/*
* 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.neural.networks;
import junit.framework.Assert;
import junit.framework.TestCase;
import org.encog.Encog;
import org.encog.engine.network.activation.ActivationSigmoid;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.data.basic.BasicMLDataSet;
import org.encog.ml.train.MLTrain;
import org.encog.neural.flat.FlatNetwork;
import org.encog.neural.networks.layers.BasicLayer;
import org.encog.neural.networks.layers.Layer;
import org.encog.neural.networks.structure.NetworkCODEC;
import org.encog.neural.networks.training.propagation.resilient.ResilientPropagation;
public class TestBiasActivation extends TestCase {
public void testLayerOutput()
{
Layer layer1, layer2;
BasicNetwork network = new BasicNetwork();
network.addLayer(layer1 = new BasicLayer(null, true,2));
network.addLayer(layer2 = new BasicLayer(new ActivationSigmoid(), true,4));
network.addLayer(new BasicLayer(new ActivationSigmoid(), false,1));
int i = 0;
i++;
layer1.setBiasActivation(0.5);
layer2.setBiasActivation(-1.0);
network.getStructure().finalizeStructure();
network.reset();
FlatNetwork flat = network.getStructure().getFlat();
Assert.assertNotNull(flat);
double[] layerOutput = flat.getLayerOutput();
Assert.assertEquals(-1, layerOutput[5], 2 );
Assert.assertEquals(0.5, layerOutput[8], 2 );
}
public void testLayerOutputPostFinalize()
{
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();
network.reset();
network.setLayerBiasActivation(0,0.5);
network.setLayerBiasActivation(1,-1.0);
FlatNetwork flat = network.getStructure().getFlat();
Assert.assertNotNull(flat);
double[] layerOutput = flat.getLayerOutput();
Assert.assertEquals(layerOutput[5], -1.0);
Assert.assertEquals(layerOutput[8], 0.5);
}
public void testTrain()
{
BasicNetwork network1 = NetworkUtil.createXORNetworkUntrained();
BasicNetwork network2 = (BasicNetwork)network1.clone();
BasicNetwork network3 = (BasicNetwork)network1.clone();
network2.setBiasActivation(-1);
network3.setBiasActivation(0.5);
MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
MLTrain rprop1 = new ResilientPropagation(network1, trainingData);
MLTrain rprop2 = new ResilientPropagation(network2, trainingData);
MLTrain rprop3 = new ResilientPropagation(network3, trainingData);
NetworkUtil.testTraining(trainingData,rprop1,0.03);
NetworkUtil.testTraining(trainingData,rprop2,0.01);
NetworkUtil.testTraining(trainingData,rprop3,0.01);
double[] w1 = NetworkCODEC.networkToArray(network1);
double[] w2 = NetworkCODEC.networkToArray(network2);
double[] w3 = NetworkCODEC.networkToArray(network3);
Assert.assertTrue(Math.abs(w1[0]-w2[0])>Encog.DEFAULT_DOUBLE_EQUAL);
Assert.assertTrue(Math.abs(w2[0]-w3[0])>Encog.DEFAULT_DOUBLE_EQUAL);
}
}