TMVA::DNN::TNet< Architecture_t, Layer_t > Member List

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

AddLayer(size_t width, EActivationFunction f, Scalar_t dropoutProbability=1.0)	TMVA::DNN::TNet< Architecture_t, Layer_t >
AddLayer(SharedLayer &layer)	TMVA::DNN::TNet< Architecture_t, Layer_t >
AddLayer(SharedLayer_t &layer)	TMVA::DNN::TNet< Architecture_t, Layer_t >	inline
Backward(const Matrix_t &X, const Matrix_t &Y, const Matrix_t &weights)	TMVA::DNN::TNet< Architecture_t, Layer_t >	inline
Clear()	TMVA::DNN::TNet< Architecture_t, Layer_t >
CreateClone(size_t batchSize)	TMVA::DNN::TNet< Architecture_t, Layer_t >
fBatchSize	TMVA::DNN::TNet< Architecture_t, Layer_t >	private
fDummy	TMVA::DNN::TNet< Architecture_t, Layer_t >	private
fInputWidth	TMVA::DNN::TNet< Architecture_t, Layer_t >	private
fJ	TMVA::DNN::TNet< Architecture_t, Layer_t >	private
fLayers	TMVA::DNN::TNet< Architecture_t, Layer_t >	private
Forward(Matrix_t &X, bool applyDropout=false)	TMVA::DNN::TNet< Architecture_t, Layer_t >	inline
fR	TMVA::DNN::TNet< Architecture_t, Layer_t >	private
fWeightDecay	TMVA::DNN::TNet< Architecture_t, Layer_t >	private
GetBatchSize() const	TMVA::DNN::TNet< Architecture_t, Layer_t >	inline
GetDepth() const	TMVA::DNN::TNet< Architecture_t, Layer_t >	inline
GetInputWidth() const	TMVA::DNN::TNet< Architecture_t, Layer_t >	inline
GetLayer(size_t i)	TMVA::DNN::TNet< Architecture_t, Layer_t >	inline
GetLayer(size_t i) const	TMVA::DNN::TNet< Architecture_t, Layer_t >	inline
GetLossFunction() const	TMVA::DNN::TNet< Architecture_t, Layer_t >	inline
GetNFlops()	TMVA::DNN::TNet< Architecture_t, Layer_t >
GetOutput()	TMVA::DNN::TNet< Architecture_t, Layer_t >	inline
GetOutputWidth() const	TMVA::DNN::TNet< Architecture_t, Layer_t >	inline
GetRegularization() const	TMVA::DNN::TNet< Architecture_t, Layer_t >	inline
GetWeightDecay() const	TMVA::DNN::TNet< Architecture_t, Layer_t >	inline
Initialize(EInitialization m)	TMVA::DNN::TNet< Architecture_t, Layer_t >	inline
InitializeGradients()	TMVA::DNN::TNet< Architecture_t, Layer_t >	inline
LayerIterator_t typedef	TMVA::DNN::TNet< Architecture_t, Layer_t >
LayersBegin()	TMVA::DNN::TNet< Architecture_t, Layer_t >	inline
LayersEnd()	TMVA::DNN::TNet< Architecture_t, Layer_t >	inline
Loss(const Matrix_t &Y, const Matrix_t &weights, bool includeRegularization=true) const	TMVA::DNN::TNet< Architecture_t, Layer_t >	inline
Loss(Matrix_t &X, const Matrix_t &Y, const Matrix_t &weights, bool applyDropout=false, bool includeRegularization=true)	TMVA::DNN::TNet< Architecture_t, Layer_t >	inline
Matrix_t typedef	TMVA::DNN::TNet< Architecture_t, Layer_t >
Prediction(Matrix_t &Y_hat, Matrix_t &X, EOutputFunction f)	TMVA::DNN::TNet< Architecture_t, Layer_t >	inline
Prediction(Matrix_t &Y_hat, EOutputFunction f) const	TMVA::DNN::TNet< Architecture_t, Layer_t >	inline
Print()	TMVA::DNN::TNet< Architecture_t, Layer_t >
Scalar_t typedef	TMVA::DNN::TNet< Architecture_t, Layer_t >
SetBatchSize(size_t batchSize)	TMVA::DNN::TNet< Architecture_t, Layer_t >	inline
SetDropoutProbabilities(const std::vector< Double_t > &probabilities)	TMVA::DNN::TNet< Architecture_t, Layer_t >
SetInputWidth(size_t inputWidth)	TMVA::DNN::TNet< Architecture_t, Layer_t >	inline
SetLossFunction(ELossFunction J)	TMVA::DNN::TNet< Architecture_t, Layer_t >	inline
SetRegularization(ERegularization R)	TMVA::DNN::TNet< Architecture_t, Layer_t >	inline
SetWeightDecay(Scalar_t weightDecay)	TMVA::DNN::TNet< Architecture_t, Layer_t >	inline
TNet()	TMVA::DNN::TNet< Architecture_t, Layer_t >
TNet(const TNet &other)	TMVA::DNN::TNet< Architecture_t, Layer_t >
TNet(size_t batchSize, const TNet< OtherArchitecture_t > &)	TMVA::DNN::TNet< Architecture_t, Layer_t >
TNet(size_t batchSize, size_t inputWidth, ELossFunction fJ, ERegularization fR=ERegularization::kNone, Scalar_t fWeightDecay=0.0)	TMVA::DNN::TNet< Architecture_t, Layer_t >