/*
* Encog(tm) Core v3.0 - Java Version
* http://www.heatonresearch.com/encog/
* http://code.google.com/p/encog-java/
* Copyright 2008-2011 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.MLMethod;
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 improve by the specified amount over a number of cycles.
*
* @author jheaton
*
*/
public class RequiredImprovementStrategy 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 last error.
*/
private double lastError = Double.NaN;
/**
* If the error is below this, then never reset.
*/
private double acceptableThreshold;
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 RequiredImprovementStrategy(final double required, final int cycles) {
this(required, 0.10, 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.
*
* @param required
* The required error rate.
* @param threshold
* The accepted threshold, don't reset if error is below this.
* @param cycles
* The number of cycles to reach that rate.
*/
public RequiredImprovementStrategy(final double required, final double threshold,
final int cycles) {
this.required = required;
this.cycles = cycles;
this.badCycleCount = 0;
this.acceptableThreshold = threshold;
}
/**
* Reset if there is not at least a 1% improvement for 5 cycles. Don't reset
* if below 10%.
*
* @param cycles
*/
public RequiredImprovementStrategy(final int cycles) {
this(0.01, 0.10, cycles);
}
/**
* 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 required improvement 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 (train.getError() > this.acceptableThreshold) {
if (!Double.isNaN(lastError)) {
double improve = (lastError - train.getError());
if (improve < this.required) {
this.badCycleCount++;
if (this.badCycleCount > this.cycles) {
EncogLogging.log(EncogLogging.LEVEL_DEBUG, "Failed to improve network, resetting.");
this.method.reset();
this.badCycleCount = 0;
this.lastError = Double.NaN;
}
} else {
this.badCycleCount = 0;
}
}
else
lastError = train.getError();
}
lastError = Math.min(this.train.getError(),lastError);
}
}