#include <Layer.hpp>

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

Collaboration diagram for mic::mlnn::Layer< eT >:

Public Member Functions
	Layer (size_t input_height_, size_t input_width_, size_t input_depth_, size_t output_height_, size_t output_width_, size_t output_depth_, LayerTypes layer_type_, std::string name_="layer")

virtual	~Layer ()

virtual void	forward (bool test=false)=0

mic::types::MatrixPtr< eT >	forward (mic::types::MatrixPtr< eT > x_, bool test=false)

virtual void	backward ()=0

mic::types::MatrixPtr< eT >	backward (mic::types::MatrixPtr< eT > dy_)

virtual void	resizeBatch (size_t batch_size_)

template<typename loss >
mic::types::MatrixPtr< eT >	calculateNumericalGradient (mic::types::MatrixPtr< eT > x_, mic::types::MatrixPtr< eT > target_y_, mic::types::MatrixPtr< eT > param_, loss loss_, eT delta_)

virtual void	resetGrads ()

virtual void	update (eT alpha_, eT decay_=0.0f)=0

size_t	inputSize ()
	Returns size (length) of inputs. More...

size_t	outputSize ()
	Returns size (length) of outputs. More...

size_t	batchSize ()
	Returns size (length) of (mini)batch. More...

const std::string	name () const
	Returns name of the layer. More...

mic::types::MatrixPtr< eT >	getParam (std::string name_)

mic::types::MatrixPtr< eT >	getState (std::string name_)

mic::types::MatrixPtr< eT >	getGradient (std::string name_)

void	setState (std::string name_, mic::types::MatrixPtr< eT > mat_ptr_)

template<typename omT >
void	setOptimization ()

const std::string	type () const

virtual std::string	streamLayerParameters ()

mic::types::MatrixPtr< eT >	lazyReturnSampleFromBatch (mic::types::MatrixPtr< eT > batch_ptr_, mic::types::MatrixArray< eT > &array_, std::string id_, size_t sample_number_, size_t sample_size_)

mic::types::MatrixPtr< eT >	lazyReturnInputSample (mic::types::MatrixPtr< eT > batch_ptr_, size_t sample_number_)

mic::types::MatrixPtr< eT >	lazyReturnOutputSample (mic::types::MatrixPtr< eT > batch_ptr_, size_t sample_number_)

mic::types::MatrixPtr< eT >	lazyReturnChannelFromSample (mic::types::MatrixPtr< eT > sample_ptr_, mic::types::MatrixArray< eT > &array_, std::string id_, size_t sample_number_, size_t channel_number_, size_t height_, size_t width_)

mic::types::MatrixPtr< eT >	lazyReturnInputChannel (mic::types::MatrixPtr< eT > sample_ptr_, size_t sample_number_, size_t channel_number_)

mic::types::MatrixPtr< eT >	lazyReturnOutputChannel (mic::types::MatrixPtr< eT > sample_ptr_, size_t sample_number_, size_t channel_number_)

void	lazyAllocateMatrixVector (std::vector< std::shared_ptr< mic::types::Matrix< eT > > > &vector_, size_t vector_size_, size_t matrix_height_, size_t matrix_width_)

virtual std::vector < std::shared_ptr < mic::types::Matrix< eT > > > &	getInputActivations ()

virtual std::vector < std::shared_ptr < mic::types::Matrix< eT > > > &	getInputGradientActivations ()

virtual std::vector < std::shared_ptr < mic::types::Matrix< eT > > > &	getOutputActivations ()

virtual std::vector < std::shared_ptr < mic::types::Matrix< eT > > > &	getOutputGradientActivations ()

Protected Member Functions
	Layer ()

Protected Attributes
size_t	input_height
	Height of the input (e.g. 28 for MNIST). More...

size_t	input_width
	Width of the input (e.g. 28 for MNIST). More...

size_t	input_depth
	Number of channels of the input (e.g. 3 for RGB images). More...

size_t	output_height
	Number of receptive fields in a single channel - vertical direction. More...

size_t	output_width
	Number of receptive fields in a single channel - horizontal direction. More...

size_t	output_depth
	Number of filters = number of output channels. More...

size_t	batch_size
	Size (length) of (mini)batch. More...

