template<typename Architecture_t, typename Layer_t = VGeneralLayer<Architecture_t>, typename DeepNet_t = TDeepNet<Architecture_t, Layer_t>>
class TMVA::DNN::TAdadelta< Architecture_t, Layer_t, DeepNet_t >

Adadelta Optimizer class.

This class represents the Adadelta Optimizer.

Definition at line 45 of file Adadelta.h.

Public Types
using	Matrix_t = typename Architecture_t::Matrix_t
using	Scalar_t = typename Architecture_t::Scalar_t

Public Member Functions
	TAdadelta (DeepNet_t &deepNet, Scalar_t learningRate=1.0, Scalar_t rho=0.95, Scalar_t epsilon=1e-8)
	Constructor.
	~TAdadelta ()=default
	Destructor.
Scalar_t	GetEpsilon () const
std::vector< std::vector< Matrix_t > > &	GetPastSquaredBiasGradients ()
std::vector< Matrix_t > &	GetPastSquaredBiasGradientsAt (size_t i)
std::vector< std::vector< Matrix_t > > &	GetPastSquaredBiasUpdates ()
std::vector< Matrix_t > &	GetPastSquaredBiasUpdatesAt (size_t i)
std::vector< std::vector< Matrix_t > > &	GetPastSquaredWeightGradients ()
std::vector< Matrix_t > &	GetPastSquaredWeightGradientsAt (size_t i)
std::vector< std::vector< Matrix_t > > &	GetPastSquaredWeightUpdates ()
std::vector< Matrix_t > &	GetPastSquaredWeightUpdatesAt (size_t i)
Scalar_t	GetRho () const
	Getters.

Protected Member Functions
void	UpdateBiases (size_t layerIndex, std::vector< Matrix_t > &biases, const std::vector< Matrix_t > &biasGradients) override
	Update the biases, given the current bias gradients.
void	UpdateWeights (size_t layerIndex, std::vector< Matrix_t > &weights, const std::vector< Matrix_t > &weightGradients) override
	Update the weights, given the current weight gradients.

Protected Attributes
Scalar_t	fEpsilon
	The Smoothing term used to avoid division by zero.
std::vector< std::vector< Matrix_t > >	fPastSquaredBiasGradients
	The accumulation of the square of the past bias gradients associated with the deep net.
std::vector< std::vector< Matrix_t > >	fPastSquaredBiasUpdates
	The accumulation of the square of the past bias updates associated with the deep net.
std::vector< std::vector< Matrix_t > >	fPastSquaredWeightGradients
	The accumulation of the square of the past weight gradients associated with the deep net.
std::vector< std::vector< Matrix_t > >	fPastSquaredWeightUpdates
	The accumulation of the square of the past weight updates associated with the deep net.
Scalar_t	fRho
	The Rho constant used by the optimizer.
std::vector< std::vector< Matrix_t > >	fWorkBiasTensor1
	working tensor used to keep a temporary copy of bias or bias gradients
std::vector< std::vector< Matrix_t > >	fWorkBiasTensor2
	working tensor used to keep a temporary copy of bias or bias gradients
std::vector< std::vector< Matrix_t > >	fWorkWeightTensor1
	working tensor used to keep a temporary copy of weights or weight gradients
std::vector< std::vector< Matrix_t > >	fWorkWeightTensor2
	working tensor used to keep a temporary copy of weights or weight gradients

#include <TMVA/DNN/Adadelta.h>

Inheritance diagram for TMVA::DNN::TAdadelta< Architecture_t, Layer_t, DeepNet_t >:

Member Typedef Documentation

◆ Matrix_t

template<typename Architecture_t, typename Layer_t = VGeneralLayer<Architecture_t>, typename DeepNet_t = TDeepNet<Architecture_t, Layer_t>>

using TMVA::DNN::TAdadelta< Architecture_t, Layer_t, DeepNet_t >::Matrix_t = typename Architecture_t::Matrix_t

Definition at line 47 of file Adadelta.h.

◆ Scalar_t

template<typename Architecture_t, typename Layer_t = VGeneralLayer<Architecture_t>, typename DeepNet_t = TDeepNet<Architecture_t, Layer_t>>

using TMVA::DNN::TAdadelta< Architecture_t, Layer_t, DeepNet_t >::Scalar_t = typename Architecture_t::Scalar_t

Definition at line 48 of file Adadelta.h.

