Definition at line 56 of file GRULayer.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 | |
| TBasicGRULayer (const TBasicGRULayer &) | |
| Copy Constructor. | |
| TBasicGRULayer (size_t batchSize, size_t stateSize, size_t inputSize, size_t timeSteps, bool rememberState=false, bool returnSequence=false, bool resetGateAfter=false, DNN::EActivationFunction f1=DNN::EActivationFunction::kSigmoid, DNN::EActivationFunction f2=DNN::EActivationFunction::kTanh, bool training=true, DNN::EInitialization fA=DNN::EInitialization::kZero) | |
| Constructor. | |
| void | AddWeightsXMLTo (void *parent) override |
| Writes the information and the weights about the layer in an XML node. | |
| void | Backward (Tensor_t &gradients_backward, const Tensor_t &activations_backward) override |
| Backpropagates the error. | |
| void | CandidateValue (const Matrix_t &input, Matrix_t &dc) |
| Decides the new candidate values (NN with Tanh). | |
| Matrix_t & | CellBackward (Matrix_t &state_gradients_backward, const Matrix_t &precStateActivations, const Matrix_t &reset_gate, const Matrix_t &update_gate, const Matrix_t &candidate_gate, const Matrix_t &input, Matrix_t &input_gradient, Matrix_t &dr, Matrix_t &du, Matrix_t &dc) |
| Backward for a single time unit a the corresponding call to Forward(...). | |
| void | CellForward (Matrix_t &updateGateValues, Matrix_t &candidateValues) |
| Forward for a single cell (time unit). | |
| 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. | |
| bool | DoesRememberState () const |
| bool | DoesReturnSequence () const |
| void | Forward (Tensor_t &input, bool isTraining=true) override |
| Computes the next hidden state and next cell state with given input matrix. | |
| DNN::EActivationFunction | GetActivationFunctionF1 () const |
| DNN::EActivationFunction | GetActivationFunctionF2 () const |
| 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 |
| Matrix_t & | GetCandidateBias () |
| const Matrix_t & | GetCandidateBias () const |
| Matrix_t & | GetCandidateBiasGradients () |
| const Matrix_t & | GetCandidateBiasGradients () const |
| Matrix_t & | GetCandidateDerivativesAt (size_t i) |
| const Matrix_t & | GetCandidateDerivativesAt (size_t i) const |
| std::vector< Matrix_t > & | GetCandidateGateTensor () |
| const std::vector< Matrix_t > & | GetCandidateGateTensor () const |
| Matrix_t & | GetCandidateGateTensorAt (size_t i) |
| const Matrix_t & | GetCandidateGateTensorAt (size_t i) const |
| Matrix_t & | GetCandidateValue () |
| const Matrix_t & | GetCandidateValue () const |
| Matrix_t & | GetCell () |
| const Matrix_t & | GetCell () const |
| size_t | GetDepth () const |
| std::vector< Matrix_t > & | GetDerivativesCandidate () |
| const std::vector< Matrix_t > & | GetDerivativesCandidate () const |
| std::vector< Matrix_t > & | GetDerivativesReset () |
| const std::vector< Matrix_t > & | GetDerivativesReset () const |
| std::vector< Matrix_t > & | GetDerivativesUpdate () |
| const std::vector< Matrix_t > & | GetDerivativesUpdate () const |
| Tensor_t & | GetDX () |
| Tensor_t & | GetDY () |
| virtual std::vector< Matrix_t > | GetExtraLayerParameters () const |
| size_t | GetHeight () const |
| EInitialization | GetInitialization () const |
| size_t | GetInputDepth () const |
| size_t | GetInputHeight () const |
| size_t | GetInputSize () const |
| Getters. | |
| 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 |
| Matrix_t & | GetResetBiasGradients () |
| const Matrix_t & | GetResetBiasGradients () const |
| Matrix_t & | GetResetDerivativesAt (size_t i) |
| const Matrix_t & | GetResetDerivativesAt (size_t i) const |
| Matrix_t & | GetResetGateBias () |
| const Matrix_t & | GetResetGateBias () const |
