org.encog.neural.freeform.training

Class FreeformBackPropagation

java.lang.Object

org.encog.ml.train.BasicTraining

org.encog.neural.freeform.training.FreeformPropagationTraining

org.encog.neural.freeform.training.FreeformBackPropagation

All Implemented Interfaces:

java.io.Serializable, MLTrain

public class FreeformBackPropagation
extends FreeformPropagationTraining
implements java.io.Serializable

Perform backpropagation for a freeform neural network.

See Also:

Serialized Form

Field Summary

Fields inherited from class org.encog.neural.freeform.training.FreeformPropagationTraining

FLAT_SPOT_CONST

Constructor Summary

Constructors

Constructor and Description
FreeformBackPropagation(FreeformNetwork theNetwork, MLDataSet theTraining, double theLearningRate, double theMomentum) Construct a back propagation trainer.

Method Summary

Modifier and Type	Method and Description
protected void	learnConnection(FreeformConnection connection) Learn for a single connection.
TrainingContinuation	pause() Pause the training to continue later.
void	resume(TrainingContinuation state) Resume training.

Methods inherited from class org.encog.neural.freeform.training.FreeformPropagationTraining

canContinue, finishTraining, getBatchSize, getError, getImplementationType, getIteration, getMethod, getTraining, isFixFlatSopt, iteration, iteration, learn, processBatches, processPureBatch, setBatchSize, setError, setFixFlatSopt, setIteration

Methods inherited from class org.encog.ml.train.BasicTraining

addStrategy, getStrategies, isTrainingDone, postIteration, preIteration, setTraining

Methods inherited from class java.lang.Object

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

Constructor Detail

FreeformBackPropagation

public FreeformBackPropagation(FreeformNetwork theNetwork,
                               MLDataSet theTraining,
                               double theLearningRate,
                               double theMomentum)

Construct a back propagation trainer.

Parameters:

theNetwork - The network to train.

theTraining - The training data to use. The coefficient for how much of the gradient is applied to each weight.

theLearningRate - The learning rate. The coefficient for how much of the previous delta is applied to each weight. In theory, prevents local minima stall.

theMomentum - The momentum.

Method Detail

learnConnection

protected void learnConnection(FreeformConnection connection)

Learn for a single connection.

Specified by:

learnConnection in class FreeformPropagationTraining

Parameters:

connection - The connection to learn from.

pause

public TrainingContinuation pause()

Pause the training to continue later.

Specified by:

pause in interface MLTrain

Returns:

A training continuation object.

resume

public void resume(TrainingContinuation state)

Resume training.

Specified by:

resume in interface MLTrain

Parameters:

state - The training continuation object to use to continue.