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TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t > Member List

This is the complete list of members for TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >, including all inherited members.

AddWeightsXMLTo(void *parent)TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >virtual
Backward(Tensor_t &gradients_backward, const Tensor_t &activations_backward)TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >inlinevirtual
CellBackward(Matrix_t &state_gradients_backward, const Matrix_t &precStateActivations, const Matrix_t &input, Matrix_t &input_gradient, Matrix_t &dF)TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >inline
CellForward(const Matrix_t &input, Matrix_t &dF)TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >inline
CopyBiases(const std::vector< Matrix_t > &otherBiases)TMVA::DNN::VGeneralLayer< Architecture_t >
CopyParameters(const VGeneralLayer< Arch > &layer)TMVA::DNN::VGeneralLayer< Architecture_t >
CopyWeights(const std::vector< Matrix_t > &otherWeights)TMVA::DNN::VGeneralLayer< Architecture_t >
DoesRememberState() constTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >inline
DoesReturnSequence() constTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >inline
fActivationDescTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >private
fActivationGradientsTMVA::DNN::VGeneralLayer< Architecture_t >protected
fBatchSizeTMVA::DNN::VGeneralLayer< Architecture_t >protected
fBiasesTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >private
fBiasGradientsTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >private
fCellTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >private
fDepthTMVA::DNN::VGeneralLayer< Architecture_t >protected
fDerivativesTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >private
fDescriptorsTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >private
fDxTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >private
fDyTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >private
fFTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >private
fHeightTMVA::DNN::VGeneralLayer< Architecture_t >protected
fInitTMVA::DNN::VGeneralLayer< Architecture_t >protected
fInputDepthTMVA::DNN::VGeneralLayer< Architecture_t >protected
fInputHeightTMVA::DNN::VGeneralLayer< Architecture_t >protected
fInputWidthTMVA::DNN::VGeneralLayer< Architecture_t >protected
fIsTrainingTMVA::DNN::VGeneralLayer< Architecture_t >protected
Forward(Tensor_t &input, bool isTraining=true)TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >virtual
fOutputTMVA::DNN::VGeneralLayer< Architecture_t >protected
fRememberStateTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >private
fReturnSequenceTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >private
fStateTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >private
fStateSizeTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >private
fTimeStepsTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >private
fWeightGradientsTMVA::DNN::VGeneralLayer< Architecture_t >protected
fWeightGradientsTensorTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >private
fWeightInputGradientsTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >private
fWeightsTMVA::DNN::VGeneralLayer< Architecture_t >protected
fWeightsInputTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >private
fWeightsStateTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >private
fWeightStateGradientsTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >private
fWeightsTensorTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >private
fWidthTMVA::DNN::VGeneralLayer< Architecture_t >protected
fWorkspaceTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >private
fXTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >private
fYTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >private
GetActivationFunction() constTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >inline
GetActivationGradients() constTMVA::DNN::VGeneralLayer< Architecture_t >inline
GetActivationGradients()TMVA::DNN::VGeneralLayer< Architecture_t >inline
GetActivationGradientsAt(size_t i)TMVA::DNN::VGeneralLayer< Architecture_t >inline
GetActivationGradientsAt(size_t i) constTMVA::DNN::VGeneralLayer< Architecture_t >inline
GetBatchSize() constTMVA::DNN::VGeneralLayer< Architecture_t >inline
GetBiases() constTMVA::DNN::VGeneralLayer< Architecture_t >inline
GetBiases()TMVA::DNN::VGeneralLayer< Architecture_t >inline
GetBiasesAt(size_t i) constTMVA::DNN::VGeneralLayer< Architecture_t >inline
GetBiasesAt(size_t i)TMVA::DNN::VGeneralLayer< Architecture_t >inline
GetBiasesState()TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >inline
GetBiasesState() constTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >inline
GetBiasGradients() constTMVA::DNN::VGeneralLayer< Architecture_t >inline
GetBiasGradients()TMVA::DNN::VGeneralLayer< Architecture_t >inline
GetBiasGradientsAt(size_t i) constTMVA::DNN::VGeneralLayer< Architecture_t >inline
GetBiasGradientsAt(size_t i)TMVA::DNN::VGeneralLayer< Architecture_t >inline
GetBiasStateGradients()TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >inline
GetBiasStateGradients() constTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >inline
GetCell()TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >inline
GetCell() constTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >inline
GetDepth() constTMVA::DNN::VGeneralLayer< Architecture_t >inline
GetDerivatives()TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >inline
GetDerivatives() constTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >inline
GetDX()TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >inline
GetDY()TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >inline
GetExtraLayerParameters() constTMVA::DNN::VGeneralLayer< Architecture_t >inlinevirtual
GetHeight() constTMVA::DNN::VGeneralLayer< Architecture_t >inline
GetInitialization() constTMVA::DNN::VGeneralLayer< Architecture_t >inline
GetInputDepth() constTMVA::DNN::VGeneralLayer< Architecture_t >inline
GetInputHeight() constTMVA::DNN::VGeneralLayer< Architecture_t >inline
GetInputSize() constTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >inline
