Java Code Examples for org.neuroph.util.TransferFunctionType#SIGMOID

The following examples show how to use org.neuroph.util.TransferFunctionType#SIGMOID . You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may check out the related API usage on the sidebar.
Example 1
Source File: MomentumBackPropagationTest.java    From NeurophFramework with Apache License 2.0 7 votes vote down vote up 
@Test
public void testXorMSE() {
    MultiLayerPerceptron myMlPerceptron = new MultiLayerPerceptron(TransferFunctionType.SIGMOID, 2, 3, 1);
    myMlPerceptron.randomizeWeights(new WeightsRandomizer(new Random(123)));

    myMlPerceptron.setLearningRule(instance);
    myMlPerceptron.learn(xorDataSet);

    MeanSquaredError mse = new MeanSquaredError();
    for (DataSetRow testSetRow : xorDataSet.getRows()) {
        myMlPerceptron.setInput(testSetRow.getInput());
        myMlPerceptron.calculate();
        double[] networkOutput = myMlPerceptron.getOutput();
        mse.addPatternError(networkOutput, testSetRow.getDesiredOutput());
    }
    assertTrue(mse.getTotalError() < maxError);
}
Example 2
Source File: BackPropagationTest.java    From NeurophFramework with Apache License 2.0 7 votes vote down vote up 
@Test
public void testXorMSE() {
    MultiLayerPerceptron myMlPerceptron = new MultiLayerPerceptron(TransferFunctionType.SIGMOID, 2, 3, 1);
    myMlPerceptron.randomizeWeights(new WeightsRandomizer(new Random(123)));

    myMlPerceptron.setLearningRule(instance);
    myMlPerceptron.learn(xorDataSet);

    MeanSquaredError mse = new MeanSquaredError();
    for (DataSetRow testSetRow : xorDataSet.getRows()) {
        myMlPerceptron.setInput(testSetRow.getInput());
        myMlPerceptron.calculate();
        double[] networkOutput = myMlPerceptron.getOutput();
        mse.addPatternError(networkOutput, testSetRow.getDesiredOutput());
    }
    assertTrue(mse.getTotalError() < maxError);
}
Example 3
Source File: SunSpots.java    From NeurophFramework with Apache License 2.0 6 votes vote down vote up 
public void run() {

		// uncomment the following line to use regular Neuroph (non-flat) processing
		//Neuroph.getInstance().setFlattenNetworks(false);
		// create neural network
		NeuralNetwork network = new MultiLayerPerceptron(TransferFunctionType.SIGMOID, WINDOW_SIZE, 10, 1);

                // normalize training data
		normalizeSunspots(0.1, 0.9);

		network.getLearningRule().addListener(this);

                // create training set
		DataSet trainingSet = generateTrainingData();
		network.learn(trainingSet);
		predict(network);

		Neuroph.getInstance().shutdown();
	}
Example 4
Source File: MomentumBackPropagationTest.java    From NeurophFramework with Apache License 2.0 5 votes vote down vote up 
@Test
public void testXorMaxError() {
    MultiLayerPerceptron myMlPerceptron = new MultiLayerPerceptron(TransferFunctionType.SIGMOID, 2, 3, 1);
    myMlPerceptron.randomizeWeights(new WeightsRandomizer(new Random(123)));

    myMlPerceptron.setLearningRule(instance);
    myMlPerceptron.learn(xorDataSet);

    assertTrue(instance.getTotalNetworkError() < maxError);
}
Example 5
Source File: BackPropagationTest.java    From NeurophFramework with Apache License 2.0 5 votes vote down vote up 
@Test
public void testXorMaxError() {
    MultiLayerPerceptron myMlPerceptron = new MultiLayerPerceptron(TransferFunctionType.SIGMOID, 2, 3, 1);
    myMlPerceptron.randomizeWeights(new WeightsRandomizer(new Random(123)));

    myMlPerceptron.setLearningRule(instance);
    myMlPerceptron.learn(xorDataSet);

    assertTrue(instance.getTotalNetworkError() < maxError);
}
Example 6
Source File: BackpropBenchmarksExample.java    From NeurophFramework with Apache License 2.0 5 votes vote down vote up 
public static void main(String[] args) throws IOException {

        BackPropBenchmarks bpb = new BackPropBenchmarks();
        bpb.setNoOfRepetitions(3);
        
        MultiLayerPerceptron mlp = new MultiLayerPerceptron(TransferFunctionType.SIGMOID, 4, 7, 3);
        
        DataSet trainingSet = DataSet.createFromFile("iris_data_normalised.txt", 4, 3, ",");

