaPath = std::move(other.lambdaPath); + activeSet = std::move(other.activeSet); + isActive = std::move(other.isActive); + ignoreSet = std::move(other.ignoreSet); + isIgnored = std::move(other.isIgnored); + return *this; +} + +inline double LARS::Train(const arma::mat& matX, + const arma::rowvec& y, + arma::vec& beta, + const bool transposeData) +{ + // Clear any previous solution information. + betaPath.clear(); + lambdaPath.clear(); + activeSet.clear(); + isActive.clear(); + ignoreSet.clear(); + isIgnored.clear(); + matUtriCholFactor.reset(); + + // Update values in case lambda1 or lambda2 changed. + lasso = (lambda1 != 0); + elasticNet = (lambda1 != 0 && lambda2 != 0); + + // This matrix may end up holding the transpose -- if necessary. + arma::mat dataTrans; + // dataRef is row-major. + const arma::mat& dataRef = (transposeData ? dataTrans : matX); + if (transposeData) + dataTrans = trans(matX); + + // Compute X' * y. + arma::vec vecXTy = trans(y * dataRef); + + // Set up active set variables. In the beginning, the active set has size 0 + // (all dimensions are inactive). + isActive.resize(dataRef.n_cols, false); + + // Set up ignores set variables. Initialized empty. + isIgnored.resize(dataRef.n_cols, false); + + // Initialize yHat and beta. + beta = arma::zeros(dataRef.n_cols); + arma::vec yHat = arma::zeros(dataRef.n_rows); + arma::vec yHatDirection(dataRef.n_rows); + + bool lassocond = false; + + // Compute the initial maximum correlation among all dimensions. + arma::vec corr = vecXTy; + double maxCorr = 0; + size_t changeInd = 0; + for (size_t i = 0; i < vecXTy.n_elem; ++i) + { + if (fabs(corr(i)) > maxCorr) + { + maxCorr = fabs(corr(i)); + changeInd = i; + } + } + + betaPath.push_back(beta); + lambdaPath.push_back(maxCorr); + + // If the maximum correlation is too small, there is no reason to continue. + if (maxCorr < lambda1) + { + lambdaPath[0] = lambda1; + return maxCorr; + } + + // Compute the Gram matrix. If this is the elastic net problem, we will add + // lambda2 * I_n to the matrix. + if (matGram->n_elem != dataRef.n_cols * dataRef.n_cols) + { + // In this case, matGram should reference matGramInternal. + matGramInternal = trans(dataRef) * dataRef; + + if (elasticNet && !useCholesky) + matGramInternal += lambda2 * arma::eye(dataRef.n_cols, dataRef.n_cols); + } + + // Main loop. + while (((activeSet.size() + ignoreSet.size()) < dataRef.n_cols) && + (maxCorr > tolerance)) + { + // Compute the maximum correlation among inactive dimensions. + maxCorr = 0; + for (size_t i = 0; i < dataRef.n_cols; ++i) + { + if ((!isActive[i]) && (!isIgnored[i]) && (fabs(corr(i)) > maxCorr)) + { + maxCorr = fabs(corr(i)); + changeInd = i; + } + } + + if (!lassocond) + { + if (useCholesky) + { + // vec newGramCol = vec(activeSet.size()); + // for (size_t i = 0; i < activeSet.size(); ++i) + // { + // newGramCol[i] = dot(matX.col(activeSet[i]), matX.col(changeInd)); + // } + // This is equivalent to the above 5 lines. + arma::vec newGramCol = matGram->elem(changeInd * dataRef.n_cols + + arma::conv_to::from(activeSet)); + + CholeskyInsert((*matGram)(changeInd, changeInd), newGramCol); + } + + // Add variable to active set. + Activate(changeInd); + } + + // Compute signs of correlations. + arma::vec s = arma::vec(activeSet.size()); + for (size_t i = 0; i < activeSet.size(); ++i) + s(i) = corr(activeSet[i]) / fabs(corr(activeSet[i])); + + // Compute the "equiangular" direction in parameter space (betaDirection). + // We use quotes because in the case of non-unit norm variables, this need + // not be equiangular. + arma::vec unnormalizedBetaDirection; + double normalization; + arma::vec betaDirection; + if (useCholesky) + { + // Check for singularity. + const double lastUtriElement = matUtriCholFactor( + matUtriCholFactor.n_cols - 1, matUtriCholFactor.n_rows - 1); + if (std::abs(lastUtriElement) > tolerance) + { + // Ok, no singularity. + /** + * Note that: + * R^T R % S^T % S = (R % S)^T (R % S) + * Now, for 1 the ones vector: + * inv( (R % S)^T (R % S) ) 1 + * = inv(R % S) inv((R % S)^T) 1 + * = inv(R % S) Solve((R % S)^T, 1) + * = inv(R % S) Solve(R^T, s) + * = Solve(R % S, Solve(R^T, s) + * = s % Solve(R, Solve(R^T, s)) + */ + unnormalizedBetaDirection = solve(trimatu(matUtriCholFactor), + solve(trimatl(trans(matUtriCholFactor)), s)); + + normalization = 1.0 / sqrt(dot(s, unnormalizedBetaDirection)); + betaDirection = normalization * unnormalizedBetaDirection; + } + else + { + // Singularity, so remove variable from active set, add to ignores set, + // and look for new variable to add. + Log::Warn << "Encountered singularity when adding variable " + << changeInd << " to active set; permanently removing." + << std::endl; + Deactivate(activeSet.size() - 1); + Ignore(changeInd); + CholeskyDelete(matUtriCholFactor.n_rows - 1); + continue; + } + } + else + { + arma::mat matGramActive = arma::mat(activeSet.size(), activeSet.size()); + for (size_t i = 0; i < activeSet.size(); ++i) + for (size_t j = 0; j < activeSet.size(); ++j) + matGramActive(i, j) = (*matGram)(activeSet[i], activeSet[j]); + + // Check for singularity. + arma::mat matS = s * arma::ones(1, activeSet.size()); + const bool solvedOk = solve(unnormalizedBetaDirection, + matGramActive % trans(matS) % matS, + arma::ones(activeSet.size(), 1)); + if (solvedOk) + { + // Ok, no singularity. + normalization = 1.0 / sqrt(sum(unnormalizedBetaDirection)); + betaDirection = normalization * unnormalizedBetaDirection % s; + } + else + { + // Singularity, so remove variable from active set, add to ignores set, + // and look for new variable to add. + Deactivate(activeSet.size() - 1); + Ignore(changeInd); + Log::Warn << "Encountered singularity when adding variable " + << changeInd << " to active set; permanently removing." + << std::endl; + continue; + } + } + + // compute "equiangular" direction in output space + ComputeYHatDirection(dataRef, betaDirection, yHatDirection); + + double gamma = maxCorr / normalization; + + // If not all variables are active. + if ((activeSet.size() + ignoreSet.size()) < dataRef.n_cols) + { + // Compute correlations with direction. + for (size_t ind = 0; ind < dataRef.n_cols; ind++) + { + if (isActive[ind] || isIgnored[ind]) + continue; + + const double dirCorr = dot(dataRef.col(ind), yHatDirection); + const double val1 = (maxCorr - corr(ind)) / (normalization - dirCorr); + const double val2 = (maxCorr + corr(ind)) / (normalization + dirCorr); + if ((val1 > 0.0) && (val1 < gamma)) + gamma = val1; + if ((val2 > 0.0) && (val2 < gamma)) + gamma = val2; + // Handle edge case where the largest actually is equal to 0. + if (std::max(val1, val2) == 0.0) + gamma = 0.0; + } + } + + // Bound gamma according to LASSO. + if (lasso) + { + lassocond = false; + double lassoboundOnGamma = DBL_MAX; + size_t activeIndToKickOut = -1; + + for (size_t i = 0; i < activeSet.size(); ++i) + { + double val = -beta(activeSet[i]) / betaDirection(i); + if ((val > 0) && (val < lassoboundOnGamma)) + { + lassoboundOnGamma = val; + activeIndToKickOut = i; + } + } + + if (lassoboundOnGamma < gamma) + { + gamma = lassoboundOnGamma; + lassocond = true; + changeInd = activeIndToKickOut; + } + } + + // Update the prediction. + yHat += gamma * yHatDirection; + + // Update the estimator. + for (size_t i = 0; i < activeSet.size(); ++i) + { + beta(activeSet[i]) += gamma * betaDirection(i); + } + + // Sanity check to make sure the kicked out dimension is actually zero. + if (lassocond) + { + if (beta(activeSet[changeInd]) != 0) + beta(activeSet[changeInd]) = 0; + } + + betaPath.push_back(beta); + + if (lassocond) + { + // Index is in position changeInd in activeSet. + if (useCholesky) + CholeskyDelete(changeInd); + + Deactivate(changeInd); + } + + corr = vecXTy - trans(dataRef) * yHat; + if (elasticNet) + corr -= lambda2 * beta; + + double curLambda = 0; + for (size_t i = 0; i < activeSet.size(); ++i) + curLambda += fabs(corr(activeSet[i])); + + curLambda /= ((double) activeSet.size()); + + lambdaPath.push_back(curLambda); + + // Time to stop for LASSO? + if (lasso) + { + if (curLambda <= lambda1) + { + InterpolateBeta(); + break; + } + } + } + + // Unfortunate copy... + beta = betaPath.back(); + + return ComputeError(matX, y, !transposeData); +} + +inline double LARS::Train(const arma::mat& data, + const arma::rowvec& responses, + const bool transposeData) +{ + arma::vec beta; + return Train(data, responses, beta, transposeData); +} + +inline void LARS::Predict(const arma::mat& points, + arma::rowvec& predictions, + const bool rowMajor) const +{ + // We really only need to store beta internally... + if (rowMajor) + predictions = trans(points * betaPath.back()); + else + predictions = betaPath.back().t() * points; +} + +// Private functions. +inline void LARS::Deactivate(const size_t activeVarInd) +{ + isActive[activeSet[activeVarInd]] = false; + activeSet.erase(activeSet.begin() + activeVarInd); +} + +inline void LARS::Activate(const size_t varInd) +{ + isActive[varInd] = true; + activeSet.push_back(varInd); +} + +inline void LARS::Ignore(const size_t varInd) +{ + isIgnored[varInd] = true; + ignoreSet.push_back(varInd); +} + +inline void LARS::ComputeYHatDirection(const arma::mat& matX, + const arma::vec& betaDirection, + arma::vec& yHatDirection) +{ + yHatDirection.fill(0); + for (size_t i = 0; i < activeSet.size(); ++i) + yHatDirection += betaDirection(i) * matX.col(activeSet[i]); +} + +inline void LARS::InterpolateBeta() +{ + int pathLength = betaPath.size(); + + // interpolate beta and stop + double ultimateLambda = lambdaPath[pathLength - 1]; + double penultimateLambda = lambdaPath[pathLength - 2]; + double interp = (penultimateLambda - lambda1) + / (penultimateLambda - ultimateLambda); + + betaPath[pathLength - 1] = (1 - interp) * (betaPath[pathLength - 2]) + + interp * betaPath[pathLength - 1]; + + lambdaPath[pathLength - 1] = lambda1; +} + +inline void LARS::CholeskyInsert(const arma::vec& newX, + const arma::mat& X) +{ + if (matUtriCholFactor.n_rows == 0) + { + matUtriCholFactor = arma::mat(1, 1); + + if (elasticNet) + matUtriCholFactor(0, 0) = sqrt(dot(newX, newX) + lambda2); + else + matUtriCholFactor(0, 0) = norm(newX, 2); + } + else + { + arma::vec newGramCol = trans(X) * newX; + CholeskyInsert(dot(newX, newX), newGramCol); + } +} + +inline void LARS::CholeskyInsert(double sqNormNewX, + const arma::vec& newGramCol) +{ + int n = matUtriCholFactor.n_rows; + + if (n == 0) + { + matUtriCholFactor = arma::mat(1, 1); + + if (elasticNet) + matUtriCholFactor(0, 0) = sqrt(sqNormNewX + lambda2); + else + matUtriCholFactor(0, 0) = sqrt(sqNormNewX); + } + else + { + arma::mat matNewR = arma::mat(n + 1, n + 1); + + if (elasticNet) + sqNormNewX += lambda2; + + arma::vec matUtriCholFactork = solve(trimatl(trans(matUtriCholFactor)), + newGramCol); + + matNewR(arma::span(0, n - 1), arma::span(0, n - 1)) = matUtriCholFactor; + matNewR(arma::span(0, n - 1), n) = matUtriCholFactork; + matNewR(n, arma::span(0, n - 1)).fill(0.0); + matNewR(n, n) = sqrt(sqNormNewX - dot(matUtriCholFactork, + matUtriCholFactork)); + + matUtriCholFactor = matNewR; + } +} + +inline void LARS::GivensRotate(const arma::vec::fixed<2>& x, + arma::vec::fixed<2>& rotatedX, + arma::mat& matG) +{ + if (x(1) == 0) + { + matG = arma::eye(2, 2); + rotatedX = x; + } + else + { + double r = norm(x, 2); + matG = arma::mat(2, 2); + + double scaledX1 = x(0) / r; + double scaledX2 = x(1) / r; + + matG(0, 0) = scaledX1; + matG(1, 0) = -scaledX2; + matG(0, 1) = scaledX2; + matG(1, 1) = scaledX1; + + rotatedX = arma::vec(2); + rotatedX(0) = r; + rotatedX(1) = 0; + } +} + +inline void LARS::CholeskyDelete(const size_t colToKill) +{ + size_t n = matUtriCholFactor.n_rows; + + if (colToKill == (n - 1)) + { + matUtriCholFactor = matUtriCholFactor(arma::span(0, n - 2), + arma::span(0, n - 2)); + } + else + { + matUtriCholFactor.shed_col(colToKill); // remove column colToKill + n--; + + for (size_t k = colToKill; k < n; ++k) + { + arma::mat matG; + arma::vec::fixed<2> rotatedVec; + GivensRotate(matUtriCholFactor(arma::span(k, k + 1), k), rotatedVec, + matG); + matUtriCholFactor(arma::span(k, k + 1), k) = rotatedVec; + if (k < n - 1) + { + matUtriCholFactor(arma::span(k, k + 1), arma::span(k + 1, n - 1)) = + matG * matUtriCholFactor(arma::span(k, k + 1), + arma::span(k + 1, n - 1)); + } + } + + matUtriCholFactor.shed_row(n); + } +} + +inline double LARS::ComputeError(const arma::mat& matX, + const arma::rowvec& y, + const bool rowMajor) +{ + if (rowMajor) + { + return arma::accu(arma::pow(y - trans(matX * betaPath.back()), 2.0)); + } + + else + { + return arma::accu(arma::pow(y - betaPath.back().t() * matX, 2.0)); + } +} + /** * Serialize the LARS model. */ From 4eb92bf96009edcc9699f7275a952cbf8d6a17d6 Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Tue, 10 May 2022 14:48:50 +0530 Subject: [PATCH 37/57] converted kde to .hpp --- src/mlpack/methods/kde/CMakeLists.txt | 1 - src/mlpack/methods/kde/kde_model.cpp | 319 ---------------------- src/mlpack/methods/kde/kde_model_impl.hpp | 304 +++++++++++++++++++++ 3 files changed, 304 insertions(+), 320 deletions(-) delete mode 100644 src/mlpack/methods/kde/kde_model.cpp diff --git a/src/mlpack/methods/kde/CMakeLists.txt b/src/mlpack/methods/kde/CMakeLists.txt index e4a6f5d0c4..e438aeab88 100644 --- a/src/mlpack/methods/kde/CMakeLists.txt +++ b/src/mlpack/methods/kde/CMakeLists.txt @@ -8,7 +8,6 @@ set(SOURCES kde_stat.hpp kde_model.hpp kde_model_impl.hpp - kde_model.cpp ) # Add directory name to sources. diff --git a/src/mlpack/methods/kde/kde_model.cpp b/src/mlpack/methods/kde/kde_model.cpp deleted file mode 100644 index 474809c822..0000000000 --- a/src/mlpack/methods/kde/kde_model.cpp +++ /dev/null @@ -1,319 +0,0 @@ -/** - * @file methods/kde/kde_model.cpp - * @author Roberto Hueso - * - * Implementation of KDE Model. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#include "kde_model.hpp" - -namespace mlpack { -namespace kde { - -//! Initialize the KDEModel with the given parameters. -KDEModel::KDEModel(const double bandwidth, - const double relError, - const double absError, - const KernelTypes kernelType, - const TreeTypes treeType, - const bool monteCarlo, - const double mcProb, - const size_t initialSampleSize, - const double mcEntryCoef, - const double mcBreakCoef) : - bandwidth(bandwidth), - relError(relError), - absError(absError), - kernelType(kernelType), - treeType(treeType), - monteCarlo(monteCarlo), - mcProb(mcProb), - initialSampleSize(initialSampleSize), - mcEntryCoef(mcEntryCoef), - mcBreakCoef(mcBreakCoef), - kdeModel(NULL) -{ - // Nothing to do. -} - -// Copy constructor. -KDEModel::KDEModel(const KDEModel& other) : - bandwidth(other.bandwidth), - relError(other.relError), - absError(other.absError), - kernelType(other.kernelType), - treeType(other.treeType), - monteCarlo(other.monteCarlo), - mcProb(other.mcProb), - initialSampleSize(other.initialSampleSize), - mcEntryCoef(other.mcEntryCoef), - mcBreakCoef(other.mcBreakCoef), - kdeModel(other.kdeModel->Clone()) -{ - // Nothing to do. -} - -// Move constructor. -KDEModel::KDEModel(KDEModel&& other) : - bandwidth(other.bandwidth), - relError(other.relError), - absError(other.absError), - kernelType(other.kernelType), - treeType(other.treeType), - monteCarlo(other.monteCarlo), - mcProb(other.mcProb), - initialSampleSize(other.initialSampleSize), - mcEntryCoef(other.mcEntryCoef), - mcBreakCoef(other.mcBreakCoef), - kdeModel(std::move(other.kdeModel)) -{ - // Reset other model. - other.bandwidth = 1.0; - other.relError = KDEDefaultParams::relError; - other.absError = KDEDefaultParams::absError; - other.kernelType = KernelTypes::GAUSSIAN_KERNEL; - other.treeType = TreeTypes::KD_TREE; - other.monteCarlo = KDEDefaultParams::monteCarlo; - other.mcProb = KDEDefaultParams::mcProb; - other.initialSampleSize = KDEDefaultParams::initialSampleSize; - other.mcEntryCoef = KDEDefaultParams::mcEntryCoef; - other.mcBreakCoef = KDEDefaultParams::mcBreakCoef; -} - -KDEModel& KDEModel::operator=(const KDEModel& other) -{ - if (this != &other) - { - delete kdeModel; - - bandwidth = other.bandwidth; - relError = other.relError; - absError = other.absError; - kernelType = other.kernelType; - treeType = other.treeType; - monteCarlo = other.monteCarlo; - mcProb = other.mcProb; - initialSampleSize = other.initialSampleSize; - mcEntryCoef = other.mcEntryCoef; - mcBreakCoef = other.mcBreakCoef; - kdeModel = other.kdeModel->Clone(); - } - - return *this; -} - -KDEModel& KDEModel::operator=(KDEModel&& other) -{ - if (this != &other) - { - delete kdeModel; - - bandwidth = other.bandwidth; - relError = other.relError; - absError = other.absError; - kernelType = other.kernelType; - treeType = other.treeType; - monteCarlo = other.monteCarlo; - mcProb = other.mcProb; - initialSampleSize = other.initialSampleSize; - mcEntryCoef = other.mcEntryCoef; - mcBreakCoef = other.mcBreakCoef; - kdeModel = std::move(other.kdeModel); - - // Reset other model. - other.bandwidth = 1.0; - other.relError = KDEDefaultParams::relError; - other.absError = KDEDefaultParams::absError; - other.kernelType = KernelTypes::GAUSSIAN_KERNEL; - other.treeType = TreeTypes::KD_TREE; - other.monteCarlo = KDEDefaultParams::monteCarlo; - other.mcProb = KDEDefaultParams::mcProb; - other.initialSampleSize = KDEDefaultParams::initialSampleSize; - other.mcEntryCoef = KDEDefaultParams::mcEntryCoef; - other.mcBreakCoef = KDEDefaultParams::mcBreakCoef; - } - - return *this; -} - -// Clean memory. -KDEModel::~KDEModel() -{ - delete kdeModel; -} - -template class TreeType> -KDEWrapperBase* InitializeModelHelper(const KDEModel::KernelTypes kernelType, - const double relError, - const double absError, - const double bandwidth) -{ - switch (kernelType) - { - case KDEModel::GAUSSIAN_KERNEL: - return new KDEWrapper( - relError, absError, kernel::GaussianKernel(bandwidth)); - - case KDEModel::EPANECHNIKOV_KERNEL: - return new KDEWrapper( - relError, absError, kernel::EpanechnikovKernel(bandwidth)); - - case KDEModel::LAPLACIAN_KERNEL: - return new KDEWrapper( - relError, absError, kernel::LaplacianKernel(bandwidth)); - - case KDEModel::SPHERICAL_KERNEL: - return new KDEWrapper( - relError, absError, kernel::SphericalKernel(bandwidth)); - - case KDEModel::TRIANGULAR_KERNEL: - return new KDEWrapper( - relError, absError, kernel::TriangularKernel(bandwidth)); - } - - // This should never happen. - return NULL; -} - -void KDEModel::InitializeModel() -{ - // Clean memory, if necessary. - delete kdeModel; - - // Build the actual model. - switch (treeType) - { - case KD_TREE: - kdeModel = InitializeModelHelper(kernelType, relError, - absError, bandwidth); - break; - - case BALL_TREE: - kdeModel = InitializeModelHelper(kernelType, relError, - absError, bandwidth); - break; - - case COVER_TREE: - kdeModel = InitializeModelHelper(kernelType, - relError, absError, bandwidth); - break; - - case OCTREE: - kdeModel = InitializeModelHelper(kernelType, relError, - absError, bandwidth); - break; - - case R_TREE: - kdeModel = InitializeModelHelper(kernelType, relError, - absError, bandwidth); - break; - } -} - -void KDEModel::BuildModel(util::Timers& timers, arma::mat&& referenceSet) -{ - InitializeModel(); - - // Set whether to use Monte Carlo estimations or not. - kdeModel->MonteCarlo() = monteCarlo; - - // Set Monte Carlo probability. - kdeModel->MCProb(mcProb); - - // Set Monte Carlo initial sample size. - kdeModel->MCInitialSampleSize() = initialSampleSize; - - // Set Monte Carlo entry coefficient. - kdeModel->MCEntryCoef(mcEntryCoef); - - // Set Monte Carlo break coefficient. - kdeModel->MCBreakCoef(mcBreakCoef); - - // Train the model. - kdeModel->Train(timers, std::move(referenceSet)); -} - -// Perform bichromatic evaluation. -void KDEModel::Evaluate(util::Timers& timers, - arma::mat&& querySet, - arma::vec& estimates) -{ - kdeModel->Evaluate(timers, std::move(querySet), estimates); -} - -// Perform monochromatic evaluation. -void KDEModel::Evaluate(util::Timers& timers, arma::vec& estimates) -{ - kdeModel->Evaluate(timers, estimates); -} - -// Clean memory. -void KDEModel::CleanMemory() -{ - delete kdeModel; -} - -// Modify model kernel bandwidth. -void KDEModel::Bandwidth(const double newBandwidth) -{ - bandwidth = newBandwidth; - kdeModel->Bandwidth(bandwidth); -} - -// Modify model relative error tolerance. -void KDEModel::RelativeError(const double newRelError) -{ - relError = newRelError; - kdeModel->RelativeError(relError); -} - -// Modify model absolute error tolerance. -void KDEModel::AbsoluteError(const double newAbsError) -{ - absError = newAbsError; - kdeModel->AbsoluteError(absError); -} - -// Modify whether Monte Carlo estimations will be used. -void KDEModel::MonteCarlo(const bool newMonteCarlo) -{ - monteCarlo = newMonteCarlo; - kdeModel->MonteCarlo() = monteCarlo; -} - -// Modify model Monte Carlo probability. -void KDEModel::MCProbability(const double newMCProb) -{ - mcProb = newMCProb; - kdeModel->MCProb(mcProb); -} - -// Modify model Monte Carlo initial sample size. -void KDEModel::MCInitialSampleSize(const size_t newSampleSize) -{ - initialSampleSize = newSampleSize; - kdeModel->MCInitialSampleSize() = initialSampleSize; -} - -// Modify model Monte Carlo entry coefficient. -void KDEModel::MCEntryCoefficient(const double newEntryCoef) -{ - mcEntryCoef = newEntryCoef; - kdeModel->MCEntryCoef(mcEntryCoef); -} - -// Modify model Monte Carlo break coefficient. -void KDEModel::MCBreakCoefficient(const double newBreakCoef) -{ - mcBreakCoef = newBreakCoef; - kdeModel->MCBreakCoef(mcBreakCoef); -} - -} // namespace kde -} // namespace mlpack diff --git a/src/mlpack/methods/kde/kde_model_impl.hpp b/src/mlpack/methods/kde/kde_model_impl.hpp index 8ff96ba666..4d6e075334 100644 --- a/src/mlpack/methods/kde/kde_model_impl.hpp +++ b/src/mlpack/methods/kde/kde_model_impl.hpp @@ -18,6 +18,310 @@ namespace mlpack { namespace kde { +//! Initialize the KDEModel with the given parameters. +inline KDEModel::KDEModel( + const double bandwidth, + const double relError, + const double absError, + const KernelTypes kernelType, + const TreeTypes treeType, + const bool monteCarlo, + const double mcProb, + const size_t initialSampleSize, + const double mcEntryCoef, + const double mcBreakCoef) : + bandwidth(bandwidth), + relError(relError), + absError(absError), + kernelType(kernelType), + treeType(treeType), + monteCarlo(monteCarlo), + mcProb(mcProb), + initialSampleSize(initialSampleSize), + mcEntryCoef(mcEntryCoef), + mcBreakCoef(mcBreakCoef), + kdeModel(NULL) +{ + // Nothing to do. +} + +// Copy constructor. +inline KDEModel::KDEModel(const KDEModel& other) : + bandwidth(other.bandwidth), + relError(other.relError), + absError(other.absError), + kernelType(other.kernelType), + treeType(other.treeType), + monteCarlo(other.monteCarlo), + mcProb(other.mcProb), + initialSampleSize(other.initialSampleSize), + mcEntryCoef(other.mcEntryCoef), + mcBreakCoef(other.mcBreakCoef), + kdeModel(other.kdeModel->Clone()) +{ + // Nothing to do. +} + +// Move constructor. +inline KDEModel::KDEModel(KDEModel&& other) : + bandwidth(other.bandwidth), + relError(other.relError), + absError(other.absError), + kernelType(other.kernelType), + treeType(other.treeType), + monteCarlo(other.monteCarlo), + mcProb(other.mcProb), + initialSampleSize(other.initialSampleSize), + mcEntryCoef(other.mcEntryCoef), + mcBreakCoef(other.mcBreakCoef), + kdeModel(std::move(other.kdeModel)) +{ + // Reset other model. + other.bandwidth = 1.0; + other.relError = KDEDefaultParams::relError; + other.absError = KDEDefaultParams::absError; + other.kernelType = KernelTypes::GAUSSIAN_KERNEL; + other.treeType = TreeTypes::KD_TREE; + other.monteCarlo = KDEDefaultParams::monteCarlo; + other.mcProb = KDEDefaultParams::mcProb; + other.initialSampleSize = KDEDefaultParams::initialSampleSize; + other.mcEntryCoef = KDEDefaultParams::mcEntryCoef; + other.mcBreakCoef = KDEDefaultParams::mcBreakCoef; +} + +inline KDEModel& KDEModel::operator=(const KDEModel& other) +{ + if (this != &other) + { + delete kdeModel; + + bandwidth = other.bandwidth; + relError = other.relError; + absError = other.absError; + kernelType = other.kernelType; + treeType = other.treeType; + monteCarlo = other.monteCarlo; + mcProb = other.mcProb; + initialSampleSize = other.initialSampleSize; + mcEntryCoef = other.mcEntryCoef; + mcBreakCoef = other.mcBreakCoef; + kdeModel = other.kdeModel->Clone(); + } + + return *this; +} + +inline KDEModel& KDEModel::operator=(KDEModel&& other) +{ + if (this != &other) + { + delete kdeModel; + + bandwidth = other.bandwidth; + relError = other.relError; + absError = other.absError; + kernelType = other.kernelType; + treeType = other.treeType; + monteCarlo = other.monteCarlo; + mcProb = other.mcProb; + initialSampleSize = other.initialSampleSize; + mcEntryCoef = other.mcEntryCoef; + mcBreakCoef = other.mcBreakCoef; + kdeModel = std::move(other.kdeModel); + + // Reset other model. + other.bandwidth = 1.0; + other.relError = KDEDefaultParams::relError; + other.absError = KDEDefaultParams::absError; + other.kernelType = KernelTypes::GAUSSIAN_KERNEL; + other.treeType = TreeTypes::KD_TREE; + other.monteCarlo = KDEDefaultParams::monteCarlo; + other.mcProb = KDEDefaultParams::mcProb; + other.initialSampleSize = KDEDefaultParams::initialSampleSize; + other.mcEntryCoef = KDEDefaultParams::mcEntryCoef; + other.mcBreakCoef = KDEDefaultParams::mcBreakCoef; + } + + return *this; +} + +// Clean memory. +inline KDEModel::~KDEModel() +{ + delete kdeModel; +} + +template class TreeType> +KDEWrapperBase* InitializeModelHelper(const KDEModel::KernelTypes kernelType, + const double relError, + const double absError, + const double bandwidth) +{ + switch (kernelType) + { + case KDEModel::GAUSSIAN_KERNEL: + return new KDEWrapper( + relError, absError, kernel::GaussianKernel(bandwidth)); + + case KDEModel::EPANECHNIKOV_KERNEL: + return new KDEWrapper( + relError, absError, kernel::EpanechnikovKernel(bandwidth)); + + case KDEModel::LAPLACIAN_KERNEL: + return new KDEWrapper( + relError, absError, kernel::LaplacianKernel(bandwidth)); + + case KDEModel::SPHERICAL_KERNEL: + return new KDEWrapper( + relError, absError, kernel::SphericalKernel(bandwidth)); + + case KDEModel::TRIANGULAR_KERNEL: + return new KDEWrapper( + relError, absError, kernel::TriangularKernel(bandwidth)); + } + + // This should never happen. + return NULL; +} + +inline void KDEModel::InitializeModel() +{ + // Clean memory, if necessary. + delete kdeModel; + + // Build the actual model. + switch (treeType) + { + case KD_TREE: + kdeModel = InitializeModelHelper(kernelType, relError, + absError, bandwidth); + break; + + case BALL_TREE: + kdeModel = InitializeModelHelper(kernelType, relError, + absError, bandwidth); + break; + + case COVER_TREE: + kdeModel = InitializeModelHelper(kernelType, + relError, absError, bandwidth); + break; + + case OCTREE: + kdeModel = InitializeModelHelper(kernelType, relError, + absError, bandwidth); + break; + + case R_TREE: + kdeModel = InitializeModelHelper(kernelType, relError, + absError, bandwidth); + break; + } +} + +inline void KDEModel::BuildModel(util::Timers& timers, + arma::mat&& referenceSet) +{ + InitializeModel(); + + // Set whether to use Monte Carlo estimations or not. + kdeModel->MonteCarlo() = monteCarlo; + + // Set Monte Carlo probability. + kdeModel->MCProb(mcProb); + + // Set Monte Carlo initial sample size. + kdeModel->MCInitialSampleSize() = initialSampleSize; + + // Set Monte Carlo