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

Generic layer class.

This generic layer class represents a dense layer of a neural network with a given width n and activation function f. The activation function of each layer is given by \(\mathbf{u} = \mathbf{W}\mathbf{x} + \boldsymbol{\theta}\).

In addition to the weight and bias matrices, each layer allocates memory for its activations and the corresponding first partial fDerivatives of the activation function as well as the gradients of the weights and biases.

The layer provides member functions for the forward propagation of activations through the given layer.

Definition at line 55 of file DenseLayer.h.

Public Types
using	Matrix_t = typename Architecture_t::Matrix_t

using	Scalar_t = typename Architecture_t::Scalar_t

Public Member Functions
	TDenseLayer (size_t BatchSize, size_t InputWidth, size_t Width, EInitialization init, Scalar_t DropoutProbability, EActivationFunction f, ERegularization reg, Scalar_t weightDecay)
	Constructor. More...

	TDenseLayer (TDenseLayer< Architecture_t > *layer)
	Copy the dense layer provided as a pointer. More...

	TDenseLayer (const TDenseLayer &)
	Copy Constructor. More...

	~TDenseLayer ()
	Destructor. More...

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

void	Backward (std::vector< Matrix_t > &gradients_backward, const std::vector< Matrix_t > &activations_backward, std::vector< Matrix_t > &inp1, std::vector< Matrix_t > &inp2)
	Compute weight, bias and activation gradients. More...

void	Forward (std::vector< Matrix_t > &input, bool applyDropout=false)
	Compute activation of the layer for the given input. More...

EActivationFunction	GetActivationFunction () const

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

std::vector< Matrix_t > &	GetDerivatives ()

Matrix_t &	GetDerivativesAt (size_t i)

const Matrix_t &	GetDerivativesAt (size_t i) const

Scalar_t	GetDropoutProbability () const
	Getters. More...

ERegularization	GetRegularization () const

Scalar_t	GetWeightDecay () const

void	Print () const
	Printing the layer info. More...

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

Public Member Functions inherited from 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)
	Constructor. More...

	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. More...

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

	VGeneralLayer (const VGeneralLayer &)
	Copy Constructor. More...

virtual	~VGeneralLayer ()
	Virtual Destructor. More...

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

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

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

std::vector< Matrix_t > &	GetActivationGradients ()

Matrix_t &	GetActivationGradientsAt (size_t i)

const Matrix_t &	GetActivationGradientsAt (size_t i) const

size_t	GetBatchSize () const
	Getters. More...

const 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)

size_t	GetDepth () const

size_t	GetHeight () const

EInitialization	GetInitialization () const

size_t	GetInputDepth () const

size_t	GetInputHeight () const

size_t	GetInputWidth () const

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

std::vector< Matrix_t > &	GetOutput ()

Matrix_t &	GetOutputAt (size_t i)

const Matrix_t &	GetOutputAt (size_t i) const

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)

size_t	GetWidth () const

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

bool	IsTraining () const

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

void	SetBatchSize (size_t batchSize)
	Setters. More...

void	SetDepth (size_t depth)

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. More...

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

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

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

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

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 More...

Private Attributes
std::vector< Matrix_t >	fDerivatives
	First fDerivatives of the activations of this layer. More...

Scalar_t	fDropoutProbability
	Probability that an input is active. More...

EActivationFunction	fF
	Activation function of the layer. More...

ERegularization	fReg
	The regularization method. More...

Scalar_t	fWeightDecay
	The weight decay. More...

Additional Inherited Members
Protected Attributes inherited from TMVA::DNN::VGeneralLayer< Architecture_t >
std::vector< Matrix_t >	fActivationGradients
	Gradients w.r.t. the activations of this layer. More...

size_t	fBatchSize
	Batch size used for training and evaluation. More...

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

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

size_t	fDepth
	The depth of the layer. More...

size_t	fHeight
	The height of the layer. More...

EInitialization	fInit
	The initialization method. More...

size_t	fInputDepth
	The depth of the previous layer or input. More...

size_t	fInputHeight
	The height of the previous layer or input. More...

size_t	fInputWidth
	The width of the previous layer or input. More...

bool	fIsTraining
	Flag indicatig the mode. More...

std::vector< Matrix_t >	fOutput
	Activations of this layer. More...

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

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

size_t	fWidth
	The width of this layer. More...

#include <TMVA/DNN/DenseLayer.h>

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

[legend]

Member Typedef Documentation

◆ Matrix_t

template<typename Architecture_t>

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

Definition at line 58 of file DenseLayer.h.

◆ Scalar_t

template<typename Architecture_t>

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

Definition at line 57 of file DenseLayer.h.

Constructor & Destructor Documentation

◆ TDenseLayer() [1/3]

template<typename Architecture_t >

TMVA::DNN::TDenseLayer< Architecture_t >::TDenseLayer	(	size_t	BatchSize,
		size_t	InputWidth,
		size_t	Width,
		EInitialization	init,
		Scalar_t	DropoutProbability,
		EActivationFunction	f,
		ERegularization	reg,
		Scalar_t	weightDecay
	)

Constructor.

Definition at line 126 of file DenseLayer.h.

◆ TDenseLayer() [2/3]

template<typename Architecture_t >

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

Copy the dense layer provided as a pointer.

Definition at line 138 of file DenseLayer.h.

◆ TDenseLayer() [3/3]

template<typename Architecture_t >

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

Copy Constructor.

Definition at line 147 of file DenseLayer.h.

◆ ~TDenseLayer()

template<typename Architecture_t >

TMVA::DNN::TDenseLayer< Architecture_t >::~TDenseLayer ( )

Destructor.

Definition at line 156 of file DenseLayer.h.

