Implements a convolutional RBM with a single greyscale input imge and a set of squared image filters. More...

#include <shark/Unsupervised/RBM/ConvolutionalRBM.h>

Inheritance diagram for shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >:

Public Types
typedef detail::ConvolutionalEnergyGradient< ConvolutionalRBM >	GradientType

typedef HiddenLayerT	HiddenType

typedef VisibleLayerT	VisibleType

typedef RngT	RngType

typedef Energy< ConvolutionalRBM< VisibleType, HiddenType, RngT > >	EnergyType

typedef base_type::BatchInputType	BatchInputType

typedef base_type::BatchOutputType	BatchOutputType

Public Types inherited from shark::AbstractModel< RealVector, RealVector >
enum	Feature

typedef RealVector	InputType
	Defines the input type of the model. More...

typedef RealVector	OutputType
	Defines the output type of the model. More...

typedef Batch< InputType >::type	BatchInputType
	defines the batch type of the input type. More...

typedef Batch< OutputType >::type	BatchOutputType
	defines the batch type of the output type More...

typedef TypedFlags< Feature >	Features

typedef TypedFeatureNotAvailableException< Feature >	FeatureNotAvailableException

Public Member Functions
	ConvolutionalRBM (RngType &rng)

std::string	name () const
	From INameable: return the class name. More...

std::size_t	numberOfParameters () const
	Returns the total number of parameters of the model. More...

RealVector	parameterVector () const
	Returns the parameters of the Model as parameter vector. More...

void	setParameterVector (const RealVector &newParameters)
	Sets the parameters of the model. More...

void	setStructure (std::size_t newInputSize1, std::size_t newInputSize2, std::size_t newNumFilters, std::size_t filterSize)
	Creates the structure of the ConvolutionalRBM. More...

HiddenType const &	hiddenNeurons () const
	Returns the layer of hidden neurons. More...

HiddenType &	hiddenNeurons ()
	Returns the layer of hidden neurons. More...

VisibleType &	visibleNeurons ()
	Returns the layer of visible neurons. More...

VisibleType const &	visibleNeurons () const
	Returns the layer of visible neurons. More...

std::size_t	numFilters () const

std::size_t	filterSize1 () const

std::size_t	filterSize2 () const

std::size_t	inputSize1 () const

std::size_t	inputSize2 () const

std::size_t	responseSize1 () const

std::size_t	responseSize2 () const

std::vector< RealMatrix > &	filters ()
	Returns the weight matrix connecting the layers. More...

std::vector< RealMatrix > const &	weightMatrix () const
	Returns the weight matrix connecting the layers. More...

EnergyType	energy () const
	Returns the energy function of the ConvolutionalRBM. More...

RngType &	rng ()
	Returns the random number generator associated with this ConvolutionalRBM. More...

void	evaluationType (bool forward, bool evalMean)
	Sets the type of evaluation, eval will perform. More...

boost::shared_ptr< State >	createState () const
	Creates an internal state of the model. More...

void	eval (BatchInputType const &patterns, BatchOutputType &outputs) const
	Passes information through/samples from an ConvolutionalRBM in a forward or backward way. More...

void	eval (BatchInputType const &patterns, BatchOutputType &outputs, State &state) const
	Standard interface for evaluating the response of the model to a batch of patterns. More...

void	inputHidden (RealMatrix &inputs, RealMatrix const &visibleStates) const
	Calculates the input of the hidden neurons given the state of the visible in a batch-vise fassion. More...

void	inputVisible (RealMatrix &inputs, RealMatrix const &hiddenStates) const
	Calculates the input of the visible neurons given the state of the hidden. More...

std::size_t	numberOfHN () const
	Returns the number of hidden Neurons, that is the number of filter responses. More...

std::size_t	numberOfVN () const
	Returns the number of visible Neurons, which is the number of pixels of the image. More...

void	read (InArchive &archive)
	Reads the network from an archive. More...

void	write (OutArchive &archive) const
	Writes the network to an archive. More...

Public Member Functions inherited from shark::AbstractModel< RealVector, RealVector >
	AbstractModel ()

virtual	~AbstractModel ()

const Features &	features () const

virtual void	updateFeatures ()

bool	hasFirstParameterDerivative () const
	Returns true when the first parameter derivative is implemented. More...

bool	hasSecondParameterDerivative () const
	Returns true when the second parameter derivative is implemented. More...

bool	hasFirstInputDerivative () const
	Returns true when the first input derivative is implemented. More...

bool	hasSecondInputDerivative () const
	Returns true when the second parameter derivative is implemented. More...

bool	isSequential () const

virtual void	eval (InputType const &pattern, OutputType &output) const
	Standard interface for evaluating the response of the model to a single pattern. More...

