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

Examples of org.encog.neural.networks.training.propagation.TrainingContinuation

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    rprop1.iteration();
   
    rprop2.iteration();
    rprop2.iteration();
   
    TrainingContinuation cont = rprop2.pause();
   
    ResilientPropagation rprop3 = new ResilientPropagation(net2,trainingSet);
    rprop3.resume(cont);
   
    rprop1.iteration();
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    rprop1.iteration();
   
    rprop2.iteration();
    rprop2.iteration();
   
    TrainingContinuation cont = rprop2.pause();
   
    EncogDirectoryPersistence.saveObject(EG_FILENAME, cont);
    TrainingContinuation cont2 = (TrainingContinuation)EncogDirectoryPersistence.loadObject(EG_FILENAME);
   
    ResilientPropagation rprop3 = new ResilientPropagation(net2,trainingSet);
    rprop3.resume(cont2);
   
    rprop1.iteration();
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          this.getParentTab().setEncogObject(this.getEncogObject());
        }
      }
     
      if( this.train.canContinue() ) {
        TrainingContinuation cont = train.pause(); 
        String name = FileUtil.getFileName(this.getEncogObject().getFile());
        name = FileUtil.forceExtension(name + "-cont", "eg");
        File path = new File(name);
        EncogWorkBench.getInstance().save(path, cont);
        EncogWorkBench.getInstance().refresh();
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        String name = FileUtil.getFileName( getEncogObject().getFile() );
        name+="-cont.eg";
        File path = new File(name);
        if( path.exists() ) {
          try {
            TrainingContinuation cont = (TrainingContinuation)EncogDirectoryPersistence.loadObject(path);
            train.resume(cont);
          } catch(Exception ex) {
            EncogWorkBench.displayError("Trainning Resume Incompatible", "Cannot use previous training data, training will begin as best it can.");
            path.delete();
          }
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    ResilientPropagation train = new ResilientPropagation(network, trainingSet);
    train.addStrategy(new RequiredImprovementStrategy(5));
   
    System.out.println("Perform initial train.");
    EncogUtility.trainToError(train,0.01);
    TrainingContinuation cont = train.pause();
    System.out.println(Arrays.toString((double[])cont.getContents().get(ResilientPropagation.LAST_GRADIENTS)));
    System.out.println(Arrays.toString((double[])cont.getContents().get(ResilientPropagation.UPDATE_VALUES)));
   
    try
    {
    cont = (TrainingContinuation)SerializeObject.load(new File("resume.ser"));
    }
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   *
   * @return A training continuation object to continue with.
   */
  @Override
  public final TrainingContinuation pause() {
    final TrainingContinuation result = new TrainingContinuation();
    result.setTrainingType(this.getClass().getSimpleName());
    final TrainFlatNetworkQPROP qprop = (TrainFlatNetworkQPROP) getFlatTraining();
    final double[] d = qprop.getLastGradient();
    result.set(QuickPropagation.LAST_GRADIENTS, d);
    return result;
  }
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   *
   * @return A training continuation object to continue with.
   */
  @Override
  public final TrainingContinuation pause() {
    final TrainingContinuation result = new TrainingContinuation();
    result.setTrainingType(this.getClass().getSimpleName());
    final TrainFlatNetworkBackPropagation backFlat = (TrainFlatNetworkBackPropagation) getFlatTraining();
    final double[] d = backFlat.getLastDelta();
    result.set(Backpropagation.LAST_DELTA, d);
    return result;
  }
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   *
   * @return A training continuation object to continue with.
   */
  @Override
  public final TrainingContinuation pause() {
    final TrainingContinuation result = new TrainingContinuation();

    result.setTrainingType(this.getClass().getSimpleName());

    result.set(ResilientPropagation.LAST_GRADIENTS,
        ((TrainFlatNetworkResilient) getFlatTraining())
            .getLastGradient());
    result.set(ResilientPropagation.UPDATE_VALUES,
        ((TrainFlatNetworkResilient) getFlatTraining())
            .getUpdateValues());

    return result;
  }
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    // train network 2, continue
    ResilientPropagation rprop2 = new ResilientPropagation(network2,trainingData);
    rprop2.iteration();
    rprop2.iteration();
    TrainingContinuation state = rprop2.pause();
    rprop2 = new ResilientPropagation(network2,trainingData);
    rprop2.resume(state);
    rprop2.iteration();
    rprop2.iteration();
   
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    // train network 2, continue
    Backpropagation rprop2 = new Backpropagation(network2,trainingData,0.4,0.4);
    rprop2.iteration();
    rprop2.iteration();
    TrainingContinuation state = rprop2.pause();
    rprop2 = new Backpropagation(network2,trainingData,0.4,0.4);
    rprop2.resume(state);
    rprop2.iteration();
    rprop2.iteration();
   
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Related Classes of org.encog.neural.networks.training.propagation.TrainingContinuation

  • org.encog.examples.neural.resume.TrainResume
  • org.encog.neural.networks.training.propagation.back.Backpropagation
  • org.encog.neural.networks.training.propagation.quick.QuickPropagation
  • org.encog.neural.networks.training.propagation.resilient.ResilientPropagation
  • org.encog.neural.networks.training.TestTrainingContinuation
  • org.encog.persist.TestPersistTrainingContinuation
  • org.encog.workbench.tabs.training.BasicTrainingProgress
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