implements the autoencoder More...

#include <shark/Models/Autoencoder.h>

Inheritance diagram for shark::Autoencoder< HiddenNeuron, OutputNeuron >:

Public Member Functions
	Autoencoder ()

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

std::size_t	inputSize () const
	Number of input neurons. More...

std::size_t	outputSize () const
	Number of output neurons. More...

std::size_t	numberOfHiddenNeurons () const
	Total number of hidden neurons. More...

RealVector const &	hiddenBias () const
	Returns the hidden bias weight vector. More...

RealVector &	hiddenBias ()
	Returns the hidden bias weight vector. More...

RealVector const &	outputBias () const
	Returns the output bias weight vector. More...

RealVector &	outputBias ()
	Returns the output bias weight vector. More...

RealMatrix const &	encoderMatrix () const
	Weight matrix for the direction input->hidden. More...

RealMatrix &	encoderMatrix ()
	Weight matrix for the direction input->hidden. More...

RealMatrix const &	decoderMatrix () const
	Weight matrix for the direction hidden->output. More...

RealMatrix &	decoderMatrix ()
	Weight matrix for the direction hidden->output. More...

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

RealVector	parameterVector () const
	returns the vector of used parameters inside the weight matrix More...

void	setParameterVector (RealVector const &newParameters)
	uses the values inside the parametervector to set the used values inside the weight matrix More...

RealMatrix const &	hiddenResponses (State const &state) const
	Returns the output of all neurons after the last call of eval. More...

HiddenNeuron const &	hiddenActivationFunction () const
	Returns the activation function of the hidden units. More...

OutputNeuron const &	outputActivationFunction () const
	Returns the activation function of the output units. More...

HiddenNeuron &	hiddenActivationFunction ()
	Returns the activation function of the hidden units. More...

OutputNeuron &	outputActivationFunction ()
	Returns the activation function of the output units. More...

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

void	evalLayer (std::size_t layer, RealMatrix const &patterns, RealMatrix &outputs) const

Data< RealVector >	evalLayer (std::size_t layer, Data< RealVector > const &patterns) const
	Returns the response of the i-th layer given the input of that layer. More...

Data< RealVector >	encode (Data< RealVector > const &patterns) const

Data< RealVector >	decode (Data< RealVector > const &patterns) const

template<class Label >
LabeledData< RealVector, Label >	encode (LabeledData< RealVector, Label > const &data) const

template<class Label >
LabeledData< RealVector, Label >	decode (LabeledData< RealVector, Label > const &data) const

void	eval (RealMatrix const &patterns, RealMatrix &output, State &state) const

void	weightedParameterDerivative (BatchInputType const &patterns, RealMatrix const &coefficients, State const &state, RealVector &gradient) const

void	weightedInputDerivative (BatchInputType const &patterns, RealMatrix const &coefficients, State const &state, BatchInputType &inputDerivative) const

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...

void	setStructure (std::size_t in, std::size_t hidden)

void	read (InArchive &archive)
	From ISerializable, reads a model from an archive. More...

void	write (OutArchive &archive) const
	From ISerializable, writes a model 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 (BatchInputType const &patterns, BatchOutputType &outputs) const
	Standard interface for evaluating the response of the model to a batch of patterns. More...

virtual void	eval (BatchInputType const &patterns, BatchOutputType &outputs, State &state) const=0
	Standard interface for evaluating the response of the model to a batch of patterns. More...

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...

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
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

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

Detailed Description

template<class HiddenNeuron, class OutputNeuron>
class shark::Autoencoder< HiddenNeuron, OutputNeuron >

implements the autoencoder

The formula is

\[ f(x) = \sigma_2(W^T\sigma_1(Wx+b_1)+b_2)\]

Where \( W, W_2, b_1 \) and \(b_2 \) are the weights and \(\sigma_1\) and \( \sigma_2\) are the activation functions for hidden and output units.

see TiedAutoencoder for the tied weights version where \( W_2=W_1^T \).

