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Reference Guide
TMVA::DNN::Settings Class Reference

Settings for the training of the neural net.

Definition at line 735 of file NeuralNet.h.

Public Member Functions

 Settings (TString name, size_t _convergenceSteps=15, size_t _batchSize=10, size_t _testRepetitions=7, double _factorWeightDecay=1e-5, TMVA::DNN::EnumRegularization _regularization=TMVA::DNN::EnumRegularization::NONE, MinimizerType _eMinimizerType=MinimizerType::fSteepest, double _learningRate=1e-5, double _momentum=0.3, int _repetitions=3, bool _multithreading=true)
 c'tor More...
 
virtual ~Settings ()
 d'tor More...
 
virtual void cycle (double progress, TString text)
 
const std::vector< double > & dropFractions () const
 
size_t dropRepetitions () const
 
virtual bool hasConverged (double testError)
 has this training converged already? More...
 
template<typename Iterator >
void setDropOut (Iterator begin, Iterator end, size_t _dropRepetitions)
 set the drop-out configuration (layer-wise) More...
 
virtual void setProgressLimits (double minProgress=0, double maxProgress=100)
 
virtual void startTrainCycle ()
 
virtual void startTraining ()
 

Public Attributes

size_t count_dE
 
size_t count_E
 
size_t count_mb_dE
 
size_t count_mb_E
 
double fLearningRate
 
MinimizerType fMinimizerType
 
double fMomentum
 
int fRepetitions
 
size_t m_batchSize
 mini-batch size More...
 
size_t m_convergenceCount
 
size_t m_convergenceSteps
 number of steps without improvement to consider the DNN to have converged More...
 
std::vector< doublem_dropOut
 
double m_dropRepetitions
 
double m_factorWeightDecay
 
size_t m_maxConvergenceCount
 
double m_maxProgress
 current limits for the progress bar More...
 
double m_minError
 
double m_minProgress
 current limits for the progress bar More...
 
EnumRegularization m_regularization
 
size_t m_testRepetitions
 
Timer m_timer
 timer for monitoring More...
 

Protected Attributes

std::shared_ptr< MonitoringfMonitoring
 
bool m_useMultithreading
 

#include <TMVA/NeuralNet.h>

Inheritance diagram for TMVA::DNN::Settings:
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Constructor & Destructor Documentation

◆ Settings()

TMVA::DNN::Settings::Settings ( TString  name,
size_t  _convergenceSteps = 15,
size_t  _batchSize = 10,
size_t  _testRepetitions = 7,
double  _factorWeightDecay = 1e-5,
TMVA::DNN::EnumRegularization  _regularization = TMVA::DNN::EnumRegularization::NONE,
MinimizerType  _eMinimizerType = MinimizerType::fSteepest,
double  _learningRate = 1e-5,
double  _momentum = 0.3,
int  _repetitions = 3,
bool  _multithreading = true 
)

c'tor

Definition at line 232 of file NeuralNet.cxx.

◆ ~Settings()

TMVA::DNN::Settings::~Settings ( )
virtual

d'tor

Definition at line 261 of file NeuralNet.cxx.

Member Function Documentation

◆ cycle()

virtual void TMVA::DNN::Settings::cycle ( double  progress,
TString  text 
)
inlinevirtual
Parameters
textadvance on the progress bar

Definition at line 805 of file NeuralNet.h.

◆ dropFractions()

const std::vector<double>& TMVA::DNN::Settings::dropFractions ( ) const
inline

Definition at line 768 of file NeuralNet.h.

◆ dropRepetitions()

size_t TMVA::DNN::Settings::dropRepetitions ( ) const
inline

Definition at line 767 of file NeuralNet.h.

◆ hasConverged()

bool TMVA::DNN::Settings::hasConverged ( double  testError)
virtual

has this training converged already?

check for convergence

Definition at line 488 of file NeuralNet.cxx.

◆ setDropOut()

template<typename Iterator >
void TMVA::DNN::Settings::setDropOut ( Iterator  begin,
Iterator  end,
size_t  _dropRepetitions 
)
inline

set the drop-out configuration (layer-wise)

Parameters
beginbegin of an array or vector denoting the drop-out probabilities for each layer
endend of an array or vector denoting the drop-out probabilities for each layer
_dropRepetitionsdenotes after how many repetitions the drop-out setting (which nodes are dropped out exactly) is changed

Definition at line 765 of file NeuralNet.h.

