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Some additional type fixes.

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This commit is contained in:
Ryan Curtin
2022-04-03 16:53:25 -04:00
parent f5b3885998
commit 83a178dffa
2 changed files with 34 additions and 32 deletions
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src/mlpack/methods/ann/ffn.hpp
+22 -21
View File
@@ -153,10 +153,10 @@ class FFN
* @return The final objective of the trained model (NaN or Inf on error).
*/
template<typename OptimizerType, typename... CallbackTypes>
double Train(MatType predictors,
MatType 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
@@ -182,9 +182,9 @@ class FFN
* @return The final objective of the trained model (NaN or Inf on error).
*/
template<typename OptimizerType = ens::RMSProp, typename... CallbackTypes>
double Train(MatType predictors,
MatType 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 be
@@ -254,7 +254,7 @@ class FFN
void SetNetworkMode(const bool training);
/**
* Perform the forward pass of the data in real batch mode.
* 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
@@ -266,7 +266,7 @@ class FFN
void Forward(const MatType& inputs, MatType& results);
/**
* Perform a partial forward pass of the data.
* 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
@@ -285,7 +285,7 @@ class FFN
const size_t end);
/**
* Perform the backward pass of the data.
* 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
@@ -307,7 +307,8 @@ class FFN
* @param predictors Input variables.
* @param responses Target outputs for input variables.
*/
double Evaluate(const MatType& predictors, const MatType& responses);
typename MatType::elem_type Evaluate(const MatType& predictors,
const MatType& responses);
//! Serialize the model.
template<typename Archive>
@@ -326,7 +327,7 @@ class FFN
*
* @param parameters Matrix model parameters.
*/
double Evaluate(const MatType& parameters);
typename MatType::elem_type Evaluate(const MatType& parameters);
/**
* Note: this function is implemented so that it can be used by ensmallen's
@@ -345,9 +346,9 @@ class FFN
* @param batchSize Number of points to be passed at a time to use for
* objective function evaluation.
*/
double Evaluate(const MatType& 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
@@ -360,8 +361,8 @@ class FFN
* @param parameters Matrix model parameters.
* @param gradient Matrix to output gradient into.
*/
double EvaluateWithGradient(const MatType& parameters,
MatType& 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
@@ -378,10 +379,10 @@ class FFN
* @param batchSize Number of points to be passed at a time to use for
* objective function evaluation.
*/
double EvaluateWithGradient(const MatType& parameters,
const size_t begin,
MatType& 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
src/mlpack/methods/ann/ffn_impl.hpp
+12 -11
View File
@@ -144,7 +144,7 @@ template<typename OutputLayerType,
typename InitializationRuleType,
typename MatType>
template<typename OptimizerType, typename... CallbackTypes>
double FFN<
typename MatType::elem_type FFN<
OutputLayerType,
InitializationRuleType,
MatType
@@ -162,7 +162,8 @@ double FFN<
// Train the model.
Timer::Start("ffn_optimization");
const double out = optimizer.Optimize(*this, parameters, callbacks...);
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
@@ -174,7 +175,7 @@ template<typename OutputLayerType,
typename InitializationRuleType,
typename MatType>
template<typename OptimizerType, typename... CallbackTypes>
double FFN<
typename MatType::elem_type FFN<
OutputLayerType,
InitializationRuleType,
MatType
@@ -341,7 +342,7 @@ typename MatType::elem_type FFN<
template<typename OutputLayerType,
typename InitializationRuleType,
typename MatType>
double FFN<
typename MatType::elem_type FFN<
OutputLayerType,
InitializationRuleType,
MatType
@@ -400,13 +401,13 @@ void FFN<
template<typename OutputLayerType,
typename InitializationRuleType,
typename MatType>
double FFN<
typename MatType::elem_type FFN<
OutputLayerType,
InitializationRuleType,
MatType
>::Evaluate(const MatType& parameters)
{
double res = 0;
typename MatType::elem_type res = 0;
for (size_t i = 0; i < predictors.n_cols; ++i)
res += Evaluate(parameters, i, 1);
@@ -416,7 +417,7 @@ double FFN<
template<typename OutputLayerType,
typename InitializationRuleType,
typename MatType>
double FFN<
typename MatType::elem_type FFN<
OutputLayerType,
InitializationRuleType,
MatType
@@ -438,13 +439,13 @@ double FFN<
template<typename OutputLayerType,
typename InitializationRuleType,
typename MatType>
double FFN<
typename MatType::elem_type FFN<
OutputLayerType,
InitializationRuleType,
MatType
>::EvaluateWithGradient(const MatType& parameters, MatType& gradient)
{
double res = 0;
typename MatType::elem_type res = 0;
res += EvaluateWithGradient(parameters, 0, gradient, 1);
for (size_t i = 1; i < predictors.n_cols; ++i)
{
@@ -459,7 +460,7 @@ double FFN<
template<typename OutputLayerType,
typename InitializationRuleType,
typename MatType>
double FFN<
typename MatType::elem_type FFN<
OutputLayerType,
InitializationRuleType,
MatType
@@ -476,7 +477,7 @@ double FFN<
network.Forward(predictors.cols(begin, begin + batchSize - 1), networkOutput);
const double obj = outputLayer.Forward(networkOutput,
const typename MatType::elem_type obj = outputLayer.Forward(networkOutput,
responses.cols(begin, begin + batchSize - 1)) + network.Loss();
// Now perform the backward pass.