org.encog.neural.networks.training.propagation
Class Propagation

java.lang.Object
  org.encog.ml.train.BasicTraining
      org.encog.neural.networks.training.propagation.Propagation

All Implemented Interfaces:

MLTrain, Train

Direct Known Subclasses:

Backpropagation, ManhattanPropagation, ResilientPropagation, ScaledConjugateGradient

public abstract class Propagation
extends BasicTraining
implements Train

Implements basic functionality that is needed by each of the propagation methods. The specifics of each of the propagation methods is implemented inside of the PropagationMethod interface implementors.

Author:

jheaton

Constructor Summary

Propagation(ContainsFlat network, MLDataSet training)
Construct a propagation object.

Method Summary

void	finishTraining() Should be called after training has completed and the iteration method will not be called any further.
FlatNetwork	getCurrentFlatNetwork()
TrainFlatNetwork	getFlatTraining()
MLMethod	getMethod() Get the current best machine learning method from the training.
int	getNumThreads()
void	iteration() Perform one training iteration.
void	iteration(int count) Perform the specified number of training iterations.
void	setFlatTraining(TrainFlatNetwork flatTraining)
void	setNumThreads(int numThreads) Set the number of threads.

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

addStrategy, getError, getImplementationType, getIteration, getStrategies, getTraining, isTrainingDone, postIteration, preIteration, setError, setIteration, setTraining

Methods inherited from class java.lang.Object

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

Methods inherited from interface org.encog.ml.train.MLTrain

addStrategy, canContinue, getError, getImplementationType, getIteration, getStrategies, getTraining, isTrainingDone, pause, resume, setError, setIteration

Constructor Detail

Propagation

public Propagation(ContainsFlat network,
                   MLDataSet training)

Construct a propagation object.

Parameters:

network - The network.

training - The training set.

Method Detail

finishTraining

public final void finishTraining()

Should be called after training has completed and the iteration method will not be called any further.

Specified by:

finishTraining in interface MLTrain

Overrides:

finishTraining in class BasicTraining

getCurrentFlatNetwork

public final FlatNetwork getCurrentFlatNetwork()

Returns:

the currentFlatNetwork

getFlatTraining

public final TrainFlatNetwork getFlatTraining()

Returns:

the flatTraining

getMethod

public final MLMethod getMethod()

Get the current best machine learning method from the training.

Specified by:

getMethod in interface MLTrain

Returns:

The best machine learningm method.

getNumThreads

public final int getNumThreads()

Returns:

The number of threads.

iteration

public final void iteration()

Perform one training iteration.

Specified by:

iteration in interface MLTrain

iteration

public final void iteration(int count)

Perform the specified number of training iterations. This can be more efficient than single training iterations. This is particularly true if you are training with a GPU.

Specified by:

iteration in interface MLTrain

Overrides:

iteration in class BasicTraining

Parameters:

count - The number of training iterations.

setFlatTraining

public final void setFlatTraining(TrainFlatNetwork flatTraining)

Parameters:

flatTraining - the flatTraining to set

setNumThreads

public final void setNumThreads(int numThreads)

Set the number of threads. Specify zero to tell Encog to automatically determine the best number of threads for the processor. If OpenCL is used as the target device, then this value is not used.

Parameters:

numThreads - The number of threads.