Constructor & Destructor Documentation

◆ TAdadelta()

template<typename Architecture_t, typename Layer_t, typename DeepNet_t>

TMVA::DNN::TAdadelta< Architecture_t, Layer_t, DeepNet_t >::TAdadelta	(	DeepNet_t &	deepNet,
		Scalar_t	learningRate = 1.0,
		Scalar_t	rho = 0.95,
		Scalar_t	epsilon = 1e-8 )

Constructor.

Definition at line 102 of file Adadelta.h.

◆ ~TAdadelta()

template<typename Architecture_t, typename Layer_t = VGeneralLayer<Architecture_t>, typename DeepNet_t = TDeepNet<Architecture_t, Layer_t>>

TMVA::DNN::TAdadelta< Architecture_t, Layer_t, DeepNet_t >::~TAdadelta ( )

default

Destructor.

Member Function Documentation

◆ GetEpsilon()

template<typename Architecture_t, typename Layer_t = VGeneralLayer<Architecture_t>, typename DeepNet_t = TDeepNet<Architecture_t, Layer_t>>

Scalar_t TMVA::DNN::TAdadelta< Architecture_t, Layer_t, DeepNet_t >::GetEpsilon ( ) const

inline

Definition at line 82 of file Adadelta.h.

◆ GetPastSquaredBiasGradients()

template<typename Architecture_t, typename Layer_t = VGeneralLayer<Architecture_t>, typename DeepNet_t = TDeepNet<Architecture_t, Layer_t>>

std::vector< std::vector< Matrix_t > > & TMVA::DNN::TAdadelta< Architecture_t, Layer_t, DeepNet_t >::GetPastSquaredBiasGradients ( )

inline

Definition at line 87 of file Adadelta.h.

◆ GetPastSquaredBiasGradientsAt()

template<typename Architecture_t, typename Layer_t = VGeneralLayer<Architecture_t>, typename DeepNet_t = TDeepNet<Architecture_t, Layer_t>>

std::vector< Matrix_t > & TMVA::DNN::TAdadelta< Architecture_t, Layer_t, DeepNet_t >::GetPastSquaredBiasGradientsAt ( size_t i )

inline

Definition at line 88 of file Adadelta.h.

◆ GetPastSquaredBiasUpdates()

template<typename Architecture_t, typename Layer_t = VGeneralLayer<Architecture_t>, typename DeepNet_t = TDeepNet<Architecture_t, Layer_t>>

std::vector< std::vector< Matrix_t > > & TMVA::DNN::TAdadelta< Architecture_t, Layer_t, DeepNet_t >::GetPastSquaredBiasUpdates ( )

inline

Definition at line 93 of file Adadelta.h.

◆ GetPastSquaredBiasUpdatesAt()

template<typename Architecture_t, typename Layer_t = VGeneralLayer<Architecture_t>, typename DeepNet_t = TDeepNet<Architecture_t, Layer_t>>

std::vector< Matrix_t > & TMVA::DNN::TAdadelta< Architecture_t, Layer_t, DeepNet_t >::GetPastSquaredBiasUpdatesAt ( size_t i )

inline

Definition at line 94 of file Adadelta.h.

◆ GetPastSquaredWeightGradients()

template<typename Architecture_t, typename Layer_t = VGeneralLayer<Architecture_t>, typename DeepNet_t = TDeepNet<Architecture_t, Layer_t>>

std::vector< std::vector< Matrix_t > > & TMVA::DNN::TAdadelta< Architecture_t, Layer_t, DeepNet_t >::GetPastSquaredWeightGradients ( )

inline

Definition at line 84 of file Adadelta.h.

◆ GetPastSquaredWeightGradientsAt()

template<typename Architecture_t, typename Layer_t = VGeneralLayer<Architecture_t>, typename DeepNet_t = TDeepNet<Architecture_t, Layer_t>>

std::vector< Matrix_t > & TMVA::DNN::TAdadelta< Architecture_t, Layer_t, DeepNet_t >::GetPastSquaredWeightGradientsAt ( size_t i )

inline

Definition at line 85 of file Adadelta.h.

