Class representing a multi-layer neural network. More...

#include <Layer.hpp>

Inheritance diagram for mic::mlnn::MultiLayerNeuralNetwork< eT >:

Public Member Functions
	MultiLayerNeuralNetwork (std::string name_="mlnn")

virtual	~MultiLayerNeuralNetwork ()

template<typename LayerType >
void	pushLayer (LayerType *layer_ptr_)

template<typename LayerType >
std::shared_ptr< LayerType >	getLayer (size_t index_)

std::shared_ptr< Layer< eT > >	getLayer (size_t index_)

void	popLayer (size_t number_of_layers_=1)

size_t	layerInputsSize (size_t layer_number_=-1)

size_t	lastLayerOutputsSize (size_t layer_number_=-1)

size_t	lastLayerBatchSize (size_t layer_number_=-1)

template<typename omT >
void	setOptimization ()

void	update (eT alpha_, eT decay_=0.0f)

void	resetGrads ()

void	resizeBatch (size_t batch_size_)

mic::types::MatrixPtr< eT >	getPredictions ()

mic::types::MatrixPtr< eT >	getPredictions (size_t layer_nr_)

size_t	countCorrectPredictions (mic::types::MatrixPtr< eT > targets_, mic::types::MatrixPtr< eT > predictions_)

bool	save (std::string filename_)

bool	load (std::string filename_)

Protected Attributes
std::vector< std::shared_ptr < mic::mlnn::Layer< eT > > >	layers

std::string	name

bool	connected
	Flag denoting whether the layers are interconnected, thus no copying between inputs and outputs of the neighboring layers will be required. More...

Private Member Functions
template<class Archive >
void	save (Archive &ar, const unsigned int version) const

template<class Archive >
void	load (Archive &ar, const unsigned int version)

Friends
class	boost::serialization::access

std::ostream &	operator<< (std::ostream &os_, const MultiLayerNeuralNetwork &obj_)

Detailed Description

template<typename eT>
class mic::mlnn::MultiLayerNeuralNetwork< eT >

Class representing a multi-layer neural network.

Author: tkornuta/kmrocki

Template Parameters

eT	Template parameter denoting precision of variables (float for calculations/double for testing).

Definition at line 86 of file Layer.hpp.

Constructor & Destructor Documentation

template<typename eT>

mic::mlnn::MultiLayerNeuralNetwork< eT >::MultiLayerNeuralNetwork ( std::string name_ = "mlnn" )

inline

Constructor. Sets the neural network name.

Parameters

name_ Name of the network.

Definition at line 65 of file MultiLayerNeuralNetwork.hpp.

template<typename eT>

virtual mic::mlnn::MultiLayerNeuralNetwork< eT >::~MultiLayerNeuralNetwork ( )

inlinevirtual

Virtual destructor - empty.

Definition at line 75 of file MultiLayerNeuralNetwork.hpp.

Member Function Documentation

template<typename eT>

size_t mic::mlnn::MultiLayerNeuralNetwork< eT >::countCorrectPredictions	(	mic::types::MatrixPtr< eT >	targets_,
		mic::types::MatrixPtr< eT >	predictions_
	)

inline

Calculated difference between the predicted and target classes. Assumes 1-ouf-of-k encoding of classes.

Parameters

predictions_	Predictions in the form of a matrix of answers, each encoded as SDR.
targets_	Desired results (targets) in the form of a matrix of answers, each encoded as SDR.

Returns

Definition at line 238 of file MultiLayerNeuralNetwork.hpp.

Referenced by main().

template<typename eT>

template<typename LayerType >

std::shared_ptr<LayerType> mic::mlnn::MultiLayerNeuralNetwork< eT >::getLayer ( size_t index_ )

inline

Returns n-th layer of the neural network.

Parameters

layer_ptr_ Pointer to the layer.

Template Parameters

layer_ptr_ Layer type.

Definition at line 94 of file MultiLayerNeuralNetwork.hpp.

