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

Definition at line 56 of file RNNLayer.h.

Public Types
using	HelperDescriptor_t = typename Architecture_t::DropoutDescriptor_t

using	LayerDescriptor_t = typename Architecture_t::RecurrentDescriptor_t

using	Matrix_t = typename Architecture_t::Matrix_t

using	RNNDescriptors_t = typename Architecture_t::RNNDescriptors_t

using	RNNWorkspace_t = typename Architecture_t::RNNWorkspace_t

using	Scalar_t = typename Architecture_t::Scalar_t

using	Tensor_t = typename Architecture_t::Tensor_t

using	TensorDescriptor_t = typename Architecture_t::TensorDescriptor_t

using	WeightsDescriptor_t = typename Architecture_t::FilterDescriptor_t

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

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

virtual	~TBasicRNNLayer ()
	Destructor.

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

void	Backward (Tensor_t &gradients_backward, const Tensor_t &activations_backward)
	Backpropagates the error.

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(...).

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

bool	DoesRememberState () const

bool	DoesReturnSequence () const

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

DNN::EActivationFunction	GetActivationFunction () const

Matrix_t &	GetBiasesState ()

const Matrix_t &	GetBiasesState () const

Matrix_t &	GetBiasStateGradients ()

const Matrix_t &	GetBiasStateGradients () const

Matrix_t &	GetCell ()

const Matrix_t &	GetCell () const

Tensor_t &	GetDerivatives ()

const Tensor_t &	GetDerivatives () const

Tensor_t &	GetDX ()

Tensor_t &	GetDY ()

size_t	GetInputSize () const

Matrix_t &	GetState ()

const Matrix_t &	GetState () const

size_t	GetStateSize () const

size_t	GetTimeSteps () const
	Getters.

Tensor_t &	GetWeightGradientsTensor ()

const Tensor_t &	GetWeightGradientsTensor () const

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

Tensor_t &	GetWeightsTensor ()

const Tensor_t &	GetWeightsTensor () const

Tensor_t &	GetX ()

Tensor_t &	GetY ()

virtual void	Initialize ()
	Initialize the weights according to the given initialization method.

void	InitState (DNN::EInitialization m=DNN::EInitialization::kZero)
	Initialize the state method.

void	InitTensors ()

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

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

void	Update (const Scalar_t learningRate)

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

	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.

	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.

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

virtual	~VGeneralLayer ()
	Virtual Destructor.

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

template<typename Arch >
void	CopyParameters (const VGeneralLayer< Arch > &layer)
	Copy all trainable weight and biases from another equivalent layer but with different architecture The function can copy also extra parameters in addition to weights and biases if they are return by the function GetExtraLayerParameters.

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

Tensor_t &	GetActivationGradients ()

const Tensor_t &	GetActivationGradients () const

Matrix_t	GetActivationGradientsAt (size_t i)

const Matrix_t &	GetActivationGradientsAt (size_t i) const

size_t	GetBatchSize () const
	Getters.

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

virtual std::vector< Matrix_t >	GetExtraLayerParameters () const

size_t	GetHeight () const

EInitialization	GetInitialization () const

size_t	GetInputDepth () const

size_t	GetInputHeight () const

size_t	GetInputWidth () const

Tensor_t &	GetOutput ()

const Tensor_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

bool	IsTraining () const

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

virtual void	ResetTraining ()
	Reset some training flags after a loop on all batches Some layer (e.g.

void	SetBatchSize (size_t batchSize)
	Setters.

void	SetDepth (size_t depth)

virtual void	SetDropoutProbability (Scalar_t)
	Set Dropout probability.

virtual void	SetExtraLayerParameters (const std::vector< Matrix_t > &)

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.

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

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

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

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

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

Private Attributes
Architecture_t::ActivationDescriptor_t	fActivationDesc

Matrix_t &	fBiases
	Biases.

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

Matrix_t	fCell
	Empty matrix for RNN.

Tensor_t	fDerivatives
	First fDerivatives of the activations.

TDescriptors *	fDescriptors = nullptr
	Keeps all the RNN descriptors.

