template<typename Architecture_t>
class TMVA::DNN::TReshapeLayer< Architecture_t >

Definition at line 41 of file ReshapeLayer.h.

Public Types
using	Matrix_t = typename Architecture_t::Matrix_t

using	Scalar_t = typename Architecture_t::Scalar_t

using	Tensor_t = typename Architecture_t::Tensor_t

Public Member Functions
	TReshapeLayer (const TReshapeLayer &)
	Copy Constructor.

	TReshapeLayer (size_t BatchSize, size_t InputDepth, size_t InputHeight, size_t InputWidth, size_t Depth, size_t Height, size_t Width, size_t OutputNSlices, size_t OutputNRows, size_t OutputNCols, bool Flattening)
	Constructor.

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

	~TReshapeLayer ()
	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)
	Backpropagates the error.

void	Forward (Tensor_t &input, bool applyDropout=false)
	The input must be in 3D tensor form with the different matrices corresponding to different events in the batch.

bool	isFlattening () const
	TODO Add documentation Does this layer flatten? (necessary for DenseLayer) B x D1 x D2 --> 1 x B x (D1 * D2)

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

Private Attributes
bool	fFlattening
	Whather the layer is doing flattening.

Additional Inherited Members
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.

#include <TMVA/DNN/ReshapeLayer.h>

Inheritance diagram for TMVA::DNN::TReshapeLayer< Architecture_t >:

[legend]

Member Typedef Documentation

◆ Matrix_t

template<typename Architecture_t >

using TMVA::DNN::TReshapeLayer< Architecture_t >::Matrix_t = typename Architecture_t::Matrix_t

Definition at line 44 of file ReshapeLayer.h.

◆ Scalar_t

template<typename Architecture_t >

using TMVA::DNN::TReshapeLayer< Architecture_t >::Scalar_t = typename Architecture_t::Scalar_t

Definition at line 45 of file ReshapeLayer.h.

◆ Tensor_t

template<typename Architecture_t >

using TMVA::DNN::TReshapeLayer< Architecture_t >::Tensor_t = typename Architecture_t::Tensor_t

Definition at line 43 of file ReshapeLayer.h.

Constructor & Destructor Documentation

◆ TReshapeLayer() [1/3]

template<typename Architecture_t >

TMVA::DNN::TReshapeLayer< Architecture_t >::TReshapeLayer	(	size_t	BatchSize,
		size_t	InputDepth,
		size_t	InputHeight,
		size_t	InputWidth,
		size_t	Depth,
		size_t	Height,
		size_t	Width,
		size_t	OutputNSlices,
		size_t	OutputNRows,
		size_t	OutputNCols,
		bool	Flattening
	)

Constructor.

Definition at line 94 of file ReshapeLayer.h.

◆ TReshapeLayer() [2/3]

template<typename Architecture_t >

TMVA::DNN::TReshapeLayer< Architecture_t >::TReshapeLayer ( TReshapeLayer< Architecture_t > * layer )

Copy the reshape layer provided as a pointer.

Definition at line 112 of file ReshapeLayer.h.

◆ TReshapeLayer() [3/3]

template<typename Architecture_t >

TMVA::DNN::TReshapeLayer< Architecture_t >::TReshapeLayer ( const TReshapeLayer< Architecture_t > & layer )

Copy Constructor.

Definition at line 119 of file ReshapeLayer.h.

◆ ~TReshapeLayer()

template<typename Architecture_t >

TMVA::DNN::TReshapeLayer< Architecture_t >::~TReshapeLayer

Destructor.

Definition at line 127 of file ReshapeLayer.h.

Member Function Documentation

◆ AddWeightsXMLTo()

template<typename Architecture_t >

auto TMVA::DNN::TReshapeLayer< 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 180 of file ReshapeLayer.h.

◆ Backward()

template<typename Architecture_t >

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

virtual

Backpropagates the error.

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

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

Definition at line 149 of file ReshapeLayer.h.

◆ Forward()

template<typename Architecture_t >

auto TMVA::DNN::TReshapeLayer< Architecture_t >::Forward	(	Tensor_t &	input,
		bool	applyDropout = `false`
	)

virtual

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

It transforms the input matrices.

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

Definition at line 134 of file ReshapeLayer.h.

◆ isFlattening()

template<typename Architecture_t >

bool TMVA::DNN::TReshapeLayer< Architecture_t >::isFlattening ( ) const

inline

TODO Add documentation Does this layer flatten? (necessary for DenseLayer) B x D1 x D2 --> 1 x B x (D1 * D2)

Definition at line 86 of file ReshapeLayer.h.

◆ Print()

template<typename Architecture_t >

auto TMVA::DNN::TReshapeLayer< Architecture_t >::Print

virtual

Prints the info about the layer.

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

Definition at line 169 of file ReshapeLayer.h.

◆ ReadWeightsFromXML()

template<typename Architecture_t >

void TMVA::DNN::TReshapeLayer< 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 195 of file ReshapeLayer.h.

Member Data Documentation

◆ fFlattening

template<typename Architecture_t >

bool TMVA::DNN::TReshapeLayer< Architecture_t >::fFlattening

private

Whather the layer is doing flattening.

Definition at line 48 of file ReshapeLayer.h.

tmva/tmva/inc/TMVA/DNN/ReshapeLayer.h

Public Types

Public Member Functions

Private Attributes

Additional Inherited Members

Member Typedef Documentation

◆ Matrix_t

◆ Scalar_t

◆ Tensor_t

Constructor & Destructor Documentation

◆ TReshapeLayer() [1/3]

◆ TReshapeLayer() [2/3]

◆ TReshapeLayer() [3/3]

◆ ~TReshapeLayer()

Member Function Documentation

◆ AddWeightsXMLTo()

◆ Backward()

◆ Forward()

◆ isFlattening()

◆ Print()

◆ ReadWeightsFromXML()

Member Data Documentation

◆ fFlattening