LayerTypes	layer_type
	Type of the layer. More...

std::string	layer_name
	Name (identifier of the type) of the layer. More...

mic::types::MatrixArray< eT >	s
	States - contains input [x] and output [y] matrices. More...

mic::types::MatrixArray< eT >	g
	Gradients - contains input [x] and output [y] matrices. More...

mic::types::MatrixArray< eT >	p
	Parameters - parameters of the layer, to be used by the derived classes. More...

mic::types::MatrixArray< eT >	m
	Memory - a list of temporal parameters, to be used by the derived classes. More...

mic::neural_nets::optimization::OptimizationArray < eT >	opt
	Array of optimization functions. More...

std::vector< std::shared_ptr < mic::types::Matrix< eT > > >	x_activations
	Vector containing activations of input neurons - used in visualization. More...

std::vector< std::shared_ptr < mic::types::Matrix< eT > > >	dx_activations
	Vector containing activations of gradients of inputs (dx) - used in visualization. More...

std::vector< std::shared_ptr < mic::types::Matrix< eT > > >	y_activations
	Vector containing activations of output neurons - used in visualization. More...

std::vector< std::shared_ptr < mic::types::Matrix< eT > > >	dy_activations
	Vector containing activations of gradients of outputs (dy) - used in visualization. More...

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

Friends
template<typename tmp >
class	MultiLayerNeuralNetwork

template<typename tmp >
class	BackpropagationNeuralNetwork

template<typename tmp >
class	HebbianNeuralNetwork

class	boost::serialization::access

std::ostream &	operator<< (std::ostream &os_, Layer &obj_)

Detailed Description

template<typename eT = float>
class mic::mlnn::Layer< eT >

Template base (abstract) class representing a layer.

Author: tkornuta/krocki

Template Parameters

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

Definition at line 94 of file Layer.hpp.

Constructor & Destructor Documentation

template<typename eT = float>

mic::mlnn::Layer< eT >::Layer	(	size_t	input_height_,
		size_t	input_width_,
		size_t	input_depth_,
		size_t	output_height_,
		size_t	output_width_,
		size_t	output_depth_,
		LayerTypes	layer_type_,
		std::string	name_ = `"layer"`
	)

inline

Default constructor of the layer parent class. Sets the input-output dimensions, layer type and name.

Parameters

input_height_	Height of the input sample.
input_width_	Width of the input sample.
input_depth_	Depth of the input sample.
output_height_	Width of the output sample.
output_width_	Height of the output sample.
output_depth_	Depth of the output sample.
layer_type_	Type of the layer.
name_	Name of the layer.

Definition at line 107 of file Layer.hpp.

template<typename eT = float>

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

inlinevirtual

Virtual destructor - required for the correct destruction of objects of derived classes.

Definition at line 154 of file Layer.hpp.

template<typename eT = float>

mic::mlnn::Layer< eT >::Layer ( )

inlineprotected

Protected constructor, used only by the derived classes during the serialization. Empty!!

Definition at line 783 of file Layer.hpp.

Member Function Documentation

template<typename eT = float>

virtual void mic::mlnn::Layer< eT >::backward ( )

pure virtual

Abstract method responsible for processing the gradients from outputs to inputs (i.e. in the opposite direction). To be overridden in the derived classes.

Implemented in mic::mlnn::convolution::Convolution< eT >, mic::mlnn::convolution::Convolution< double >, mic::mlnn::convolution::Convolution< float >, mic::mlnn::convolution::MaxPooling< eT >, mic::mlnn::fully_connected::Linear< eT >, mic::mlnn::fully_connected::Linear< double >, mic::mlnn::fully_connected::Linear< float >, mic::mlnn::fully_connected::BinaryCorrelator< eT >, mic::mlnn::cost_function::Softmax< eT >, mic::mlnn::cost_function::Softmax< float >, mic::mlnn::fully_connected::HebbianLinear< eT >, mic::mlnn::convolution::Padding< eT >, mic::mlnn::experimental::ConvHebbian< eT >, mic::mlnn::convolution::Cropping< eT >, mic::mlnn::regularisation::Dropout< eT >, mic::mlnn::activation_function::ReLU< eT >, mic::mlnn::activation_function::ELU< eT >, mic::mlnn::activation_function::Sigmoid< eT >, and mic::mlnn::fully_connected::SparseLinear< eT >.

