TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t > Member List

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

AddWeightsXMLTo(void *parent)	TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >	virtual
Backward(std::vector< Matrix_t > &gradients_backward, const std::vector< Matrix_t > &activations_backward, std::vector< Matrix_t > &inp1, std::vector< Matrix_t > &inp2)	TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >	virtual
calculateDimension(size_t imgDim, size_t fltDim, size_t padding, size_t stride)	TMVA::DNN::CNN::TConvLayer< Architecture_t >	private
calculateNLocalViewPixels(size_t depth, size_t height, size_t width)	TMVA::DNN::CNN::TConvLayer< Architecture_t >	inlineprivate
calculateNLocalViews(size_t inputHeight, size_t filterHeight, size_t paddingHeight, size_t strideRows, size_t inputWidth, size_t filterWidth, size_t paddingWidth, size_t strideCols)	TMVA::DNN::CNN::TConvLayer< Architecture_t >	private
CopyBiases(const std::vector< Matrix_t > &otherBiases)	TMVA::DNN::VGeneralLayer< Architecture_t >
CopyWeights(const std::vector< Matrix_t > &otherWeights)	TMVA::DNN::VGeneralLayer< Architecture_t >
fActivationGradients	TMVA::DNN::VGeneralLayer< Architecture_t >	protected
fBackwardIndices	TMVA::DNN::CNN::TConvLayer< Architecture_t >	private
fBatchSize	TMVA::DNN::VGeneralLayer< Architecture_t >	protected
fBiases	TMVA::DNN::VGeneralLayer< Architecture_t >	protected
fBiasGradients	TMVA::DNN::VGeneralLayer< Architecture_t >	protected
fDepth	TMVA::DNN::VGeneralLayer< Architecture_t >	protected
fDerivatives	TMVA::DNN::CNN::TConvLayer< Architecture_t >	private
fDropoutProbability	TMVA::DNN::CNN::TConvLayer< Architecture_t >	protected
fF	TMVA::DNN::CNN::TConvLayer< Architecture_t >	private
fFilterDepth	TMVA::DNN::CNN::TConvLayer< Architecture_t >	protected
fFilterHeight	TMVA::DNN::CNN::TConvLayer< Architecture_t >	protected
fFilterWidth	TMVA::DNN::CNN::TConvLayer< Architecture_t >	protected
fForwardMatrices	TMVA::DNN::CNN::TConvLayer< Architecture_t >	private
fHeight	TMVA::DNN::VGeneralLayer< Architecture_t >	protected
fInit	TMVA::DNN::VGeneralLayer< Architecture_t >	protected
fInputDepth	TMVA::DNN::VGeneralLayer< Architecture_t >	protected
fInputHeight	TMVA::DNN::VGeneralLayer< Architecture_t >	protected
fInputWidth	TMVA::DNN::VGeneralLayer< Architecture_t >	protected
fIsTraining	TMVA::DNN::VGeneralLayer< Architecture_t >	protected
fNLocalViewPixels	TMVA::DNN::CNN::TConvLayer< Architecture_t >	protected
fNLocalViews	TMVA::DNN::CNN::TConvLayer< Architecture_t >	protected
Forward(std::vector< Matrix_t > &input, bool applyDropout=false)	TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >	virtual
fOutput	TMVA::DNN::VGeneralLayer< Architecture_t >	protected
fPaddingHeight	TMVA::DNN::CNN::TConvLayer< Architecture_t >	private
fPaddingWidth	TMVA::DNN::CNN::TConvLayer< Architecture_t >	private
fReg	TMVA::DNN::CNN::TConvLayer< Architecture_t >	private
fStrideCols	TMVA::DNN::CNN::TConvLayer< Architecture_t >	protected
fStrideRows	TMVA::DNN::CNN::TConvLayer< Architecture_t >	protected
fWeightDecay	TMVA::DNN::CNN::TConvLayer< Architecture_t >	private
fWeightGradients	TMVA::DNN::VGeneralLayer< Architecture_t >	protected
fWeights	TMVA::DNN::VGeneralLayer< Architecture_t >	protected
fWidth	TMVA::DNN::VGeneralLayer< Architecture_t >	protected
GetActivationFunction() const	TMVA::DNN::CNN::TConvLayer< Architecture_t >	inline
