| AddBasicGRULayer(size_t stateSize, size_t inputSize, size_t timeSteps, bool rememberState=false, bool returnSequence=false, bool resetGateAfter=false) | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | |
| AddBasicGRULayer(TBasicGRULayer< Architecture_t > *basicGRULayer) | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | |
| AddBasicLSTMLayer(size_t stateSize, size_t inputSize, size_t timeSteps, bool rememberState=false, bool returnSequence=false) | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | |
| AddBasicLSTMLayer(TBasicLSTMLayer< Architecture_t > *basicLSTMLayer) | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | |
| AddBasicRNNLayer(size_t stateSize, size_t inputSize, size_t timeSteps, bool rememberState=false, bool returnSequence=false, EActivationFunction f=EActivationFunction::kTanh) | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | |
| AddBasicRNNLayer(TBasicRNNLayer< Architecture_t > *basicRNNLayer) | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | |
| AddBatchNormLayer(Scalar_t momentum=-1, Scalar_t epsilon=0.0001) | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | |
| AddConvLayer(size_t depth, size_t filterHeight, size_t filterWidth, size_t strideRows, size_t strideCols, size_t paddingHeight, size_t paddingWidth, EActivationFunction f, Scalar_t dropoutProbability=1.0) | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | |
| AddConvLayer(TConvLayer< Architecture_t > *convLayer) | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | |
| AddDenseLayer(size_t width, EActivationFunction f, Scalar_t dropoutProbability=1.0) | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | |
| AddDenseLayer(TDenseLayer< Architecture_t > *denseLayer) | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | |
| AddMaxPoolLayer(size_t frameHeight, size_t frameWidth, size_t strideRows, size_t strideCols, Scalar_t dropoutProbability=1.0) | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | |
| AddMaxPoolLayer(CNN::TMaxPoolLayer< Architecture_t > *maxPoolLayer) | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | |
| AddReshapeLayer(size_t depth, size_t height, size_t width, bool flattening) | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | |
| AddReshapeLayer(TReshapeLayer< Architecture_t > *reshapeLayer) | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | |
| Backward(const Tensor_t &input, const Matrix_t &groundTruth, const Matrix_t &weights) | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | |
| calculateDimension(int imgDim, int fltDim, int padding, int stride) | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | private |
| Clear() | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | inline |
| fBatchDepth | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | private |
| fBatchHeight | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | private |
| fBatchSize | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | private |
| fBatchWidth | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | private |
| fI | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | private |
| fInputDepth | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | private |
| fInputHeight | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | private |
| fInputWidth | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | private |
| fIsTraining | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | private |
| fJ | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | private |
| fLayers | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | private |
| Forward(Tensor_t &input, bool applyDropout=false) | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | |
| fR | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | private |
| fWeightDecay | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | private |
| GetBatchDepth() const | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | inline |
| GetBatchHeight() const | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | inline |
| GetBatchSize() const | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | inline |
| GetBatchWidth() const | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | inline |
| GetDepth() const | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | inline |
| GetInitialization() const | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | inline |
| GetInputDepth() const | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | inline |
| GetInputHeight() const | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | inline |
| GetInputWidth() const | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | inline |
| GetLayerAt(size_t i) | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | inline |
| GetLayerAt(size_t i) const | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | inline |
| GetLayers() | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | inline |
| GetLayers() const | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | inline |
| GetLossFunction() const | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | inline |
| GetOutputWidth() const | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | inline |
| GetRegularization() const | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | inline |
| GetWeightDecay() const | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | inline |
| Initialize() | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | |
| isInteger(Scalar_t x) const | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | inlineprivate |
| IsTraining() const | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | inline |
| Loss(const Matrix_t &groundTruth, const Matrix_t &weights, bool includeRegularization=true) const | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | |
| Loss(Tensor_t &input, const Matrix_t &groundTruth, const Matrix_t &weights, bool inTraining=false, bool includeRegularization=true) | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | |
| Matrix_t typedef | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | |
| Prediction(Matrix_t &predictions, EOutputFunction f) const | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | |
| Prediction(Matrix_t &predictions, Tensor_t &input, EOutputFunction f) | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | |
| Print() const | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | |
| RegularizationTerm() const | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | |
| ResetTraining() | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | |
| Scalar_t typedef | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | |
| SetBatchDepth(size_t batchDepth) | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | inline |
| SetBatchHeight(size_t batchHeight) | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | inline |
| SetBatchSize(size_t batchSize) | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | inline |
| SetBatchWidth(size_t batchWidth) | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | inline |
| SetDropoutProbabilities(const std::vector< Double_t > &probabilities) | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | |
| SetInitialization(EInitialization I) | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | inline |
| SetInputDepth(size_t inputDepth) | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | inline |
| SetInputHeight(size_t inputHeight) | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | inline |
| SetInputWidth(size_t inputWidth) | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | inline |
| SetLossFunction(ELossFunction J) | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | inline |
| SetRegularization(ERegularization R) | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | inline |
| SetWeightDecay(Scalar_t weightDecay) | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | inline |
| TDeepNet() | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | |
| TDeepNet(size_t BatchSize, size_t InputDepth, size_t InputHeight, size_t InputWidth, size_t BatchDepth, size_t BatchHeight, size_t BatchWidth, ELossFunction fJ, EInitialization fI=EInitialization::kZero, ERegularization fR=ERegularization::kNone, Scalar_t fWeightDecay=0.0, bool isTraining=false) | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | |
| TDeepNet(const TDeepNet &) | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | |
| Tensor_t typedef | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | |
| Update(Scalar_t learningRate) | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | |
| ~TDeepNet() | TMVA::DNN::TDeepNet< Architecture_t, Layer_t > | |