Tensor_t	fDx
	cached gradient on the input (output of backward) as T x B x I

Tensor_t	fDy
	cached activation gradient (input of backward) as T x B x S

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

bool	fRememberState
	Remember state in next pass.

bool	fReturnSequence = false
	Return in output full sequence or just last element in time.

Matrix_t	fState
	Hidden State.

size_t	fStateSize
	Hidden state size of RNN.

size_t	fTimeSteps
	Timesteps for RNN.

Tensor_t	fWeightGradientsTensor

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

Matrix_t &	fWeightsInput
	Input weights, fWeights[0].

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

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

Tensor_t	fWeightsTensor

TWorkspace *	fWorkspace = nullptr

Tensor_t	fX
	cached input tensor as T x B x I

Tensor_t	fY
	cached output tensor as T x B x S

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

size_t	fBatchSize
	Batch size used for training and evaluation.

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

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

size_t	fDepth
	The depth of the layer.

size_t	fHeight
	The height of the layer.

EInitialization	fInit
	The initialization method.

size_t	fInputDepth
	The depth of the previous layer or input.

size_t	fInputHeight
	The height of the previous layer or input.

size_t	fInputWidth
	The width of the previous layer or input.

bool	fIsTraining
	Flag indicating the mode.

Tensor_t	fOutput
	Activations of this layer.

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

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

size_t	fWidth
	The width of this layer.

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

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

[legend]

Member Typedef Documentation

◆ HelperDescriptor_t

template<typename Architecture_t >

using TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::HelperDescriptor_t = typename Architecture_t::DropoutDescriptor_t

Definition at line 68 of file RNNLayer.h.

◆ LayerDescriptor_t

template<typename Architecture_t >

using TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::LayerDescriptor_t = typename Architecture_t::RecurrentDescriptor_t

Definition at line 65 of file RNNLayer.h.

◆ Matrix_t

template<typename Architecture_t >

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

Definition at line 62 of file RNNLayer.h.

◆ RNNDescriptors_t

template<typename Architecture_t >

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

Definition at line 71 of file RNNLayer.h.

◆ RNNWorkspace_t

template<typename Architecture_t >

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

Definition at line 70 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 63 of file RNNLayer.h.

◆ Tensor_t

template<typename Architecture_t >

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

Definition at line 61 of file RNNLayer.h.

◆ TensorDescriptor_t

template<typename Architecture_t >

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

Definition at line 67 of file RNNLayer.h.

◆ WeightsDescriptor_t

template<typename Architecture_t >

using TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::WeightsDescriptor_t = typename Architecture_t::FilterDescriptor_t

Definition at line 66 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`,
		bool	returnSequence = `false`,
		DNN::EActivationFunction	f = `DNN::EActivationFunction::kTanh`,
		bool	training = `true`,
		DNN::EInitialization	fA = `DNN::EInitialization::kZero`
	)

Constructor.

Definition at line 213 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 231 of file RNNLayer.h.

◆ ~TBasicRNNLayer()

template<typename Architecture_t >

TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::~TBasicRNNLayer

virtual

Destructor.

Definition at line 249 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 594 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
	)

inlinevirtual

Backpropagates the error.

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

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

Definition at line 426 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 582 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 403 of file RNNLayer.h.

◆ DoesRememberState()

template<typename Architecture_t >

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

inline

Definition at line 171 of file RNNLayer.h.

◆ DoesReturnSequence()

template<typename Architecture_t >

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

inline

Definition at line 172 of file RNNLayer.h.

◆ Forward()

template<typename Architecture_t >

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

virtual

Compute and return the next state with given input matrix.

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

Definition at line 322 of file RNNLayer.h.

◆ GetActivationFunction()

template<typename Architecture_t >

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

inline

Definition at line 173 of file RNNLayer.h.

◆ GetBiasesState() [1/2]

template<typename Architecture_t >

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

inline

Definition at line 188 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 189 of file RNNLayer.h.

◆ GetBiasStateGradients() [1/2]

template<typename Architecture_t >

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

inline

Definition at line 190 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 191 of file RNNLayer.h.

◆ GetCell() [1/2]

template<typename Architecture_t >

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

inline

Definition at line 176 of file RNNLayer.h.

◆ GetCell() [2/2]

template<typename Architecture_t >

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

inline

Definition at line 177 of file RNNLayer.h.