Definition at line 46 of file Autoencoder.h.

Constructor & Destructor Documentation

◆ Autoencoder()

template<class HiddenNeuron, class OutputNeuron>

shark::Autoencoder< HiddenNeuron, OutputNeuron >::Autoencoder ( )

inline

Definition at line 55 of file Autoencoder.h.

References shark::AbstractModel< RealVector, RealVector >::HAS_FIRST_INPUT_DERIVATIVE, shark::AbstractModel< RealVector, RealVector >::HAS_FIRST_PARAMETER_DERIVATIVE, and shark::AbstractModel< RealVector, RealVector >::m_features.

Member Function Documentation

◆ createState()

template<class HiddenNeuron, class OutputNeuron>

boost::shared_ptr<State> shark::Autoencoder< HiddenNeuron, OutputNeuron >::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 165 of file Autoencoder.h.

◆ decode() [1/2]

template<class HiddenNeuron, class OutputNeuron>

Data<RealVector> shark::Autoencoder< HiddenNeuron, OutputNeuron >::decode ( Data< RealVector > const & patterns ) const

inline

Definition at line 209 of file Autoencoder.h.

References shark::Autoencoder< HiddenNeuron, OutputNeuron >::evalLayer().

Referenced by shark::Autoencoder< HiddenNeuron, OutputNeuron >::decode().

◆ decode() [2/2]

template<class HiddenNeuron, class OutputNeuron>

template<class Label >

LabeledData<RealVector,Label> shark::Autoencoder< HiddenNeuron, OutputNeuron >::decode ( LabeledData< RealVector, Label > const & data ) const

inline

Definition at line 221 of file Autoencoder.h.

References shark::Autoencoder< HiddenNeuron, OutputNeuron >::decode(), shark::LabeledData< InputT, LabelT >::inputs(), and shark::LabeledData< InputT, LabelT >::labels().

◆ decoderMatrix() [1/2]

template<class HiddenNeuron, class OutputNeuron>

RealMatrix const& shark::Autoencoder< HiddenNeuron, OutputNeuron >::decoderMatrix ( ) const

inline

Weight matrix for the direction hidden->output.

Definition at line 108 of file Autoencoder.h.

Referenced by shark::Autoencoder< HiddenNeuron, OutputNeuron >::evalLayer(), and shark::Autoencoder< HiddenNeuron, OutputNeuron >::write().

◆ decoderMatrix() [2/2]

template<class HiddenNeuron, class OutputNeuron>

RealMatrix& shark::Autoencoder< HiddenNeuron, OutputNeuron >::decoderMatrix ( )

inline

Weight matrix for the direction hidden->output.

Definition at line 112 of file Autoencoder.h.

◆ encode() [1/2]

template<class HiddenNeuron, class OutputNeuron>

Data<RealVector> shark::Autoencoder< HiddenNeuron, OutputNeuron >::encode ( Data< RealVector > const & patterns ) const

inline

Definition at line 205 of file Autoencoder.h.

References shark::Autoencoder< HiddenNeuron, OutputNeuron >::evalLayer().

Referenced by shark::Autoencoder< HiddenNeuron, OutputNeuron >::encode().

◆ encode() [2/2]

template<class HiddenNeuron, class OutputNeuron>

template<class Label >

LabeledData<RealVector,Label> shark::Autoencoder< HiddenNeuron, OutputNeuron >::encode ( LabeledData< RealVector, Label > const & data ) const

inline

Definition at line 214 of file Autoencoder.h.

References shark::Autoencoder< HiddenNeuron, OutputNeuron >::encode(), shark::LabeledData< InputT, LabelT >::inputs(), and shark::LabeledData< InputT, LabelT >::labels().

◆ encoderMatrix() [1/2]

template<class HiddenNeuron, class OutputNeuron>

RealMatrix const& shark::Autoencoder< HiddenNeuron, OutputNeuron >::encoderMatrix ( ) const

inline

Weight matrix for the direction input->hidden.

