DeepNet_t typedef | TMVA::DNN::TDLGradientDescent< Architecture_t > | |
fBatchSize | TMVA::DNN::TDLGradientDescent< Architecture_t > | private |
fConvergenceCount | TMVA::DNN::TDLGradientDescent< Architecture_t > | private |
fConvergenceSteps | TMVA::DNN::TDLGradientDescent< Architecture_t > | private |
fLearningRate | TMVA::DNN::TDLGradientDescent< Architecture_t > | private |
fMinimumError | TMVA::DNN::TDLGradientDescent< Architecture_t > | private |
fStepCount | TMVA::DNN::TDLGradientDescent< Architecture_t > | private |
fTestError | TMVA::DNN::TDLGradientDescent< Architecture_t > | private |
fTestInterval | TMVA::DNN::TDLGradientDescent< Architecture_t > | private |
fTrainingError | TMVA::DNN::TDLGradientDescent< Architecture_t > | private |
GetConvergenceCount() const | TMVA::DNN::TDLGradientDescent< Architecture_t > | inline |
GetConvergenceSteps() const | TMVA::DNN::TDLGradientDescent< Architecture_t > | inline |
GetTestError() const | TMVA::DNN::TDLGradientDescent< Architecture_t > | inline |
GetTestInterval() const | TMVA::DNN::TDLGradientDescent< Architecture_t > | inline |
GetTrainingError() const | TMVA::DNN::TDLGradientDescent< Architecture_t > | inline |
HasConverged() | TMVA::DNN::TDLGradientDescent< Architecture_t > | |
HasConverged(Scalar_t testError) | TMVA::DNN::TDLGradientDescent< Architecture_t > | |
Matrix_t typedef | TMVA::DNN::TDLGradientDescent< Architecture_t > | |
Reset() | TMVA::DNN::TDLGradientDescent< Architecture_t > | inline |
Scalar_t typedef | TMVA::DNN::TDLGradientDescent< Architecture_t > | |
SetBatchSize(Scalar_t rate) | TMVA::DNN::TDLGradientDescent< Architecture_t > | inline |
SetConvergenceSteps(size_t steps) | TMVA::DNN::TDLGradientDescent< Architecture_t > | inline |
SetLearningRate(Scalar_t rate) | TMVA::DNN::TDLGradientDescent< Architecture_t > | inline |
SetTestInterval(size_t interval) | TMVA::DNN::TDLGradientDescent< Architecture_t > | inline |
Step(DeepNet_t &deepNet, std::vector< Matrix_t > &input, const Matrix_t &output, const Matrix_t &weights) | TMVA::DNN::TDLGradientDescent< Architecture_t > | |
Step(DeepNet_t &master, std::vector< DeepNet_t > &nets, std::vector< TTensorBatch< Architecture_t >> &batches) | TMVA::DNN::TDLGradientDescent< Architecture_t > | |
StepLoss(DeepNet_t &deepNet, std::vector< Matrix_t > &input, const Matrix_t &output, const Matrix_t &weights) | TMVA::DNN::TDLGradientDescent< Architecture_t > | |
StepMomentum(DeepNet_t &master, std::vector< DeepNet_t > &nets, std::vector< TTensorBatch< Architecture_t >> &batches, Scalar_t momentum) | TMVA::DNN::TDLGradientDescent< Architecture_t > | |
StepNesterov(DeepNet_t &master, std::vector< DeepNet_t > &nets, std::vector< TTensorBatch< Architecture_t >> &batches, Scalar_t momentum) | TMVA::DNN::TDLGradientDescent< Architecture_t > | |
StepReducedWeights(DeepNet_t &deepNet, std::vector< Matrix_t > &input, const Matrix_t &output, const Matrix_t &weights) | TMVA::DNN::TDLGradientDescent< Architecture_t > | |
StepReducedWeightsLoss(DeepNet_t &deepNet, std::vector< Matrix_t > &input, const Matrix_t &output, const Matrix_t &weights) | TMVA::DNN::TDLGradientDescent< Architecture_t > | |
TDLGradientDescent() | TMVA::DNN::TDLGradientDescent< Architecture_t > | |
TDLGradientDescent(Scalar_t learningRate, size_t convergenceSteps, size_t testInterval) | TMVA::DNN::TDLGradientDescent< Architecture_t > | |