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ROOT 6.14/05 Reference Guide |
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 |