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 void recognizer(List<File> files) { FeatureExtractor fe = new FeatureExtractor(); MLDataSet trainingSet = new BasicMLDataSet(); for (File f : files) { // System.out.println(f.getAbsolutePath()); List<double[]> data; try { data = fe.fileProcessor(f); MLData mldataIn = new BasicMLData(data.get(0)); double[] out = new double[NUM_OUT]; Integer index = new Integer(Labeler.getLabel(f)); // System.out.println(index+""+data.get(0)); out[index] = 1.; System.out.println(out.toString()); MLData mldataout = new BasicMLData(out); trainingSet.add(mldataIn, mldataout); } catch (FileNotFoundException e) { // TODO Auto-generated catch block e.printStackTrace(); } } BasicNetwork network = new BasicNetwork(); network.addLayer(new BasicLayer(NUM_IN)); network.addLayer(new BasicLayer(new ActivationSigmoid(), true, 4 * NUM_IN)); // network.addLayer(new BasicLayer(new ActivationSigmoid(), true, 2 * NUM_IN)); network.addLayer(new BasicLayer(new ActivationSigmoid(), false, NUM_OUT)); network.getStructure().finalizeStructure(); network.reset(); // train the neural network ResilientPropagation train = new ResilientPropagation(network, trainingSet); System.out.println("Training Set: " + trainingSet.size()); int epoch = 1; do { train.iteration(); System.out.println("Epoch:" + epoch + " Error-->" + train.getError()); epoch++; } while (train.getError() > 0.001); train.finishTraining(); // test the neural network System.out.println("Neural Network Results:"); for (MLDataPair pair : trainingSet) { final MLData output = network.compute(pair.getInput()); System.out.println( "actual-->" + Labeler.getWord(output) + ", ideal-->" + Labeler.getWord(pair.getIdeal())); } Encog.getInstance().shutdown(); }
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; }
/** * The main method. * * @param args No arguments are used. */ public static void main(final String args[]) { // create a neural network, without using a factory BasicNetwork network = new BasicNetwork(); network.addLayer(new BasicLayer(null, true, 2)); network.addLayer(new BasicLayer(new ActivationSigmoid(), true, 3)); network.addLayer(new BasicLayer(new ActivationSigmoid(), false, 1)); network.getStructure().finalizeStructure(); network.reset(); // create training data MLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL); // train the neural network final ResilientPropagation train = new ResilientPropagation(network, trainingSet); int epoch = 1; do { train.iteration(); System.out.println("Epoch #" + epoch + " Error:" + train.getError()); epoch++; } while (train.getError() > 0.01); train.finishTraining(); // test the neural network System.out.println("Neural Network Results:"); for (MLDataPair pair : trainingSet) { final MLData output = network.compute(pair.getInput()); System.out.println( pair.getInput().getData(0) + "," + pair.getInput().getData(1) + ", actual=" + output.getData(0) + ",ideal=" + pair.getIdeal().getData(0)); } 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..."); }