/*
* Encog(tm) Examples 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.examples.neural.activation;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.data.basic.BasicMLDataSet;
import org.encog.ml.train.MLTrain;
import org.encog.ml.train.strategy.RequiredImprovementStrategy;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.layers.BasicLayer;
import org.encog.neural.networks.training.propagation.resilient.ResilientPropagation;
import org.encog.util.simple.EncogUtility;
/**
* This example shows how to use a custom activation function.
*/
public class CustomActivation {
public static double XOR_INPUT[][] = { { 0.0, 0.0 }, { 1.0, 0.0 },
{ 0.0, 1.0 }, { 1.0, 1.0 } };
public static double XOR_IDEAL[][] = { { 0.0 }, { 1.0 }, { 1.0 }, { 0.0 } };
public static void main(final String args[]) {
final BasicNetwork network = new BasicNetwork();
network.addLayer(new BasicLayer(null, true, 2));
network.addLayer(new BasicLayer(new ActivationSigmoidPosNeg(), true, 4));
network.addLayer(new BasicLayer(new ActivationSigmoidPosNeg(), true, 1));
network.getStructure().finalizeStructure();
network.reset();
final MLDataSet trainingSet = new BasicMLDataSet(
CustomActivation.XOR_INPUT, CustomActivation.XOR_IDEAL);
// train the neural network
final MLTrain train = new ResilientPropagation(network, trainingSet);
// reset if improve is less than 1% over 5 cycles
train.addStrategy(new RequiredImprovementStrategy(5));
EncogUtility.trainToError(train, 0.01);
EncogUtility.evaluate(network, trainingSet);
}
}