org.encog.ml.train.strategy.end

Class EarlyStoppingStrategy

java.lang.Object

org.encog.ml.train.strategy.end.EarlyStoppingStrategy

All Implemented Interfaces:

EndTrainingStrategy, Strategy

public class EarlyStoppingStrategy
extends Object
implements EndTrainingStrategy

Stop early when validation set no longer improves. Based on the following paper: techreport{Prechelt94c, author = {Lutz Prechelt}, title = {{PROBEN1} --- {A} Set of Benchmarks and Benchmarking Rules for Neural Network Training Algorithms}, institution = {Fakult\"at f\"ur Informatik, Universit\"at Karlsruhe}, year = {1994}, number = {21/94}, address = {D-76128 Karlsruhe, Germany}, month = sep, note = {Anonymous FTP: /pub/pa\-pers/tech\-reports/1994/1994-21.ps.Z on ftp.ira.uka.de}, }

Constructor Summary

Constructors

Constructor and Description
EarlyStoppingStrategy(MLDataSet theValidationSet, MLDataSet theTestSet) Construct the early stopping strategy.
EarlyStoppingStrategy(MLDataSet theValidationSet, MLDataSet theTestSet, int theStripLength, double theAlpha, double theMinEfficiency) Construct the early stopping strategy.

Method Summary

Methods

Modifier and Type	Method and Description
double	geteOpt()
double	getGl()
double	getMinEfficiency()
double	getStripEfficiency()
int	getStripLength()
double	getStripOpt()
double	getTestError()
double	getTrainingError()
double	getValidationError()
void	init(MLTrain theTrain) Initialize this strategy.
void	postIteration() Called just after a training iteration.
void	preIteration() Called just before a training iteration.
boolean	shouldStop()

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

EarlyStoppingStrategy

public EarlyStoppingStrategy(MLDataSet theValidationSet,
                     MLDataSet theTestSet)

Construct the early stopping strategy. Use default operating parameters.

Parameters:

theValidationSet - The validation set.

theTestSet - The test set.

EarlyStoppingStrategy

public EarlyStoppingStrategy(MLDataSet theValidationSet,
                     MLDataSet theTestSet,
                     int theStripLength,
                     double theAlpha,
                     double theMinEfficiency)

Construct the early stopping strategy.

Parameters:

theValidationSet -

theTestSet -

theStripLength - The number of training set elements to validate.

theAlpha - Stop once GL is below this value.

theMinEfficiency - The minimum training efficiency to stop.

Method Detail

init

public void init(MLTrain theTrain)

Initialize this strategy.

Specified by:

init in interface Strategy

Parameters:

theTrain - The training algorithm.

preIteration

public void preIteration()

Called just before a training iteration.

Specified by:

preIteration in interface Strategy

postIteration

public void postIteration()

Called just after a training iteration.

Specified by:

postIteration in interface Strategy

shouldStop

public boolean shouldStop()

Specified by:

shouldStop in interface EndTrainingStrategy

Returns:

Returns true if we should stop.

getTrainingError

public double getTrainingError()

Returns:

the trainingError

getTestError

public double getTestError()

Returns:

the testError

getValidationError

public double getValidationError()

Returns:

the validationError

geteOpt

public double geteOpt()

Returns:

the eOpt

getGl

public double getGl()

Returns:

the gl

getStripLength

public int getStripLength()

Returns:

the stripLength

getStripOpt

public double getStripOpt()

Returns:

the stripOpt

getStripEfficiency

public double getStripEfficiency()

Returns:

the stripEfficiency

getMinEfficiency

public double getMinEfficiency()

Returns:

the minEfficiency