◆ setProgressLimits()

virtual void TMVA::DNN::Settings::setProgressLimits ( double  minProgress = 0,
double  maxProgress = 100 
)
inlinevirtual
Parameters
maxProgressfor monitoring and logging (set the current "progress" limits for the display of the progress)

Definition at line 796 of file NeuralNet.h.

◆ startTrainCycle()

virtual void TMVA::DNN::Settings::startTrainCycle ( )
inlinevirtual

Reimplemented in TMVA::DNN::ClassificationSettings.

Definition at line 788 of file NeuralNet.h.

◆ startTraining()

virtual void TMVA::DNN::Settings::startTraining ( )
inlinevirtual

Definition at line 801 of file NeuralNet.h.

Member Data Documentation

◆ count_dE

size_t TMVA::DNN::Settings::count_dE

Definition at line 849 of file NeuralNet.h.

◆ count_E

size_t TMVA::DNN::Settings::count_E

Definition at line 848 of file NeuralNet.h.

◆ count_mb_dE

size_t TMVA::DNN::Settings::count_mb_dE

Definition at line 851 of file NeuralNet.h.

◆ count_mb_E

size_t TMVA::DNN::Settings::count_mb_E

Definition at line 850 of file NeuralNet.h.

◆ fLearningRate

double TMVA::DNN::Settings::fLearningRate

Definition at line 858 of file NeuralNet.h.

◆ fMinimizerType

MinimizerType TMVA::DNN::Settings::fMinimizerType

Definition at line 861 of file NeuralNet.h.

◆ fMomentum

double TMVA::DNN::Settings::fMomentum

Definition at line 859 of file NeuralNet.h.

◆ fMonitoring

std::shared_ptr<Monitoring> TMVA::DNN::Settings::fMonitoring
protected

Definition at line 871 of file NeuralNet.h.

◆ fRepetitions

int TMVA::DNN::Settings::fRepetitions

Definition at line 860 of file NeuralNet.h.

◆ m_batchSize

size_t TMVA::DNN::Settings::m_batchSize

mini-batch size

Definition at line 844 of file NeuralNet.h.

◆ m_convergenceCount

size_t TMVA::DNN::Settings::m_convergenceCount

Definition at line 863 of file NeuralNet.h.

◆ m_convergenceSteps

size_t TMVA::DNN::Settings::m_convergenceSteps

number of steps without improvement to consider the DNN to have converged

Definition at line 843 of file NeuralNet.h.

◆ m_dropOut

std::vector<double> TMVA::DNN::Settings::m_dropOut

Definition at line 856 of file NeuralNet.h.

◆ m_dropRepetitions

double TMVA::DNN::Settings::m_dropRepetitions

Definition at line 855 of file NeuralNet.h.

◆ m_factorWeightDecay

double TMVA::DNN::Settings::m_factorWeightDecay

Definition at line 846 of file NeuralNet.h.

◆ m_maxConvergenceCount

size_t TMVA::DNN::Settings::m_maxConvergenceCount

Definition at line 864 of file NeuralNet.h.

◆ m_maxProgress

double TMVA::DNN::Settings::m_maxProgress

current limits for the progress bar

Definition at line 840 of file NeuralNet.h.

◆ m_minError

double TMVA::DNN::Settings::m_minError

Definition at line 865 of file NeuralNet.h.

◆ m_minProgress

double TMVA::DNN::Settings::m_minProgress

current limits for the progress bar

Definition at line 839 of file NeuralNet.h.

◆ m_regularization

EnumRegularization TMVA::DNN::Settings::m_regularization

Definition at line 853 of file NeuralNet.h.

◆ m_testRepetitions

size_t TMVA::DNN::Settings::m_testRepetitions

Definition at line 845 of file NeuralNet.h.

◆ m_timer

Timer TMVA::DNN::Settings::m_timer

timer for monitoring

Definition at line 838 of file NeuralNet.h.

◆ m_useMultithreading

bool TMVA::DNN::Settings::m_useMultithreading
protected

Definition at line 869 of file NeuralNet.h.

Libraries for TMVA::DNN::Settings:
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The documentation for this class was generated from the following files: