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TMVA::DNN::Settings Class Reference

Settings for the training of the neural net.

Definition at line 729 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
 
virtual ~Settings ()
 d'tor
 
void addPoint (std::string histoName, double x)
 for monitoring
 
void addPoint (std::string histoName, double x, double y)
 for monitoring
 
size_t batchSize () const
 mini-batch size
 
void clear (std::string histoName)
 for monitoring
 
virtual void computeResult (const Net &, std::vector< double > &)
 callback for monitoring and logging
 
size_t convergenceCount () const
 returns the current convergence count
 
size_t convergenceSteps () const
 how many steps until training is deemed to have converged
 
void create (std::string histoName, int bins, double min, double max)
 for monitoring
 
void create (std::string histoName, int bins, double min, double max, int bins2, double min2, double max2)
 for monitoring
 
virtual void cycle (double progress, TString text)
 
virtual void drawSample (const std::vector< double > &, const std::vector< double > &, const std::vector< double > &, double)
 callback for monitoring and logging
 
const std::vector< double > & dropFractions () const
 
size_t dropRepetitions () const
 
virtual void endTestCycle ()
 callback for monitoring and loggging
 
virtual void endTrainCycle (double)
 callback for monitoring and logging
 
bool exists (std::string histoName)
 for monitoring
 
double factorWeightDecay () const
 get the weight-decay factor
 
virtual bool hasConverged (double testError)
 has this training converged already?
 
double learningRate () const
 get the learning rate
 
size_t maxConvergenceCount () const
 returns the max convergence count so far
 
size_t minError () const
 returns the smallest error so far
 
MinimizerType minimizerType () const
 which minimizer shall be used (e.g. SGD)
 
double momentum () const
 get the momentum (e.g. for SGD)
 
void pads (int numPads)
 preparation for monitoring
 
void plot (std::string histoName, std::string options, int pad, EColor color)
 for monitoring
 
EnumRegularization regularization () const
 some regularization of the DNN is turned on?
 
int repetitions () const
 how many steps have to be gone until the batch is changed
 
template<typename Iterator >
void setDropOut (Iterator begin, Iterator end, size_t _dropRepetitions)
 set the drop-out configuration (layer-wise)
 
void setMonitoring (std::shared_ptr< Monitoring > ptrMonitoring)
 prepared for monitoring
 
virtual void setProgressLimits (double minProgress=0, double maxProgress=100)
 
virtual void startTestCycle ()
 callback for monitoring and loggging
 
virtual void startTrainCycle ()
 
virtual void startTraining ()
 
virtual void testIteration ()
 callback for monitoring and loggging
 
size_t testRepetitions () const
 how often is the test data tested
 
virtual void testSample (double, double, double, double)
 virtual function to be used for monitoring (callback)
 
bool useMultithreading () const
 is multithreading turned on?
 

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
 
size_t m_convergenceCount
 
size_t m_convergenceSteps
 number of steps without improvement to consider the DNN to have converged
 
std::vector< doublem_dropOut
 
double m_dropRepetitions
 
double m_factorWeightDecay
 
size_t m_maxConvergenceCount
 
double m_maxProgress
 current limits for the progress bar
 
double m_minError
 
double m_minProgress
 current limits for the progress bar
 
EnumRegularization m_regularization
 
size_t m_testRepetitions
 
Timer m_timer
 timer for monitoring
 

Protected Attributes

std::shared_ptr< MonitoringfMonitoring
 
bool m_useMultithreading
 

#include <TMVA/NeuralNet.h>

Inheritance diagram for TMVA::DNN::Settings:
[legend]

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

◆ addPoint() [1/2]

void TMVA::DNN::Settings::addPoint ( std::string  histoName,
double  x 
)
inline

for monitoring

Definition at line 821 of file NeuralNet.h.

◆ addPoint() [2/2]

void TMVA::DNN::Settings::addPoint ( std::string  histoName,
double  x,
double  y 
)
inline

for monitoring

Definition at line 822 of file NeuralNet.h.

◆ batchSize()

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

mini-batch size

Definition at line 767 of file NeuralNet.h.

◆ clear()

void TMVA::DNN::Settings::clear ( std::string  histoName)
inline

for monitoring

Definition at line 824 of file NeuralNet.h.