| std::vector< Matrix_t > & | GetResetGateTensor () |
| const std::vector< Matrix_t > & | GetResetGateTensor () const |
| Matrix_t & | GetResetGateTensorAt (size_t i) |
| const Matrix_t & | GetResetGateTensorAt (size_t i) const |
| Matrix_t & | GetResetGateValue () |
| const Matrix_t & | GetResetGateValue () const |
| Matrix_t & | GetState () |
| const Matrix_t & | GetState () const |
| size_t | GetStateSize () const |
| size_t | GetTimeSteps () const |
| Matrix_t & | GetUpdateBiasGradients () |
| const Matrix_t & | GetUpdateBiasGradients () const |
| Matrix_t & | GetUpdateDerivativesAt (size_t i) |
| const Matrix_t & | GetUpdateDerivativesAt (size_t i) const |
| Matrix_t & | GetUpdateGateBias () |
| const Matrix_t & | GetUpdateGateBias () const |
| std::vector< Matrix_t > & | GetUpdateGateTensor () |
| const std::vector< Matrix_t > & | GetUpdateGateTensor () const |
| Matrix_t & | GetUpdateGateTensorAt (size_t i) |
| const Matrix_t & | GetUpdateGateTensorAt (size_t i) const |
| Matrix_t & | GetUpdateGateValue () |
| const Matrix_t & | GetUpdateGateValue () 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 |
| Tensor_t & | GetWeightGradientsTensor () |
| const Tensor_t & | GetWeightGradientsTensor () 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 |
| Matrix_t & | GetWeightsCandidate () |
| const Matrix_t & | GetWeightsCandidate () const |
| Matrix_t & | GetWeightsCandidateGradients () |
| const Matrix_t & | GetWeightsCandidateGradients () const |
| Matrix_t & | GetWeightsCandidateState () |
| const Matrix_t & | GetWeightsCandidateState () const |
| Matrix_t & | GetWeightsCandidateStateGradients () |
| const Matrix_t & | GetWeightsCandidateStateGradients () const |
| Matrix_t & | GetWeightsResetGate () |
| const Matrix_t & | GetWeightsResetGate () const |
| Matrix_t & | GetWeightsResetGateState () |
| const Matrix_t & | GetWeightsResetGateState () const |
| Matrix_t & | GetWeightsResetGradients () |
| const Matrix_t & | GetWeightsResetGradients () const |
| Matrix_t & | GetWeightsResetStateGradients () |
| const Matrix_t & | GetWeightsResetStateGradients () const |
| Tensor_t & | GetWeightsTensor () |
| const Tensor_t & | GetWeightsTensor () const |
| Matrix_t & | GetWeightsUpdateGate () |
| const Matrix_t & | GetWeightsUpdateGate () const |
| Matrix_t & | GetWeightsUpdateGateState () |
| const Matrix_t & | GetWeightsUpdateGateState () const |
| Matrix_t & | GetWeightsUpdateGradients () |
| const Matrix_t & | GetWeightsUpdateGradients () const |
| Matrix_t & | GetWeightsUpdateStateGradients () |
| const Matrix_t & | GetWeigthsUpdateStateGradients () const |
| size_t | GetWidth () const |
| Tensor_t & | GetX () |
| Tensor_t & | GetY () |
| void | Initialize () override |
| Initialize the weights according to the given initialization method. | |
| void | InitState (DNN::EInitialization m=DNN::EInitialization::kZero) |
| Initialize the hidden state and cell state method. | |
| bool | IsTraining () const |
| void | Print () const override |
| Prints the info about the layer. | |
| void | ReadMatrixXML (void *node, const char *name, Matrix_t &matrix) |
| void | ReadWeightsFromXML (void *parent) override |
| Read the information and the weights about the layer from XML node. | |
| void | ResetGate (const Matrix_t &input, Matrix_t &di) |
| Decides the values we'll update (NN with Sigmoid). | |
| 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) |
| 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 | UpdateGate (const Matrix_t &input, Matrix_t &df) |
| Forgets the past values (NN with Sigmoid). | |
| 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 | |
Protected Attributes | |