Data< OutputType >	operator() (Data< InputType > const &patterns) const
	Model evaluation as an operator for a whole dataset. This is a convenience function. More...

OutputType	operator() (InputType const &pattern) const
	Model evaluation as an operator for a single pattern. This is a convenience function. More...

BatchOutputType	operator() (BatchInputType const &patterns) const
	Model evaluation as an operator for a single pattern. This is a convenience function. More...

virtual void	weightedParameterDerivative (BatchInputType const &pattern, BatchOutputType const &coefficients, State const &state, RealVector &derivative) const
	calculates the weighted sum of derivatives w.r.t the parameters. More...

virtual void	weightedParameterDerivative (BatchInputType const &pattern, BatchOutputType const &coefficients, Batch< RealMatrix >::type const &errorHessian, State const &state, RealVector &derivative, RealMatrix &hessian) const
	calculates the weighted sum of derivatives w.r.t the parameters More...

virtual void	weightedInputDerivative (BatchInputType const &pattern, BatchOutputType const &coefficients, State const &state, BatchInputType &derivative) const
	calculates the weighted sum of derivatives w.r.t the inputs More...

virtual void	weightedInputDerivative (BatchInputType const &pattern, BatchOutputType const &coefficients, typename Batch< RealMatrix >::type const &errorHessian, State const &state, RealMatrix &derivative, Batch< RealMatrix >::type &hessian) const
	calculates the weighted sum of derivatives w.r.t the inputs More...

virtual void	weightedDerivatives (BatchInputType const &patterns, BatchOutputType const &coefficients, State const &state, RealVector &parameterDerivative, BatchInputType &inputDerivative) const
	calculates weighted input and parameter derivative at the same time More...

Public Member Functions inherited from shark::IParameterizable
virtual	~IParameterizable ()

Public Member Functions inherited from shark::INameable
virtual	~INameable ()

Public Member Functions inherited from shark::ISerializable
virtual	~ISerializable ()
	Virtual d'tor. More...

void	load (InArchive &archive, unsigned int version)
	Versioned loading of components, calls read(...). More...

void	save (OutArchive &archive, unsigned int version) const
	Versioned storing of components, calls write(...). More...

	BOOST_SERIALIZATION_SPLIT_MEMBER ()

Additional Inherited Members
Protected Attributes inherited from shark::AbstractModel< RealVector, RealVector >
Features	m_features

Detailed Description

template<class VisibleLayerT, class HiddenLayerT, class RngT>
class shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >

Implements a convolutional RBM with a single greyscale input imge and a set of squared image filters.

This class implements a simple RBM which interprets is input as images and instead of learning arbitrary filters, learns a convolution Thus the ConvolutionalRBM is to an RBM what a ConvolutionalFFNet is to an FFNet.

Definition at line 46 of file ConvolutionalRBM.h.

Member Typedef Documentation

◆ BatchInputType

template<class VisibleLayerT , class HiddenLayerT , class RngT >

typedef base_type::BatchInputType shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::BatchInputType

Definition at line 56 of file ConvolutionalRBM.h.

◆ BatchOutputType

template<class VisibleLayerT , class HiddenLayerT , class RngT >

typedef base_type::BatchOutputType shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::BatchOutputType

Definition at line 57 of file ConvolutionalRBM.h.

◆ EnergyType

template<class VisibleLayerT , class HiddenLayerT , class RngT >

typedef Energy<ConvolutionalRBM<VisibleType,HiddenType,RngT> > shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::EnergyType

Definition at line 54 of file ConvolutionalRBM.h.

◆ GradientType

template<class VisibleLayerT , class HiddenLayerT , class RngT >

typedef detail::ConvolutionalEnergyGradient<ConvolutionalRBM> shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::GradientType

Definition at line 50 of file ConvolutionalRBM.h.

◆ HiddenType

template<class VisibleLayerT , class HiddenLayerT , class RngT >

typedef HiddenLayerT shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::HiddenType

Definition at line 51 of file ConvolutionalRBM.h.

◆ RngType

template<class VisibleLayerT , class HiddenLayerT , class RngT >

typedef RngT shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::RngType

Definition at line 53 of file ConvolutionalRBM.h.

◆ VisibleType

template<class VisibleLayerT , class HiddenLayerT , class RngT >

typedef VisibleLayerT shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::VisibleType

Definition at line 52 of file ConvolutionalRBM.h.