Referenced by mic::mlnn::Layer< float >::backward().

template<typename eT = float>

mic::types::MatrixPtr<eT> mic::mlnn::Layer< eT >::backward ( mic::types::MatrixPtr< eT > dy_ )

inline

Backward pass - backpropagation.

Definition at line 184 of file Layer.hpp.

template<typename eT = float>

size_t mic::mlnn::Layer< eT >::batchSize ( )

inline

Returns size (length) of (mini)batch.

Definition at line 265 of file Layer.hpp.

template<typename eT = float>

template<typename loss >

mic::types::MatrixPtr<eT> mic::mlnn::Layer< eT >::calculateNumericalGradient	(	mic::types::MatrixPtr< eT >	x_,
		mic::types::MatrixPtr< eT >	target_y_,
		mic::types::MatrixPtr< eT >	param_,
		loss	loss_,
		eT	delta_
	)

inline

Calculates the numerical gradient.

Parameters

x_	Input vector.
target_y_	Target (desired) output.
loss_	Loss function.
delta_	Delta.

Template Parameters

loss	Loss function type.

Definition at line 219 of file Layer.hpp.

template<typename eT = float>

virtual void mic::mlnn::Layer< eT >::forward ( bool test = false )

pure virtual

Abstract method responsible for processing the data from the inputs to outputs. To be overridden in the derived classes.

Parameters

test	Test mode - used for dropout-alike regularization techniques.

Implemented in mic::mlnn::convolution::Convolution< eT >, mic::mlnn::convolution::Convolution< double >, mic::mlnn::convolution::Convolution< float >, mic::mlnn::fully_connected::BinaryCorrelator< eT >, mic::mlnn::fully_connected::Linear< eT >, mic::mlnn::fully_connected::Linear< double >, mic::mlnn::fully_connected::Linear< float >, mic::mlnn::fully_connected::HebbianLinear< eT >, mic::mlnn::cost_function::Softmax< eT >, mic::mlnn::cost_function::Softmax< float >, mic::mlnn::experimental::ConvHebbian< eT >, mic::mlnn::convolution::MaxPooling< eT >, mic::mlnn::activation_function::ELU< eT >, mic::mlnn::activation_function::ReLU< eT >, mic::mlnn::activation_function::Sigmoid< eT >, mic::mlnn::regularisation::Dropout< eT >, mic::mlnn::convolution::Cropping< eT >, and mic::mlnn::convolution::Padding< eT >.

Referenced by mic::mlnn::Layer< float >::calculateNumericalGradient(), and mic::mlnn::Layer< float >::forward().

template<typename eT = float>

mic::types::MatrixPtr<eT> mic::mlnn::Layer< eT >::forward	(	mic::types::MatrixPtr< eT >	x_,
		bool	test = `false`
	)

inline

Forwards the activations of the neural network.

Definition at line 165 of file Layer.hpp.

template<typename eT = float>

mic::types::MatrixPtr<eT> mic::mlnn::Layer< eT >::getGradient ( std::string name_ )

inline

Returns the pointer to gradient (matrix) (or throws an exception!)

Definition at line 291 of file Layer.hpp.

template<typename eT = float>

virtual std::vector< std::shared_ptr <mic::types::Matrix<eT> > >& mic::mlnn::Layer< eT >::getInputActivations ( )

inlinevirtual

Normalizes the matrix. to the range <-1.0, 1.0>, e.g. for the visualization purposes. DEPRICATED - functionality moved to mi-visualization!

Parameters

matrix_ Matrix to be normalized.

Returns activations of input neurons.

Definition at line 586 of file Layer.hpp.

template<typename eT = float>

virtual std::vector< std::shared_ptr <mic::types::Matrix<eT> > >& mic::mlnn::Layer< eT >::getInputGradientActivations ( )

inlinevirtual

Returns activations of input gradients (dx).