Definition at line 99 of file Autoencoder.h.

Referenced by shark::Autoencoder< HiddenNeuron, OutputNeuron >::evalLayer(), main(), shark::Autoencoder< HiddenNeuron, OutputNeuron >::numberOfHiddenNeurons(), unsupervisedPreTraining(), and shark::Autoencoder< HiddenNeuron, OutputNeuron >::write().

◆ encoderMatrix() [2/2]

template<class HiddenNeuron, class OutputNeuron>

RealMatrix& shark::Autoencoder< HiddenNeuron, OutputNeuron >::encoderMatrix ( )

inline

Weight matrix for the direction input->hidden.

Definition at line 103 of file Autoencoder.h.

◆ eval()

template<class HiddenNeuron, class OutputNeuron>

void shark::Autoencoder< HiddenNeuron, OutputNeuron >::eval	(	RealMatrix const &	patterns,
		RealMatrix &	output,
		State &	state
	)		const

inline

Definition at line 228 of file Autoencoder.h.

References shark::Autoencoder< HiddenNeuron, OutputNeuron >::evalLayer(), and shark::State::toState().

◆ evalLayer() [1/2]

template<class HiddenNeuron, class OutputNeuron>

void shark::Autoencoder< HiddenNeuron, OutputNeuron >::evalLayer	(	std::size_t	layer,
		RealMatrix const &	patterns,
		RealMatrix &	outputs
	)		const

inline

Definition at line 169 of file Autoencoder.h.

References shark::Autoencoder< HiddenNeuron, OutputNeuron >::decoderMatrix(), shark::Autoencoder< HiddenNeuron, OutputNeuron >::encoderMatrix(), shark::Autoencoder< HiddenNeuron, OutputNeuron >::hiddenBias(), remora::noalias(), shark::Autoencoder< HiddenNeuron, OutputNeuron >::outputBias(), remora::prod(), remora::repeat(), SIZE_CHECK, and remora::trans().

Referenced by shark::Autoencoder< HiddenNeuron, OutputNeuron >::decode(), shark::Autoencoder< HiddenNeuron, OutputNeuron >::encode(), shark::Autoencoder< HiddenNeuron, OutputNeuron >::eval(), shark::Autoencoder< HiddenNeuron, OutputNeuron >::evalLayer(), and unsupervisedPreTraining().

◆ evalLayer() [2/2]

template<class HiddenNeuron, class OutputNeuron>

Data<RealVector> shark::Autoencoder< HiddenNeuron, OutputNeuron >::evalLayer	(	std::size_t	layer,
		Data< RealVector > const &	patterns
	)		const

inline

Returns the response of the i-th layer given the input of that layer.

this is usefull if only a portion of the network needs to be evaluated be aware that this only works without shortcuts in the network

Definition at line 195 of file Autoencoder.h.

References shark::Data< Type >::batch(), shark::Autoencoder< HiddenNeuron, OutputNeuron >::evalLayer(), shark::Data< Type >::numberOfBatches(), SHARK_PARALLEL_FOR, and SIZE_CHECK.

◆ hiddenActivationFunction() [1/2]

template<class HiddenNeuron, class OutputNeuron>

HiddenNeuron const& shark::Autoencoder< HiddenNeuron, OutputNeuron >::hiddenActivationFunction ( ) const

inline

Returns the activation function of the hidden units.

Definition at line 148 of file Autoencoder.h.

◆ hiddenActivationFunction() [2/2]

template<class HiddenNeuron, class OutputNeuron>

HiddenNeuron& shark::Autoencoder< HiddenNeuron, OutputNeuron >::hiddenActivationFunction ( )

inline

Returns the activation function of the hidden units.

Definition at line 157 of file Autoencoder.h.

◆ hiddenBias() [1/2]

template<class HiddenNeuron, class OutputNeuron>

RealVector const& shark::Autoencoder< HiddenNeuron, OutputNeuron >::hiddenBias ( ) const

inline

Returns the hidden bias weight vector.

