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Reference Guide
TMVA::DNN::TDeepNet< Architecture_t, Layer_t > Member List

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

AddBasicRNNLayer(size_t stateSize, size_t inputSize, size_t timeSteps, bool rememberState=false)TMVA::DNN::TDeepNet< Architecture_t, Layer_t >
AddBasicRNNLayer(TBasicRNNLayer< Architecture_t > *basicRNNLayer)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(std::vector< Matrix_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
fBatchDepthTMVA::DNN::TDeepNet< Architecture_t, Layer_t >private
fBatchHeightTMVA::DNN::TDeepNet< Architecture_t, Layer_t >private
fBatchSizeTMVA::DNN::TDeepNet< Architecture_t, Layer_t >private
fBatchWidthTMVA::DNN::TDeepNet< Architecture_t, Layer_t >private
fITMVA::DNN::TDeepNet< Architecture_t, Layer_t >private
fInputDepthTMVA::DNN::TDeepNet< Architecture_t, Layer_t >private
fInputHeightTMVA::DNN::TDeepNet< Architecture_t, Layer_t >private
fInputWidthTMVA::DNN::TDeepNet< Architecture_t, Layer_t >private
fIsTrainingTMVA::DNN::TDeepNet< Architecture_t, Layer_t >private
fJTMVA::DNN::TDeepNet< Architecture_t, Layer_t >private
fLayersTMVA::DNN::TDeepNet< Architecture_t, Layer_t >private
Forward(std::vector< Matrix_t > &input, bool applyDropout=false)TMVA::DNN::TDeepNet< Architecture_t, Layer_t >
fRTMVA::DNN::TDeepNet< Architecture_t, Layer_t >private
fWeightDecayTMVA::DNN::TDeepNet< Architecture_t, Layer_t >private
GetBatchDepth() constTMVA::DNN::TDeepNet< Architecture_t, Layer_t >inline
GetBatchHeight() constTMVA::DNN::TDeepNet< Architecture_t, Layer_t >inline
GetBatchSize() constTMVA::DNN::TDeepNet< Architecture_t, Layer_t >inline
GetBatchWidth() constTMVA::DNN::TDeepNet< Architecture_t, Layer_t >inline
GetDepth() constTMVA::DNN::TDeepNet< Architecture_t, Layer_t >inline
GetInitialization() constTMVA::DNN::TDeepNet< Architecture_t, Layer_t >inline
GetInputDepth() constTMVA::DNN::TDeepNet< Architecture_t, Layer_t >inline
GetInputHeight() constTMVA::DNN::TDeepNet< Architecture_t, Layer_t >inline
GetInputWidth() constTMVA::DNN::TDeepNet< Architecture_t, Layer_t >inline
GetLayerAt(size_t i)TMVA::DNN::TDeepNet< Architecture_t, Layer_t >inline
GetLayerAt(size_t i) constTMVA::DNN::TDeepNet< Architecture_t, Layer_t >inline
GetLayers()TMVA::DNN::TDeepNet< Architecture_t, Layer_t >inline
GetLayers() constTMVA::DNN::TDeepNet< Architecture_t, Layer_t >inline
GetLossFunction() constTMVA::DNN::TDeepNet< Architecture_t, Layer_t >inline
GetOutputWidth() constTMVA::DNN::TDeepNet< Architecture_t, Layer_t >inline
GetRegularization() constTMVA::DNN::TDeepNet< Architecture_t, Layer_t >inline
GetWeightDecay() constTMVA::DNN::TDeepNet< Architecture_t, Layer_t >inline
Initialize()TMVA::DNN::TDeepNet< Architecture_t, Layer_t >
isInteger(Scalar_t x) constTMVA::DNN::TDeepNet< Architecture_t, Layer_t >inlineprivate
IsTraining() constTMVA::DNN::TDeepNet< Architecture_t, Layer_t >inline
Loss(const Matrix_t &groundTruth, const Matrix_t &weights, bool includeRegularization=true) constTMVA::DNN::TDeepNet< Architecture_t, Layer_t >
Loss(std::vector< Matrix_t > &input, const Matrix_t &groundTruth, const Matrix_t &weights, bool applyDropout=false, bool includeRegularization=true)TMVA::DNN::TDeepNet< Architecture_t, Layer_t >
Matrix_t typedefTMVA::DNN::TDeepNet< Architecture_t, Layer_t >
ParallelBackward(std::vector< TDeepNet< Architecture_t, Layer_t >> &nets, std::vector< TTensorBatch< Architecture_t >> &batches, Scalar_t learningRate)TMVA::DNN::TDeepNet< Architecture_t, Layer_t >
ParallelBackwardMomentum(std::vector< TDeepNet< Architecture_t, Layer_t >> &nets, std::vector< TTensorBatch< Architecture_t >> &batches, Scalar_t learningRate, Scalar_t momentum)TMVA::DNN::TDeepNet< Architecture_t, Layer_t >
ParallelBackwardNestorov(std::vector< TDeepNet< Architecture_t, Layer_t >> &nets, std::vector< TTensorBatch< Architecture_t >> &batches, Scalar_t learningRate, Scalar_t momentum)TMVA::DNN::TDeepNet< Architecture_t, Layer_t >
ParallelForward(std::vector< TDeepNet< Architecture_t, Layer_t >> &nets, std::vector< TTensorBatch< Architecture_t >> &batches, bool applyDropout=false)TMVA::DNN::TDeepNet< Architecture_t, Layer_t >
Prediction(Matrix_t &predictions, EOutputFunction f) constTMVA::DNN::TDeepNet< Architecture_t, Layer_t >
Prediction(Matrix_t &predictions, std::vector< Matrix_t > input, EOutputFunction f)TMVA::DNN::TDeepNet< Architecture_t, Layer_t >
Print() constTMVA::DNN::TDeepNet< Architecture_t, Layer_t >
Scalar_t typedefTMVA::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
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 >
Update(Scalar_t learningRate)TMVA::DNN::TDeepNet< Architecture_t, Layer_t >
~TDeepNet()TMVA::DNN::TDeepNet< Architecture_t, Layer_t >