| fBatchSize | TMVA::DNN::TGradientDescent< Architecture_t > | private |
| fConvergenceCount | TMVA::DNN::TGradientDescent< Architecture_t > | private |
| fConvergenceSteps | TMVA::DNN::TGradientDescent< Architecture_t > | private |
| fLearningRate | TMVA::DNN::TGradientDescent< Architecture_t > | private |
| fMinimumError | TMVA::DNN::TGradientDescent< Architecture_t > | private |
| fStepCount | TMVA::DNN::TGradientDescent< Architecture_t > | private |
| fTestError | TMVA::DNN::TGradientDescent< Architecture_t > | private |
| fTestInterval | TMVA::DNN::TGradientDescent< Architecture_t > | private |
| fTrainingError | TMVA::DNN::TGradientDescent< Architecture_t > | private |
| GetConvergenceCount() const | TMVA::DNN::TGradientDescent< Architecture_t > | inline |
| GetConvergenceSteps() const | TMVA::DNN::TGradientDescent< Architecture_t > | inline |
| GetTestError() const | TMVA::DNN::TGradientDescent< Architecture_t > | inline |
| GetTestInterval() const | TMVA::DNN::TGradientDescent< Architecture_t > | inline |
| GetTrainingError() const | TMVA::DNN::TGradientDescent< Architecture_t > | inline |
| HasConverged() | TMVA::DNN::TGradientDescent< Architecture_t > | inline |
| HasConverged(Scalar_t testError) | TMVA::DNN::TGradientDescent< Architecture_t > | inline |
| Matrix_t typedef | TMVA::DNN::TGradientDescent< Architecture_t > | |
| Reset() | TMVA::DNN::TGradientDescent< Architecture_t > | inline |
| Scalar_t typedef | TMVA::DNN::TGradientDescent< Architecture_t > | |
| SetBatchSize(Scalar_t rate) | TMVA::DNN::TGradientDescent< Architecture_t > | inline |
| SetConvergenceSteps(size_t steps) | TMVA::DNN::TGradientDescent< Architecture_t > | inline |
| SetLearningRate(Scalar_t rate) | TMVA::DNN::TGradientDescent< Architecture_t > | inline |
| SetTestInterval(size_t interval) | TMVA::DNN::TGradientDescent< Architecture_t > | inline |
| Step(Net_t &net, Matrix_t &input, const Matrix_t &output, const Matrix_t &weights) | TMVA::DNN::TGradientDescent< Architecture_t > | inline |
| Step(Net_t &master, std::vector< Net_t > &nets, std::vector< TBatch< Architecture_t > > &batches) | TMVA::DNN::TGradientDescent< Architecture_t > | inline |
| StepLoss(Net_t &net, Matrix_t &input, const Matrix_t &output, const Matrix_t &weights) | TMVA::DNN::TGradientDescent< Architecture_t > | |
| StepLoss(Net_t &net, Matrix_t &input, const Matrix_t &output, const Matrix_t &weights) -> Scalar_t | TMVA::DNN::TGradientDescent< Architecture_t > | inline |
| StepMomentum(Net_t &master, std::vector< Net_t > &nets, std::vector< TBatch< Architecture_t > > &batches, Scalar_t momentum) | TMVA::DNN::TGradientDescent< Architecture_t > | inline |
| StepNesterov(Net_t &master, std::vector< Net_t > &nets, std::vector< TBatch< Architecture_t > > &batches, Scalar_t momentum) | TMVA::DNN::TGradientDescent< Architecture_t > | inline |
| StepReducedWeights(Net_t &net, Matrix_t &input, const Matrix_t &output) | TMVA::DNN::TGradientDescent< Architecture_t > | inline |
| StepReducedWeightsLoss(Net_t &net, Matrix_t &input, const Matrix_t &output, const Matrix_t &weights) | TMVA::DNN::TGradientDescent< Architecture_t > | |
| StepReducedWeightsLoss(Net_t &net, Matrix_t &input, const Matrix_t &output, const Matrix_t &weights) -> Scalar_t | TMVA::DNN::TGradientDescent< Architecture_t > | inline |
| TGradientDescent() | TMVA::DNN::TGradientDescent< Architecture_t > | |
| TGradientDescent(Scalar_t learningRate, size_t convergenceSteps, size_t testInterval) | TMVA::DNN::TGradientDescent< Architecture_t > | |
| Train(const Data_t &TrainingDataIn, size_t nTrainingSamples, const Data_t &TestDataIn, size_t nTestSamples, Net_t &net, size_t nThreads=1) | TMVA::DNN::TGradientDescent< Architecture_t > | |
| Train(const Data_t &trainingData, size_t nTrainingSamples, const Data_t &testData, size_t nTestSamples, Net_t &net, size_t nThreads) -> Scalar_t | TMVA::DNN::TGradientDescent< Architecture_t > | |
| TrainMomentum(const Data_t &TrainingDataIn, size_t nTrainingSamples, const Data_t &TestDataIn, size_t nTestSamples, Net_t &net, Scalar_t momentum, size_t nThreads=1) | TMVA::DNN::TGradientDescent< Architecture_t > | |
| TrainMomentum(const Data_t &trainingData, size_t nTrainingSamples, const Data_t &testData, size_t nTestSamples, Net_t &net, Scalar_t momentum, size_t nThreads) -> Scalar_t | TMVA::DNN::TGradientDescent< Architecture_t > | |
