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);
}
private void processNetwork() throws IOException {
System.out.println("Downsampling images...");
for (final ImagePair pair : this.imageList) {
final NeuralData ideal = new BasicNeuralData(this.outputCount);
final int idx = pair.getIdentity();
for (int i = 0; i < this.outputCount; i++) {
if (i == idx) {
ideal.setData(i, 1);
} else {
ideal.setData(i, -1);
}
}
final Image img = ImageIO.read(pair.getFile());
final ImageNeuralData data = new ImageNeuralData(img);
this.training.add(data, ideal);
}
final String strHidden1 = getArg("hidden1");
final String strHidden2 = getArg("hidden2");
this.training.downsample(this.downsampleHeight, this.downsampleWidth);
final int hidden1 = Integer.parseInt(strHidden1);
final int hidden2 = Integer.parseInt(strHidden2);
this.network =
EncogUtility.simpleFeedForward(
this.training.getInputSize(), hidden1, hidden2, this.training.getIdealSize(), true);
System.out.println("Created network: " + this.network.toString());
}
public void testAnalyze() {
BasicNetwork network = EncogUtility.simpleFeedForward(2, 2, 0, 1, false);
double[] weights = new double[network.encodedArrayLength()];
EngineArray.fill(weights, 1.0);
network.decodeFromArray(weights);
AnalyzeNetwork analyze = new AnalyzeNetwork(network);
Assert.assertEquals(weights.length, analyze.getWeightsAndBias().getSamples());
Assert.assertEquals(3, analyze.getBias().getSamples());
Assert.assertEquals(6, analyze.getWeights().getSamples());
}
public void testCompleteTrain() {
MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT, XOR.XOR_IDEAL);
BasicNetwork network = EncogUtility.simpleFeedForward(2, 5, 7, 1, true);
(new ConsistentRandomizer(-1, 1)).randomize(network);
MLTrain rprop = new ResilientPropagation(network, trainingData);
int iteration = 0;
do {
rprop.iteration();
iteration++;
} while (iteration < 5000 && rprop.getError() > 0.01);
Assert.assertTrue(iteration < 40);
}
public PredictSIN() {
this.setTitle("SIN Wave Predict");
this.setSize(640, 480);
Container content = this.getContentPane();
content.setLayout(new BorderLayout());
content.add(graph = new GraphPanel(), BorderLayout.CENTER);
network = EncogUtility.simpleFeedForward(INPUT_WINDOW, PREDICT_WINDOW * 2, 0, 1, true);
network.reset();
graph.setNetwork(network);
this.trainingData = generateTraining();
this.train = new ResilientPropagation(this.network, this.trainingData);
btnTrain = new JButton("Train");
this.btnTrain.addActionListener(this);
content.add(btnTrain, BorderLayout.SOUTH);
graph.setError(network.calculateError(this.trainingData));
}