| 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. | |
Private Attributes | |
| std::vector< Matrix_t > | candidate_gate_value |
| Candidate gate value for every time step. | |
| Matrix_t & | fCandidateBias |
| Candidate Gate bias. | |
| Matrix_t & | fCandidateBiasGradients |
| Gradients w.r.t the candidate gate - bias weights. | |
| Matrix_t | fCandidateValue |
| Computed candidate values. | |
| Matrix_t | fCell |
| Empty matrix for GRU. | |
| std::vector< Matrix_t > | fDerivativesCandidate |
| First fDerivatives of the activations candidate gate. | |
| std::vector< Matrix_t > | fDerivativesReset |
| First fDerivatives of the activations reset gate. | |
| std::vector< Matrix_t > | fDerivativesUpdate |
| First fDerivatives of the activations update gate. | |
| 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 | fF1 |
| Activation function: sigmoid. | |
| DNN::EActivationFunction | fF2 |
| Activation function: tanh. | |
| bool | fRememberState |
| Remember state in next pass. | |
| Matrix_t & | fResetBiasGradients |
| Gradients w.r.t the reset gate - bias weights. | |
| bool | fResetGateAfter = false |
| GRU variant to Apply the reset gate multiplication afterwards (used by cuDNN). | |
| Matrix_t & | fResetGateBias |
| Input Gate bias. | |
| Matrix_t | fResetValue |
| Computed reset gate values. | |
| bool | fReturnSequence = false |
| Return in output full sequence or just last element. | |
| Matrix_t | fState |
| Hidden state of GRU. | |
| size_t | fStateSize |
| Hidden state size for GRU. | |
| size_t | fTimeSteps |
| Timesteps for GRU. | |
| Matrix_t & | fUpdateBiasGradients |
| Gradients w.r.t the update gate - bias weights. | |
| Matrix_t & | fUpdateGateBias |
| Update Gate bias. | |
| Matrix_t | fUpdateValue |
| Computed forget gate values. | |
| Tensor_t | fWeightGradientsTensor |
| Tensor for all weight gradients. | |
| Matrix_t & | fWeightsCandidate |
| Candidate Gate weights for input, fWeights[4]. | |
| Matrix_t & | fWeightsCandidateGradients |
| Gradients w.r.t the candidate gate - input weights. | |
| Matrix_t & | fWeightsCandidateState |
| Candidate Gate weights for prev state, fWeights[5]. | |
| Matrix_t & | fWeightsCandidateStateGradients |
| Gradients w.r.t the candidate gate - hidden state weights. | |
| Matrix_t & | fWeightsResetGate |
| Reset Gate weights for input, fWeights[0]. | |
| Matrix_t & | fWeightsResetGateState |
| Input Gate weights for prev state, fWeights[1]. | |
| Matrix_t & | fWeightsResetGradients |
| Gradients w.r.t the reset gate - input weights. | |
| Matrix_t & | fWeightsResetStateGradients |
| Gradients w.r.t the reset gate - hidden state weights. | |
| Tensor_t | fWeightsTensor |
| Tensor for all weights. | |
| Matrix_t & | fWeightsUpdateGate |
| Update Gate weights for input, fWeights[2]. | |
| Matrix_t & | fWeightsUpdateGateState |
| Update Gate weights for prev state, fWeights[3]. | |
| Matrix_t & | fWeightsUpdateGradients |
| Gradients w.r.t the update gate - input weights. | |
| Matrix_t & | fWeightsUpdateStateGradients |
| Gradients w.r.t the update gate - hidden state weights. | |
| 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 | |
| std::vector< Matrix_t > | reset_gate_value |
| Reset gate value for every time step. | |
| std::vector< Matrix_t > | update_gate_value |
| Update gate value for every time step. | |
#include <TMVA/DNN/RNN/GRULayer.h>
| using TMVA::DNN::RNN::TBasicGRULayer< Architecture_t >::HelperDescriptor_t = typename Architecture_t::DropoutDescriptor_t |
Definition at line 68 of file GRULayer.h.
| using TMVA::DNN::RNN::TBasicGRULayer< Architecture_t >::LayerDescriptor_t = typename Architecture_t::RecurrentDescriptor_t |
Definition at line 65 of file GRULayer.h.
