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 (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...

	TBasicRNNLayer (const TBasicRNNLayer &)
	Copy 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 (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...

	VGeneralLayer (const VGeneralLayer &)
	Copy Constructor. More...

virtual	~VGeneralLayer ()
	Virtual Destructor. 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...

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

std::vector< Matrix_t > &	GetActivationGradients ()

Matrix_t &	GetActivationGradientsAt (size_t i)

const Matrix_t &	GetActivationGradientsAt (size_t i) const

size_t	GetBatchSize () const
	Getters. More...

const 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)

size_t	GetDepth () const

size_t	GetHeight () const

EInitialization	GetInitialization () const

size_t	GetInputDepth () const

size_t	GetInputHeight () const

size_t	GetInputWidth () const

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

std::vector< Matrix_t > &	GetOutput ()

Matrix_t &	GetOutputAt (size_t i)

const Matrix_t &	GetOutputAt (size_t i) const

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)

size_t	GetWidth () const

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

bool	IsTraining () const

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

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

void	SetDepth (size_t depth)

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 ( ) const

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.

Libraries for TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >:

[legend]

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

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

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