template<typename Architecture_t>
class TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >

Definition at line 55 of file RNNLayer.h.

Public Types
using	Matrix_t = typename Architecture_t::Matrix_t

using	Scalar_t = typename Architecture_t::Scalar_t

using	Tensor_t = std::vector< Matrix_t >

Public Member Functions
	TBasicRNNLayer (const TBasicRNNLayer &)
	Copy Constructor. More...

	TBasicRNNLayer (size_t batchSize, size_t stateSize, size_t inputSize, size_t timeSteps, bool rememberState=false, DNN::EActivationFunction f=DNN::EActivationFunction::kTanh, bool training=true, DNN::EInitialization fA=DNN::EInitialization::kZero)
	Constructor. More...

virtual void	AddWeightsXMLTo (void *parent)
	Writes the information and the weights about the layer in an XML node. More...

void	Backward (Tensor_t &gradients_backward, const Tensor_t &activations_backward, std::vector< Matrix_t > &inp1, std::vector< Matrix_t > &inp2)
	Backpropagates the error. More...

Matrix_t &	CellBackward (Matrix_t &state_gradients_backward, const Matrix_t &precStateActivations, const Matrix_t &input, Matrix_t &input_gradient, Matrix_t &dF)
	Backward for a single time unit a the corresponding call to Forward(...). More...

void	CellForward (const Matrix_t &input, Matrix_t &dF)
	Forward for a single cell (time unit) More...

void	Forward (Tensor_t &input, bool isTraining=true)
	Compute and return the next state with given input matrix. More...

DNN::EActivationFunction	GetActivationFunction () const

Matrix_t &	GetBiasesState ()

const Matrix_t &	GetBiasesState () const

Matrix_t &	GetBiasStateGradients ()

const Matrix_t &	GetBiasStateGradients () const

std::vector< Matrix_t > &	GetDerivatives ()

const std::vector< Matrix_t > &	GetDerivatives () const

Matrix_t &	GetDerivativesAt (size_t i)

const Matrix_t &	GetDerivativesAt (size_t i) const

size_t	GetInputSize () const

Matrix_t &	GetState ()

const Matrix_t &	GetState () const

size_t	GetStateSize () const

size_t	GetTimeSteps () const
	Getters. More...

Matrix_t &	GetWeightInputGradients ()

const Matrix_t &	GetWeightInputGradients () const

Matrix_t &	GetWeightsInput ()

const Matrix_t &	GetWeightsInput () const

Matrix_t &	GetWeightsState ()

const Matrix_t &	GetWeightsState () const

Matrix_t &	GetWeightStateGradients ()

const Matrix_t &	GetWeightStateGradients () const

void	InitState (DNN::EInitialization m=DNN::EInitialization::kZero)
	Initialize the weights according to the given initialization method. More...

bool	IsRememberState () const

void	Print () const
	Prints the info about the layer. More...

virtual void	ReadWeightsFromXML (void *parent)
	Read the information and the weights about the layer from XML node. More...

void	Update (const Scalar_t learningRate)

Public Member Functions inherited from TMVA::DNN::VGeneralLayer< Architecture_t >
	VGeneralLayer (const VGeneralLayer &)
	Copy Constructor. More...

	VGeneralLayer (size_t BatchSize, size_t InputDepth, size_t InputHeight, size_t InputWidth, size_t Depth, size_t Height, size_t Width, size_t WeightsNSlices, size_t WeightsNRows, size_t WeightsNCols, size_t BiasesNSlices, size_t BiasesNRows, size_t BiasesNCols, size_t OutputNSlices, size_t OutputNRows, size_t OutputNCols, EInitialization Init)
	Constructor. More...