entry coefficient. + kdeModel->MCEntryCoef(mcEntryCoef); + + // Set Monte Carlo break coefficient. + kdeModel->MCBreakCoef(mcBreakCoef); + + // Train the model. + kdeModel->Train(timers, std::move(referenceSet)); +} + +// Perform bichromatic evaluation. +inline void KDEModel::Evaluate(util::Timers& timers, + arma::mat&& querySet, + arma::vec& estimates) +{ + kdeModel->Evaluate(timers, std::move(querySet), estimates); +} + +// Perform monochromatic evaluation. +inline void KDEModel::Evaluate(util::Timers& timers, + arma::vec& estimates) +{ + kdeModel->Evaluate(timers, estimates); +} + +// Clean memory. +inline void KDEModel::CleanMemory() +{ + delete kdeModel; +} + +// Modify model kernel bandwidth. +inline void KDEModel::Bandwidth(const double newBandwidth) +{ + bandwidth = newBandwidth; + kdeModel->Bandwidth(bandwidth); +} + +// Modify model relative error tolerance. +inline void KDEModel::RelativeError(const double newRelError) +{ + relError = newRelError; + kdeModel->RelativeError(relError); +} + +// Modify model absolute error tolerance. +inline void KDEModel::AbsoluteError(const double newAbsError) +{ + absError = newAbsError; + kdeModel->AbsoluteError(absError); +} + +// Modify whether Monte Carlo estimations will be used. +inline void KDEModel::MonteCarlo(const bool newMonteCarlo) +{ + monteCarlo = newMonteCarlo; + kdeModel->MonteCarlo() = monteCarlo; +} + +// Modify model Monte Carlo probability. +inline void KDEModel::MCProbability(const double newMCProb) +{ + mcProb = newMCProb; + kdeModel->MCProb(mcProb); +} + +// Modify model Monte Carlo initial sample size. +inline void KDEModel::MCInitialSampleSize(const size_t newSampleSize) +{ + initialSampleSize = newSampleSize; + kdeModel->MCInitialSampleSize() = initialSampleSize; +} + +// Modify model Monte Carlo entry coefficient. +inline void KDEModel::MCEntryCoefficient(const double newEntryCoef) +{ + mcEntryCoef = newEntryCoef; + kdeModel->MCEntryCoef(mcEntryCoef); +} + +// Modify model Monte Carlo break coefficient. +inline void KDEModel::MCBreakCoefficient(const double newBreakCoef) +{ + mcBreakCoef = newBreakCoef; + kdeModel->MCBreakCoef(mcBreakCoef); +} + //! Train the model (build the tree). template Date: Tue, 10 May 2022 14:49:31 +0530 Subject: [PATCH 38/57] added missing include in lcc --- src/mlpack/methods/local_coordinate_coding/lcc.hpp | 1 + 1 file changed, 1 insertion(+) diff --git a/src/mlpack/methods/local_coordinate_coding/lcc.hpp b/src/mlpack/methods/local_coordinate_coding/lcc.hpp index 373907586b..b489fb07f4 100644 --- a/src/mlpack/methods/local_coordinate_coding/lcc.hpp +++ b/src/mlpack/methods/local_coordinate_coding/lcc.hpp @@ -15,6 +15,7 @@ #include #include +#include // Include three simple dictionary initializers from sparse coding. #include From 1e41d704f786766045295dcd4b833c9d33dee1f0 Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Tue, 10 May 2022 15:01:58 +0530 Subject: [PATCH 39/57] converted hoeffding tree model to .hpp --- .../methods/hoeffding_trees/CMakeLists.txt | 2 +- .../hoeffding_trees/hoeffding_tree_model.hpp | 4 ++ ...odel.cpp => hoeffding_tree_model_impl.hpp} | 51 +++++++++++-------- 3 files changed, 35 insertions(+), 22 deletions(-) rename src/mlpack/methods/hoeffding_trees/{hoeffding_tree_model.cpp => hoeffding_tree_model_impl.hpp} (84%) diff --git a/src/mlpack/methods/hoeffding_trees/CMakeLists.txt b/src/mlpack/methods/hoeffding_trees/CMakeLists.txt index 69f390f6ea..acf4f47b8a 100644 --- a/src/mlpack/methods/hoeffding_trees/CMakeLists.txt +++ b/src/mlpack/methods/hoeffding_trees/CMakeLists.txt @@ -13,7 +13,7 @@ set(SOURCES hoeffding_tree.hpp hoeffding_tree_impl.hpp hoeffding_tree_model.hpp - hoeffding_tree_model.cpp + hoeffding_tree_model_impl.hpp information_gain.hpp numeric_split_info.hpp typedef.hpp diff --git a/src/mlpack/methods/hoeffding_trees/hoeffding_tree_model.hpp b/src/mlpack/methods/hoeffding_trees/hoeffding_tree_model.hpp index f375a622c2..1ae7dee758 100644 --- a/src/mlpack/methods/hoeffding_trees/hoeffding_tree_model.hpp +++ b/src/mlpack/methods/hoeffding_trees/hoeffding_tree_model.hpp @@ -15,6 +15,7 @@ #include "hoeffding_tree.hpp" #include "binary_numeric_split.hpp" #include "information_gain.hpp" +#include namespace mlpack { namespace tree { @@ -220,4 +221,7 @@ class HoeffdingTreeModel } // namespace tree } // namespace mlpack +// Include implementation. +#include "hoeffding_tree_model_impl.hpp" + #endif diff --git a/src/mlpack/methods/hoeffding_trees/hoeffding_tree_model.cpp b/src/mlpack/methods/hoeffding_trees/hoeffding_tree_model_impl.hpp similarity index 84% rename from src/mlpack/methods/hoeffding_trees/hoeffding_tree_model.cpp rename to src/mlpack/methods/hoeffding_trees/hoeffding_tree_model_impl.hpp index 2dfe857edf..a02eebec65 100644 --- a/src/mlpack/methods/hoeffding_trees/hoeffding_tree_model.cpp +++ b/src/mlpack/methods/hoeffding_trees/hoeffding_tree_model_impl.hpp @@ -1,5 +1,5 @@ /** - * @param hoeffding_tree_model.cpp + * @file methods/hoeffding_trees/hoeffding_tree_model_impl.hpp * @author Ryan Curtin * * Implementation of the HoeffdingTreeModel class. @@ -9,15 +9,16 @@ * 3-clause BSD license along with mlpack. If not, see * http://www.opensource.org/licenses/BSD-3-Clause for more information. */ +#ifndef MLPACK_METHODS_HOEFFDING_TREE_HOEFFDING_TREE_MODEL_IMPL_HPP +#define MLPACK_METHODS_HOEFFDING_TREE_HOEFFDING_TREE_MODEL_IMPL_HPP + #include "hoeffding_tree_model.hpp" -#include - -using namespace mlpack; -using namespace mlpack::tree; +namespace mlpack { +namespace tree { // Constructor. -HoeffdingTreeModel::HoeffdingTreeModel(const TreeType& type) : +inline HoeffdingTreeModel::HoeffdingTreeModel(const TreeType& type) : type(type), giniHoeffdingTree(NULL), giniBinaryTree(NULL), @@ -28,7 +29,7 @@ HoeffdingTreeModel::HoeffdingTreeModel(const TreeType& type) : } // Copy constructor. -HoeffdingTreeModel::HoeffdingTreeModel(const HoeffdingTreeModel& other) : +inline HoeffdingTreeModel::HoeffdingTreeModel(const HoeffdingTreeModel& other) : type(other.type), giniHoeffdingTree(other.giniHoeffdingTree ? new GiniHoeffdingTreeType( *other.giniHoeffdingTree) : NULL), @@ -43,7 +44,7 @@ HoeffdingTreeModel::HoeffdingTreeModel(const HoeffdingTreeModel& other) : } // Move constructor. -HoeffdingTreeModel::HoeffdingTreeModel(HoeffdingTreeModel&& other) : +inline HoeffdingTreeModel::HoeffdingTreeModel(HoeffdingTreeModel&& other) : type(other.type), giniHoeffdingTree(other.giniHoeffdingTree), giniBinaryTree(other.giniBinaryTree), @@ -59,7 +60,7 @@ HoeffdingTreeModel::HoeffdingTreeModel(HoeffdingTreeModel&& other) : } // Copy operator. -HoeffdingTreeModel& HoeffdingTreeModel::operator=( +inline HoeffdingTreeModel& HoeffdingTreeModel::operator=( const HoeffdingTreeModel& other) { if (this != &other) @@ -90,7 +91,8 @@ HoeffdingTreeModel& HoeffdingTreeModel::operator=( } // Move operator. -HoeffdingTreeModel& HoeffdingTreeModel::operator=(HoeffdingTreeModel&& other) +inline HoeffdingTreeModel& HoeffdingTreeModel::operator=( + HoeffdingTreeModel&& other) { if (this != &other) { @@ -117,7 +119,7 @@ HoeffdingTreeModel& HoeffdingTreeModel::operator=(HoeffdingTreeModel&& other) } // Destructor. -HoeffdingTreeModel::~HoeffdingTreeModel() +inline HoeffdingTreeModel::~HoeffdingTreeModel() { delete giniHoeffdingTree; delete giniBinaryTree; @@ -126,7 +128,7 @@ HoeffdingTreeModel::~HoeffdingTreeModel() } // Create the model. -void HoeffdingTreeModel::BuildModel( +inline void HoeffdingTreeModel::BuildModel( const arma::mat& dataset, const data::DatasetInfo& datasetInfo, const arma::Row& labels, @@ -189,9 +191,9 @@ void HoeffdingTreeModel::BuildModel( } // Train the model on one pass of the dataset. -void HoeffdingTreeModel::Train(const arma::mat& dataset, - const arma::Row& labels, - const bool batchTraining) +inline void HoeffdingTreeModel::Train(const arma::mat& dataset, + const arma::Row& labels, + const bool batchTraining) { // Depending on the type, pass through once. switch (type) @@ -215,8 +217,9 @@ void HoeffdingTreeModel::Train(const arma::mat& dataset, } // Classify the given points. -void HoeffdingTreeModel::Classify(const arma::mat& dataset, - arma::Row& predictions) const +inline void HoeffdingTreeModel::Classify(const arma::mat& dataset, + arma::Row& predictions) + const { // Call Classify() with the right model. switch (type) @@ -240,9 +243,10 @@ void HoeffdingTreeModel::Classify(const arma::mat& dataset, } // Classify the given points. -void HoeffdingTreeModel::Classify(const arma::mat& dataset, - arma::Row& predictions, - arma::rowvec& probabilities) const +inline void HoeffdingTreeModel::Classify(const arma::mat& dataset, + arma::Row& predictions, + arma::rowvec& probabilities) + const { // Call Classify() with the right model. switch (type) @@ -286,7 +290,7 @@ size_t CountNodes(TreeType& tree) } // Get the number of nodes in the tree. -size_t HoeffdingTreeModel::NumNodes() const +inline size_t HoeffdingTreeModel::NumNodes() const { // Call CountNodes() with the right type of tree. switch (type) @@ -303,3 +307,8 @@ size_t HoeffdingTreeModel::NumNodes() const return 0; // This should never happen! } + +} // namespace tree +} // namespace mlpack + +#endif From 836a80111e77599c4c1c9a69b95924b8a510df6b Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Tue, 10 May 2022 15:38:54 +0530 Subject: [PATCH 40/57] converted gmm to .hpp --- src/mlpack/methods/gmm/CMakeLists.txt | 2 - src/mlpack/methods/gmm/diagonal_gmm.cpp | 230 ----------------- src/mlpack/methods/gmm/diagonal_gmm.hpp | 1 + src/mlpack/methods/gmm/diagonal_gmm_impl.hpp | 212 ++++++++++++++++ src/mlpack/methods/gmm/gmm.cpp | 247 ------------------- src/mlpack/methods/gmm/gmm.hpp | 6 +- src/mlpack/methods/gmm/gmm_impl.hpp | 226 +++++++++++++++++ 7 files changed, 443 insertions(+), 481 deletions(-) delete mode 100644 src/mlpack/methods/gmm/diagonal_gmm.cpp delete mode 100644 src/mlpack/methods/gmm/gmm.cpp diff --git a/src/mlpack/methods/gmm/CMakeLists.txt b/src/mlpack/methods/gmm/CMakeLists.txt index abbfb90668..c351d45607 100644 --- a/src/mlpack/methods/gmm/CMakeLists.txt +++ b/src/mlpack/methods/gmm/CMakeLists.txt @@ -2,10 +2,8 @@ # Anything not in this list will not be compiled into mlpack. set(SOURCES gmm.hpp - gmm.cpp gmm_impl.hpp diagonal_gmm.hpp - diagonal_gmm.cpp diagonal_gmm_impl.hpp em_fit.hpp em_fit_impl.hpp diff --git a/src/mlpack/methods/gmm/diagonal_gmm.cpp b/src/mlpack/methods/gmm/diagonal_gmm.cpp deleted file mode 100644 index ab7c30e1ac..0000000000 --- a/src/mlpack/methods/gmm/diagonal_gmm.cpp +++ /dev/null @@ -1,230 +0,0 @@ -/** - * @file methods/gmm/diagonal_gmm.cpp - * @author Kim SangYeon - * - * Implementation of template-based GMM methods. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ - -#include "diagonal_gmm.hpp" -#include - -namespace mlpack { -namespace gmm { - -/** - * Create a DiagonalGMM with the given number of Gaussians, each of which have - * the specified dimensionality. The means and covariances will be set to 0. - * - * @param gaussians Number of Gaussians in this GMM. - * @param dimensionality Dimensionality of each Gaussian. - */ -DiagonalGMM::DiagonalGMM(const size_t gaussians, const size_t dimensionality) : - gaussians(gaussians), - dimensionality(dimensionality), - dists(gaussians, - distribution::DiagonalGaussianDistribution(dimensionality)), - weights(gaussians) -{ - // Set equal weights. Technically this model is still valid, but only barely. - weights.fill(1.0 / gaussians); -} - -// Copy constructor for when the other GMM uses the same fitting type. -DiagonalGMM::DiagonalGMM(const DiagonalGMM& other) : - gaussians(other.Gaussians()), - dimensionality(other.dimensionality), - dists(other.dists), - weights(other.weights) { /* Nothing to do. */ } - -DiagonalGMM& DiagonalGMM::operator=(const DiagonalGMM& other) -{ - gaussians = other.gaussians; - dimensionality = other.dimensionality; - dists = other.dists; - weights = other.weights; - - return *this; -} - -/** - * Return the log probability of the given observation being from this GMM. - */ -double DiagonalGMM::LogProbability(const arma::vec& observation) const -{ - // Sum the probability for each Gaussian in our mixture (and we have to - // multiply by the prior for each Gaussian too). - double sum = -std::numeric_limits::infinity(); - for (size_t i = 0; i < gaussians; ++i) - { - sum = math::LogAdd(sum, log(weights[i]) + - dists[i].LogProbability(observation)); - } - return sum; -} - -/** - * Return the log probability of the given observation GMM matrix. - * - * @param observation Observation matrix to compute log-probabilty. - * @param logProbs Stores the value of log-probability for input. - */ -void DiagonalGMM::LogProbability(const arma::mat& observation, - arma::vec& logProbs) const -{ - // Sum the probability for each Gaussian in our mixture (and we have to - // multiply by the prior for each Gaussian too). - logProbs.set_size(observation.n_cols); - - // Store log-probability value in a matrix. - arma::mat logProb(observation.n_cols, gaussians); - - // Assign value to the matrix. - for (size_t i = 0; i < gaussians; i++) - { - arma::vec temp(logProb.colptr(i), observation.n_cols, false, true); - dists[i].LogProbability(observation, temp); - } - - // Save log(weights) as a vector. - arma::vec logWeights = arma::log(weights); - - // Compute log-probability. - logProb += repmat(logWeights.t(), logProb.n_rows, 1); - math::LogSumExp(logProb, logProbs); -} - -/** - * Return the probability of the given observation being from this GMM. - */ -double DiagonalGMM::Probability(const arma::vec& observation) const -{ - return exp(LogProbability(observation)); -} - -/** - * Return the probability of the given observation GMM matrix. - * - * @param observation Observation matrix to compute probabilty. - * @param probs Stores the value of probability for observation. - */ -void DiagonalGMM::Probability(const arma::mat& observation, - arma::vec& probs) const -{ - LogProbability(observation, probs); - probs = exp(probs); -} - - -/** - * Return the log probability of the given observation being from the given - * component in the mixture. - */ -double DiagonalGMM::LogProbability(const arma::vec& observation, - const size_t component) const -{ - // We are only considering one Gaussian component -- so we only need to call - // Probability() once. We do consider the prior probability! - return log(weights[component]) + - dists[component].LogProbability(observation); -} - -/** - * Return the probability of the given observation being from the given - * component in the mixture. - */ -double DiagonalGMM::Probability(const arma::vec& observation, - const size_t component) const -{ - return exp(LogProbability(observation, component)); -} - -/** - * Return a randomly generated observation according to the probability - * distribution defined by this object. - */ -arma::vec DiagonalGMM::Random() const -{ - // Determine which Gaussian it will be coming from. - double gaussRand = math::Random(); - size_t gaussian = 0; - - double sumProb = 0; - for (size_t g = 0; g < gaussians; g++) - { - sumProb += weights(g); - if (gaussRand <= sumProb) - { - gaussian = g; - break; - } - } - - return arma::sqrt(dists[gaussian].Covariance()) % - arma::randn(dimensionality) + dists[gaussian].Mean(); -} - -/** - * Classify the given observations as being from an individual component in - * this GMM. - */ -void DiagonalGMM::Classify(const arma::mat& observations, - arma::Row& labels) const -{ - // This is not the best way to do this! - - // We should not have to fill this with values, because each one should be - // overwritten. - labels.set_size(observations.n_cols); - for (size_t i = 0; i < observations.n_cols; ++i) - { - // Find maximum probability component. - double probability = 0; - for (size_t j = 0; j < gaussians; ++j) - { - double newProb = Probability(observations.unsafe_col(i), j); - if (newProb >= probability) - { - probability = newProb; - labels[i] = j; - } - } - } -} - -/** - * Get the log-likelihood of this data's fit to the model. - */ -double DiagonalGMM::LogLikelihood( - const arma::mat& observations, - const std::vector& dists, - const arma::vec& weights) const -{ - double logLikelihood = 0; - arma::vec phis; - arma::mat likelihoods(gaussians, observations.n_cols); - - for (size_t i = 0; i < gaussians; ++i) - { - dists[i].Probability(observations, phis); - likelihoods.row(i) = weights(i) * trans(phis); - } - - // Now sum over every point. - for (size_t j = 0; j < observations.n_cols; ++j) - { - if (accu(likelihoods.col(j)) == 0) - Log::Info << "Likelihood of point " << j << " is 0! It is probably an " - << "outlier." << std::endl; - logLikelihood += log(accu(likelihoods.col(j))); - } - - return logLikelihood; -} - -} // namespace gmm -} // namespace mlpack diff --git a/src/mlpack/methods/gmm/diagonal_gmm.hpp b/src/mlpack/methods/gmm/diagonal_gmm.hpp index ef8fe9445a..937b4662b5 100644 --- a/src/mlpack/methods/gmm/diagonal_gmm.hpp +++ b/src/mlpack/methods/gmm/diagonal_gmm.hpp @@ -16,6 +16,7 @@ #include #include +#include // This is the default fitting method class. #include "em_fit.hpp" diff --git a/src/mlpack/methods/gmm/diagonal_gmm_impl.hpp b/src/mlpack/methods/gmm/diagonal_gmm_impl.hpp index 61a172c654..fc33988d4f 100644 --- a/src/mlpack/methods/gmm/diagonal_gmm_impl.hpp +++ b/src/mlpack/methods/gmm/diagonal_gmm_impl.hpp @@ -101,6 +101,218 @@ double DiagonalGMM::Train(const arma::mat& observations, return bestLikelihood; } +/** + * Create a DiagonalGMM with the given number of Gaussians, each of which have + * the specified dimensionality. The means and covariances will be set to 0. + * + * @param gaussians Number of Gaussians in this GMM. + * @param dimensionality Dimensionality of each Gaussian. + */ +inline DiagonalGMM::DiagonalGMM( + const size_t gaussians, + const size_t dimensionality) : + gaussians(gaussians), + dimensionality(dimensionality), + dists(gaussians, + distribution::DiagonalGaussianDistribution(dimensionality)), + weights(gaussians) +{ + // Set equal weights. Technically this model is still valid, but only barely. + weights.fill(1.0 / gaussians); +} + +// Copy constructor for when the other GMM uses the same fitting type. +inline DiagonalGMM::DiagonalGMM(const DiagonalGMM& other) : + gaussians(other.Gaussians()), + dimensionality(other.dimensionality), + dists(other.dists), + weights(other.weights) { /* Nothing to do. */ } + +inline DiagonalGMM& DiagonalGMM::operator=(const DiagonalGMM& other) +{ + gaussians = other.gaussians; + dimensionality = other.dimensionality; + dists = other.dists; + weights = other.weights; + + return *this; +} + +/** + * Return the log probability of the given observation being from this GMM. + */ +inline double DiagonalGMM::LogProbability(const arma::vec& observation) const +{ + // Sum the probability for each Gaussian in our mixture (and we have to + // multiply by the prior for each Gaussian too). + double sum = -std::numeric_limits::infinity(); + for (size_t i = 0; i < gaussians; ++i) + { + sum = math::LogAdd(sum, log(weights[i]) + + dists[i].LogProbability(observation)); + } + return sum; +} + +/** + * Return the log probability of the given observation GMM matrix. + * + * @param observation Observation matrix to compute log-probabilty. + * @param logProbs Stores the value of log-probability for input. + */ +inline void DiagonalGMM::LogProbability(const arma::mat& observation, + arma::vec& logProbs) const +{ + // Sum the probability for each Gaussian in our mixture (and we have to + // multiply by the prior for each Gaussian too). + logProbs.set_size(observation.n_cols); + + // Store log-probability value in a matrix. + arma::mat logProb(observation.n_cols, gaussians); + + // Assign value to the matrix. + for (size_t i = 0; i < gaussians; i++) + { + arma::vec temp(logProb.colptr(i), observation.n_cols, false, true); + dists[i].LogProbability(observation, temp); + } + + // Save log(weights) as a vector. + arma::vec logWeights = arma::log(weights); + + // Compute log-probability. + logProb += repmat(logWeights.t(), logProb.n_rows, 1); + math::LogSumExp(logProb, logProbs); +} + +/** + * Return the probability of the given observation being from this GMM. + */ +inline double DiagonalGMM::Probability(const arma::vec& observation) const +{ + return exp(LogProbability(observation)); +} + +/** + * Return the probability of the given observation GMM matrix. + * + * @param observation Observation matrix to compute probabilty. + * @param probs Stores the value of probability for observation. + */ +inline void DiagonalGMM::Probability(const arma::mat& observation, + arma::vec& probs) const +{ + LogProbability(observation, probs); + probs = exp(probs); +} + + +/** + * Return the log probability of the given observation being from the given + * component in the mixture. + */ +inline double DiagonalGMM::LogProbability(const arma::vec& observation, + const size_t component) const +{ + // We are only considering one Gaussian component -- so we only need to call + // Probability() once. We do consider the prior probability! + return log(weights[component]) + + dists[component].LogProbability(observation); +} + +/** + * Return the probability of the given observation being from the given + * component in the mixture. + */ +inline double DiagonalGMM::Probability(const arma::vec& observation, + const size_t component) const +{ + return exp(LogProbability(observation, component)); +} + +/** + * Return a randomly generated observation according to the probability + * distribution defined by this object. + */ +inline arma::vec DiagonalGMM::Random() const +{ + // Determine which Gaussian it will be coming from. + double gaussRand = math::Random(); + size_t gaussian = 0; + + double sumProb = 0; + for (size_t g = 0; g < gaussians; g++) + { + sumProb += weights(g); + if (gaussRand <= sumProb) + { + gaussian = g; + break; + } + } + + return arma::sqrt(dists[gaussian].Covariance()) % + arma::randn(dimensionality) + dists[gaussian].Mean(); +} + +/** + * Classify the given observations as being from an individual component in + * this GMM. + */ +inline void DiagonalGMM::Classify(const arma::mat& observations, + arma::Row& labels) const +{ + // This is not the best way to do this! + + // We should not have to fill this with values, because each one should be + // overwritten. + labels.set_size(observations.n_cols); + for (size_t i = 0; i < observations.n_cols; ++i) + { + // Find maximum probability component. + double probability = 0; + for (size_t j = 0; j < gaussians; ++j) + { + double newProb = Probability(observations.unsafe_col(i), j); + if (newProb >= probability) + { + probability = newProb; + labels[i] = j; + } + } + } +} + +/** + * Get the log-likelihood of this data's fit to the model. + */ +inline double DiagonalGMM::LogLikelihood( + const arma::mat& observations, + const std::vector& dists, + const arma::vec& weights) const +{ + double logLikelihood = 0; + arma::vec phis; + arma::mat likelihoods(gaussians, observations.n_cols); + + for (size_t i = 0; i < gaussians; ++i) + { + dists[i].Probability(observations, phis); + likelihoods.row(i) = weights(i) * trans(phis); + } + + // Now sum over every point. + for (size_t j = 0; j < observations.n_cols; ++j) + { + if (accu(likelihoods.col(j)) == 0) + Log::Info << "Likelihood of point " << j << " is 0! It is probably an " + << "outlier." << std::endl; + logLikelihood += log(accu(likelihoods.col(j))); + } + + return logLikelihood; +} + /** * Fit the DiagonalGMM to the given observations, each of which has a certain * probability of being from this distribution. diff --git a/src/mlpack/methods/gmm/gmm.cpp b/src/mlpack/methods/gmm/gmm.cpp deleted file mode 100644 index 42603d45c7..0000000000 --- a/src/mlpack/methods/gmm/gmm.cpp +++ /dev/null @@ -1,247 +0,0 @@ -/** - * @file methods/gmm/gmm.cpp - * @author Parikshit Ram (pram@cc.gatech.edu) - * @author Ryan Curtin - * @author Michael Fox - * - * Implementation of template-based GMM methods. