public static void train(File dataDir) {
final File networkFile = new File(dataDir, Config.NETWORK_FILE);
final File trainingFile = new File(dataDir, Config.TRAINING_FILE);
// network file
if (!networkFile.exists()) {
System.out.println("Can't read file: " + networkFile.getAbsolutePath());
return;
}
BasicNetwork network = (BasicNetwork) EncogDirectoryPersistence.loadObject(networkFile);
// training file
if (!trainingFile.exists()) {
System.out.println("Can't read file: " + trainingFile.getAbsolutePath());
return;
}
final MLDataSet trainingSet = EncogUtility.loadEGB2Memory(trainingFile);
// train the neural network
EncogUtility.trainConsole(network, trainingSet, Config.TRAINING_MINUTES);
System.out.println("Final Error: " + (float) network.calculateError(trainingSet));
System.out.println("Training complete, saving network.");
EncogDirectoryPersistence.saveObject(networkFile, network);
System.out.println("Network saved.");
Encog.getInstance().shutdown();
}
private void processTrain() throws IOException {
final String strMode = getArg("mode");
final String strMinutes = getArg("minutes");
final String strStrategyError = getArg("strategyerror");
final String strStrategyCycles = getArg("strategycycles");
System.out.println("Training Beginning... Output patterns=" + this.outputCount);
final double strategyError = Double.parseDouble(strStrategyError);
final int strategyCycles = Integer.parseInt(strStrategyCycles);
final ResilientPropagation train = new ResilientPropagation(this.network, this.training);
train.addStrategy(new ResetStrategy(strategyError, strategyCycles));
if (strMode.equalsIgnoreCase("gui")) {
EncogUtility.trainDialog(train, this.network, this.training);
} else {
final int minutes = Integer.parseInt(strMinutes);
EncogUtility.trainConsole(train, this.network, this.training, minutes);
}
System.out.println("Training Stopped...");
}
/**
* Train the neural network, using SCG training, and output status to the console.
*
* @param network The network to train.
* @param trainingSet The training set.
* @param minutes The number of minutes to train for.
*/
public static void trainConsole(
final BasicNetwork network, final MLDataSet trainingSet, final int minutes) {
final Propagation train = new ResilientPropagation(network, trainingSet);
train.setThreadCount(0);
EncogUtility.trainConsole(train, network, trainingSet, minutes);
}