	VGeneralLayer (size_t BatchSize, size_t InputDepth, size_t InputHeight, size_t InputWidth, size_t Depth, size_t Height, size_t Width, size_t WeightsNSlices, std::vector< size_t > WeightsNRows, std::vector< size_t > WeightsNCols, size_t BiasesNSlices, std::vector< size_t > BiasesNRows, std::vector< size_t > BiasesNCols, size_t OutputNSlices, size_t OutputNRows, size_t OutputNCols, EInitialization Init)
	General Constructor with different weights dimension. More...

	VGeneralLayer (VGeneralLayer< Architecture_t > *layer)
	Copy the layer provided as a pointer. More...

virtual	~VGeneralLayer ()
	Virtual Destructor. More...

virtual void	AddWeightsXMLTo (void *parent)=0
	Writes the information and the weights about the layer in an XML node. More...

virtual void	Backward (std::vector< Matrix_t > &gradients_backward, const std::vector< Matrix_t > &activations_backward, std::vector< Matrix_t > &inp1, std::vector< Matrix_t > &inp2)=0
	Backpropagates the error. More...

void	CopyBiases (const std::vector< Matrix_t > &otherBiases)
	Copies the biases provided as an input. More...

void	CopyWeights (const std::vector< Matrix_t > &otherWeights)
	Copies the weights provided as an input. More...

virtual void	Forward (std::vector< Matrix_t > &input, bool applyDropout=false)=0
	Computes activation of the layer for the given input. More...

std::vector< Matrix_t > &	GetActivationGradients ()

const std::vector< Matrix_t > &	GetActivationGradients () const

Matrix_t &	GetActivationGradientsAt (size_t i)

const Matrix_t &	GetActivationGradientsAt (size_t i) const

size_t	GetBatchSize () const
	Getters. More...

std::vector< Matrix_t > &	GetBiases ()

const std::vector< Matrix_t > &	GetBiases () const

Matrix_t &	GetBiasesAt (size_t i)

const Matrix_t &	GetBiasesAt (size_t i) const

std::vector< Matrix_t > &	GetBiasGradients ()

const std::vector< Matrix_t > &	GetBiasGradients () const

Matrix_t &	GetBiasGradientsAt (size_t i)

const Matrix_t &	GetBiasGradientsAt (size_t i) const

size_t	GetDepth () const

size_t	GetHeight () const

EInitialization	GetInitialization () const

size_t	GetInputDepth () const

size_t	GetInputHeight () const

size_t	GetInputWidth () const

std::vector< Matrix_t > &	GetOutput ()

const std::vector< Matrix_t > &	GetOutput () const

Matrix_t &	GetOutputAt (size_t i)

const Matrix_t &	GetOutputAt (size_t i) const

std::vector< Matrix_t > &	GetWeightGradients ()

const std::vector< Matrix_t > &	GetWeightGradients () const

Matrix_t &	GetWeightGradientsAt (size_t i)

const Matrix_t &	GetWeightGradientsAt (size_t i) const

std::vector< Matrix_t > &	GetWeights ()

const std::vector< Matrix_t > &	GetWeights () const

Matrix_t &	GetWeightsAt (size_t i)

const Matrix_t &	GetWeightsAt (size_t i) const

size_t	GetWidth () const

void	Initialize ()
	Initialize the weights and biases according to the given initialization method. More...

bool	IsTraining () const

virtual void	Print () const =0
	Prints the info about the layer. More...

void	ReadMatrixXML (void node, const char name, Matrix_t &matrix)

virtual void	ReadWeightsFromXML (void *parent)=0
	Read the information and the weights about the layer from XML node. More...

void	SetBatchSize (size_t batchSize)
	Setters. More...

void	SetDepth (size_t depth)

virtual void	SetDropoutProbability (Scalar_t)
	Set Dropout probability. More...

void	SetHeight (size_t height)

void	SetInputDepth (size_t inputDepth)

void	SetInputHeight (size_t inputHeight)

void	SetInputWidth (size_t inputWidth)

void	SetIsTraining (bool isTraining)

void	SetWidth (size_t width)

void	Update (const Scalar_t learningRate)
	Updates the weights and biases, given the learning rate. More...

