template<typename Architecture_t>
class TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >

Generic Max Pooling Layer class.

This generic Max Pooling Layer Class represents a pooling layer of a CNN. It inherits all of the properties of the convolutional layer TConvLayer, but it overrides the propagation methods. In a sense, max pooling can be seen as non-linear convolution: a filter slides over the input and produces one element as a function of the the elements within the receptive field. In addition to that, it contains a matrix of winning units.

The height and width of the weights and biases is set to 0, since this layer does not contain any weights.

Definition at line 59 of file MaxPoolLayer.h.

Public Types
using	AlgorithmBackward_t = typename Architecture_t::AlgorithmBackward_t

using	AlgorithmDataType_t = typename Architecture_t::AlgorithmDataType_t

using	AlgorithmForward_t = typename Architecture_t::AlgorithmForward_t

using	AlgorithmHelper_t = typename Architecture_t::AlgorithmHelper_t

using	HelperDescriptor_t = typename Architecture_t::DropoutDescriptor_t

using	LayerDescriptor_t = typename Architecture_t::PoolingDescriptor_t

using	Matrix_t = typename Architecture_t::Matrix_t

using	ReduceTensorDescriptor_t = typename Architecture_t::ReduceTensorDescriptor_t

using	Scalar_t = typename Architecture_t::Scalar_t

using	Tensor_t = typename Architecture_t::Tensor_t

using	WeightsDescriptor_t = typename Architecture_t::EmptyDescriptor_t

Public Member Functions
	TMaxPoolLayer (const TMaxPoolLayer &)
	Copy constructor.

	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)
	Constructor.

	TMaxPoolLayer (TMaxPoolLayer< Architecture_t > *layer)
	Copy the max pooling layer provided as a pointer.

virtual	~TMaxPoolLayer ()
	Destructor.

virtual void	AddWeightsXMLTo (void *parent)
	Writes the information and the weights about the layer in an XML node.

void	Backward (Tensor_t &gradients_backward, const Tensor_t &activations_backward)
	Depending on the winning units determined during the Forward pass, it only forwards the derivatives to the right units in the previous layer.

void	Forward (Tensor_t &input, bool applyDropout=true)
	Computes activation of the layer for the given input.

TDescriptors *	GetDescriptors ()

const TDescriptors *	GetDescriptors () const

Scalar_t	GetDropoutProbability () const

size_t	GetFilterDepth () const
	Getters.

size_t	GetFilterHeight () const

size_t	GetFilterWidth () const

Tensor_t &	GetIndexTensor ()

const Tensor_t &	GetIndexTensor () const

size_t	GetNLocalViews () const

size_t	GetStrideCols () const

size_t	GetStrideRows () const

TWorkspace *	GetWorkspace ()

const TWorkspace *	GetWorkspace () const

void	Print () const
	Prints the info about the layer.

virtual void	ReadWeightsFromXML (void *parent)
	Read the information and the weights about the layer from XML node.

Public Member Functions inherited from TMVA::DNN::VGeneralLayer< Architecture_t >
	VGeneralLayer (const VGeneralLayer &)
	Copy Constructor.

	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)
	Constructor.

	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)
	General Constructor with different weights dimension.

	VGeneralLayer (VGeneralLayer< Architecture_t > *layer)
	Copy the layer provided as a pointer.

virtual	~VGeneralLayer ()
	Virtual Destructor.

void	CopyBiases (const std::vector< Matrix_t > &otherBiases)
	Copies the biases provided as an input.

template<typename Arch >
void	CopyParameters (const VGeneralLayer< Arch > &layer)
	Copy all trainable weight and biases from another equivalent layer but with different architecture The function can copy also extra parameters in addition to weights and biases if they are return by the function GetExtraLayerParameters.

void	CopyWeights (const std::vector< Matrix_t > &otherWeights)
	Copies the weights provided as an input.