Definition at line 621 of file Layer.hpp.

template<typename eT = float>

virtual std::vector< std::shared_ptr <mic::types::Matrix<eT> > >& mic::mlnn::Layer< eT >::getOutputActivations ( )

inlinevirtual

Returns activations of output neurons.

Reimplemented in mic::mlnn::experimental::ConvHebbian< eT >.

Definition at line 656 of file Layer.hpp.

template<typename eT = float>

virtual std::vector< std::shared_ptr <mic::types::Matrix<eT> > >& mic::mlnn::Layer< eT >::getOutputGradientActivations ( )

inlinevirtual

Returns activations of gradients of output neurons.

Definition at line 691 of file Layer.hpp.

template<typename eT = float>

mic::types::MatrixPtr<eT> mic::mlnn::Layer< eT >::getParam ( std::string name_ )

inline

Returns the pointer to a parameter (matrix) (or throws an exception!)

Definition at line 277 of file Layer.hpp.

template<typename eT = float>

mic::types::MatrixPtr<eT> mic::mlnn::Layer< eT >::getState ( std::string name_ )

inline

Returns the pointer to state (matrix) (or throws an exception!)

Definition at line 284 of file Layer.hpp.

template<typename eT = float>

size_t mic::mlnn::Layer< eT >::inputSize ( )

inline

Returns size (length) of inputs.

Definition at line 255 of file Layer.hpp.

Referenced by mic::mlnn::convolution::MaxPooling< eT >::forward(), mic::mlnn::fully_connected::HebbianLinear< eT >::getActivations(), mic::mlnn::fully_connected::BinaryCorrelator< eT >::getActivations(), mic::neural_nets::unit_tests::Linear50x100Double::Linear50x100Double(), mic::neural_nets::unit_tests::Linear50x100Double::SetUp(), mic::mlnn::convolution::Convolution< float >::streamLayerParameters(), and mic::mlnn::Layer< float >::streamLayerParameters().

template<typename eT = float>

void mic::mlnn::Layer< eT >::lazyAllocateMatrixVector	(	std::vector< std::shared_ptr< mic::types::Matrix< eT > > > &	vector_,
		size_t	vector_size_,
		size_t	matrix_height_,
		size_t	matrix_width_
	)

inline

Allocates memory to a matrix vector (lazy).

Parameters

vector_	Vector that will store the matrices.
vector_size	Number of matrices to be added.
matrix_height_	Height of matrices.
matrix_width_	Width of matrices.

Definition at line 543 of file Layer.hpp.

template<typename eT = float>

mic::types::MatrixPtr<eT> mic::mlnn::Layer< eT >::lazyReturnChannelFromSample	(	mic::types::MatrixPtr< eT >	sample_ptr_,
		mic::types::MatrixArray< eT > &	array_,
		std::string	id_,
		size_t	sample_number_,
		size_t	channel_number_,
		size_t	height_,
		size_t	width_
	)

inline

Returns channel from a sample. If a given memory array does not contain such a memory pointer - it (lazy) allocates it. Assumes that: a) a sample is a column vector and b) there is one "channel memory ptr" for each sample (so the whole batch can be processed in parallel). OMP secured - critical section inside.

Parameters

sample_ptr	Pointer to a sample.
array_	Array of matrices where a given matrix (channel) is/will be stored.
id_	Channel id (variable prefix).
sample_number_	Number of the sample in batch.
channel_number_	Number of the channel.
height_	Height of the channel.
width_	Width of the channel.

Definition at line 487 of file Layer.hpp.

Referenced by mic::mlnn::Layer< float >::lazyReturnInputChannel(), and mic::mlnn::Layer< float >::lazyReturnOutputChannel().

template<typename eT = float>

mic::types::MatrixPtr<eT> mic::mlnn::Layer< eT >::lazyReturnInputChannel	(	mic::types::MatrixPtr< eT >	sample_ptr_,
		size_t	sample_number_,
		size_t	channel_number_
	)

inline

Returns input channel, with lazy matrix ptr allocation.

Parameters

batch_ptr_	Pointer to a batch.
sample_number_	Number of the sample in batch.
channel_number_	Number of the channel in sample.

Definition at line 519 of file Layer.hpp.