void	UpdateBiases (const std::vector< Matrix_t > &biasGradients, const Scalar_t learningRate)
	Updates the biases, given the gradients and the learning rate. More...

void	UpdateBiasGradients (const std::vector< Matrix_t > &biasGradients, const Scalar_t learningRate)
	Updates the bias gradients, given some other weight gradients and learning rate. More...

void	UpdateWeightGradients (const std::vector< Matrix_t > &weightGradients, const Scalar_t learningRate)
	Updates the weight gradients, given some other weight gradients and learning rate. More...

void	UpdateWeights (const std::vector< Matrix_t > &weightGradients, const Scalar_t learningRate)
	Updates the weights, given the gradients and the learning rate,. More...

void	WriteMatrixToXML (void node, const char name, const Matrix_t &matrix)

void	WriteTensorToXML (void node, const char name, const std::vector< Matrix_t > &tensor)
	helper functions for XML More...

Private Attributes
Matrix_t &	fBiases
	Biases. More...

Matrix_t &	fBiasGradients
	Gradients w.r.t. the bias values. More...

std::vector< Matrix_t >	fDerivatives
	First fDerivatives of the activations. More...

DNN::EActivationFunction	fF
	Activation function of the hidden state. More...

bool	fRememberState
	Remember state in next pass. More...

Matrix_t	fState
	Hidden State. More...

size_t	fStateSize
	Hidden state size of RNN. More...

size_t	fTimeSteps
	Timesteps for RNN. More...

Matrix_t &	fWeightInputGradients
	Gradients w.r.t. the input weights. More...

Matrix_t &	fWeightsInput
	Input weights, fWeights[0]. More...

Matrix_t &	fWeightsState
	Prev state weights, fWeights[1]. More...

Matrix_t &	fWeightStateGradients
	Gradients w.r.t. the recurring weights. More...

Additional Inherited Members
Protected Attributes inherited from TMVA::DNN::VGeneralLayer< Architecture_t >
std::vector< Matrix_t >	fActivationGradients
	Gradients w.r.t. the activations of this layer. More...

size_t	fBatchSize
	Batch size used for training and evaluation. More...

std::vector< Matrix_t >	fBiases
	The biases associated to the layer. More...

std::vector< Matrix_t >	fBiasGradients
	Gradients w.r.t. the bias values of the layer. More...

size_t	fDepth
	The depth of the layer. More...

size_t	fHeight
	The height of the layer. More...

EInitialization	fInit
	The initialization method. More...

size_t	fInputDepth
	The depth of the previous layer or input. More...

size_t	fInputHeight
	The height of the previous layer or input. More...

size_t	fInputWidth
	The width of the previous layer or input. More...

bool	fIsTraining
	Flag indicatig the mode. More...

std::vector< Matrix_t >	fOutput
	Activations of this layer. More...

std::vector< Matrix_t >	fWeightGradients
	Gradients w.r.t. the weights of the layer. More...

std::vector< Matrix_t >	fWeights
	The weights associated to the layer. More...

size_t	fWidth
	The width of this layer. More...

#include <TMVA/DNN/RNN/RNNLayer.h>

Inheritance diagram for TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >:

[legend]

Member Typedef Documentation

◆ Matrix_t

template<typename Architecture_t >

using TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::Matrix_t = typename Architecture_t::Matrix_t

Definition at line 60 of file RNNLayer.h.

◆ Scalar_t

template<typename Architecture_t >

using TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::Scalar_t = typename Architecture_t::Scalar_t

Definition at line 61 of file RNNLayer.h.

◆ Tensor_t

template<typename Architecture_t >

using TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::Tensor_t = std::vector<Matrix_t>

Definition at line 62 of file RNNLayer.h.