Tensor_t &	GetActivationGradients ()

const Tensor_t &	GetActivationGradients () const

Matrix_t	GetActivationGradientsAt (size_t i)

const Matrix_t &	GetActivationGradientsAt (size_t i) const

size_t	GetBatchSize () const
	Getters.

std::vector< Matrix_t > &	GetBiases ()

const std::vector< Matrix_t > &	GetBiases () const

Matrix_t &	GetBiasesAt (size_t i)

const Matrix_t &	GetBiasesAt (size_t i) const

std::vector< Matrix_t > &	GetBiasGradients ()

const std::vector< Matrix_t > &	GetBiasGradients () const

Matrix_t &	GetBiasGradientsAt (size_t i)

const Matrix_t &	GetBiasGradientsAt (size_t i) const

size_t	GetDepth () const

virtual std::vector< Matrix_t >	GetExtraLayerParameters () const

size_t	GetHeight () const

EInitialization	GetInitialization () const

size_t	GetInputDepth () const

size_t	GetInputHeight () const

size_t	GetInputWidth () const

Tensor_t &	GetOutput ()

const Tensor_t &	GetOutput () const

Matrix_t	GetOutputAt (size_t i)

const Matrix_t &	GetOutputAt (size_t i) const

std::vector< Matrix_t > &	GetWeightGradients ()

const std::vector< Matrix_t > &	GetWeightGradients () const

Matrix_t &	GetWeightGradientsAt (size_t i)

const Matrix_t &	GetWeightGradientsAt (size_t i) const

std::vector< Matrix_t > &	GetWeights ()

const std::vector< Matrix_t > &	GetWeights () const

Matrix_t &	GetWeightsAt (size_t i)

const Matrix_t &	GetWeightsAt (size_t i) const

size_t	GetWidth () const

virtual void	Initialize ()
	Initialize the weights and biases according to the given initialization method.

bool	IsTraining () const

void	ReadMatrixXML (void node, const char name, Matrix_t &matrix)

virtual void	ResetTraining ()
	Reset some training flags after a loop on all batches Some layer (e.g.

void	SetBatchSize (size_t batchSize)
	Setters.

void	SetDepth (size_t depth)

virtual void	SetDropoutProbability (Scalar_t)
	Set Dropout probability.

virtual void	SetExtraLayerParameters (const std::vector< Matrix_t > &)

void	SetHeight (size_t height)

void	SetInputDepth (size_t inputDepth)

void	SetInputHeight (size_t inputHeight)

void	SetInputWidth (size_t inputWidth)

void	SetIsTraining (bool isTraining)

void	SetWidth (size_t width)

void	Update (const Scalar_t learningRate)
	Updates the weights and biases, given the learning rate.

void	UpdateBiases (const std::vector< Matrix_t > &biasGradients, const Scalar_t learningRate)
	Updates the biases, given the gradients and the learning rate.

void	UpdateBiasGradients (const std::vector< Matrix_t > &biasGradients, const Scalar_t learningRate)
	Updates the bias gradients, given some other weight gradients and learning rate.

void	UpdateWeightGradients (const std::vector< Matrix_t > &weightGradients, const Scalar_t learningRate)
	Updates the weight gradients, given some other weight gradients and learning rate.

void	UpdateWeights (const std::vector< Matrix_t > &weightGradients, const Scalar_t learningRate)
	Updates the weights, given the gradients and the learning rate,.

void	WriteMatrixToXML (void node, const char name, const Matrix_t &matrix)

void	WriteTensorToXML (void node, const char name, const std::vector< Matrix_t > &tensor)
	helper functions for XML

Protected Attributes
TDescriptors *	fDescriptors = nullptr
	Keeps the convolution, activations and filter descriptors.

Scalar_t	fDropoutProbability
	Probability that an input is active.

size_t	fFilterDepth
	The depth of the filter.

size_t	fFilterHeight
	The height of the filter.

size_t	fFilterWidth
	The width of the filter.

size_t	fNLocalViewPixels
	The number of pixels in one local image view.

size_t	fNLocalViews
	The number of local views in one image.

size_t	fStrideCols
	The number of column pixels to slid the filter each step.

size_t	fStrideRows
	The number of row pixels to slid the filter each step.

TWorkspace *	fWorkspace = nullptr

Protected Attributes inherited from TMVA::DNN::VGeneralLayer< Architecture_t >
Tensor_t	fActivationGradients
	Gradients w.r.t. the activations of this layer.

size_t	fBatchSize
	Batch size used for training and evaluation.

std::vector< Matrix_t >	fBiases
	The biases associated to the layer.

std::vector< Matrix_t >	fBiasGradients
	Gradients w.r.t. the bias values of the layer.

size_t	fDepth
	The depth of the layer.

size_t	fHeight
	The height of the layer.

EInitialization	fInit
	The initialization method.

size_t	fInputDepth
	The depth of the previous layer or input.

size_t	fInputHeight
	The height of the previous layer or input.

size_t	fInputWidth
	The width of the previous layer or input.

bool	fIsTraining
	Flag indicating the mode.

