Java Code Examples for org.neuroph.nnet.learning.MomentumBackpropagation#setMaxIterations()

The following examples show how to use org.neuroph.nnet.learning.MomentumBackpropagation#setMaxIterations() . 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: TrainNetwork.java    From NeurophFramework with Apache License 2.0 5 votes vote down vote up 
public void createNeuralNetwork() {
    System.out.println("Creating neural network... ");
    MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(config.getInputCount(), config.getFirstHiddenLayerCount(), config.getSecondHiddenLayerCount(), config.getOutputCount());
    MomentumBackpropagation learningRule = (MomentumBackpropagation) neuralNet.getLearningRule();
    learningRule.setLearningRate(0.01);
    learningRule.setMaxError(0.1);
    learningRule.setMaxIterations(1000);
    System.out.println("Saving neural network to file... ");
    neuralNet.save(config.getTrainedNetworkFileName());
    System.out.println("Neural network successfully saved!");
}
Example 2
Source File: Model.java    From o2oa with GNU Affero General Public License v3.0 5 votes vote down vote up 
private MomentumBackpropagation createMomentumBackpropagation(Double maxError, Integer maxIteration,
		Double learningRate, Double momentum) {
	MomentumBackpropagation momentumBackpropagation = new MomentumBackpropagation();
	momentumBackpropagation.setMaxError(maxError);
	momentumBackpropagation.setMaxIterations(maxIteration);
	momentumBackpropagation.setLearningRate(learningRate);
	momentumBackpropagation.setMomentum(momentum);
	return momentumBackpropagation;
}
Example 3
Source File: DiabetesSample.java    From NeurophFramework with Apache License 2.0 5 votes vote down vote up 
public void run() {
    String dataSetFile = "data_sets/diabetes.txt";
    int inputsCount = 8;
    int outputsCount = 1;

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

    // Creatinig training set (70%) and test set (30%)
    DataSet[] trainTestSplit = dataSet.split(0.7, 0.3);
    DataSet trainingSet = trainTestSplit[0];
    DataSet testSet = trainTestSplit[1];

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

    System.out.println("Creating neural network...");
    //Create MultiLayerPerceptron neural network
    MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(inputsCount, 20, 10, outputsCount);
    //attach listener to learning rule
    MomentumBackpropagation learningRule = (MomentumBackpropagation) neuralNet.getLearningRule();
    learningRule.addListener(this);

    learningRule.setLearningRate(0.6);
    learningRule.setMaxError(0.07);
    learningRule.setMaxIterations(100000);

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

    System.out.println("Testing network...");
    testNeuralNetwork(neuralNet, testSet);

}
Example 4
Source File: BreastCancerSample.java    From NeurophFramework with Apache License 2.0 5 votes vote down vote up 
public void run() {

        System.out.println("Creating training and test set from file...");
        String dataSetFile = "data_sets/breast_cancer.txt";
        int numInputs = 30;
        int numOutputs = 1;

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

        //Creatinig training set (70%) and test set (30%)
        DataSet[] trainTestSplit = dataSet.split(0.7, 0.3);
        DataSet trainingSet = trainTestSplit[0];
        DataSet testSet = trainTestSplit[1];

        //Normalizing data set
        Normalizer normalizer = new MaxNormalizer(trainingSet);
        normalizer.normalize(trainingSet);
        normalizer.normalize(testSet);

        //Create MultiLayerPerceptron neural network
        MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(numInputs, 16, numOutputs);

        //attach listener to learning rule
        MomentumBackpropagation learningRule = (MomentumBackpropagation) neuralNet.getLearningRule();
        learningRule.addListener(this);

        learningRule.setLearningRate(0.3);
        learningRule.setMaxError(0.01);
        learningRule.setMaxIterations(500);

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

        System.out.println("Testing network...");
        testNeuralNetwork(neuralNet, testSet);