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#include "gmm.hpp" -#include - -namespace mlpack { -namespace gmm { - -/** - * Create a GMM with the given number of Gaussians, each of which have the - * specified dimensionality. The means and covariances will be set to 0. - * - * @param gaussians Number of Gaussians in this GMM. - * @param dimensionality Dimensionality of each Gaussian. - */ -GMM::GMM(const size_t gaussians, const size_t dimensionality) : - gaussians(gaussians), - dimensionality(dimensionality), - dists(gaussians, distribution::GaussianDistribution(dimensionality)), - weights(gaussians) -{ - // Set equal weights. Technically this model is still valid, but only barely. - weights.fill(1.0 / gaussians); -} - -// Copy constructor for when the other GMM uses the same fitting type. -GMM::GMM(const GMM& other) : - gaussians(other.Gaussians()), - dimensionality(other.dimensionality), - dists(other.dists), - weights(other.weights) { /* Nothing to do. */ } - -GMM& GMM::operator=(const GMM& other) -{ - gaussians = other.gaussians; - dimensionality = other.dimensionality; - dists = other.dists; - weights = other.weights; - - return *this; -} - -/** - * Return the log probability of the given observation being from this GMM. - * - * @param observation Observation vector to compute log-probabilty. - */ -double GMM::LogProbability(const arma::vec& observation) const -{ - // Sum the probability for each Gaussian in our mixture (and we have to - // multiply by the prior for each Gaussian too). - double sum = -std::numeric_limits::infinity(); - for (size_t i = 0; i < gaussians; ++i) - sum = math::LogAdd(sum, log(weights[i]) + - dists[i].LogProbability(observation)); - - return sum; -} - -/** - * Return the log probability of the given observation GMM matrix. - * - * @param observation Observation matrix to compute log-probabilty. - * @param logProbs Stores the value of log-probability for Observation. - */ -void GMM::LogProbability(const arma::mat& observation, - arma::vec& logProbs) const -{ - // Sum the probability for each Gaussian in our mixture (and we have to - // multiply by the prior for each Gaussian too). - logProbs.set_size(observation.n_cols); - - // Store log-probability value in a matrix. - arma::mat logProb(observation.n_cols, gaussians); - - // Assign value to the matrix. - for (size_t i = 0; i < gaussians; i++) - { - arma::vec temp(logProb.colptr(i), observation.n_cols, false, true); - dists[i].LogProbability(observation, temp); - } - - // Save log(weights) as a vector. - arma::vec logWeights = arma::log(weights); - - // Compute log-probability. - logProb += repmat(logWeights.t(), logProb.n_rows, 1); - math::LogSumExp(logProb, logProbs); -} - -/** - * Return the probability of the given observation being from this GMM. - * - * @param observation Observation vector to compute probabilty. - */ -double GMM::Probability(const arma::vec& observation) const -{ - return exp(LogProbability(observation)); -} - -/** - * Return the probability of the given observation GMM matrix. - * - * @param observation Observation matrix to compute probabilty. - * @param probs Stores the value of probability for x. - */ -void GMM::Probability(const arma::mat& observation, - arma::vec& probs) const -{ - LogProbability(observation, probs); - probs = exp(probs); -} - - -/** - * Return the log probability of the given observation being from the given - * component in the mixture. - * - * @param observation Observation vector to compute log-probabilty. - * @param component Calculate the log-probability for given observation vector. - */ -double GMM::LogProbability(const arma::vec& observation, - const size_t component) const -{ - // We are only considering one Gaussian component -- so we only need to call - // Probability() once. We do consider the prior probability! - return log(weights[component]) + dists[component].LogProbability(observation); -} - -/** - * Return the probability of the given observation being from the given - * component in the mixture. - * - * @param observation Observation matrix to compute probabilty. - * @param component Calculate the probability for given component. - */ -double GMM::Probability(const arma::vec& observation, - const size_t component) const -{ - return exp(LogProbability(observation, component)); -} - -/** - * Return a randomly generated observation according to the probability - * distribution defined by this object. - */ -arma::vec GMM::Random() const -{ - // Determine which Gaussian it will be coming from. - double gaussRand = math::Random(); - size_t gaussian = 0; - - double sumProb = 0; - for (size_t g = 0; g < gaussians; g++) - { - sumProb += weights(g); - if (gaussRand <= sumProb) - { - gaussian = g; - break; - } - } - - arma::mat cholDecomp; - if (!arma::chol(cholDecomp, dists[gaussian].Covariance())) - { - Log::Fatal << "Cholesky decomposition failed." << std::endl; - } - return trans(cholDecomp) * - arma::randn(dimensionality) + dists[gaussian].Mean(); -} - -/** - * Classify the given observations as being from an individual component in this - * GMM. - * - * @param observation Observation matrix for classification. - * @param labels Save the labels for the given observation matrix. - */ -void GMM::Classify(const arma::mat& observations, - arma::Row& labels) const -{ - // This is not the best way to do this! - - // We should not have to fill this with values, because each one should be - // overwritten. - labels.set_size(observations.n_cols); - for (size_t i = 0; i < observations.n_cols; ++i) - { - // Find maximum probability component. - double probability = -std::numeric_limits::infinity(); - for (size_t j = 0; j < gaussians; ++j) - { - // We have to use LogProbability() otherwise Probability() would overflow - // easily. - double newProb = LogProbability(observations.unsafe_col(i), j); - if (newProb >= probability) - { - probability = newProb; - labels[i] = j; - } - } - } -} - -/** - * Get the log-likelihood of this data's fit to the model. - * - * @param data Data matrix to compute log-likelihood. - * @parma distsL Vector of Gaussian distribution. - * @param weightsL Vector of weights for computing likelihoods. - */ -double GMM::LogLikelihood( - const arma::mat& data, - const std::vector& distsL, - const arma::vec& weightsL) const -{ - double loglikelihood = 0; - arma::vec logPhis; - arma::mat logLikelihoods(gaussians, data.n_cols); - - // It has to be LogProbability() otherwise Probability() would overflow easily - for (size_t i = 0; i < gaussians; ++i) - { - distsL[i].LogProbability(data, logPhis); - logLikelihoods.row(i) = log(weightsL(i)) + trans(logPhis); - } - - // Now sum over every point. - for (size_t j = 0; j < data.n_cols; ++j) - loglikelihood += mlpack::math::AccuLog(logLikelihoods.col(j)); - return loglikelihood; -} - -} // namespace gmm -} // namespace mlpack diff --git a/src/mlpack/methods/gmm/gmm.hpp b/src/mlpack/methods/gmm/gmm.hpp index b8436ce995..05d2b61a32 100644 --- a/src/mlpack/methods/gmm/gmm.hpp +++ b/src/mlpack/methods/gmm/gmm.hpp @@ -10,14 +10,16 @@ * 3-clause BSD license along with mlpack. If not, see * http://www.opensource.org/licenses/BSD-3-Clause for more information. */ -#ifndef MLPACK_METHODS_MOG_MOG_EM_HPP -#define MLPACK_METHODS_MOG_MOG_EM_HPP +#ifndef MLPACK_METHODS_GMM_GMM_HPP +#define MLPACK_METHODS_GMM_GMM_HPP #include // This is the default fitting method class. #include "em_fit.hpp" +#include + namespace mlpack { namespace gmm /** Gaussian Mixture Models. */ { diff --git a/src/mlpack/methods/gmm/gmm_impl.hpp b/src/mlpack/methods/gmm/gmm_impl.hpp index 876e2a068a..b332284560 100644 --- a/src/mlpack/methods/gmm/gmm_impl.hpp +++ b/src/mlpack/methods/gmm/gmm_impl.hpp @@ -20,6 +20,232 @@ namespace mlpack { namespace gmm { +/** + * Create a GMM with the given number of Gaussians, each of which have the + * specified dimensionality. The means and covariances will be set to 0. + * + * @param gaussians Number of Gaussians in this GMM. + * @param dimensionality Dimensionality of each Gaussian. + */ +inline GMM::GMM(const size_t gaussians, const size_t dimensionality) : + gaussians(gaussians), + dimensionality(dimensionality), + dists(gaussians, distribution::GaussianDistribution(dimensionality)), + weights(gaussians) +{ + // Set equal weights. Technically this model is still valid, but only barely. + weights.fill(1.0 / gaussians); +} + +// Copy constructor for when the other GMM uses the same fitting type. +inline GMM::GMM(const GMM& other) : + gaussians(other.Gaussians()), + dimensionality(other.dimensionality), + dists(other.dists), + weights(other.weights) { /* Nothing to do. */ } + +inline GMM& GMM::operator=(const GMM& other) +{ + gaussians = other.gaussians; + dimensionality = other.dimensionality; + dists = other.dists; + weights = other.weights; + + return *this; +} + +/** + * Return the log probability of the given observation being from this GMM. + * + * @param observation Observation vector to compute log-probabilty. + */ +inline double GMM::LogProbability(const arma::vec& observation) const +{ + // Sum the probability for each Gaussian in our mixture (and we have to + // multiply by the prior for each Gaussian too). + double sum = -std::numeric_limits::infinity(); + for (size_t i = 0; i < gaussians; ++i) + sum = math::LogAdd(sum, log(weights[i]) + + dists[i].LogProbability(observation)); + + return sum; +} + +/** + * Return the log probability of the given observation GMM matrix. + * + * @param observation Observation matrix to compute log-probabilty. + * @param logProbs Stores the value of log-probability for Observation. + */ +inline void GMM::LogProbability(const arma::mat& observation, + arma::vec& logProbs) const +{ + // Sum the probability for each Gaussian in our mixture (and we have to + // multiply by the prior for each Gaussian too). + logProbs.set_size(observation.n_cols); + + // Store log-probability value in a matrix. + arma::mat logProb(observation.n_cols, gaussians); + + // Assign value to the matrix. + for (size_t i = 0; i < gaussians; i++) + { + arma::vec temp(logProb.colptr(i), observation.n_cols, false, true); + dists[i].LogProbability(observation, temp); + } + + // Save log(weights) as a vector. + arma::vec logWeights = arma::log(weights); + + // Compute log-probability. + logProb += repmat(logWeights.t(), logProb.n_rows, 1); + math::LogSumExp(logProb, logProbs); +} + +/** + * Return the probability of the given observation being from this GMM. + * + * @param observation Observation vector to compute probabilty. + */ +inline double GMM::Probability(const arma::vec& observation) const +{ + return exp(LogProbability(observation)); +} + +/** + * Return the probability of the given observation GMM matrix. + * + * @param observation Observation matrix to compute probabilty. + * @param probs Stores the value of probability for x. + */ +inline void GMM::Probability(const arma::mat& observation, + arma::vec& probs) const +{ + LogProbability(observation, probs); + probs = exp(probs); +} + + +/** + * Return the log probability of the given observation being from the given + * component in the mixture. + * + * @param observation Observation vector to compute log-probabilty. + * @param component Calculate the log-probability for given observation vector. + */ +inline double GMM::LogProbability(const arma::vec& observation, + const size_t component) const +{ + // We are only considering one Gaussian component -- so we only need to call + // Probability() once. We do consider the prior probability! + return log(weights[component]) + dists[component].LogProbability(observation); +} + +/** + * Return the probability of the given observation being from the given + * component in the mixture. + * + * @param observation Observation matrix to compute probabilty. + * @param component Calculate the probability for given component. + */ +inline double GMM::Probability(const arma::vec& observation, + const size_t component) const +{ + return exp(LogProbability(observation, component)); +} + +/** + * Return a randomly generated observation according to the probability + * distribution defined by this object. + */ +inline arma::vec GMM::Random() const +{ + // Determine which Gaussian it will be coming from. + double gaussRand = math::Random(); + size_t gaussian = 0; + + double sumProb = 0; + for (size_t g = 0; g < gaussians; g++) + { + sumProb += weights(g); + if (gaussRand <= sumProb) + { + gaussian = g; + break; + } + } + + arma::mat cholDecomp; + if (!arma::chol(cholDecomp, dists[gaussian].Covariance())) + { + Log::Fatal << "Cholesky decomposition failed." << std::endl; + } + return trans(cholDecomp) * + arma::randn(dimensionality) + dists[gaussian].Mean(); +} + +/** + * Classify the given observations as being from an individual component in this + * GMM. + * + * @param observation Observation matrix for classification. + * @param labels Save the labels for the given observation matrix. + */ +inline void GMM::Classify(const arma::mat& observations, + arma::Row& labels) const +{ + // This is not the best way to do this! + + // We should not have to fill this with values, because each one should be + // overwritten. + labels.set_size(observations.n_cols); + for (size_t i = 0; i < observations.n_cols; ++i) + { + // Find maximum probability component. + double probability = -std::numeric_limits::infinity(); + for (size_t j = 0; j < gaussians; ++j) + { + // We have to use LogProbability() otherwise Probability() would overflow + // easily. + double newProb = LogProbability(observations.unsafe_col(i), j); + if (newProb >= probability) + { + probability = newProb; + labels[i] = j; + } + } + } +} + +/** + * Get the log-likelihood of this data's fit to the model. + * + * @param data Data matrix to compute log-likelihood. + * @parma distsL Vector of Gaussian distribution. + * @param weightsL Vector of weights for computing likelihoods. + */ +inline double GMM::LogLikelihood( + const arma::mat& data, + const std::vector& distsL, + const arma::vec& weightsL) const +{ + double loglikelihood = 0; + arma::vec logPhis; + arma::mat logLikelihoods(gaussians, data.n_cols); + + // It has to be LogProbability() otherwise Probability() would overflow easily + for (size_t i = 0; i < gaussians; ++i) + { + distsL[i].LogProbability(data, logPhis); + logLikelihoods.row(i) = log(weightsL(i)) + trans(logPhis); + } + + // Now sum over every point. + for (size_t j = 0; j < data.n_cols; ++j) + loglikelihood += mlpack::math::AccuLog(logLikelihoods.col(j)); + return loglikelihood; +} + /** * Fit the GMM to the given observations. */ From 018eb427e96860de19c74df2d2a9241c05b031d0 Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Tue, 10 May 2022 15:53:39 +0530 Subject: [PATCH 41/57] converted fastMKS model to .hpp --- src/mlpack/methods/fastmks/CMakeLists.txt | 1 - src/mlpack/methods/fastmks/fastmks_model.cpp | 318 ------------------ .../methods/fastmks/fastmks_model_impl.hpp | 304 +++++++++++++++++ 3 files changed, 304 insertions(+), 319 deletions(-) delete mode 100644 src/mlpack/methods/fastmks/fastmks_model.cpp diff --git a/src/mlpack/methods/fastmks/CMakeLists.txt b/src/mlpack/methods/fastmks/CMakeLists.txt index cefe42a02e..8e1bc0ad22 100644 --- a/src/mlpack/methods/fastmks/CMakeLists.txt +++ b/src/mlpack/methods/fastmks/CMakeLists.txt @@ -5,7 +5,6 @@ set(SOURCES fastmks_impl.hpp fastmks_model.hpp fastmks_model_impl.hpp - fastmks_model.cpp fastmks_rules.hpp fastmks_rules_impl.hpp fastmks_stat.hpp diff --git a/src/mlpack/methods/fastmks/fastmks_model.cpp b/src/mlpack/methods/fastmks/fastmks_model.cpp deleted file mode 100644 index f7f3e9f126..0000000000 --- a/src/mlpack/methods/fastmks/fastmks_model.cpp +++ /dev/null @@ -1,318 +0,0 @@ -/** - * @file methods/fastmks/fastmks_model.cpp - * @author Ryan Curtin - * - * Implementation of non-templatized functions of FastMKSModel. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#include "fastmks_model.hpp" - -using namespace mlpack; -using namespace mlpack::fastmks; -using namespace mlpack::kernel; - -FastMKSModel::FastMKSModel(const int kernelType) : - kernelType(kernelType), - linear(NULL), - polynomial(NULL), - cosine(NULL), - gaussian(NULL), - epan(NULL), - triangular(NULL), - hyptan(NULL) -{ - // Nothing to do. -} - -FastMKSModel::FastMKSModel(const FastMKSModel& other) : - kernelType(other.kernelType), - linear(other.linear == NULL ? NULL : - new FastMKS(*other.linear)), - polynomial(other.polynomial == NULL ? NULL : - new FastMKS(*other.polynomial)), - cosine(other.cosine == NULL ? NULL : - new FastMKS(*other.cosine)), - gaussian(other.gaussian == NULL ? NULL : - new FastMKS(*other.gaussian)), - epan(other.epan == NULL ? NULL : - new FastMKS(*other.epan)), - triangular(other.triangular == NULL ? NULL : - new FastMKS(*other.triangular)), - hyptan(other.hyptan == NULL ? NULL : - new FastMKS(*other.hyptan)) -{ - // Nothing to do. -} - -FastMKSModel::FastMKSModel(FastMKSModel&& other) : - kernelType(other.kernelType), - linear(other.linear), - polynomial(other.polynomial), - cosine(other.cosine), - gaussian(other.gaussian), - epan(other.epan), - triangular(other.triangular), - hyptan(other.hyptan) -{ - // Clear other object. - other.kernelType = KernelTypes::LINEAR_KERNEL; - other.linear = NULL; - other.polynomial = NULL; - other.cosine = NULL; - other.gaussian = NULL; - other.epan = NULL; - other.triangular = NULL; - other.hyptan = NULL; -} - -FastMKSModel& FastMKSModel::operator=(const FastMKSModel& other) -{ - if (this != &other) - { - // Clear memory. - delete linear; - delete polynomial; - delete cosine; - delete gaussian; - delete epan; - delete triangular; - delete hyptan; - - // Set pointers to null. - linear = NULL; - polynomial = NULL; - cosine = NULL; - gaussian = NULL; - epan = NULL; - triangular = NULL; - hyptan = NULL; - - kernelType = other.kernelType; - if (other.linear) - linear = new FastMKS(*other.linear); - if (other.polynomial) - polynomial = new FastMKS(*other.polynomial); - if (other.cosine) - cosine = new FastMKS(*other.cosine); - if (other.gaussian) - gaussian = new FastMKS(*other.gaussian); - if (other.epan) - epan = new FastMKS(*other.epan); - if (other.triangular) - triangular = new FastMKS(*other.triangular); - if (other.hyptan) - hyptan = new FastMKS(*other.hyptan); - } - return *this; -} - -FastMKSModel& FastMKSModel::operator=(FastMKSModel&& other) -{ - if (this != &other) - { - kernelType = other.kernelType; - linear = other.linear; - polynomial = other.polynomial; - cosine = other.cosine; - gaussian = other.gaussian; - epan = other.epan; - triangular = other.triangular; - hyptan = other.hyptan; - - // Clear other object. - other.kernelType = KernelTypes::LINEAR_KERNEL; - other.linear = nullptr; - other.polynomial = nullptr; - other.cosine = nullptr; - other.gaussian = nullptr; - other.epan = nullptr; - other.triangular = nullptr; - other.hyptan = nullptr; - } - return *this; -} - -FastMKSModel::~FastMKSModel() -{ - // Clean memory. - if (linear) - delete linear; - if (polynomial) - delete polynomial; - if (cosine) - delete cosine; - if (gaussian) - delete gaussian; - if (epan) - delete epan; - if (triangular) - delete triangular; - if (hyptan) - delete hyptan; -} - -bool FastMKSModel::Naive() const -{ - switch (kernelType) - { - case LINEAR_KERNEL: - return linear->Naive(); - case POLYNOMIAL_KERNEL: - return polynomial->Naive(); - case COSINE_DISTANCE: - return cosine->Naive(); - case GAUSSIAN_KERNEL: - return gaussian->Naive(); - case EPANECHNIKOV_KERNEL: - return epan->Naive(); - case TRIANGULAR_KERNEL: - return triangular->Naive(); - case HYPTAN_KERNEL: - return hyptan->Naive(); - } - - throw std::runtime_error("invalid model type"); -} - -bool& FastMKSModel::Naive() -{ - switch (kernelType) - { - case LINEAR_KERNEL: - return linear->Naive(); - case POLYNOMIAL_KERNEL: - return polynomial->Naive(); - case COSINE_DISTANCE: - return cosine->Naive(); - case GAUSSIAN_KERNEL: - return gaussian->Naive(); - case EPANECHNIKOV_KERNEL: - return epan->Naive(); - case TRIANGULAR_KERNEL: - return triangular->Naive(); - case HYPTAN_KERNEL: - return hyptan->Naive(); - } - - throw std::runtime_error("invalid model type"); -} - -bool FastMKSModel::SingleMode() const -{ - switch (kernelType) - { - case LINEAR_KERNEL: - return linear->SingleMode(); - case POLYNOMIAL_KERNEL: - return polynomial->SingleMode(); - case COSINE_DISTANCE: - return cosine->SingleMode(); - case GAUSSIAN_KERNEL: - return gaussian->SingleMode(); - case EPANECHNIKOV_KERNEL: - return epan->SingleMode(); - case TRIANGULAR_KERNEL: - return triangular->SingleMode(); - case HYPTAN_KERNEL: - return hyptan->SingleMode(); - } - - throw std::runtime_error("invalid model type"); -} - -bool& FastMKSModel::SingleMode() -{ - switch (kernelType) - { - case LINEAR_KERNEL: - return linear->SingleMode(); - case POLYNOMIAL_KERNEL: - return polynomial->SingleMode(); - case COSINE_DISTANCE: - return cosine->SingleMode(); - case GAUSSIAN_KERNEL: - return gaussian->SingleMode(); - case EPANECHNIKOV_KERNEL: - return epan->SingleMode(); - case TRIANGULAR_KERNEL: - return triangular->SingleMode(); - case HYPTAN_KERNEL: - return hyptan->SingleMode(); - } - - throw std::runtime_error("invalid model type"); -} - -void FastMKSModel::Search(util::Timers& timers, - const arma::mat& querySet, - const size_t k, - arma::Mat& indices, - arma::mat& kernels, - const double base) -{ - switch (kernelType) - { - case LINEAR_KERNEL: - Search(timers, *linear, querySet, k, indices, kernels, base); - break; - case POLYNOMIAL_KERNEL: - Search(timers, *polynomial, querySet, k, indices, kernels, base); - break; - case COSINE_DISTANCE: - Search(timers, *cosine, querySet, k, indices, kernels, base); - break; - case GAUSSIAN_KERNEL: - Search(timers, *gaussian, querySet, k, indices, kernels, base); - break; - case EPANECHNIKOV_KERNEL: - Search(timers, *epan, querySet, k, indices, kernels, base); - break; - case TRIANGULAR_KERNEL: - Search(timers, *triangular, querySet, k, indices, kernels, base); - break; - case HYPTAN_KERNEL: - Search(timers, *hyptan, querySet, k, indices, kernels, base); - break; - default: - throw std::runtime_error("invalid model type"); - } -} - -void FastMKSModel::Search(util::Timers& timers, - const size_t k, - arma::Mat& indices, - arma::mat& kernels) -{ - timers.Start("computing_products"); - switch (kernelType) - { - case LINEAR_KERNEL: - linear->Search(k, indices, kernels); - break; - case POLYNOMIAL_KERNEL: - polynomial->Search(k, indices, kernels); - break; - case COSINE_DISTANCE: - cosine->Search(k, indices, kernels); - break; - case GAUSSIAN_KERNEL: - gaussian->Search(k, indices, kernels); - break; - case EPANECHNIKOV_KERNEL: - epan->Search(k, indices, kernels); - break; - case TRIANGULAR_KERNEL: - triangular->Search(k, indices, kernels); - break; - case HYPTAN_KERNEL: - hyptan->Search(k, indices, kernels); - break; - default: - throw std::invalid_argument("invalid model type"); - } - timers.Stop("computing_products"); -} diff --git a/src/mlpack/methods/fastmks/fastmks_model_impl.hpp b/src/mlpack/methods/fastmks/fastmks_model_impl.hpp index 1713832da6..eec9e56b81 100644 --- a/src/mlpack/methods/fastmks/fastmks_model_impl.hpp +++ b/src/mlpack/methods/fastmks/fastmks_model_impl.hpp @@ -17,6 +17,238 @@ namespace mlpack { namespace fastmks { +inline FastMKSModel::FastMKSModel(const int kernelType) : + kernelType(kernelType), + linear(NULL), + polynomial(NULL), + cosine(NULL), + gaussian(NULL), + epan(NULL), + triangular(NULL), + hyptan(NULL) +{ + // Nothing to do. +} + +inline FastMKSModel::FastMKSModel(const FastMKSModel& other) : + kernelType(other.kernelType), + linear(other.linear == NULL ? NULL : + new FastMKS(*other.linear)), + polynomial(other.polynomial == NULL ? NULL : + new FastMKS(*other.polynomial)), + cosine(other.cosine == NULL ? NULL : + new FastMKS(*other.cosine)), + gaussian(other.gaussian == NULL ? NULL : + new FastMKS(*other.gaussian)), + epan(other.epan == NULL ? NULL : + new FastMKS(*other.epan)), + triangular(other.triangular == NULL ? NULL : + new FastMKS(*other.triangular)), + hyptan(other.hyptan == NULL ? NULL : + new FastMKS(*other.hyptan)) +{ + // Nothing to do. +} + +inline FastMKSModel::FastMKSModel(FastMKSModel&& other) : + kernelType(other.kernelType), + linear(other.linear), + polynomial(other.polynomial), + cosine(other.cosine), + gaussian(other.gaussian), + epan(other.epan), + triangular(other.triangular), + hyptan(other.hyptan) +{ + // Clear other object. + other.kernelType = KernelTypes::LINEAR_KERNEL; + other.linear = NULL; + other.polynomial = NULL; + other.cosine = NULL; + other.gaussian = NULL; + other.epan = NULL; + other.triangular = NULL; + other.hyptan = NULL; +} + +inline FastMKSModel& FastMKSModel::operator=(const FastMKSModel& other) +{ + if (this != &other) + { + // Clear memory. + delete linear; + delete polynomial; + delete cosine; + delete gaussian; + delete epan; + delete triangular; + delete hyptan; + + // Set pointers to null. + linear = NULL; + polynomial = NULL; + cosine = NULL; + gaussian = NULL; + epan = NULL; + triangular = NULL; + hyptan = NULL; + + kernelType = other.kernelType; + if (other.linear) + linear = new FastMKS(*other.linear); + if (other.polynomial) + polynomial = new FastMKS(*other.polynomial); + if (other.cosine) + cosine = new FastMKS(*other.cosine); + if (other.gaussian) + gaussian = new FastMKS(*other.gaussian); + if (other.epan) + epan = new FastMKS(*other.epan); + if (other.triangular) + triangular = new FastMKS(*other.triangular); + if (other.hyptan) + hyptan = new FastMKS(*other.hyptan); + } + return *this; +} + +inline FastMKSModel& FastMKSModel::operator=(FastMKSModel&& other) +{ + if (this != &other) + { + kernelType = other.kernelType; + linear = other.linear; + polynomial = other.polynomial; + cosine = other.cosine; + gaussian = other.gaussian; + epan = other.epan; + triangular = other.triangular; + hyptan = other.hyptan; + + // Clear other object. + other.kernelType = KernelTypes::LINEAR_KERNEL; + other.linear = nullptr; + other.polynomial = nullptr; + other.cosine = nullptr; + other.gaussian = nullptr; + other.epan = nullptr; + other.triangular = nullptr; + other.hyptan = nullptr; + } + return *this; +} + +inline FastMKSModel::~FastMKSModel() +{ + // Clean memory. + if (linear) + delete linear; + if (polynomial) + delete polynomial; + if (cosine) + delete cosine; + if (gaussian) + delete gaussian; + if (epan) + delete epan; + if (triangular) + delete triangular; + if (hyptan) + delete hyptan; +} + +inline bool FastMKSModel::Naive() const +{ + switch (kernelType) + { + case LINEAR_KERNEL: + return linear->Naive(); + case POLYNOMIAL_KERNEL: + return polynomial->Naive(); + case COSINE_DISTANCE: + return cosine->Naive(); + case GAUSSIAN_KERNEL: + return gaussian->Naive(); + case EPANECHNIKOV_KERNEL: + return epan->Naive(); + case TRIANGULAR_KERNEL: + return triangular->Naive(); + case HYPTAN_KERNEL: + return hyptan->Naive(); + } + + throw std::runtime_error("invalid model type"); +} + +inline bool& FastMKSModel::Naive() +{ + switch (kernelType) + { + case LINEAR_KERNEL: + return linear->Naive(); + case POLYNOMIAL_KERNEL: + return polynomial->Naive(); + case COSINE_DISTANCE: + return cosine->Naive(); + case GAUSSIAN_KERNEL: + return gaussian->Naive(); + case EPANECHNIKOV_KERNEL: + return epan->Naive(); + case TRIANGULAR_KERNEL: + return triangular->Naive(); + case HYPTAN_KERNEL: + return hyptan->Naive(); + } + + throw std::runtime_error("invalid model type"); +} + +inline bool FastMKSModel::SingleMode() const +{ + switch (kernelType) + { + case LINEAR_KERNEL: + return linear->SingleMode(); + case POLYNOMIAL_KERNEL: + return polynomial->SingleMode(); + case COSINE_DISTANCE: + return cosine->SingleMode(); + case GAUSSIAN_KERNEL: + return gaussian->SingleMode(); + case EPANECHNIKOV_KERNEL: + return epan->SingleMode(); + case TRIANGULAR_KERNEL: + return triangular->SingleMode(); + case HYPTAN_KERNEL: + return hyptan->SingleMode(); + } + + throw std::runtime_error("invalid model type"); +} + +inline bool& FastMKSModel::SingleMode() +{ + switch (kernelType) + { + case LINEAR_KERNEL: + return linear->SingleMode(); + case POLYNOMIAL_KERNEL: + return polynomial->SingleMode(); + case COSINE_DISTANCE: + return cosine->SingleMode(); + case GAUSSIAN_KERNEL: + return gaussian->SingleMode(); + case EPANECHNIKOV_KERNEL: + return epan->SingleMode(); + case TRIANGULAR_KERNEL: + return triangular->SingleMode(); + case HYPTAN_KERNEL: + return hyptan->SingleMode(); + } + + throw std::runtime_error("invalid model type"); +} + //! This is called when the KernelType is the same as the model. template void BuildFastMKSModel(util::Timers& timers, @@ -229,6 +461,78 @@ void FastMKSModel::Search(util::Timers& timers, } } +inline void FastMKSModel::Search( + util::Timers& timers, + const arma::mat& querySet, + const size_t k, + arma::Mat& indices, + arma::mat& kernels, + const double base) +{ + switch (kernelType) + { + case LINEAR_KERNEL: + Search(timers, *linear, querySet, k, indices, kernels, base); + break; + case POLYNOMIAL_KERNEL: + Search(timers, *polynomial, querySet, k, indices, kernels, base); + break; + case COSINE_DISTANCE: + Search(timers, *cosine, querySet, k, indices, kernels, base); + break; + case GAUSSIAN_KERNEL: + Search(timers, *gaussian, querySet, k, indices, kernels, base); + break; + case EPANECHNIKOV_KERNEL: + Search(timers, *epan, querySet, k, indices, kernels, base); + break; + case TRIANGULAR_KERNEL: + Search(timers, *triangular, querySet, k, indices, kernels, base); + break; + case HYPTAN_KERNEL: + Search(timers, *hyptan, querySet, k, indices, kernels, base); + break; + default: + throw std::runtime_error("invalid model type"); + } +} + +inline void FastMKSModel::Search( + util::Timers& timers, + const size_t k, + arma::Mat& indices, + arma::mat& kernels) +{ + timers.Start("computing_products"); + switch (kernelType) + { + case LINEAR_KERNEL: + linear->Search(k, indices, kernels); + break; + case POLYNOMIAL_KERNEL: + polynomial->Search(k, indices, kernels); + break; + case COSINE_DISTANCE: + cosine->Search(k, indices, kernels); + break; + case GAUSSIAN_KERNEL: + gaussian->Search(k, indices, kernels); + break; + case EPANECHNIKOV_KERNEL: + epan->Search(k, indices, kernels); + break; + case TRIANGULAR_KERNEL: + triangular->Search(k, indices, kernels); + break; + case HYPTAN_KERNEL: + hyptan->Search(k, indices, kernels); + break; + default: + throw std::invalid_argument("invalid model type"); + } + timers.Stop("computing_products"); +} + } // namespace fastmks } // namespace mlpack From 0d8fac31971d80c7a918f8c4eed787adec6d3f34 Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Tue, 10 May 2022 16:00:49 +0530 Subject: [PATCH 42/57] converted cf model to .hpp --- src/mlpack/methods/cf/CMakeLists.txt | 1 - src/mlpack/methods/cf/cf_model.cpp | 202 ------------------------ src/mlpack/methods/cf/cf_model_impl.hpp | 188 ++++++++++++++++++++++ 3 files changed, 188 insertions(+), 203 deletions(-) delete mode 100644 src/mlpack/methods/cf/cf_model.cpp diff --git a/src/mlpack/methods/cf/CMakeLists.txt b/src/mlpack/methods/cf/CMakeLists.txt index 1cf4e79e5d..3032109856 100644 --- a/src/mlpack/methods/cf/CMakeLists.txt +++ b/src/mlpack/methods/cf/CMakeLists.txt @@ -5,7 +5,6 @@ set(SOURCES cf_impl.hpp cf_model.hpp cf_model_impl.hpp - cf_model.cpp svd_wrapper.hpp svd_wrapper_impl.hpp ) diff --git a/src/mlpack/methods/cf/cf_model.cpp b/src/mlpack/methods/cf/cf_model.cpp deleted file mode 100644 index 424726953e..0000000000 --- a/src/mlpack/methods/cf/cf_model.cpp +++ /dev/null @@ -1,202 +0,0 @@ -/** - * @file methods/cf/cf_model_impl.hpp - * @author Wenhao Huang - * - * A serializable CF model, used by the main program. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#include "cf_model.hpp" - -namespace mlpack { -namespace cf { - -CFModel::CFModel() : - decompositionType(NMF), - normalizationType(NO_NORMALIZATION), - cf(NULL) -{ - // Nothing else to do. -} - -CFModel::CFModel(const CFModel& other) : - decompositionType(other.decompositionType), - normalizationType(other.normalizationType), - cf(other.cf->Clone()) -{ - // Nothing else to do. -} - -CFModel::CFModel(CFModel&& other) : - decompositionType(other.decompositionType), - normalizationType(other.normalizationType), - cf(std::move(other.cf)) -{ - // Reset properties of the other one. - other.decompositionType = NMF; - other.normalizationType = NO_NORMALIZATION; -} - -CFModel& CFModel::operator=(const CFModel& other) -{ - if (this != &other) - { - decompositionType = other.decompositionType; - normalizationType = other.normalizationType; - cf = other.cf->Clone(); - } - - return *this; -} - -CFModel& CFModel::operator=(CFModel&& other) -{ - if (this != &other) - { - decompositionType = other.decompositionType; - normalizationType = other.normalizationType; - cf = std::move(other.cf); - - // Reset the other object. - other.decompositionType = NMF; - other.normalizationType = NO_NORMALIZATION; - } - - return *this; -} - -CFModel::~CFModel() -{ - delete cf; -} - -template -CFWrapperBase* TrainHelper(const DecompositionPolicy& decomposition, - const CFModel::NormalizationTypes normalizationType, - const arma::mat& data, - const size_t numUsersForSimilarity, - const size_t rank, - const size_t maxIterations, - const double minResidue, - const bool mit) -{ - switch (normalizationType) - { - case CFModel::NO_NORMALIZATION: - return new CFWrapper(data, - decomposition, numUsersForSimilarity, rank, maxIterations, minResidue, - mit); - - case CFModel::ITEM_MEAN_NORMALIZATION: - return new CFWrapper(data, - decomposition, numUsersForSimilarity, rank, maxIterations, minResidue, - mit); - - case CFModel::USER_MEAN_NORMALIZATION: - return new CFWrapper(data, - decomposition, numUsersForSimilarity, rank, maxIterations, minResidue, - mit); - - case CFModel::OVERALL_MEAN_NORMALIZATION: - return new CFWrapper(data, - decomposition, numUsersForSimilarity, rank, maxIterations, minResidue, - mit); - - case CFModel::Z_SCORE_NORMALIZATION: - return new CFWrapper(data, - decomposition, numUsersForSimilarity, rank, maxIterations, minResidue, - mit); - } - - // This shouldn't ever happen. - return NULL; -} - -void CFModel::Train(const arma::mat& data, - const size_t numUsersForSimilarity, - const size_t rank, - const size_t maxIterations, - const double minResidue, - const bool mit) -{ - // Delete the current CFType object, if there is one. - delete cf; - - switch (decompositionType) - { - case NMF: - cf = TrainHelper(NMFPolicy(), normalizationType, data, - numUsersForSimilarity, rank, maxIterations, minResidue, mit); - break; - - case BATCH_SVD: - cf = TrainHelper(BatchSVDPolicy(), normalizationType, data, - numUsersForSimilarity, rank, maxIterations, minResidue, mit); - break; - - case RANDOMIZED_SVD: - cf = TrainHelper(RandomizedSVDPolicy(), normalizationType, data, - numUsersForSimilarity, rank, maxIterations, minResidue, mit); - break; - - case REG_SVD: - cf = TrainHelper(RegSVDPolicy(), normalizationType, data, - numUsersForSimilarity, rank, maxIterations, minResidue, mit); - break; - - case SVD_COMPLETE: - cf = TrainHelper(SVDCompletePolicy(), normalizationType, data, - numUsersForSimilarity, rank, maxIterations, minResidue, mit); - break; - - case SVD_INCOMPLETE: - cf = TrainHelper(SVDIncompletePolicy(), normalizationType, data, - numUsersForSimilarity, rank, maxIterations, minResidue, mit); - break; - - case BIAS_SVD: - cf = TrainHelper(BiasSVDPolicy(), normalizationType, data, - numUsersForSimilarity, rank, maxIterations, minResidue, mit); - break; - - case SVD_PLUS_PLUS: - cf = TrainHelper(SVDPlusPlusPolicy(), normalizationType, data, - numUsersForSimilarity, rank, maxIterations, minResidue, mit); - break; - } -} - -//! Make predictions. -void CFModel::Predict(const NeighborSearchTypes nsType, - const InterpolationTypes interpolationType, - const arma::Mat& combinations, - arma::vec& predictions) -{ - cf->Predict(nsType, interpolationType, combinations, predictions); -} - -//! Compute recommendations for queried users. -void CFModel::GetRecommendations(const NeighborSearchTypes nsType, - const InterpolationTypes interpolationType, - const size_t numRecs, - arma::Mat& recommendations, - const arma::Col& users) -{ - cf->GetRecommendations(nsType, interpolationType, numRecs, recommendations, - users); -} - -//! Compute recommendations for all users. -void CFModel::GetRecommendations(const NeighborSearchTypes nsType, - const InterpolationTypes interpolationType, - const size_t numRecs, - arma::Mat& recommendations) -{ - cf->GetRecommendations(nsType, interpolationType, numRecs, recommendations); -} - -} // namespace cf -} // namespace mlpack diff --git a/src/mlpack/methods/cf/cf_model_impl.hpp b/src/mlpack/methods/cf/cf_model_impl.hpp index fa2634a823..0ed1d55077 100644 --- a/src/mlpack/methods/cf/cf_model_impl.hpp +++ b/src/mlpack/methods/cf/cf_model_impl.hpp @@ -40,6 +40,107 @@ namespace mlpack { namespace cf { +inline CFModel::CFModel() : + decompositionType(NMF), + normalizationType(NO_NORMALIZATION), + cf(NULL) +{ + // Nothing else to do. +} + +inline CFModel::CFModel(const CFModel& other) : + decompositionType(other.decompositionType), + normalizationType(other.normalizationType), + cf(other.cf->Clone()) +{ + // Nothing else to do. +} + +inline CFModel::CFModel(CFModel&& other) : + decompositionType(other.decompositionType), + normalizationType(other.normalizationType), + cf(std::move(other.cf)) +{ + // Reset properties of the other one. + other.decompositionType = NMF; + other.normalizationType = NO_NORMALIZATION; +} + +inline CFModel& CFModel::operator=(const CFModel& other) +{ + if (this != &other) + { + decompositionType = other.decompositionType; + normalizationType = other.normalizationType; + cf = other.cf->Clone(); + } + + return *this; +} + +inline CFModel& CFModel::operator=(CFModel&& other) +{ + if (this != &other) + { + decompositionType = other.decompositionType; + normalizationType = other.normalizationType; + cf = std::move(other.cf); + + // Reset the other object. + other.decompositionType = NMF; + other.normalizationType = NO_NORMALIZATION; + } + + return *this; +} + +inline CFModel::~CFModel() +{ + delete cf; +} + +template +CFWrapperBase* TrainHelper(const DecompositionPolicy& decomposition, + const CFModel::NormalizationTypes normalizationType, + const arma::mat& data, + const size_t numUsersForSimilarity, + const size_t rank, + const size_t maxIterations, + const double minResidue, + const bool mit) +{ + switch (normalizationType) + { + case CFModel::NO_NORMALIZATION: + return new CFWrapper(data, + decomposition, numUsersForSimilarity, rank, maxIterations, minResidue, + mit); + + case CFModel::ITEM_MEAN_NORMALIZATION: + return new CFWrapper(data, + decomposition, numUsersForSimilarity, rank, maxIterations, minResidue, + mit); + + case CFModel::USER_MEAN_NORMALIZATION: + return new CFWrapper(data, + decomposition, numUsersForSimilarity, rank, maxIterations, minResidue, + mit); + + case CFModel::OVERALL_MEAN_NORMALIZATION: + return new CFWrapper(data, + decomposition, numUsersForSimilarity, rank, maxIterations, minResidue, + mit); + + case CFModel::Z_SCORE_NORMALIZATION: + return new CFWrapper(data, + decomposition, numUsersForSimilarity, rank, maxIterations, minResidue, + mit); + } + + // This shouldn't ever happen. + return NULL; +} + template void PredictHelper(CFType& cf, const InterpolationTypes interpolationType, @@ -321,6 +422,93 @@ void SerializeHelper(Archive& ar, } } +inline void CFModel::Train( + const arma::mat& data, + const size_t numUsersForSimilarity, + const size_t rank, + const size_t maxIterations, + const double minResidue, + const bool mit) +{ + // Delete the current CFType object, if there is one. + delete cf; + + switch (decompositionType) + { + case NMF: + cf = TrainHelper(NMFPolicy(), normalizationType, data, + numUsersForSimilarity, rank, maxIterations, minResidue, mit); + break; + + case BATCH_SVD: + cf = TrainHelper(BatchSVDPolicy(), normalizationType, data, + numUsersForSimilarity, rank, maxIterations, minResidue, mit); + break; + + case RANDOMIZED_SVD: + cf = TrainHelper(RandomizedSVDPolicy(), normalizationType, data, + numUsersForSimilarity, rank, maxIterations, minResidue, mit); + break; + + case REG_SVD: + cf = TrainHelper(RegSVDPolicy(), normalizationType, data, + numUsersForSimilarity, rank, maxIterations, minResidue, mit); + break; + + case SVD_COMPLETE: + cf = TrainHelper(SVDCompletePolicy(), normalizationType, data, + numUsersForSimilarity, rank, maxIterations, minResidue, mit); + break; + + case SVD_INCOMPLETE: + cf = TrainHelper(SVDIncompletePolicy(), normalizationType, data, + numUsersForSimilarity, rank, maxIterations, minResidue, mit); + break; + + case BIAS_SVD: + cf = TrainHelper(BiasSVDPolicy(), normalizationType, data, + numUsersForSimilarity, rank, maxIterations, minResidue, mit); + break; + + case SVD_PLUS_PLUS: + cf = TrainHelper(SVDPlusPlusPolicy(), normalizationType, data, + numUsersForSimilarity, rank, maxIterations, minResidue, mit); + break; + } +} + +//! Make predictions. +inline void CFModel::Predict( + const NeighborSearchTypes nsType, + const InterpolationTypes interpolationType, + const arma::Mat& combinations, + arma::vec& predictions) +{ + cf->Predict(nsType, interpolationType, combinations, predictions); +} + +//! Compute recommendations for queried users. +inline void CFModel::GetRecommendations( + const NeighborSearchTypes nsType, + const InterpolationTypes interpolationType, + const size_t numRecs, + arma::Mat& recommendations, + const arma::Col& users) +{ + cf->GetRecommendations(nsType, interpolationType, numRecs, recommendations, + users); +} + +//! Compute recommendations for all users. +inline void CFModel::GetRecommendations( + const NeighborSearchTypes nsType, + const InterpolationTypes interpolationType, + const size_t numRecs, + arma::Mat& recommendations) +{ + cf->GetRecommendations(nsType, interpolationType, numRecs, recommendations); +} + template void CFModel::serialize(Archive& ar, const uint32_t /* version */) { From d72568861dc40bb9035ee545b91e79fa29188f70 Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Tue, 10 May 2022 16:06:11 +0530 Subject: [PATCH 43/57] converted krylov svd to .hpp --- .../methods/block_krylov_svd/CMakeLists.txt | 2 +- .../randomized_block_krylov_svd.hpp | 3 ++ ...p => randomized_block_krylov_svd_impl.hpp} | 36 +++++++++++-------- 3 files changed, 25 insertions(+), 16 deletions(-) rename src/mlpack/methods/block_krylov_svd/{randomized_block_krylov_svd.cpp => randomized_block_krylov_svd_impl.hpp} (68%) diff --git a/src/mlpack/methods/block_krylov_svd/CMakeLists.txt b/src/mlpack/methods/block_krylov_svd/CMakeLists.txt index 6380befb28..fef06ef13d 100644 --- a/src/mlpack/methods/block_krylov_svd/CMakeLists.txt +++ b/src/mlpack/methods/block_krylov_svd/CMakeLists.txt @@ -2,7 +2,7 @@ # Anything not in this list will not be compiled into mlpack. set(SOURCES randomized_block_krylov_svd.hpp - randomized_block_krylov_svd.cpp + randomized_block_krylov_svd_impl.hpp ) # Add directory name to sources. diff --git a/src/mlpack/methods/block_krylov_svd/randomized_block_krylov_svd.hpp b/src/mlpack/methods/block_krylov_svd/randomized_block_krylov_svd.hpp index 0979cf4613..cae4c7e89f 100644 --- a/src/mlpack/methods/block_krylov_svd/randomized_block_krylov_svd.hpp +++ b/src/mlpack/methods/block_krylov_svd/randomized_block_krylov_svd.hpp @@ -125,4 +125,7 @@ class RandomizedBlockKrylovSVD } // namespace svd } // namespace mlpack +// Include implementation. +#include "randomized_block_krylov_svd_impl.hpp" + #endif diff --git a/src/mlpack/methods/block_krylov_svd/randomized_block_krylov_svd.cpp b/src/mlpack/methods/block_krylov_svd/randomized_block_krylov_svd_impl.hpp similarity index 68% rename from src/mlpack/methods/block_krylov_svd/randomized_block_krylov_svd.cpp rename to src/mlpack/methods/block_krylov_svd/randomized_block_krylov_svd_impl.hpp index 101d7cd477..e908065129 100644 --- a/src/mlpack/methods/block_krylov_svd/randomized_block_krylov_svd.cpp +++ b/src/mlpack/methods/block_krylov_svd/randomized_block_krylov_svd_impl.hpp @@ -1,5 +1,5 @@ /** - * @file methods/block_krylov_svd/randomized_block_krylov_svd.cpp + * @file methods/block_krylov_svd/randomized_block_krylov_svd_impl.hpp * @author Marcus Edel * * Implementation of the randomized block krylov SVD method. @@ -9,19 +9,22 @@ * 3-clause BSD license along with mlpack. If not, see * http://www.opensource.org/licenses/BSD-3-Clause for more information. */ +#ifndef MLPACK_METHODS_BLOCK_KRYLOV_SVD_RANDOMIZED_BLOCK_KRYLOV_SVD_IMPL_HPP +#define MLPACK_METHODS_BLOCK_KRYLOV_SVD_RANDOMIZED_BLOCK_KRYLOV_SVD_IMPL_HPP #include "randomized_block_krylov_svd.hpp" namespace mlpack { namespace svd { -RandomizedBlockKrylovSVD::RandomizedBlockKrylovSVD(const arma::mat& data, - arma::mat& u, - arma::vec& s, - arma::mat& v, - const size_t maxIterations, - const size_t rank, - const size_t blockSize) : +inline RandomizedBlockKrylovSVD::RandomizedBlockKrylovSVD( + const arma::mat& data, + arma::mat& u, + arma::vec& s, + arma::mat& v, + const size_t maxIterations, + const size_t rank, + const size_t blockSize) : maxIterations(maxIterations), blockSize(blockSize) { @@ -35,19 +38,20 @@ RandomizedBlockKrylovSVD::RandomizedBlockKrylovSVD(const arma::mat& data, } } -RandomizedBlockKrylovSVD::RandomizedBlockKrylovSVD(const size_t maxIterations, - const size_t blockSize) : +inline RandomizedBlockKrylovSVD::RandomizedBlockKrylovSVD( + const size_t maxIterations, + const size_t blockSize) : maxIterations(maxIterations), blockSize(blockSize) { /* Nothing to do here */ } -void RandomizedBlockKrylovSVD::Apply(const arma::mat& data, - arma::mat& u, - arma::vec& s, - arma::mat& v, - const size_t rank) +inline void RandomizedBlockKrylovSVD::Apply(const arma::mat& data, + arma::mat& u, + arma::vec& s, + arma::mat& v, + const size_t rank) { arma::mat Q, R, block, blockIteration; @@ -94,3 +98,5 @@ void RandomizedBlockKrylovSVD::Apply(const arma::mat& data, } // namespace svd } // namespace mlpack + +#endif From 6180f2d6f6a0d61689fdf518cedd93a99455fd0d Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Tue, 10 May 2022 16:17:36 +0530 Subject: [PATCH 44/57] converted bayesian lin. regression to .hpp --- .../bayesian_linear_regression/CMakeLists.txt | 1 - .../bayesian_linear_regression.cpp | 184 ------------------ .../bayesian_linear_regression.hpp | 2 + .../bayesian_linear_regression_impl.hpp | 170 ++++++++++++++++ 4 files changed, 172 insertions(+), 185 deletions(-) delete mode 100644 src/mlpack/methods/bayesian_linear_regression/bayesian_linear_regression.cpp diff --git a/src/mlpack/methods/bayesian_linear_regression/CMakeLists.txt b/src/mlpack/methods/bayesian_linear_regression/CMakeLists.txt index c12a86af74..a5e8febd38 100644 --- a/src/mlpack/methods/bayesian_linear_regression/CMakeLists.txt +++ b/src/mlpack/methods/bayesian_linear_regression/CMakeLists.txt @@ -3,7 +3,6 @@ set(SOURCES bayesian_linear_regression.hpp bayesian_linear_regression_impl.hpp - bayesian_linear_regression.cpp ) # add directory name to sources diff --git a/src/mlpack/methods/bayesian_linear_regression/bayesian_linear_regression.cpp b/src/mlpack/methods/bayesian_linear_regression/bayesian_linear_regression.cpp deleted file mode 100644 index 33e1c53b5c..0000000000 --- a/src/mlpack/methods/bayesian_linear_regression/bayesian_linear_regression.cpp +++ /dev/null @@ -1,184 +0,0 @@ -/** - * @file methods/bayesian_linear_regression/bayesian_linear_regression.cpp - * @author Clement Mercier - * - * Implementation of Bayesian linear regression. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#include "bayesian_linear_regression.hpp" -#include -#include - -using namespace mlpack; -using namespace mlpack::regression; - -BayesianLinearRegression::BayesianLinearRegression(const bool centerData, - const bool scaleData, - const size_t maxIterations, - const double tolerance) : - centerData(centerData), - scaleData(scaleData), - maxIterations(maxIterations), - tolerance(tolerance), - responsesOffset(0.0), - alpha(0.0), - beta(0.0), - gamma(0.0) -{/* Nothing to do */} - -double BayesianLinearRegression::Train(const arma::mat& data, - const arma::rowvec& responses) -{ - arma::mat phi; - arma::rowvec t; - arma::colvec eigVal; - arma::mat eigVec; - - // Preprocess the data. Center and scale. - responsesOffset = CenterScaleData(data, responses, phi, t); - - if (!arma::eig_sym(eigVal, eigVec, arma::symmatu(phi * phi.t()))) - { - Log::Fatal << "BayesianLinearRegression::Train(): Eigendecomposition " - << "of covariance failed!" << std::endl; - } - - // Compute this quantities once and for all. - const arma::mat eigVecInv = inv(eigVec); - const arma::colvec eigVecInvPhitT = eigVecInv * phi * t.t(); - - // Initialize the hyperparameters and begin with an infinitely broad prior. - alpha = 1e-6; - beta = 1 / (var(t, 1) * 0.1); - - unsigned short i = 0; - double deltaAlpha = 1.0, crit = 1.0; - - while ((crit > tolerance) && (i < maxIterations)) - { - deltaAlpha = -alpha; - double deltaBeta = -beta; - - // Update the solution. - omega = eigVec * diagmat(1 / (eigVal + (alpha / beta))) * eigVecInvPhitT; - - // Update alpha. - gamma = sum(eigVal / (alpha / beta + eigVal)); - alpha = gamma / dot(omega, omega); - - // Update beta. - const arma::rowvec temp = t - omega.t() * phi; - beta = (data.n_cols - gamma) / dot(temp, temp); - - // Compute the stopping criterion. - deltaAlpha += alpha; - deltaBeta += beta; - crit = std::abs(deltaAlpha / alpha + deltaBeta / beta); - i++; - } - // Compute the covariance matrix for the uncertainties later. - matCovariance = eigVec * diagmat(1 / (beta * eigVal + alpha)) * eigVecInv; - - return RMSE(data, responses); -} - -void BayesianLinearRegression::Predict(const arma::mat& points, - arma::rowvec& predictions) const -{ - // Center and scale the points before applying the model. - arma::mat matX; - CenterScaleDataPred(points, matX); - predictions = omega.t() * matX + responsesOffset; -} - -void BayesianLinearRegression::Predict(const arma::mat& points, - arma::rowvec& predictions, - arma::rowvec& std) const -{ - // Center and scale the points before applying the model. - arma::mat matX; - CenterScaleDataPred(points, matX); - predictions = omega.t() * matX + responsesOffset; - // Compute the standard deviation for each point. - std = sqrt(Variance() + sum(matX % (matCovariance * matX), 0)); -} - -double BayesianLinearRegression::RMSE(const arma::mat& data, - const arma::rowvec& responses) const -{ - arma::rowvec predictions; - Predict(data, predictions); - return sqrt(mean(square(responses - predictions))); -} - -double BayesianLinearRegression::CenterScaleData(const arma::mat& data, - const arma::rowvec& responses, - arma::mat& dataProc, - arma::rowvec& responsesProc) -{ - if (!centerData && !scaleData) - { - dataProc = arma::mat(const_cast(data.memptr()), data.n_rows, - data.n_cols, false, true); - responsesProc = arma::rowvec(const_cast(responses.memptr()), - responses.n_elem, false, - true); - } - - else if (centerData && !scaleData) - { - dataOffset = mean(data, 1); - responsesOffset = mean(responses); - dataProc = data.each_col() - dataOffset; - responsesProc = responses - responsesOffset; - } - - else if (!centerData && scaleData) - { - dataScale = stddev(data, 0, 1); - dataProc = data.each_col() / dataScale; - responsesProc = arma::rowvec(const_cast(responses.memptr()), - responses.n_elem, false, - true); - } - - else - { - dataOffset = mean(data, 1); - dataScale = stddev(data, 0, 1); - responsesOffset = mean(responses); - dataProc = (data.each_col() - dataOffset).each_col() / dataScale; - responsesProc = responses - responsesOffset; - } - return responsesOffset; -} - -void BayesianLinearRegression::CenterScaleDataPred( - const arma::mat& data, - arma::mat& dataProc) const -{ - if (!centerData && !scaleData) - { - dataProc = arma::mat(const_cast(data.memptr()), data.n_rows, - data.n_cols, false, true); - } - - else if (centerData && !scaleData) - { - dataProc = data.each_col() - dataOffset; - } - - else if (!centerData && scaleData) - { - dataProc = data.each_col() / dataScale; - } - - else - { - dataProc = (data.each_col() - dataOffset).each_col() / dataScale; - } -} diff --git a/src/mlpack/methods/bayesian_linear_regression/bayesian_linear_regression.hpp b/src/mlpack/methods/bayesian_linear_regression/bayesian_linear_regression.hpp index 417e795bff..7665c3e9a6 100644 --- a/src/mlpack/methods/bayesian_linear_regression/bayesian_linear_regression.hpp +++ b/src/mlpack/methods/bayesian_linear_regression/bayesian_linear_regression.hpp @@ -16,6 +16,8 @@ #define MLPACK_METHODS_BAYESIAN_LINEAR_REGRESSION_HPP #include +#include +#include namespace mlpack { namespace regression { diff --git a/src/mlpack/methods/bayesian_linear_regression/bayesian_linear_regression_impl.hpp b/src/mlpack/methods/bayesian_linear_regression/bayesian_linear_regression_impl.hpp index 65a4d70998..928bfc17f6 100644 --- a/src/mlpack/methods/bayesian_linear_regression/bayesian_linear_regression_impl.hpp +++ b/src/mlpack/methods/bayesian_linear_regression/bayesian_linear_regression_impl.hpp @@ -17,6 +17,176 @@ namespace mlpack { namespace regression { +inline BayesianLinearRegression::BayesianLinearRegression( + const bool centerData, + const bool scaleData, + const size_t maxIterations, + const double tolerance) : + centerData(centerData), + scaleData(scaleData), + maxIterations(maxIterations), + tolerance(tolerance), + responsesOffset(0.0), + alpha(0.0), + beta(0.0), + gamma(0.0) +{/* Nothing to do */} + +inline double BayesianLinearRegression::Train(const arma::mat& data, + const arma::rowvec& responses) +{ + arma::mat phi; + arma::rowvec t; + arma::colvec eigVal; + arma::mat eigVec; + + // Preprocess the data. Center and scale. + responsesOffset = CenterScaleData(data, responses, phi, t); + + if (!arma::eig_sym(eigVal, eigVec, arma::symmatu(phi * phi.t()))) + { + Log::Fatal << "BayesianLinearRegression::Train(): Eigendecomposition " + << "of covariance failed!" << std::endl; + } + + // Compute this quantities once and for all. + const arma::mat eigVecInv = inv(eigVec); + const arma::colvec eigVecInvPhitT = eigVecInv * phi * t.t(); + + // Initialize the hyperparameters and begin with an infinitely broad prior. + alpha = 1e-6; + beta = 1 / (var(t, 1) * 0.1); + + unsigned short i = 0; + double deltaAlpha = 1.0, crit = 1.0; + + while ((crit > tolerance) && (i < maxIterations)) + { + deltaAlpha = -alpha; + double deltaBeta = -beta; + + // Update the solution. + omega = eigVec * diagmat(1 / (eigVal + (alpha / beta))) * eigVecInvPhitT; + + // Update alpha. + gamma = sum(eigVal / (alpha / beta + eigVal)); + alpha = gamma / dot(omega, omega); + + // Update beta. + const arma::rowvec temp = t - omega.t() * phi; + beta = (data.n_cols - gamma) / dot(temp, temp); + + // Compute the stopping criterion. + deltaAlpha += alpha; + deltaBeta += beta; + crit = std::abs(deltaAlpha / alpha + deltaBeta / beta); + i++; + } + // Compute the covariance matrix for the uncertainties later. + matCovariance = eigVec * diagmat(1 / (beta * eigVal + alpha)) * eigVecInv; + + return RMSE(data, responses); +} + +inline void BayesianLinearRegression::Predict(const arma::mat& points, + arma::rowvec& predictions) const +{ + // Center and scale the points before applying the model. + arma::mat matX; + CenterScaleDataPred(points, matX); + predictions = omega.t() * matX + responsesOffset; +} + +inline void BayesianLinearRegression::Predict(const arma::mat& points, + arma::rowvec& predictions, + arma::rowvec& std) const +{ + // Center and scale the points before applying the model. + arma::mat matX; + CenterScaleDataPred(points, matX); + predictions = omega.t() * matX + responsesOffset; + // Compute the standard deviation for each point. + std = sqrt(Variance() + sum(matX % (matCovariance * matX), 0)); +} + +inline double BayesianLinearRegression::RMSE( + const arma::mat& data, + const arma::rowvec& responses) const +{ + arma::rowvec predictions; + Predict(data, predictions); + return sqrt(mean(square(responses - predictions))); +} + +inline double BayesianLinearRegression::CenterScaleData( + const arma::mat& data, + const arma::rowvec& responses, + arma::mat& dataProc, + arma::rowvec& responsesProc) +{ + if (!centerData && !scaleData) + { + dataProc = arma::mat(const_cast(data.memptr()), data.n_rows, + data.n_cols, false, true); + responsesProc = arma::rowvec(const_cast(responses.memptr()), + responses.n_elem, false, + true); + } + + else if (centerData && !scaleData) + { + dataOffset = mean(data, 1); + responsesOffset = mean(responses); + dataProc = data.each_col() - dataOffset; + responsesProc = responses - responsesOffset; + } + + else if (!centerData && scaleData) + { + dataScale = stddev(data, 0, 1); + dataProc = data.each_col() / dataScale; + responsesProc = arma::rowvec(const_cast(responses.memptr()), + responses.n_elem, false, + true); + } + + else + { + dataOffset = mean(data, 1); + dataScale = stddev(data, 0, 1); + responsesOffset = mean(responses); + dataProc = (data.each_col() - dataOffset).each_col() / dataScale; + responsesProc = responses - responsesOffset; + } + return responsesOffset; +} + +inline void BayesianLinearRegression::CenterScaleDataPred( + const arma::mat& data, + arma::mat& dataProc) const +{ + if (!centerData && !scaleData) + { + dataProc = arma::mat(const_cast(data.memptr()), data.n_rows, + data.n_cols, false, true); + } + + else if (centerData && !scaleData) + { + dataProc = data.each_col() - dataOffset; + } + + else if (!centerData && scaleData) + { + dataProc = data.each_col() / dataScale; + } + + else + { + dataProc = (data.each_col() - dataOffset).each_col() / dataScale; + } +} + /** * Serialize the Bayesian linear regression model. */ From 6eb2b0b2a319f6356ce75d45096a0b4613c81652 Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Tue, 10 May 2022 16:32:42 +0530 Subject: [PATCH 45/57] converted adaboost model to .hpp --- src/mlpack/methods/adaboost/CMakeLists.txt | 2 +- .../methods/adaboost/adaboost_model.hpp | 3 + ...oost_model.cpp => adaboost_model_impl.hpp} | 74 ++++++++++--------- 3 files changed, 43 insertions(+), 36 deletions(-) rename src/mlpack/methods/adaboost/{adaboost_model.cpp => adaboost_model_impl.hpp} (59%) diff --git a/src/mlpack/methods/adaboost/CMakeLists.txt b/src/mlpack/methods/adaboost/CMakeLists.txt index 0a6bf73ceb..3e5d71f191 100644 --- a/src/mlpack/methods/adaboost/CMakeLists.txt +++ b/src/mlpack/methods/adaboost/CMakeLists.txt @@ -4,7 +4,7 @@ set(SOURCES adaboost.hpp adaboost_impl.hpp adaboost_model.hpp - adaboost_model.cpp + adaboost_model_impl.hpp ) # Add directory name to sources. diff --git a/src/mlpack/methods/adaboost/adaboost_model.hpp b/src/mlpack/methods/adaboost/adaboost_model.hpp index 36743c4e18..23fb651c01 100644 --- a/src/mlpack/methods/adaboost/adaboost_model.hpp +++ b/src/mlpack/methods/adaboost/adaboost_model.hpp @@ -126,4 +126,7 @@ class AdaBoostModel } // namespace adaboost } // namespace mlpack +// Include implementation. +#include "adaboost_model_impl.hpp" + #endif diff --git a/src/mlpack/methods/adaboost/adaboost_model.cpp b/src/mlpack/methods/adaboost/adaboost_model_impl.hpp similarity index 59% rename from src/mlpack/methods/adaboost/adaboost_model.cpp rename to src/mlpack/methods/adaboost/adaboost_model_impl.hpp index d71b857d74..57a68090eb 100644 --- a/src/mlpack/methods/adaboost/adaboost_model.cpp +++ b/src/mlpack/methods/adaboost/adaboost_model_impl.hpp @@ -1,5 +1,5 @@ /** - * @file methods/adaboost/adaboost_model.cpp + * @file methods/adaboost/adaboost_model_impl.hpp * @author Ryan Curtin * * A serializable AdaBoost model, used by the main program. @@ -9,18 +9,17 @@ * 3-clause BSD license along with mlpack. If not, see * http://www.opensource.org/licenses/BSD-3-Clause for more information. */ +#ifndef MLPACK_METHODS_ADABOOST_ADABOOST_MODEL_IMPL_HPP +#define MLPACK_METHODS_ADABOOST_ADABOOST_MODEL_IMPL_HPP + #include "adaboost.hpp" #include "adaboost_model.hpp" -using namespace mlpack; -using namespace std; -using namespace arma; -using namespace mlpack::adaboost; -using namespace mlpack::tree; -using namespace mlpack::perceptron; +namespace mlpack { +namespace adaboost { //! Create an empty AdaBoost model. -AdaBoostModel::AdaBoostModel() : +inline AdaBoostModel::AdaBoostModel() : weakLearnerType(0), dsBoost(NULL), pBoost(NULL), @@ -30,8 +29,8 @@ AdaBoostModel::AdaBoostModel() : } //! Create the AdaBoost model with the given mappings and type. -AdaBoostModel::AdaBoostModel( - const Col& mappings, +inline AdaBoostModel::AdaBoostModel( + const arma::Col& mappings, const size_t weakLearnerType) : mappings(mappings), weakLearnerType(weakLearnerType), @@ -43,20 +42,20 @@ AdaBoostModel::AdaBoostModel( } //! Copy constructor. -AdaBoostModel::AdaBoostModel(const AdaBoostModel& other) : +inline AdaBoostModel::AdaBoostModel(const AdaBoostModel& other) : mappings(other.mappings), weakLearnerType(other.weakLearnerType), dsBoost(other.dsBoost == NULL ? NULL : - new AdaBoost(*other.dsBoost)), + new AdaBoost(*other.dsBoost)), pBoost(other.pBoost == NULL ? NULL : - new AdaBoost>(*other.pBoost)), + new AdaBoost>(*other.pBoost)), dimensionality(other.dimensionality) { // Nothing to do. } //! Move constructor. -AdaBoostModel::AdaBoostModel(AdaBoostModel&& other) : +inline AdaBoostModel::AdaBoostModel(AdaBoostModel&& other) : mappings(std::move(other.mappings)), weakLearnerType(other.weakLearnerType), dsBoost(other.dsBoost), @@ -70,7 +69,7 @@ AdaBoostModel::AdaBoostModel(AdaBoostModel&& other) : } //! Copy assignment operator. -AdaBoostModel& AdaBoostModel::operator=(const AdaBoostModel& other) +inline AdaBoostModel& AdaBoostModel::operator=(const AdaBoostModel& other) { if (this != &other) { @@ -79,11 +78,11 @@ AdaBoostModel& AdaBoostModel::operator=(const AdaBoostModel& other) delete dsBoost; dsBoost = (other.dsBoost == NULL) ? NULL : - new AdaBoost(*other.dsBoost); + new AdaBoost(*other.dsBoost); delete pBoost; pBoost = (other.pBoost == NULL) ? NULL : - new AdaBoost>(*other.pBoost); + new AdaBoost>(*other.pBoost); dimensionality = other.dimensionality; } @@ -91,7 +90,7 @@ AdaBoostModel& AdaBoostModel::operator=(const AdaBoostModel& other) } //! Move assignment operator. -AdaBoostModel& AdaBoostModel::operator=(AdaBoostModel&& other) +inline AdaBoostModel& AdaBoostModel::operator=(AdaBoostModel&& other) { if (this != &other) { @@ -109,40 +108,40 @@ AdaBoostModel& AdaBoostModel::operator=(AdaBoostModel&& other) return *this; } -AdaBoostModel::~AdaBoostModel() +inline AdaBoostModel::~AdaBoostModel() { delete dsBoost; delete pBoost; } //! Train the model. -void AdaBoostModel::Train(const mat& data, - const Row& labels, - const size_t numClasses, - const size_t iterations, - const double tolerance) +inline void AdaBoostModel::Train(const arma::mat& data, + const arma::Row& labels, + const size_t numClasses, + const size_t iterations, + const double tolerance) { dimensionality = data.n_rows; if (weakLearnerType == WeakLearnerTypes::DECISION_STUMP) { delete dsBoost; - ID3DecisionStump ds(data, labels, max(labels) + 1); - dsBoost = new AdaBoost(data, labels, numClasses, ds, - iterations, tolerance); + tree::ID3DecisionStump ds(data, labels, max(labels) + 1); + dsBoost = new AdaBoost(data, labels, numClasses, + ds, iterations, tolerance); } else if (weakLearnerType == WeakLearnerTypes::PERCEPTRON) { delete pBoost; - Perceptron<> p(data, labels, max(labels) + 1); - pBoost = new AdaBoost>(data, labels, numClasses, p, iterations, - tolerance); + perceptron::Perceptron<> p(data, labels, max(labels) + 1); + pBoost = new AdaBoost>(data, labels, numClasses, + p, iterations, tolerance); } } //! Classify test points. -void AdaBoostModel::Classify(const mat& testData, - Row& predictions, - mat& probabilities) +inline void AdaBoostModel::Classify(const arma::mat& testData, + arma::Row& predictions, + arma::mat& probabilities) { if (weakLearnerType == WeakLearnerTypes::DECISION_STUMP) dsBoost->Classify(testData, predictions, probabilities); @@ -151,11 +150,16 @@ void AdaBoostModel::Classify(const mat& testData, } //! Classify test points. -void AdaBoostModel::Classify(const mat& testData, - Row& predictions) +inline void AdaBoostModel::Classify(const arma::mat& testData, + arma::Row& predictions) { if (weakLearnerType == WeakLearnerTypes::DECISION_STUMP) dsBoost->Classify(testData, predictions); else if (weakLearnerType == WeakLearnerTypes::PERCEPTRON) pBoost->Classify(testData, predictions); } + +} // namespace adaboost +} // namespace mlpack + +#endif From 685a38803b2fff1909e18abecc342cd08087dc47 Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Tue, 10 May 2022 16:55:43 +0530 Subject: [PATCH 46/57] converted linear regression to .hpp --- .../methods/linear_regression/CMakeLists.txt | 2 +- .../linear_regression/linear_regression.hpp | 5 ++ ...ression.cpp => linear_regression_impl.hpp} | 60 +++++++++++-------- 3 files changed, 41 insertions(+), 26 deletions(-) rename src/mlpack/methods/linear_regression/{linear_regression.cpp => linear_regression_impl.hpp} (76%) diff --git a/src/mlpack/methods/linear_regression/CMakeLists.txt b/src/mlpack/methods/linear_regression/CMakeLists.txt index 4e39c5e42b..da87ce69a7 100644 --- a/src/mlpack/methods/linear_regression/CMakeLists.txt +++ b/src/mlpack/methods/linear_regression/CMakeLists.txt @@ -3,7 +3,7 @@ # Do not include test programs here set(SOURCES linear_regression.hpp - linear_regression.cpp + linear_regression_impl.hpp ) # add directory name to sources diff --git a/src/mlpack/methods/linear_regression/linear_regression.hpp b/src/mlpack/methods/linear_regression/linear_regression.hpp index 3ce6f78d4c..e9baaa8966 100644 --- a/src/mlpack/methods/linear_regression/linear_regression.hpp +++ b/src/mlpack/methods/linear_regression/linear_regression.hpp @@ -14,6 +14,8 @@ #define MLPACK_METHODS_LINEAR_REGRESSION_LINEAR_REGRESSION_HPP #include +#include +#include namespace mlpack { namespace regression /** Regression methods. */ { @@ -167,4 +169,7 @@ class LinearRegression } // namespace regression } // namespace mlpack +// Include implementation. +#include "linear_regression_impl.hpp" + #endif // MLPACK_METHODS_LINEAR_REGRESSION_HPP diff --git a/src/mlpack/methods/linear_regression/linear_regression.cpp b/src/mlpack/methods/linear_regression/linear_regression_impl.hpp similarity index 76% rename from src/mlpack/methods/linear_regression/linear_regression.cpp rename to src/mlpack/methods/linear_regression/linear_regression_impl.hpp index 774425ebc7..e6d7428318 100644 --- a/src/mlpack/methods/linear_regression/linear_regression.cpp +++ b/src/mlpack/methods/linear_regression/linear_regression_impl.hpp @@ -1,5 +1,5 @@ /** - * @file methods/linear_regression/linear_regression.cpp + * @file methods/linear_regression/linear_regression_impl.hpp * @author James Cline * @author Michael Fox * @@ -10,42 +10,45 @@ * 3-clause BSD license along with mlpack. If not, see * http://www.opensource.org/licenses/BSD-3-Clause for more information. */ +#ifndef MLPACK_METHODS_LINEAR_REGRESSION_LINEAR_REGRESSION_IMPL_HPP +#define MLPACK_METHODS_LINEAR_REGRESSION_LINEAR_REGRESSION_IMPL_HPP + #include "linear_regression.hpp" -#include -#include -using namespace mlpack; -using namespace mlpack::regression; +namespace mlpack { +namespace regression { -LinearRegression::LinearRegression(const arma::mat& predictors, - const arma::rowvec& responses, - const double lambda, - const bool intercept) : +inline LinearRegression::LinearRegression( + const arma::mat& predictors, + const arma::rowvec& responses, + const double lambda, + const bool intercept) : LinearRegression(predictors, responses, arma::rowvec(), lambda, intercept) -{} +{ /* Nothing to do. */ } -LinearRegression::LinearRegression(const arma::mat& predictors, - const arma::rowvec& responses, - const arma::rowvec& weights, - const double lambda, - const bool intercept) : +inline LinearRegression::LinearRegression( + const arma::mat& predictors, + const arma::rowvec& responses, + const arma::rowvec& weights, + const double lambda, + const bool intercept) : lambda(lambda), intercept(intercept) { Train(predictors, responses, weights, intercept); } -double LinearRegression::Train(const arma::mat& predictors, - const arma::rowvec& responses, - const bool intercept) +inline double LinearRegression::Train(const arma::mat& predictors, + const arma::rowvec& responses, + const bool intercept) { return Train(predictors, responses, arma::rowvec(), intercept); } -double LinearRegression::Train(const arma::mat& predictors, - const arma::rowvec& responses, - const arma::rowvec& weights, - const bool intercept) +inline double LinearRegression::Train(const arma::mat& predictors, + const arma::rowvec& responses, + const arma::rowvec& weights, + const bool intercept) { this->intercept = intercept; @@ -93,7 +96,8 @@ double LinearRegression::Train(const arma::mat& predictors, return ComputeError(predictors, responses); } -void LinearRegression::Predict(const arma::mat& points, +inline void LinearRegression::Predict( + const arma::mat& points, arma::rowvec& predictions) const { if (intercept) @@ -122,8 +126,9 @@ void LinearRegression::Predict(const arma::mat& points, } } -double LinearRegression::ComputeError(const arma::mat& predictors, - const arma::rowvec& responses) const +inline double LinearRegression::ComputeError( + const arma::mat& predictors, + const arma::rowvec& responses) const { // Sanity check on data. util::CheckSameSizes(predictors, responses, "LinearRegression::Train()"); @@ -160,3 +165,8 @@ double LinearRegression::ComputeError(const arma::mat& predictors, return cost; } + +} // namespace regression +} // namespace mlpack + +#endif From 155c269fbb7403a1002b26a17a814005074b5535 Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Tue, 10 May 2022 17:11:39 +0530 Subject: [PATCH 47/57] indendation corrections --- .../diagonal_gaussian_distribution_impl.hpp | 2 +- .../core/dists/discrete_distribution_impl.hpp | 2 +- src/mlpack/core/dists/gamma_distribution.hpp | 4 +-- .../core/dists/gamma_distribution_impl.hpp | 25 ++++++++++--------- .../core/dists/gaussian_distribution_impl.hpp | 13 ++++++---- .../core/dists/laplace_distribution_impl.hpp | 7 +++--- .../dists/regression_distribution_impl.hpp | 11 ++++---- 7 files changed, 35 insertions(+), 29 deletions(-) diff --git a/src/mlpack/core/dists/diagonal_gaussian_distribution_impl.hpp b/src/mlpack/core/dists/diagonal_gaussian_distribution_impl.hpp index c3e03ac689..817327330a 100644 --- a/src/mlpack/core/dists/diagonal_gaussian_distribution_impl.hpp +++ b/src/mlpack/core/dists/diagonal_gaussian_distribution_impl.hpp @@ -99,7 +99,7 @@ inline void DiagonalGaussianDistribution::Train(const arma::mat& observations) } inline void DiagonalGaussianDistribution::Train(const arma::mat& observations, - const arma::vec& probabilities) + const arma::vec& probabilities) { if (observations.n_cols > 0) { diff --git a/src/mlpack/core/dists/discrete_distribution_impl.hpp b/src/mlpack/core/dists/discrete_distribution_impl.hpp index 09533ae170..7e3c6db1f4 100644 --- a/src/mlpack/core/dists/discrete_distribution_impl.hpp +++ b/src/mlpack/core/dists/discrete_distribution_impl.hpp @@ -111,7 +111,7 @@ inline void DiscreteDistribution::Train(const arma::mat& observations) * given probabilities that each observation is from this distribution. */ inline void DiscreteDistribution::Train(const arma::mat& observations, - const arma::vec& probObs) + const arma::vec& probObs) { // Make sure the observations have same dimension as the probabilities. if (observations.n_rows != probabilities.size()) diff --git a/src/mlpack/core/dists/gamma_distribution.hpp b/src/mlpack/core/dists/gamma_distribution.hpp index 631178ad6a..7c7e97def6 100644 --- a/src/mlpack/core/dists/gamma_distribution.hpp +++ b/src/mlpack/core/dists/gamma_distribution.hpp @@ -16,8 +16,8 @@ * 3-clause BSD license along with mlpack. If not, see * http://www.opensource.org/licenses/BSD-3-Clause for more information. */ -#ifndef _MLPACK_CORE_DISTRIBUTIONS_GAMMA_DISTRIBUTION_HPP -#define _MLPACK_CORE_DISTRIBUTIONS_GAMMA_DISTRIBUTION_HPP +#ifndef MLPACK_CORE_DISTRIBUTIONS_GAMMA_DISTRIBUTION_HPP +#define MLPACK_CORE_DISTRIBUTIONS_GAMMA_DISTRIBUTION_HPP #include #include diff --git a/src/mlpack/core/dists/gamma_distribution_impl.hpp b/src/mlpack/core/dists/gamma_distribution_impl.hpp index e42a73f309..e1187dedbb 100644 --- a/src/mlpack/core/dists/gamma_distribution_impl.hpp +++ b/src/mlpack/core/dists/gamma_distribution_impl.hpp @@ -10,8 +10,8 @@ * 3-clause BSD license along with mlpack. If not, see * http://www.opensource.org/licenses/BSD-3-Clause for more information. */ -#ifndef _MLPACK_CORE_DISTRIBUTIONS_GAMMA_DISTRIBUTION_IMPL_HPP -#define _MLPACK_CORE_DISTRIBUTIONS_GAMMA_DISTRIBUTION_IMPL_HPP +#ifndef MLPACK_CORE_DISTRIBUTIONS_GAMMA_DISTRIBUTION_IMPL_HPP +#define MLPACK_CORE_DISTRIBUTIONS_GAMMA_DISTRIBUTION_IMPL_HPP #include "gamma_distribution.hpp" @@ -26,13 +26,13 @@ inline GammaDistribution::GammaDistribution(const size_t dimensionality) } inline GammaDistribution::GammaDistribution(const arma::mat& data, - const double tol) + const double tol) { Train(data, tol); } inline GammaDistribution::GammaDistribution(const arma::vec& alpha, - const arma::vec& beta) + const arma::vec& beta) { if (beta.n_elem != alpha.n_elem) throw std::runtime_error("Alpha and beta vector dimensions mismatch."); @@ -68,8 +68,8 @@ inline void GammaDistribution::Train(const arma::mat& rdata, const double tol) // Fits an alpha and beta parameter according to observation probabilities. inline void GammaDistribution::Train(const arma::mat& rdata, - const arma::vec& probabilities, - const double tol) + const arma::vec& probabilities, + const double tol) { // If fittingSet is empty, nothing to do. if (arma::size(rdata) == arma::size(arma::mat())) @@ -98,9 +98,9 @@ inline void GammaDistribution::Train(const arma::mat& rdata, // Fits an alpha and beta parameter to each dimension of the data. inline void GammaDistribution::Train(const arma::vec& logMeanxVec, - const arma::vec& meanLogxVec, - const arma::vec& meanxVec, - const double tol) + const arma::vec& meanLogxVec, + const arma::vec& meanxVec, + const double tol) { using std::log; @@ -159,7 +159,7 @@ inline void GammaDistribution::Train(const arma::vec& logMeanxVec, // Returns the probability of the provided observations. inline void GammaDistribution::Probability(const arma::mat& observations, - arma::vec& probabilities) const + arma::vec& probabilities) const { size_t numObs = observations.n_cols; @@ -194,8 +194,9 @@ inline double GammaDistribution::Probability(double x, size_t dim) const } // Returns the log probability of the provided observations. -inline void GammaDistribution::LogProbability(const arma::mat& observations, - arma::vec& logProbabilities) const +inline void GammaDistribution::LogProbability( + const arma::mat& observations, + arma::vec& logProbabilities) const { size_t numObs = observations.n_cols; diff --git a/src/mlpack/core/dists/gaussian_distribution_impl.hpp b/src/mlpack/core/dists/gaussian_distribution_impl.hpp index 54ee51b259..3ff9d5cb99 100644 --- a/src/mlpack/core/dists/gaussian_distribution_impl.hpp +++ b/src/mlpack/core/dists/gaussian_distribution_impl.hpp @@ -19,9 +19,11 @@ namespace mlpack { namespace distribution /** Probability distributions. */ { -inline GaussianDistribution::GaussianDistribution(const arma::vec& mean, - const arma::mat& covariance) - : mean(mean), logDetCov(0.0) +inline GaussianDistribution::GaussianDistribution( + const arma::vec& mean, + const arma::mat& covariance) : + mean(mean), + logDetCov(0.0) { Covariance(covariance); } @@ -70,7 +72,8 @@ inline void GaussianDistribution::FactorCovariance() logDetCov *= 2; } -inline double GaussianDistribution::LogProbability(const arma::vec& observation) const +inline double GaussianDistribution::LogProbability( + const arma::vec& observation) const { const size_t k = observation.n_elem; const arma::vec diff = mean - observation; @@ -130,7 +133,7 @@ inline void GaussianDistribution::Train(const arma::mat& observations) * distribution. */ inline void GaussianDistribution::Train(const arma::mat& observations, - const arma::vec& probabilities) + const arma::vec& probabilities) { if (observations.n_cols > 0) { diff --git a/src/mlpack/core/dists/laplace_distribution_impl.hpp b/src/mlpack/core/dists/laplace_distribution_impl.hpp index beb3ebcb2c..1bc41c4945 100644 --- a/src/mlpack/core/dists/laplace_distribution_impl.hpp +++ b/src/mlpack/core/dists/laplace_distribution_impl.hpp @@ -21,7 +21,8 @@ namespace distribution /** Probability distributions. */ { /** * Return the log probability of the given observation. */ -inline double LaplaceDistribution::LogProbability(const arma::vec& observation) const +inline double LaplaceDistribution::LogProbability( + const arma::vec& observation) const { // Evaluate the PDF of the Laplace distribution to determine // the log probability. @@ -35,7 +36,7 @@ inline double LaplaceDistribution::LogProbability(const arma::vec& observation) * @param probabilities Output probabilities for each input observation. */ inline void LaplaceDistribution::Probability(const arma::mat& x, - arma::vec& probabilities) const + arma::vec& probabilities) const { probabilities.set_size(x.n_cols); for (size_t i = 0; i < x.n_cols; ++i) @@ -83,7 +84,7 @@ inline void LaplaceDistribution::Estimate(const arma::mat& observations) * this distribution. */ inline void LaplaceDistribution::Estimate(const arma::mat& observations, - const arma::vec& probabilities) + const arma::vec& probabilities) { // I am not completely sure that this change results in a valid maximum // likelihood estimator given probabilities of points. diff --git a/src/mlpack/core/dists/regression_distribution_impl.hpp b/src/mlpack/core/dists/regression_distribution_impl.hpp index 6130ec66e4..ce7d3723ee 100644 --- a/src/mlpack/core/dists/regression_distribution_impl.hpp +++ b/src/mlpack/core/dists/regression_distribution_impl.hpp @@ -40,13 +40,13 @@ inline void RegressionDistribution::Train(const arma::mat& observations) * @param weights Probability that given observation is from distribution. */ inline void RegressionDistribution::Train(const arma::mat& observations, - const arma::vec& weights) + const arma::vec& weights) { Train(observations, arma::rowvec(weights.t())); } inline void RegressionDistribution::Train(const arma::mat& observations, - const arma::rowvec& weights) + const arma::rowvec& weights) { regression::LinearRegression lr(observations.rows(1, observations.n_rows - 1), arma::rowvec(observations.row(0)), weights, 0, true); @@ -61,7 +61,8 @@ inline void RegressionDistribution::Train(const arma::mat& observations, * * @param observation Point to evaluate probability at. */ -inline double RegressionDistribution::Probability(const arma::vec& observation) const +inline double RegressionDistribution::Probability( + const arma::vec& observation) const { arma::rowvec fitted; rf.Predict(observation.rows(1, observation.n_rows-1), fitted); @@ -69,7 +70,7 @@ inline double RegressionDistribution::Probability(const arma::vec& observation) } inline void RegressionDistribution::Predict(const arma::mat& points, - arma::vec& predictions) const + arma::vec& predictions) const { arma::rowvec rowPredictions; Predict(points, rowPredictions); @@ -77,7 +78,7 @@ inline void RegressionDistribution::Predict(const arma::mat& points, } inline void RegressionDistribution::Predict(const arma::mat& points, - arma::rowvec& predictions) const + arma::rowvec& predictions) const { rf.Predict(points, predictions); } From 6ce2580bf69bb8443a7e9d16a8f6913338dd4f33 Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Tue, 10 May 2022 18:09:35 +0530 Subject: [PATCH 48/57] header only util tried to make log header only changing init of assert adding comment cause checks not started adding corrections cleanup converted version to header only converted timers to header only converted prefixoutsream to .hpp converted program doc to .hpp removing circular dependency removing another circular dependency trying forward decl trying another fwd decl idk now :( forward decl trying correcting dependencies --- src/mlpack/core/util/CMakeLists.txt | 10 +- src/mlpack/core/util/binding_details.hpp | 3 +- src/mlpack/core/util/io.hpp | 8 +- src/mlpack/core/util/log.cpp | 40 --- src/mlpack/core/util/log.hpp | 138 +++++++--- src/mlpack/core/util/log_impl.hpp | 70 +++++ src/mlpack/core/util/param.hpp | 2 + src/mlpack/core/util/prefixedoutstream.cpp | 127 --------- .../core/util/prefixedoutstream_impl.hpp | 104 ++++++++ src/mlpack/core/util/program_doc.hpp | 3 + .../{program_doc.cpp => program_doc_impl.hpp} | 35 ++- src/mlpack/core/util/singletons.cpp | 46 ---- src/mlpack/core/util/timers.cpp | 231 ---------------- src/mlpack/core/util/timers.hpp | 3 + src/mlpack/core/util/timers_impl.hpp | 249 ++++++++++++++++++ src/mlpack/core/util/version.hpp | 3 + .../util/{version.cpp => version_impl.hpp} | 9 +- 17 files changed, 576 insertions(+), 505 deletions(-) delete mode 100644 src/mlpack/core/util/log.cpp create mode 100644 src/mlpack/core/util/log_impl.hpp delete mode 100644 src/mlpack/core/util/prefixedoutstream.cpp rename src/mlpack/core/util/{program_doc.cpp => program_doc_impl.hpp} (75%) delete mode 100644 src/mlpack/core/util/singletons.cpp delete mode 100644 src/mlpack/core/util/timers.cpp create mode 100644 src/mlpack/core/util/timers_impl.hpp rename src/mlpack/core/util/{version.cpp => version_impl.hpp} (82%) diff --git a/src/mlpack/core/util/CMakeLists.txt b/src/mlpack/core/util/CMakeLists.txt index 4ffe767d47..fec4137c57 100644 --- a/src/mlpack/core/util/CMakeLists.txt +++ b/src/mlpack/core/util/CMakeLists.txt @@ -13,7 +13,7 @@ set(SOURCES hyphenate_string.hpp is_std_vector.hpp log.hpp - log.cpp + log_impl.hpp mlpack_main.hpp nulloutstream.hpp param.hpp @@ -24,18 +24,16 @@ set(SOURCES params_impl.hpp params.cpp prefixedoutstream.hpp - prefixedoutstream.cpp prefixedoutstream_impl.hpp program_doc.hpp - program_doc.cpp + program_doc_impl.hpp size_checks.hpp sfinae_utility.hpp - singletons.cpp timers.hpp - timers.cpp + timers_impl.hpp to_lower.hpp version.hpp - version.cpp + version_impl.hpp ) # add directory name to sources diff --git a/src/mlpack/core/util/binding_details.hpp b/src/mlpack/core/util/binding_details.hpp index 2320aed8cb..05ef8aa90d 100644 --- a/src/mlpack/core/util/binding_details.hpp +++ b/src/mlpack/core/util/binding_details.hpp @@ -13,11 +13,10 @@ #define MLPACK_CORE_UTIL_BINDING_DETAILS_HPP #include -#include "program_doc.hpp" +// #include "program_doc.hpp" namespace mlpack { namespace util { - /** * This structure holds all of the information about bindings documentation. */ diff --git a/src/mlpack/core/util/io.hpp b/src/mlpack/core/util/io.hpp index 0b45563b6c..22346f1e04 100644 --- a/src/mlpack/core/util/io.hpp +++ b/src/mlpack/core/util/io.hpp @@ -20,9 +20,7 @@ #include -#include "timers.hpp" #include "binding_details.hpp" -#include "program_doc.hpp" #include "version.hpp" #include "param_data.hpp" @@ -35,6 +33,12 @@ // into src/mlpack/bindings/util/. namespace mlpack { +class Timer; + +namespace util { + class Timers; +} + // TODO: completely go through this documentation and clean it up /** * @brief Parses the command line for parameters and holds user-specified diff --git a/src/mlpack/core/util/log.cpp b/src/mlpack/core/util/log.cpp deleted file mode 100644 index d8b2767025..0000000000 --- a/src/mlpack/core/util/log.cpp +++ /dev/null @@ -1,40 +0,0 @@ -/** - * @file core/util/log.cpp - * @author Matthew Amidon - * - * Implementation of the Log class. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#include "log.hpp" - -#ifdef HAS_BFD_DL - #include "backtrace.hpp" -#endif - -using namespace mlpack; -using namespace mlpack::util; - -// Only do anything for Assert() if in debugging mode. -#ifdef DEBUG -void Log::Assert(bool condition, const std::string& message) -{ - if (!condition) - { -#ifdef HAS_BFD_DL - Backtrace bt; - - Log::Debug << bt.ToString(); -#endif - Log::Debug << message << std::endl; - - throw std::runtime_error("Log::Assert() failed: " + message); - } -} -#else -void Log::Assert(bool /* condition */, const std::string& /* message */) -{ } -#endif diff --git a/src/mlpack/core/util/log.hpp b/src/mlpack/core/util/log.hpp index 351e362c88..a2e6dbaa99 100644 --- a/src/mlpack/core/util/log.hpp +++ b/src/mlpack/core/util/log.hpp @@ -1,6 +1,7 @@ /** * @file core/util/log.hpp * @author Matthew Amidon + * @author Shubham Agrawal * * Definition of the Log class. * @@ -17,6 +18,21 @@ #include "prefixedoutstream.hpp" #include "nulloutstream.hpp" +#include + +#ifndef _WIN32 + #define BASH_RED "\033[0;31m" + #define BASH_GREEN "\033[0;32m" + #define BASH_YELLOW "\033[0;33m" + #define BASH_CYAN "\033[0;36m" + #define BASH_CLEAR "\033[0m" +#else + #define BASH_RED "" + #define BASH_GREEN "" + #define BASH_YELLOW "" + #define BASH_CYAN "" + #define BASH_CLEAR "" +#endif namespace mlpack { @@ -53,46 +69,98 @@ namespace mlpack { * * @see PrefixedOutStream, NullOutStream, IO */ -class Log -{ - public: - /** - * Checks if the specified condition is true. - * If not, halts program execution and prints a custom error message. - * Does nothing in non-debug mode. - */ - static void Assert(bool condition, - const std::string& message = "Assert Failed."); - /** - * MLPACK_EXPORT is required for global variables, so that they are properly - * exported by the Windows compiler. - */ - - // We only use PrefixedOutStream if the program is compiled with debug - // symbols. +/** + * MLPACK_EXPORT is required for global variables, so that they are properly + * exported by the Windows compiler. + */ +#if __cplusplus < 201703L + template + struct Log_ + { #ifdef DEBUG - //! Prints debug output with the appropriate tag: [DEBUG]. - static MLPACK_EXPORT util::PrefixedOutStream Debug; + //! Prints debug output with the appropriate tag: [DEBUG]. + static MLPACK_EXPORT util::PrefixedOutStream Debug; #else - //! Dumps debug output into the bit nether regions. - static MLPACK_EXPORT util::NullOutStream Debug; + //! Dumps debug output into the bit nether regions. + static MLPACK_EXPORT util::NullOutStream Debug; +#endif + //! Prints informational messages if --verbose is specified, prefixed with + //! [INFO ]. + static MLPACK_EXPORT util::PrefixedOutStream Info; + + //! Prints warning messages prefixed with [WARN ]. + static MLPACK_EXPORT util::PrefixedOutStream Warn; + + //! Prints fatal messages prefixed with [FATAL], then terminates the program. + static MLPACK_EXPORT util::PrefixedOutStream Fatal; + + //! Reference to cout, if necessary. + static std::ostream& cout; + + /** + * Checks if the specified condition is true. + * If not, halts program execution and prints a custom error message. + * Does nothing in non-debug mode. + */ + static void Assert(bool condition, + const std::string& message = "Assert Failed."); + }; +#ifdef DEBUG + template + util::PrefixedOutStream Log_::Debug = util::PrefixedOutStream(MLPACK_COUT_STREAM, + BASH_CYAN "[DEBUG] " BASH_CLEAR); +#else + template + util::NullOutStream Log_::Debug = util::NullOutStream(); +#endif + template + util::PrefixedOutStream Log_::Info = util::PrefixedOutStream(MLPACK_COUT_STREAM, + BASH_GREEN "[INFO ] " BASH_CLEAR, true /* unless --verbose */, false); + template + util::PrefixedOutStream Log_::Warn = util::PrefixedOutStream(MLPACK_COUT_STREAM, + BASH_YELLOW "[WARN ] " BASH_CLEAR, false, false); + template + util::PrefixedOutStream Log_::Fatal = util::PrefixedOutStream(MLPACK_CERR_STREAM, + BASH_RED "[FATAL] " BASH_CLEAR, false, true /* fatal */); + using Log = Log_; +#else + namespace Log { +#ifdef DEBUG + //! Prints debug output with the appropriate tag: [DEBUG]. + inline util::PrefixedOutStream Debug = util::PrefixedOutStream(MLPACK_COUT_STREAM, + BASH_CYAN "[DEBUG] " BASH_CLEAR); +#else + //! Dumps debug output into the bit nether regions. + inline util::NullOutStream Debug = util::NullOutStream(); +#endif + + //! Prints informational messages if --verbose is specified, prefixed with + //! [INFO ]. + inline util::PrefixedOutStream Info = util::PrefixedOutStream(MLPACK_COUT_STREAM, + BASH_GREEN "[INFO ] " BASH_CLEAR, true /* unless --verbose */, false); + + //! Prints warning messages prefixed with [WARN ]. + inline util::PrefixedOutStream Warn = util::PrefixedOutStream(MLPACK_COUT_STREAM, + BASH_YELLOW "[WARN ] " BASH_CLEAR, false, false); + + //! Prints fatal messages prefixed with [FATAL], then terminates the program. + inline util::PrefixedOutStream Fatal = util::PrefixedOutStream(MLPACK_CERR_STREAM, + BASH_RED "[FATAL] " BASH_CLEAR, false, true /* fatal */); + + /** + * Checks if the specified condition is true. + * If not, halts program execution and prints a custom error message. + * Does nothing in non-debug mode. + */ + void Assert(bool condition, + const std::string& message = "Assert Failed."); + } // namespace Log #endif - //! Prints informational messages if --verbose is specified, prefixed with - //! [INFO ]. - static MLPACK_EXPORT util::PrefixedOutStream Info; +} // namespace mlpack - //! Prints warning messages prefixed with [WARN ]. - static MLPACK_EXPORT util::PrefixedOutStream Warn; - - //! Prints fatal messages prefixed with [FATAL], then terminates the program. - static MLPACK_EXPORT util::PrefixedOutStream Fatal; - - //! Reference to cout, if necessary. - static std::ostream& cout; -}; - -}; // namespace mlpack +// Include implementation. +#include "log_impl.hpp" #endif diff --git a/src/mlpack/core/util/log_impl.hpp b/src/mlpack/core/util/log_impl.hpp new file mode 100644 index 0000000000..0333a3f01f --- /dev/null +++ b/src/mlpack/core/util/log_impl.hpp @@ -0,0 +1,70 @@ +/** + * @file core/util/log_impl.hpp + * @author Matthew Amidon + * + * Implementation of the Log class. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_CORE_UTIL_LOG_IMPL_HPP +#define MLPACK_CORE_UTIL_LOG_IMPL_HPP + +#include "log.hpp" + +#ifdef HAS_BFD_DL + #include "backtrace.hpp" +#endif + +namespace mlpack { + +#if __cplusplus < 201703L +// Only do anything for Assert() if in debugging mode. +#ifdef DEBUG +template +void Log_::Assert(bool condition, const std::string& message) +{ + if (!condition) + { +#ifdef HAS_BFD_DL + Backtrace bt; + + Log::Debug << bt.ToString(); +#endif + Log::Debug << message << std::endl; + + throw std::runtime_error("Log::Assert() failed: " + message); + } +} +#else +template +void Log_::Assert(bool /* condition */, const std::string& /* message */) +{ /* Nothing to do. */ } +#endif +#else +// Only do anything for Assert() if in debugging mode. +#ifdef DEBUG +inline void Log::Assert(bool condition, const std::string& message) +{ + if (!condition) + { +#ifdef HAS_BFD_DL + Backtrace bt; + + Log::Debug << bt.ToString(); +#endif + Log::Debug << message << std::endl; + + throw std::runtime_error("Log::Assert() failed: " + message); + } +} +#else +inline void Log::Assert(bool /* condition */, const std::string& /* message */) +{ /* Nothing to do. */ } +#endif +#endif // __cplusplus < 201703L +} // namespace mlpack + +#endif diff --git a/src/mlpack/core/util/param.hpp b/src/mlpack/core/util/param.hpp index c2dbe7e2aa..7cdf975098 100644 --- a/src/mlpack/core/util/param.hpp +++ b/src/mlpack/core/util/param.hpp @@ -15,6 +15,8 @@ #ifndef MLPACK_CORE_UTIL_PARAM_HPP #define MLPACK_CORE_UTIL_PARAM_HPP +#include "program_doc.hpp" + // Required forward declarations. namespace mlpack { namespace data { diff --git a/src/mlpack/core/util/prefixedoutstream.cpp b/src/mlpack/core/util/prefixedoutstream.cpp deleted file mode 100644 index 283cdcd10f..0000000000 --- a/src/mlpack/core/util/prefixedoutstream.cpp +++ /dev/null @@ -1,127 +0,0 @@ -/** - * @file core/util/prefixedoutstream.cpp - * @author Ryan Curtin - * @author Matthew Amidon - * - * Implementation of PrefixedOutStream methods. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#include - -#include "prefixedoutstream.hpp" - -using namespace mlpack::util; - -/** - * These are all necessary because gcc's template mechanism does not seem smart - * enough to figure out what I want to pass into operator<< without these. That - * may not be the actual case, but it works when these is here. - */ - -PrefixedOutStream& PrefixedOutStream::operator<<(bool val) -{ - BaseLogic(val); - return *this; -} - -PrefixedOutStream& PrefixedOutStream::operator<<(short val) -{ - BaseLogic(val); - return *this; -} - -PrefixedOutStream& PrefixedOutStream::operator<<(unsigned short val) -{ - BaseLogic(val); - return *this; -} - -PrefixedOutStream& PrefixedOutStream::operator<<(int val) -{ - BaseLogic(val); - return *this; -} - -PrefixedOutStream& PrefixedOutStream::operator<<(unsigned int val) -{ - BaseLogic(val); - return *this; -} - -PrefixedOutStream& PrefixedOutStream::operator<<(long val) -{ - BaseLogic(val); - return *this; -} - -PrefixedOutStream& PrefixedOutStream::operator<<(unsigned long val) -{ - BaseLogic(val); - return *this; -} - -PrefixedOutStream& PrefixedOutStream::operator<<(float val) -{ - BaseLogic(val); - return *this; -} - -PrefixedOutStream& PrefixedOutStream::operator<<(double val) -{ - BaseLogic(val); - return *this; -} - -PrefixedOutStream& PrefixedOutStream::operator<<(long double val) -{ - BaseLogic(val); - return *this; -} - -PrefixedOutStream& PrefixedOutStream::operator<<(void* val) -{ - BaseLogic(val); - return *this; -} - -PrefixedOutStream& PrefixedOutStream::operator<<(const char* str) -{ - BaseLogic(str); - return *this; -} - -PrefixedOutStream& PrefixedOutStream::operator<<(std::string& str) -{ - BaseLogic(str); - return *this; -} - -PrefixedOutStream& PrefixedOutStream::operator<<(std::streambuf* sb) -{ - BaseLogic(sb); - return *this; -} - -PrefixedOutStream& PrefixedOutStream::operator<<( - std::ostream& (*pf)(std::ostream&)) -{ - BaseLogic(pf); - return *this; -} - -PrefixedOutStream& PrefixedOutStream::operator<<(std::ios& (*pf)(std::ios&)) -{ - BaseLogic(pf); - return *this; -} - -PrefixedOutStream& PrefixedOutStream::operator<<( - std::ios_base& (*pf) (std::ios_base&)) -{ - BaseLogic(pf); - return *this; -} diff --git a/src/mlpack/core/util/prefixedoutstream_impl.hpp b/src/mlpack/core/util/prefixedoutstream_impl.hpp index 3cb9eea353..3189a1c66a 100644 --- a/src/mlpack/core/util/prefixedoutstream_impl.hpp +++ b/src/mlpack/core/util/prefixedoutstream_impl.hpp @@ -33,6 +33,110 @@ PrefixedOutStream& PrefixedOutStream::operator<<(const T& s) return *this; } +inline PrefixedOutStream& PrefixedOutStream::operator<<(bool val) +{ + BaseLogic(val); + return *this; +} + +inline PrefixedOutStream& PrefixedOutStream::operator<<(short val) +{ + BaseLogic(val); + return *this; +} + +inline PrefixedOutStream& PrefixedOutStream::operator<<(unsigned short val) +{ + BaseLogic(val); + return *this; +} + +inline PrefixedOutStream& PrefixedOutStream::operator<<(int val) +{ + BaseLogic(val); + return *this; +} + +inline PrefixedOutStream& PrefixedOutStream::operator<<(unsigned int val) +{ + BaseLogic(val); + return *this; +} + +inline PrefixedOutStream& PrefixedOutStream::operator<<(long val) +{ + BaseLogic(val); + return *this; +} + +inline PrefixedOutStream& PrefixedOutStream::operator<<(unsigned long val) +{ + BaseLogic(val); + return *this; +} + +inline PrefixedOutStream& PrefixedOutStream::operator<<(float val) +{ + BaseLogic(val); + return *this; +} + +inline PrefixedOutStream& PrefixedOutStream::operator<<(double val) +{ + BaseLogic(val); + return *this; +} + +inline PrefixedOutStream& PrefixedOutStream::operator<<(long double val) +{ + BaseLogic(val); + return *this; +} + +inline PrefixedOutStream& PrefixedOutStream::operator<<(void* val) +{ + BaseLogic(val); + return *this; +} + +inline PrefixedOutStream& PrefixedOutStream::operator<<(const char* str) +{ + BaseLogic(str); + return *this; +} + +inline PrefixedOutStream& PrefixedOutStream::operator<<(std::string& str) +{ + BaseLogic(str); + return *this; +} + +inline PrefixedOutStream& PrefixedOutStream::operator<<(std::streambuf* sb) +{ + BaseLogic(sb); + return *this; +} + +inline PrefixedOutStream& PrefixedOutStream::operator<<( + std::ostream& (*pf)(std::ostream&)) +{ + BaseLogic(pf); + return *this; +} + +inline PrefixedOutStream& PrefixedOutStream::operator<<(std::ios& (*pf)(std::ios&)) +{ + BaseLogic(pf); + return *this; +} + +inline PrefixedOutStream& PrefixedOutStream::operator<<( + std::ios_base& (*pf) (std::ios_base&)) +{ + BaseLogic(pf); + return *this; +} + // For non-Armadillo types. template typename std::enable_if::value>::type diff --git a/src/mlpack/core/util/program_doc.hpp b/src/mlpack/core/util/program_doc.hpp index 7d6f64d7c4..5dc21826f5 100644 --- a/src/mlpack/core/util/program_doc.hpp +++ b/src/mlpack/core/util/program_doc.hpp @@ -97,4 +97,7 @@ class SeeAlso } // namespace util } // namespace mlpack +// Include implementation. +#include "program_doc_impl.hpp" + #endif diff --git a/src/mlpack/core/util/program_doc.cpp b/src/mlpack/core/util/program_doc_impl.hpp similarity index 75% rename from src/mlpack/core/util/program_doc.cpp rename to src/mlpack/core/util/program_doc_impl.hpp index f95f560c96..6c34d65022 100644 --- a/src/mlpack/core/util/program_doc.cpp +++ b/src/mlpack/core/util/program_doc_impl.hpp @@ -1,5 +1,5 @@ /** - * @file core/util/program_doc.cpp + * @file core/util/program_doc_impl.hpp * @author Yashwant Singh Parihar * @author Ryan Curtin * @@ -11,14 +11,16 @@ * 3-clause BSD license along with mlpack. If not, see * http://www.opensource.org/licenses/BSD-3-Clause for more information. */ +#ifndef MLPACK_CORE_UTIL_PROGRAM_DOC_IMPL_HPP +#define MLPACK_CORE_UTIL_PROGRAM_DOC_IMPL_HPP + #include "io.hpp" #include "program_doc.hpp" #include -using namespace mlpack; -using namespace mlpack::util; -using namespace std; +namespace mlpack { +namespace util { /** * Construct a BindingName object. When constructed, it will register itself @@ -28,8 +30,8 @@ using namespace std; * @param bindingName Name of the binding. * @param name Name displayed to user of the binding. */ -BindingName::BindingName(const std::string& bindingName, - const std::string& name) +inline BindingName::BindingName(const std::string& bindingName, + const std::string& name) { // Register this with IO. IO::AddBindingName(bindingName, name); @@ -44,8 +46,8 @@ BindingName::BindingName(const std::string& bindingName, * @param shortDescription A short two-sentence description of the binding, * what it does, and what it is useful for. */ -ShortDescription::ShortDescription(const std::string& bindingName, - const std::string& shortDescription) +inline ShortDescription::ShortDescription(const std::string& bindingName, + const std::string& shortDescription) { // Register this with IO. IO::AddShortDescription(bindingName, shortDescription); @@ -61,7 +63,7 @@ ShortDescription::ShortDescription(const std::string& bindingName, * what it is. No newline characters are necessary; this is * taken care of by IO later. */ -LongDescription::LongDescription( +inline LongDescription::LongDescription( const std::string& bindingName, const std::function& longDescription) { @@ -76,8 +78,8 @@ LongDescription::LongDescription( * @param bindingName Name of the binding. * @param example Documentation on how to use the binding. */ -Example::Example(const std::string& bindingName, - const std::function& example) +inline Example::Example(const std::string& bindingName, + const std::function& example) { // Register this with IO. IO::AddExample(bindingName, example); @@ -91,10 +93,15 @@ Example::Example(const std::string& bindingName, * @param description Description of SeeAlso. * @param link Link of SeeAlso. */ -SeeAlso::SeeAlso(const std::string& bindingName, - const std::string& description, - const std::string& link) +inline SeeAlso::SeeAlso(const std::string& bindingName, + const std::string& description, + const std::string& link) { // Register this with IO. IO::AddSeeAlso(bindingName, description, link); } + +} // namespace util +} // namespace mlpack + +#endif diff --git a/src/mlpack/core/util/singletons.cpp b/src/mlpack/core/util/singletons.cpp deleted file mode 100644 index 2e653ef3e8..0000000000 --- a/src/mlpack/core/util/singletons.cpp +++ /dev/null @@ -1,46 +0,0 @@ -/** - * @file core/util/singletons.cpp - * @author Ryan Curtin - * - * Declaration of singletons in libmlpack.so. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#include "io.hpp" -#include "log.hpp" -#include - -using namespace mlpack; -using namespace mlpack::util; - -// Color code escape sequences -- but not on Windows. -#ifndef _WIN32 - #define BASH_RED "\033[0;31m" - #define BASH_GREEN "\033[0;32m" - #define BASH_YELLOW "\033[0;33m" - #define BASH_CYAN "\033[0;36m" - #define BASH_CLEAR "\033[0m" -#else - #define BASH_RED "" - #define BASH_GREEN "" - #define BASH_YELLOW "" - #define BASH_CYAN "" - #define BASH_CLEAR "" -#endif - -#ifdef DEBUG -PrefixedOutStream Log::Debug = PrefixedOutStream(MLPACK_COUT_STREAM, - BASH_CYAN "[DEBUG] " BASH_CLEAR); -#else -NullOutStream Log::Debug = NullOutStream(); -#endif - -PrefixedOutStream Log::Info = PrefixedOutStream(MLPACK_COUT_STREAM, - BASH_GREEN "[INFO ] " BASH_CLEAR, true /* unless --verbose */, false); -PrefixedOutStream Log::Warn = PrefixedOutStream(MLPACK_COUT_STREAM, - BASH_YELLOW "[WARN ] " BASH_CLEAR, false, false); -PrefixedOutStream Log::Fatal = PrefixedOutStream(MLPACK_CERR_STREAM, - BASH_RED "[FATAL] " BASH_CLEAR, false, true /* fatal */); diff --git a/src/mlpack/core/util/timers.cpp b/src/mlpack/core/util/timers.cpp deleted file mode 100644 index b93f5731fe..0000000000 --- a/src/mlpack/core/util/timers.cpp +++ /dev/null @@ -1,231 +0,0 @@ -/** - * @file core/util/timers.cpp - * @author Matthew Amidon - * @author Marcus Edel - * @author Ryan Curtin - * - * Implementation of timers. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#include "timers.hpp" -#include "io.hpp" -#include "log.hpp" - -#include -#include - -using namespace mlpack; -using namespace mlpack::util; -using namespace std; -using namespace chrono; - -/** - * Start the given timer. - */ -void Timer::Start(const string& name) -{ - IO::GetSingleton().timer.Start(name, this_thread::get_id()); -} - -/** - * Stop the given timer. - */ -void Timer::Stop(const string& name) -{ - IO::GetSingleton().timer.Stop(name, this_thread::get_id()); -} - -/** - * Get the given timer, summing over all threads. - */ -microseconds Timer::Get(const string& name) -{ - return IO::GetSingleton().timer.Get(name); -} - -// Enable timing. -void Timer::EnableTiming() -{ - IO::GetSingleton().timer.Enabled() = true; -} - -// Disable timing. -void Timer::DisableTiming() -{ - IO::GetSingleton().timer.Enabled() = false; -} - -// Reset all timers. Save state of enabled. -void Timer::ResetAll() -{ - IO::GetSingleton().timer.Reset(); -} - -std::map Timer::GetAllTimers() -{ - return IO::GetSingleton().timer.GetAllTimers(); -} - -// Reset a Timers object. -void Timers::Reset() -{ - lock_guard lock(timersMutex); - timers.clear(); - timerStartTime.clear(); -} - -map Timers::GetAllTimers() -{ - // Make a copy of the timer. - lock_guard lock(timersMutex); - return timers; -} - -microseconds Timers::Get(const string& timerName) -{ - if (!enabled) - return microseconds(0); - - lock_guard lock(timersMutex); - return timers[timerName]; -} - -std::string Timers::Print(const microseconds& totalDuration) -{ - // Convert microseconds to seconds. - seconds totalDurationSec = duration_cast(totalDuration); - microseconds totalDurationMicroSec = - duration_cast(totalDuration % seconds(1)); - - std::ostringstream oss; - oss << totalDurationSec.count() << "." << setw(6) - << setfill('0') << totalDurationMicroSec.count() << "s"; - - // Also output convenient day/hr/min/sec. - // The following line is a custom duration for a day. - typedef duration> days; - days d = duration_cast(totalDuration); - hours h = duration_cast(totalDuration % days(1)); - minutes m = duration_cast(totalDuration % hours(1)); - seconds s = duration_cast(totalDuration % minutes(1)); - // No output if it didn't even take a minute. - if (!(d.count() == 0 && h.count() == 0 && m.count() == 0)) - { - bool output = false; // Denotes if we have output anything yet. - oss << " ("; - - // Only output units if they have nonzero values (yes, a bit tedious). - if (d.count() > 0) - { - oss << d.count() << " days"; - output = true; - } - - if (h.count() > 0) - { - if (output) - oss << ", "; - oss << h.count() << " hrs"; - output = true; - } - - if (m.count() > 0) - { - if (output) - oss << ", "; - oss << m.count() << " mins"; - output = true; - } - - if (s.count() > 0) - { - if (output) - oss << ", "; - oss << s.count() << "." << setw(1) - << (totalDurationMicroSec.count() / 100000) << " secs"; - } - - oss << ")"; - } - - oss << endl; - return oss.str(); -} - -void Timers::StopAllTimers() -{ - // Terminate the program timers. Don't use StopTimer() since that modifies - // the map and would invalidate our iterators. - lock_guard lock(timersMutex); - - high_resolution_clock::time_point currTime = high_resolution_clock::now(); - for (auto it : timerStartTime) - for (auto it2 : it.second) - timers[it2.first] += duration_cast(currTime - it2.second); - - // If all timers are stopped, we can clear the maps. - timerStartTime.clear(); -} - -void Timers::Start(const string& timerName, - const thread::id& threadId) -{ - // Don't do anything if we aren't timing. - if (!enabled) - return; - - lock_guard lock(timersMutex); - - if ((timerStartTime.count(threadId) > 0) && - (timerStartTime[threadId].count(timerName))) - { - ostringstream error; - error << "Timer::Start(): timer '" << timerName - << "' has already been started"; - throw runtime_error(error.str()); - } - - high_resolution_clock::time_point currTime = high_resolution_clock::now(); - - // If the timer is added for the first time. - if (timers.count(timerName) == 0) - { - timers[timerName] = (microseconds) 0; - } - - timerStartTime[threadId][timerName] = currTime; -} - -void Timers::Stop(const string& timerName, - const thread::id& threadId) -{ - // Don't do anything if we aren't timing. - if (!enabled) - return; - - lock_guard lock(timersMutex); - - if ((timerStartTime.count(threadId) == 0) || - (timerStartTime[threadId].count(timerName) == 0)) - { - ostringstream error; - error << "Timer::Stop(): no timer with name '" << timerName - << "' currently running"; - throw runtime_error(error.str()); - } - - high_resolution_clock::time_point currTime = high_resolution_clock::now(); - - // Calculate the delta time. - timers[timerName] += duration_cast(currTime - - timerStartTime[threadId][timerName]); - - // Remove the entries. - timerStartTime[threadId].erase(timerName); - if (timerStartTime[threadId].empty()) - timerStartTime.erase(threadId); -} diff --git a/src/mlpack/core/util/timers.hpp b/src/mlpack/core/util/timers.hpp index 177850de52..ee7279c004 100644 --- a/src/mlpack/core/util/timers.hpp +++ b/src/mlpack/core/util/timers.hpp @@ -182,4 +182,7 @@ class Timers } // namespace util } // namespace mlpack +// Include implementation. +#include "timers_impl.hpp" + #endif // MLPACK_CORE_UTILITIES_TIMERS_HPP diff --git a/src/mlpack/core/util/timers_impl.hpp b/src/mlpack/core/util/timers_impl.hpp new file mode 100644 index 0000000000..f9237bdc48 --- /dev/null +++ b/src/mlpack/core/util/timers_impl.hpp @@ -0,0 +1,249 @@ +/** + * @file core/util/timers_impl.hpp + * @author Matthew Amidon + * @author Marcus Edel + * @author Ryan Curtin + * + * Implementation of timers. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#ifndef MLPACK_CORE_UTILITIES_TIMERS_IMPL_HPP +#define MLPACK_CORE_UTILITIES_TIMERS_IMPL_HPP + +#include "timers.hpp" +#include "io.hpp" + +#include +#include + +namespace mlpack { + +/** + * Start the given timer. + */ +inline void Timer::Start(const std::string& name) +{ + IO::GetSingleton().timer.Start(name, std::this_thread::get_id()); +} + +/** + * Stop the given timer. + */ +inline void Timer::Stop(const std::string& name) +{ + IO::GetSingleton().timer.Stop(name, std::this_thread::get_id()); +} + +/** + * Get the given timer, summing over all threads. + */ +inline std::chrono::microseconds Timer::Get(const std::string& name) +{ + return IO::GetSingleton().timer.Get(name); +} + +// Enable timing. +inline void Timer::EnableTiming() +{ + IO::GetSingleton().timer.Enabled() = true; +} + +// Disable timing. +inline void Timer::DisableTiming() +{ + IO::GetSingleton().timer.Enabled() = false; +} + +// Reset all timers. Save state of enabled. +inline void Timer::ResetAll() +{ + IO::GetSingleton().timer.Reset(); +} + +inline std::map Timer::GetAllTimers() +{ + return IO::GetSingleton().timer.GetAllTimers(); +} + +namespace util { +// Reset a Timers object. +inline void Timers::Reset() +{ + std::lock_guard lock(timersMutex); + timers.clear(); + timerStartTime.clear(); +} + +inline std::map Timers::GetAllTimers() +{ + // Make a copy of the timer. + std::lock_guard lock(timersMutex); + return timers; +} + +inline std::chrono::microseconds Timers::Get(const std::string& timerName) +{ + if (!enabled) + return std::chrono::microseconds(0); + + std::lock_guard lock(timersMutex); + return timers[timerName]; +} + +inline std::string Timers::Print(const std::chrono::microseconds& totalDuration) +{ + // Convert microseconds to seconds. + std::chrono::seconds totalDurationSec = + std::chrono::duration_cast(totalDuration); + std::chrono::microseconds totalDurationMicroSec = + std::chrono::duration_cast( + totalDuration % std::chrono::seconds(1)); + + std::ostringstream oss; + oss << totalDurationSec.count() << "." << std::setw(6) + << std::setfill('0') << totalDurationMicroSec.count() << "s"; + + // Also output convenient day/hr/min/sec. + // The following line is a custom duration for a day. + typedef std::chrono::duration> days; + days d = std::chrono::duration_cast(totalDuration); + std::chrono::hours h = + std::chrono::duration_cast(totalDuration % days(1)); + std::chrono::minutes m = + std::chrono::duration_cast( + totalDuration % std::chrono::hours(1)); + std::chrono::seconds s = + std::chrono::duration_cast( + totalDuration % std::chrono::minutes(1)); + // No output if it didn't even take a minute. + if (!