◆ GetPastSquaredWeightUpdates()

template<typename Architecture_t, typename Layer_t = VGeneralLayer<Architecture_t>, typename DeepNet_t = TDeepNet<Architecture_t, Layer_t>>

std::vector< std::vector< Matrix_t > > & TMVA::DNN::TAdadelta< Architecture_t, Layer_t, DeepNet_t >::GetPastSquaredWeightUpdates ( )

inline

Definition at line 90 of file Adadelta.h.

◆ GetPastSquaredWeightUpdatesAt()

template<typename Architecture_t, typename Layer_t = VGeneralLayer<Architecture_t>, typename DeepNet_t = TDeepNet<Architecture_t, Layer_t>>

std::vector< Matrix_t > & TMVA::DNN::TAdadelta< Architecture_t, Layer_t, DeepNet_t >::GetPastSquaredWeightUpdatesAt ( size_t i )

inline

Definition at line 91 of file Adadelta.h.

◆ GetRho()

template<typename Architecture_t, typename Layer_t = VGeneralLayer<Architecture_t>, typename DeepNet_t = TDeepNet<Architecture_t, Layer_t>>

Scalar_t TMVA::DNN::TAdadelta< Architecture_t, Layer_t, DeepNet_t >::GetRho ( ) const

inline

Getters.

Definition at line 81 of file Adadelta.h.

◆ UpdateBiases()

template<typename Architecture_t, typename Layer_t, typename DeepNet_t>

auto TMVA::DNN::TAdadelta< Architecture_t, Layer_t, DeepNet_t >::UpdateBiases	(	size_t	layerIndex,
		std::vector< Matrix_t > &	biases,
		const std::vector< Matrix_t > &	biasGradients )

overrideprotected

Update the biases, given the current bias gradients.

Definition at line 206 of file Adadelta.h.

◆ UpdateWeights()

template<typename Architecture_t, typename Layer_t, typename DeepNet_t>

auto TMVA::DNN::TAdadelta< Architecture_t, Layer_t, DeepNet_t >::UpdateWeights	(	size_t	layerIndex,
		std::vector< Matrix_t > &	weights,
		const std::vector< Matrix_t > &	weightGradients )

overrideprotected

Update the weights, given the current weight gradients.

Definition at line 147 of file Adadelta.h.

Member Data Documentation

◆ fEpsilon

template<typename Architecture_t, typename Layer_t = VGeneralLayer<Architecture_t>, typename DeepNet_t = TDeepNet<Architecture_t, Layer_t>>

Scalar_t TMVA::DNN::TAdadelta< Architecture_t, Layer_t, DeepNet_t >::fEpsilon

protected

The Smoothing term used to avoid division by zero.

Definition at line 52 of file Adadelta.h.

◆ fPastSquaredBiasGradients

template<typename Architecture_t, typename Layer_t = VGeneralLayer<Architecture_t>, typename DeepNet_t = TDeepNet<Architecture_t, Layer_t>>

std::vector<std::vector<Matrix_t> > TMVA::DNN::TAdadelta< Architecture_t, Layer_t, DeepNet_t >::fPastSquaredBiasGradients

protected

The accumulation of the square of the past bias gradients associated with the deep net.

Definition at line 55 of file Adadelta.h.

◆ fPastSquaredBiasUpdates

template<typename Architecture_t, typename Layer_t = VGeneralLayer<Architecture_t>, typename DeepNet_t = TDeepNet<Architecture_t, Layer_t>>

std::vector<std::vector<Matrix_t> > TMVA::DNN::TAdadelta< Architecture_t, Layer_t, DeepNet_t >::fPastSquaredBiasUpdates

protected

The accumulation of the square of the past bias updates associated with the deep net.

Definition at line 60 of file Adadelta.h.

◆ fPastSquaredWeightGradients

template<typename Architecture_t, typename Layer_t = VGeneralLayer<Architecture_t>, typename DeepNet_t = TDeepNet<Architecture_t, Layer_t>>

std::vector<std::vector<Matrix_t> > TMVA::DNN::TAdadelta< Architecture_t, Layer_t, DeepNet_t >::fPastSquaredWeightGradients

protected

The accumulation of the square of the past weight gradients associated with the deep net.

