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, std::vector< Matrix_t > &inp1, std::vector< Matrix_t > &inp2)	TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >	inline
TMVA::DNN::VGeneralLayer::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	TMVA::DNN::VGeneralLayer< Architecture_t >	pure virtual
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 >
CopyWeights(const std::vector< Matrix_t > &otherWeights)	TMVA::DNN::VGeneralLayer< Architecture_t >
fActivationGradients	TMVA::DNN::VGeneralLayer< Architecture_t >	protected
fBatchSize	TMVA::DNN::VGeneralLayer< Architecture_t >	protected
fBiases	TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >	private
fBiasGradients	TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >	private
fDepth	TMVA::DNN::VGeneralLayer< Architecture_t >	protected
fDerivatives	TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >	private
fF	TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >	private
fHeight	TMVA::DNN::VGeneralLayer< Architecture_t >	protected
fInit	TMVA::DNN::VGeneralLayer< Architecture_t >	protected
fInputDepth	TMVA::DNN::VGeneralLayer< Architecture_t >	protected
fInputHeight	TMVA::DNN::VGeneralLayer< Architecture_t >	protected
fInputWidth	TMVA::DNN::VGeneralLayer< Architecture_t >	protected
fIsTraining	TMVA::DNN::VGeneralLayer< Architecture_t >	protected
Forward(Tensor_t &input, bool isTraining=true)	TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >	inline
TMVA::DNN::VGeneralLayer::Forward(std::vector< Matrix_t > &input, bool applyDropout=false)=0	TMVA::DNN::VGeneralLayer< Architecture_t >	pure virtual
fOutput	TMVA::DNN::VGeneralLayer< Architecture_t >	protected
fRememberState	TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >	private
fState	TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >	private
fStateSize	TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >	private
fTimeSteps	TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >	private
fWeightGradients	TMVA::DNN::VGeneralLayer< Architecture_t >	protected
fWeightInputGradients	TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >	private
fWeights	TMVA::DNN::VGeneralLayer< Architecture_t >	protected
fWeightsInput	TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >	private
fWeightsState	TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >	private
fWeightStateGradients	TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >	private
fWidth	TMVA::DNN::VGeneralLayer< Architecture_t >	protected
GetActivationFunction() const	TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >	inline
GetActivationGradients() const	TMVA::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) const	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetBatchSize() const	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetBiases() const	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetBiases()	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetBiasesAt(size_t i) const	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetBiasesAt(size_t i)	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetBiasesState()	TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >	inline
GetBiasesState() const	TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >	inline
GetBiasGradients() const	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetBiasGradients()	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetBiasGradientsAt(size_t i) const	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetBiasGradientsAt(size_t i)	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetBiasStateGradients()	TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >	inline
GetBiasStateGradients() const	TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >	inline
GetDepth() const	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetDerivatives()	TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >	inline
GetDerivatives() const	TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >	inline
GetDerivativesAt(size_t i)	TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >	inline
GetDerivativesAt(size_t i) const	TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >	inline
GetHeight() const	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetInitialization() const	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetInputDepth() const	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetInputHeight() const	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetInputSize() const	TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >	inline
GetInputWidth() const	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetOutput() const	TMVA::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) const	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetState()	TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >	inline
GetState() const	TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >	inline
GetStateSize() const	TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >	inline
GetTimeSteps() const	TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >	inline
GetWeightGradients() const	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetWeightGradients()	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetWeightGradientsAt(size_t i) const	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetWeightGradientsAt(size_t i)	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetWeightInputGradients()	TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >	inline
GetWeightInputGradients() const	TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >	inline
GetWeights() const	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetWeights()	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetWeightsAt(size_t i) const	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetWeightsAt(size_t i)	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetWeightsInput()	TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >	inline
GetWeightsInput() const	TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >	inline
GetWeightsState()	TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >	inline
GetWeightsState() const	TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >	inline
GetWeightStateGradients()	TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >	inline
GetWeightStateGradients() const	TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >	inline
GetWidth() const	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
Initialize()	TMVA::DNN::VGeneralLayer< Architecture_t >
InitState(DNN::EInitialization m=DNN::EInitialization::kZero)	TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >
IsRememberState() const	TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >	inline
IsTraining() const	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
Matrix_t typedef	TMVA::DNN::RNN::TBasicRNNLayer< Architecture_t >
Print() const	TMVA::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
Scalar_t typedef	TMVA::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
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, 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 typedef	TMVA::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 >
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 >
~VGeneralLayer()	TMVA::DNN::VGeneralLayer< Architecture_t >	virtual