(d.count() == 0 && h.count() == 0 && m.count() == 0)) + { + bool output = false; // Denotes if we have output anything yet. + oss << " ("; + + // Only output units if they have nonzero values (yes, a bit tedious). + if (d.count() > 0) + { + oss << d.count() << " days"; + output = true; + } + + if (h.count() > 0) + { + if (output) + oss << ", "; + oss << h.count() << " hrs"; + output = true; + } + + if (m.count() > 0) + { + if (output) + oss << ", "; + oss << m.count() << " mins"; + output = true; + } + + if (s.count() > 0) + { + if (output) + oss << ", "; + oss << s.count() << "." << std::setw(1) + << (totalDurationMicroSec.count() / 100000) << " secs"; + } + + oss << ")"; + } + + oss << std::endl; + return oss.str(); +} + +inline void Timers::StopAllTimers() +{ + // Terminate the program timers. Don't use StopTimer() since that modifies + // the map and would invalidate our iterators. + std::lock_guard lock(timersMutex); + + std::chrono::high_resolution_clock::time_point currTime = + std::chrono::high_resolution_clock::now(); + for (auto it : timerStartTime) + for (auto it2 : it.second) + timers[it2.first] += + std::chrono::duration_cast( + currTime - it2.second); + + // If all timers are stopped, we can clear the maps. + timerStartTime.clear(); +} + +inline void Timers::Start(const std::string& timerName, + const std::thread::id& threadId) +{ + // Don't do anything if we aren't timing. + if (!enabled) + return; + + std::lock_guard lock(timersMutex); + + if ((timerStartTime.count(threadId) > 0) && + (timerStartTime[threadId].count(timerName))) + { + std::ostringstream error; + error << "Timer::Start(): timer '" << timerName + << "' has already been started"; + throw std::runtime_error(error.str()); + } + + std::chrono::high_resolution_clock::time_point currTime = + std::chrono::high_resolution_clock::now(); + + // If the timer is added for the first time. + if (timers.count(timerName) == 0) + { + timers[timerName] = (std::chrono::microseconds) 0; + } + + timerStartTime[threadId][timerName] = currTime; +} + +inline void Timers::Stop(const std::string& timerName, + const std::thread::id& threadId) +{ + // Don't do anything if we aren't timing. + if (!enabled) + return; + + std::lock_guard lock(timersMutex); + + if ((timerStartTime.count(threadId) == 0) || + (timerStartTime[threadId].count(timerName) == 0)) + { + std::ostringstream error; + error << "Timer::Stop(): no timer with name '" << timerName + << "' currently running"; + throw std::runtime_error(error.str()); + } + + std::chrono::high_resolution_clock::time_point currTime = + std::chrono::high_resolution_clock::now(); + + // Calculate the delta time. + timers[timerName] += + std::chrono::duration_cast( + currTime - timerStartTime[threadId][timerName]); + + // Remove the entries. + timerStartTime[threadId].erase(timerName); + if (timerStartTime[threadId].empty()) + timerStartTime.erase(threadId); +} + +} // namespace timers +} // namespace mlpack + +#endif diff --git a/src/mlpack/core/util/version.hpp b/src/mlpack/core/util/version.hpp index 5292b64999..e47043494e 100644 --- a/src/mlpack/core/util/version.hpp +++ b/src/mlpack/core/util/version.hpp @@ -33,4 +33,7 @@ std::string GetVersion(); } // namespace util } // namespace mlpack +// Include implementation. +#include "version_impl.hpp" + #endif diff --git a/src/mlpack/core/util/version.cpp b/src/mlpack/core/util/version_impl.hpp similarity index 82% rename from src/mlpack/core/util/version.cpp rename to src/mlpack/core/util/version_impl.hpp index 2e0dfd1cf2..1bbfd66d9f 100644 --- a/src/mlpack/core/util/version.cpp +++ b/src/mlpack/core/util/version_impl.hpp @@ -1,5 +1,5 @@ /** - * @file core/util/version.cpp + * @file core/util/version_impl.hpp * @author Ryan Curtin * * The implementation of GetVersion(). @@ -9,13 +9,16 @@ * 3-clause BSD license along with mlpack. If not, see * http://www.opensource.org/licenses/BSD-3-Clause for more information. */ +#ifndef MLPACK_CORE_UTIL_VERSION_IMPL_HPP +#define MLPACK_CORE_UTIL_VERSION_IMPL_HPP + #include "version.hpp" #include // If we are not a git revision, just use the macros to assemble the version // name. -std::string mlpack::util::GetVersion() +inline std::string mlpack::util::GetVersion() { #ifndef MLPACK_GIT_VERSION std::stringstream o; @@ -28,3 +31,5 @@ std::string mlpack::util::GetVersion() #include "gitversion.hpp" #endif } + +#endif From c6ac6af6a9d5bf8e6276433f6fd6acd5467d85b9 Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Wed, 11 May 2022 11:06:27 +0530 Subject: [PATCH 49/57] Revert "header only util" This reverts commit 6ce2580bf69bb8443a7e9d16a8f6913338dd4f33. --- src/mlpack/core/util/CMakeLists.txt | 10 +- src/mlpack/core/util/binding_details.hpp | 3 +- src/mlpack/core/util/io.hpp | 8 +- src/mlpack/core/util/log.cpp | 40 +++ src/mlpack/core/util/log.hpp | 138 +++------- src/mlpack/core/util/log_impl.hpp | 70 ----- src/mlpack/core/util/param.hpp | 2 - src/mlpack/core/util/prefixedoutstream.cpp | 127 +++++++++ .../core/util/prefixedoutstream_impl.hpp | 104 -------- .../{program_doc_impl.hpp => program_doc.cpp} | 35 +-- src/mlpack/core/util/program_doc.hpp | 3 - src/mlpack/core/util/singletons.cpp | 46 ++++ src/mlpack/core/util/timers.cpp | 231 ++++++++++++++++ src/mlpack/core/util/timers.hpp | 3 - src/mlpack/core/util/timers_impl.hpp | 249 ------------------ .../util/{version_impl.hpp => version.cpp} | 9 +- src/mlpack/core/util/version.hpp | 3 - 17 files changed, 505 insertions(+), 576 deletions(-) create mode 100644 src/mlpack/core/util/log.cpp delete mode 100644 src/mlpack/core/util/log_impl.hpp create mode 100644 src/mlpack/core/util/prefixedoutstream.cpp rename src/mlpack/core/util/{program_doc_impl.hpp => program_doc.cpp} (75%) create mode 100644 src/mlpack/core/util/singletons.cpp create mode 100644 src/mlpack/core/util/timers.cpp delete mode 100644 src/mlpack/core/util/timers_impl.hpp rename src/mlpack/core/util/{version_impl.hpp => version.cpp} (82%) diff --git a/src/mlpack/core/util/CMakeLists.txt b/src/mlpack/core/util/CMakeLists.txt index fec4137c57..4ffe767d47 100644 --- a/src/mlpack/core/util/CMakeLists.txt +++ b/src/mlpack/core/util/CMakeLists.txt @@ -13,7 +13,7 @@ set(SOURCES hyphenate_string.hpp is_std_vector.hpp log.hpp - log_impl.hpp + log.cpp mlpack_main.hpp nulloutstream.hpp param.hpp @@ -24,16 +24,18 @@ set(SOURCES params_impl.hpp params.cpp prefixedoutstream.hpp + prefixedoutstream.cpp prefixedoutstream_impl.hpp program_doc.hpp - program_doc_impl.hpp + program_doc.cpp size_checks.hpp sfinae_utility.hpp + singletons.cpp timers.hpp - timers_impl.hpp + timers.cpp to_lower.hpp version.hpp - version_impl.hpp + version.cpp ) # add directory name to sources diff --git a/src/mlpack/core/util/binding_details.hpp b/src/mlpack/core/util/binding_details.hpp index 05ef8aa90d..2320aed8cb 100644 --- a/src/mlpack/core/util/binding_details.hpp +++ b/src/mlpack/core/util/binding_details.hpp @@ -13,10 +13,11 @@ #define MLPACK_CORE_UTIL_BINDING_DETAILS_HPP #include -// #include "program_doc.hpp" +#include "program_doc.hpp" namespace mlpack { namespace util { + /** * This structure holds all of the information about bindings documentation. */ diff --git a/src/mlpack/core/util/io.hpp b/src/mlpack/core/util/io.hpp index 22346f1e04..0b45563b6c 100644 --- a/src/mlpack/core/util/io.hpp +++ b/src/mlpack/core/util/io.hpp @@ -20,7 +20,9 @@ #include +#include "timers.hpp" #include "binding_details.hpp" +#include "program_doc.hpp" #include "version.hpp" #include "param_data.hpp" @@ -33,12 +35,6 @@ // into src/mlpack/bindings/util/. namespace mlpack { -class Timer; - -namespace util { - class Timers; -} - // TODO: completely go through this documentation and clean it up /** * @brief Parses the command line for parameters and holds user-specified diff --git a/src/mlpack/core/util/log.cpp b/src/mlpack/core/util/log.cpp new file mode 100644 index 0000000000..d8b2767025 --- /dev/null +++ b/src/mlpack/core/util/log.cpp @@ -0,0 +1,40 @@ +/** + * @file core/util/log.cpp + * @author Matthew Amidon + * + * Implementation of the Log class. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#include "log.hpp" + +#ifdef HAS_BFD_DL + #include "backtrace.hpp" +#endif + +using namespace mlpack; +using namespace mlpack::util; + +// Only do anything for Assert() if in debugging mode. +#ifdef DEBUG +void Log::Assert(bool condition, const std::string& message) +{ + if (!condition) + { +#ifdef HAS_BFD_DL + Backtrace bt; + + Log::Debug << bt.ToString(); +#endif + Log::Debug << message << std::endl; + + throw std::runtime_error("Log::Assert() failed: " + message); + } +} +#else +void Log::Assert(bool /* condition */, const std::string& /* message */) +{ } +#endif diff --git a/src/mlpack/core/util/log.hpp b/src/mlpack/core/util/log.hpp index a2e6dbaa99..351e362c88 100644 --- a/src/mlpack/core/util/log.hpp +++ b/src/mlpack/core/util/log.hpp @@ -1,7 +1,6 @@ /** * @file core/util/log.hpp * @author Matthew Amidon - * @author Shubham Agrawal * * Definition of the Log class. * @@ -18,21 +17,6 @@ #include "prefixedoutstream.hpp" #include "nulloutstream.hpp" -#include - -#ifndef _WIN32 - #define BASH_RED "\033[0;31m" - #define BASH_GREEN "\033[0;32m" - #define BASH_YELLOW "\033[0;33m" - #define BASH_CYAN "\033[0;36m" - #define BASH_CLEAR "\033[0m" -#else - #define BASH_RED "" - #define BASH_GREEN "" - #define BASH_YELLOW "" - #define BASH_CYAN "" - #define BASH_CLEAR "" -#endif namespace mlpack { @@ -69,98 +53,46 @@ namespace mlpack { * * @see PrefixedOutStream, NullOutStream, IO */ +class Log +{ + public: + /** + * Checks if the specified condition is true. + * If not, halts program execution and prints a custom error message. + * Does nothing in non-debug mode. + */ + static void Assert(bool condition, + const std::string& message = "Assert Failed."); -/** - * MLPACK_EXPORT is required for global variables, so that they are properly - * exported by the Windows compiler. - */ -#if __cplusplus < 201703L - template - struct Log_ - { + /** + * MLPACK_EXPORT is required for global variables, so that they are properly + * exported by the Windows compiler. + */ + + // We only use PrefixedOutStream if the program is compiled with debug + // symbols. #ifdef DEBUG - //! Prints debug output with the appropriate tag: [DEBUG]. - static MLPACK_EXPORT util::PrefixedOutStream Debug; + //! Prints debug output with the appropriate tag: [DEBUG]. + static MLPACK_EXPORT util::PrefixedOutStream Debug; #else - //! Dumps debug output into the bit nether regions. - static MLPACK_EXPORT util::NullOutStream Debug; -#endif - //! Prints informational messages if --verbose is specified, prefixed with - //! [INFO ]. - static MLPACK_EXPORT util::PrefixedOutStream Info; - - //! Prints warning messages prefixed with [WARN ]. - static MLPACK_EXPORT util::PrefixedOutStream Warn; - - //! Prints fatal messages prefixed with [FATAL], then terminates the program. - static MLPACK_EXPORT util::PrefixedOutStream Fatal; - - //! Reference to cout, if necessary. - static std::ostream& cout; - - /** - * Checks if the specified condition is true. - * If not, halts program execution and prints a custom error message. - * Does nothing in non-debug mode. - */ - static void Assert(bool condition, - const std::string& message = "Assert Failed."); - }; -#ifdef DEBUG - template - util::PrefixedOutStream Log_::Debug = util::PrefixedOutStream(MLPACK_COUT_STREAM, - BASH_CYAN "[DEBUG] " BASH_CLEAR); -#else - template - util::NullOutStream Log_::Debug = util::NullOutStream(); -#endif - template - util::PrefixedOutStream Log_::Info = util::PrefixedOutStream(MLPACK_COUT_STREAM, - BASH_GREEN "[INFO ] " BASH_CLEAR, true /* unless --verbose */, false); - template - util::PrefixedOutStream Log_::Warn = util::PrefixedOutStream(MLPACK_COUT_STREAM, - BASH_YELLOW "[WARN ] " BASH_CLEAR, false, false); - template - util::PrefixedOutStream Log_::Fatal = util::PrefixedOutStream(MLPACK_CERR_STREAM, - BASH_RED "[FATAL] " BASH_CLEAR, false, true /* fatal */); - using Log = Log_; -#else - namespace Log { -#ifdef DEBUG - //! Prints debug output with the appropriate tag: [DEBUG]. - inline util::PrefixedOutStream Debug = util::PrefixedOutStream(MLPACK_COUT_STREAM, - BASH_CYAN "[DEBUG] " BASH_CLEAR); -#else - //! Dumps debug output into the bit nether regions. - inline util::NullOutStream Debug = util::NullOutStream(); -#endif - - //! Prints informational messages if --verbose is specified, prefixed with - //! [INFO ]. - inline util::PrefixedOutStream Info = util::PrefixedOutStream(MLPACK_COUT_STREAM, - BASH_GREEN "[INFO ] " BASH_CLEAR, true /* unless --verbose */, false); - - //! Prints warning messages prefixed with [WARN ]. - inline util::PrefixedOutStream Warn = util::PrefixedOutStream(MLPACK_COUT_STREAM, - BASH_YELLOW "[WARN ] " BASH_CLEAR, false, false); - - //! Prints fatal messages prefixed with [FATAL], then terminates the program. - inline util::PrefixedOutStream Fatal = util::PrefixedOutStream(MLPACK_CERR_STREAM, - BASH_RED "[FATAL] " BASH_CLEAR, false, true /* fatal */); - - /** - * Checks if the specified condition is true. - * If not, halts program execution and prints a custom error message. - * Does nothing in non-debug mode. - */ - void Assert(bool condition, - const std::string& message = "Assert Failed."); - } // namespace Log + //! Dumps debug output into the bit nether regions. + static MLPACK_EXPORT util::NullOutStream Debug; #endif -} // namespace mlpack + //! Prints informational messages if --verbose is specified, prefixed with + //! [INFO ]. + static MLPACK_EXPORT util::PrefixedOutStream Info; -// Include implementation. -#include "log_impl.hpp" + //! Prints warning messages prefixed with [WARN ]. + static MLPACK_EXPORT util::PrefixedOutStream Warn; + + //! Prints fatal messages prefixed with [FATAL], then terminates the program. + static MLPACK_EXPORT util::PrefixedOutStream Fatal; + + //! Reference to cout, if necessary. + static std::ostream& cout; +}; + +}; // namespace mlpack #endif diff --git a/src/mlpack/core/util/log_impl.hpp b/src/mlpack/core/util/log_impl.hpp deleted file mode 100644 index 0333a3f01f..0000000000 --- a/src/mlpack/core/util/log_impl.hpp +++ /dev/null @@ -1,70 +0,0 @@ -/** - * @file core/util/log_impl.hpp - * @author Matthew Amidon - * - * Implementation of the Log class. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_CORE_UTIL_LOG_IMPL_HPP -#define MLPACK_CORE_UTIL_LOG_IMPL_HPP - -#include "log.hpp" - -#ifdef HAS_BFD_DL - #include "backtrace.hpp" -#endif - -namespace mlpack { - -#if __cplusplus < 201703L -// Only do anything for Assert() if in debugging mode. -#ifdef DEBUG -template -void Log_::Assert(bool condition, const std::string& message) -{ - if (!condition) - { -#ifdef HAS_BFD_DL - Backtrace bt; - - Log::Debug << bt.ToString(); -#endif - Log::Debug << message << std::endl; - - throw std::runtime_error("Log::Assert() failed: " + message); - } -} -#else -template -void Log_::Assert(bool /* condition */, const std::string& /* message */) -{ /* Nothing to do. */ } -#endif -#else -// Only do anything for Assert() if in debugging mode. -#ifdef DEBUG -inline void Log::Assert(bool condition, const std::string& message) -{ - if (!condition) - { -#ifdef HAS_BFD_DL - Backtrace bt; - - Log::Debug << bt.ToString(); -#endif - Log::Debug << message << std::endl; - - throw std::runtime_error("Log::Assert() failed: " + message); - } -} -#else -inline void Log::Assert(bool /* condition */, const std::string& /* message */) -{ /* Nothing to do. */ } -#endif -#endif // __cplusplus < 201703L -} // namespace mlpack - -#endif diff --git a/src/mlpack/core/util/param.hpp b/src/mlpack/core/util/param.hpp index 7cdf975098..c2dbe7e2aa 100644 --- a/src/mlpack/core/util/param.hpp +++ b/src/mlpack/core/util/param.hpp @@ -15,8 +15,6 @@ #ifndef MLPACK_CORE_UTIL_PARAM_HPP #define MLPACK_CORE_UTIL_PARAM_HPP -#include "program_doc.hpp" - // Required forward declarations. namespace mlpack { namespace data { diff --git a/src/mlpack/core/util/prefixedoutstream.cpp b/src/mlpack/core/util/prefixedoutstream.cpp new file mode 100644 index 0000000000..283cdcd10f --- /dev/null +++ b/src/mlpack/core/util/prefixedoutstream.cpp @@ -0,0 +1,127 @@ +/** + * @file core/util/prefixedoutstream.cpp + * @author Ryan Curtin + * @author Matthew Amidon + * + * Implementation of PrefixedOutStream methods. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#include + +#include "prefixedoutstream.hpp" + +using namespace mlpack::util; + +/** + * These are all necessary because gcc's template mechanism does not seem smart + * enough to figure out what I want to pass into operator<< without these. That + * may not be the actual case, but it works when these is here. + */ + +PrefixedOutStream& PrefixedOutStream::operator<<(bool val) +{ + BaseLogic(val); + return *this; +} + +PrefixedOutStream& PrefixedOutStream::operator<<(short val) +{ + BaseLogic(val); + return *this; +} + +PrefixedOutStream& PrefixedOutStream::operator<<(unsigned short val) +{ + BaseLogic(val); + return *this; +} + +PrefixedOutStream& PrefixedOutStream::operator<<(int val) +{ + BaseLogic(val); + return *this; +} + +PrefixedOutStream& PrefixedOutStream::operator<<(unsigned int val) +{ + BaseLogic(val); + return *this; +} + +PrefixedOutStream& PrefixedOutStream::operator<<(long val) +{ + BaseLogic(val); + return *this; +} + +PrefixedOutStream& PrefixedOutStream::operator<<(unsigned long val) +{ + BaseLogic(val); + return *this; +} + +PrefixedOutStream& PrefixedOutStream::operator<<(float val) +{ + BaseLogic(val); + return *this; +} + +PrefixedOutStream& PrefixedOutStream::operator<<(double val) +{ + BaseLogic(val); + return *this; +} + +PrefixedOutStream& PrefixedOutStream::operator<<(long double val) +{ + BaseLogic(val); + return *this; +} + +PrefixedOutStream& PrefixedOutStream::operator<<(void* val) +{ + BaseLogic(val); + return *this; +} + +PrefixedOutStream& PrefixedOutStream::operator<<(const char* str) +{ + BaseLogic(str); + return *this; +} + +PrefixedOutStream& PrefixedOutStream::operator<<(std::string& str) +{ + BaseLogic(str); + return *this; +} + +PrefixedOutStream& PrefixedOutStream::operator<<(std::streambuf* sb) +{ + BaseLogic(sb); + return *this; +} + +PrefixedOutStream& PrefixedOutStream::operator<<( + std::ostream& (*pf)(std::ostream&)) +{ + BaseLogic(pf); + return *this; +} + +PrefixedOutStream& PrefixedOutStream::operator<<(std::ios& (*pf)(std::ios&)) +{ + BaseLogic(pf); + return *this; +} + +PrefixedOutStream& PrefixedOutStream::operator<<( + std::ios_base& (*pf) (std::ios_base&)) +{ + BaseLogic(pf); + return *this; +} diff --git a/src/mlpack/core/util/prefixedoutstream_impl.hpp b/src/mlpack/core/util/prefixedoutstream_impl.hpp index 3189a1c66a..3cb9eea353 100644 --- a/src/mlpack/core/util/prefixedoutstream_impl.hpp +++ b/src/mlpack/core/util/prefixedoutstream_impl.hpp @@ -33,110 +33,6 @@ PrefixedOutStream& PrefixedOutStream::operator<<(const T& s) return *this; } -inline PrefixedOutStream& PrefixedOutStream::operator<<(bool val) -{ - BaseLogic(val); - return *this; -} - -inline PrefixedOutStream& PrefixedOutStream::operator<<(short val) -{ - BaseLogic(val); - return *this; -} - -inline PrefixedOutStream& PrefixedOutStream::operator<<(unsigned short val) -{ - BaseLogic(val); - return *this; -} - -inline PrefixedOutStream& PrefixedOutStream::operator<<(int val) -{ - BaseLogic(val); - return *this; -} - -inline PrefixedOutStream& PrefixedOutStream::operator<<(unsigned int val) -{ - BaseLogic(val); - return *this; -} - -inline PrefixedOutStream& PrefixedOutStream::operator<<(long val) -{ - BaseLogic(val); - return *this; -} - -inline PrefixedOutStream& PrefixedOutStream::operator<<(unsigned long val) -{ - BaseLogic(val); - return *this; -} - -inline PrefixedOutStream& PrefixedOutStream::operator<<(float val) -{ - BaseLogic(val); - return *this; -} - -inline PrefixedOutStream& PrefixedOutStream::operator<<(double val) -{ - BaseLogic(val); - return *this; -} - -inline PrefixedOutStream& PrefixedOutStream::operator<<(long double val) -{ - BaseLogic(val); - return *this; -} - -inline PrefixedOutStream& PrefixedOutStream::operator<<(void* val) -{ - BaseLogic(val); - return *this; -} - -inline PrefixedOutStream& PrefixedOutStream::operator<<(const char* str) -{ - BaseLogic(str); - return *this; -} - -inline PrefixedOutStream& PrefixedOutStream::operator<<(std::string& str) -{ - BaseLogic(str); - return *this; -} - -inline PrefixedOutStream& PrefixedOutStream::operator<<(std::streambuf* sb) -{ - BaseLogic(sb); - return *this; -} - -inline PrefixedOutStream& PrefixedOutStream::operator<<( - std::ostream& (*pf)(std::ostream&)) -{ - BaseLogic(pf); - return *this; -} - -inline PrefixedOutStream& PrefixedOutStream::operator<<(std::ios& (*pf)(std::ios&)) -{ - BaseLogic(pf); - return *this; -} - -inline PrefixedOutStream& PrefixedOutStream::operator<<( - std::ios_base& (*pf) (std::ios_base&)) -{ - BaseLogic(pf); - return *this; -} - // For non-Armadillo types. template typename std::enable_if::value>::type diff --git a/src/mlpack/core/util/program_doc_impl.hpp b/src/mlpack/core/util/program_doc.cpp similarity index 75% rename from src/mlpack/core/util/program_doc_impl.hpp rename to src/mlpack/core/util/program_doc.cpp index 6c34d65022..f95f560c96 100644 --- a/src/mlpack/core/util/program_doc_impl.hpp +++ b/src/mlpack/core/util/program_doc.cpp @@ -1,5 +1,5 @@ /** - * @file core/util/program_doc_impl.hpp + * @file core/util/program_doc.cpp * @author Yashwant Singh Parihar * @author Ryan Curtin * @@ -11,16 +11,14 @@ * 3-clause BSD license along with mlpack. If not, see * http://www.opensource.org/licenses/BSD-3-Clause for more information. */ -#ifndef MLPACK_CORE_UTIL_PROGRAM_DOC_IMPL_HPP -#define MLPACK_CORE_UTIL_PROGRAM_DOC_IMPL_HPP - #include "io.hpp" #include "program_doc.hpp" #include -namespace mlpack { -namespace util { +using namespace mlpack; +using namespace mlpack::util; +using namespace std; /** * Construct a BindingName object. When constructed, it will register itself @@ -30,8 +28,8 @@ namespace util { * @param bindingName Name of the binding. * @param name Name displayed to user of the binding. */ -inline BindingName::BindingName(const std::string& bindingName, - const std::string& name) +BindingName::BindingName(const std::string& bindingName, + const std::string& name) { // Register this with IO. IO::AddBindingName(bindingName, name); @@ -46,8 +44,8 @@ inline BindingName::BindingName(const std::string& bindingName, * @param shortDescription A short two-sentence description of the binding, * what it does, and what it is useful for. */ -inline ShortDescription::ShortDescription(const std::string& bindingName, - const std::string& shortDescription) +ShortDescription::ShortDescription(const std::string& bindingName, + const std::string& shortDescription) { // Register this with IO. IO::AddShortDescription(bindingName, shortDescription); @@ -63,7 +61,7 @@ inline ShortDescription::ShortDescription(const std::string& bindingName, * what it is. No newline characters are necessary; this is * taken care of by IO later. */ -inline LongDescription::LongDescription( +LongDescription::LongDescription( const std::string& bindingName, const std::function& longDescription) { @@ -78,8 +76,8 @@ inline LongDescription::LongDescription( * @param bindingName Name of the binding. * @param example Documentation on how to use the binding. */ -inline Example::Example(const std::string& bindingName, - const std::function& example) +Example::Example(const std::string& bindingName, + const std::function& example) { // Register this with IO. IO::AddExample(bindingName, example); @@ -93,15 +91,10 @@ inline Example::Example(const std::string& bindingName, * @param description Description of SeeAlso. * @param link Link of SeeAlso. */ -inline SeeAlso::SeeAlso(const std::string& bindingName, - const std::string& description, - const std::string& link) +SeeAlso::SeeAlso(const std::string& bindingName, + const std::string& description, + const std::string& link) { // Register this with IO. IO::AddSeeAlso(bindingName, description, link); } - -} // namespace util -} // namespace mlpack - -#endif diff --git a/src/mlpack/core/util/program_doc.hpp b/src/mlpack/core/util/program_doc.hpp index 5dc21826f5..7d6f64d7c4 100644 --- a/src/mlpack/core/util/program_doc.hpp +++ b/src/mlpack/core/util/program_doc.hpp @@ -97,7 +97,4 @@ class SeeAlso } // namespace util } // namespace mlpack -// Include implementation. -#include "program_doc_impl.hpp" - #endif diff --git a/src/mlpack/core/util/singletons.cpp b/src/mlpack/core/util/singletons.cpp new file mode 100644 index 0000000000..2e653ef3e8 --- /dev/null +++ b/src/mlpack/core/util/singletons.cpp @@ -0,0 +1,46 @@ +/** + * @file core/util/singletons.cpp + * @author Ryan Curtin + * + * Declaration of singletons in libmlpack.so. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#include "io.hpp" +#include "log.hpp" +#include + +using namespace mlpack; +using namespace mlpack::util; + +// Color code escape sequences -- but not on Windows. +#ifndef _WIN32 + #define BASH_RED "\033[0;31m" + #define BASH_GREEN "\033[0;32m" + #define BASH_YELLOW "\033[0;33m" + #define BASH_CYAN "\033[0;36m" + #define BASH_CLEAR "\033[0m" +#else + #define BASH_RED "" + #define BASH_GREEN "" + #define BASH_YELLOW "" + #define BASH_CYAN "" + #define BASH_CLEAR "" +#endif + +#ifdef DEBUG +PrefixedOutStream Log::Debug = PrefixedOutStream(MLPACK_COUT_STREAM, + BASH_CYAN "[DEBUG] " BASH_CLEAR); +#else +NullOutStream Log::Debug = NullOutStream(); +#endif + +PrefixedOutStream Log::Info = PrefixedOutStream(MLPACK_COUT_STREAM, + BASH_GREEN "[INFO ] " BASH_CLEAR, true /* unless --verbose */, false); +PrefixedOutStream Log::Warn = PrefixedOutStream(MLPACK_COUT_STREAM, + BASH_YELLOW "[WARN ] " BASH_CLEAR, false, false); +PrefixedOutStream Log::Fatal = PrefixedOutStream(MLPACK_CERR_STREAM, + BASH_RED "[FATAL] " BASH_CLEAR, false, true /* fatal */); diff --git a/src/mlpack/core/util/timers.cpp b/src/mlpack/core/util/timers.cpp new file mode 100644 index 0000000000..b93f5731fe --- /dev/null +++ b/src/mlpack/core/util/timers.cpp @@ -0,0 +1,231 @@ +/** + * @file core/util/timers.cpp + * @author Matthew Amidon + * @author Marcus Edel + * @author Ryan Curtin + * + * Implementation of timers. + * + * mlpack is free software; you may redistribute it and/or modify it under the + * terms of the 3-clause BSD license. You should have received a copy of the + * 3-clause BSD license along with mlpack. If not, see + * http://www.opensource.org/licenses/BSD-3-Clause for more information. + */ +#include "timers.hpp" +#include "io.hpp" +#include "log.hpp" + +#include +#include + +using namespace mlpack; +using namespace mlpack::util; +using namespace std; +using namespace chrono; + +/** + * Start the given timer. + */ +void Timer::Start(const string& name) +{ + IO::GetSingleton().timer.Start(name, this_thread::get_id()); +} + +/** + * Stop the given timer. + */ +void Timer::Stop(const string& name) +{ + IO::GetSingleton().timer.Stop(name, this_thread::get_id()); +} + +/** + * Get the given timer, summing over all threads. + */ +microseconds Timer::Get(const string& name) +{ + return IO::GetSingleton().timer.Get(name); +} + +// Enable timing. +void Timer::EnableTiming() +{ + IO::GetSingleton().timer.Enabled() = true; +} + +// Disable timing. +void Timer::DisableTiming() +{ + IO::GetSingleton().timer.Enabled() = false; +} + +// Reset all timers. Save state of enabled. +void Timer::ResetAll() +{ + IO::GetSingleton().timer.Reset(); +} + +std::map Timer::GetAllTimers() +{ + return IO::GetSingleton().timer.GetAllTimers(); +} + +// Reset a Timers object. +void Timers::Reset() +{ + lock_guard lock(timersMutex); + timers.clear(); + timerStartTime.clear(); +} + +map Timers::GetAllTimers() +{ + // Make a copy of the timer. + lock_guard lock(timersMutex); + return timers; +} + +microseconds Timers::Get(const string& timerName) +{ + if (!enabled) + return microseconds(0); + + lock_guard lock(timersMutex); + return timers[timerName]; +} + +std::string Timers::Print(const microseconds& totalDuration) +{ + // Convert microseconds to seconds. + seconds totalDurationSec = duration_cast(totalDuration); + microseconds totalDurationMicroSec = + duration_cast(totalDuration % seconds(1)); + + std::ostringstream oss; + oss << totalDurationSec.count() << "." << setw(6) + << setfill('0') << totalDurationMicroSec.count() << "s"; + + // Also output convenient day/hr/min/sec. + // The following line is a custom duration for a day. + typedef duration> days; + days d = duration_cast(totalDuration); + hours h = duration_cast(totalDuration % days(1)); + minutes m = duration_cast(totalDuration % hours(1)); + seconds s = duration_cast(totalDuration % minutes(1)); + // No output if it didn't even take a minute. + if (!