GetInputWidth() constTMVA::DNN::VGeneralLayer< Architecture_t >inline
GetOutput() constTMVA::DNN::VGeneralLayer< Architecture_t >inline
GetOutput()TMVA::DNN::VGeneralLayer< Architecture_t >inline
GetOutputAt(size_t i)TMVA::DNN::VGeneralLayer< Architecture_t >inline
GetOutputAt(size_t i) constTMVA::DNN::VGeneralLayer< Architecture_t >inline
GetState()TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >inline
GetState() constTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >inline
GetStateSize() constTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >inline
GetTimeSteps() constTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >inline
GetWeightGradients() constTMVA::DNN::VGeneralLayer< Architecture_t >inline
GetWeightGradients()TMVA::DNN::VGeneralLayer< Architecture_t >inline
GetWeightGradientsAt(size_t i) constTMVA::DNN::VGeneralLayer< Architecture_t >inline
GetWeightGradientsAt(size_t i)TMVA::DNN::VGeneralLayer< Architecture_t >inline
GetWeightGradientsTensor()TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >inline
GetWeightGradientsTensor() constTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >inline
GetWeightInputGradients()TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >inline
GetWeightInputGradients() constTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >inline
GetWeights() constTMVA::DNN::VGeneralLayer< Architecture_t >inline
GetWeights()TMVA::DNN::VGeneralLayer< Architecture_t >inline
GetWeightsAt(size_t i) constTMVA::DNN::VGeneralLayer< Architecture_t >inline
GetWeightsAt(size_t i)TMVA::DNN::VGeneralLayer< Architecture_t >inline
GetWeightsInput()TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >inline
GetWeightsInput() constTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >inline
GetWeightsState()TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >inline
GetWeightsState() constTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >inline
GetWeightStateGradients()TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >inline
GetWeightStateGradients() constTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >inline
GetWeightsTensor()TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >inline
GetWeightsTensor() constTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >inline
GetWidth() constTMVA::DNN::VGeneralLayer< Architecture_t >inline
GetX()TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >inline
GetY()TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >inline
HelperDescriptor_t typedefTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >
Initialize()TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >virtual
InitState(DNN::EInitialization m=DNN::EInitialization::kZero)TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >
InitTensors()TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >
IsTraining() constTMVA::DNN::VGeneralLayer< Architecture_t >inline
LayerDescriptor_t typedefTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >
Matrix_t typedefTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >
Print() constTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >virtual
ReadMatrixXML(void *node, const char *name, Matrix_t &matrix)TMVA::DNN::VGeneralLayer< Architecture_t >
ReadWeightsFromXML(void *parent)TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >virtual
ResetTraining()TMVA::DNN::VGeneralLayer< Architecture_t >inlinevirtual
RNNDescriptors_t typedefTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >
RNNWorkspace_t typedefTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >
Scalar_t typedefTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >
SetBatchSize(size_t batchSize)TMVA::DNN::VGeneralLayer< Architecture_t >inline
SetDepth(size_t depth)TMVA::DNN::VGeneralLayer< Architecture_t >inline
SetDropoutProbability(Scalar_t)TMVA::DNN::VGeneralLayer< Architecture_t >inlinevirtual
SetExtraLayerParameters(const std::vector< Matrix_t > &)TMVA::DNN::VGeneralLayer< Architecture_t >inlinevirtual
SetHeight(size_t height)TMVA::DNN::VGeneralLayer< Architecture_t >inline
SetInputDepth(size_t inputDepth)TMVA::DNN::VGeneralLayer< Architecture_t >inline
SetInputHeight(size_t inputHeight)TMVA::DNN::VGeneralLayer< Architecture_t >inline
SetInputWidth(size_t inputWidth)TMVA::DNN::VGeneralLayer< Architecture_t >inline
SetIsTraining(bool isTraining)TMVA::DNN::VGeneralLayer< Architecture_t >inline
SetWidth(size_t width)TMVA::DNN::VGeneralLayer< Architecture_t >inline
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)TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >
TBasicRNNLayer(const TBasicRNNLayer &)TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >
Tensor_t typedefTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >
TensorDescriptor_t typedefTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >
Update(const Scalar_t learningRate)TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >
UpdateBiases(const std::vector< Matrix_t > &biasGradients, const Scalar_t learningRate)TMVA::DNN::VGeneralLayer< Architecture_t >
UpdateBiasGradients(const std::vector< Matrix_t > &biasGradients, const Scalar_t learningRate)TMVA::DNN::VGeneralLayer< Architecture_t >
UpdateWeightGradients(const std::vector< Matrix_t > &weightGradients, const Scalar_t learningRate)TMVA::DNN::VGeneralLayer< Architecture_t >
UpdateWeights(const std::vector< Matrix_t > &weightGradients, const Scalar_t learningRate)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)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, 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)TMVA::DNN::VGeneralLayer< Architecture_t >
VGeneralLayer(VGeneralLayer< Architecture_t > *layer)TMVA::DNN::VGeneralLayer< Architecture_t >
VGeneralLayer(const VGeneralLayer &)TMVA::DNN::VGeneralLayer< Architecture_t >
WeightsDescriptor_t typedefTMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >
WriteMatrixToXML(void *node, const char *name, const Matrix_t &matrix)TMVA::DNN::VGeneralLayer< Architecture_t >
WriteTensorToXML(void *node, const char *name, const std::vector< Matrix_t > &tensor)TMVA::DNN::VGeneralLayer< Architecture_t >
~TBasicRNNLayer()TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >virtual
~VGeneralLayer()TMVA::DNN::VGeneralLayer< Architecture_t >virtual