Referenced by mic::mlnn::convolution::MaxPooling< eT >::forward().

template<typename eT = float>

mic::types::MatrixPtr<eT> mic::mlnn::Layer< eT >::lazyReturnInputSample	(	mic::types::MatrixPtr< eT >	batch_ptr_,
		size_t	sample_number_
	)

inline

Returns input sample, with lazy matrix ptr allocation.

Parameters

batch_ptr_	Pointer to a batch.
sample_number_	Number of the sample in batch.

Definition at line 460 of file Layer.hpp.

Referenced by mic::mlnn::convolution::MaxPooling< eT >::forward().

template<typename eT = float>

mic::types::MatrixPtr<eT> mic::mlnn::Layer< eT >::lazyReturnOutputChannel	(	mic::types::MatrixPtr< eT >	sample_ptr_,
		size_t	sample_number_,
		size_t	channel_number_
	)

inline

Returns output sample, with lazy matrix ptr allocation.

Parameters

batch_ptr_	Pointer to a batch.
sample_number_	Number of the sample in batch.
channel_number_	Number of the channel in sample.

Definition at line 530 of file Layer.hpp.

template<typename eT = float>

mic::types::MatrixPtr<eT> mic::mlnn::Layer< eT >::lazyReturnOutputSample	(	mic::types::MatrixPtr< eT >	batch_ptr_,
		size_t	sample_number_
	)

inline

Returns output sample, with lazy matrix ptr allocation.

Parameters

batch_ptr_	Pointer to a batch.
sample_number_	Number of the sample in batch.

Definition at line 470 of file Layer.hpp.

template<typename eT = float>

mic::types::MatrixPtr<eT> mic::mlnn::Layer< eT >::lazyReturnSampleFromBatch	(	mic::types::MatrixPtr< eT >	batch_ptr_,
		mic::types::MatrixArray< eT > &	array_,
		std::string	id_,
		size_t	sample_number_,
		size_t	sample_size_
	)

inline

Returns sample from batch. If a given memory array does not contain such a memory pointer - it (lazy) allocates it. OMP secured - critical section inside.

Parameters

batch_ptr_	Pointer to a batch.
array_	Array of matrices where a given matrix (sample) is/will be stored.
id_	Sample id (variable prefix).
sample_number_	Number of the sample in batch.
sample_size_	Size of the sample.

Definition at line 433 of file Layer.hpp.

Referenced by mic::mlnn::Layer< float >::lazyReturnInputSample(), and mic::mlnn::Layer< float >::lazyReturnOutputSample().

template<typename eT = float>

const std::string mic::mlnn::Layer< eT >::name ( ) const

inline

Returns name of the layer.

Definition at line 270 of file Layer.hpp.

template<typename eT = float>

size_t mic::mlnn::Layer< eT >::outputSize ( )

inline

Returns size (length) of outputs.

Definition at line 260 of file Layer.hpp.

Referenced by mic::mlnn::fully_connected::SparseLinear< eT >::backward(), mic::mlnn::convolution::MaxPooling< eT >::forward(), mic::mlnn::fully_connected::HebbianLinear< eT >::getActivations(), mic::mlnn::fully_connected::BinaryCorrelator< eT >::getActivations(), mic::neural_nets::unit_tests::Linear50x100Double::Linear50x100Double(), mic::neural_nets::unit_tests::Linear50x100Double::SetUp(), mic::mlnn::fully_connected::SparseLinear< eT >::SparseLinear(), mic::mlnn::convolution::Convolution< float >::streamLayerParameters(), and mic::mlnn::Layer< float >::streamLayerParameters().

template<typename eT = float>

virtual void mic::mlnn::Layer< eT >::resetGrads ( )

inlinevirtual

Reset gradients. Virtual empty method - to be implemented by the inherited classes.

Reimplemented in mic::mlnn::convolution::Convolution< eT >, mic::mlnn::convolution::Convolution< double >, mic::mlnn::convolution::Convolution< float >, mic::mlnn::fully_connected::Linear< eT >, mic::mlnn::fully_connected::Linear< double >, and mic::mlnn::fully_connected::Linear< float >.

