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

Adagrad Optimizer class.

This class represents the Adagrad Optimizer.

Definition at line 44 of file Adagrad.h.

Public Types
using	Matrix_t = typename Architecture_t::Matrix_t

using	Scalar_t = typename Architecture_t::Scalar_t

Public Types inherited from TMVA::DNN::VOptimizer< Architecture_t, VGeneralLayer< Architecture_t >, TDeepNet< Architecture_t, VGeneralLayer< Architecture_t > > >
using	Matrix_t = typename Architecture_t::Matrix_t

using	Scalar_t = typename Architecture_t::Scalar_t

Public Member Functions
	TAdagrad (DeepNet_t &deepNet, Scalar_t learningRate=0.01, Scalar_t epsilon=1e-8)
	Constructor. More...

	~TAdagrad ()=default
	Destructor. More...

Scalar_t	GetEpsilon () const
	Getters. More...

std::vector< std::vector< Matrix_t > > &	GetPastSquaredBiasGradients ()

std::vector< Matrix_t > &	GetPastSquaredBiasGradientsAt (size_t i)

std::vector< std::vector< Matrix_t > > &	GetPastSquaredWeightGradients ()

std::vector< Matrix_t > &	GetPastSquaredWeightGradientsAt (size_t i)

Public Member Functions inherited from TMVA::DNN::VOptimizer< Architecture_t, VGeneralLayer< Architecture_t >, TDeepNet< Architecture_t, VGeneralLayer< Architecture_t > > >
	VOptimizer (Scalar_t learningRate, TDeepNet< Architecture_t, VGeneralLayer< Architecture_t > > &deepNet)
	Constructor. More...

virtual	~VOptimizer ()=default
	Virtual Destructor. More...

size_t	GetGlobalStep () const

VGeneralLayer< Architecture_t > *	GetLayerAt (size_t i)

std::vector< VGeneralLayer< Architecture_t > * > &	GetLayers ()

Scalar_t	GetLearningRate () const
	Getters. More...

void	IncrementGlobalStep ()
	Increments the global step. More...

void	SetLearningRate (size_t learningRate)
	Setters. More...

void	Step ()
	Performs one step of optimization. More...

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

void	UpdateWeights (size_t layerIndex, std::vector< Matrix_t > &weights, const std::vector< Matrix_t > &weightGradients)
	Update the weights, given the current weight gradients. More...

virtual void	UpdateBiases (size_t layerIndex, std::vector< Matrix_t > &biases, const std::vector< Matrix_t > &biasGradients)=0
	Update the biases, given the current bias gradients. More...

virtual void	UpdateWeights (size_t layerIndex, std::vector< Matrix_t > &weights, const std::vector< Matrix_t > &weightGradients)=0
	Update the weights, given the current weight gradients. More...

Protected Attributes
Scalar_t	fEpsilon
	The Smoothing term used to avoid division by zero. More...

std::vector< std::vector< Matrix_t > >	fPastSquaredBiasGradients
	The sum of the square of the past bias gradients associated with the deep net. More...

std::vector< std::vector< Matrix_t > >	fPastSquaredWeightGradients
	The sum of the square of the past weight gradients associated with the deep net. More...

Protected Attributes inherited from TMVA::DNN::VOptimizer< Architecture_t, VGeneralLayer< Architecture_t >, TDeepNet< Architecture_t, VGeneralLayer< Architecture_t > > >
TDeepNet< Architecture_t, VGeneralLayer< Architecture_t > > &	fDeepNet
	The reference to the deep net. More...

size_t	fGlobalStep
	The current global step count during training. More...

Scalar_t	fLearningRate
	The learning rate used for training. More...

#include <TMVA/DNN/Adagrad.h>

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

[legend]

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::TAdagrad< Architecture_t, Layer_t, DeepNet_t >::Matrix_t = typename Architecture_t::Matrix_t

Definition at line 46 of file Adagrad.h.

◆ Scalar_t

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

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

Definition at line 47 of file Adagrad.h.

Constructor & Destructor Documentation

◆ TAdagrad()

template<typename Architecture_t , typename Layer_t , typename DeepNet_t >

TMVA::DNN::TAdagrad< Architecture_t, Layer_t, DeepNet_t >::TAdagrad	(	DeepNet_t &	deepNet,
		Scalar_t	learningRate = `0.01`,
		Scalar_t	epsilon = `1e-8`
	)

Constructor.

Definition at line 85 of file Adagrad.h.

◆ ~TAdagrad()

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

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

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::TAdagrad< Architecture_t, Layer_t, DeepNet_t >::GetEpsilon ( ) const

inline

Getters.

Definition at line 71 of file Adagrad.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::TAdagrad< Architecture_t, Layer_t, DeepNet_t >::GetPastSquaredBiasGradients ( )

inline

Definition at line 76 of file Adagrad.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::TAdagrad< Architecture_t, Layer_t, DeepNet_t >::GetPastSquaredBiasGradientsAt ( size_t i )

inline

Definition at line 77 of file Adagrad.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::TAdagrad< Architecture_t, Layer_t, DeepNet_t >::GetPastSquaredWeightGradients ( )

inline

Definition at line 73 of file Adagrad.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::TAdagrad< Architecture_t, Layer_t, DeepNet_t >::GetPastSquaredWeightGradientsAt ( size_t i )

inline

Definition at line 74 of file Adagrad.h.

◆ UpdateBiases()

template<typename Architecture_t , typename Layer_t , typename DeepNet_t >

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

protectedvirtual

Update the biases, given the current bias gradients.

Implements TMVA::DNN::VOptimizer< Architecture_t, VGeneralLayer< Architecture_t >, TDeepNet< Architecture_t, VGeneralLayer< Architecture_t > > >.

Definition at line 149 of file Adagrad.h.

◆ UpdateWeights()

template<typename Architecture_t , typename Layer_t , typename DeepNet_t >

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

protectedvirtual

Update the weights, given the current weight gradients.

Implements TMVA::DNN::VOptimizer< Architecture_t, VGeneralLayer< Architecture_t >, TDeepNet< Architecture_t, VGeneralLayer< Architecture_t > > >.

Definition at line 120 of file Adagrad.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::TAdagrad< Architecture_t, Layer_t, DeepNet_t >::fEpsilon

protected

The Smoothing term used to avoid division by zero.

Definition at line 50 of file Adagrad.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::TAdagrad< Architecture_t, Layer_t, DeepNet_t >::fPastSquaredBiasGradients

protected

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

Definition at line 55 of file Adagrad.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::TAdagrad< Architecture_t, Layer_t, DeepNet_t >::fPastSquaredWeightGradients

protected

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

Definition at line 53 of file Adagrad.h.

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

tmva/tmva/inc/TMVA/DNN/Adagrad.h

Public Types

Public Member Functions

Protected Member Functions

Protected Attributes

Member Typedef Documentation

◆ Matrix_t

◆ Scalar_t

Constructor & Destructor Documentation

◆ TAdagrad()

◆ ~TAdagrad()

Member Function Documentation

◆ GetEpsilon()

◆ GetPastSquaredBiasGradients()

◆ GetPastSquaredBiasGradientsAt()

◆ GetPastSquaredWeightGradients()

◆ GetPastSquaredWeightGradientsAt()

◆ UpdateBiases()

◆ UpdateWeights()

Member Data Documentation

◆ fEpsilon

◆ fPastSquaredBiasGradients

◆ fPastSquaredWeightGradients