    }
Example 5
Source File: WheatSeeds.java    From NeurophFramework with Apache License 2.0 5 votes vote down vote up 
public void run() throws InterruptedException, ExecutionException {
    System.out.println("Creating training set...");
    // get path to training set
    String dataSetFile = "data_sets/seeds.txt";
    int inputsCount = 7;
    int outputsCount = 3;

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

    System.out.println("Creating neural network...");
    MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(inputsCount, 15, 2, outputsCount);

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

    // set learning rate and max error
    learningRule.setLearningRate(0.1);
    learningRule.setMaxError(0.01);
    learningRule.setMaxIterations(1000);

    String[] classLabels = new String[]{"Cama", "Rosa", "Canadian"};
    neuralNet.setOutputLabels(classLabels);
    KFoldCrossValidation crossVal = new KFoldCrossValidation(neuralNet, dataSet, 10);
    EvaluationResult totalResult= crossVal.run();
     List<FoldResult> cflist= crossVal.getResultsByFolds();
}
Example 6
Source File: WineQuality.java    From NeurophFramework with Apache License 2.0 5 votes vote down vote up 
public void run() throws InterruptedException, ExecutionException {
    System.out.println("Creating training set...");
    // get path to training set
    String dataSetFile = "data_sets/wine.txt";
    int inputsCount = 11;
    int outputsCount = 10;

    // create training set from file
    DataSet dataSet = DataSet.createFromFile(dataSetFile, inputsCount, outputsCount, "\t", true);
    Normalizer norm = new MaxNormalizer(dataSet);
    norm.normalize(dataSet);
    dataSet.shuffle();

    System.out.println("Creating neural network...");
    MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(inputsCount, 20, 15, outputsCount);

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

    // set learning rate and max error
    learningRule.setLearningRate(0.1);
    learningRule.setMaxIterations(10);

    String classLabels[] = new String[]{"1", "2", "3", "4", "5", "6", "7", "8", "9", "10"};
    neuralNet.setOutputLabels(classLabels);
    KFoldCrossValidation crossVal = new KFoldCrossValidation(neuralNet, dataSet, 10);
    EvaluationResult totalResult= crossVal.run();
    List<FoldResult> cflist= crossVal.getResultsByFolds();
}
Example 7
Source File: MomentumTraining.java    From NeurophFramework with Apache License 2.0 5 votes vote down vote up 
/**
 * Create instance of learning rule and setup given parameters
 *
 * @return returns learning rule with predefined parameters
 */

@Override
public LearningRule setParameters() {
    MomentumBackpropagation mbp = new MomentumBackpropagation();
    mbp.setBatchMode(getSettings().isBatchMode());
    mbp.setLearningRate(getSettings().getLearningRate());
    mbp.setMaxError(getSettings().getMaxError());
    mbp.setMaxIterations(getSettings().getMaxIterations());
    mbp.setMomentum(getSettings().getMomentum());
    return mbp;
}
Example 8
Source File: BrestCancerSample.java    From NeurophFramework with Apache License 2.0 4 votes vote down vote up 
public void run() {

        System.out.println("Creating training and test set from file...");
        String dataSetFile = "data_sets/breast cancer.txt";
        int inputsCount = 30;
        int outputsCount = 2; // use onlz one output - binarz classification, transform dat aset

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

        //Creatinig training set (70%) and test set (30%)
        DataSet[] trainTestSplit = dataSet.split(0.7, 0.3);
        DataSet trainingSet = trainTestSplit[0];
        DataSet testSet = trainTestSplit[1];

        //Normalizing data set
        Normalizer normalizer = new MaxNormalizer(trainingSet);
        normalizer.normalize(trainingSet);
        normalizer.normalize(testSet);

        System.out.println("Creating neural network...");
        //Create MultiLayerPerceptron neural network
        MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(inputsCount, 16, outputsCount);

        //attach listener to learning rule
        MomentumBackpropagation learningRule = (MomentumBackpropagation) neuralNet.getLearningRule();
        learningRule.addListener(this);

        learningRule.setLearningRate(0.3);
        learningRule.setMaxError(0.001);
        learningRule.setMaxIterations(5000);