Definition at line 245 of file Layer.hpp.

template<typename eT = float>

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

inlinevirtual

Changes the size of the batch. By default it resizes state (x,y) and gradients (x,y).

Parameters

New	size of the batch.

Reimplemented in mic::mlnn::cost_function::Softmax< eT >, mic::mlnn::cost_function::Softmax< float >, mic::mlnn::convolution::MaxPooling< eT >, and mic::mlnn::regularisation::Dropout< eT >.

Definition at line 199 of file Layer.hpp.

Referenced by mic::mlnn::regularisation::Dropout< eT >::resizeBatch(), mic::mlnn::convolution::MaxPooling< eT >::resizeBatch(), and mic::mlnn::cost_function::Softmax< float >::resizeBatch().

template<typename eT = float>

template<class Archive >

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

inlineprivate

Serializes the layer to and from archive.

Parameters

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

Definition at line 800 of file Layer.hpp.

template<typename eT = float>

template<typename omT >

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

inline

Sets the optimization method.

Template Parameters

omT	Optimization method type

Definition at line 307 of file Layer.hpp.

template<typename eT = float>

void mic::mlnn::Layer< eT >::setState	(	std::string	name_,
		mic::types::MatrixPtr< eT >	mat_ptr_
	)

inline

Sets state (matrix) (or throws an exception!)

Definition at line 298 of file Layer.hpp.

template<typename eT = float>

virtual std::string mic::mlnn::Layer< eT >::streamLayerParameters ( )

inlinevirtual

Stream layer parameters.

Returns: Ostream object.

Reimplemented in mic::mlnn::convolution::Convolution< eT >, mic::mlnn::convolution::Convolution< double >, and mic::mlnn::convolution::Convolution< float >.

Definition at line 363 of file Layer.hpp.

template<typename eT = float>

const std::string mic::mlnn::Layer< eT >::type ( ) const

inline

Returns the type of layer.

Definition at line 323 of file Layer.hpp.

Referenced by mic::mlnn::convolution::Convolution< float >::streamLayerParameters(), and mic::mlnn::Layer< float >::streamLayerParameters().

template<typename eT = float>

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

pure virtual

Performs the update according to the calculated gradients and injected optimization method. Abstract.

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).

Implemented in mic::mlnn::convolution::Convolution< eT >, mic::mlnn::convolution::Convolution< double >, mic::mlnn::convolution::Convolution< float >, mic::mlnn::convolution::MaxPooling< eT >, mic::mlnn::fully_connected::Linear< eT >, mic::mlnn::fully_connected::Linear< double >, mic::mlnn::fully_connected::Linear< float >, mic::mlnn::convolution::Cropping< eT >, mic::mlnn::convolution::Padding< eT >, mic::mlnn::cost_function::Softmax< eT >, mic::mlnn::cost_function::Softmax< float >, mic::mlnn::fully_connected::BinaryCorrelator< eT >, mic::mlnn::fully_connected::HebbianLinear< eT >, mic::mlnn::experimental::ConvHebbian< eT >, mic::mlnn::regularisation::Dropout< eT >, mic::mlnn::activation_function::ReLU< eT >, mic::mlnn::activation_function::ELU< eT >, mic::mlnn::activation_function::Sigmoid< eT >, and mic::mlnn::fully_connected::SparseLinear< eT >.

Friends And Related Function Documentation

template<typename eT = float>

template<typename tmp >

friend class BackpropagationNeuralNetwork

friend

Definition at line 788 of file Layer.hpp.

template<typename eT = float>

friend class boost::serialization::access

friend

Definition at line 792 of file Layer.hpp.

template<typename eT = float>

template<typename tmp >

friend class HebbianNeuralNetwork

friend

Definition at line 789 of file Layer.hpp.

template<typename eT = float>

template<typename tmp >

friend class MultiLayerNeuralNetwork

friend

Definition at line 787 of file Layer.hpp.

template<typename eT = float>

std::ostream& operator<<	(	std::ostream &	os_,
		Layer< eT > &	obj_
	)

friend

Stream operator enabling to print neural network.

Parameters

os_	Ostream object.
obj_	Tensor object.

Definition at line 385 of file Layer.hpp.