Tensor_t	fOutput
	Activations of this layer.

std::vector< Matrix_t >	fWeightGradients
	Gradients w.r.t. the weights of the layer.

std::vector< Matrix_t >	fWeights
	The weights associated to the layer.

size_t	fWidth
	The width of this layer.

Private Member Functions
void	FreeWorkspace ()

void	InitializeDescriptors ()

void	InitializeWorkspace ()

void	ReleaseDescriptors ()

Private Attributes
Tensor_t	fIndexTensor
	Matrix of indices for the backward pass.

#include <TMVA/DNN/CNN/MaxPoolLayer.h>

Inheritance diagram for TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >:

[legend]

Member Typedef Documentation

◆ AlgorithmBackward_t

template<typename Architecture_t >

using TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::AlgorithmBackward_t = typename Architecture_t::AlgorithmBackward_t

Definition at line 72 of file MaxPoolLayer.h.

◆ AlgorithmDataType_t

template<typename Architecture_t >

using TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::AlgorithmDataType_t = typename Architecture_t::AlgorithmDataType_t

Definition at line 76 of file MaxPoolLayer.h.

◆ AlgorithmForward_t

template<typename Architecture_t >

using TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::AlgorithmForward_t = typename Architecture_t::AlgorithmForward_t

Definition at line 71 of file MaxPoolLayer.h.

◆ AlgorithmHelper_t

template<typename Architecture_t >

using TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::AlgorithmHelper_t = typename Architecture_t::AlgorithmHelper_t

Definition at line 73 of file MaxPoolLayer.h.

◆ HelperDescriptor_t

template<typename Architecture_t >

using TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::HelperDescriptor_t = typename Architecture_t::DropoutDescriptor_t

Definition at line 68 of file MaxPoolLayer.h.

◆ LayerDescriptor_t

template<typename Architecture_t >

using TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::LayerDescriptor_t = typename Architecture_t::PoolingDescriptor_t

Definition at line 66 of file MaxPoolLayer.h.

◆ Matrix_t

template<typename Architecture_t >

using TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::Matrix_t = typename Architecture_t::Matrix_t

Definition at line 63 of file MaxPoolLayer.h.

◆ ReduceTensorDescriptor_t

template<typename Architecture_t >

using TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::ReduceTensorDescriptor_t = typename Architecture_t::ReduceTensorDescriptor_t

Definition at line 78 of file MaxPoolLayer.h.

◆ Scalar_t

template<typename Architecture_t >

using TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::Scalar_t = typename Architecture_t::Scalar_t

Definition at line 64 of file MaxPoolLayer.h.

◆ Tensor_t

template<typename Architecture_t >

using TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::Tensor_t = typename Architecture_t::Tensor_t

Definition at line 62 of file MaxPoolLayer.h.

◆ WeightsDescriptor_t

template<typename Architecture_t >

using TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::WeightsDescriptor_t = typename Architecture_t::EmptyDescriptor_t

Definition at line 67 of file MaxPoolLayer.h.

Constructor & Destructor Documentation

◆ TMaxPoolLayer() [1/3]

template<typename Architecture_t >

TMVA::DNN::CNN::TMaxPoolLayer< 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
	)

Constructor.

Definition at line 168 of file MaxPoolLayer.h.

◆ TMaxPoolLayer() [2/3]

template<typename Architecture_t >

TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::TMaxPoolLayer ( TMaxPoolLayer< Architecture_t > * layer )

Copy the max pooling layer provided as a pointer.

Definition at line 192 of file MaxPoolLayer.h.

◆ TMaxPoolLayer() [3/3]

template<typename Architecture_t >

TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::TMaxPoolLayer ( const TMaxPoolLayer< Architecture_t > & layer )

Copy constructor.

Definition at line 204 of file MaxPoolLayer.h.

◆ ~TMaxPoolLayer()

template<typename Architecture_t >

TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::~TMaxPoolLayer

virtual

Destructor.

Definition at line 216 of file MaxPoolLayer.h.

Member Function Documentation

◆ AddWeightsXMLTo()

template<typename Architecture_t >

void TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::AddWeightsXMLTo ( void * parent )

virtual

Writes the information and the weights about the layer in an XML node.

Implements TMVA::DNN::VGeneralLayer< Architecture_t >.

