Droput layer - a layer used for the regularization of neural network by randomly dropping neurons during training. More...

#include <Dropout.hpp>

Inheritance diagram for mic::mlnn::regularisation::Dropout< eT >:

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Collaboration diagram for mic::mlnn::regularisation::Dropout< eT >:

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Public Member Functions
	Dropout (size_t inputs_, float ratio_, std::string name_="Dropout")

virtual	~Dropout ()

virtual void	resizeBatch (size_t batch_size_)

void	forward (bool test=false)

void	backward ()

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

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_)

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 ()

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 Attributes
eT	keep_ratio

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
	Dropout ()

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::regularisation::Dropout< eT >

Droput layer - a layer used for the regularization of neural network by randomly dropping neurons during training.

Author: tkornuta/krocki

Template Parameters

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

Definition at line 39 of file Dropout.hpp.

Constructor & Destructor Documentation

template<typename eT = float>

mic::mlnn::regularisation::Dropout< eT >::Dropout	(	size_t	inputs_,
		float	ratio_,
		std::string	name_ = `"Dropout< eT >"`
	)

inline

Parameters

ratio_ Keep ratio denoting the probability of activations to be passed.

Definition at line 45 of file Dropout.hpp.

References mic::mlnn::Layer< eT >::m.

template<typename eT = float>

virtual mic::mlnn::regularisation::Dropout< eT >::~Dropout ( )

inlinevirtual

Definition at line 56 of file Dropout.hpp.

template<typename eT = float>

mic::mlnn::regularisation::Dropout< eT >::Dropout ( )

inlineprivate

Private constructor, used only during the serialization.

Definition at line 143 of file Dropout.hpp.

Member Function Documentation

template<typename eT = float>

void mic::mlnn::regularisation::Dropout< eT >::backward ( )

inlinevirtual

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

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

Definition at line 101 of file Dropout.hpp.

References mic::mlnn::Layer< eT >::g, and mic::mlnn::Layer< eT >::m.

template<typename eT = float>

void mic::mlnn::regularisation::Dropout< eT >::forward ( bool test = false )

inlinevirtual

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.

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

Definition at line 71 of file Dropout.hpp.

References mic::mlnn::regularisation::Dropout< eT >::keep_ratio, mic::mlnn::Layer< eT >::m, and mic::mlnn::Layer< eT >::s.

template<typename eT = float>

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

inlinevirtual

Changes the size of the batch - calls base Layer class resize and additionally resizes the cache size.

Parameters

New	size of the batch.

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

Definition at line 62 of file Dropout.hpp.

References mic::mlnn::Layer< eT >::m, and mic::mlnn::Layer< eT >::resizeBatch().

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template<typename eT = float>

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

inlinevirtual

Performs the update according to the calculated gradients and injected optimization method. Empty as this is a "const" layer.

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 116 of file Dropout.hpp.

Friends And Related Function Documentation

template<typename eT = float>

template<typename tmp >

friend class mic::mlnn::MultiLayerNeuralNetwork

friend

Definition at line 138 of file Dropout.hpp.

Member Data Documentation

template<typename eT = float>

eT mic::mlnn::regularisation::Dropout< eT >::keep_ratio

protected

Ratio denoting the probability of activations to be passed.

Definition at line 134 of file Dropout.hpp.

Referenced by mic::mlnn::regularisation::Dropout< eT >::forward().

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

src/mlnn/regularisation/Dropout.hpp

Public Member Functions

Protected Attributes

Private Member Functions

Friends

Additional Inherited Members

Detailed Description

template<typename eT = float> class mic::mlnn::regularisation::Dropout< eT >

Constructor & Destructor Documentation

Member Function Documentation

Friends And Related Function Documentation

Member Data Documentation

template<typename eT = float>
class mic::mlnn::regularisation::Dropout< eT >