Package org.encog.neural.networks.training.propagation.resilient

Examples of org.encog.neural.networks.training.propagation.resilient.ResilientPropagation.iteration()

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    // 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);
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    train.addStrategy(new RequiredImprovementStrategy(5));
   
    int epoch = 1;

    do {
      train.iteration();
      System.out
          .println("Epoch #" + epoch + " Error:" + train.getError());
      epoch++;
    } while(train.getError() > 0.01);
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    train.setNumThreads(1);
    long start = System.currentTimeMillis();
    System.out.println("Training 20 Iterations with RPROP");
    for(int i=1;i<=20;i++)
    {
      train.iteration();
      System.out.println("Iteration #" + i + " Error:" + train.getError());
    }
    train.finishTraining();
    long stop = System.currentTimeMillis();
    double diff = ((double)(stop - start))/1000.0;
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    train.setNumThreads(0);
    long start = System.currentTimeMillis();
    System.out.println("Training 20 Iterations with MPROP");
    for(int i=1;i<=20;i++)
    {
      train.iteration();
      System.out.println("Iteration #" + i + " Error:" + train.getError());
    }
    train.finishTraining();
    long stop = System.currentTimeMillis();
    double diff = ((double)(stop - start))/1000.0;
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  public static double evaluate(BasicNetwork network, MLDataSet training) {
    ResilientPropagation rprop = new ResilientPropagation(network, training);
    double startingError = network.calculateError(training);
    for (int i = 0; i < ITERATIONS; i++) {
      rprop.iteration();
    }
    double finalError = network.calculateError(training);
    return startingError - finalError;
  }
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    final ResilientPropagation train = new ResilientPropagation(network, trainingSet);

    int epoch = 1;

    do {
      train.iteration();
      System.out
          .println("Epoch #" + epoch + " Error:" + train.getError());
      epoch++;
    } while(train.getError() > 0.01);
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    ResilientPropagation rprop = new ResilientPropagation(network,training);
    rprop.setNumThreads(thread);
    for(int i=0;i<5;i++)
    {
      rprop.iteration();
    }
    long stop = System.currentTimeMillis();
    System.out.println("Result with " + thread + " was " + (stop-start));
  }
 
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    // 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);
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    BasicNetwork network2 = NetworkUtil.createXORNetworkUntrained();
    MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
   
    // train network 1, no continue
    ResilientPropagation rprop1 = new ResilientPropagation(network1,trainingData);
    rprop1.iteration();
    rprop1.iteration();
    rprop1.iteration();
    rprop1.iteration();
   
    // train network 2, continue
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    MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
   
    // train network 1, no continue
    ResilientPropagation rprop1 = new ResilientPropagation(network1,trainingData);
    rprop1.iteration();
    rprop1.iteration();
    rprop1.iteration();
    rprop1.iteration();
   
    // train network 2, continue
    ResilientPropagation rprop2 = new ResilientPropagation(network2,trainingData);
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