GetActivationGradients() const	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetActivationGradients()	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetActivationGradientsAt(size_t i)	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetActivationGradientsAt(size_t i) const	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetBatchSize() const	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetBiases() const	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetBiases()	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetBiasesAt(size_t i) const	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetBiasesAt(size_t i)	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetBiasGradients() const	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetBiasGradients()	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetBiasGradientsAt(size_t i) const	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetBiasGradientsAt(size_t i)	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetDepth() const	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetDerivatives() const	TMVA::DNN::CNN::TConvLayer< Architecture_t >	inline
GetDerivatives()	TMVA::DNN::CNN::TConvLayer< Architecture_t >	inline
GetDerivativesAt(size_t i)	TMVA::DNN::CNN::TConvLayer< Architecture_t >	inline
GetDerivativesAt(size_t i) const	TMVA::DNN::CNN::TConvLayer< Architecture_t >	inline
GetDropoutProbability() const	TMVA::DNN::CNN::TConvLayer< Architecture_t >	inline
GetFilterDepth() const	TMVA::DNN::CNN::TConvLayer< Architecture_t >	inline
GetFilterHeight() const	TMVA::DNN::CNN::TConvLayer< Architecture_t >	inline
GetFilterWidth() const	TMVA::DNN::CNN::TConvLayer< Architecture_t >	inline
GetForwardMatrices() const	TMVA::DNN::CNN::TConvLayer< Architecture_t >	inline
GetForwardMatrices()	TMVA::DNN::CNN::TConvLayer< Architecture_t >	inline
GetHeight() const	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetIndexMatrix() const	TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >	inline
GetIndexMatrix()	TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >	inline
GetInitialization() const	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetInputDepth() const	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetInputHeight() const	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetInputWidth() const	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetNLocalViewPixels() const	TMVA::DNN::CNN::TConvLayer< Architecture_t >	inline
GetNLocalViews() const	TMVA::DNN::CNN::TConvLayer< Architecture_t >	inline
GetOutput() const	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetOutput()	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetOutputAt(size_t i)	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetOutputAt(size_t i) const	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetPaddingHeight() const	TMVA::DNN::CNN::TConvLayer< Architecture_t >	inline
GetPaddingWidth() const	TMVA::DNN::CNN::TConvLayer< Architecture_t >	inline
GetRegularization() const	TMVA::DNN::CNN::TConvLayer< Architecture_t >	inline
GetStrideCols() const	TMVA::DNN::CNN::TConvLayer< Architecture_t >	inline
GetStrideRows() const	TMVA::DNN::CNN::TConvLayer< Architecture_t >	inline
GetWeightDecay() const	TMVA::DNN::CNN::TConvLayer< Architecture_t >	inline
GetWeightGradients() const	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetWeightGradients()	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetWeightGradientsAt(size_t i) const	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetWeightGradientsAt(size_t i)	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetWeights() const	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetWeights()	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetWeightsAt(size_t i) const	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetWeightsAt(size_t i)	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
GetWidth() const	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
indexMatrix	TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >	private
Initialize()	TMVA::DNN::VGeneralLayer< Architecture_t >
IsTraining() const	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
Matrix_t typedef	TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >
Print() const	TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >	virtual
ReadMatrixXML(void *node, const char *name, Matrix_t &matrix)	TMVA::DNN::VGeneralLayer< Architecture_t >
ReadWeightsFromXML(void *parent)	TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >	virtual
