/*
* 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.
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package org.encog.neural.networks;
import junit.framework.Assert;
import org.encog.engine.network.activation.ActivationSigmoid;
import org.encog.mathutil.randomize.ConsistentRandomizer;
import org.encog.mathutil.randomize.NguyenWidrowRandomizer;
import org.encog.ml.MLError;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.train.MLTrain;
import org.encog.neural.freeform.FreeformLayer;
import org.encog.neural.freeform.FreeformNetwork;
import org.encog.neural.networks.layers.BasicLayer;
public class NetworkUtil {
public static BasicNetwork createXORNetworkUntrained()
{
// random matrix data. However, it provides a constant starting point
// for the unit tests.
BasicNetwork network = new BasicNetwork();
network.addLayer(new BasicLayer(null,true,2));
network.addLayer(new BasicLayer(new ActivationSigmoid(),true,4));
network.addLayer(new BasicLayer(new ActivationSigmoid(),false,1));
network.getStructure().finalizeStructure();
(new ConsistentRandomizer(-1,1)).randomize(network);
return network;
}
public static BasicNetwork createXORNetworknNguyenWidrowUntrained()
{
// random matrix data. However, it provides a constant starting point
// for the unit tests.
BasicNetwork network = new BasicNetwork();
network.addLayer(new BasicLayer(null,true,2));
network.addLayer(new BasicLayer(new ActivationSigmoid(),true,3));
network.addLayer(new BasicLayer(new ActivationSigmoid(),false,3));
network.addLayer(new BasicLayer(null,false,1));
network.getStructure().finalizeStructure();
(new NguyenWidrowRandomizer()).randomize( network );
return network;
}
public static void testTraining(MLDataSet dataSet, MLTrain train, double requiredImprove)
{
train.iteration();
double error1 = train.getError();
for(int i=0;i<10;i++)
train.iteration();
double error2 = train.getError();
if( train.getMethod() instanceof MLError ) {
double error3 = ((MLError)train.getMethod()).calculateError(dataSet);
double improve = (error1-error3)/error1;
Assert.assertTrue("Improve rate too low for " + train.getClass().getSimpleName() +
",Improve="+improve+",Needed="+requiredImprove, improve>=requiredImprove);
}
double improve = (error1-error2)/error1;
Assert.assertTrue("Improve rate too low for " + train.getClass().getSimpleName() +
",Improve="+improve+",Needed="+requiredImprove, improve>=requiredImprove);
}
public static FreeformNetwork createXORFreeformNetworkUntrained() {
FreeformNetwork network = new FreeformNetwork();
FreeformLayer inputLayer = network.createInputLayer(2);
FreeformLayer hiddenLayer1 = network.createLayer(3);
FreeformLayer outputLayer = network.createOutputLayer(1);
network.connectLayers(inputLayer, hiddenLayer1, new ActivationSigmoid(), 1.0, false);
network.connectLayers(hiddenLayer1, outputLayer, new ActivationSigmoid(), 1.0, false);
network.reset(1000);
return network;
}
}