| 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 |
| SetDropoutProbability(Scalar_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, 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 |
ROOT 6.18/05 - Reference Guide Generated on Wed Apr 13 2022 01:59:45 (GVA Time) using Doxygen 1.9.4 (15dae504a167d838873053cc96417d8beae78df7).