Scalar_t typedef	TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >
SetBatchSize(size_t batchSize)	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
SetDepth(size_t depth)	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
SetDropoutProbability(Scalar_t)	TMVA::DNN::VGeneralLayer< Architecture_t >	inlinevirtual
SetHeight(size_t height)	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
SetInputDepth(size_t inputDepth)	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
SetInputHeight(size_t inputHeight)	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
SetInputWidth(size_t inputWidth)	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
SetIsTraining(bool isTraining)	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
SetWidth(size_t width)	TMVA::DNN::VGeneralLayer< Architecture_t >	inline
TConvLayer(size_t BatchSize, size_t InputDepth, size_t InputHeight, size_t InputWidth, size_t Depth, EInitialization Init, size_t FilterHeight, size_t FilterWidth, size_t StrideRows, size_t StrideCols, size_t PaddingHeight, size_t PaddingWidth, Scalar_t DropoutProbability, EActivationFunction f, ERegularization Reg, Scalar_t WeightDecay)	TMVA::DNN::CNN::TConvLayer< Architecture_t >
TConvLayer(TConvLayer< Architecture_t > *layer)	TMVA::DNN::CNN::TConvLayer< Architecture_t >
TConvLayer(const TConvLayer &)	TMVA::DNN::CNN::TConvLayer< Architecture_t >
TMaxPoolLayer(size_t BatchSize, size_t InputDepth, size_t InputHeight, size_t InputWidth, size_t FilterHeight, size_t FilterWidth, size_t StrideRows, size_t StrideCols, Scalar_t DropoutProbability)	TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >
TMaxPoolLayer(TMaxPoolLayer< Architecture_t > *layer)	TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >
TMaxPoolLayer(const TMaxPoolLayer &)	TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >
Update(const Scalar_t learningRate)	TMVA::DNN::VGeneralLayer< Architecture_t >
UpdateBiases(const std::vector< Matrix_t > &biasGradients, const Scalar_t learningRate)	TMVA::DNN::VGeneralLayer< Architecture_t >
UpdateBiasGradients(const std::vector< Matrix_t > &biasGradients, const Scalar_t learningRate)	TMVA::DNN::VGeneralLayer< Architecture_t >
UpdateWeightGradients(const std::vector< Matrix_t > &weightGradients, const Scalar_t learningRate)	TMVA::DNN::VGeneralLayer< Architecture_t >
UpdateWeights(const std::vector< Matrix_t > &weightGradients, const Scalar_t learningRate)	TMVA::DNN::VGeneralLayer< Architecture_t >
VGeneralLayer(size_t BatchSize, size_t InputDepth, size_t InputHeight, size_t InputWidth, size_t Depth, size_t Height, size_t Width, size_t WeightsNSlices, size_t WeightsNRows, size_t WeightsNCols, size_t BiasesNSlices, size_t BiasesNRows, size_t BiasesNCols, size_t OutputNSlices, size_t OutputNRows, size_t OutputNCols, EInitialization Init)	TMVA::DNN::VGeneralLayer< Architecture_t >
VGeneralLayer(size_t BatchSize, size_t InputDepth, size_t InputHeight, size_t InputWidth, size_t Depth, size_t Height, size_t Width, size_t WeightsNSlices, std::vector< size_t > WeightsNRows, std::vector< size_t > WeightsNCols, size_t BiasesNSlices, std::vector< size_t > BiasesNRows, std::vector< size_t > BiasesNCols, size_t OutputNSlices, size_t OutputNRows, size_t OutputNCols, EInitialization Init)	TMVA::DNN::VGeneralLayer< Architecture_t >
VGeneralLayer(VGeneralLayer< Architecture_t > *layer)	TMVA::DNN::VGeneralLayer< Architecture_t >
VGeneralLayer(const VGeneralLayer &)	TMVA::DNN::VGeneralLayer< Architecture_t >
WriteMatrixToXML(void *node, const char *name, const Matrix_t &matrix)	TMVA::DNN::VGeneralLayer< Architecture_t >
WriteTensorToXML(void *node, const char *name, const std::vector< Matrix_t > &tensor)	TMVA::DNN::VGeneralLayer< Architecture_t >
~TConvLayer()	TMVA::DNN::CNN::TConvLayer< Architecture_t >
~TMaxPoolLayer()	TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >
~VGeneralLayer()	TMVA::DNN::VGeneralLayer< Architecture_t >	virtual