Definition at line 80 of file Autoencoder.h.

Referenced by shark::Autoencoder< HiddenNeuron, OutputNeuron >::evalLayer(), and unsupervisedPreTraining().

◆ hiddenBias() [2/2]

template<class HiddenNeuron, class OutputNeuron>

RealVector& shark::Autoencoder< HiddenNeuron, OutputNeuron >::hiddenBias ( )

inline

Returns the hidden bias weight vector.

Definition at line 85 of file Autoencoder.h.

◆ hiddenResponses()

template<class HiddenNeuron, class OutputNeuron>

RealMatrix const& shark::Autoencoder< HiddenNeuron, OutputNeuron >::hiddenResponses ( State const & state ) const

inline

Returns the output of all neurons after the last call of eval.

Parameters

state last result of eval

Returns: Output value of the neurons.

Definition at line 142 of file Autoencoder.h.

References shark::State::toState().

◆ inputSize()

template<class HiddenNeuron, class OutputNeuron>

std::size_t shark::Autoencoder< HiddenNeuron, OutputNeuron >::inputSize ( ) const

inline

Number of input neurons.

Definition at line 66 of file Autoencoder.h.

References shark::Autoencoder< HiddenNeuron, OutputNeuron >::outputSize().

Referenced by initializeFFNet(), main(), shark::Autoencoder< HiddenNeuron, OutputNeuron >::numberOfParameters(), and shark::Autoencoder< HiddenNeuron, OutputNeuron >::write().

◆ name()

template<class HiddenNeuron, class OutputNeuron>

std::string shark::Autoencoder< HiddenNeuron, OutputNeuron >::name ( ) const

inlinevirtual

From INameable: return the class name.

Reimplemented from shark::INameable.

Definition at line 61 of file Autoencoder.h.

Referenced by trainAutoencoderModel().

◆ numberOfHiddenNeurons()

template<class HiddenNeuron, class OutputNeuron>

std::size_t shark::Autoencoder< HiddenNeuron, OutputNeuron >::numberOfHiddenNeurons ( ) const

inline

Total number of hidden neurons.

Definition at line 75 of file Autoencoder.h.

References shark::Autoencoder< HiddenNeuron, OutputNeuron >::encoderMatrix().

Referenced by initializeFFNet(), main(), shark::Autoencoder< HiddenNeuron, OutputNeuron >::numberOfParameters(), and shark::Autoencoder< HiddenNeuron, OutputNeuron >::write().

◆ numberOfParameters()

template<class HiddenNeuron, class OutputNeuron>

std::size_t shark::Autoencoder< HiddenNeuron, OutputNeuron >::numberOfParameters ( ) const

inlinevirtual

Returns the total number of parameters of the network.

Reimplemented from shark::IParameterizable.

Definition at line 117 of file Autoencoder.h.

References shark::Autoencoder< HiddenNeuron, OutputNeuron >::inputSize(), and shark::Autoencoder< HiddenNeuron, OutputNeuron >::numberOfHiddenNeurons().

Referenced by initializeFFNet(), main(), shark::Autoencoder< HiddenNeuron, OutputNeuron >::setParameterVector(), and shark::Autoencoder< HiddenNeuron, OutputNeuron >::write().

◆ outputActivationFunction() [1/2]

template<class HiddenNeuron, class OutputNeuron>

OutputNeuron const& shark::Autoencoder< HiddenNeuron, OutputNeuron >::outputActivationFunction ( ) const

inline

Returns the activation function of the output units.

Definition at line 152 of file Autoencoder.h.

◆ outputActivationFunction() [2/2]

template<class HiddenNeuron, class OutputNeuron>

OutputNeuron& shark::Autoencoder< HiddenNeuron, OutputNeuron >::outputActivationFunction ( )

inline

Returns the activation function of the output units.

Definition at line 161 of file Autoencoder.h.

◆ outputBias() [1/2]

template<class HiddenNeuron, class OutputNeuron>

RealVector const& shark::Autoencoder< HiddenNeuron, OutputNeuron >::outputBias ( ) const

inline

Returns the output bias weight vector.

