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

public void train() {
    // get the path to file with data
    String inputFileName = "C:\\timeseries\\BSW15";
    
    // create MultiLayerPerceptron neural network
    neuralNet = new MultiLayerPerceptron(TransferFunctionType.TANH, 5, 10, 1);
    MomentumBackpropagation learningRule = (MomentumBackpropagation)neuralNet.getLearningRule();
    learningRule.setLearningRate(0.2);
    learningRule.setMomentum(0.5);
    // learningRule.addObserver(this);
    learningRule.addListener(this);        
    
    // create training set from file
     trainingSet = DataSet.createFromFile(inputFileName, 5, 1, "\t", false);
    // train the network with training set
    neuralNet.learn(trainingSet);         
          
    System.out.println("Done training.");          
}
 

/**
 * Create and run MLP with XOR training set
 */
public static void main(String[] args) {
    // 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}));

    MultiLayerPerceptron nnet = new MultiLayerPerceptron( TransferFunctionType.TANH ,2, 3, 1);
    MatrixMultiLayerPerceptron mnet = new MatrixMultiLayerPerceptron(nnet);

    System.out.println("Training network...");

    mnet.learn(trainingSet);

    System.out.println("Done training network.");
}
 

public void run() throws InterruptedException, ExecutionException {
    System.out.println("Creating training set...");
    // get path to training set
    String dataSetFile = "data_sets/iris_data_normalised.txt";
    int inputsCount = 4;
    int outputsCount = 3;

    // create training set from file
    DataSet dataSet = DataSet.createFromFile(dataSetFile, inputsCount, outputsCount, ",");
 //   dataSet.setColumnNames(new String[]{"sepal.length", "sepal.width", "petal.length", "petal.width",  "setosa", "versicolor", "virginica"});
    dataSet.setColumnNames(new String[]{"setosa", "versicolor", "virginica"});
    dataSet.shuffle();

    System.out.println("Creating neural network...");
    MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(TransferFunctionType.TANH, inputsCount, 5, outputsCount);

    String[] classLabels = new String[]{"setosa", "versicolor", "virginica"};
    neuralNet.setOutputLabels(classLabels);

    KFoldCrossValidation crossVal = new KFoldCrossValidation(neuralNet, dataSet, 5);
    EvaluationResult totalResult= crossVal.run();
    List<FoldResult> cflist= crossVal.getResultsByFolds();

}
 

public static void main(String[] args) {

        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}));

        MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(TransferFunctionType.TANH, 2, 3, 1);
        neuralNet.learn(trainingSet);

        Evaluation.runFullEvaluation(neuralNet, trainingSet);
    }
 

public void run() {
    System.out.println("Creating data set...");
    String dataSetFile = "data_sets/ml10standard/irisdatanormalised.txt";
    int inputsCount = 4;
    int outputsCount = 3;

    // create data set from file
    DataSet dataSet = DataSet.createFromFile(dataSetFile, inputsCount, outputsCount, ",");

    // split data into training and test set
    DataSet[] trainTestSplit = dataSet.split(0.6, 0.4);
    DataSet trainingSet = trainTestSplit[0];
    DataSet testSet = trainTestSplit[1];

    System.out.println("Creating neural network...");
    MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(TransferFunctionType.TANH, inputsCount, 2, outputsCount);

    neuralNet.setLearningRule(new MomentumBackpropagation());
    MomentumBackpropagation learningRule = (MomentumBackpropagation) neuralNet.getLearningRule();
    learningRule.addListener((event)->{
        MomentumBackpropagation bp = (MomentumBackpropagation) event.getSource();
        System.out.println(bp.getCurrentIteration() + ". iteration | Total network error: " + bp.getTotalNetworkError());
    });

    // set learning rate and max error
    learningRule.setLearningRate(0.2);
    learningRule.setMaxError(0.03);
    System.out.println("Training network...");
    // train the network with training set
    neuralNet.learn(trainingSet);
    System.out.println("Training completed.");
    System.out.println("Testing network...");

    System.out.println("Network performance on the test set");
    evaluate(neuralNet, testSet);

    System.out.println("Saving network");
    // save neural network to file
    neuralNet.save("nn1.nnet");

    System.out.println("Done.");
}
 

public void run() {
    System.out.println("Creating data set...");
    String dataSetFile = "data_sets/ml10standard/databanknote.txt";
    int inputsCount = 4;
    int outputsCount = 1;