Referenced by batch_function().

template<typename eT>

std::shared_ptr<Layer<eT> > mic::mlnn::MultiLayerNeuralNetwork< eT >::getLayer ( size_t index_ )

inline

Returns n-th layer of the neural network.

Parameters

layer_ptr_ Pointer to the layer.

Template Parameters

layer_ptr_ Layer type.

Definition at line 105 of file MultiLayerNeuralNetwork.hpp.

template<typename eT>

mic::types::MatrixPtr<eT> mic::mlnn::MultiLayerNeuralNetwork< eT >::getPredictions ( )

inline

Returns the predictions (output of the forward processing) of the last layer in the form of a matrix of size [output_size x batch_size].

Definition at line 217 of file MultiLayerNeuralNetwork.hpp.

Referenced by mic::applications::MNISTPatchReconstructionApplication::collectTestStatistics(), main(), mic::applications::MNISTPatchReconstructionApplication::performLearningStep(), mic::applications::MNISTPatchSoftmaxApplication::performLearningStep(), mic::mlnn::BackpropagationNeuralNetwork< float >::test(), and mic::mlnn::BackpropagationNeuralNetwork< float >::train().

template<typename eT>

mic::types::MatrixPtr<eT> mic::mlnn::MultiLayerNeuralNetwork< eT >::getPredictions ( size_t layer_nr_ )

inline

Returns the predictions (output of the forward processing) of a given layer in the form of a matrix of size [output_size x batch_size].

Parameters

layer_nr_ Layer number.

Definition at line 225 of file MultiLayerNeuralNetwork.hpp.

template<typename eT>

size_t mic::mlnn::MultiLayerNeuralNetwork< eT >::lastLayerBatchSize ( size_t layer_number_ = -1 )

inline

Returns the size of the batch of a given layer.

Parameters

layer_number Number of layer. As default(= -1) returns the size of the batch of LAST layer.

Definition at line 154 of file MultiLayerNeuralNetwork.hpp.

template<typename eT>

size_t mic::mlnn::MultiLayerNeuralNetwork< eT >::lastLayerOutputsSize ( size_t layer_number_ = -1 )

inline

Returns the size of the output of a given layer.

Parameters

layer_number Number of layer. As default(= -1) returns the size of the output of LAST layer.

Definition at line 141 of file MultiLayerNeuralNetwork.hpp.

template<typename eT>

size_t mic::mlnn::MultiLayerNeuralNetwork< eT >::layerInputsSize ( size_t layer_number_ = -1 )

inline

Returns the size of the input od a given layer.

Parameters

layer_number Number of layer. As default(= -1) returns the size of the input of LAST layer.

Definition at line 128 of file MultiLayerNeuralNetwork.hpp.

template<typename eT>

bool mic::mlnn::MultiLayerNeuralNetwork< eT >::load ( std::string filename_ )

inline

Loads network from the file using serialization.

Parameters

filename_ Name of the file.

Definition at line 306 of file MultiLayerNeuralNetwork.hpp.

Referenced by mic::applications::MNISTPatchReconstructionApplication::initializePropertyDependentVariables(), mic::applications::MNISTPatchSoftmaxApplication::initializePropertyDependentVariables(), and mic::neural_nets::unit_tests::TEST_F().

template<typename eT>

template<class Archive >

void mic::mlnn::MultiLayerNeuralNetwork< eT >::load	(	Archive &	ar,
		const unsigned int	version
	)

inlineprivate

Serialization load - loads the neural net object to archive.

Parameters

ar	Used archive.
version	Version of the neural net class (not used currently).

Definition at line 377 of file MultiLayerNeuralNetwork.hpp.

template<typename eT>

void mic::mlnn::MultiLayerNeuralNetwork< eT >::popLayer ( size_t number_of_layers_ = 1 )

inline

Removes several last layers of the neural network.

Parameters

number_of_layers_ Number of layers to be removed.