Definition at line 90 of file Autoencoder.h.

Referenced by shark::Autoencoder< HiddenNeuron, OutputNeuron >::evalLayer(), and shark::Autoencoder< HiddenNeuron, OutputNeuron >::outputSize().

◆ outputBias() [2/2]

template<class HiddenNeuron, class OutputNeuron>

RealVector& shark::Autoencoder< HiddenNeuron, OutputNeuron >::outputBias ( )

inline

Returns the output bias weight vector.

Definition at line 94 of file Autoencoder.h.

◆ outputSize()

template<class HiddenNeuron, class OutputNeuron>

std::size_t shark::Autoencoder< HiddenNeuron, OutputNeuron >::outputSize ( ) const

inline

Number of output neurons.

Definition at line 70 of file Autoencoder.h.

References shark::Autoencoder< HiddenNeuron, OutputNeuron >::outputBias().

Referenced by initializeFFNet(), shark::Autoencoder< HiddenNeuron, OutputNeuron >::inputSize(), main(), shark::Autoencoder< HiddenNeuron, OutputNeuron >::weightedDerivatives(), shark::Autoencoder< HiddenNeuron, OutputNeuron >::weightedInputDerivative(), shark::Autoencoder< HiddenNeuron, OutputNeuron >::weightedParameterDerivative(), and shark::Autoencoder< HiddenNeuron, OutputNeuron >::write().

◆ parameterVector()

template<class HiddenNeuron, class OutputNeuron>

RealVector shark::Autoencoder< HiddenNeuron, OutputNeuron >::parameterVector ( ) const

inlinevirtual

returns the vector of used parameters inside the weight matrix

Reimplemented from shark::IParameterizable.

Definition at line 122 of file Autoencoder.h.

References remora::to_vector().

◆ read()

template<class HiddenNeuron, class OutputNeuron>

void shark::Autoencoder< HiddenNeuron, OutputNeuron >::read ( InArchive & archive )

inlinevirtual

From ISerializable, reads a model from an archive.

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

Definition at line 285 of file Autoencoder.h.

◆ setParameterVector()

template<class HiddenNeuron, class OutputNeuron>

void shark::Autoencoder< HiddenNeuron, OutputNeuron >::setParameterVector ( RealVector const & newParameters )

inlinevirtual

uses the values inside the parametervector to set the used values inside the weight matrix

Reimplemented from shark::IParameterizable.

Definition at line 126 of file Autoencoder.h.

References remora::noalias(), shark::Autoencoder< HiddenNeuron, OutputNeuron >::numberOfParameters(), SIZE_CHECK, remora::subrange(), and remora::to_vector().

Referenced by initializeFFNet(), and trainAutoencoderModel().

◆ setStructure()

template<class HiddenNeuron, class OutputNeuron>

void shark::Autoencoder< HiddenNeuron, OutputNeuron >::setStructure	(	std::size_t	in,
		std::size_t	hidden
	)

inline

Definition at line 275 of file Autoencoder.h.

Referenced by main(), and trainAutoencoderModel().

◆ weightedDerivatives()

template<class HiddenNeuron, class OutputNeuron>

virtual void shark::Autoencoder< HiddenNeuron, OutputNeuron >::weightedDerivatives	(	BatchInputType const &	patterns,
		BatchOutputType const &	coefficients,
		State const &	state,
		RealVector &	parameterDerivative,
		BatchInputType &	inputDerivative
	)		const

inlinevirtual

calculates weighted input and parameter derivative at the same time

Sometimes, both derivatives are needed at the same time. But sometimes, when calculating the weighted parameter derivative, the input derivative can be calculated for free. This is for example true for the feed-forward neural networks. However, there exists the obvious default implementation to just calculate the derivatives one after another.