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

        System.out.println("Testing network...\n\n");
        testNeuralNetwork(neuralNet, testSet);

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

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

    }
Example 9
Source File: GermanCreditDataSample.java    From NeurophFramework with Apache License 2.0 4 votes vote down vote up 
public void run() {

        System.out.println("Creating training and test set from file...");
        String dataSetFile = "data_sets/german credit data.txt";
        int inputsCount = 24;
        int outputsCount = 2;

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

        //Normalizing data set
        Normalizer normalizer = new MaxNormalizer(dataSet);
        normalizer.normalize(dataSet);

        //Creatinig training set (70%) and test set (30%)
        DataSet[] trainingAndTestSet = dataSet.createTrainingAndTestSubsets(70, 30);
        DataSet trainingSet = trainingAndTestSet[0];
        DataSet testSet = trainingAndTestSet[1];

        System.out.println("Creating neural network...");
        //Create MultiLayerPerceptron neural network
        MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(inputsCount, 12, 6, outputsCount);

        //attach listener to learning rule
        MomentumBackpropagation learningRule = (MomentumBackpropagation) neuralNet.getLearningRule();
        learningRule.addListener(this);

        learningRule.setLearningRate(0.01);
        learningRule.setMaxError(0.001);
        learningRule.setMaxIterations(10000);

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

        System.out.println("Testing network...\n\n");
        testNeuralNetwork(neuralNet, testSet);

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

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

    }
Example 10
Source File: Abalone.java    From NeurophFramework with Apache License 2.0 4 votes vote down vote up 
public void run() {
    System.out.println("Creating data set...");
    String dataSetFile = "data_sets/ml10standard/abalonerings.txt";
    int inputsCount = 8;
    int outputsCount = 29;

    // create training set from file
    DataSet dataSet = DataSet.createFromFile(dataSetFile, inputsCount, outputsCount, "\t", true);
    DataSet[] trainTestSplit = dataSet.split(0.7, 0.3);
    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(inputsCount, 15, 10, 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.setMaxIterations(5000);

    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.");
}
Example 11
Source File: WineQualityClassification.java    From NeurophFramework with Apache License 2.0 4 votes vote down vote up 
public void run() {
    System.out.println("Creating data set...");
    String dataSetFile = "data_sets/ml10standard/wine.txt";
    int inputsCount = 11;
    int outputsCount = 10;

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

    // split data into train and test set
    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(inputsCount, 20, 15, 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.setMaxIterations(5000);

    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);
}
Example 12
Source File: WheatSeeds.java    From NeurophFramework with Apache License 2.0 4 votes vote down vote up 
public void run() {
    System.out.println("Creating data set...");
    String dataSetFile = "data_sets/ml10standard/seeds.txt";
    int inputsCount = 7;
    int outputsCount = 3;

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

    // split data into train 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(inputsCount, 15, 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(0.1);
    learningRule.setMaxError(0.01);
    learningRule.setMaxIterations(5000);
    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);
}
Example 13
Source File: IonosphereSample2.java    From NeurophFramework with Apache License 2.0 4 votes vote down vote up 
public void run() {

        System.out.println("Creating training and test set from file...");
        String dataSetFile = "data_sets/ionosphere.csv";
        int inputsCount = 34;
        int outputsCount = 1;

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

        //Normalizing data set
        Normalizer normalizer = new MaxNormalizer(dataSet);
        normalizer.normalize(dataSet);

        //Creatinig training set (70%) and test set (30%)
        DataSet[] trainingAndTestSet = dataSet.createTrainingAndTestSubsets(70, 30);
        DataSet trainingSet = trainingAndTestSet[0];
        DataSet testSet = trainingAndTestSet[1];

        // ovde ubaci u petlju sa hidden neuronima i learning rates

        System.out.println("Creating neural network...");
        //Create MultiLayerPerceptron neural network
        MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(inputsCount, 10, 8, outputsCount);

        //attach listener to learning rule
        MomentumBackpropagation learningRule = (MomentumBackpropagation) neuralNet.getLearningRule();
        learningRule.addListener(this);

        learningRule.setLearningRate(0.4);
        learningRule.setMaxError(0.01);
        learningRule.setMaxIterations(10000);