| using TMVA::DNN::RNN::TBasicGRULayer< Architecture_t >::Matrix_t = typename Architecture_t::Matrix_t |
Definition at line 61 of file GRULayer.h.
| using TMVA::DNN::RNN::TBasicGRULayer< Architecture_t >::RNNDescriptors_t = typename Architecture_t::RNNDescriptors_t |
Definition at line 71 of file GRULayer.h.
| using TMVA::DNN::RNN::TBasicGRULayer< Architecture_t >::RNNWorkspace_t = typename Architecture_t::RNNWorkspace_t |
Definition at line 70 of file GRULayer.h.
| using TMVA::DNN::RNN::TBasicGRULayer< Architecture_t >::Scalar_t = typename Architecture_t::Scalar_t |
Definition at line 62 of file GRULayer.h.
| using TMVA::DNN::RNN::TBasicGRULayer< Architecture_t >::Tensor_t = typename Architecture_t::Tensor_t |
Definition at line 63 of file GRULayer.h.
| using TMVA::DNN::RNN::TBasicGRULayer< Architecture_t >::TensorDescriptor_t = typename Architecture_t::TensorDescriptor_t |
Definition at line 67 of file GRULayer.h.
| using TMVA::DNN::RNN::TBasicGRULayer< Architecture_t >::WeightsDescriptor_t = typename Architecture_t::FilterDescriptor_t |
Definition at line 66 of file GRULayer.h.
| TMVA::DNN::RNN::TBasicGRULayer< Architecture_t >::TBasicGRULayer | ( | size_t | batchSize, |
| size_t | stateSize, | ||
| size_t | inputSize, | ||
| size_t | timeSteps, | ||
| bool | rememberState = false, | ||
| bool | returnSequence = false, | ||
| bool | resetGateAfter = false, | ||
| DNN::EActivationFunction | f1 = DNN::EActivationFunction::kSigmoid, | ||
| DNN::EActivationFunction | f2 = DNN::EActivationFunction::kTanh, | ||
| bool | training = true, | ||
| DNN::EInitialization | fA = DNN::EInitialization::kZero ) |
Constructor.
Definition at line 308 of file GRULayer.h.
| TMVA::DNN::RNN::TBasicGRULayer< Architecture_t >::TBasicGRULayer | ( | const TBasicGRULayer< Architecture_t > & | layer | ) |
Copy Constructor.
Definition at line 344 of file GRULayer.h.
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Writes the information and the weights about the layer in an XML node.
Implements TMVA::DNN::VGeneralLayer< Architecture_t >.
Definition at line 821 of file GRULayer.h.
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Backpropagates the error.
Must only be called directly at the corresponding call to Forward(...).
Implements TMVA::DNN::VGeneralLayer< Architecture_t >.
Definition at line 611 of file GRULayer.h.
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Decides the new candidate values (NN with Tanh).
Definition at line 459 of file GRULayer.h.
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Backward for a single time unit a the corresponding call to Forward(...).
Definition at line 776 of file GRULayer.h.
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Forward for a single cell (time unit).
Definition at line 591 of file GRULayer.h.
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Copies the biases provided as an input.
Definition at line 468 of file GeneralLayer.h.
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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.
Definition at line 478 of file GeneralLayer.h.
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Copies the weights provided as an input.
Definition at line 458 of file GeneralLayer.h.
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Definition at line 204 of file GRULayer.h.
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Definition at line 205 of file GRULayer.h.
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Computes the next hidden state and next cell state with given input matrix.
Implements TMVA::DNN::VGeneralLayer< Architecture_t >.
Definition at line 494 of file GRULayer.h.
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Definition at line 207 of file GRULayer.h.
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Definition at line 208 of file GRULayer.h.
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Definition at line 200 of file GeneralLayer.h.
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Definition at line 205 of file GeneralLayer.h.
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Definition at line 206 of file GeneralLayer.h.
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Getters.
Definition at line 163 of file GeneralLayer.h.
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Definition at line 179 of file GeneralLayer.h.
