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

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

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    private int trainNeuralNetwork() {
        final Train train = new ResilientPropagation(network, trainingData);

        int epoch = 1;
        do {
            train.iteration();
            //System.out.println("Epoch #" + epoch + " Error: " + train.getError());
            epoch++;
            if (epoch > 500) {
                return 1;
            }
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    ResilientPropagation rprop2 = new ResilientPropagation(net2,trainingSet);
   
    rprop1.iteration();
    rprop1.iteration();
   
    rprop2.iteration();
    rprop2.iteration();
   
    TrainingContinuation cont = rprop2.pause();
   
    ResilientPropagation rprop3 = new ResilientPropagation(net2,trainingSet);
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    rprop1.iteration();
    rprop1.iteration();
   
    rprop2.iteration();
    rprop2.iteration();
   
    TrainingContinuation cont = rprop2.pause();
   
    ResilientPropagation rprop3 = new ResilientPropagation(net2,trainingSet);
    rprop3.resume(cont);
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    ResilientPropagation rprop3 = new ResilientPropagation(net2,trainingSet);
    rprop3.resume(cont);
   
    rprop1.iteration();
    rprop3.iteration();
   
   
    for(int i=0;i<net1.getFlat().getWeights().length;i++) {
      Assert.assertEquals(net1.getFlat().getWeights()[i], net2.getFlat().getWeights()[i],0.0001);
    }
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    ResilientPropagation rprop2 = new ResilientPropagation(net2,trainingSet);
   
    rprop1.iteration();
    rprop1.iteration();
   
    rprop2.iteration();
    rprop2.iteration();
   
    TrainingContinuation cont = rprop2.pause();
   
    EncogDirectoryPersistence.saveObject(EG_FILENAME, cont);
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    rprop1.iteration();
    rprop1.iteration();
   
    rprop2.iteration();
    rprop2.iteration();
   
    TrainingContinuation cont = rprop2.pause();
   
    EncogDirectoryPersistence.saveObject(EG_FILENAME, cont);
    TrainingContinuation cont2 = (TrainingContinuation)EncogDirectoryPersistence.loadObject(EG_FILENAME);
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    ResilientPropagation rprop3 = new ResilientPropagation(net2,trainingSet);
    rprop3.resume(cont2);
   
    rprop1.iteration();
    rprop3.iteration();
   
   
    for(int i=0;i<net1.getFlat().getWeights().length;i++) {
      Assert.assertEquals(net1.getFlat().getWeights()[i], net2.getFlat().getWeights()[i],0.0001);
    }
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      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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      train.setRPROPType(RPROPType.iRPROPp);

      int epoch = 1;

      do {
        train.iteration();
        epoch++;
      } while (train.getError() > 0.01 && epoch<1000 );
     
      if( epoch>900 ) {
        failureCount++;
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    final MLTrain train = new ResilientPropagation(network, training);

    int epoch = 1;

    do {
      train.iteration();
      System.out
          .println("Epoch #" + epoch + " Error:" + train.getError());
      epoch++;
    } while(train.getError() > MAX_ERROR);
  }
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