    // create training set from file
    DataSet dataSet = DataSet.createFromFile(dataSetFile, inputsCount, outputsCount, ",", false);
    DataSet[] trainTestSplit = dataSet.split(0.6, 0.4);
    DataSet trainingSet = trainTestSplit[0];
    DataSet testSet = trainTestSplit[1];

    Normalizer norm = new MaxNormalizer(trainingSet);
    norm.normalize(trainingSet);
    norm.normalize(testSet);

    System.out.println("Creating neural network...");
    MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(TransferFunctionType.TANH, inputsCount, 1, outputsCount);

    neuralNet.setLearningRule(new MomentumBackpropagation());
    MomentumBackpropagation learningRule = (MomentumBackpropagation) neuralNet.getLearningRule();
    learningRule.addListener((event) -> {
        MomentumBackpropagation bp = (MomentumBackpropagation) event.getSource();
        System.out.println(bp.getCurrentIteration() + ". iteration | Total network error: " + bp.getTotalNetworkError());
    });

    // set learning rate and max error
    learningRule.setLearningRate(0.1);
    learningRule.setMaxError(0.01);
    System.out.println("Training network...");
    // train the network with training set
    neuralNet.learn(trainingSet);
    System.out.println("Training completed.");
    System.out.println("Testing network...");

    System.out.println("Network performance on the test set");
    evaluate(neuralNet, testSet);

    System.out.println("Saving network");
    // save neural network to file
    neuralNet.save("nn1.nnet");

    System.out.println("Done.");
}
 

public void run() {
    System.out.println("Creating data set...");
    String dataSetFile = "data_sets/ml10standard/bostonhouse.txt";
    int inputsCount = 13;
    int outputsCount = 1;

    // create data set from file
    DataSet dataSet = DataSet.createFromFile(dataSetFile, inputsCount, outputsCount, ",");

    // split data into training and test set
    DataSet[] trainTestSplit = dataSet.split(0.6, 0.4);
    DataSet trainingSet = trainTestSplit[0];
    DataSet testSet = trainTestSplit[1];

    // normalize data
    Normalizer norm = new MaxNormalizer(trainingSet);
    norm.normalize(trainingSet);
    norm.normalize(testSet);

    System.out.println("Creating neural network...");
    MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(TransferFunctionType.TANH, inputsCount, 2, 2, outputsCount);

    neuralNet.setLearningRule(new MomentumBackpropagation());
    MomentumBackpropagation learningRule = (MomentumBackpropagation) neuralNet.getLearningRule();
    learningRule.addListener(event -> {
        MomentumBackpropagation bp = (MomentumBackpropagation) event.getSource();
        System.out.println(bp.getCurrentIteration() + ". iteration | Total network error: " + bp.getTotalNetworkError());
    });

    System.out.println("Training network...");
    // train the network with training set
    neuralNet.learn(trainingSet);
    System.out.println("Training completed.");
    System.out.println("Testing network...");

    System.out.println("Network performance on the test set");
    evaluate(neuralNet, testSet);

    System.out.println("Saving network");
    // save neural network to file
    neuralNet.save("nn1.nnet");

    System.out.println("Done.");

}
 

public void run() {
    String dataSetFile = "data_sets/ml10standard/sonardata.txt";
    int numInputs = 60;
    int numOutputs = 1;

    // create data set from csv file
    DataSet dataSet = DataSet.createFromFile(dataSetFile, numInputs, numOutputs, ",");

    // split data into train and test set
    DataSet[] trainTestSplit = dataSet.split(0.6, 0.4);
    DataSet trainingSet = trainTestSplit[0];
    DataSet testSet = trainTestSplit[1];

    // create neural network
    MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(TransferFunctionType.TANH, numInputs, 35, 15, numOutputs);

    // set learning rule and add listener
    neuralNet.setLearningRule(new MomentumBackpropagation());
    MomentumBackpropagation learningRule = (MomentumBackpropagation) neuralNet.getLearningRule();
    learningRule.addListener((event) -> {
        MomentumBackpropagation bp = (MomentumBackpropagation) event.getSource();
        System.out.println(bp.getCurrentIteration() + ". iteration | Total network error: " + bp.getTotalNetworkError());
    });

    // set learning rate and max error
    learningRule.setLearningRate(0.01);
    learningRule.setMaxError(0.01);

    // train the network with training set
    neuralNet.learn(trainingSet);