Constructor & Destructor Documentation

◆ ConvolutionalRBM()

template<class VisibleLayerT , class HiddenLayerT , class RngT >

shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::ConvolutionalRBM ( RngType & rng )

inline

Definition at line 118 of file ConvolutionalRBM.h.

Member Function Documentation

◆ createState()

template<class VisibleLayerT , class HiddenLayerT , class RngT >

boost::shared_ptr<State> shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::createState ( ) const

inlinevirtual

Creates an internal state of the model.

The state is needed when the derivatives are to be calculated. Eval can store a state which is then reused to speed up the calculations of the derivatives. This also allows eval to be evaluated in parallel!

Reimplemented from shark::AbstractModel< RealVector, RealVector >.

Definition at line 253 of file ConvolutionalRBM.h.

◆ energy()

template<class VisibleLayerT , class HiddenLayerT , class RngT >

EnergyType shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::energy ( ) const

inline

Returns the energy function of the ConvolutionalRBM.

Definition at line 228 of file ConvolutionalRBM.h.

◆ eval() [1/2]

template<class VisibleLayerT , class HiddenLayerT , class RngT >

void shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::eval	(	BatchInputType const &	patterns,
		BatchOutputType &	outputs
	)		const

inlinevirtual

Passes information through/samples from an ConvolutionalRBM in a forward or backward way.

Eval performs its operation based on the given evaluation type. There are two ways to pass data through an ConvolutionalRBM: either forward, setting the states of the visible neurons and sample the hidden states or backwards, where the state of the hidden is fixed and the visible are sampled. Instead of the state of the hidden/visible, one often wants the mean of the state \( E_{p(h|v)}\left(h\right)\). By default, the ConvolutionalRBM uses the forward evaluation and returns the mean of the state, but other evaluation modes can be set by evaluationType().

Parameters

patterns	the batch of (visible or hidden) inputs
outputs	the batch of (visible or hidden) outputs

Reimplemented from shark::AbstractModel< RealVector, RealVector >.

Definition at line 269 of file ConvolutionalRBM.h.

Referenced by shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::eval().

◆ eval() [2/2]

template<class VisibleLayerT , class HiddenLayerT , class RngT >

void shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::eval	(	BatchInputType const &	patterns,
		BatchOutputType &	outputs,
		State &	state
	)		const

inlinevirtual

Standard interface for evaluating the response of the model to a batch of patterns.

Parameters

patterns	the inputs of the model
outputs	the predictions or response of the model to every pattern
state	intermediate results stored by eval which can be reused for derivative computation.

Implements shark::AbstractModel< RealVector, RealVector >.

Definition at line 279 of file ConvolutionalRBM.h.

References shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::eval().

◆ evaluationType()

template<class VisibleLayerT , class HiddenLayerT , class RngT >

void shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::evaluationType	(	bool	forward,
		bool	evalMean
	)

inline

Sets the type of evaluation, eval will perform.

Eval performs its operation based on the state of this function. There are two ways to pass data through an ConvolutionalRBM: either forward, setting the states of the visible neurons and sample the hidden states or backwards, where the state of the hidden is fixed and the visible are sampled. Instead of the state of the hidden/visible, one often wants the mean of the state \( E_{p(h|v)}\left(h\right)\). By default, the ConvolutionalRBM uses the forward evaluation and returns the mean of the state

Parameters

forward	whether the forward view should be used false=backwards
evalMean	whether the mean state should be returned. false=a sample is returned

Definition at line 248 of file ConvolutionalRBM.h.

◆ filters()

template<class VisibleLayerT , class HiddenLayerT , class RngT >

std::vector<RealMatrix>& shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::filters ( )

inline

Returns the weight matrix connecting the layers.

Definition at line 219 of file ConvolutionalRBM.h.

◆ filterSize1()

template<class VisibleLayerT , class HiddenLayerT , class RngT >

std::size_t shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::filterSize1 ( ) const

inline

Definition at line 195 of file ConvolutionalRBM.h.

Referenced by shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::numberOfParameters().

◆ filterSize2()

template<class VisibleLayerT , class HiddenLayerT , class RngT >

std::size_t shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::filterSize2 ( ) const

inline

Definition at line 198 of file ConvolutionalRBM.h.

Referenced by shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::numberOfParameters().

◆ hiddenNeurons() [1/2]

template<class VisibleLayerT , class HiddenLayerT , class RngT >

HiddenType const& shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::hiddenNeurons ( ) const

inline

Returns the layer of hidden neurons.

Definition at line 176 of file ConvolutionalRBM.h.

