/*
* Encog(tm) Core Unit Tests 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.
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package org.encog.neural.networks.training;
import junit.framework.TestCase;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.data.basic.BasicMLDataSet;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.NetworkUtil;
import org.encog.neural.networks.XOR;
import org.encog.neural.networks.structure.NetworkCODEC;
import org.encog.neural.networks.training.propagation.TrainingContinuation;
import org.encog.neural.networks.training.propagation.back.Backpropagation;
import org.encog.neural.networks.training.propagation.resilient.ResilientPropagation;
import org.junit.Assert;
public class TestTrainingContinuation extends TestCase {
public void testContRPROP()
{
BasicNetwork network1 = NetworkUtil.createXORNetworkUntrained();
BasicNetwork network2 = NetworkUtil.createXORNetworkUntrained();
MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
// train network 1, no continue
ResilientPropagation rprop1 = new ResilientPropagation(network1,trainingData);
rprop1.iteration();
rprop1.iteration();
rprop1.iteration();
rprop1.iteration();
// train network 2, continue
ResilientPropagation rprop2 = new ResilientPropagation(network2,trainingData);
rprop2.iteration();
rprop2.iteration();
TrainingContinuation state = rprop2.pause();
rprop2 = new ResilientPropagation(network2,trainingData);
rprop2.resume(state);
rprop2.iteration();
rprop2.iteration();
// verify weights are the same
double[] weights1 = NetworkCODEC.networkToArray(network1);
double[] weights2 = NetworkCODEC.networkToArray(network2);
Assert.assertEquals(rprop1.getError(), rprop2.getError(), 0.01);
Assert.assertEquals(weights1.length, weights2.length);
Assert.assertArrayEquals(weights1, weights2, 0.01);
}
public void testContBackprop()
{
BasicNetwork network1 = NetworkUtil.createXORNetworkUntrained();
BasicNetwork network2 = NetworkUtil.createXORNetworkUntrained();
MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
// train network 1, no continue
Backpropagation rprop1 = new Backpropagation(network1,trainingData,0.4,0.4);
rprop1.iteration();
rprop1.iteration();
rprop1.iteration();
rprop1.iteration();
// train network 2, continue
Backpropagation rprop2 = new Backpropagation(network2,trainingData,0.4,0.4);
rprop2.iteration();
rprop2.iteration();
TrainingContinuation state = rprop2.pause();
rprop2 = new Backpropagation(network2,trainingData,0.4,0.4);
rprop2.resume(state);
rprop2.iteration();
rprop2.iteration();
// verify weights are the same
double[] weights1 = NetworkCODEC.networkToArray(network1);
double[] weights2 = NetworkCODEC.networkToArray(network2);
Assert.assertEquals(rprop1.getError(), rprop2.getError(), 0.01);
Assert.assertEquals(weights1.length, weights2.length);
Assert.assertArrayEquals(weights1, weights2, 0.01);
}
}