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Reference Guide |
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ROOT
Reference Guide |
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This is the complete list of members for TMVA::DNN::Net, including all inherited members.
| addLayer(Layer &layer) | TMVA::DNN::Net | inline |
| addLayer(Layer &&layer) | TMVA::DNN::Net | inline |
| backPropagate(std::vector< std::vector< LayerData > > &layerPatternData, const Settings &settings, size_t trainFromLayer, size_t totalNumWeights) const | TMVA::DNN::Net | |
| begin_end_type typedef | TMVA::DNN::Net | |
| clear() | TMVA::DNN::Net | inline |
| compute(const std::vector< double > &input, const Weights &weights) const | TMVA::DNN::Net | |
| computeError(const Settings &settings, std::vector< LayerData > &lastLayerData, Batch &batch, ItWeight itWeightBegin, ItWeight itWeightEnd) const | TMVA::DNN::Net | |
| container_type typedef | TMVA::DNN::Net | |
| dE() | TMVA::DNN::Net | |
| dropOutWeightFactor(WeightsType &weights, const DropProbabilities &drops, bool inverse=false) | TMVA::DNN::Net | |
| E() | TMVA::DNN::Net | |
| errorFunction(LayerData &layerData, Container truth, ItWeight itWeight, ItWeight itWeightEnd, double patternWeight, double factorWeightDecay, EnumRegularization eRegularization) const | TMVA::DNN::Net | |
| fetchOutput(const LayerData &lastLayerData, OutputContainer &outputContainer) const | TMVA::DNN::Net | |
| fetchOutput(const std::vector< LayerData > &layerPatternData, OutputContainer &outputContainer) const | TMVA::DNN::Net | |
| fExitFromTraining | TMVA::DNN::Net | protected |
| fillDropContainer(DropContainer &dropContainer, double dropFraction, size_t numNodes) const | TMVA::DNN::Net | protected |
| fInteractive | TMVA::DNN::Net | protected |
| fIPyCurrentIter | TMVA::DNN::Net | protected |
| fIPyMaxIter | TMVA::DNN::Net | protected |
| forward_backward(LayerContainer &layers, PassThrough &settingsAndBatch, ItWeight itWeightBegin, ItWeight itWeightEnd, ItGradient itGradientBegin, ItGradient itGradientEnd, size_t trainFromLayer, OutContainer &outputContainer, bool fetchOutput) const | TMVA::DNN::Net | |
| forwardBatch(const LayerContainer &_layers, LayerPatternContainer &layerPatternData, std::vector< double > &valuesMean, std::vector< double > &valuesStdDev, size_t trainFromLayer) const | TMVA::DNN::Net | |
| forwardPattern(const LayerContainer &_layers, std::vector< LayerData > &layerData) const | TMVA::DNN::Net | |
| initializeWeights(WeightInitializationStrategy eInitStrategy, OutIterator itWeight) | TMVA::DNN::Net | |
| inputSize() const | TMVA::DNN::Net | inline |
| iterator_type typedef | TMVA::DNN::Net | |
| layers() const | TMVA::DNN::Net | inline |
| layers() | TMVA::DNN::Net | inline |
| m_eErrorFunction | TMVA::DNN::Net | private |
| m_layers | TMVA::DNN::Net | private |
| m_sizeInput | TMVA::DNN::Net | private |
| m_sizeOutput | TMVA::DNN::Net | private |
| Net() | TMVA::DNN::Net | inline |
| Net(const Net &other) | TMVA::DNN::Net | inline |
| numNodes(size_t trainingStartLayer=0) const | TMVA::DNN::Net | |
| numWeights(size_t trainingStartLayer=0) const | TMVA::DNN::Net | |
| operator()(PassThrough &settingsAndBatch, const Weights &weights) const | TMVA::DNN::Net | |
| operator()(PassThrough &settingsAndBatch, const Weights &weights, ModeOutput eFetch, OutContainer &outputContainer) const | TMVA::DNN::Net | |
| operator()(PassThrough &settingsAndBatch, Weights &weights, Gradients &gradients) const | TMVA::DNN::Net | |
| operator()(PassThrough &settingsAndBatch, Weights &weights, Gradients &gradients, ModeOutput eFetch, OutContainer &outputContainer) const | TMVA::DNN::Net | |
| outputSize() const | TMVA::DNN::Net | inline |
| prepareLayerData(LayerContainer &layers, Batch &batch, const DropContainer &dropContainer, ItWeight itWeightBegin, ItWeight itWeightEnd, ItGradient itGradientBegin, ItGradient itGradientEnd, size_t &totalNumWeights) const | TMVA::DNN::Net | |
| preTrain(std::vector< double > &weights, std::vector< Pattern > &trainPattern, const std::vector< Pattern > &testPattern, Minimizer &minimizer, Settings &settings) | TMVA::DNN::Net | |
| removeLayer() | TMVA::DNN::Net | inline |
| setErrorFunction(ModeErrorFunction eErrorFunction) | TMVA::DNN::Net | inline |
| setInputSize(size_t sizeInput) | TMVA::DNN::Net | inline |
| SetIpythonInteractive(IPythonInteractive *fI, bool *fE, UInt_t *M, UInt_t *C) | TMVA::DNN::Net | inline |
| setOutputSize(size_t sizeOutput) | TMVA::DNN::Net | inline |
| train(std::vector< double > &weights, std::vector< Pattern > &trainPattern, const std::vector< Pattern > &testPattern, Minimizer &minimizer, Settings &settings) | TMVA::DNN::Net | |
| trainCycle(Minimizer &minimizer, std::vector< double > &weights, Iterator itPatternBegin, Iterator itPatternEnd, Settings &settings, DropContainer &dropContainer) | TMVA::DNN::Net | inline |