◆ computeResult()

virtual void TMVA::DNN::Settings::computeResult ( const Net ,
std::vector< double > &   
)
inlinevirtual

callback for monitoring and logging

Definition at line 809 of file NeuralNet.h.

◆ convergenceCount()

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

returns the current convergence count

Definition at line 827 of file NeuralNet.h.

◆ convergenceSteps()

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

how many steps until training is deemed to have converged

Definition at line 766 of file NeuralNet.h.

◆ create() [1/2]

void TMVA::DNN::Settings::create ( std::string  histoName,
int  bins,
double  min,
double  max 
)
inline

for monitoring

Definition at line 819 of file NeuralNet.h.

◆ create() [2/2]

void TMVA::DNN::Settings::create ( std::string  histoName,
int  bins,
double  min,
double  max,
int  bins2,
double  min2,
double  max2 
)
inline

for monitoring

Definition at line 820 of file NeuralNet.h.

◆ cycle()

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

Definition at line 799 of file NeuralNet.h.

◆ drawSample()

virtual void TMVA::DNN::Settings::drawSample ( const std::vector< double > &  ,
const std::vector< double > &  ,
const std::vector< double > &  ,
double   
)
inlinevirtual

callback for monitoring and logging

Definition at line 807 of file NeuralNet.h.

◆ dropFractions()

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

Definition at line 762 of file NeuralNet.h.

◆ dropRepetitions()

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

Definition at line 761 of file NeuralNet.h.

◆ endTestCycle()

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

callback for monitoring and loggging

Reimplemented in TMVA::DNN::ClassificationSettings.

Definition at line 805 of file NeuralNet.h.

◆ endTrainCycle()

virtual void TMVA::DNN::Settings::endTrainCycle ( double  )
inlinevirtual

callback for monitoring and logging

Reimplemented in TMVA::DNN::ClassificationSettings.

Definition at line 788 of file NeuralNet.h.

◆ exists()

bool TMVA::DNN::Settings::exists ( std::string  histoName)
inline

for monitoring

Definition at line 825 of file NeuralNet.h.

◆ factorWeightDecay()

double TMVA::DNN::Settings::factorWeightDecay ( ) const
inline

get the weight-decay factor

Definition at line 769 of file NeuralNet.h.

◆ hasConverged()

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

has this training converged already?

check for convergence

Definition at line 485 of file NeuralNet.cxx.

◆ learningRate()

double TMVA::DNN::Settings::learningRate ( ) const
inline

get the learning rate

Definition at line 771 of file NeuralNet.h.

◆ maxConvergenceCount()

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

returns the max convergence count so far

Definition at line 828 of file NeuralNet.h.

◆ minError()

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

returns the smallest error so far

Definition at line 829 of file NeuralNet.h.

◆ minimizerType()

MinimizerType TMVA::DNN::Settings::minimizerType ( ) const
inline

which minimizer shall be used (e.g. SGD)

Definition at line 774 of file NeuralNet.h.

◆ momentum()

double TMVA::DNN::Settings::momentum ( ) const
inline

get the momentum (e.g. for SGD)

Definition at line 772 of file NeuralNet.h.

◆ pads()

void TMVA::DNN::Settings::pads ( int  numPads)
inline

preparation for monitoring

Definition at line 818 of file NeuralNet.h.

◆ plot()

void TMVA::DNN::Settings::plot ( std::string  histoName,
std::string  options,
int  pad,
EColor  color 
)
inline

for monitoring

Definition at line 823 of file NeuralNet.h.

◆ regularization()

EnumRegularization TMVA::DNN::Settings::regularization ( ) const
inline

some regularization of the DNN is turned on?

Definition at line 813 of file NeuralNet.h.

◆ repetitions()

int TMVA::DNN::Settings::repetitions ( ) const
inline

how many steps have to be gone until the batch is changed

Definition at line 773 of file NeuralNet.h.

◆ 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 759 of file NeuralNet.h.

◆ setMonitoring()

void TMVA::DNN::Settings::setMonitoring ( std::shared_ptr< Monitoring ptrMonitoring)
inline

prepared for monitoring

Definition at line 764 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)
minProgressminimum value
maxProgressmaximum value

Definition at line 790 of file NeuralNet.h.