◆ hiddenNeurons() [2/2]

template<class VisibleLayerT , class HiddenLayerT , class RngT >

HiddenType& shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::hiddenNeurons ( )

inline

Returns the layer of hidden neurons.

Definition at line 180 of file ConvolutionalRBM.h.

◆ inputHidden()

template<class VisibleLayerT , class HiddenLayerT , class RngT >

void shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::inputHidden	(	RealMatrix &	inputs,
		RealMatrix const &	visibleStates
	)		const

inline

Calculates the input of the hidden neurons given the state of the visible in a batch-vise fassion.

Parameters

inputs	the batch of vectors the input of the hidden neurons is stored in
visibleStates	the batch of states of the visible neurons

Definition at line 287 of file ConvolutionalRBM.h.

References shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::inputSize1(), shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::inputSize2(), shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::numberOfHN(), shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::numberOfVN(), shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::responseSize1(), shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::responseSize2(), remora::row(), SIZE_CHECK, remora::subrange(), remora::sum(), and remora::to_matrix().

◆ inputSize1()

template<class VisibleLayerT , class HiddenLayerT , class RngT >

std::size_t shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::inputSize1 ( ) const

inline

Definition at line 202 of file ConvolutionalRBM.h.

Referenced by shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::inputHidden().

◆ inputSize2()

template<class VisibleLayerT , class HiddenLayerT , class RngT >

std::size_t shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::inputSize2 ( ) const

inline

Definition at line 206 of file ConvolutionalRBM.h.

Referenced by shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::inputHidden().

◆ inputVisible()

template<class VisibleLayerT , class HiddenLayerT , class RngT >

void shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::inputVisible	(	RealMatrix &	inputs,
		RealMatrix const &	hiddenStates
	)		const

inline

Calculates the input of the visible neurons given the state of the hidden.

Parameters

inputs	the vector the input of the visible neurons is stored in
hiddenStates	the state of the hidden neurons

Definition at line 316 of file ConvolutionalRBM.h.

References shark::AbstractModel< RealVector, RealVector >::eval(), remora::noalias(), shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::numberOfHN(), shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::numberOfVN(), shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::responseSize1(), shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::responseSize2(), remora::row(), SIZE_CHECK, remora::subrange(), and remora::to_matrix().

◆ name()

template<class VisibleLayerT , class HiddenLayerT , class RngT >

std::string shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::name ( ) const

inlinevirtual

From INameable: return the class name.

Reimplemented from shark::INameable.

Definition at line 122 of file ConvolutionalRBM.h.

◆ numberOfHN()

template<class VisibleLayerT , class HiddenLayerT , class RngT >

std::size_t shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::numberOfHN ( ) const

inline

Returns the number of hidden Neurons, that is the number of filter responses.

Definition at line 351 of file ConvolutionalRBM.h.

Referenced by shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::inputHidden(), and shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::inputVisible().

◆ numberOfParameters()

template<class VisibleLayerT , class HiddenLayerT , class RngT >

std::size_t shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::numberOfParameters ( ) const

inlinevirtual

Returns the total number of parameters of the model.

Reimplemented from shark::IParameterizable.

Definition at line 126 of file ConvolutionalRBM.h.

References shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::filterSize1(), shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::filterSize2(), shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::numFilters(), and remora::parameters().

Referenced by shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::parameterVector().

◆ numberOfVN()

template<class VisibleLayerT , class HiddenLayerT , class RngT >

std::size_t shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::numberOfVN ( ) const

inline

Returns the number of visible Neurons, which is the number of pixels of the image.

Definition at line 355 of file ConvolutionalRBM.h.

Referenced by shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::inputHidden(), and shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::inputVisible().

◆ numFilters()

template<class VisibleLayerT , class HiddenLayerT , class RngT >

std::size_t shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::numFilters ( ) const

inline

Definition at line 192 of file ConvolutionalRBM.h.

Referenced by shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::numberOfParameters().

◆ parameterVector()

template<class VisibleLayerT , class HiddenLayerT , class RngT >

RealVector shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::parameterVector ( ) const

inlinevirtual

Returns the parameters of the Model as parameter vector.

Reimplemented from shark::IParameterizable.

Definition at line 134 of file ConvolutionalRBM.h.

References remora::init(), remora::matrixSet(), shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::numberOfParameters(), and remora::parameters().

◆ read()

template<class VisibleLayerT , class HiddenLayerT , class RngT >

void shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::read ( InArchive & archive )

inlinevirtual

Reads the network from an archive.

