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

Generic layer class.

This generic layer class represents a 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 fWeights and fBiases.

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

Definition at line 52 of file Layer.h.

Public Types
using	Matrix_t = typename Architecture_t::Matrix_t

using	Scalar_t = typename Architecture_t::Scalar_t

Public Member Functions
	TLayer (size_t BatchSize, size_t InputWidth, size_t Width, EActivationFunction f, Scalar_t dropoutProbability)

	TLayer (const TLayer &)

void	Backward (Matrix_t &gradients_backward, const Matrix_t &activations_backward, ERegularization r, Scalar_t weightDecay)
	Compute weight, bias and activation gradients. More...

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

EActivationFunction	GetActivationFunction () const

Matrix_t &	GetActivationGradients ()

const Matrix_t &	GetActivationGradients () const

size_t	GetBatchSize () const

Matrix_t &	GetBiases ()

const Matrix_t &	GetBiases () const

Matrix_t &	GetBiasGradients ()

const Matrix_t &	GetBiasGradients () const

size_t	GetDropoutProbability () const

size_t	GetInputWidth () const

Matrix_t &	GetOutput ()

const Matrix_t &	GetOutput () const

Matrix_t &	GetWeightGradients ()

const Matrix_t &	GetWeightGradients () const

Matrix_t &	GetWeights ()

const Matrix_t &	GetWeights () const

size_t	GetWidth () const

void	Initialize (EInitialization m)
	Initialize fWeights according to the given initialization method. More...

void	Print () const

void	SetDropoutProbability (Scalar_t p)

Private Attributes
Matrix_t	fActivationGradients
	Gradients w.r.t. the activations of this layer. More...

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

Matrix_t	fBiases
	The bias values of this layer. More...

Matrix_t	fBiasGradients
	Gradients w.r.t. the bias values of this layer. More...

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...

size_t	fInputWidth
	Number of neurons of the previous layer. More...

Matrix_t	fOutput
	Activations of this layer. More...

Matrix_t	fWeightGradients
	Gradients w.r.t. the weigths of this layer. More...

Matrix_t	fWeights
	The fWeights of this layer. More...

size_t	fWidth
	Number of neurons of this layer. More...

#include <TMVA/DNN/Layer.h>

Member Typedef Documentation

◆ Matrix_t

template<typename Architecture_t>

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

Definition at line 57 of file Layer.h.

◆ Scalar_t

template<typename Architecture_t>

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

Definition at line 56 of file Layer.h.

Constructor & Destructor Documentation

◆ TLayer() [1/2]

template<typename Architecture_t >

TMVA::DNN::TLayer< Architecture_t >::TLayer	(	size_t	BatchSize,
		size_t	InputWidth,
		size_t	Width,
		EActivationFunction	f,
		Scalar_t	dropoutProbability
	)

Definition at line 223 of file Layer.h.

◆ TLayer() [2/2]

template<typename Architecture_t >

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

Definition at line 239 of file Layer.h.

Member Function Documentation

◆ Backward()

template<typename Architecture_t >

auto TMVA::DNN::TLayer< Architecture_t >::Backward	(	Matrix_t &	gradients_backward,
		const Matrix_t &	activations_backward,
		ERegularization	r,
		Scalar_t	weightDecay
	)

inline

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(...).

Definition at line 280 of file Layer.h.

◆ Forward()

template<typename Architecture_t >

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

inline

Compute activation of the layer for the given input.

The input must be in matrix form with the different rows corresponding to different events in the batch. Computes activations as well as the first partial derivative of the activation function at those activations.

Definition at line 265 of file Layer.h.

◆ GetActivationFunction()

template<typename Architecture_t>

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

inline

Definition at line 113 of file Layer.h.

◆ GetActivationGradients() [1/2]

template<typename Architecture_t>

Matrix_t& TMVA::DNN::TLayer< Architecture_t >::GetActivationGradients ( )

inline

Definition at line 121 of file Layer.h.

