/*
* 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.neural.flat.train.prop;
import org.encog.engine.network.activation.ActivationFunction;
import org.encog.mathutil.error.ErrorCalculation;
import org.encog.ml.data.MLDataPair;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.data.basic.BasicMLDataPair;
import org.encog.neural.error.ErrorFunction;
import org.encog.neural.flat.FlatNetwork;
import org.encog.util.EngineArray;
import org.encog.util.concurrency.EngineTask;
/**
* Worker class for the mulithreaded training of flat networks.
*/
public class GradientWorker implements EngineTask {
/**
* The network to train.
*/
private final FlatNetwork network;
/**
* The error calculation method.
*/
private final ErrorCalculation errorCalculation = new ErrorCalculation();
/**
* The actual values from the neural network.
*/
private final double[] actual;
/**
* The deltas for each layer.
*/
private final double[] layerDelta;
/**
* The neuron counts, per layer.
*/
private final int[] layerCounts;
/**
* The feed counts, per layer.
*/
private final int[] layerFeedCounts;
/**
* The layer indexes.
*/
private final int[] layerIndex;
/**
* The index to each layer's weights and thresholds.
*/
private final int[] weightIndex;
/**
* The output from each layer.
*/
private final double[] layerOutput;
/**
* The sums.
*/
private final double[] layerSums;
/**
* The gradients.
*/
private final double[] gradients;
/**
* The weights and thresholds.
*/
private final double[] weights;
/**
* The pair to use for training.
*/
private final MLDataPair pair;
/**
* The training data.
*/
private final MLDataSet training;
/**
* The high end of the training data.
*/
private final int low;
/**
* The low end of the training.
*/
private final int high;
/**
* The owner.
*/
private final TrainFlatNetworkProp owner;
/**
* Derivative add constant. Used to combat flat spot.
*/
private double[] flatSpot;
/**
* The error function to use.
*/
private final ErrorFunction errorFunction;
/**
* Construct a gradient worker.
*
* @param theNetwork
* The network to train.
* @param theOwner
* The owner that is doing the training.
* @param theTraining
* The training data.
* @param theLow
* The low index to use in the training data.
* @param theHigh
* The high index to use in the training data.
*/
public GradientWorker(final FlatNetwork theNetwork,
final TrainFlatNetworkProp theOwner,
final MLDataSet theTraining, final int theLow,
final int theHigh, final double[] flatSpot,
ErrorFunction ef) {
this.network = theNetwork;
this.training = theTraining;
this.low = theLow;
this.high = theHigh;
this.owner = theOwner;
this.flatSpot = flatSpot;
this.errorFunction = ef;
this.layerDelta = new double[network.getLayerOutput().length];
this.gradients = new double[network.getWeights().length];
this.actual = new double[network.getOutputCount()];
this.weights = network.getWeights();
this.layerIndex = network.getLayerIndex();
this.layerCounts = network.getLayerCounts();
this.weightIndex = network.getWeightIndex();
this.layerOutput = network.getLayerOutput();
this.layerSums = network.getLayerSums();
this.layerFeedCounts = network.getLayerFeedCounts();
this.pair = BasicMLDataPair.createPair(network.getInputCount(), network
.getOutputCount());
}
/**
* @return The network being processed.
*/
public final FlatNetwork getNetwork() {
return this.network;
}
/**
* @return The weights for this network.
*/
public final double[] getWeights() {
return this.weights;
}
/**
* Process one training set element.
*
* @param input
* The network input.
* @param ideal
* The ideal values.
* @param s The significance.
*/
private void process(final double[] input, final double[] ideal, double s) {
this.network.compute(input, this.actual);
this.errorCalculation.updateError(this.actual, ideal, s);
this.errorFunction.calculateError(ideal, actual, this.layerDelta);
for (int i = 0; i < this.actual.length; i++) {
this.layerDelta[i] = ((this.network.getActivationFunctions()[0]
.derivativeFunction(this.layerSums[i],this.layerOutput[i]) + this.flatSpot[0]))
* (this.layerDelta[i] * s);
}
for (int i = this.network.getBeginTraining(); i < this.network
.getEndTraining(); i++) {
processLevel(i);
}
}
/**
* Process one level.
*
* @param currentLevel
* The level.
*/
private void processLevel(final int currentLevel) {
final int fromLayerIndex = this.layerIndex[currentLevel + 1];
final int toLayerIndex = this.layerIndex[currentLevel];
final int fromLayerSize = this.layerCounts[currentLevel + 1];
final int toLayerSize = this.layerFeedCounts[currentLevel];
final int index = this.weightIndex[currentLevel];
final ActivationFunction activation = this.network
.getActivationFunctions()[currentLevel + 1];
final double currentFlatSpot = this.flatSpot[currentLevel + 1];
// handle weights
int yi = fromLayerIndex;
for (int y = 0; y < fromLayerSize; y++) {
final double output = this.layerOutput[yi];
double sum = 0;
int xi = toLayerIndex;
int wi = index + y;
for (int x = 0; x < toLayerSize; x++) {
this.gradients[wi] += output * this.layerDelta[xi];
sum += this.weights[wi] * this.layerDelta[xi];
wi += fromLayerSize;
xi++;
}
this.layerDelta[yi] = sum
* (activation.derivativeFunction(this.layerSums[yi],this.layerOutput[yi])+currentFlatSpot);
yi++;
}
}
/**
* Perform the gradient calculation for the specified index range.
*/
public final void run() {
try {
this.errorCalculation.reset();
for (int i = this.low; i <= this.high; i++) {
this.training.getRecord(i, this.pair);
process(this.pair.getInputArray(), this.pair.getIdealArray(),pair.getSignificance());
}
final double error = this.errorCalculation.calculate();
this.owner.report(this.gradients, error, null);
EngineArray.fill(this.gradients, 0);
} catch (final Throwable ex) {
this.owner.report(null, 0, ex);
}
}
}