public void trainAndSave() {
System.out.println("Training XOR network to under 1% error rate.");
BasicNetwork network = new BasicNetwork();
network.addLayer(new BasicLayer(2));
network.addLayer(new BasicLayer(2));
network.addLayer(new BasicLayer(1));
network.getStructure().finalizeStructure();
network.reset();
NeuralDataSet trainingSet = new BasicNeuralDataSet(XOR_INPUT, XOR_IDEAL);
// train the neural network
final Train train = new ResilientPropagation(network, trainingSet);
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");
final EncogPersistedCollection encog = new EncogPersistedCollection(FILENAME);
encog.create();
encog.add("network", network);
}
public void testFeedforwardPersist() throws Throwable {
NeuralDataSet trainingData = new BasicNeuralDataSet(XOR.XOR_INPUT, XOR.XOR_IDEAL);
BasicNetwork network = createNetwork();
Train train = new Backpropagation(network, trainingData, 0.7, 0.9);
for (int i = 0; i < 5000; i++) {
train.iteration();
network = (BasicNetwork) train.getNetwork();
}
TestCase.assertTrue("Error too high for backpropagation", train.getError() < 0.1);
TestCase.assertTrue("XOR outputs not correct", XOR.verifyXOR(network, 0.1));
EncogPersistedCollection encog = new EncogPersistedCollection();
encog.add(network);
encog.save("encogtest.xml");
EncogPersistedCollection encog2 = new EncogPersistedCollection();
encog2.load("encogtest.xml");
new File("encogtest.xml").delete();
BasicNetwork n = (BasicNetwork) encog2.getList().get(0);
TestCase.assertTrue("Error too high for load", n.calculateError(trainingData) < 0.1);
}
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();
}
public void loadAndEvaluate() {
System.out.println("Loading network");
final EncogPersistedCollection encog = new EncogPersistedCollection(FILENAME);
BasicNetwork network = (BasicNetwork) encog.find("network");
NeuralDataSet trainingSet = new BasicNeuralDataSet(XOR_INPUT, XOR_IDEAL);
double e = network.calculateError(trainingSet);
System.out.println("Loaded network's error is(should be same as above): " + e);
}
public void performEvaluate() {
try {
EvaluateDialog dialog = new EvaluateDialog(EncogWorkBench.getInstance().getMainWindow());
if (dialog.process()) {
BasicNetwork network = dialog.getNetwork();
NeuralDataSet training = dialog.getTrainingSet();
double error = network.calculateError(training);
EncogWorkBench.displayMessage("Error For this Network", "" + Format.formatPercent(error));
}
} catch (Throwable t) {
EncogWorkBench.displayError("Error Evaluating Network", t);
}
}
public static double evaluateMPROP(BasicNetwork network, NeuralDataSet data) {
ResilientPropagation train = new ResilientPropagation(network, data);
train.setNumThreads(0);
long start = System.currentTimeMillis();
System.out.println("Training 20 Iterations with MPROP");
for (int i = 1; i <= 20; i++) {
train.iteration();
System.out.println("Iteration #" + i + " Error:" + train.getError());
}
train.finishTraining();
long stop = System.currentTimeMillis();
double diff = ((double) (stop - start)) / 1000.0;
System.out.println("MPROP Result:" + diff + " seconds.");
System.out.println("Final MPROP error: " + network.calculateError(data));
return diff;
}
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));
}
public static void main(String args[]) {
int inputNeurons = CHAR_WIDTH * CHAR_HEIGHT;
int outputNeurons = DIGITS.length;
BasicNetwork network = new BasicNetwork();
Layer inputLayer = new BasicLayer(new ActivationLinear(), false, inputNeurons);
Layer outputLayer = new BasicLayer(new ActivationLinear(), true, outputNeurons);
network.addLayer(inputLayer);
network.addLayer(outputLayer);
network.getStructure().finalizeStructure();
(new RangeRandomizer(-0.5, 0.5)).randomize(network);
// train it
NeuralDataSet training = generateTraining();
Train train = new TrainAdaline(network, training, 0.01);
int epoch = 1;
do {
train.iteration();
System.out.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]));
for (int j = 0; j < CHAR_HEIGHT; j++) {
if (j == CHAR_HEIGHT - 1) System.out.println(DIGITS[i][j] + " -> " + output);
else System.out.println(DIGITS[i][j]);
}
System.out.println();
}
}
@Override
public double getError(EnsembleDataSet testset) {
return ml.calculateError(testset);
}