Definition at line 53 of file Adadelta.h.

◆ fPastSquaredWeightUpdates

template<typename Architecture_t, typename Layer_t = VGeneralLayer<Architecture_t>, typename DeepNet_t = TDeepNet<Architecture_t, Layer_t>>

std::vector<std::vector<Matrix_t> > TMVA::DNN::TAdadelta< Architecture_t, Layer_t, DeepNet_t >::fPastSquaredWeightUpdates

protected

The accumulation of the square of the past weight updates associated with the deep net.

Definition at line 58 of file Adadelta.h.

◆ fRho

template<typename Architecture_t, typename Layer_t = VGeneralLayer<Architecture_t>, typename DeepNet_t = TDeepNet<Architecture_t, Layer_t>>

Scalar_t TMVA::DNN::TAdadelta< Architecture_t, Layer_t, DeepNet_t >::fRho

protected

The Rho constant used by the optimizer.

Definition at line 51 of file Adadelta.h.

◆ fWorkBiasTensor1

template<typename Architecture_t, typename Layer_t = VGeneralLayer<Architecture_t>, typename DeepNet_t = TDeepNet<Architecture_t, Layer_t>>

std::vector<std::vector<Matrix_t> > TMVA::DNN::TAdadelta< Architecture_t, Layer_t, DeepNet_t >::fWorkBiasTensor1

protected

working tensor used to keep a temporary copy of bias or bias gradients

Definition at line 63 of file Adadelta.h.

◆ fWorkBiasTensor2

template<typename Architecture_t, typename Layer_t = VGeneralLayer<Architecture_t>, typename DeepNet_t = TDeepNet<Architecture_t, Layer_t>>

std::vector<std::vector<Matrix_t> > TMVA::DNN::TAdadelta< Architecture_t, Layer_t, DeepNet_t >::fWorkBiasTensor2

protected

working tensor used to keep a temporary copy of bias or bias gradients

Definition at line 65 of file Adadelta.h.

◆ fWorkWeightTensor1

template<typename Architecture_t, typename Layer_t = VGeneralLayer<Architecture_t>, typename DeepNet_t = TDeepNet<Architecture_t, Layer_t>>

std::vector<std::vector<Matrix_t> > TMVA::DNN::TAdadelta< Architecture_t, Layer_t, DeepNet_t >::fWorkWeightTensor1

protected

working tensor used to keep a temporary copy of weights or weight gradients

Definition at line 62 of file Adadelta.h.

◆ fWorkWeightTensor2

template<typename Architecture_t, typename Layer_t = VGeneralLayer<Architecture_t>, typename DeepNet_t = TDeepNet<Architecture_t, Layer_t>>

std::vector<std::vector<Matrix_t> > TMVA::DNN::TAdadelta< Architecture_t, Layer_t, DeepNet_t >::fWorkWeightTensor2

protected

working tensor used to keep a temporary copy of weights or weight gradients

Definition at line 64 of file Adadelta.h.

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

tmva/tmva/inc/TMVA/DNN/Adadelta.h

Public Types

Public Member Functions

Protected Member Functions

Protected Attributes

Member Typedef Documentation

◆ Matrix_t

◆ Scalar_t

Constructor & Destructor Documentation

◆ TAdadelta()

◆ ~TAdadelta()

Member Function Documentation

◆ GetEpsilon()

◆ GetPastSquaredBiasGradients()

◆ GetPastSquaredBiasGradientsAt()

◆ GetPastSquaredBiasUpdates()

◆ GetPastSquaredBiasUpdatesAt()

◆ GetPastSquaredWeightGradients()

◆ GetPastSquaredWeightGradientsAt()

◆ GetPastSquaredWeightUpdates()

◆ GetPastSquaredWeightUpdatesAt()

◆ GetRho()

◆ UpdateBiases()

◆ UpdateWeights()

Member Data Documentation

◆ fEpsilon

◆ fPastSquaredBiasGradients

◆ fPastSquaredBiasUpdates

◆ fPastSquaredWeightGradients

◆ fPastSquaredWeightUpdates

◆ fRho

◆ fWorkBiasTensor1

◆ fWorkBiasTensor2

◆ fWorkWeightTensor1

◆ fWorkWeightTensor2