addPoint(std::string histoName, double x) | TMVA::DNN::Settings | inline |
addPoint(std::string histoName, double x, double y) | TMVA::DNN::Settings | inline |
batchSize() const | TMVA::DNN::Settings | inline |
clear(std::string histoName) | TMVA::DNN::Settings | inline |
computeResult(const Net &, std::vector< double > &) | TMVA::DNN::Settings | inlinevirtual |
convergenceCount() const | TMVA::DNN::Settings | inline |
convergenceSteps() const | TMVA::DNN::Settings | inline |
count_dE | TMVA::DNN::Settings | |
count_E | TMVA::DNN::Settings | |
count_mb_dE | TMVA::DNN::Settings | |
count_mb_E | TMVA::DNN::Settings | |
create(std::string histoName, int bins, double min, double max) | TMVA::DNN::Settings | inline |
create(std::string histoName, int bins, double min, double max, int bins2, double min2, double max2) | TMVA::DNN::Settings | inline |
cycle(double progress, TString text) | TMVA::DNN::Settings | inlinevirtual |
drawSample(const std::vector< double > &, const std::vector< double > &, const std::vector< double > &, double) | TMVA::DNN::Settings | inlinevirtual |
dropFractions() const | TMVA::DNN::Settings | inline |
dropRepetitions() const | TMVA::DNN::Settings | inline |
endTestCycle() | TMVA::DNN::Settings | inlinevirtual |
endTrainCycle(double) | TMVA::DNN::Settings | inlinevirtual |
exists(std::string histoName) | TMVA::DNN::Settings | inline |
factorWeightDecay() const | TMVA::DNN::Settings | inline |
fLearningRate | TMVA::DNN::Settings | |
fMinimizerType | TMVA::DNN::Settings | |
fMomentum | TMVA::DNN::Settings | |
fMonitoring | TMVA::DNN::Settings | protected |
fRepetitions | TMVA::DNN::Settings | |
hasConverged(double testError) | TMVA::DNN::Settings | virtual |
learningRate() const | TMVA::DNN::Settings | inline |
m_batchSize | TMVA::DNN::Settings | |
m_convergenceCount | TMVA::DNN::Settings | |
m_convergenceSteps | TMVA::DNN::Settings | |
m_dropOut | TMVA::DNN::Settings | |
m_dropRepetitions | TMVA::DNN::Settings | |
m_factorWeightDecay | TMVA::DNN::Settings | |
m_maxConvergenceCount | TMVA::DNN::Settings | |
m_maxProgress | TMVA::DNN::Settings | |
m_minError | TMVA::DNN::Settings | |
m_minProgress | TMVA::DNN::Settings | |
m_regularization | TMVA::DNN::Settings | |
m_testRepetitions | TMVA::DNN::Settings | |
m_timer | TMVA::DNN::Settings | |
m_useMultithreading | TMVA::DNN::Settings | protected |
maxConvergenceCount() const | TMVA::DNN::Settings | inline |
minError() const | TMVA::DNN::Settings | inline |
minimizerType() const | TMVA::DNN::Settings | inline |
momentum() const | TMVA::DNN::Settings | inline |
pads(int numPads) | TMVA::DNN::Settings | inline |
plot(std::string histoName, std::string options, int pad, EColor color) | TMVA::DNN::Settings | inline |
regularization() const | TMVA::DNN::Settings | inline |
repetitions() const | TMVA::DNN::Settings | inline |
setDropOut(Iterator begin, Iterator end, size_t _dropRepetitions) | TMVA::DNN::Settings | inline |
setMonitoring(std::shared_ptr< Monitoring > ptrMonitoring) | TMVA::DNN::Settings | inline |
setProgressLimits(double minProgress=0, double maxProgress=100) | TMVA::DNN::Settings | inlinevirtual |
Settings(TString name, size_t _convergenceSteps=15, size_t _batchSize=10, size_t _testRepetitions=7, double _factorWeightDecay=1e-5, TMVA::DNN::EnumRegularization _regularization=TMVA::DNN::EnumRegularization::NONE, MinimizerType _eMinimizerType=MinimizerType::fSteepest, double _learningRate=1e-5, double _momentum=0.3, int _repetitions=3, bool _multithreading=true) | TMVA::DNN::Settings | |
startTestCycle() | TMVA::DNN::Settings | inlinevirtual |
startTrainCycle() | TMVA::DNN::Settings | inlinevirtual |
startTraining() | TMVA::DNN::Settings | inlinevirtual |
testIteration() | TMVA::DNN::Settings | inlinevirtual |
testRepetitions() const | TMVA::DNN::Settings | inline |
testSample(double, double, double, double) | TMVA::DNN::Settings | inlinevirtual |
useMultithreading() const | TMVA::DNN::Settings | inline |
~Settings() | TMVA::DNN::Settings | virtual |