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::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