◆ GetActivationGradients() [2/2]

template<typename Architecture_t>

const Matrix_t& TMVA::DNN::TLayer< Architecture_t >::GetActivationGradients ( ) const

inline

Definition at line 122 of file Layer.h.

◆ GetBatchSize()

template<typename Architecture_t>

size_t TMVA::DNN::TLayer< Architecture_t >::GetBatchSize ( ) const

inline

Definition at line 106 of file Layer.h.

◆ GetBiases() [1/2]

template<typename Architecture_t>

Matrix_t& TMVA::DNN::TLayer< Architecture_t >::GetBiases ( )

inline

Definition at line 119 of file Layer.h.

◆ GetBiases() [2/2]

template<typename Architecture_t>

const Matrix_t& TMVA::DNN::TLayer< Architecture_t >::GetBiases ( ) const

inline

Definition at line 120 of file Layer.h.

◆ GetBiasGradients() [1/2]

template<typename Architecture_t>

Matrix_t& TMVA::DNN::TLayer< Architecture_t >::GetBiasGradients ( )

inline

Definition at line 123 of file Layer.h.

◆ GetBiasGradients() [2/2]

template<typename Architecture_t>

const Matrix_t& TMVA::DNN::TLayer< Architecture_t >::GetBiasGradients ( ) const

inline

Definition at line 124 of file Layer.h.

◆ GetDropoutProbability()

template<typename Architecture_t>

size_t TMVA::DNN::TLayer< Architecture_t >::GetDropoutProbability ( ) const

inline

Definition at line 109 of file Layer.h.

◆ GetInputWidth()

template<typename Architecture_t>

size_t TMVA::DNN::TLayer< Architecture_t >::GetInputWidth ( ) const

inline

Definition at line 107 of file Layer.h.

◆ GetOutput() [1/2]

template<typename Architecture_t>

Matrix_t& TMVA::DNN::TLayer< Architecture_t >::GetOutput ( )

inline

Definition at line 115 of file Layer.h.

◆ GetOutput() [2/2]

template<typename Architecture_t>

const Matrix_t& TMVA::DNN::TLayer< Architecture_t >::GetOutput ( ) const

inline

Definition at line 116 of file Layer.h.

◆ GetWeightGradients() [1/2]

template<typename Architecture_t>

Matrix_t& TMVA::DNN::TLayer< Architecture_t >::GetWeightGradients ( )

inline

Definition at line 125 of file Layer.h.

◆ GetWeightGradients() [2/2]

template<typename Architecture_t>

const Matrix_t& TMVA::DNN::TLayer< Architecture_t >::GetWeightGradients ( ) const

inline

Definition at line 126 of file Layer.h.

◆ GetWeights() [1/2]

template<typename Architecture_t>

Matrix_t& TMVA::DNN::TLayer< Architecture_t >::GetWeights ( )

inline

Definition at line 117 of file Layer.h.

◆ GetWeights() [2/2]

template<typename Architecture_t>

const Matrix_t& TMVA::DNN::TLayer< Architecture_t >::GetWeights ( ) const

inline

Definition at line 118 of file Layer.h.

◆ GetWidth()

template<typename Architecture_t>

size_t TMVA::DNN::TLayer< Architecture_t >::GetWidth ( ) const

inline

Definition at line 108 of file Layer.h.

◆ Initialize()

template<typename Architecture_t >

auto TMVA::DNN::TLayer< Architecture_t >::Initialize ( EInitialization m )

Initialize fWeights according to the given initialization method.

Definition at line 256 of file Layer.h.

◆ Print()

template<typename Architecture_t >

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

Definition at line 300 of file Layer.h.

◆ SetDropoutProbability()

template<typename Architecture_t>

void TMVA::DNN::TLayer< Architecture_t >::SetDropoutProbability ( Scalar_t p )

inline

Definition at line 111 of file Layer.h.