Definition at line 283 of file MaxPoolLayer.h.

◆ Backward()

template<typename Architecture_t >

auto TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::Backward	(	Tensor_t &	gradients_backward,
		const Tensor_t &	activations_backward
	)

virtual

Depending on the winning units determined during the Forward pass, it only forwards the derivatives to the right units in the previous layer.

Must only be called directly at the corresponding call to Forward(...).

Implements TMVA::DNN::VGeneralLayer< Architecture_t >.

Definition at line 247 of file MaxPoolLayer.h.

◆ Forward()

template<typename Architecture_t >

auto TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::Forward	(	Tensor_t &	input,
		bool	applyDropout = `true`
	)

virtual

Computes activation of the layer for the given input.

The input must be in 3D tensor form with the different matrices corresponding to different events in the batch. It spatially downsamples the input matrices.

Implements TMVA::DNN::VGeneralLayer< Architecture_t >.

Definition at line 233 of file MaxPoolLayer.h.

◆ FreeWorkspace()

template<typename Architecture_t >

void TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::FreeWorkspace

private

Definition at line 326 of file MaxPoolLayer.h.

◆ GetDescriptors() [1/2]

template<typename Architecture_t >

TDescriptors * TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::GetDescriptors ( )

inline

Definition at line 159 of file MaxPoolLayer.h.

◆ GetDescriptors() [2/2]

template<typename Architecture_t >

const TDescriptors * TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::GetDescriptors ( ) const

inline

Definition at line 160 of file MaxPoolLayer.h.

◆ GetDropoutProbability()

template<typename Architecture_t >

Scalar_t TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::GetDropoutProbability ( ) const

inline

Definition at line 153 of file MaxPoolLayer.h.

◆ GetFilterDepth()

template<typename Architecture_t >

size_t TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::GetFilterDepth ( ) const

inline

Getters.

Definition at line 144 of file MaxPoolLayer.h.

◆ GetFilterHeight()

template<typename Architecture_t >

size_t TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::GetFilterHeight ( ) const

inline

Definition at line 145 of file MaxPoolLayer.h.

◆ GetFilterWidth()

template<typename Architecture_t >

size_t TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::GetFilterWidth ( ) const

inline

Definition at line 146 of file MaxPoolLayer.h.

◆ GetIndexTensor() [1/2]

template<typename Architecture_t >

Tensor_t & TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::GetIndexTensor ( )

inline

Definition at line 156 of file MaxPoolLayer.h.

◆ GetIndexTensor() [2/2]

template<typename Architecture_t >

const Tensor_t & TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::GetIndexTensor ( ) const

inline

Definition at line 155 of file MaxPoolLayer.h.

◆ GetNLocalViews()

template<typename Architecture_t >

size_t TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::GetNLocalViews ( ) const

inline

Definition at line 151 of file MaxPoolLayer.h.

◆ GetStrideCols()

template<typename Architecture_t >

size_t TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::GetStrideCols ( ) const

inline

Definition at line 149 of file MaxPoolLayer.h.

◆ GetStrideRows()

template<typename Architecture_t >

size_t TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::GetStrideRows ( ) const

inline

Definition at line 148 of file MaxPoolLayer.h.

◆ GetWorkspace() [1/2]

template<typename Architecture_t >

TWorkspace * TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::GetWorkspace ( )

inline

Definition at line 162 of file MaxPoolLayer.h.

◆ GetWorkspace() [2/2]

template<typename Architecture_t >

const TWorkspace * TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::GetWorkspace ( ) const

inline

Definition at line 163 of file MaxPoolLayer.h.

◆ InitializeDescriptors()

template<typename Architecture_t >

void TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::InitializeDescriptors

private

Definition at line 304 of file MaxPoolLayer.h.

◆ InitializeWorkspace()

template<typename Architecture_t >

void TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::InitializeWorkspace

private

Definition at line 315 of file MaxPoolLayer.h.

◆ Print()

template<typename Architecture_t >

auto TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::Print

virtual

Prints the info about the layer.

Implements TMVA::DNN::VGeneralLayer< Architecture_t >.

Definition at line 264 of file MaxPoolLayer.h.

◆ ReadWeightsFromXML()

template<typename Architecture_t >

void TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::ReadWeightsFromXML ( void * parent )

virtual

Read the information and the weights about the layer from XML node.

Implements TMVA::DNN::VGeneralLayer< Architecture_t >.

