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ROOT 6.08/07 Reference Guide |
Settings for the training of the neural net.
Definition at line 736 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... | |
void | addPoint (std::string histoName, double x) |
for monitoring More... | |
void | addPoint (std::string histoName, double x, double y) |
for monitoring More... | |
size_t | batchSize () const |
mini-batch size More... | |
void | clear (std::string histoName) |
for monitoring More... | |
virtual void | computeResult (const Net &, std::vector< double > &) |
callback for monitoring and loggging More... | |
size_t | convergenceCount () const |
returns the current convergence count More... | |
size_t | convergenceSteps () const |
how many steps until training is deemed to have converged More... | |
void | create (std::string histoName, int bins, double min, double max) |
for monitoring More... | |
void | create (std::string histoName, int bins, double min, double max, int bins2, double min2, double max2) |
for monitoring More... | |
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 loggging More... | |
const std::vector< double > & | dropFractions () const |
size_t | dropRepetitions () const |
virtual void | endTestCycle () |
callback for monitoring and loggging More... | |
virtual void | endTrainCycle (double) |
callback for monitoring and logging More... | |
bool | exists (std::string histoName) |
for monitoring More... | |
double | factorWeightDecay () const |
get the weight-decay factor More... | |
virtual bool | hasConverged (double testError) |
has this training converged already? More... | |
double | learningRate () const |
get the learning rate More... | |
size_t | maxConvergenceCount () const |
returns the max convergence count so far More... | |
size_t | minError () const |
returns the smallest error so far More... | |
MinimizerType | minimizerType () const |
which minimizer shall be used (e.g. SGD) More... | |
double | momentum () const |
get the momentum (e.g. for SGD) More... | |
void | pads (int numPads) |
preparation for monitoring More... | |
void | plot (std::string histoName, std::string options, int pad, EColor color) |
for monitoring More... | |
EnumRegularization | regularization () const |
some regularization of the DNN is turned on? More... | |
int | repetitions () const |
how many steps have to be gone until the batch is changed More... | |
template<typename Iterator > | |
void | setDropOut (Iterator begin, Iterator end, size_t _dropRepetitions) |
set the drop-out configuration (layer-wise) More... | |
void | setMonitoring (std::shared_ptr< Monitoring > ptrMonitoring) |
prepared for monitoring More... | |
virtual void | setProgressLimits (double minProgress=0, double maxProgress=100) |
virtual void | startTestCycle () |
callback for monitoring and loggging More... | |
virtual void | startTrainCycle () |
virtual void | startTraining () |
virtual void | testIteration () |
callback for monitoring and loggging More... | |
size_t | testRepetitions () const |
how often is the test data tested More... | |
virtual void | testSample (double, double, double, double) |
virtual function to be used for monitoring (callback) More... | |
bool | useMultithreading () const |
is multithreading turned on? More... | |
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< double > | m_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< Monitoring > | fMonitoring |
bool | m_useMultithreading |
#include <TMVA/NeuralNet.h>
TMVA::DNN::Settings::Settings | ( | TString | name, |
size_t | _convergenceSteps = 15 , |
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size_t | _batchSize = 10 , |
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size_t | _testRepetitions = 7 , |
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double | _factorWeightDecay = 1e-5 , |
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TMVA::DNN::EnumRegularization | _regularization = TMVA::DNN::EnumRegularization::NONE , |
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MinimizerType | _eMinimizerType = MinimizerType::fSteepest , |
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double | _learningRate = 1e-5 , |
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double | _momentum = 0.3 , |
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int | _repetitions = 3 , |
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bool | _multithreading = true |
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c'tor
Definition at line 210 of file NeuralNet.cxx.
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d'tor
Definition at line 239 of file NeuralNet.cxx.
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for monitoring
Definition at line 828 of file NeuralNet.h.
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for monitoring
Definition at line 829 of file NeuralNet.h.
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mini-batch size
Definition at line 774 of file NeuralNet.h.
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for monitoring
Definition at line 831 of file NeuralNet.h.
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callback for monitoring and loggging
Definition at line 816 of file NeuralNet.h.
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returns the current convergence count
Definition at line 834 of file NeuralNet.h.
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how many steps until training is deemed to have converged
Definition at line 773 of file NeuralNet.h.
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for monitoring
Definition at line 826 of file NeuralNet.h.
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for monitoring
Definition at line 827 of file NeuralNet.h.
text | advance on the progress bar |
Definition at line 806 of file NeuralNet.h.
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callback for monitoring and loggging
Definition at line 814 of file NeuralNet.h.
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Definition at line 769 of file NeuralNet.h.
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Definition at line 768 of file NeuralNet.h.
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callback for monitoring and loggging
Reimplemented in TMVA::DNN::ClassificationSettings.
Definition at line 812 of file NeuralNet.h.
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callback for monitoring and logging
Reimplemented in TMVA::DNN::ClassificationSettings.
Definition at line 795 of file NeuralNet.h.