Definition at line 115 of file MultiLayerNeuralNetwork.hpp.

Referenced by mic::applications::MNISTPatchSoftmaxApplication::initializePropertyDependentVariables().

template<typename eT>

template<typename LayerType >

void mic::mlnn::MultiLayerNeuralNetwork< eT >::pushLayer ( LayerType * layer_ptr_ )

inline

Adds layer to neural network.

Parameters

layer_ptr_ Pointer to the newly created layer.

Template Parameters

layer_ptr_ Layer type.

Definition at line 83 of file MultiLayerNeuralNetwork.hpp.

Referenced by batch_function(), mic::applications::MNISTPatchReconstructionApplication::initializePropertyDependentVariables(), mic::applications::MNISTPatchSoftmaxApplication::initializePropertyDependentVariables(), main(), mic::neural_nets::unit_tests::Simple2LayerRegressionNN::Simple2LayerRegressionNN(), and mic::neural_nets::unit_tests::Tutorial2LayerNN::Tutorial2LayerNN().

template<typename eT>

void mic::mlnn::MultiLayerNeuralNetwork< eT >::resetGrads ( )

inline

Resets the gradients of all layers.

Definition at line 194 of file MultiLayerNeuralNetwork.hpp.

Referenced by mic::neural_nets::unit_tests::Simple2LayerRegressionNN::SetUp(), and mic::neural_nets::unit_tests::Tutorial2LayerNN::SetUp().

template<typename eT>

void mic::mlnn::MultiLayerNeuralNetwork< eT >::resizeBatch ( size_t batch_size_ )

inline

Changes the size of the batch.

Parameters

New	size of the batch.

Definition at line 203 of file MultiLayerNeuralNetwork.hpp.

Referenced by mic::mlnn::HebbianNeuralNetwork< eT >::forward(), mic::mlnn::BackpropagationNeuralNetwork< float >::forward(), main(), mic::neural_nets::unit_tests::Simple2LayerRegressionNN::SetUp(), and mic::neural_nets::unit_tests::Tutorial2LayerNN::SetUp().

template<typename eT>

bool mic::mlnn::MultiLayerNeuralNetwork< eT >::save ( std::string filename_ )

inline

Saves network to file using serialization.

Parameters

filename_ Name of the file.

Definition at line 279 of file MultiLayerNeuralNetwork.hpp.

Referenced by main(), mic::applications::MNISTPatchReconstructionApplication::populateTestStatistics(), and mic::applications::MNISTPatchSoftmaxApplication::populateTestStatistics().

template<typename eT>

template<class Archive >

void mic::mlnn::MultiLayerNeuralNetwork< eT >::save	(	Archive &	ar,
		const unsigned int	version
	)		const

inlineprivate

Serialization save - saves the neural net object to archive.

Parameters

ar	Used archive.
version	Version of the neural net class (not used currently).

Definition at line 352 of file MultiLayerNeuralNetwork.hpp.

template<typename eT>

template<typename omT >

void mic::mlnn::MultiLayerNeuralNetwork< eT >::setOptimization ( )

inline

Sets the optimization method.

Template Parameters

omT	Optimization method type

Definition at line 169 of file MultiLayerNeuralNetwork.hpp.

Referenced by batch_function(), and main().

template<typename eT>

void mic::mlnn::MultiLayerNeuralNetwork< eT >::update	(	eT	alpha_,
		eT	decay_ = `0.0f`
	)

inline

Performs the network training by updating parameters of all layers according to gradients computed by back-propagation.

Parameters

alpha_	Learning rate - passed to the optimization functions of all layers.
decay_	Weight decay rate (determining that the "unused/unupdated" weights will decay to 0) (DEFAULT=0.0 - no decay).

Definition at line 181 of file MultiLayerNeuralNetwork.hpp.

Referenced by mic::mlnn::HebbianNeuralNetwork< eT >::train(), and mic::mlnn::BackpropagationNeuralNetwork< float >::train().