    // evaluate network performance on test set
    evaluate(neuralNet, testSet);

    // save neural network to file
    neuralNet.save("nn1.nnet");

    System.out.println("Done.");

    //testNeuralNetwork(neuralNet, testSet);
}
 

public void run() {
    System.out.println("Creating data set...");
    String dataSetFile = "data_sets/ml10standard/pimadata.txt";
    int inputsCount = 8;
    int outputsCount = 1;

    // create data set from file
    DataSet dataSet = DataSet.createFromFile(dataSetFile, inputsCount, outputsCount, "\t", false);

    // split data into training and test set
    DataSet[] trainTestSplit = dataSet.split(0.6, 0.4);
    DataSet trainingSet = trainTestSplit[0];
    DataSet testSet = trainTestSplit[1];

    // normalize training and test set
    Normalizer norm = new MaxNormalizer(trainingSet);
    norm.normalize(trainingSet);
    norm.normalize(testSet);

    System.out.println("Creating neural network...");
    MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(TransferFunctionType.TANH, inputsCount, 15, 5, outputsCount);

    neuralNet.setLearningRule(new MomentumBackpropagation());
    MomentumBackpropagation learningRule = (MomentumBackpropagation) neuralNet.getLearningRule();
    learningRule.addListener((event) -> {
        MomentumBackpropagation bp = (MomentumBackpropagation) event.getSource();
        System.out.println(bp.getCurrentIteration() + ". iteration | Total network error: " + bp.getTotalNetworkError());
    });

    // set learning rate and max error
    learningRule.setLearningRate(0.1);
    learningRule.setMaxError(0.03);
    
    System.out.println("Training network...");
    // train the network with training set
    neuralNet.learn(trainingSet);
    System.out.println("Training completed.");
    System.out.println("Testing network...");

    System.out.println("Network performance on the test set");
    evaluate(neuralNet, testSet);

    System.out.println("Saving network");
    // save neural network to file
    neuralNet.save("nn1.nnet");

    System.out.println("Done.");
}
 

public void run() {
    System.out.println("Creating training set...");
    // get path to training set
    String trainingSetFileName = "data_sets/irisdatanormalised.txt";
    int inputsCount = 4;
    int outputsCount = 3;

    // create training set from file
    DataSet dataSet = DataSet.createFromFile(trainingSetFileName, inputsCount, outputsCount, ",");

    // splid data into training and test set
    DataSet[] trainTestSplit = dataSet.split(0.6, 0.4);
    DataSet trainingSet = trainTestSplit[0];
    DataSet testSet = trainTestSplit[1];

    System.out.println("Creating neural network...");
    MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(TransferFunctionType.TANH, inputsCount, 2, outputsCount);

    neuralNet.setLearningRule(new MomentumBackpropagation());
    MomentumBackpropagation learningRule = (MomentumBackpropagation) neuralNet.getLearningRule();
    learningRule.addListener(this);

    // set learning rate and max error
    learningRule.setLearningRate(0.2);
    learningRule.setMaxError(0.03);
    System.out.println("Training network...");
    // train the network with training set
    neuralNet.learn(trainingSet);
    System.out.println("Training completed.");
    System.out.println("Testing network...");

    System.out.println("Network performance on the test set");
    evaluate(neuralNet, testSet);

    System.out.println("Saving network");
    // save neural network to file
    neuralNet.save("nn1.nnet");

    System.out.println("Done.");

    System.out.println();
    System.out.println("Network outputs for test set");
    testNeuralNetwork(neuralNet, testSet);
}
 

public void run() {
    System.out.println("Creating training set...");
    // get path to training set
    String trainingSetFileName = "data_sets/databanknote.txt";
    int inputsCount = 4;
    int outputsCount = 1;

    // create training set from file
    DataSet dataSet = DataSet.createFromFile(trainingSetFileName, inputsCount, outputsCount, ",", false);
    DataSet[] trainTestSplit = dataSet.split(0.6, 0.4);
    DataSet trainingSet = trainTestSplit[0];
    DataSet testSet = trainTestSplit[1];

    Normalizer norm = new MaxNormalizer(trainingSet);
    norm.normalize(trainingSet);
    norm.normalize(testSet);

    System.out.println("Creating neural network...");
    MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(TransferFunctionType.TANH, inputsCount, 1, outputsCount);

    neuralNet.setLearningRule(new MomentumBackpropagation());
    MomentumBackpropagation learningRule = (MomentumBackpropagation) neuralNet.getLearningRule();
    learningRule.addListener(this);

    // set learning rate and max error
    learningRule.setLearningRate(0.1);
    learningRule.setMaxError(0.01);
    System.out.println("Training network...");
    // train the network with training set
    neuralNet.learn(trainingSet);
    System.out.println("Training completed.");
    System.out.println("Testing network...");

    System.out.println("Network performance on the test set");
    evaluate(neuralNet, testSet);

    System.out.println("Saving network");
    // save neural network to file
    neuralNet.save("nn1.nnet");

    System.out.println("Done.");

    System.out.println();
    System.out.println("Network outputs for test set");
    testNeuralNetwork(neuralNet, testSet);
}
 

public void run() {
    System.out.println("Creating training set...");
    // get path to training set
    String trainingSetFileName = "data_sets/bostonhouse.txt";
    int inputsCount = 13;
    int outputsCount = 1;

    // create training set from file
    DataSet dataSet = DataSet.createFromFile(trainingSetFileName, inputsCount, outputsCount, ",");
    DataSet[] trainTestSplit = dataSet.split(0.6, 0.4);
    DataSet trainingSet = trainTestSplit[0];
    DataSet testSet = trainTestSplit[1];

    // normalize data
    Normalizer norm = new MaxNormalizer(trainingSet);
    norm.normalize(trainingSet);
    norm.normalize(testSet);
    System.out.println("Creating neural network...");
    MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(TransferFunctionType.TANH, inputsCount, 2, 2, outputsCount);

    neuralNet.setLearningRule(new MomentumBackpropagation());
    MomentumBackpropagation learningRule = (MomentumBackpropagation) neuralNet.getLearningRule();
    learningRule.addListener(this);

    System.out.println("Training network...");
    // train the network with training set
    neuralNet.learn(trainingSet);
    System.out.println("Training completed.");
    System.out.println("Testing network...");

    System.out.println("Network performance on the test set");
    evaluate(neuralNet, testSet);

    System.out.println("Saving network");
    // save neural network to file
    neuralNet.save("nn1.nnet");

    System.out.println("Done.");

    System.out.println();
    System.out.println("Network outputs for test set");
    testNeuralNetwork(neuralNet, testSet);
}
 

public void run() {
    System.out.println("Creating training set...");
    // get path to training set
    String trainingSetFileName = "data_sets/pimadata.txt";
    int inputsCount = 8;
    int outputsCount = 1;

    // create training set from file
    DataSet dataSet = DataSet.createFromFile(trainingSetFileName, inputsCount, outputsCount, "\t", false);

    // split data into training and test set
    DataSet[] trainTestSplit = dataSet.split(0.6, 0.4);
    DataSet trainingSet = trainTestSplit[0];
    DataSet testSet = trainTestSplit[1];

    // normalize training and test set
    Normalizer norm = new MaxNormalizer(trainingSet);
    norm.normalize(trainingSet);
    norm.normalize(testSet);

    System.out.println("Creating neural network...");
    MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(TransferFunctionType.TANH, inputsCount, 15, 5, outputsCount);

    neuralNet.setLearningRule(new MomentumBackpropagation());
    MomentumBackpropagation learningRule = (MomentumBackpropagation) neuralNet.getLearningRule();
    learningRule.addListener(this);

    // set learning rate and max error
    learningRule.setLearningRate(0.1);
    learningRule.setMaxError(0.03);
    System.out.println("Training network...");
    // train the network with training set
    neuralNet.learn(trainingSet);
    System.out.println("Training completed.");
    System.out.println("Testing network...");

    System.out.println("Network performance on the test set");
    evaluate(neuralNet, testSet);

    System.out.println("Saving network");
    // save neural network to file
    neuralNet.save("nn1.nnet");

    System.out.println("Done.");

    System.out.println();
    System.out.println("Network outputs for test set");
    testNeuralNetwork(neuralNet, testSet);
}
 

@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);
}
 

/**
 * Returns multilayer percepton learned to approximate the XOR gate.
 * @return
 */
public static MultiLayerPerceptron getNetwork() {

	MultiLayerPerceptron mlPerceptron = new MultiLayerPerceptron(TransferFunctionType.TANH, 2, 3, 1);


	mlPerceptron.learn( getTrainingData() );

	return mlPerceptron;
}