apaam/mlpack
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Add RMVA class and function documentation.

Browse Source
This commit is contained in:
Marcus Edel
2016-05-21 00:33:29 +02:00
parent f1bf339c3e
commit 39eefded8c
2 changed files with 70 additions and 27 deletions
Download Patch File Download Diff File Expand all files Collapse all files
src/mlpack/methods/rmva/rmva.hpp
+67 -23
View File
@@ -2,7 +2,8 @@
* @file rmva.hpp
* @author Marcus Edel
*
* Definition of the RNN class, which implements feed forward neural networks.
* Definition of the RecurrentNeuralAttention class, which implements the
* Recurrent Model for Visual Attention.
*/
#ifndef __MLPACK_METHODS_RMVA_RMVA_HPP
#define __MLPACK_METHODS_RMVA_RMVA_HPP
@@ -23,11 +24,32 @@ namespace mlpack {
namespace ann /** Artificial Neural Network. */ {
/**
* An implementation of a standard feed forward network.
* This class implements the Recurrent Model for Visual Attention, using a
* variety of possible layer implementations.
*
* @tparam LayerTypes Contains all layer modules used to construct the network.
* @tparam OutputLayerType The outputlayer type used to evaluate the network.
* @tparam PerformanceFunction Performance strategy used to claculate the error.
* For more information, see the following paper.
*
* @code
* @article{MnihHGK14,
* title={Recurrent Models of Visual Attention},
* author={Volodymyr Mnih, Nicolas Heess, Alex Graves, Koray Kavukcuoglu},
* journal={CoRR},
* volume={abs/1406.6247},
* year={2014}
* }
* @endcode
*
* @tparam LocatorType Type of locator network.
* @tparam LocationSensorType Type of location sensor network.
* @tparam GlimpseSensorType Type of glimpse sensor network.
* @tparam GlimpseType Type of glimpse network.
* @tparam StartType Type of start network.
* @tparam FeedbackType Type of feedback network.
* @tparam TransferType Type of transfer network.
* @tparam ClassifierType Type of classifier network.
* @tparam RewardPredictorType Type of reward predictor network.
* @tparam InitializationRuleType Rule used to initialize the weight matrix.
* @tparam MatType Matrix type (arma::mat or arma::sp_mat).
*/
template<
typename LocatorType,
@@ -60,11 +82,20 @@ class RecurrentNeuralAttention
MatType>;
/**
* Construct the RNN object, which will construct a recurrent neural
* network with the specified layers.
* Construct the RecurrentNeuralAttention object, which will construct the
* recurrent model for visual attentionh using the specified networks.
*
* @param network The network modules used to construct the network.
* @param outputLayer The outputlayer used to evaluate the network.
* @param locator The locator network.
* @param locationSensor The location sensor network.
* @param glimpseSensor The glimpse sensor network.
* @param glimpse The glimpse network.
* @param start The start network.
* @param feedback The feedback network.
* @param transfer The transfer network.
* @param classifier The classifier network.
* @param rewardPredictor The reward predictor network.
* @param nStep Number of steps for the back-propagate through time.
* @param initializeRule Rule used to initialize the weight matrix.
*/
template<typename TypeLocator,
typename TypeLocationSensor,
@@ -85,8 +116,20 @@ class RecurrentNeuralAttention
TypeClassifier&& classifier,
TypeRewardPredictor&& rewardPredictor,
const size_t nStep,
InitializationRuleType initializeRule = InitializationRuleType());
InitializationRuleType initializeRule =
InitializationRuleType());
/**
* Train the 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.
*
* @tparam OptimizerType Type of optimizer to use to train the model.
* @param predictors Input training variables.
* @param responses Outputs results from input training variables.
* @param optimizer Instantiated optimizer used to train the model.
*/
template<
template<typename> class OptimizerType = mlpack::optimization::RMSprop
>
@@ -105,8 +148,8 @@ class RecurrentNeuralAttention
void Predict(arma::mat& predictors, arma::mat& responses);
/**
* Evaluate the feedforward network with the given parameters. This function
* is usually called by the optimizer to train the model.