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Reimplemented in TMVA::DNN::TBatchNormLayer< Architecture_t >, TMVA::DNN::TBatchNormLayer< TCpu< AReal > >, and TMVA::DNN::TBatchNormLayer< TCuda< AReal > >.
Definition at line 210 of file GeneralLayer.h.
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Getters.
Definition at line 200 of file GRULayer.h.
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Definition at line 253 of file GRULayer.h.
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Definition at line 256 of file GRULayer.h.
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Definition at line 255 of file GRULayer.h.
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Definition at line 215 of file GRULayer.h.
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Definition at line 214 of file GRULayer.h.
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inlineinherited |
Definition at line 185 of file GeneralLayer.h.
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inlineinherited |
Definition at line 184 of file GeneralLayer.h.
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inlineinherited |
Definition at line 188 of file GeneralLayer.h.
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inlineinherited |
Definition at line 187 of file GeneralLayer.h.
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Definition at line 292 of file GRULayer.h.
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inline |
Definition at line 293 of file GRULayer.h.
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inlineinherited |
Definition at line 173 of file GeneralLayer.h.
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inlineinherited |
Definition at line 172 of file GeneralLayer.h.
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inlineinherited |
Definition at line 176 of file GeneralLayer.h.
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inlineinherited |
Definition at line 175 of file GeneralLayer.h.
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Definition at line 225 of file GRULayer.h.
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Definition at line 224 of file GRULayer.h.
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Definition at line 284 of file GRULayer.h.
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Definition at line 283 of file GRULayer.h.
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Definition at line 234 of file GRULayer.h.
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Definition at line 233 of file GRULayer.h.
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Definition at line 286 of file GRULayer.h.
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Definition at line 285 of file GRULayer.h.
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Definition at line 223 of file GRULayer.h.
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Definition at line 222 of file GRULayer.h.
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Definition at line 230 of file GRULayer.h.
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Definition at line 229 of file GRULayer.h.
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Definition at line 272 of file GRULayer.h.
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Definition at line 271 of file GRULayer.h.
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Definition at line 274 of file GRULayer.h.
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Definition at line 273 of file GRULayer.h.
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Definition at line 290 of file GRULayer.h.
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Definition at line 291 of file GRULayer.h.
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Definition at line 227 of file GRULayer.h.
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Definition at line 226 of file GRULayer.h.
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Definition at line 232 of file GRULayer.h.
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Definition at line 231 of file GRULayer.h.
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Definition at line 278 of file GRULayer.h.
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Definition at line 277 of file GRULayer.h.
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Definition at line 280 of file GRULayer.h.
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Definition at line 279 of file GRULayer.h.
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inlineinherited |
Definition at line 169 of file GeneralLayer.h.
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inline |
Definition at line 295 of file GRULayer.h.
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Definition at line 296 of file GRULayer.h.
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overridevirtual |
Initialize the weights according to the given initialization method.
Reimplemented from TMVA::DNN::VGeneralLayer< Architecture_t >.
Definition at line 409 of file GRULayer.h.
| auto TMVA::DNN::RNN::TBasicGRULayer< Architecture_t >::InitState | ( | DNN::EInitialization | m = DNN::EInitialization::kZero | ) |
Initialize the hidden state and cell state method.
Definition at line 801 of file GRULayer.h.
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inlineinherited |
Definition at line 170 of file GeneralLayer.h.
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overridevirtual |
Prints the info about the layer.
Implements TMVA::DNN::VGeneralLayer< Architecture_t >.
Definition at line 809 of file GRULayer.h.
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inherited |
Definition at line 544 of file GeneralLayer.h.
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inlineoverridevirtual |
Read the information and the weights about the layer from XML node.
Implements TMVA::DNN::VGeneralLayer< Architecture_t >.
Definition at line 848 of file GRULayer.h.
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Decides the values we'll update (NN with Sigmoid).
Definition at line 423 of file GRULayer.h.
