/*
* Encog(tm) Core v3.3 - Java Version
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-core
* Copyright 2008-2014 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.mathutil.matrices.hessian;
import org.encog.mathutil.IntRange;
import org.encog.mathutil.matrices.Matrix;
import org.encog.ml.data.MLDataSet;
import org.encog.neural.networks.BasicNetwork;
import org.encog.util.EngineArray;
import org.encog.util.concurrency.DetermineWorkload;
import org.encog.util.concurrency.EngineConcurrency;
import org.encog.util.concurrency.MultiThreadable;
import org.encog.util.concurrency.TaskGroup;
/**
* Calculate the Hessian matrix using the chain rule method.
*
*/
public class HessianCR extends BasicHessian implements MultiThreadable {
/**
* The number of threads to use.
*/
private int numThreads;
/**
* The workers.
*/
private ChainRuleWorker[] workers;
/**
* {@inheritDoc}
*/
public void init(BasicNetwork theNetwork, MLDataSet theTraining) {
super.init(theNetwork,theTraining);
int weightCount = theNetwork.getStructure().getFlat().getWeights().length;
this.training = theTraining;
this.network = theNetwork;
this.hessianMatrix = new Matrix(weightCount,weightCount);
this.hessian = this.hessianMatrix.getData();
// create worker(s)
final DetermineWorkload determine = new DetermineWorkload(
this.numThreads, (int) this.training.getRecordCount());
this.workers = new ChainRuleWorker[determine.getThreadCount()];
int index = 0;
// handle CPU
for (final IntRange r : determine.calculateWorkers()) {
this.workers[index++] = new ChainRuleWorker(this.flat.clone(),
this.training.openAdditional(), r.getLow(),
r.getHigh());
}
}
/**
* {@inheritDoc}
*/
public void compute() {
clear();
double e = 0;
int weightCount = this.network.getFlat().getWeights().length;
for (int outputNeuron = 0; outputNeuron < this.network.getOutputCount(); outputNeuron++) {
// handle context
if (this.flat.getHasContext()) {
this.workers[0].getNetwork().clearContext();
}
if (this.workers.length > 1) {
final TaskGroup group = EngineConcurrency.getInstance()
.createTaskGroup();
for (final ChainRuleWorker worker : this.workers) {
worker.setOutputNeuron(outputNeuron);
EngineConcurrency.getInstance().processTask(worker, group);
}
group.waitForComplete();
} else {
this.workers[0].setOutputNeuron(outputNeuron);
this.workers[0].run();
}
// aggregate workers
for (final ChainRuleWorker worker : this.workers) {
e+=worker.getError();
for(int i=0;i<weightCount;i++) {
this.gradients[i] += worker.getGradients()[i];
}
EngineArray.arrayAdd(this.getHessian(),worker.getHessian());
}
}
sse= e/2;
}
/**
* 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.
*
* @param numThreads
* The number of threads.
*/
@Override
public final void setThreadCount(final int numThreads) {
this.numThreads = numThreads;
}
/**
* @return The thread count.
*/
@Override
public int getThreadCount() {
return this.numThreads;
}
}