(d.count() == 0 && h.count() == 0 && m.count() == 0)) + { + bool output = false; // Denotes if we have output anything yet. + oss << " ("; + + // Only output units if they have nonzero values (yes, a bit tedious). + if (d.count() > 0) + { + oss << d.count() << " days"; + output = true; + } + + if (h.count() > 0) + { + if (output) + oss << ", "; + oss << h.count() << " hrs"; + output = true; + } + + if (m.count() > 0) + { + if (output) + oss << ", "; + oss << m.count() << " mins"; + output = true; + } + + if (s.count() > 0) + { + if (output) + oss << ", "; + oss << s.count() << "." << setw(1) + << (totalDurationMicroSec.count() / 100000) << " secs"; + } + + oss << ")"; + } + + oss << endl; + return oss.str(); +} + +void Timers::StopAllTimers() +{ + // Terminate the program timers. Don't use StopTimer() since that modifies + // the map and would invalidate our iterators. + lock_guard lock(timersMutex); + + high_resolution_clock::time_point currTime = high_resolution_clock::now(); + for (auto it : timerStartTime) + for (auto it2 : it.second) + timers[it2.first] += duration_cast(currTime - it2.second); + + // If all timers are stopped, we can clear the maps. + timerStartTime.clear(); +} + +void Timers::Start(const string& timerName, + const thread::id& threadId) +{ + // Don't do anything if we aren't timing. + if (!enabled) + return; + + lock_guard lock(timersMutex); + + if ((timerStartTime.count(threadId) > 0) && + (timerStartTime[threadId].count(timerName))) + { + ostringstream error; + error << "Timer::Start(): timer '" << timerName + << "' has already been started"; + throw runtime_error(error.str()); + } + + high_resolution_clock::time_point currTime = high_resolution_clock::now(); + + // If the timer is added for the first time. + if (timers.count(timerName) == 0) + { + timers[timerName] = (microseconds) 0; + } + + timerStartTime[threadId][timerName] = currTime; +} + +void Timers::Stop(const string& timerName, + const thread::id& threadId) +{ + // Don't do anything if we aren't timing. + if (!enabled) + return; + + lock_guard lock(timersMutex); + + if ((timerStartTime.count(threadId) == 0) || + (timerStartTime[threadId].count(timerName) == 0)) + { + ostringstream error; + error << "Timer::Stop(): no timer with name '" << timerName + << "' currently running"; + throw runtime_error(error.str()); + } + + high_resolution_clock::time_point currTime = high_resolution_clock::now(); + + // Calculate the delta time. + timers[timerName] += duration_cast(currTime - + timerStartTime[threadId][timerName]); + + // Remove the entries. + timerStartTime[threadId].erase(timerName); + if (timerStartTime[threadId].empty()) + timerStartTime.erase(threadId); +} diff --git a/src/mlpack/core/util/timers.hpp b/src/mlpack/core/util/timers.hpp index ee7279c004..177850de52 100644 --- a/src/mlpack/core/util/timers.hpp +++ b/src/mlpack/core/util/timers.hpp @@ -182,7 +182,4 @@ class Timers } // namespace util } // namespace mlpack -// Include implementation. -#include "timers_impl.hpp" - #endif // MLPACK_CORE_UTILITIES_TIMERS_HPP diff --git a/src/mlpack/core/util/timers_impl.hpp b/src/mlpack/core/util/timers_impl.hpp deleted file mode 100644 index f9237bdc48..0000000000 --- a/src/mlpack/core/util/timers_impl.hpp +++ /dev/null @@ -1,249 +0,0 @@ -/** - * @file core/util/timers_impl.hpp - * @author Matthew Amidon - * @author Marcus Edel - * @author Ryan Curtin - * - * Implementation of timers. - * - * mlpack is free software; you may redistribute it and/or modify it under the - * terms of the 3-clause BSD license. You should have received a copy of the - * 3-clause BSD license along with mlpack. If not, see - * http://www.opensource.org/licenses/BSD-3-Clause for more information. - */ -#ifndef MLPACK_CORE_UTILITIES_TIMERS_IMPL_HPP -#define MLPACK_CORE_UTILITIES_TIMERS_IMPL_HPP - -#include "timers.hpp" -#include "io.hpp" - -#include -#include - -namespace mlpack { - -/** - * Start the given timer. - */ -inline void Timer::Start(const std::string& name) -{ - IO::GetSingleton().timer.Start(name, std::this_thread::get_id()); -} - -/** - * Stop the given timer. - */ -inline void Timer::Stop(const std::string& name) -{ - IO::GetSingleton().timer.Stop(name, std::this_thread::get_id()); -} - -/** - * Get the given timer, summing over all threads. - */ -inline std::chrono::microseconds Timer::Get(const std::string& name) -{ - return IO::GetSingleton().timer.Get(name); -} - -// Enable timing. -inline void Timer::EnableTiming() -{ - IO::GetSingleton().timer.Enabled() = true; -} - -// Disable timing. -inline void Timer::DisableTiming() -{ - IO::GetSingleton().timer.Enabled() = false; -} - -// Reset all timers. Save state of enabled. -inline void Timer::ResetAll() -{ - IO::GetSingleton().timer.Reset(); -} - -inline std::map Timer::GetAllTimers() -{ - return IO::GetSingleton().timer.GetAllTimers(); -} - -namespace util { -// Reset a Timers object. -inline void Timers::Reset() -{ - std::lock_guard lock(timersMutex); - timers.clear(); - timerStartTime.clear(); -} - -inline std::map Timers::GetAllTimers() -{ - // Make a copy of the timer. - std::lock_guard lock(timersMutex); - return timers; -} - -inline std::chrono::microseconds Timers::Get(const std::string& timerName) -{ - if (!enabled) - return std::chrono::microseconds(0); - - std::lock_guard lock(timersMutex); - return timers[timerName]; -} - -inline std::string Timers::Print(const std::chrono::microseconds& totalDuration) -{ - // Convert microseconds to seconds. - std::chrono::seconds totalDurationSec = - std::chrono::duration_cast(totalDuration); - std::chrono::microseconds totalDurationMicroSec = - std::chrono::duration_cast( - totalDuration % std::chrono::seconds(1)); - - std::ostringstream oss; - oss << totalDurationSec.count() << "." << std::setw(6) - << std::setfill('0') << totalDurationMicroSec.count() << "s"; - - // Also output convenient day/hr/min/sec. - // The following line is a custom duration for a day. - typedef std::chrono::duration> days; - days d = std::chrono::duration_cast(totalDuration); - std::chrono::hours h = - std::chrono::duration_cast(totalDuration % days(1)); - std::chrono::minutes m = - std::chrono::duration_cast( - totalDuration % std::chrono::hours(1)); - std::chrono::seconds s = - std::chrono::duration_cast( - totalDuration % std::chrono::minutes(1)); - // No output if it didn't even take a minute. - if (!(d.count() == 0 && h.count() == 0 && m.count() == 0)) - { - bool output = false; // Denotes if we have output anything yet. - oss << " ("; - - // Only output units if they have nonzero values (yes, a bit tedious). - if (d.count() > 0) - { - oss << d.count() << " days"; - output = true; - } - - if (h.count() > 0) - { - if (output) - oss << ", "; - oss << h.count() << " hrs"; - output = true; - } - - if (m.count() > 0) - { - if (output) - oss << ", "; - oss << m.count() << " mins"; - output = true; - } - - if (s.count() > 0) - { - if (output) - oss << ", "; - oss << s.count() << "." << std::setw(1) - << (totalDurationMicroSec.count() / 100000) << " secs"; - } - - oss << ")"; - } - - oss << std::endl; - return oss.str(); -} - -inline void Timers::StopAllTimers() -{ - // Terminate the program timers. Don't use StopTimer() since that modifies - // the map and would invalidate our iterators. - std::lock_guard lock(timersMutex); - - std::chrono::high_resolution_clock::time_point currTime = - std::chrono::high_resolution_clock::now(); - for (auto it : timerStartTime) - for (auto it2 : it.second) - timers[it2.first] += - std::chrono::duration_cast( - currTime - it2.second); - - // If all timers are stopped, we can clear the maps. - timerStartTime.clear(); -} - -inline void Timers::Start(const std::string& timerName, - const std::thread::id& threadId) -{ - // Don't do anything if we aren't timing. - if (!enabled) - return; - - std::lock_guard lock(timersMutex); - - if ((timerStartTime.count(threadId) > 0) && - (timerStartTime[threadId].count(timerName))) - { - std::ostringstream error; - error << "Timer::Start(): timer '" << timerName - << "' has already been started"; - throw std::runtime_error(error.str()); - } - - std::chrono::high_resolution_clock::time_point currTime = - std::chrono::high_resolution_clock::now(); - - // If the timer is added for the first time. - if (timers.count(timerName) == 0) - { - timers[timerName] = (std::chrono::microseconds) 0; - } - - timerStartTime[threadId][timerName] = currTime; -} - -inline void Timers::Stop(const std::string& timerName, - const std::thread::id& threadId) -{ - // Don't do anything if we aren't timing. - if (!enabled) - return; - - std::lock_guard lock(timersMutex); - - if ((timerStartTime.count(threadId) == 0) || - (timerStartTime[threadId].count(timerName) == 0)) - { - std::ostringstream error; - error << "Timer::Stop(): no timer with name '" << timerName - << "' currently running"; - throw std::runtime_error(error.str()); - } - - std::chrono::high_resolution_clock::time_point currTime = - std::chrono::high_resolution_clock::now(); - - // Calculate the delta time. - timers[timerName] += - std::chrono::duration_cast( - currTime - timerStartTime[threadId][timerName]); - - // Remove the entries. - timerStartTime[threadId].erase(timerName); - if (timerStartTime[threadId].empty()) - timerStartTime.erase(threadId); -} - -} // namespace timers -} // namespace mlpack - -#endif diff --git a/src/mlpack/core/util/version_impl.hpp b/src/mlpack/core/util/version.cpp similarity index 82% rename from src/mlpack/core/util/version_impl.hpp rename to src/mlpack/core/util/version.cpp index 1bbfd66d9f..2e0dfd1cf2 100644 --- a/src/mlpack/core/util/version_impl.hpp +++ b/src/mlpack/core/util/version.cpp @@ -1,5 +1,5 @@ /** - * @file core/util/version_impl.hpp + * @file core/util/version.cpp * @author Ryan Curtin * * The implementation of GetVersion(). @@ -9,16 +9,13 @@ * 3-clause BSD license along with mlpack. If not, see * http://www.opensource.org/licenses/BSD-3-Clause for more information. */ -#ifndef MLPACK_CORE_UTIL_VERSION_IMPL_HPP -#define MLPACK_CORE_UTIL_VERSION_IMPL_HPP - #include "version.hpp" #include // If we are not a git revision, just use the macros to assemble the version // name. -inline std::string mlpack::util::GetVersion() +std::string mlpack::util::GetVersion() { #ifndef MLPACK_GIT_VERSION std::stringstream o; @@ -31,5 +28,3 @@ inline std::string mlpack::util::GetVersion() #include "gitversion.hpp" #endif } - -#endif diff --git a/src/mlpack/core/util/version.hpp b/src/mlpack/core/util/version.hpp index e47043494e..5292b64999 100644 --- a/src/mlpack/core/util/version.hpp +++ b/src/mlpack/core/util/version.hpp @@ -33,7 +33,4 @@ std::string GetVersion(); } // namespace util } // namespace mlpack -// Include implementation. -#include "version_impl.hpp" - #endif From 45417a5df43c10b26e6a84b1ad172d34ecddb1f8 Mon Sep 17 00:00:00 2001 From: Shubham Agrawal <58412969+shubham1206agra@users.noreply.github.com> Date: Mon, 16 May 2022 11:30:54 +0530 Subject: [PATCH 50/57] Apply suggestions from code review Co-authored-by: Ryan Curtin --- src/mlpack/core/dists/laplace_distribution.hpp | 1 + src/mlpack/core/math/columns_to_blocks_impl.hpp | 8 ++++---- src/mlpack/methods/radical/radical_impl.hpp | 2 +- 3 files changed, 6 insertions(+), 5 deletions(-) diff --git a/src/mlpack/core/dists/laplace_distribution.hpp b/src/mlpack/core/dists/laplace_distribution.hpp index 9af82652eb..eb651fbb08 100644 --- a/src/mlpack/core/dists/laplace_distribution.hpp +++ b/src/mlpack/core/dists/laplace_distribution.hpp @@ -15,6 +15,7 @@ #define MLPACK_CORE_DISTRIBUTIONS_LAPLACE_DISTRIBUTION_HPP #include + namespace mlpack { namespace distribution /** Probability distributions. */ { diff --git a/src/mlpack/core/math/columns_to_blocks_impl.hpp b/src/mlpack/core/math/columns_to_blocks_impl.hpp index cc4fd2d95c..a33441782e 100644 --- a/src/mlpack/core/math/columns_to_blocks_impl.hpp +++ b/src/mlpack/core/math/columns_to_blocks_impl.hpp @@ -18,9 +18,9 @@ namespace mlpack { namespace math { inline ColumnsToBlocks::ColumnsToBlocks(const size_t rows, - const size_t cols, - const size_t blockHeight, - const size_t blockWidth) : + const size_t cols, + const size_t blockHeight, + const size_t blockWidth) : blockHeight(blockHeight), blockWidth(blockWidth), bufSize(1), @@ -40,7 +40,7 @@ inline bool ColumnsToBlocks::IsPerfectSquare(const size_t value) const } inline void ColumnsToBlocks::Transform(const arma::mat& maximalInputs, - arma::mat& output) + arma::mat& output) { //! TODO: Maybe replace std::runtime_error with Log::Fatal. if (!IsPerfectSquare(maximalInputs.n_rows)) diff --git a/src/mlpack/methods/radical/radical_impl.hpp b/src/mlpack/methods/radical/radical_impl.hpp index 8914c11000..6a492e0972 100644 --- a/src/mlpack/methods/radical/radical_impl.hpp +++ b/src/mlpack/methods/radical/radical_impl.hpp @@ -19,7 +19,7 @@ namespace radical { // Set the parameters to RADICAL. inline Radical::Radical( - const double noiseStdDev, + const double noiseStdDev, const size_t replicates, const size_t angles, const size_t sweeps, From 458a18b2468b1f64075113db680629070dfdb206 Mon Sep 17 00:00:00 2001 From: Shubham Agrawal <58412969+shubham1206agra@users.noreply.github.com> Date: Mon, 16 May 2022 11:31:38 +0530 Subject: [PATCH 51/57] Update src/mlpack/methods/sparse_autoencoder/sparse_autoencoder_impl.hpp Co-authored-by: Ryan Curtin --- .../methods/sparse_autoencoder/sparse_autoencoder_impl.hpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/mlpack/methods/sparse_autoencoder/sparse_autoencoder_impl.hpp b/src/mlpack/methods/sparse_autoencoder/sparse_autoencoder_impl.hpp index d21e554e9f..4fcd6d860d 100644 --- a/src/mlpack/methods/sparse_autoencoder/sparse_autoencoder_impl.hpp +++ b/src/mlpack/methods/sparse_autoencoder/sparse_autoencoder_impl.hpp @@ -73,7 +73,7 @@ SparseAutoencoder::SparseAutoencoder(const arma::mat& data, } inline void SparseAutoencoder::GetNewFeatures(arma::mat& data, - arma::mat& features) + arma::mat& features) { const size_t l1 = hiddenSize; const size_t l2 = visibleSize; From a851ea76a5e69cd44d76412f023f6f85724e1c2d Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Mon, 16 May 2022 12:25:48 +0530 Subject: [PATCH 52/57] trying to remove static build warnings --- .../bayesian_linear_regression_impl.hpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/mlpack/methods/bayesian_linear_regression/bayesian_linear_regression_impl.hpp b/src/mlpack/methods/bayesian_linear_regression/bayesian_linear_regression_impl.hpp index 928bfc17f6..5cfc914623 100644 --- a/src/mlpack/methods/bayesian_linear_regression/bayesian_linear_regression_impl.hpp +++ b/src/mlpack/methods/bayesian_linear_regression/bayesian_linear_regression_impl.hpp @@ -58,11 +58,11 @@ inline double BayesianLinearRegression::Train(const arma::mat& data, beta = 1 / (var(t, 1) * 0.1); unsigned short i = 0; - double deltaAlpha = 1.0, crit = 1.0; + double crit = 1.0; while ((crit > tolerance) && (i < maxIterations)) { - deltaAlpha = -alpha; + double deltaAlpha = -alpha; double deltaBeta = -beta; // Update the solution. From 1256ba41b75b25c3548c5b6a0b3f9d3e36644e37 Mon Sep 17 00:00:00 2001 From: Shubham Agrawal Date: Sat, 4 Jun 2022 17:12:41 +0530 Subject: [PATCH 53/57] changed random code --- .../core/dists/gamma_distribution_impl.hpp | 2 +- src/mlpack/core/math/random.hpp | 67 +++++++------------ .../bias_svd/bias_svd_function_impl.hpp | 2 +- .../regularized_svd_function_impl.hpp | 2 +- .../svdplusplus/svdplusplus_function_impl.hpp | 2 +- src/mlpack/tests/distribution_test.cpp | 16 ++--- 6 files changed, 36 insertions(+), 55 deletions(-) diff --git a/src/mlpack/core/dists/gamma_distribution_impl.hpp b/src/mlpack/core/dists/gamma_distribution_impl.hpp index e1187dedbb..6da0f9763e 100644 --- a/src/mlpack/core/dists/gamma_distribution_impl.hpp +++ b/src/mlpack/core/dists/gamma_distribution_impl.hpp @@ -240,7 +240,7 @@ inline arma::vec GammaDistribution::Random() const { std::gamma_distribution dist(alpha(d), beta(d)); // Use the mlpack random object. - randVec(d) = dist(mlpack::math::GlobalRandomVariables::randGen); + randVec(d) = dist(mlpack::math::RandGen()); } return randVec; diff --git a/src/mlpack/core/math/random.hpp b/src/mlpack/core/math/random.hpp index 78adc7f4bf..ce919ceb18 100644 --- a/src/mlpack/core/math/random.hpp +++ b/src/mlpack/core/math/random.hpp @@ -23,42 +23,23 @@ namespace math /** Miscellaneous math routines. */ { * correctly on Windows. */ -#ifndef MLPACK_CORE_MATH_RANDOM_GLOBAL -#define MLPACK_CORE_MATH_RANDOM_GLOBAL +inline std::mt19937& RandGen() +{ + static thread_local std::mt19937 randGen; + return randGen; +} -#if __cplusplus < 201703L - template - struct GlobalRandomVariables_ - { - // Global random object. - static std::mt19937 randGen; - // Global uniform distribution. - static std::uniform_real_distribution<> randUniformDist; - // Global normal distribution. - static std::normal_distribution<> randNormalDist; - }; - template - // Global random object. - std::mt19937 GlobalRandomVariables_::randGen; - template - // Global uniform distribution. - std::uniform_real_distribution<> GlobalRandomVariables_::randUniformDist(0.0, 1.0); - template - // Global normal distribution. - std::normal_distribution<> GlobalRandomVariables_::randNormalDist(0.0, 1.0); - using GlobalRandomVariables = GlobalRandomVariables_; -#else - namespace GlobalRandomVariables { - // Global random object. - inline std::mt19937 randGen; - // Global uniform distribution. - inline std::uniform_real_distribution<> randUniformDist(0.0, 1.0); - // Global normal distribution. - inline std::normal_distribution<> randNormalDist(0.0, 1.0); - } -#endif +inline std::uniform_real_distribution<>& RandUniformDist() +{ + static thread_local std::uniform_real_distribution<> randUniformDist(0.0, 0.1); + return randUniformDist; +} -#endif +inline std::normal_distribution<>& RandNormalDist() +{ + static thread_local std::normal_distribution<> randNormalDist(0.0, 0.1); + return randNormalDist; +} /** * Set the random seed used by the random functions (Random() and RandInt()). @@ -70,7 +51,7 @@ namespace math /** Miscellaneous math routines. */ { inline void RandomSeed(const size_t seed) { #if (!defined(BINDING_TYPE) || BINDING_TYPE != BINDING_TYPE_TEST) - GlobalRandomVariables::randGen.seed((uint32_t) seed); + RandGen().seed((uint32_t) seed); #if (BINDING_TYPE == BINDING_TYPE_R) // To suppress Found 'srand', possibly from 'srand' (C). (void) seed; @@ -94,14 +75,14 @@ inline void RandomSeed(const size_t seed) inline void FixedRandomSeed() { const static size_t seed = rand(); - GlobalRandomVariables::randGen.seed((uint32_t) seed); + RandGen().seed((uint32_t) seed); srand((unsigned int) seed); arma::arma_rng::set_seed(seed); } inline void CustomRandomSeed(const size_t seed) { - GlobalRandomVariables::randGen.seed((uint32_t) seed); + RandGen().seed((uint32_t) seed); srand((unsigned int) seed); arma::arma_rng::set_seed(seed); } @@ -112,7 +93,7 @@ inline void CustomRandomSeed(const size_t seed) */ inline double Random() { - return GlobalRandomVariables::randUniformDist(GlobalRandomVariables::randGen); + return RandUniformDist()(RandGen()); } /** @@ -120,7 +101,7 @@ inline double Random() */ inline double Random(const double lo, const double hi) { - return lo + (hi - lo) * GlobalRandomVariables::randUniformDist(GlobalRandomVariables::randGen); + return lo + (hi - lo) * RandUniformDist()(RandGen()); } /** @@ -139,7 +120,7 @@ inline double RandBernoulli(const double input) */ inline int RandInt(const int hiExclusive) { - return (int) std::floor((double) hiExclusive * GlobalRandomVariables::randUniformDist(GlobalRandomVariables::randGen)); + return (int) std::floor((double) hiExclusive * RandUniformDist()(RandGen())); } /** @@ -148,7 +129,7 @@ inline int RandInt(const int hiExclusive) inline int RandInt(const int lo, const int hiExclusive) { return lo + (int) std::floor((double) (hiExclusive - lo) - * GlobalRandomVariables::randUniformDist(GlobalRandomVariables::randGen)); + * RandUniformDist()(RandGen())); } /** @@ -156,7 +137,7 @@ inline int RandInt(const int lo, const int hiExclusive) */ inline double RandNormal() { - return GlobalRandomVariables::randNormalDist(GlobalRandomVariables::randGen); + return RandNormalDist()(RandGen()); } /** @@ -168,7 +149,7 @@ inline double RandNormal() */ inline double RandNormal(const double mean, const double variance) { - return variance * GlobalRandomVariables::randNormalDist(GlobalRandomVariables::randGen) + mean; + return variance * RandNormalDist()(RandGen()) + mean; } /** diff --git a/src/mlpack/methods/bias_svd/bias_svd_function_impl.hpp b/src/mlpack/methods/bias_svd/bias_svd_function_impl.hpp index fcd85a8140..004638b442 100644 --- a/src/mlpack/methods/bias_svd/bias_svd_function_impl.hpp +++ b/src/mlpack/methods/bias_svd/bias_svd_function_impl.hpp @@ -317,7 +317,7 @@ inline double ParallelSGD::Optimize( if (shuffle) // Determine order of visitation. std::shuffle(visitationOrder.begin(), visitationOrder.end(), - mlpack::math::GlobalRandomVariables::randGen); + mlpack::math::RandGen()); #pragma omp parallel { diff --git a/src/mlpack/methods/regularized_svd/regularized_svd_function_impl.hpp b/src/mlpack/methods/regularized_svd/regularized_svd_function_impl.hpp index 36a3d87297..1a3ec638be 100644 --- a/src/mlpack/methods/regularized_svd/regularized_svd_function_impl.hpp +++ b/src/mlpack/methods/regularized_svd/regularized_svd_function_impl.hpp @@ -272,7 +272,7 @@ inline double ParallelSGD::Optimize( if (shuffle) // Determine order of visitation. std::shuffle(visitationOrder.begin(), visitationOrder.end(), - mlpack::math::GlobalRandomVariables::randGen); + mlpack::math::RandGen()); #pragma omp parallel { diff --git a/src/mlpack/methods/svdplusplus/svdplusplus_function_impl.hpp b/src/mlpack/methods/svdplusplus/svdplusplus_function_impl.hpp index 867090fa00..d575848902 100644 --- a/src/mlpack/methods/svdplusplus/svdplusplus_function_impl.hpp +++ b/src/mlpack/methods/svdplusplus/svdplusplus_function_impl.hpp @@ -454,7 +454,7 @@ inline double ParallelSGD::Optimize( if (shuffle) // Determine order of visitation. std::shuffle(visitationOrder.begin(), visitationOrder.end(), - mlpack::math::GlobalRandomVariables::randGen); + mlpack::math::RandGen()); #pragma omp parallel { diff --git a/src/mlpack/tests/distribution_test.cpp b/src/mlpack/tests/distribution_test.cpp index 4df4630313..35298ee1d8 100644 --- a/src/mlpack/tests/distribution_test.cpp +++ b/src/mlpack/tests/distribution_test.cpp @@ -669,7 +669,7 @@ TEST_CASE("GammaDistributionTrainTest", "[DistributionTest]") // Random generation of gamma-like points. for (size_t j = 0; j < d; ++j) for (size_t i = 0; i < N; ++i) - rdata(j, i) = dist(math::GlobalRandomVariables::randGen); + rdata(j, i) = dist(math::RandGen()); // Create Gamma object and call Train() on reference set. GammaDistribution gDist; @@ -687,7 +687,7 @@ TEST_CASE("GammaDistributionTrainTest", "[DistributionTest]") // Random generation of gamma-like points. for (size_t j = 0; j < d2; ++j) for (size_t i = 0; i < N2; ++i) - rdata2(j, i) = dist(math::GlobalRandomVariables::randGen); + rdata2(j, i) = dist(math::RandGen()); // Fit results using old object. gDist.Train(rdata2); @@ -715,7 +715,7 @@ TEST_CASE("GammaDistributionTrainWithProbabilitiesTest", "[DistributionTest]") for (size_t j = 0; j < d; ++j) for (size_t i = 0; i < N; ++i) - rdata(j, i) = dist(math::GlobalRandomVariables::randGen); + rdata(j, i) = dist(math::RandGen()); // Fill the probabilities randomly. arma::vec probabilities(N, arma::fill::randu); @@ -759,7 +759,7 @@ TEST_CASE("GammaDistributionTrainAllProbabilities1Test", "[DistributionTest]") for (size_t j = 0; j < d; ++j) for (size_t i = 0; i < N; ++i) - rdata(j, i) = dist(math::GlobalRandomVariables::randGen); + rdata(j, i) = dist(math::RandGen()); // Fit results with only data. GammaDistribution gDist; @@ -808,9 +808,9 @@ TEST_CASE("GammaDistributionTrainTwoDistProbabilities1Test", for (size_t i = 0; i < N; ++i) { if (i % 2 == 0) - rdata(j, i) = dist(math::GlobalRandomVariables::randGen); + rdata(j, i) = dist(math::RandGen()); else - rdata(j, i) = dist2(math::GlobalRandomVariables::randGen); + rdata(j, i) = dist2(math::RandGen()); } } @@ -859,7 +859,7 @@ TEST_CASE("GammaDistributionFittingTest", "[DistributionTest]") arma::mat rdata(d, N); for (size_t j = 0; j < d; ++j) for (size_t i = 0; i < N; ++i) - rdata(j, i) = dist(math::GlobalRandomVariables::randGen); + rdata(j, i) = dist(math::RandGen()); // Create Gamma object and call Train() on reference set. GammaDistribution gDist; @@ -880,7 +880,7 @@ TEST_CASE("GammaDistributionFittingTest", "[DistributionTest]") arma::mat rdata2(d, N); for (size_t j = 0; j < d; ++j) for (size_t i = 0; i < N; ++i) - rdata2(j, i) = dist2(math::GlobalRandomVariables::randGen); + rdata2(j, i) = dist2(math::RandGen()); // Create Gamma object and call Train() on reference set. GammaDistribution gDist2; From ac5daca8d5392cc57759ff8ea1194175b952d9a9 Mon Sep 17 00:00:00 2001 From: shubham1206agra Date: Sun, 5 Jun 2022 10:35:40 +0530 Subject: [PATCH 54/57] add documentation. --- src/mlpack/core/math/random.hpp | 3 +++ 1 file changed, 3 insertions(+) diff --git a/src/mlpack/core/math/random.hpp b/src/mlpack/core/math/random.hpp index ce919ceb18..637ae16cdd 100644 --- a/src/mlpack/core/math/random.hpp +++ b/src/mlpack/core/math/random.hpp @@ -23,18 +23,21 @@ namespace math /** Miscellaneous math routines. */ { * correctly on Windows. */ +//! Global random object. inline std::mt19937& RandGen() { static thread_local std::mt19937 randGen; return randGen; } +//! Global uniform distribution. inline std::uniform_real_distribution<>& RandUniformDist() { static thread_local std::uniform_real_distribution<> randUniformDist(0.0, 0.1); return randUniformDist; } +//! Global normal distribution. inline std::normal_distribution<>& RandNormalDist() { static thread_local std::normal_distribution<> randNormalDist(0.0, 0.1); From 6dba88badbf33bd3cc791c8143427a575b0d3e46 Mon Sep 17 00:00:00 2001 From: Shubham Agrawal Date: Sun, 5 Jun 2022 20:37:38 +0530 Subject: [PATCH 55/57] removing thread_local to test failing tests. --- src/mlpack/core/math/random.hpp | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/mlpack/core/math/random.hpp b/src/mlpack/core/math/random.hpp index 637ae16cdd..291f3cc0f4 100644 --- a/src/mlpack/core/math/random.hpp +++ b/src/mlpack/core/math/random.hpp @@ -26,21 +26,21 @@ namespace math /** Miscellaneous math routines. */ { //! Global random object. inline std::mt19937& RandGen() { - static thread_local std::mt19937 randGen; + static std::mt19937 randGen; return randGen; } //! Global uniform distribution. inline std::uniform_real_distribution<>& RandUniformDist() { - static thread_local std::uniform_real_distribution<> randUniformDist(0.0, 0.1); + static std::uniform_real_distribution<> randUniformDist(0.0, 0.1); return randUniformDist; } //! Global normal distribution. inline std::normal_distribution<>& RandNormalDist() { - static thread_local std::normal_distribution<> randNormalDist(0.0, 0.1); + static std::normal_distribution<> randNormalDist(0.0, 0.1); return randNormalDist; } From 769187e68e232abfb1fa2d78f544978bb7c92a97 Mon Sep 17 00:00:00 2001 From: Shubham Agrawal <58412969+shubham1206agra@users.noreply.github.com> Date: Thu, 9 Jun 2022 09:16:31 +0530 Subject: [PATCH 56/57] Apply suggestions from code review Co-authored-by: Ryan Curtin --- src/mlpack/core/math/random.hpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/mlpack/core/math/random.hpp b/src/mlpack/core/math/random.hpp index 291f3cc0f4..f130c1f8f9 100644 --- a/src/mlpack/core/math/random.hpp +++ b/src/mlpack/core/math/random.hpp @@ -33,14 +33,14 @@ inline std::mt19937& RandGen() //! Global uniform distribution. inline std::uniform_real_distribution<>& RandUniformDist() { - static std::uniform_real_distribution<> randUniformDist(0.0, 0.1); + static std::uniform_real_distribution<> randUniformDist(0.0, 1.0); return randUniformDist; } //! Global normal distribution. inline std::normal_distribution<>& RandNormalDist() { - static std::normal_distribution<> randNormalDist(0.0, 0.1); + static std::normal_distribution<> randNormalDist(0.0, 1.0); return randNormalDist; } From 9e5d62f28e220a77419c607e50c2ac39d2fce271 Mon Sep 17 00:00:00 2001 From: Shubham Agrawal Date: Thu, 9 Jun 2022 10:02:31 +0530 Subject: [PATCH 57/57] added back --- src/mlpack/core/math/random.hpp | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/mlpack/core/math/random.hpp b/src/mlpack/core/math/random.hpp index f130c1f8f9..613f966fb2 100644 --- a/src/mlpack/core/math/random.hpp +++ b/src/mlpack/core/math/random.hpp @@ -26,21 +26,21 @@ namespace math /** Miscellaneous math routines. */ { //! Global random object. inline std::mt19937& RandGen() { - static std::mt19937 randGen; + static thread_local std::mt19937 randGen; return randGen; } //! Global uniform distribution. inline std::uniform_real_distribution<>& RandUniformDist() { - static std::uniform_real_distribution<> randUniformDist(0.0, 1.0); + static thread_local std::uniform_real_distribution<> randUniformDist(0.0, 1.0); return randUniformDist; } //! Global normal distribution. inline std::normal_distribution<>& RandNormalDist() { - static std::normal_distribution<> randNormalDist(0.0, 1.0); + static thread_local std::normal_distribution<> randNormalDist(0.0, 1.0); return randNormalDist; }