Member Data Documentation

template<typename eT = float>

size_t mic::mlnn::Layer< eT >::batch_size

protected

Size (length) of (mini)batch.

Definition at line 744 of file Layer.hpp.

template<typename eT = float>

std::vector< std::shared_ptr <mic::types::Matrix<eT> > > mic::mlnn::Layer< eT >::dx_activations

protected

Vector containing activations of gradients of inputs (dx) - used in visualization.

Definition at line 771 of file Layer.hpp.

Referenced by mic::mlnn::Layer< float >::getInputGradientActivations().

template<typename eT = float>

std::vector< std::shared_ptr <mic::types::Matrix<eT> > > mic::mlnn::Layer< eT >::dy_activations

protected

Vector containing activations of gradients of outputs (dy) - used in visualization.

Definition at line 777 of file Layer.hpp.

Referenced by mic::mlnn::Layer< float >::getOutputGradientActivations().

template<typename eT = float>

mic::types::MatrixArray<eT> mic::mlnn::Layer< eT >::g

protected

Gradients - contains input [x] and output [y] matrices.

Definition at line 756 of file Layer.hpp.

template<typename eT = float>

size_t mic::mlnn::Layer< eT >::input_depth

protected

Number of channels of the input (e.g. 3 for RGB images).

Definition at line 732 of file Layer.hpp.

template<typename eT = float>

size_t mic::mlnn::Layer< eT >::input_height

protected

Height of the input (e.g. 28 for MNIST).

Definition at line 726 of file Layer.hpp.

Referenced by mic::mlnn::convolution::Convolution< float >::backpropagade_dy_to_dW(), mic::mlnn::convolution::Convolution< float >::backpropagade_dy_to_dx(), mic::mlnn::convolution::Cropping< eT >::backward(), mic::mlnn::convolution::Padding< eT >::backward(), mic::mlnn::fully_connected::Linear< float >::calculateMeanReconstructionError(), mic::mlnn::convolution::Convolution< float >::Convolution(), mic::mlnn::convolution::Padding< eT >::forward(), mic::mlnn::convolution::Cropping< eT >::forward(), mic::mlnn::convolution::MaxPooling< eT >::forward(), mic::mlnn::convolution::Convolution< float >::forward(), mic::mlnn::Layer< float >::getInputActivations(), mic::mlnn::Layer< float >::getInputGradientActivations(), mic::mlnn::fully_connected::Linear< float >::getInverseOutputActivations(), mic::mlnn::fully_connected::Linear< float >::getInverseWeightActivations(), mic::mlnn::experimental::ConvHebbian< eT >::getOutputReconstruction(), mic::mlnn::Layer< float >::inputSize(), mic::mlnn::Layer< float >::Layer(), mic::mlnn::Layer< float >::lazyReturnInputChannel(), mic::mlnn::Layer< float >::serialize(), mic::mlnn::convolution::Convolution< float >::streamLayerParameters(), and mic::mlnn::Layer< float >::streamLayerParameters().

template<typename eT = float>

size_t mic::mlnn::Layer< eT >::input_width

protected

Width of the input (e.g. 28 for MNIST).

Definition at line 729 of file Layer.hpp.

Referenced by mic::mlnn::convolution::Convolution< float >::backpropagade_dy_to_dW(), mic::mlnn::convolution::Convolution< float >::backpropagade_dy_to_dx(), mic::mlnn::convolution::Cropping< eT >::backward(), mic::mlnn::convolution::Padding< eT >::backward(), mic::mlnn::fully_connected::Linear< float >::calculateMeanReconstructionError(), mic::mlnn::convolution::Convolution< float >::Convolution(), mic::mlnn::convolution::Padding< eT >::forward(), mic::mlnn::convolution::Cropping< eT >::forward(), mic::mlnn::convolution::MaxPooling< eT >::forward(), mic::mlnn::experimental::ConvHebbian< eT >::forward(), mic::mlnn::convolution::Convolution< float >::forward(), mic::mlnn::Layer< float >::getInputActivations(), mic::mlnn::Layer< float >::getInputGradientActivations(), mic::mlnn::fully_connected::Linear< float >::getInverseOutputActivations(), mic::mlnn::fully_connected::Linear< float >::getInverseWeightActivations(), mic::mlnn::experimental::ConvHebbian< eT >::getOutputReconstruction(), mic::mlnn::Layer< float >::inputSize(), mic::mlnn::Layer< float >::Layer(), mic::mlnn::Layer< float >::lazyReturnInputChannel(), mic::mlnn::Layer< float >::serialize(), mic::mlnn::convolution::Convolution< float >::streamLayerParameters(), and mic::mlnn::Layer< float >::streamLayerParameters().