◆ startTestCycle()

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

callback for monitoring and loggging

Reimplemented in TMVA::DNN::ClassificationSettings.

Definition at line 804 of file NeuralNet.h.

◆ startTrainCycle()

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

Reimplemented in TMVA::DNN::ClassificationSettings.

Definition at line 782 of file NeuralNet.h.

◆ startTraining()

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

Definition at line 795 of file NeuralNet.h.

◆ testIteration()

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

callback for monitoring and loggging

Reimplemented in TMVA::DNN::ClassificationSettings.

Definition at line 806 of file NeuralNet.h.

◆ testRepetitions()

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

how often is the test data tested

Definition at line 768 of file NeuralNet.h.

◆ testSample()

virtual void TMVA::DNN::Settings::testSample ( double  ,
double  ,
double  ,
double   
)
inlinevirtual

virtual function to be used for monitoring (callback)

Reimplemented in TMVA::DNN::ClassificationSettings.

Definition at line 781 of file NeuralNet.h.

◆ useMultithreading()

bool TMVA::DNN::Settings::useMultithreading ( ) const
inline

is multithreading turned on?

Definition at line 815 of file NeuralNet.h.

Member Data Documentation

◆ count_dE

size_t TMVA::DNN::Settings::count_dE

Definition at line 843 of file NeuralNet.h.

◆ count_E

size_t TMVA::DNN::Settings::count_E

Definition at line 842 of file NeuralNet.h.

◆ count_mb_dE

size_t TMVA::DNN::Settings::count_mb_dE

Definition at line 845 of file NeuralNet.h.

◆ count_mb_E

size_t TMVA::DNN::Settings::count_mb_E

Definition at line 844 of file NeuralNet.h.

◆ fLearningRate

double TMVA::DNN::Settings::fLearningRate

Definition at line 852 of file NeuralNet.h.

◆ fMinimizerType

MinimizerType TMVA::DNN::Settings::fMinimizerType

Definition at line 855 of file NeuralNet.h.

◆ fMomentum

double TMVA::DNN::Settings::fMomentum

Definition at line 853 of file NeuralNet.h.

◆ fMonitoring

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

Definition at line 865 of file NeuralNet.h.

◆ fRepetitions

int TMVA::DNN::Settings::fRepetitions

Definition at line 854 of file NeuralNet.h.

◆ m_batchSize

size_t TMVA::DNN::Settings::m_batchSize

mini-batch size

Definition at line 838 of file NeuralNet.h.

◆ m_convergenceCount

size_t TMVA::DNN::Settings::m_convergenceCount

Definition at line 857 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 837 of file NeuralNet.h.

◆ m_dropOut

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

Definition at line 850 of file NeuralNet.h.

◆ m_dropRepetitions

double TMVA::DNN::Settings::m_dropRepetitions

Definition at line 849 of file NeuralNet.h.

◆ m_factorWeightDecay

double TMVA::DNN::Settings::m_factorWeightDecay

Definition at line 840 of file NeuralNet.h.

◆ m_maxConvergenceCount

size_t TMVA::DNN::Settings::m_maxConvergenceCount

Definition at line 858 of file NeuralNet.h.

◆ m_maxProgress

double TMVA::DNN::Settings::m_maxProgress

current limits for the progress bar

Definition at line 834 of file NeuralNet.h.

◆ m_minError

double TMVA::DNN::Settings::m_minError

Definition at line 859 of file NeuralNet.h.

◆ m_minProgress

double TMVA::DNN::Settings::m_minProgress

current limits for the progress bar

Definition at line 833 of file NeuralNet.h.

◆ m_regularization

EnumRegularization TMVA::DNN::Settings::m_regularization

Definition at line 847 of file NeuralNet.h.

◆ m_testRepetitions

size_t TMVA::DNN::Settings::m_testRepetitions

Definition at line 839 of file NeuralNet.h.

◆ m_timer

Timer TMVA::DNN::Settings::m_timer

timer for monitoring

Definition at line 832 of file NeuralNet.h.

◆ m_useMultithreading

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

Definition at line 863 of file NeuralNet.h.

Libraries for TMVA::DNN::Settings:

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