Package org.encog.examples.neural.persist

Source Code of org.encog.examples.neural.persist.EncogPersistence

/*
* 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.persist;

import java.io.File;

import org.encog.ml.data.MLDataSet;
import org.encog.ml.data.basic.BasicMLDataSet;
import org.encog.ml.train.MLTrain;
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.persist.EncogDirectoryPersistence;

public class EncogPersistence {

  public static final String FILENAME = "encogexample.eg";

  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 void trainAndSave() {
    System.out.println("Training XOR network to under 1% error rate.");
    BasicNetwork network = new BasicNetwork();
    network.addLayer(new BasicLayer(2));
    network.addLayer(new BasicLayer(2));
    network.addLayer(new BasicLayer(1));
    network.getStructure().finalizeStructure();
    network.reset();

    MLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);

    // train the neural network
    final MLTrain train = new ResilientPropagation(network, trainingSet);

    do {
      train.iteration();
    } while (train.getError() > 0.009);

    double e = network.calculateError(trainingSet);
    System.out.println("Network traiined to error: " + e);

    System.out.println("Saving network");
    EncogDirectoryPersistence.saveObject(new File(FILENAME), network);
  }

  public void loadAndEvaluate() {
    System.out.println("Loading network");

    BasicNetwork network = (BasicNetwork)EncogDirectoryPersistence.loadObject(new File(FILENAME));

    MLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);
    double e = network.calculateError(trainingSet);
    System.out
        .println("Loaded network's error is(should be same as above): "
            + e);
  }

  public static void main(String[] args) {
    try {
      EncogPersistence program = new EncogPersistence();
      program.trainAndSave();
      program.loadAndEvaluate();
    } catch (Throwable t) {
      t.printStackTrace();
    }

  }
}
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