TMVA::DNN::TGradientDescent< Architecture_t > Member List

This is the complete list of members for TMVA::DNN::TGradientDescent< Architecture_t >, including all inherited members.

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 >