◆ GetDerivatives() [1/2]

template<typename Architecture_t >

Tensor_t & TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::GetDerivatives ( )

inline

Definition at line 183 of file RNNLayer.h.

◆ GetDerivatives() [2/2]

template<typename Architecture_t >

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

inline

Definition at line 184 of file RNNLayer.h.

◆ GetDX()

template<typename Architecture_t >

Tensor_t & TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::GetDX ( )

inline

Definition at line 204 of file RNNLayer.h.

◆ GetDY()

template<typename Architecture_t >

Tensor_t & TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::GetDY ( )

inline

Definition at line 205 of file RNNLayer.h.

◆ GetInputSize()

template<typename Architecture_t >

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

inline

Definition at line 170 of file RNNLayer.h.

◆ GetState() [1/2]

template<typename Architecture_t >

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

inline

Definition at line 174 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 175 of file RNNLayer.h.

◆ GetStateSize()

template<typename Architecture_t >

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

inline

Definition at line 169 of file RNNLayer.h.

◆ GetTimeSteps()

template<typename Architecture_t >

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

inline

Getters.

Definition at line 168 of file RNNLayer.h.

◆ GetWeightGradientsTensor() [1/2]

template<typename Architecture_t >

Tensor_t & TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::GetWeightGradientsTensor ( )

inline

Definition at line 199 of file RNNLayer.h.

◆ GetWeightGradientsTensor() [2/2]

template<typename Architecture_t >

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

inline

Definition at line 200 of file RNNLayer.h.

◆ GetWeightInputGradients() [1/2]

template<typename Architecture_t >

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

inline

Definition at line 192 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 193 of file RNNLayer.h.

◆ GetWeightsInput() [1/2]

template<typename Architecture_t >

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

inline

Definition at line 179 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 180 of file RNNLayer.h.

◆ GetWeightsState() [1/2]

template<typename Architecture_t >

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

inline

Definition at line 181 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 182 of file RNNLayer.h.

◆ GetWeightStateGradients() [1/2]

template<typename Architecture_t >

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

inline

Definition at line 194 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 195 of file RNNLayer.h.

◆ GetWeightsTensor() [1/2]

template<typename Architecture_t >

Tensor_t & TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::GetWeightsTensor ( )

inline

Definition at line 197 of file RNNLayer.h.

◆ GetWeightsTensor() [2/2]

template<typename Architecture_t >

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

inline

Definition at line 198 of file RNNLayer.h.

◆ GetX()

template<typename Architecture_t >

Tensor_t & TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::GetX ( )

inline

Definition at line 202 of file RNNLayer.h.

◆ GetY()

template<typename Architecture_t >

Tensor_t & TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::GetY ( )

inline

Definition at line 203 of file RNNLayer.h.

◆ Initialize()

template<typename Architecture_t >

void TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::Initialize

virtual

Initialize the weights according to the given initialization method.

Reimplemented from TMVA::DNN::VGeneralLayer< Architecture_t >.

Definition at line 264 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 state method.

Definition at line 286 of file RNNLayer.h.

◆ InitTensors()

template<typename Architecture_t >

void TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::InitTensors

Definition at line 279 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 295 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 615 of file RNNLayer.h.

◆ Update()

template<typename Architecture_t >

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

Member Data Documentation

◆ fActivationDesc

template<typename Architecture_t >

Architecture_t::ActivationDescriptor_t TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::fActivationDesc

private

Definition at line 95 of file RNNLayer.h.

◆ fBiases

template<typename Architecture_t >

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

private

Biases.

Definition at line 85 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 90 of file RNNLayer.h.

◆ fCell

template<typename Architecture_t >

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

private

Empty matrix for RNN.

Definition at line 100 of file RNNLayer.h.

◆ fDerivatives

template<typename Architecture_t >

Tensor_t TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::fDerivatives

private

First fDerivatives of the activations.

Definition at line 87 of file RNNLayer.h.

◆ fDescriptors

template<typename Architecture_t >

TDescriptors* TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::fDescriptors = nullptr

private

Keeps all the RNN descriptors.

Definition at line 97 of file RNNLayer.h.