Constructor & Destructor Documentation

◆ TBasicRNNLayer() [1/2]

template<typename Architecture_t >

TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::TBasicRNNLayer	(	size_t	batchSize,
		size_t	stateSize,
		size_t	inputSize,
		size_t	timeSteps,
		bool	rememberState = `false`,
		DNN::EActivationFunction	f = `DNN::EActivationFunction::kTanh`,
		bool	training = `true`,
		DNN::EInitialization	fA = `DNN::EInitialization::kZero`
	)

Constructor.

Definition at line 165 of file RNNLayer.h.

◆ TBasicRNNLayer() [2/2]

template<typename Architecture_t >

TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::TBasicRNNLayer ( const TBasicRNNLayer< Architecture_t > & layer )

Copy Constructor.

Definition at line 192 of file RNNLayer.h.

Member Function Documentation

◆ AddWeightsXMLTo()

template<typename Architecture_t >

void TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::AddWeightsXMLTo ( void * parent )

virtual

Writes the information and the weights about the layer in an XML node.

Implements TMVA::DNN::VGeneralLayer< Architecture_t >.

Definition at line 372 of file RNNLayer.h.

◆ Backward()

template<typename Architecture_t >

auto TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::Backward	(	Tensor_t &	gradients_backward,
		const Tensor_t &	activations_backward,
		std::vector< Matrix_t > &	inp1,
		std::vector< Matrix_t > &	inp2
	)

inline

Backpropagates the error.

Must only be called directly at the corresponding call to Forward(...).

Definition at line 294 of file RNNLayer.h.

◆ CellBackward()

template<typename Architecture_t >

auto TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::CellBackward	(	Matrix_t &	state_gradients_backward,
		const Matrix_t &	precStateActivations,
		const Matrix_t &	input,
		Matrix_t &	input_gradient,
		Matrix_t &	dF
	)

inline

Backward for a single time unit a the corresponding call to Forward(...).

Definition at line 360 of file RNNLayer.h.

◆ CellForward()

template<typename Architecture_t >

auto TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::CellForward	(	const Matrix_t &	input,
		Matrix_t &	dF
	)

inline

Forward for a single cell (time unit)

Definition at line 278 of file RNNLayer.h.

◆ Forward()

template<typename Architecture_t >

auto TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::Forward	(	Tensor_t &	input,
		bool	isTraining = `true`
	)

inline

Compute and return the next state with given input matrix.

Definition at line 254 of file RNNLayer.h.

◆ GetActivationFunction()

template<typename Architecture_t >

DNN::EActivationFunction TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::GetActivationFunction ( ) const

inline

Definition at line 139 of file RNNLayer.h.

◆ GetBiasesState() [1/2]

template<typename Architecture_t >

Matrix_t & TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::GetBiasesState ( )

inline

Definition at line 150 of file RNNLayer.h.

◆ GetBiasesState() [2/2]

template<typename Architecture_t >

const Matrix_t & TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::GetBiasesState ( ) const

inline

Definition at line 151 of file RNNLayer.h.

◆ GetBiasStateGradients() [1/2]

template<typename Architecture_t >

Matrix_t & TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::GetBiasStateGradients ( )

inline

Definition at line 152 of file RNNLayer.h.

◆ GetBiasStateGradients() [2/2]

template<typename Architecture_t >

const Matrix_t & TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::GetBiasStateGradients ( ) const

inline

Definition at line 153 of file RNNLayer.h.

◆ GetDerivatives() [1/2]

template<typename Architecture_t >

std::vector< Matrix_t > & TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::GetDerivatives ( )

inline

Definition at line 146 of file RNNLayer.h.

◆ GetDerivatives() [2/2]

template<typename Architecture_t >

const std::vector< Matrix_t > & TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::GetDerivatives ( ) const

inline

Definition at line 147 of file RNNLayer.h.

◆ GetDerivativesAt() [1/2]

template<typename Architecture_t >

Matrix_t & TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::GetDerivativesAt ( size_t i )

inline

Definition at line 148 of file RNNLayer.h.

