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 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);
}
/**
* Calculate the error for the given method and dataset.
*
* @param method The method to use.
* @param data The data to use.
* @return The error.
*/
public double calculateError(MLMethod method, MLDataSet data) {
if (this.dataset.getNormHelper().getOutputColumns().size() == 1) {
ColumnDefinition cd = this.dataset.getNormHelper().getOutputColumns().get(0);
if (cd.getDataType() == ColumnType.nominal) {
return EncogUtility.calculateClassificationError((MLClassification) method, data);
}
}
return EncogUtility.calculateRegressionError((MLRegression) method, data);
}
/**
* Evaluate the network and display (to the console) the output for every value in the training
* set. Displays ideal and actual.
*
* @param network The network to evaluate.
* @param training The training set to evaluate.
*/
public static void evaluate(final MLRegression network, final MLDataSet training) {
for (final MLDataPair pair : training) {
final MLData output = network.compute(pair.getInput());
System.out.println(
"Input="
+ EncogUtility.formatNeuralData(pair.getInput())
+ ", Actual="
+ EncogUtility.formatNeuralData(output)
+ ", Ideal="
+ EncogUtility.formatNeuralData(pair.getIdeal()));
}
}
/** {@inheritDoc} */
@Override
public boolean executeCommand(final String args) {
// get filenames
final String sourceID =
getProp().getPropertyString(ScriptProperties.GENERATE_CONFIG_SOURCE_FILE);
final String targetID =
getProp().getPropertyString(ScriptProperties.GENERATE_CONFIG_TARGET_FILE);
final CSVFormat format = getAnalyst().getScript().determineFormat();
EncogLogging.log(EncogLogging.LEVEL_DEBUG, "Beginning generate");
EncogLogging.log(EncogLogging.LEVEL_DEBUG, "source file:" + sourceID);
EncogLogging.log(EncogLogging.LEVEL_DEBUG, "target file:" + targetID);
final File sourceFile = getScript().resolveFilename(sourceID);
final File targetFile = getScript().resolveFilename(targetID);
// mark generated
getScript().markGenerated(targetID);
// read file
final boolean headers = getScript().expectInputHeaders(sourceID);
final CSVHeaders headerList = new CSVHeaders(sourceFile, headers, format);
final int[] input = determineInputFields(headerList);
final int[] ideal = determineIdealFields(headerList);
EncogUtility.convertCSV2Binary(sourceFile, format, targetFile, input, ideal, headers);
return false;
}
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());
}
/**
* Train the method, to a specific error, send the output to the console.
*
* @param method The method to train.
* @param dataSet The training set to use.
* @param error The error level to train to.
*/
public static void trainToError(
final MLMethod method, final MLDataSet dataSet, final double error) {
MLTrain train;
if (method instanceof SVM) {
train = new SVMTrain((SVM) method, dataSet);
} else {
train = new ResilientPropagation((ContainsFlat) method, dataSet);
}
EncogUtility.trainToError(train, error);
}
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);
}
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...");
}
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));
}
/**
* Calculate the error for this neural network.
*
* @param data The training set.
* @return The error percentage.
*/
@Override
public double calculateError(final MLDataSet data) {
return EncogUtility.calculateRegressionError(this, data);
}
/**
* 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);
}