Reimplemented from shark::AbstractModel< RealVector, RealVector >.

Definition at line 360 of file ConvolutionalRBM.h.

◆ responseSize1()

template<class VisibleLayerT , class HiddenLayerT , class RngT >

std::size_t shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::responseSize1 ( ) const

inline

Definition at line 211 of file ConvolutionalRBM.h.

Referenced by shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::inputHidden(), and shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::inputVisible().

◆ responseSize2()

template<class VisibleLayerT , class HiddenLayerT , class RngT >

std::size_t shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::responseSize2 ( ) const

inline

Definition at line 214 of file ConvolutionalRBM.h.

Referenced by shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::inputHidden(), and shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::inputVisible().

◆ rng()

template<class VisibleLayerT , class HiddenLayerT , class RngT >

RngType& shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::rng ( )

inline

Returns the random number generator associated with this ConvolutionalRBM.

Definition at line 233 of file ConvolutionalRBM.h.

◆ setParameterVector()

template<class VisibleLayerT , class HiddenLayerT , class RngT >

void shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::setParameterVector ( const RealVector & newParameters )

inlinevirtual

Sets the parameters of the model.

Parameters

newParameters vector of parameters

Reimplemented from shark::IParameterizable.

Definition at line 143 of file ConvolutionalRBM.h.

References remora::init(), remora::matrixSet(), and remora::parameters().

◆ setStructure()

template<class VisibleLayerT , class HiddenLayerT , class RngT >

void shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::setStructure	(	std::size_t	newInputSize1,
		std::size_t	newInputSize2,
		std::size_t	newNumFilters,
		std::size_t	filterSize
	)

inline

Creates the structure of the ConvolutionalRBM.

Parameters

newInputSize1	width of input image
newInputSize2	height of input image
newNumFilters	number of filters to train
filterSize	size of the sides of the filter

Definition at line 153 of file ConvolutionalRBM.h.

References SIZE_CHECK.

◆ visibleNeurons() [1/2]

template<class VisibleLayerT , class HiddenLayerT , class RngT >

VisibleType& shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::visibleNeurons ( )

inline

Returns the layer of visible neurons.

Definition at line 184 of file ConvolutionalRBM.h.

◆ visibleNeurons() [2/2]

template<class VisibleLayerT , class HiddenLayerT , class RngT >

VisibleType const& shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::visibleNeurons ( ) const

inline

Returns the layer of visible neurons.

Definition at line 188 of file ConvolutionalRBM.h.

◆ weightMatrix()

template<class VisibleLayerT , class HiddenLayerT , class RngT >

std::vector<RealMatrix> const& shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::weightMatrix ( ) const

inline

Returns the weight matrix connecting the layers.

Definition at line 223 of file ConvolutionalRBM.h.

◆ write()

template<class VisibleLayerT , class HiddenLayerT , class RngT >

void shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >::write ( OutArchive & archive ) const

inlinevirtual

Writes the network to an archive.

Reimplemented from shark::AbstractModel< RealVector, RealVector >.

Definition at line 374 of file ConvolutionalRBM.h.

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

include/shark/Unsupervised/RBM/ConvolutionalRBM.h

Public Types

Public Member Functions

Additional Inherited Members

Detailed Description

template<class VisibleLayerT, class HiddenLayerT, class RngT> class shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >

Member Typedef Documentation

◆ BatchInputType

◆ BatchOutputType

◆ EnergyType

◆ GradientType

◆ HiddenType

◆ RngType

◆ VisibleType

Constructor & Destructor Documentation

◆ ConvolutionalRBM()

Member Function Documentation

◆ createState()

◆ energy()

◆ eval() [1/2]

◆ eval() [2/2]

◆ evaluationType()

◆ filters()

◆ filterSize1()

◆ filterSize2()

◆ hiddenNeurons() [1/2]

◆ hiddenNeurons() [2/2]

◆ inputHidden()

◆ inputSize1()

◆ inputSize2()

◆ inputVisible()

◆ name()

◆ numberOfHN()

◆ numberOfParameters()

◆ numberOfVN()

◆ numFilters()

◆ parameterVector()

◆ read()

◆ responseSize1()

◆ responseSize2()

◆ rng()

◆ setParameterVector()

◆ setStructure()

◆ visibleNeurons() [1/2]

◆ visibleNeurons() [2/2]

◆ weightMatrix()

◆ write()

template<class VisibleLayerT, class HiddenLayerT, class RngT>
class shark::ConvolutionalRBM< VisibleLayerT, HiddenLayerT, RngT >