* Evaluate the network with the given parameters. This function is usually
* called by the optimizer to train the model.
*
* @param parameters Matrix model parameters.
* @param i Index of point to use for objective function evaluation.
@@ -118,8 +161,8 @@ class RecurrentNeuralAttention
const bool deterministic = true);
/**
* Evaluate the gradient of the feedforward network with the given parameters,
* and with respect to only one point in the dataset. This is useful for
* Evaluate the gradient of the 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.
@@ -174,7 +217,8 @@ class RecurrentNeuralAttention
ResetParameter(rewardPredictor);
ResetParameter(start);
// Aample an initial starting actions by forwarding zeros through the locator.
// Sample an initial starting actions by forwarding zeros through the
// locator.
locatorInput.push_back(new arma::cube(arma::zeros<arma::cube>(inputSize, 1,
input.n_slices)));
@@ -378,7 +422,7 @@ class RecurrentNeuralAttention
template<typename T, typename P>
typename std::enable_if<
HasRecurrentParameterCheck<T, P&(T::*)()>::value, void>::type
Save(const size_t layerNumber,
Save(const size_t /* layerNumber */,
boost::ptr_vector<MatType>& activations,
T& layer,
P& /* unused */)
@@ -626,7 +670,7 @@ class RecurrentNeuralAttention
template<size_t I = 1, typename DataType, typename... Tp>
typename std::enable_if<I == (sizeof...(Tp)), void>::type
BackwardTail(const DataType& error, std::tuple<Tp...>& t)
BackwardTail(const DataType& /* error */, std::tuple<Tp...>& t)
{
std::get<sizeof...(Tp) - I>(t).Backward(
std::get<sizeof...(Tp) - I>(t).OutputParameter(),
@@ -740,15 +784,15 @@ class RecurrentNeuralAttention
// Nothing to do here
}
//! The locator network.
LocatorType locator;
//! The location sensor network.
LocationSensorType locationSensor;
//! The glimpse sensor network.
GlimpseSensorType glimpseSensor;
//! The locator network.
LocatorType locator;
//! The glimpse network.
GlimpseType glimpse;
@@ -905,8 +949,8 @@ class RecurrentNeuralAttention
arma::mat evaluationLocation;
}; // class RecurrentNeuralAttention
}; // namespace ann
}; // namespace mlpack
} // namespace ann
} // namespace mlpack
// Include implementation.
#include "rmva_impl.hpp"
src/mlpack/methods/rmva/rmva_impl.hpp
+3 -4
View File
@@ -2,7 +2,7 @@
* @file rmva_impl.hpp
* @author Marcus Edel
*
* Definition of the FFN class, which implements feed forward neural networks.
* Implementation of the Recurrent Model for Visual Attention.
*/
#ifndef __MLPACK_METHODS_RMVA_RMVA_IMPL_HPP
#define __MLPACK_METHODS_RMVA_RMVA_IMPL_HPP
@@ -13,7 +13,6 @@
namespace mlpack {
namespace ann /** Artificial Neural Network. */ {
template<
typename LocatorType,
typename LocationSensorType,
@@ -270,7 +269,7 @@ double RecurrentNeuralAttention<
location.clear();
feedbackActivationsInput.clear();
// Aample an initial starting actions by forwarding zeros through the locator.
// Sample an initial starting actions by forwarding zeros through the locator.
locatorInput.push_back(new arma::cube(arma::zeros<arma::cube>(inputSize, 1,
input.n_slices)));
@@ -583,7 +582,7 @@ void RecurrentNeuralAttention<
UpdateGradients(std::get<0>(transfer).Delta(), locationSensor);
UpdateGradients(std::get<0>(transfer).Delta(), glimpseSensor);
// feedback module
// Feedback module.
if (step != 0)
{
UpdateGradients(feedbackActivationsInput[step - 1],