Member Function Documentation

◆ AddWeightsXMLTo()

template<typename Architecture_t >

void TMVA::DNN::TDenseLayer< 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 215 of file DenseLayer.h.

◆ Backward()

template<typename Architecture_t >

auto TMVA::DNN::TDenseLayer< Architecture_t >::Backward	(	std::vector< Matrix_t > &	gradients_backward,
		const std::vector< Matrix_t > &	activations_backward,
		std::vector< Matrix_t > &	inp1,
		std::vector< Matrix_t > &	inp2
	)

virtual

Compute weight, bias and activation gradients.

Uses the precomputed first partial derviatives of the activation function computed during forward propagation and modifies them. Must only be called directly a the corresponding call to Forward(...).

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

Definition at line 176 of file DenseLayer.h.

◆ Forward()

template<typename Architecture_t >

auto TMVA::DNN::TDenseLayer< Architecture_t >::Forward	(	std::vector< Matrix_t > &	input,
		bool	applyDropout = `false`
	)

virtual

Compute 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. Computes activations as well as the first partial derivative of the activation function at those activations.

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

Definition at line 163 of file DenseLayer.h.

◆ GetActivationFunction()

template<typename Architecture_t>

EActivationFunction TMVA::DNN::TDenseLayer< Architecture_t >::GetActivationFunction ( ) const

inline

Definition at line 116 of file DenseLayer.h.

◆ GetDerivatives() [1/2]

template<typename Architecture_t>

const std::vector<Matrix_t>& TMVA::DNN::TDenseLayer< Architecture_t >::GetDerivatives ( ) const

inline

Definition at line 110 of file DenseLayer.h.

◆ GetDerivatives() [2/2]

template<typename Architecture_t>

std::vector<Matrix_t>& TMVA::DNN::TDenseLayer< Architecture_t >::GetDerivatives ( )

inline

Definition at line 111 of file DenseLayer.h.

◆ GetDerivativesAt() [1/2]

template<typename Architecture_t>

Matrix_t& TMVA::DNN::TDenseLayer< Architecture_t >::GetDerivativesAt ( size_t i )

inline

Definition at line 113 of file DenseLayer.h.

◆ GetDerivativesAt() [2/2]

template<typename Architecture_t>

const Matrix_t& TMVA::DNN::TDenseLayer< Architecture_t >::GetDerivativesAt ( size_t i ) const

inline

Definition at line 114 of file DenseLayer.h.

◆ GetDropoutProbability()

template<typename Architecture_t>

Scalar_t TMVA::DNN::TDenseLayer< Architecture_t >::GetDropoutProbability ( ) const

inline

Getters.

Definition at line 108 of file DenseLayer.h.

◆ GetRegularization()

template<typename Architecture_t>

ERegularization TMVA::DNN::TDenseLayer< Architecture_t >::GetRegularization ( ) const

inline

Definition at line 117 of file DenseLayer.h.

◆ GetWeightDecay()

template<typename Architecture_t>

Scalar_t TMVA::DNN::TDenseLayer< Architecture_t >::GetWeightDecay ( ) const

inline

Definition at line 118 of file DenseLayer.h.

◆ Print()

template<typename Architecture_t >

void TMVA::DNN::TDenseLayer< Architecture_t >::Print ( ) const

virtual

Printing the layer info.

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

Definition at line 199 of file DenseLayer.h.

◆ ReadWeightsFromXML()

template<typename Architecture_t >

void TMVA::DNN::TDenseLayer< 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 233 of file DenseLayer.h.

Member Data Documentation

◆ fDerivatives

template<typename Architecture_t>

std::vector<Matrix_t> TMVA::DNN::TDenseLayer< Architecture_t >::fDerivatives

private

First fDerivatives of the activations of this layer.

Definition at line 61 of file DenseLayer.h.

◆ fDropoutProbability

template<typename Architecture_t>

Scalar_t TMVA::DNN::TDenseLayer< Architecture_t >::fDropoutProbability

private

Probability that an input is active.

Definition at line 63 of file DenseLayer.h.

◆ fF

template<typename Architecture_t>

EActivationFunction TMVA::DNN::TDenseLayer< Architecture_t >::fF

private

Activation function of the layer.

Definition at line 65 of file DenseLayer.h.

◆ fReg

template<typename Architecture_t>

ERegularization TMVA::DNN::TDenseLayer< Architecture_t >::fReg

private

The regularization method.

Definition at line 66 of file DenseLayer.h.

◆ fWeightDecay

template<typename Architecture_t>

Scalar_t TMVA::DNN::TDenseLayer< Architecture_t >::fWeightDecay

private

The weight decay.

Definition at line 67 of file DenseLayer.h.

Libraries for TMVA::DNN::TDenseLayer< Architecture_t >:

[legend]

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

Public Types

Public Member Functions

Private Attributes

Additional Inherited Members

Member Typedef Documentation

◆ Matrix_t

◆ Scalar_t

Constructor & Destructor Documentation

◆ TDenseLayer() [1/3]

◆ TDenseLayer() [2/3]

◆ TDenseLayer() [3/3]

◆ ~TDenseLayer()

Member Function Documentation

◆ AddWeightsXMLTo()

◆ Backward()

◆ Forward()

◆ GetActivationFunction()

◆ GetDerivatives() [1/2]

◆ GetDerivatives() [2/2]

◆ GetDerivativesAt() [1/2]

◆ GetDerivativesAt() [2/2]

◆ GetDropoutProbability()

◆ GetRegularization()

◆ GetWeightDecay()

◆ Print()

◆ ReadWeightsFromXML()

Member Data Documentation

◆ fDerivatives

◆ fDropoutProbability

◆ fF

◆ fReg

◆ fWeightDecay

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