◆ GetDerivativesAt() [2/2]

template<typename Architecture_t >

const Matrix_t & TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::GetDerivativesAt ( size_t i ) const

inline

Definition at line 149 of file RNNLayer.h.

◆ GetInputSize()

template<typename Architecture_t >

size_t TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::GetInputSize ( ) const

inline

Definition at line 137 of file RNNLayer.h.

◆ GetState() [1/2]

template<typename Architecture_t >

Matrix_t & TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::GetState ( )

inline

Definition at line 140 of file RNNLayer.h.

◆ GetState() [2/2]

template<typename Architecture_t >

const Matrix_t & TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::GetState ( ) const

inline

Definition at line 141 of file RNNLayer.h.

◆ GetStateSize()

template<typename Architecture_t >

size_t TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::GetStateSize ( ) const

inline

Definition at line 136 of file RNNLayer.h.

◆ GetTimeSteps()

template<typename Architecture_t >

size_t TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::GetTimeSteps ( ) const

inline

Getters.

Definition at line 135 of file RNNLayer.h.

◆ GetWeightInputGradients() [1/2]

template<typename Architecture_t >

Matrix_t & TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::GetWeightInputGradients ( )

inline

Definition at line 154 of file RNNLayer.h.

◆ GetWeightInputGradients() [2/2]

template<typename Architecture_t >

const Matrix_t & TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::GetWeightInputGradients ( ) const

inline

Definition at line 155 of file RNNLayer.h.

◆ GetWeightsInput() [1/2]

template<typename Architecture_t >

Matrix_t & TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::GetWeightsInput ( )

inline

Definition at line 142 of file RNNLayer.h.

◆ GetWeightsInput() [2/2]

template<typename Architecture_t >

const Matrix_t & TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::GetWeightsInput ( ) const

inline

Definition at line 143 of file RNNLayer.h.

◆ GetWeightsState() [1/2]

template<typename Architecture_t >

Matrix_t & TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::GetWeightsState ( )

inline

Definition at line 144 of file RNNLayer.h.

◆ GetWeightsState() [2/2]

template<typename Architecture_t >

const Matrix_t & TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::GetWeightsState ( ) const

inline

Definition at line 145 of file RNNLayer.h.

◆ GetWeightStateGradients() [1/2]

template<typename Architecture_t >

Matrix_t & TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::GetWeightStateGradients ( )

inline

Definition at line 156 of file RNNLayer.h.

◆ GetWeightStateGradients() [2/2]

template<typename Architecture_t >

const Matrix_t & TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::GetWeightStateGradients ( ) const

inline

Definition at line 157 of file RNNLayer.h.

◆ InitState()

template<typename Architecture_t >

auto TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::InitState ( DNN::EInitialization m = DNN::EInitialization::kZero )

Initialize the weights according to the given initialization method.

Initialize the state method.

Definition at line 220 of file RNNLayer.h.

◆ IsRememberState()

template<typename Architecture_t >

bool TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::IsRememberState ( ) const

inline

Definition at line 138 of file RNNLayer.h.

◆ Print()

template<typename Architecture_t >

auto TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::Print

virtual

Prints the info about the layer.

Implements TMVA::DNN::VGeneralLayer< Architecture_t >.

Definition at line 227 of file RNNLayer.h.

◆ ReadWeightsFromXML()

template<typename Architecture_t >

void TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::ReadWeightsFromXML ( void * parent )

virtual

Read the information and the weights about the layer from XML node.

Implements TMVA::DNN::VGeneralLayer< Architecture_t >.

Definition at line 392 of file RNNLayer.h.

◆ Update()

template<typename Architecture_t >

void TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::Update ( const Scalar_t learningRate )

Member Data Documentation

◆ fBiases

template<typename Architecture_t >

Matrix_t& TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::fBiases

private

Biases.

Definition at line 75 of file RNNLayer.h.

