Package org.encog.neural.networks

Examples of org.encog.neural.networks.BasicNetwork.calculateError()

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    // train the neural network
    EncogUtility.trainConsole( jordanNetwork, trainingSet, 1);
    EncogUtility.trainConsole( ffNetwork, trainingSet, 1);
   
    System.out.println("Final Jordan Error: " + Format.formatPercent(jordanNetwork.calculateError(trainingSet)));
    System.out.println("Final Feedforwd Error: " + Format.formatPercent(jordanNetwork.calculateError(trainingSet)));
   
    System.out.println("However, the error rate can be misleading.  Consider the evaluations of each network.");
   
    System.out.println("Feedforward Evaluation:");
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    // train the neural network
    EncogUtility.trainConsole( jordanNetwork, trainingSet, 1);
    EncogUtility.trainConsole( ffNetwork, trainingSet, 1);
   
    System.out.println("Final Jordan Error: " + Format.formatPercent(jordanNetwork.calculateError(trainingSet)));
    System.out.println("Final Feedforwd Error: " + Format.formatPercent(jordanNetwork.calculateError(trainingSet)));
   
    System.out.println("However, the error rate can be misleading.  Consider the evaluations of each network.");
   
    System.out.println("Feedforward Evaluation:");
    EncogUtility.evaluate(ffNetwork, trainingSet);
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    do {
      train.iteration();
    } while (train.getError() > 0.009);

    double e = network.calculateError(trainingSet);
    System.out.println("Network traiined to error: " + e);

    System.out.println("Saving network");
    SerializeObject.save(new File(FILENAME), network);
  }
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  public void loadAndEvaluate() throws IOException, ClassNotFoundException {
    System.out.println("Loading network");
    BasicNetwork network = (BasicNetwork) SerializeObject.load(new File(FILENAME));
    MLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);
    double e = network.calculateError(trainingSet);
    System.out
        .println("Loaded network's error is(should be same as above): "
            + e);
  }
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          .println("Epoch #" + epoch + " Error:" + train.getError());
      epoch++;
    } while(train.getError() > 0.01);
   
    //
    System.out.println("Error:" + network.calculateError(training));
   
    // test it
    for(int i=0;i<DIGITS.length;i++)
    {
      int output = network.winner(image2data(DIGITS[i]));
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    do {
      train.iteration();
    } while (train.getError() > 0.009);

    double e = network.calculateError(trainingSet);
    System.out.println("Network traiined to error: " + e);

    System.out.println("Saving network");
    EncogDirectoryPersistence.saveObject(new File(FILENAME), network);
  }
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    System.out.println("Loading network");

    BasicNetwork network = (BasicNetwork)EncogDirectoryPersistence.loadObject(new File(FILENAME));

    MLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);
    double e = network.calculateError(trainingSet);
    System.out
        .println("Loaded network's error is(should be same as above): "
            + e);
  }
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