template<typename eT = float>

std::string mic::mlnn::Layer< eT >::layer_name

protected

Name (identifier of the type) of the layer.

Definition at line 750 of file Layer.hpp.

Referenced by mic::mlnn::Layer< float >::name(), mic::mlnn::Layer< float >::serialize(), and mic::mlnn::Layer< float >::streamLayerParameters().

template<typename eT = float>

LayerTypes mic::mlnn::Layer< eT >::layer_type

protected

Type of the layer.

Definition at line 747 of file Layer.hpp.

Referenced by mic::mlnn::Layer< float >::serialize(), and mic::mlnn::Layer< float >::type().

template<typename eT = float>

mic::types::MatrixArray<eT> mic::mlnn::Layer< eT >::m

protected

Memory - a list of temporal parameters, to be used by the derived classes.

Definition at line 762 of file Layer.hpp.

template<typename eT = float>

mic::neural_nets::optimization::OptimizationArray<eT> mic::mlnn::Layer< eT >::opt

protected

Array of optimization functions.

Definition at line 765 of file Layer.hpp.

Referenced by mic::mlnn::Layer< float >::setOptimization(), mic::mlnn::fully_connected::SparseLinear< eT >::update(), mic::mlnn::experimental::ConvHebbian< eT >::update(), mic::mlnn::fully_connected::HebbianLinear< eT >::update(), mic::mlnn::fully_connected::BinaryCorrelator< eT >::update(), mic::mlnn::fully_connected::Linear< float >::update(), and mic::mlnn::convolution::Convolution< float >::update().

template<typename eT = float>

size_t mic::mlnn::Layer< eT >::output_depth

protected

Number of filters = number of output channels.

Definition at line 741 of file Layer.hpp.

template<typename eT = float>

size_t mic::mlnn::Layer< eT >::output_height

protected

Number of receptive fields in a single channel - vertical direction.

Definition at line 735 of file Layer.hpp.

template<typename eT = float>

size_t mic::mlnn::Layer< eT >::output_width

protected

Number of receptive fields in a single channel - horizontal direction.

Definition at line 738 of file Layer.hpp.

template<typename eT = float>

mic::types::MatrixArray<eT> mic::mlnn::Layer< eT >::p

protected

Parameters - parameters of the layer, to be used by the derived classes.

Definition at line 759 of file Layer.hpp.

template<typename eT = float>

mic::types::MatrixArray<eT> mic::mlnn::Layer< eT >::s

protected

States - contains input [x] and output [y] matrices.

Definition at line 753 of file Layer.hpp.

template<typename eT = float>

std::vector< std::shared_ptr <mic::types::Matrix<eT> > > mic::mlnn::Layer< eT >::x_activations

protected

Vector containing activations of input neurons - used in visualization.

Definition at line 768 of file Layer.hpp.

Referenced by mic::mlnn::Layer< float >::getInputActivations().

template<typename eT = float>

std::vector< std::shared_ptr <mic::types::Matrix<eT> > > mic::mlnn::Layer< eT >::y_activations

protected

Vector containing activations of output neurons - used in visualization.

Definition at line 774 of file Layer.hpp.

Referenced by mic::mlnn::Layer< float >::getOutputActivations().

The documentation for this class was generated from the following file:

src/mlnn/layer/Layer.hpp

Public Member Functions

Protected Member Functions

Protected Attributes

Private Member Functions

Friends

Detailed Description

template<typename eT = float> class mic::mlnn::Layer< eT >

Constructor & Destructor Documentation

Member Function Documentation

Friends And Related Function Documentation

Member Data Documentation

template<typename eT = float>
class mic::mlnn::Layer< eT >