        TrainingSettingsGenerator generator = new TrainingSettingsGenerator();
      
        Properties prop = new Properties();
        prop.setProperty(BackpropagationSettings.MIN_LEARNING_RATE, "0.1");
        prop.setProperty(BackpropagationSettings.MAX_LEARNING_RATE, "0.4");
        prop.setProperty(BackpropagationSettings.LEARNING_RATE_STEP, "0.5");
        prop.setProperty(BackpropagationSettings.MIN_HIDDEN_NEURONS, "9");
        prop.setProperty(BackpropagationSettings.MAX_HIDDEN_NEURONS, "10");
        prop.setProperty(BackpropagationSettings.HIDDEN_NEURONS_STEP, "1");
        prop.setProperty(BackpropagationSettings.MOMENTUM, "0.5");
        prop.setProperty(BackpropagationSettings.MAX_ERROR, "0.1");
        prop.setProperty(BackpropagationSettings.MAX_ITERATIONS, "10000");
        prop.setProperty(BackpropagationSettings.BATCH_MODE, "true");
      
        generator.setSettings(prop);

        List<TrainingSettings> settingsCollection = generator.generateSettings();
        List<Class<? extends AbstractTraining>> trainingTypeCollection = new ArrayList<>();
        trainingTypeCollection.add(BackpropagationTraining.class);
        trainingTypeCollection.add(MomentumTraining.class);
        
        bpb.startBenchmark(trainingTypeCollection, settingsCollection, trainingSet, mlp);
        bpb.saveResults("C:\\Users\\Mladen\\Desktop\\test123");
      
    }
Example 7
Source File: AbstractTraining.java    From NeurophFramework with Apache License 2.0 5 votes vote down vote up 
/**
 * Create instance of training with new neural network
 *
 * @param dataset
 * @param settings
 */
public AbstractTraining(DataSet dataset, TrainingSettings settings) {
    this.dataset = dataset;
    this.settings = settings;
    this.stats = new TrainingStatistics();
    this.neuralNet = new MultiLayerPerceptron(TransferFunctionType.SIGMOID, dataset.getInputSize(), settings.getHiddenNeurons(), dataset.getOutputSize());
}
Example 8
Source File: XorResilientPropagationSample.java    From NeurophFramework with Apache License 2.0 5 votes vote down vote up 
/**
 * Runs this sample
 */
public  void run() {

    // create training set (logical XOR function)
    DataSet trainingSet = new DataSet(2, 1);
    trainingSet.add(new DataSetRow(new double[]{0, 0}, new double[]{0}));
    trainingSet.add(new DataSetRow(new double[]{0, 1}, new double[]{1}));
    trainingSet.add(new DataSetRow(new double[]{1, 0}, new double[]{1}));
    trainingSet.add(new DataSetRow(new double[]{1, 1}, new double[]{0}));

    // create multi layer perceptron
    MultiLayerPerceptron myMlPerceptron = new MultiLayerPerceptron(TransferFunctionType.SIGMOID, 2, 3, 1);
    // set ResilientPropagation learning rule
    myMlPerceptron.setLearningRule(new ResilientPropagation());
    LearningRule learningRule = myMlPerceptron.getLearningRule();
    learningRule.addListener(this);

    // learn the training set
    System.out.println("Training neural network...");
    myMlPerceptron.learn(trainingSet);

    int iterations = ((SupervisedLearning)myMlPerceptron.getLearningRule()).getCurrentIteration();
    System.out.println("Learned in "+iterations+" iterations");

    // test perceptron
    System.out.println("Testing trained neural network");
    testNeuralNetwork(myMlPerceptron, trainingSet);

}
Example 9
Source File: AutoTrainer.java    From NeurophFramework with Apache License 2.0 4 votes vote down vote up 
/**
 *
 */
public AutoTrainer() {
    trainingSettingsList = new ArrayList<>();
    results = new ArrayList<>();
    transferFunction = TransferFunctionType.SIGMOID;
}
Example 10
Source File: XorMultiLayerPerceptronSample.java    From NeurophFramework with Apache License 2.0 4 votes vote down vote up 
/**
     * Runs this sample
     */
    public void run() {

        // create training set (logical XOR function)
        DataSet trainingSet = new DataSet(2, 1);
        trainingSet.add(new DataSetRow(new double[]{0, 0}, new double[]{0}));
        trainingSet.add(new DataSetRow(new double[]{0, 1}, new double[]{1}));
        trainingSet.add(new DataSetRow(new double[]{1, 0}, new double[]{1}));
        trainingSet.add(new DataSetRow(new double[]{1, 1}, new double[]{0}));

