Class representing a convolution layer, with "valid padding" and variable stride. More...

#include <Convolution.hpp>

Inheritance diagram for mic::mlnn::convolution::Convolution< eT >:

Collaboration diagram for mic::mlnn::convolution::Convolution< eT >:

Public Member Functions
	Convolution (size_t input_height_, size_t input_width_, size_t input_channels_, size_t number_of_filters_, size_t filter_size_, size_t stride_, std::string name_="Convolution")

virtual	~Convolution ()

virtual std::string	streamLayerParameters ()

void	forward (bool test=false)

void	backward ()

void	backpropagade_dy_to_dx ()

void	backpropagade_dy_to_dW ()

void	backpropagade_dy_to_db ()

void	resetGrads ()

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

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

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

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

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

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

eT	cosineSimilarity (eT A, eT B, size_t length_)

Public Member Functions inherited from 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")

virtual	~Layer ()

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

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_)

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

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 Attributes
size_t	filter_size
	Size of filters (assuming square filters). Filter_size^2 = length of the output vector. More...

size_t	stride
	Stride (assuming equal vertical and horizontal strides). More...

Protected Attributes inherited from mic::mlnn::Layer< eT >
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
	Convolution ()

Private Attributes
std::vector < mic::types::MatrixPtr< eT > >	w_activations
	Vector containing activations of weights/filters. More...

std::vector < mic::types::MatrixPtr< eT > >	dw_activations
	Vector containing activations of gradients of weights (dW). More...

std::vector < mic::types::MatrixPtr< eT > >	xrf_activations
	Vector containing receptive fields. More...

std::vector < mic::types::MatrixPtr< eT > >	irf_activations
	Vector containing inverse receptive fields. More...

Friends
template<typename tmp >
class	mic::mlnn::MultiLayerNeuralNetwork

Additional Inherited Members
Protected Member Functions inherited from mic::mlnn::Layer< eT >
	Layer ()

Detailed Description

template<typename eT = float>
class mic::mlnn::convolution::Convolution< eT >

Class representing a convolution layer, with "valid padding" and variable stride.

Author: tkornuta

Template Parameters

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

Definition at line 41 of file Convolution.hpp.

Constructor & Destructor Documentation

template<typename eT = float>

mic::mlnn::convolution::Convolution< eT >::Convolution	(	size_t	input_height_,
		size_t	input_width_,
		size_t	input_channels_,
		size_t	number_of_filters_,
		size_t	filter_size_,
		size_t	stride_,
		std::string	name_ = `"Convolution< eT >"`
	)

inline

Creates a convolutional layer.

Parameters

input_height_	Height of the input / rows (e.g. 28 for MNIST).
input_width_	Width of the input / columns (e.g. 28 for MNIST).
input_channels_	Number of channels of the input (e.g. 3 for RGB images).
number_of_filters_	Number of filters = Length of the output vector.
filter_size_	Size of filters (assuming square filters).
stride_	Stride (assuming equal vertical and horizontal strides).
name_	Name of the layer.

Definition at line 54 of file Convolution.hpp.

template<typename eT = float>

virtual mic::mlnn::convolution::Convolution< eT >::~Convolution ( )

inlinevirtual

Virtual destructor - empty.

Definition at line 159 of file Convolution.hpp.

template<typename eT = float>

mic::mlnn::convolution::Convolution< eT >::Convolution ( )

inlineprivate

Private constructor, used only during the serialization.

Definition at line 838 of file Convolution.hpp.

Member Function Documentation

template<typename eT = float>

void mic::mlnn::convolution::Convolution< eT >::backpropagade_dy_to_db ( )

inline

Back-propagates the gradients from dy to dx.

Definition at line 558 of file Convolution.hpp.

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

template<typename eT = float>

void mic::mlnn::convolution::Convolution< eT >::backpropagade_dy_to_dW ( )

inline

Back-propagates the gradients from dy to dW.

Definition at line 449 of file Convolution.hpp.

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

template<typename eT = float>

void mic::mlnn::convolution::Convolution< eT >::backpropagade_dy_to_dx ( )

inline

Back-propagates the gradients from dy to dx.

Definition at line 335 of file Convolution.hpp.

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

template<typename eT = float>

void mic::mlnn::convolution::Convolution< eT >::backward ( )

inlinevirtual

Back-propagates the gradients through the layer.

Implements mic::mlnn::Layer< eT >.

Definition at line 296 of file Convolution.hpp.

template<typename eT = float>

eT mic::mlnn::convolution::Convolution< eT >::cosineSimilarity	(	eT *	A,
		eT *	B,
		size_t	length_
	)

inline

Calculates the cosine similarity between two vectors/matrices.

Parameters

A	Pointer to a memory with the first vector.
B	Pointer to a memory with the second vector.
length_	Length of (both) vector(s).

Definition at line 777 of file Convolution.hpp.

Referenced by mic::mlnn::convolution::Convolution< float >::getFilterSimilarityMatrix().

template<typename eT = float>

void mic::mlnn::convolution::Convolution< eT >::forward ( bool test = false )

inlinevirtual

Performs forward pass through the filters. Can process batches.

Implements mic::mlnn::Layer< eT >.

Definition at line 186 of file Convolution.hpp.

template<typename eT = float>

mic::types::MatrixPtr<eT> mic::mlnn::convolution::Convolution< eT >::getFilterSimilarityMatrix ( )

inline

Returns a matrix containing filter similarities. Note: in diagonal zeros.

Definition at line 738 of file Convolution.hpp.

template<typename eT = float>

std::vector< std::shared_ptr <mic::types::Matrix<eT> > >& mic::mlnn::convolution::Convolution< eT >::getInverseReceptiveFields ( )

inline

Returns activations of inverse receptive fields. Limitation: displays receptive fields of the last sample from batch!

Definition at line 707 of file Convolution.hpp.

template<typename eT = float>

std::vector< std::shared_ptr <mic::types::Matrix<eT> > >& mic::mlnn::convolution::Convolution< eT >::getReceptiveFields ( )

inline

Returns activations of receptive fields. Limitation: displays receptive fields of the last sample from batch!

Definition at line 675 of file Convolution.hpp.

template<typename eT = float>

std::vector< std::shared_ptr <mic::types::Matrix<eT> > >& mic::mlnn::convolution::Convolution< eT >::getWeightActivations ( )

inline

Returns activations of weights.

Definition at line 613 of file Convolution.hpp.

template<typename eT = float>

std::vector< std::shared_ptr <mic::types::Matrix<eT> > >& mic::mlnn::convolution::Convolution< eT >::getWeightGradientActivations ( )

inline

Returns activations of weight gradients (dx).

Definition at line 644 of file Convolution.hpp.

template<typename eT = float>

void mic::mlnn::convolution::Convolution< eT >::resetGrads ( )

inlinevirtual

Resets all gradients. Warning: including gx and gy!

Reimplemented from mic::mlnn::Layer< eT >.

Definition at line 579 of file Convolution.hpp.

template<typename eT = float>

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

inlinevirtual

Stream layer parameters.

Returns: Ostream object.

Reimplemented from mic::mlnn::Layer< eT >.

Definition at line 165 of file Convolution.hpp.

Referenced by mic::mlnn::convolution::Convolution< float >::Convolution().

template<typename eT = float>

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

inlinevirtual

Applies the gradient update, using the selected optimization method.

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).

Implements mic::mlnn::Layer< eT >.

Definition at line 590 of file Convolution.hpp.