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Reference Guide
TMVA::DNN::Net Member List

This is the complete list of members for TMVA::DNN::Net, including all inherited members.

addLayer(Layer &layer)TMVA::DNN::Netinline
addLayer(Layer &&layer)TMVA::DNN::Netinline
backPropagate(std::vector< std::vector< LayerData > > &layerPatternData, const Settings &settings, size_t trainFromLayer, size_t totalNumWeights) constTMVA::DNN::Net
begin_end_type typedefTMVA::DNN::Net
clear()TMVA::DNN::Netinline
compute(const std::vector< double > &input, const Weights &weights) constTMVA::DNN::Net
computeError(const Settings &settings, std::vector< LayerData > &lastLayerData, Batch &batch, ItWeight itWeightBegin, ItWeight itWeightEnd) constTMVA::DNN::Net
container_type typedefTMVA::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) constTMVA::DNN::Net
fetchOutput(const LayerData &lastLayerData, OutputContainer &outputContainer) constTMVA::DNN::Net
fetchOutput(const std::vector< LayerData > &layerPatternData, OutputContainer &outputContainer) constTMVA::DNN::Net
fExitFromTrainingTMVA::DNN::Netprotected
fillDropContainer(DropContainer &dropContainer, double dropFraction, size_t numNodes) constTMVA::DNN::Netprotected
fInteractiveTMVA::DNN::Netprotected
fIPyCurrentIterTMVA::DNN::Netprotected
fIPyMaxIterTMVA::DNN::Netprotected
forward_backward(LayerContainer &layers, PassThrough &settingsAndBatch, ItWeight itWeightBegin, ItWeight itWeightEnd, ItGradient itGradientBegin, ItGradient itGradientEnd, size_t trainFromLayer, OutContainer &outputContainer, bool fetchOutput) constTMVA::DNN::Net
forwardBatch(const LayerContainer &_layers, LayerPatternContainer &layerPatternData, std::vector< double > &valuesMean, std::vector< double > &valuesStdDev, size_t trainFromLayer) constTMVA::DNN::Net
forwardPattern(const LayerContainer &_layers, std::vector< LayerData > &layerData) constTMVA::DNN::Net
initializeWeights(WeightInitializationStrategy eInitStrategy, OutIterator itWeight)TMVA::DNN::Net
inputSize() constTMVA::DNN::Netinline
iterator_type typedefTMVA::DNN::Net
layers() constTMVA::DNN::Netinline
layers()TMVA::DNN::Netinline
m_eErrorFunctionTMVA::DNN::Netprivate
m_layersTMVA::DNN::Netprivate
m_sizeInputTMVA::DNN::Netprivate
m_sizeOutputTMVA::DNN::Netprivate
Net()TMVA::DNN::Netinline
Net(const Net &other)TMVA::DNN::Netinline
numNodes(size_t trainingStartLayer=0) constTMVA::DNN::Net
numWeights(size_t trainingStartLayer=0) constTMVA::DNN::Net
operator()(PassThrough &settingsAndBatch, const Weights &weights) constTMVA::DNN::Net
operator()(PassThrough &settingsAndBatch, const Weights &weights, ModeOutput eFetch, OutContainer &outputContainer) constTMVA::DNN::Net
operator()(PassThrough &settingsAndBatch, Weights &weights, Gradients &gradients) constTMVA::DNN::Net
operator()(PassThrough &settingsAndBatch, Weights &weights, Gradients &gradients, ModeOutput eFetch, OutContainer &outputContainer) constTMVA::DNN::Net
outputSize() constTMVA::DNN::Netinline
prepareLayerData(LayerContainer &layers, Batch &batch, const DropContainer &dropContainer, ItWeight itWeightBegin, ItWeight itWeightEnd, ItGradient itGradientBegin, ItGradient itGradientEnd, size_t &totalNumWeights) constTMVA::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::Netinline
setErrorFunction(ModeErrorFunction eErrorFunction)TMVA::DNN::Netinline
setInputSize(size_t sizeInput)TMVA::DNN::Netinline
SetIpythonInteractive(IPythonInteractive *fI, bool *fE, UInt_t *M, UInt_t *C)TMVA::DNN::Netinline
setOutputSize(size_t sizeOutput)TMVA::DNN::Netinline
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::Netinline