Steepest Gradient Descent algorithm (SGD)

Implements a steepest gradient descent minimization algorithm

Definition at line 333 of file NeuralNet.h.

Public Member Functions
	Steepest (double learningRate=1e-4, double momentum=0.5, size_t repetitions=10)
	c'tor

template<typename Function , typename Weights , typename PassThrough >
double	operator() (Function &fitnessFunction, Weights &weights, PassThrough &passThrough)
	operator to call the steepest gradient descent algorithm

Public Attributes
double	m_alpha
	internal parameter (learningRate)

double	m_beta
	internal parameter (momentum)

std::vector< double >	m_localGradients
	local gradients for reuse in thread.

std::vector< double >	m_localWeights
	local weights for reuse in thread.

std::vector< double >	m_prevGradients
	vector remembers the gradients of the previous step

size_t	m_repetitions

#include <TMVA/NeuralNet.h>

Constructor & Destructor Documentation

◆ Steepest()

TMVA::DNN::Steepest::Steepest	(	double	learningRate = `1e-4`,
		double	momentum = `0.5`,
		size_t	repetitions = `10`
	)

inline

c'tor

C'tor

Parameters

learningRate	denotes the learning rate for the SGD algorithm
momentum	fraction of the velocity which is taken over from the last step
repetitions	re-compute the gradients each "repetitions" steps

Definition at line 348 of file NeuralNet.h.

Member Function Documentation

◆ operator()()

template<typename Function , typename Weights , typename PassThrough >

double TMVA::DNN::Steepest::operator()	(	Function &	fitnessFunction,
		Weights &	weights,
		PassThrough &	passThrough
	)

operator to call the steepest gradient descent algorithm

implementation of the steepest gradient descent algorithm

entry point to start the minimization procedure

Parameters

fitnessFunction	(templated) function which has to be provided. This function is minimized
weights	(templated) a reference to a container of weights. The result of the minimization procedure is returned via this reference (needs to support std::begin and std::end
passThrough	(templated) object which can hold any data which the fitness function needs. This object is not touched by the minimizer; This object is provided to the fitness function when called

Can be used with multithreading (i.e. "HogWild!" style); see call in trainCycle

Definition at line 271 of file NeuralNet.icc.

Member Data Documentation

◆ m_alpha

double TMVA::DNN::Steepest::m_alpha

internal parameter (learningRate)

Definition at line 371 of file NeuralNet.h.

◆ m_beta

double TMVA::DNN::Steepest::m_beta

internal parameter (momentum)

Definition at line 372 of file NeuralNet.h.

◆ m_localGradients

std::vector<double> TMVA::DNN::Steepest::m_localGradients

local gradients for reuse in thread.

Definition at line 376 of file NeuralNet.h.

◆ m_localWeights

std::vector<double> TMVA::DNN::Steepest::m_localWeights

local weights for reuse in thread.

Definition at line 375 of file NeuralNet.h.

◆ m_prevGradients

std::vector<double> TMVA::DNN::Steepest::m_prevGradients

vector remembers the gradients of the previous step

Definition at line 373 of file NeuralNet.h.

◆ m_repetitions

size_t TMVA::DNN::Steepest::m_repetitions

Definition at line 337 of file NeuralNet.h.

Libraries for TMVA::DNN::Steepest:

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

tmva/tmva/inc/TMVA/NeuralNet.h
tmva/tmva/inc/TMVA/NeuralNet.icc

Public Member Functions

Public Attributes