◆ fBiasGradients

template<typename Architecture_t >

Matrix_t& TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::fBiasGradients

private

Gradients w.r.t. the bias values.

Definition at line 80 of file RNNLayer.h.

◆ fDerivatives

template<typename Architecture_t >

std::vector<Matrix_t> TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::fDerivatives

private

First fDerivatives of the activations.

Definition at line 77 of file RNNLayer.h.

◆ fF

template<typename Architecture_t >

DNN::EActivationFunction TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::fF

private

Activation function of the hidden state.

Definition at line 70 of file RNNLayer.h.

◆ fRememberState

template<typename Architecture_t >

bool TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::fRememberState

private

Remember state in next pass.

Definition at line 68 of file RNNLayer.h.

◆ fState

template<typename Architecture_t >

Matrix_t TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::fState

private

Hidden State.

Definition at line 72 of file RNNLayer.h.

◆ fStateSize

template<typename Architecture_t >

size_t TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::fStateSize

private

Hidden state size of RNN.

Definition at line 67 of file RNNLayer.h.

◆ fTimeSteps

template<typename Architecture_t >

size_t TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::fTimeSteps

private

Timesteps for RNN.

Definition at line 66 of file RNNLayer.h.

◆ fWeightInputGradients

template<typename Architecture_t >

Matrix_t& TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::fWeightInputGradients

private

Gradients w.r.t. the input weights.

Definition at line 78 of file RNNLayer.h.

◆ fWeightsInput

template<typename Architecture_t >

Matrix_t& TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::fWeightsInput

private

Input weights, fWeights[0].

Definition at line 73 of file RNNLayer.h.

◆ fWeightsState

template<typename Architecture_t >

Matrix_t& TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::fWeightsState

private

Prev state weights, fWeights[1].

Definition at line 74 of file RNNLayer.h.

◆ fWeightStateGradients

template<typename Architecture_t >

Matrix_t& TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::fWeightStateGradients

private

Gradients w.r.t. the recurring weights.

Definition at line 79 of file RNNLayer.h.

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

tmva/tmva/inc/TMVA/DNN/RNN/RNNLayer.h

Public Types

Public Member Functions

Private Attributes

Additional Inherited Members

Member Typedef Documentation

◆ Matrix_t

◆ Scalar_t

◆ Tensor_t

Constructor & Destructor Documentation

◆ TBasicRNNLayer() [1/2]

◆ TBasicRNNLayer() [2/2]

Member Function Documentation

◆ AddWeightsXMLTo()

◆ Backward()

◆ CellBackward()

◆ CellForward()

◆ Forward()

◆ GetActivationFunction()

◆ GetBiasesState() [1/2]

◆ GetBiasesState() [2/2]

◆ GetBiasStateGradients() [1/2]

◆ GetBiasStateGradients() [2/2]

◆ GetDerivatives() [1/2]

◆ GetDerivatives() [2/2]

◆ GetDerivativesAt() [1/2]

◆ GetDerivativesAt() [2/2]

◆ GetInputSize()

◆ GetState() [1/2]

◆ GetState() [2/2]

◆ GetStateSize()

◆ GetTimeSteps()

◆ GetWeightInputGradients() [1/2]

◆ GetWeightInputGradients() [2/2]

◆ GetWeightsInput() [1/2]

◆ GetWeightsInput() [2/2]

◆ GetWeightsState() [1/2]

◆ GetWeightsState() [2/2]

◆ GetWeightStateGradients() [1/2]

◆ GetWeightStateGradients() [2/2]

◆ InitState()

◆ IsRememberState()

◆ Print()

◆ ReadWeightsFromXML()

◆ Update()

Member Data Documentation

◆ fBiases

◆ fBiasGradients

◆ fDerivatives

◆ fF

◆ fRememberState

◆ fState

◆ fStateSize

◆ fTimeSteps

◆ fWeightInputGradients

◆ fWeightsInput

◆ fWeightsState

◆ fWeightStateGradients