Definition at line 297 of file MaxPoolLayer.h.

◆ ReleaseDescriptors()

template<typename Architecture_t >

void TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::ReleaseDescriptors

private

Definition at line 309 of file MaxPoolLayer.h.

Member Data Documentation

◆ fDescriptors

template<typename Architecture_t >

TDescriptors* TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::fDescriptors = nullptr

protected

Keeps the convolution, activations and filter descriptors.

Definition at line 93 of file MaxPoolLayer.h.

◆ fDropoutProbability

template<typename Architecture_t >

Scalar_t TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::fDropoutProbability

protected

Probability that an input is active.

Definition at line 91 of file MaxPoolLayer.h.

◆ fFilterDepth

template<typename Architecture_t >

size_t TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::fFilterDepth

protected

The depth of the filter.

Definition at line 81 of file MaxPoolLayer.h.

◆ fFilterHeight

template<typename Architecture_t >

size_t TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::fFilterHeight

protected

The height of the filter.

Definition at line 82 of file MaxPoolLayer.h.

◆ fFilterWidth

template<typename Architecture_t >

size_t TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::fFilterWidth

protected

The width of the filter.

Definition at line 83 of file MaxPoolLayer.h.

◆ fIndexTensor

template<typename Architecture_t >

Tensor_t TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::fIndexTensor

private

Matrix of indices for the backward pass.

Definition at line 98 of file MaxPoolLayer.h.

◆ fNLocalViewPixels

template<typename Architecture_t >

size_t TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::fNLocalViewPixels

protected

The number of pixels in one local image view.

Definition at line 88 of file MaxPoolLayer.h.

◆ fNLocalViews

template<typename Architecture_t >

size_t TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::fNLocalViews

protected

The number of local views in one image.

Definition at line 89 of file MaxPoolLayer.h.

◆ fStrideCols

template<typename Architecture_t >

size_t TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::fStrideCols

protected

The number of column pixels to slid the filter each step.

Definition at line 86 of file MaxPoolLayer.h.

◆ fStrideRows

template<typename Architecture_t >

size_t TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::fStrideRows

protected

The number of row pixels to slid the filter each step.

Definition at line 85 of file MaxPoolLayer.h.

◆ fWorkspace

template<typename Architecture_t >

TWorkspace* TMVA::DNN::CNN::TMaxPoolLayer< Architecture_t >::fWorkspace = nullptr

protected

Definition at line 95 of file MaxPoolLayer.h.

tmva/tmva/inc/TMVA/DNN/CNN/MaxPoolLayer.h

Public Types

Public Member Functions

Protected Attributes

Private Member Functions

Private Attributes

Member Typedef Documentation

◆ AlgorithmBackward_t

◆ AlgorithmDataType_t

◆ AlgorithmForward_t

◆ AlgorithmHelper_t

◆ HelperDescriptor_t

◆ LayerDescriptor_t

◆ Matrix_t

◆ ReduceTensorDescriptor_t

◆ Scalar_t

◆ Tensor_t

◆ WeightsDescriptor_t

Constructor & Destructor Documentation

◆ TMaxPoolLayer() [1/3]

◆ TMaxPoolLayer() [2/3]

◆ TMaxPoolLayer() [3/3]

◆ ~TMaxPoolLayer()

Member Function Documentation

◆ AddWeightsXMLTo()

◆ Backward()

◆ Forward()

◆ FreeWorkspace()

◆ GetDescriptors() [1/2]

◆ GetDescriptors() [2/2]

◆ GetDropoutProbability()

◆ GetFilterDepth()

◆ GetFilterHeight()

◆ GetFilterWidth()

◆ GetIndexTensor() [1/2]

◆ GetIndexTensor() [2/2]

◆ GetNLocalViews()

◆ GetStrideCols()

◆ GetStrideRows()

◆ GetWorkspace() [1/2]

◆ GetWorkspace() [2/2]

◆ InitializeDescriptors()

◆ InitializeWorkspace()

◆ Print()

◆ ReadWeightsFromXML()

◆ ReleaseDescriptors()

Member Data Documentation

◆ fDescriptors

◆ fDropoutProbability

◆ fFilterDepth

◆ fFilterHeight

◆ fFilterWidth

◆ fIndexTensor

◆ fNLocalViewPixels

◆ fNLocalViews

◆ fStrideCols

◆ fStrideRows

◆ fWorkspace