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TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t > Class Template Reference

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. More...
 
 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. More...
 
virtual ~TBasicRNNLayer ()
 Destructor. 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)
 Backpropagates the error. More...
 
Matrix_tCellBackward (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...
 
bool DoesRememberState () const
 
bool DoesReturnSequence () const
 
void Forward (Tensor_t &input, bool isTraining=true)
 Compute and return the next state with given input matrix. More...
 
DNN::EActivationFunction GetActivationFunction () const
 
Matrix_tGetBiasesState ()
 
const Matrix_tGetBiasesState () const
 
Matrix_tGetBiasStateGradients ()
 
const Matrix_tGetBiasStateGradients () const
 
Matrix_tGetCell ()
 
const Matrix_tGetCell () const
 
Tensor_tGetDerivatives ()
 
const Tensor_tGetDerivatives () const
 
Tensor_tGetDX ()
 
Tensor_tGetDY ()
 
size_t GetInputSize () const
 
Matrix_tGetState ()
 
const Matrix_tGetState () const
 
size_t GetStateSize () const
 
size_t GetTimeSteps () const
 Getters. More...
 
Tensor_tGetWeightGradientsTensor ()
 
const Tensor_tGetWeightGradientsTensor () const
 
Matrix_tGetWeightInputGradients ()
 
const Matrix_tGetWeightInputGradients () const
 
Matrix_tGetWeightsInput ()
 
const Matrix_tGetWeightsInput () const
 
Matrix_tGetWeightsState ()
 
const Matrix_tGetWeightsState () const
 
Matrix_tGetWeightStateGradients ()
 
const Matrix_tGetWeightStateGradients () const
 
Tensor_tGetWeightsTensor ()
 
const Tensor_tGetWeightsTensor () const
 
Tensor_tGetX ()
 
Tensor_tGetY ()
 
virtual void Initialize ()
 Initialize the weights according to the given initialization method. More...
 
void InitState (DNN::EInitialization m=DNN::EInitialization::kZero)
 Initialize the state method. More...
 
void InitTensors ()
 
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...
 
void CopyBiases (const std::vector< Matrix_t > &otherBiases)
 Copies the biases provided as an input. More...
 
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. More...
 
void CopyWeights (const std::vector< Matrix_t > &otherWeights)
 Copies the weights provided as an input. More...
 
Tensor_tGetActivationGradients ()
 
const Tensor_tGetActivationGradients () const
 
Matrix_t GetActivationGradientsAt (size_t i)
 
const Matrix_tGetActivationGradientsAt (size_t i) const
 
size_t GetBatchSize () const
 Getters. More...
 
std::vector< Matrix_t > & GetBiases ()
 
const std::vector< Matrix_t > & GetBiases () const
 
Matrix_tGetBiasesAt (size_t i)
 
const Matrix_tGetBiasesAt (size_t i) const
 
std::vector< Matrix_t > & GetBiasGradients ()
 
const std::vector< Matrix_t > & GetBiasGradients () const
 
Matrix_tGetBiasGradientsAt (size_t i)
 
const Matrix_tGetBiasGradientsAt (size_t i) const
 
size_t GetDepth () const
 
virtual std::vector< Matrix_tGetExtraLayerParameters () const
 
size_t GetHeight () const
 
EInitialization GetInitialization () const
 
size_t GetInputDepth () const
 
size_t GetInputHeight () const
 
size_t GetInputWidth () const
 
Tensor_tGetOutput ()
 
const Tensor_tGetOutput () const
 
Matrix_t GetOutputAt (size_t i)
 
const Matrix_tGetOutputAt (size_t i) const
 
std::vector< Matrix_t > & GetWeightGradients ()
 
const std::vector< Matrix_t > & GetWeightGradients () const
 
Matrix_tGetWeightGradientsAt (size_t i)
 
const Matrix_tGetWeightGradientsAt (size_t i) const
 
std::vector< Matrix_t > & GetWeights ()
 
const std::vector< Matrix_t > & GetWeights () const
 
Matrix_tGetWeightsAt (size_t i)
 
const Matrix_tGetWeightsAt (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. More...
 
void SetBatchSize (size_t batchSize)
 Setters. More...
 
void SetDepth (size_t depth)
 
virtual void SetDropoutProbability (Scalar_t)
 Set Dropout probability. More...
 
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. 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

Architecture_t::ActivationDescriptor_t fActivationDesc
 
Matrix_tfBiases
 Biases. More...
 
Matrix_tfBiasGradients
 Gradients w.r.t. the bias values. More...
 
Matrix_t fCell
 Empty matrix for RNN. More...
 
Tensor_t fDerivatives
 First fDerivatives of the activations. More...
 
TDescriptorsfDescriptors = nullptr
 Keeps all the RNN descriptors. More...
 
Tensor_t fDx
 cached gradient on the input (output of backward) as T x B x I More...
 
Tensor_t fDy
 cached activation gradient (input of backward) as T x B x S More...
 
DNN::EActivationFunction fF
 Activation function of the hidden state. More...
 
bool fRememberState
 Remember state in next pass. More...
 
bool fReturnSequence = false
 Return in output full sequence or just last element in time. More...
 
Matrix_t fState
 Hidden State. More...
 
size_t fStateSize
 Hidden state size of RNN. More...
 
size_t fTimeSteps
 Timesteps for RNN. More...
 
Tensor_t fWeightGradientsTensor
 
Matrix_tfWeightInputGradients
 Gradients w.r.t. the input weights. More...
 
Matrix_tfWeightsInput
 Input weights, fWeights[0]. More...
 
Matrix_tfWeightsState
 Prev state weights, fWeights[1]. More...
 
Matrix_tfWeightStateGradients
 Gradients w.r.t. the recurring weights. More...
 
Tensor_t fWeightsTensor
 
TWorkspacefWorkspace = nullptr
 
Tensor_t fX
 cached input tensor as T x B x I More...
 
Tensor_t fY
 cached output tensor as T x B x S More...
 

Additional Inherited Members

- Protected Attributes inherited from TMVA::DNN::VGeneralLayer< Architecture_t >
Tensor_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_tfBiases
 The biases associated to the layer. More...
 
std::vector< Matrix_tfBiasGradients
 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 indicating the mode. More...
 
Tensor_t fOutput
 Activations of this layer. More...
 
std::vector< Matrix_tfWeightGradients
 Gradients w.r.t. the weights of the layer. More...
 
std::vector< Matrix_tfWeights
 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

◆ 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 ( void  )
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.


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