Parameters

patterns	the patterns to evaluate
coefficients	the coefficients which are used to calculate the weighted sum
state	intermediate results stored by eval to sped up calculations of the derivatives
parameterDerivative	the calculated parameter derivative as sum over all derivates of all patterns
inputDerivative	the calculated derivative for every pattern

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

Definition at line 259 of file Autoencoder.h.

References shark::Autoencoder< HiddenNeuron, OutputNeuron >::outputSize(), and SIZE_CHECK.

◆ weightedInputDerivative()

template<class HiddenNeuron, class OutputNeuron>

void shark::Autoencoder< HiddenNeuron, OutputNeuron >::weightedInputDerivative	(	BatchInputType const &	patterns,
		RealMatrix const &	coefficients,
		State const &	state,
		BatchInputType &	inputDerivative
	)		const

inline

Definition at line 248 of file Autoencoder.h.

References shark::Autoencoder< HiddenNeuron, OutputNeuron >::outputSize(), and SIZE_CHECK.

◆ weightedParameterDerivative()

template<class HiddenNeuron, class OutputNeuron>

void shark::Autoencoder< HiddenNeuron, OutputNeuron >::weightedParameterDerivative	(	BatchInputType const &	patterns,
		RealMatrix const &	coefficients,
		State const &	state,
		RealVector &	gradient
	)		const

inline

Definition at line 236 of file Autoencoder.h.

References shark::Autoencoder< HiddenNeuron, OutputNeuron >::outputSize(), and SIZE_CHECK.

◆ write()

template<class HiddenNeuron, class OutputNeuron>

void shark::Autoencoder< HiddenNeuron, OutputNeuron >::write ( OutArchive & archive ) const

inlinevirtual

From ISerializable, writes a model to an archive.

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

Definition at line 293 of file Autoencoder.h.

References shark::Autoencoder< HiddenNeuron, OutputNeuron >::decoderMatrix(), shark::Autoencoder< HiddenNeuron, OutputNeuron >::encoderMatrix(), shark::Autoencoder< HiddenNeuron, OutputNeuron >::inputSize(), remora::noalias(), shark::Autoencoder< HiddenNeuron, OutputNeuron >::numberOfHiddenNeurons(), shark::Autoencoder< HiddenNeuron, OutputNeuron >::numberOfParameters(), shark::Autoencoder< HiddenNeuron, OutputNeuron >::outputSize(), remora::prod(), remora::subrange(), remora::bindings::sum_rows(), remora::to_matrix(), shark::State::toState(), and remora::trans().

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

include/shark/Models/Autoencoder.h

Public Member Functions

Additional Inherited Members

Detailed Description

template<class HiddenNeuron, class OutputNeuron> class shark::Autoencoder< HiddenNeuron, OutputNeuron >

Constructor & Destructor Documentation

◆ Autoencoder()

Member Function Documentation

◆ createState()

◆ decode() [1/2]

◆ decode() [2/2]

◆ decoderMatrix() [1/2]

◆ decoderMatrix() [2/2]

◆ encode() [1/2]

◆ encode() [2/2]

◆ encoderMatrix() [1/2]

◆ encoderMatrix() [2/2]

◆ eval()

◆ evalLayer() [1/2]

◆ evalLayer() [2/2]

◆ hiddenActivationFunction() [1/2]

◆ hiddenActivationFunction() [2/2]

◆ hiddenBias() [1/2]

◆ hiddenBias() [2/2]

◆ hiddenResponses()

◆ inputSize()

◆ name()

◆ numberOfHiddenNeurons()

◆ numberOfParameters()

◆ outputActivationFunction() [1/2]

◆ outputActivationFunction() [2/2]

◆ outputBias() [1/2]

◆ outputBias() [2/2]

◆ outputSize()

◆ parameterVector()

◆ read()

◆ setParameterVector()

◆ setStructure()

◆ weightedDerivatives()

◆ weightedInputDerivative()

◆ weightedParameterDerivative()

◆ write()

template<class HiddenNeuron, class OutputNeuron>
class shark::Autoencoder< HiddenNeuron, OutputNeuron >