org.encog.ml.train.strategy

Class ResetStrategy

java.lang.Object

org.encog.ml.train.strategy.ResetStrategy

All Implemented Interfaces:

Strategy

public class ResetStrategy
extends java.lang.Object
implements Strategy

The reset strategy will reset the weights if the neural network fails to fall below a specified error by a specified number of cycles. This can be useful to throw out initially "bad/hard" random initializations of the weight matrix.

Constructor Summary

Constructors

Constructor and Description
ResetStrategy(double required, int cycles) Construct a reset strategy.

Method Summary

Modifier and Type	Method and Description
void	init(MLTrain train) Initialize this strategy.
void	postIteration() Called just after a training iteration.
void	preIteration() Called just before a training iteration.

Methods inherited from class java.lang.Object

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

Constructor Detail

ResetStrategy

public ResetStrategy(double required,
                     int cycles)

Construct a reset strategy. The error rate must fall below the required rate in the specified number of cycles, or the neural network will be reset to random weights and bias values.

Parameters:

required - The required error rate.

cycles - The number of cycles to reach that rate.

Method Detail

init

public void init(MLTrain train)

Initialize this strategy.

Specified by:

init in interface Strategy

Parameters:

train - The training algorithm.

postIteration

public void postIteration()

Called just after a training iteration.

Specified by:

postIteration in interface Strategy

preIteration

public void preIteration()

Called just before a training iteration.

Specified by:

preIteration in interface Strategy