        // create multi layer perceptron
        MultiLayerPerceptron myMlPerceptron = new MultiLayerPerceptron(TransferFunctionType.SIGMOID, 2, 3, 1);
        myMlPerceptron.randomizeWeights(new WeightsRandomizer(new Random(123)));

        System.out.println(Arrays.toString(myMlPerceptron.getWeights()));

        myMlPerceptron.setLearningRule(new BackPropagation());

        myMlPerceptron.getLearningRule().setLearningRate(0.5);
        // enable batch if using MomentumBackpropagation
//        if( myMlPerceptron.getLearningRule() instanceof MomentumBackpropagation )
//        	((MomentumBackpropagation)myMlPerceptron.getLearningRule()).setBatchMode(false);

        LearningRule learningRule = myMlPerceptron.getLearningRule();
        learningRule.addListener(this);

        // learn the training set
        System.out.println("Training neural network...");
        myMlPerceptron.learn(trainingSet);

        // test perceptron
        System.out.println("Testing trained neural network");
        testNeuralNetwork(myMlPerceptron, trainingSet);

        // save trained neural network
        myMlPerceptron.save("myMlPerceptron.nnet");

        // load saved neural network
        NeuralNetwork loadedMlPerceptron = NeuralNetwork.createFromFile("myMlPerceptron.nnet");

        // test loaded neural network
        System.out.println("Testing loaded neural network");
        testNeuralNetwork(loadedMlPerceptron, trainingSet);
    }
Example 11
Source File: NetworkcreatorController.java    From FakeImageDetection with GNU General Public License v3.0 4 votes vote down vote up 
@FXML
private void saveNeuralNet(ActionEvent event) {
    String neuralNetLbl = neuralNetLabel.getText();
    Dimension samplingDimension = new Dimension(Integer.parseInt(width.getText()),
            Integer.parseInt(height.getText()));
    ColorMode mode;
    switch (colorMode.getSelectionModel().getSelectedItem()) {
        case "COLOR_RGB":
            mode = ColorMode.COLOR_RGB;
            break;
        case "COLOR_HSL":
            mode = ColorMode.COLOR_HSL;
            break;
        case "BLACK_AND_WHITE":
            mode = ColorMode.COLOR_RGB;
            break;
        default:
            mode = ColorMode.COLOR_RGB;
            break;
    }
    TransferFunctionType tFunction;
    switch (transferFunction.getSelectionModel().getSelectedItem()) {
        case "LINEAR":
            tFunction = TransferFunctionType.LINEAR;
            break;
        case "RAMP":
            tFunction = TransferFunctionType.RAMP;
            break;
        case "STEP":
            tFunction = TransferFunctionType.STEP;
            break;
        case "SIGMOID":
            tFunction = TransferFunctionType.SIGMOID;
            break;
        case "TANH":
            tFunction = TransferFunctionType.TANH;
            break;
        case "GAUSSIAN":
            tFunction = TransferFunctionType.GAUSSIAN;
            break;
        case "TRAPEZOID":
            tFunction = TransferFunctionType.TRAPEZOID;
            break;
        case "SGN":
            tFunction = TransferFunctionType.SGN;
            break;
        case "SIN":
            tFunction = TransferFunctionType.SIN;
            break;
        case "LOG":
            tFunction = TransferFunctionType.LOG;
            break;
        default:
            tFunction = TransferFunctionType.GAUSSIAN;
            break;
    }

    ArrayList<String> neuronLabels = new ArrayList(Arrays.asList(neuronLabelList.getText().split("[,]")));
    ArrayList<Integer> neuronCounts = new ArrayList();

    for (String neuronCount : neuronCountList.getText().split("[,]")) {
        neuronCounts.add(Integer.parseInt(neuronCount.replaceAll(" ", "")));
        System.out.println("neuronCounts = " + neuronCount);
    }

    //Show File save dialog
    FileChooser fileChooser = new FileChooser();
    fileChooser.setTitle("Save Neural Network");
    File file = fileChooser.showSaveDialog(rootPane.getScene().getWindow());
    if (file == null) {
        Calert.showAlert("Not a valid File", "Select target again", Alert.AlertType.ERROR);
        return;
    }
    MLPNetworkMaker maker = new MLPNetworkMaker(neuralNetLbl, samplingDimension, mode, neuronLabels, neuronCounts, tFunction, file.getAbsolutePath());
    maker.setListener(this);
    Thread nnetCreator = new Thread(maker);
    nnetCreator.start();
    loadingSpinner.setVisible(true);
}