Examples of org.encog.ml.train.strategy.StopTrainingStrategy

org.encog.ml.train.strategy.StopTrainingStrategy
This strategy will indicate once training is no longer improving the neural network by a specified amount, over a specified number of cycles. This allows the program to automatically determine when to stop training. @author jheaton

        network, score, 10, 2, 100);


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


    ((Propagation)trainMain).setNumThreads(1);
    final StopTrainingStrategy stop = new StopTrainingStrategy();
    trainMain.addStrategy(new Greedy());
    trainMain.addStrategy(new HybridStrategy(trainAlt));
    trainMain.addStrategy(stop);


    int epoch = 0;
    while (!stop.shouldStop()) {
      trainMain.iteration();
      System.out.println("Training " + what + ", Epoch #" + epoch
          + " Error:" + trainMain.getError());
      epoch++;
    }

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    double error = Double.POSITIVE_INFINITY;
    for (int z = 0; z < this.weightTries; z++) {
      network.reset();
      final Propagation train = new ResilientPropagation(network,
          useTraining);
      final StopTrainingStrategy strat = new StopTrainingStrategy(0.001,
          5);


      train.addStrategy(strat);
      train.setNumThreads(1); // force single thread mode


      for (int i = 0; (i < this.iterations) && !getShouldStop()
          && !strat.shouldStop(); i++) {
        train.iteration();
      }


      error = Math.min(error, train.getError());
    }

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        network, score, 10, 2, 100);


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


    ((Propagation)trainMain).setNumThreads(1);
    final StopTrainingStrategy stop = new StopTrainingStrategy();
    trainMain.addStrategy(new Greedy());
    trainMain.addStrategy(new HybridStrategy(trainAlt));
    trainMain.addStrategy(stop);


    int epoch = 0;
    while (!stop.shouldStop()) {
      trainMain.iteration();
      System.out.println("Training " + what + ", Epoch #" + epoch
          + " Error:" + trainMain.getError());
      epoch++;
    }

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import org.junit.Assert;


public class TestStrategy extends TestCase{
  public void testDone()
  {
    StopTrainingStrategy strategy = new StopTrainingStrategy(0.01,2);
    MockTrain mock = new MockTrain();
    mock.addStrategy(strategy);
    mock.setError(0.05);
    Assert.assertFalse(strategy.shouldStop());
    mock.iteration();
    mock.iteration();
    mock.iteration();
    mock.iteration();
    Assert.assertTrue(strategy.shouldStop());
  }

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    double error = Double.POSITIVE_INFINITY;
    for (int z = 0; z < this.weightTries; z++) {
      network.reset();
      final Propagation train = new ResilientPropagation(network,
          useTraining);
      final StopTrainingStrategy strat = new StopTrainingStrategy(0.001,
          5);


      train.addStrategy(strat);
      train.setThreadCount(1); // force single thread mode


      for (int i = 0; (i < this.iterations) && !getShouldStop()
          && !strat.shouldStop(); i++) {
        train.iteration();
      }


      error = Math.min(error, train.getError());
    }

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import org.junit.Assert;


public class TestStrategy extends TestCase{
  public void testDone()
  {
    StopTrainingStrategy strategy = new StopTrainingStrategy(0.01,2);
    MockTrain mock = new MockTrain();
    mock.addStrategy(strategy);
    mock.setError(0.05);
    Assert.assertFalse(strategy.shouldStop());
    mock.iteration();
    mock.iteration();
    mock.iteration();
    mock.iteration();
    Assert.assertTrue(strategy.shouldStop());
  }

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