Package org.encog.neural.networks.training.anneal

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

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

  }
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    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;
  }
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    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;
  }
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  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);
  }
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  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);
  }
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Related Classes of org.encog.neural.networks.training.anneal.NeuralSimulatedAnnealing

  • org.encog.examples.neural.lunar.LunarLander
  • org.encog.examples.neural.recurrent.elman.ElmanXOR
  • org.encog.examples.neural.recurrent.jordan.JordanXOR
  • org.encog.ml.factory.train.AnnealFactory
  • org.encog.neural.freeform.TestFreeformTraining
  • org.encog.neural.networks.training.TestTraining
  • org.encog.neural.networks.training.TrainingError
  • org.encog.workbench.process.TrainBasicNetwork
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