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inlinevirtualinherited |
Reset some training flags after a loop on all batches Some layer (e.g.
batchnormalization) might need to implement the function in case some operations are needed after looping an all batches
Reimplemented in TMVA::DNN::TBatchNormLayer< Architecture_t >, TMVA::DNN::TBatchNormLayer< TCpu< AReal > >, and TMVA::DNN::TBatchNormLayer< TCuda< AReal > >.
Definition at line 121 of file GeneralLayer.h.
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Setters.
Definition at line 217 of file GeneralLayer.h.
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inlineinherited |
Definition at line 221 of file GeneralLayer.h.
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inlinevirtualinherited |
Set Dropout probability.
Reimplemented for layers supporting droput
Reimplemented in TMVA::DNN::TDenseLayer< Architecture_t >.
Definition at line 160 of file GeneralLayer.h.
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inlinevirtualinherited |
Reimplemented in TMVA::DNN::TBatchNormLayer< Architecture_t >, TMVA::DNN::TBatchNormLayer< TCpu< AReal > >, and TMVA::DNN::TBatchNormLayer< TCuda< AReal > >.
Definition at line 212 of file GeneralLayer.h.
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inlineinherited |
Definition at line 222 of file GeneralLayer.h.
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inlineinherited |
Definition at line 218 of file GeneralLayer.h.
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inlineinherited |
Definition at line 219 of file GeneralLayer.h.
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inlineinherited |
Definition at line 220 of file GeneralLayer.h.
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inlineinherited |
Definition at line 224 of file GeneralLayer.h.
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inlineinherited |
Definition at line 223 of file GeneralLayer.h.
| void TMVA::DNN::RNN::TBasicGRULayer< Architecture_t >::Update | ( | const Scalar_t | learningRate | ) |
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inherited |
Updates the biases, given the gradients and the learning rate.
Definition at line 428 of file GeneralLayer.h.
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inherited |
Updates the bias gradients, given some other weight gradients and learning rate.
Definition at line 448 of file GeneralLayer.h.
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inline |
Forgets the past values (NN with Sigmoid).
Definition at line 441 of file GRULayer.h.
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inherited |
Updates the weight gradients, given some other weight gradients and learning rate.
Definition at line 438 of file GeneralLayer.h.
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inherited |
Updates the weights, given the gradients and the learning rate,.
Definition at line 418 of file GeneralLayer.h.
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inherited |
Definition at line 521 of file GeneralLayer.h.
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inherited |
helper functions for XML
Definition at line 496 of file GeneralLayer.h.
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private |
Candidate gate value for every time step.
Definition at line 106 of file GRULayer.h.
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protectedinherited |
Gradients w.r.t. the activations of this layer.
Definition at line 78 of file GeneralLayer.h.
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protectedinherited |
Batch size used for training and evaluation.
Definition at line 59 of file GeneralLayer.h.
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protectedinherited |
The biases associated to the layer.
Definition at line 72 of file GeneralLayer.h.
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protectedinherited |
Gradients w.r.t. the bias values of the layer.
Definition at line 75 of file GeneralLayer.h.
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private |
Candidate Gate bias.
Definition at line 101 of file GRULayer.h.
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private |
Gradients w.r.t the candidate gate - bias weights.
Definition at line 120 of file GRULayer.h.
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private |
Computed candidate values.
Definition at line 87 of file GRULayer.h.
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private |
Empty matrix for GRU.
Definition at line 122 of file GRULayer.h.
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protectedinherited |
The depth of the layer.
Definition at line 65 of file GeneralLayer.h.
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private |
First fDerivatives of the activations candidate gate.
Definition at line 110 of file GRULayer.h.
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private |
First fDerivatives of the activations reset gate.
Definition at line 108 of file GRULayer.h.
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private |
First fDerivatives of the activations update gate.
Definition at line 109 of file GRULayer.h.
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private |
Keeps all the RNN descriptors.
Definition at line 134 of file GRULayer.h.
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cached gradient on the input (output of backward) as T x B x I
Definition at line 131 of file GRULayer.h.
|
private |
cached activation gradient (input of backward) as T x B x S
Definition at line 132 of file GRULayer.h.
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private |
Activation function: sigmoid.
Definition at line 82 of file GRULayer.h.
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Activation function: tanh.
Definition at line 83 of file GRULayer.h.
