Examples of NeuralSimulatedAnnealing

org.encog.neural.networks.training.anneal.NeuralSimulatedAnnealing
This class implements a simulated annealing training algorithm for neural networks. It is based on the generic SimulatedAnnealing class. It is used in the same manner as any other training class that implements the Train interface. There are essentially two ways you can make use of this class. Either way, you will need a score object. The score object tells the simulated annealing algorithm how well suited a neural network is. If you would like to use simulated annealing with a training set you should make use TrainingSetScore class. This score object uses a training set to score your neural network. If you would like to be more abstract, and not use a training set, you can create your own implementation of the CalculateScore method. This class can then score the networks any way that you like.

Examples of org.encog.neural.networks.training.anneal.NeuralSimulatedAnnealing


  public static double trainNetwork(final String what,
      final BasicNetwork network, final MLDataSet trainingSet) {
    // train the neural network
    CalculateScore score = new TrainingSetScore(trainingSet);
    final MLTrain trainAlt = new NeuralSimulatedAnnealing(
        network, score, 10, 2, 100);


    final MLTrain trainMain = new Backpropagation(network, trainingSet,0.000001, 0.0);


    ((Propagation)trainMain).setNumThreads(1);

View Full Code Here

Examples of org.encog.neural.networks.training.anneal.NeuralSimulatedAnnealing


  public static double trainNetwork(final String what,
      final BasicNetwork network, final MLDataSet trainingSet) {
    // train the neural network
    CalculateScore score = new TrainingSetScore(trainingSet);
    final MLTrain trainAlt = new NeuralSimulatedAnnealing(
        network, score, 10, 2, 100);


    final MLTrain trainMain = new Backpropagation(network, trainingSet,0.000001, 0.0);


    ((Propagation)trainMain).setNumThreads(1);

View Full Code Here

Examples of org.encog.neural.networks.training.anneal.NeuralSimulatedAnnealing

  public void testAnneal() throws Throwable
  {
    MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);    
    BasicNetwork network = NetworkUtil.createXORNetworkUntrained();
    CalculateScore score = new TrainingSetScore(trainingData);
    NeuralSimulatedAnnealing anneal = new NeuralSimulatedAnnealing(network,score,10,2,100);
    NetworkUtil.testTraining(anneal,0.01);
  }

View Full Code Here

Examples of org.encog.neural.networks.training.anneal.NeuralSimulatedAnnealing

    
    MLTrain train;
    
    if( args.length>0 && args[0].equalsIgnoreCase("anneal"))
    {
      train = new NeuralSimulatedAnnealing(
          network, new PilotScore(), 10, 2, 100);
    }
    else
    {
      train = new NeuralGeneticAlgorithm(

View Full Code Here

Examples of org.encog.neural.networks.training.anneal.NeuralSimulatedAnnealing

      final double startTemp = dialog.getStartTemp().getValue();
      final double stopTemp = dialog.getStartTemp().getValue();
      final int cycles = dialog.getCycles().getValue();


      CalculateScore score = new TrainingSetScore(trainingData);
      final NeuralSimulatedAnnealing train = new NeuralSimulatedAnnealing(
          (BasicNetwork) file.getObject(), score, startTemp,
          stopTemp, cycles);
      train.setTraining(trainingData);
      startup(file, train, dialog.getMaxError().getValue() / 100.0);
    }


  }

View Full Code Here

Examples of org.encog.neural.networks.training.anneal.NeuralSimulatedAnnealing

    final double stopTemp = holder.getDouble(
        MLTrainFactory.PROPERTY_TEMPERATURE_STOP, false, 2);


    final int cycles = holder.getInt(MLTrainFactory.CYCLES, false, 100);


    final MLTrain train = new NeuralSimulatedAnnealing(
        (BasicNetwork) method, score, startTemp, stopTemp, cycles);


    return train;
  }

View Full Code Here

Examples of org.encog.neural.networks.training.anneal.NeuralSimulatedAnnealing

    final double stopTemp = holder.getDouble(
        MLTrainFactory.PROPERTY_TEMPERATURE_STOP, false, 2);


    final int cycles = holder.getInt(MLTrainFactory.CYCLES, false, 100);


    final MLTrain train = new NeuralSimulatedAnnealing(
        (BasicNetwork) method, score, startTemp, stopTemp, cycles);


    return train;
  }

View Full Code Here

Examples of org.encog.neural.networks.training.anneal.NeuralSimulatedAnnealing

  public void testAnneal() throws Throwable
  {
    MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);    
    BasicNetwork network = NetworkUtil.createXORNetworkUntrained();
    CalculateScore score = new TrainingSetScore(trainingData);
    NeuralSimulatedAnnealing anneal = new NeuralSimulatedAnnealing(network,score,10,2,100);
    NetworkUtil.testTraining(trainingData,anneal,0.01);
  }

View Full Code Here

Examples of org.encog.neural.networks.training.anneal.NeuralSimulatedAnnealing

  public void testAnneal() throws Throwable
  {
    MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);    
    FreeformNetwork network = NetworkUtil.createXORFreeformNetworkUntrained();
    CalculateScore score = new TrainingSetScore(trainingData);
    NeuralSimulatedAnnealing anneal = new NeuralSimulatedAnnealing(network,score,10,2,100);
    NetworkUtil.testTraining(trainingData,anneal,0.01);
  }

View Full Code Here

TOP

All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.