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for monitoring
Definition at line 832 of file NeuralNet.h.
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get the weight-decay factor
Definition at line 776 of file NeuralNet.h.
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has this training converged already?
check for convergence
Definition at line 466 of file NeuralNet.cxx.
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get the learning rate
Definition at line 778 of file NeuralNet.h.
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returns the max convergence count so far
Definition at line 835 of file NeuralNet.h.
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returns the smallest error so far
Definition at line 836 of file NeuralNet.h.
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which minimizer shall be used (e.g. SGD)
Definition at line 781 of file NeuralNet.h.
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get the momentum (e.g. for SGD)
Definition at line 779 of file NeuralNet.h.
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preparation for monitoring
Definition at line 825 of file NeuralNet.h.
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for monitoring
Definition at line 830 of file NeuralNet.h.
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some regularization of the DNN is turned on?
Definition at line 820 of file NeuralNet.h.
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how many steps have to be gone until the batch is changed
Definition at line 780 of file NeuralNet.h.
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set the drop-out configuration (layer-wise)
begin | begin of an array or vector denoting the drop-out probabilities for each layer |
end | end of an array or vector denoting the drop-out probabilities for each layer |
_dropRepetitions | denotes after how many repetitions the drop-out setting (which nodes are dropped out exactly) is changed |
Definition at line 766 of file NeuralNet.h.
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prepared for monitoring
Definition at line 771 of file NeuralNet.h.
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maxProgress | for monitoring and logging (set the current "progress" limits for the display of the progress) |
Definition at line 797 of file NeuralNet.h.
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callback for monitoring and loggging
Reimplemented in TMVA::DNN::ClassificationSettings.
Definition at line 811 of file NeuralNet.h.
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Reimplemented in TMVA::DNN::ClassificationSettings.
Definition at line 789 of file NeuralNet.h.
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Definition at line 802 of file NeuralNet.h.
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callback for monitoring and loggging
Reimplemented in TMVA::DNN::ClassificationSettings.
Definition at line 813 of file NeuralNet.h.
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how often is the test data tested
Definition at line 775 of file NeuralNet.h.
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virtual function to be used for monitoring (callback)
Reimplemented in TMVA::DNN::ClassificationSettings.
Definition at line 788 of file NeuralNet.h.
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is multithreading turned on?
Definition at line 822 of file NeuralNet.h.
size_t TMVA::DNN::Settings::count_dE |
Definition at line 850 of file NeuralNet.h.
size_t TMVA::DNN::Settings::count_E |
Definition at line 849 of file NeuralNet.h.
size_t TMVA::DNN::Settings::count_mb_dE |
Definition at line 852 of file NeuralNet.h.
size_t TMVA::DNN::Settings::count_mb_E |
Definition at line 851 of file NeuralNet.h.
double TMVA::DNN::Settings::fLearningRate |
Definition at line 859 of file NeuralNet.h.
MinimizerType TMVA::DNN::Settings::fMinimizerType |
Definition at line 862 of file NeuralNet.h.
double TMVA::DNN::Settings::fMomentum |
Definition at line 860 of file NeuralNet.h.
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Definition at line 872 of file NeuralNet.h.
int TMVA::DNN::Settings::fRepetitions |
Definition at line 861 of file NeuralNet.h.
size_t TMVA::DNN::Settings::m_batchSize |
mini-batch size
Definition at line 845 of file NeuralNet.h.
size_t TMVA::DNN::Settings::m_convergenceCount |
Definition at line 864 of file NeuralNet.h.
size_t TMVA::DNN::Settings::m_convergenceSteps |
number of steps without improvement to consider the DNN to have converged
Definition at line 844 of file NeuralNet.h.
std::vector<double> TMVA::DNN::Settings::m_dropOut |
Definition at line 857 of file NeuralNet.h.
double TMVA::DNN::Settings::m_dropRepetitions |
Definition at line 856 of file NeuralNet.h.
double TMVA::DNN::Settings::m_factorWeightDecay |
Definition at line 847 of file NeuralNet.h.
size_t TMVA::DNN::Settings::m_maxConvergenceCount |
Definition at line 865 of file NeuralNet.h.
double TMVA::DNN::Settings::m_maxProgress |
current limits for the progress bar
Definition at line 841 of file NeuralNet.h.
double TMVA::DNN::Settings::m_minError |
Definition at line 866 of file NeuralNet.h.
double TMVA::DNN::Settings::m_minProgress |
current limits for the progress bar
Definition at line 840 of file NeuralNet.h.
EnumRegularization TMVA::DNN::Settings::m_regularization |
Definition at line 854 of file NeuralNet.h.
size_t TMVA::DNN::Settings::m_testRepetitions |
Definition at line 846 of file NeuralNet.h.
Timer TMVA::DNN::Settings::m_timer |
timer for monitoring
Definition at line 839 of file NeuralNet.h.
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Definition at line 870 of file NeuralNet.h.