public static void main(String[] args) {
Logging.stopConsoleLogging();
NeuralDataSet trainingSet = new BasicNeuralDataSet(XOR_INPUT, XOR_IDEAL);
BasicNetwork network = EncogUtility.simpleFeedForward(2, 4, 0, 1, false);
ResilientPropagation train = new ResilientPropagation(network, trainingSet);
train.addStrategy(new RequiredImprovementStrategy(5));
System.out.println("Perform initial train.");
EncogUtility.trainToError(train, network, trainingSet, 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 {
SerializeObject.save("resume.ser", cont);
cont = (TrainingContinuation) SerializeObject.load("resume.ser");
} catch (Exception ex) {
ex.printStackTrace();
}
System.out.println(
"Now trying a second train, with continue from the first. Should stop after one iteration");
ResilientPropagation train2 = new ResilientPropagation(network, trainingSet);
train2.resume(cont);
EncogUtility.trainToError(train2, network, trainingSet, 0.01);
}
public static void main(String args[]) {
Logging.stopConsoleLogging();
BasicNetwork network = generateNetwork();
NeuralDataSet data = generateTraining();
double rprop = evaluateRPROP(network, data);
double mprop = evaluateMPROP(network, data);
double factor = rprop / mprop;
System.out.println("Factor improvement:" + factor);
}
public static void main(String[] args) {
Logging.stopConsoleLogging();
try {
EncogPersistence program = new EncogPersistence();
program.trainAndSave();
program.loadAndEvaluate();
} catch (Throwable t) {
t.printStackTrace();
}
}
public static void main(final String[] args) {
Logging.stopConsoleLogging();
if (args.length < 1) {
System.out.println("Must specify command file. See source for format.");
} else {
try {
final ImageNeuralNetwork program = new ImageNeuralNetwork();
program.execute(args[0]);
} catch (final Exception e) {
e.printStackTrace();
}
}
}
public static void main(final String args[]) {
Logging.stopConsoleLogging();
final TemporalXOR temp = new TemporalXOR();
final NeuralDataSet trainingSet = temp.generate(100);
final BasicNetwork elmanNetwork = ElmanXOR.createElmanNetwork();
final BasicNetwork feedforwardNetwork = ElmanXOR.createFeedforwardNetwork();
final double elmanError = ElmanXOR.trainNetwork("Elman", elmanNetwork, trainingSet);
final double feedforwardError =
ElmanXOR.trainNetwork("Feedforward", feedforwardNetwork, trainingSet);
System.out.println("Best error rate with Elman Network: " + elmanError);
System.out.println("Best error rate with Feedforward Network: " + feedforwardError);
System.out.println(
"Elman should be able to get into the 30% range,\nfeedforward should not go below 50%.\nThe recurrent Elment net can learn better in this case.");
System.out.println(
"If your results are not as good, try rerunning, or perhaps training longer.");
}
/**
* The main method.
*
* @param args Args not really used.
*/
public static void main(final String args[]) {
Logging.stopConsoleLogging();
(new OCR()).setVisible(true);
}