◆ fDx

template<typename Architecture_t >

Tensor_t TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::fDx

private

cached gradient on the input (output of backward) as T x B x I

Definition at line 105 of file RNNLayer.h.

◆ fDy

template<typename Architecture_t >

Tensor_t TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::fDy

private

cached activation gradient (input of backward) as T x B x S

Definition at line 106 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 80 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 77 of file RNNLayer.h.

◆ fReturnSequence

template<typename Architecture_t >

bool TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::fReturnSequence = false

private

Return in output full sequence or just last element in time.

Definition at line 78 of file RNNLayer.h.

◆ fState

template<typename Architecture_t >

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

private

Hidden State.

Definition at line 82 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 76 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 75 of file RNNLayer.h.

◆ fWeightGradientsTensor

template<typename Architecture_t >

Tensor_t TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::fWeightGradientsTensor

private

Definition at line 93 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 88 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 83 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 84 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 89 of file RNNLayer.h.

◆ fWeightsTensor

template<typename Architecture_t >

Tensor_t TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::fWeightsTensor

private

Definition at line 92 of file RNNLayer.h.

◆ fWorkspace

template<typename Architecture_t >

TWorkspace* TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::fWorkspace = nullptr

private

Definition at line 98 of file RNNLayer.h.

◆ fX

template<typename Architecture_t >

Tensor_t TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::fX

private

cached input tensor as T x B x I

Definition at line 103 of file RNNLayer.h.

◆ fY

template<typename Architecture_t >

Tensor_t TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >::fY

private

cached output tensor as T x B x S

Definition at line 104 of file RNNLayer.h.

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

Public Types

Public Member Functions

Private Attributes

Additional Inherited Members

Member Typedef Documentation

◆ HelperDescriptor_t

◆ LayerDescriptor_t

◆ Matrix_t

◆ RNNDescriptors_t

◆ RNNWorkspace_t

◆ Scalar_t

◆ Tensor_t

◆ TensorDescriptor_t

◆ WeightsDescriptor_t

Constructor & Destructor Documentation

◆ TBasicRNNLayer() [1/2]

◆ TBasicRNNLayer() [2/2]

◆ ~TBasicRNNLayer()

Member Function Documentation

◆ AddWeightsXMLTo()

◆ Backward()

◆ CellBackward()

◆ CellForward()

◆ DoesRememberState()

◆ DoesReturnSequence()

◆ Forward()

◆ GetActivationFunction()

◆ GetBiasesState() [1/2]

◆ GetBiasesState() [2/2]

◆ GetBiasStateGradients() [1/2]

◆ GetBiasStateGradients() [2/2]

◆ GetCell() [1/2]

◆ GetCell() [2/2]

◆ GetDerivatives() [1/2]

◆ GetDerivatives() [2/2]

◆ GetDX()

◆ GetDY()

◆ GetInputSize()

◆ GetState() [1/2]

◆ GetState() [2/2]

◆ GetStateSize()

◆ GetTimeSteps()

◆ GetWeightGradientsTensor() [1/2]

◆ GetWeightGradientsTensor() [2/2]

◆ GetWeightInputGradients() [1/2]

◆ GetWeightInputGradients() [2/2]

◆ GetWeightsInput() [1/2]

◆ GetWeightsInput() [2/2]

◆ GetWeightsState() [1/2]

◆ GetWeightsState() [2/2]

◆ GetWeightStateGradients() [1/2]

◆ GetWeightStateGradients() [2/2]

◆ GetWeightsTensor() [1/2]

◆ GetWeightsTensor() [2/2]

◆ GetX()

◆ GetY()

◆ Initialize()

◆ InitState()

◆ InitTensors()

◆ Print()

◆ ReadWeightsFromXML()

◆ Update()

Member Data Documentation

◆ fActivationDesc

◆ fBiases

◆ fBiasGradients

◆ fCell

◆ fDerivatives

◆ fDescriptors

◆ fDx

◆ fDy

◆ fF

◆ fRememberState

◆ fReturnSequence

◆ fState

◆ fStateSize

◆ fTimeSteps

◆ fWeightGradientsTensor

◆ fWeightInputGradients

◆ fWeightsInput