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protectedinherited |
The height of the layer.
Definition at line 66 of file GeneralLayer.h.
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protectedinherited |
The initialization method.
Definition at line 80 of file GeneralLayer.h.
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protectedinherited |
The depth of the previous layer or input.
Definition at line 61 of file GeneralLayer.h.
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protectedinherited |
The height of the previous layer or input.
Definition at line 62 of file GeneralLayer.h.
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protectedinherited |
The width of the previous layer or input.
Definition at line 63 of file GeneralLayer.h.
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protectedinherited |
Flag indicating the mode.
Definition at line 69 of file GeneralLayer.h.
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protectedinherited |
Activations of this layer.
Definition at line 77 of file GeneralLayer.h.
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private |
Remember state in next pass.
Definition at line 78 of file GRULayer.h.
|
private |
Gradients w.r.t the reset gate - bias weights.
Definition at line 114 of file GRULayer.h.
|
private |
GRU variant to Apply the reset gate multiplication afterwards (used by cuDNN).
Definition at line 80 of file GRULayer.h.
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Input Gate bias.
Definition at line 93 of file GRULayer.h.
|
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Computed reset gate values.
Definition at line 85 of file GRULayer.h.
|
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Return in output full sequence or just last element.
Definition at line 79 of file GRULayer.h.
|
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Hidden state of GRU.
Definition at line 88 of file GRULayer.h.
|
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Hidden state size for GRU.
Definition at line 75 of file GRULayer.h.
|
private |
Timesteps for GRU.
Definition at line 76 of file GRULayer.h.
|
private |
Gradients w.r.t the update gate - bias weights.
Definition at line 117 of file GRULayer.h.
|
private |
Update Gate bias.
Definition at line 97 of file GRULayer.h.
|
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Computed forget gate values.
Definition at line 86 of file GRULayer.h.
|
protectedinherited |
Gradients w.r.t. the weights of the layer.
Definition at line 74 of file GeneralLayer.h.
|
private |
Tensor for all weight gradients.
Definition at line 126 of file GRULayer.h.
|
protectedinherited |
The weights associated to the layer.
Definition at line 71 of file GeneralLayer.h.
|
private |
Candidate Gate weights for input, fWeights[4].
Definition at line 99 of file GRULayer.h.
|
private |
Gradients w.r.t the candidate gate - input weights.
Definition at line 118 of file GRULayer.h.
|
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Candidate Gate weights for prev state, fWeights[5].
Definition at line 100 of file GRULayer.h.
|
private |
Gradients w.r.t the candidate gate - hidden state weights.
Definition at line 119 of file GRULayer.h.
|
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Reset Gate weights for input, fWeights[0].
Definition at line 91 of file GRULayer.h.
|
private |
Input Gate weights for prev state, fWeights[1].
Definition at line 92 of file GRULayer.h.
|
private |
Gradients w.r.t the reset gate - input weights.
Definition at line 112 of file GRULayer.h.
|
private |
Gradients w.r.t the reset gate - hidden state weights.
Definition at line 113 of file GRULayer.h.
|
private |
Tensor for all weights.
Definition at line 125 of file GRULayer.h.
|
private |
Update Gate weights for input, fWeights[2].
Definition at line 95 of file GRULayer.h.
|
private |
Update Gate weights for prev state, fWeights[3].
Definition at line 96 of file GRULayer.h.
|
private |
Gradients w.r.t the update gate - input weights.
Definition at line 115 of file GRULayer.h.
|
private |
Gradients w.r.t the update gate - hidden state weights.
Definition at line 116 of file GRULayer.h.
|
protectedinherited |
The width of this layer.
Definition at line 67 of file GeneralLayer.h.
|
private |
Definition at line 135 of file GRULayer.h.
|
private |
cached input tensor as T x B x I
Definition at line 129 of file GRULayer.h.
|
private |
cached output tensor as T x B x S
Definition at line 130 of file GRULayer.h.
|
private |
Reset gate value for every time step.
Definition at line 104 of file GRULayer.h.
|
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Update gate value for every time step.
Definition at line 105 of file GRULayer.h.