Member Data Documentation

◆ fActivationGradients

template<typename Architecture_t>

Matrix_t TMVA::DNN::TLayer< Architecture_t >::fActivationGradients

private

Gradients w.r.t. the activations of this layer.

Definition at line 73 of file Layer.h.

◆ fBatchSize

template<typename Architecture_t>

size_t TMVA::DNN::TLayer< Architecture_t >::fBatchSize

private

Batch size used for training and evaluation.

Definition at line 61 of file Layer.h.

◆ fBiases

template<typename Architecture_t>

Matrix_t TMVA::DNN::TLayer< Architecture_t >::fBiases

private

The bias values of this layer.

Definition at line 68 of file Layer.h.

◆ fBiasGradients

template<typename Architecture_t>

Matrix_t TMVA::DNN::TLayer< Architecture_t >::fBiasGradients

private

Gradients w.r.t. the bias values of this layer.

Definition at line 72 of file Layer.h.

◆ fDerivatives

template<typename Architecture_t>

Matrix_t TMVA::DNN::TLayer< Architecture_t >::fDerivatives

private

First fDerivatives of the activations of this layer.

Definition at line 70 of file Layer.h.

◆ fDropoutProbability

template<typename Architecture_t>

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

private

Probability that an input is active.

Definition at line 65 of file Layer.h.

◆ fF

template<typename Architecture_t>

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

private

Activation function of the layer.

Definition at line 75 of file Layer.h.

◆ fInputWidth

template<typename Architecture_t>

size_t TMVA::DNN::TLayer< Architecture_t >::fInputWidth

private

Number of neurons of the previous layer.

Definition at line 62 of file Layer.h.

◆ fOutput

template<typename Architecture_t>

Matrix_t TMVA::DNN::TLayer< Architecture_t >::fOutput

private

Activations of this layer.

Definition at line 69 of file Layer.h.

◆ fWeightGradients

template<typename Architecture_t>

Matrix_t TMVA::DNN::TLayer< Architecture_t >::fWeightGradients

private

Gradients w.r.t. the weigths of this layer.

Definition at line 71 of file Layer.h.

◆ fWeights

template<typename Architecture_t>

Matrix_t TMVA::DNN::TLayer< Architecture_t >::fWeights

private

The fWeights of this layer.

Definition at line 67 of file Layer.h.

◆ fWidth

template<typename Architecture_t>

size_t TMVA::DNN::TLayer< Architecture_t >::fWidth

private

Number of neurons of this layer.

Definition at line 63 of file Layer.h.

The documentation for this class was generated from the following file:

tmva/tmva/inc/TMVA/DNN/Layer.h

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

Public Types

Public Member Functions

Private Attributes

Member Typedef Documentation

◆ Matrix_t

◆ Scalar_t

Constructor & Destructor Documentation

◆ TLayer() [1/2]

◆ TLayer() [2/2]

Member Function Documentation

◆ Backward()

◆ Forward()

◆ GetActivationFunction()

◆ GetActivationGradients() [1/2]

◆ GetActivationGradients() [2/2]

◆ GetBatchSize()

◆ GetBiases() [1/2]

◆ GetBiases() [2/2]

◆ GetBiasGradients() [1/2]

◆ GetBiasGradients() [2/2]

◆ GetDropoutProbability()

◆ GetInputWidth()

◆ GetOutput() [1/2]

◆ GetOutput() [2/2]

◆ GetWeightGradients() [1/2]

◆ GetWeightGradients() [2/2]

◆ GetWeights() [1/2]

◆ GetWeights() [2/2]

◆ GetWidth()

◆ Initialize()

◆ Print()

◆ SetDropoutProbability()

Member Data Documentation

◆ fActivationGradients

◆ fBatchSize

◆ fBiases

◆ fBiasGradients

◆ fDerivatives

◆ fDropoutProbability

◆ fF

◆ fInputWidth

◆ fOutput

◆ fWeightGradients

◆ fWeights

◆ fWidth

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