/*
* Encog(tm) Core v3.4 - Java Version
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-core
* Copyright 2008-2016 Heaton Research, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* For more information on Heaton Research copyrights, licenses
* and trademarks visit:
* http://www.heatonresearch.com/copyright
*/
package org.encog.ml.train.strategy;
import org.encog.ml.MLResettable;
import org.encog.ml.train.MLTrain;
import org.encog.neural.networks.training.TrainingError;
import org.encog.util.logging.EncogLogging;
/**
* 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.
*
* @author jheaton
*
*/
public class ResetStrategy implements Strategy {
/**
* The required minimum error.
*/
private final double required;
/**
* The number of cycles to reach the required minimum error.
*/
private final int cycles;
/**
* The training algorithm that is using this strategy.
*/
private MLTrain train;
/**
* How many bad cycles have there been so far.
*/
private int badCycleCount;
/**
* The method that is being trained.
*/
private MLResettable method;
/**
* 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.
* @param required The required error rate.
* @param cycles The number of cycles to reach that rate.
*/
public ResetStrategy(final double required, final int cycles) {
this.required = required;
this.cycles = cycles;
this.badCycleCount = 0;
}
/**
* Initialize this strategy.
*
* @param train
* The training algorithm.
*/
public void init(final MLTrain train) {
this.train = train;
if( !(train.getMethod() instanceof MLResettable) ) {
throw new TrainingError("To use the reset strategy the machine learning method must support MLResettable.");
}
this.method = (MLResettable)this.train.getMethod();
}
/**
* Called just after a training iteration.
*/
public void postIteration() {
}
/**
* Called just before a training iteration.
*/
public void preIteration() {
if (this.train.getError() > this.required) {
this.badCycleCount++;
if (this.badCycleCount > this.cycles) {
EncogLogging.log(EncogLogging.LEVEL_DEBUG, "Failed to imrove network, resetting.");
this.method.reset();
this.badCycleCount = 0;
}
} else {
this.badCycleCount = 0;
}
}
}