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

//        System.out.println("Testing network...\n\n");
//        testNeuralNetwork(neuralNet, testSet);

        System.out.println("Done.");
        System.out.println("**************************************************");
//        }
    }
Example 14
Source File: IonosphereSample.java    From NeurophFramework with Apache License 2.0 4 votes vote down vote up 
public void run() {

        System.out.println("Creating training and test set from file...");
        String dataSetFile = "data_sets/ionosphere.csv";
        int inputsCount = 34;
        int outputsCount = 1;

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

        //Normalizing data set
        Normalizer normalizer = new MaxNormalizer(dataSet);
        normalizer.normalize(dataSet);

        //Creatinig training set (70%) and test set (30%)
        DataSet[] trainingAndTestSet = dataSet.createTrainingAndTestSubsets(70, 30);
        DataSet trainingSet = trainingAndTestSet[0];
        DataSet testSet = trainingAndTestSet[1];
//        for (int i = 0; i < 21; i++) {
        System.out.println("Creating neural network...");
        //Create MultiLayerPerceptron neural network
        MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(inputsCount, 16, 8, outputsCount);
//            System.out.println("HIDDEN COUNT: " + i);
        //attach listener to learning rule
        MomentumBackpropagation learningRule = (MomentumBackpropagation) neuralNet.getLearningRule();
        learningRule.addListener(this);

        learningRule.setLearningRate(0.2);
        learningRule.setMaxError(0.01);
        learningRule.setMaxIterations(10000);

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

        System.out.println("Testing network...\n\n");
        testNeuralNetwork(neuralNet, testSet);

        System.out.println("Done.");
        System.out.println("**************************************************");
//        }
    }
Example 15
Source File: SegmentChallengeSample.java    From NeurophFramework with Apache License 2.0 4 votes vote down vote up 
public void run() {

        System.out.println("Creating training and test set from file...");
        String dataSetFile = "data_sets/segment challenge.txt";
        String testSetFileName = "data_sets/segment test.txt";
        int inputsCount = 19;
        int outputsCount = 7;

        //Create training data set from file
        DataSet trainingSet = DataSet.createFromFile(dataSetFile, inputsCount, outputsCount, ",");
        System.out.println("Training set size: " + trainingSet.getRows().size());
        trainingSet.shuffle();

        //Normalizing training data set
        Normalizer normalizer = new MaxNormalizer(trainingSet);
        normalizer.normalize(trainingSet);

        //Create test data set from file
        DataSet testSet = DataSet.createFromFile(testSetFileName, inputsCount, outputsCount, ",");
        System.out.println("Test set size: " + testSet.getRows().size());
        System.out.println("--------------------------------------------------");
        testSet.shuffle();

        //Normalizing training data set
        normalizer.normalize(testSet);

        System.out.println("Creating neural network...");
        //Create MultiLayerPerceptron neural network
        MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(inputsCount, 17, 10, outputsCount);
        //attach listener to learning rule
        MomentumBackpropagation learningRule = (MomentumBackpropagation) neuralNet.getLearningRule();
        learningRule.addListener((event) -> {
            BackPropagation bp = (BackPropagation) event.getSource();
            if (event.getEventType().equals(LearningEvent.Type.LEARNING_STOPPED)) {
                double error = bp.getTotalNetworkError();
                System.out.println("Training completed in " + bp.getCurrentIteration() + " iterations, ");
                System.out.println("With total error: " + formatDecimalNumber(error));
            } else {
                System.out.println("Iteration: " + bp.getCurrentIteration() + " | Network error: " + bp.getTotalNetworkError());
            }
        });

        learningRule.setLearningRate(0.01);
        learningRule.setMaxError(0.001);
        learningRule.setMaxIterations(12000);

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

        System.out.println("Testing network...\n\n");
        testNeuralNetwork(neuralNet, testSet);

        System.out.println("Done.");
        System.out.println("**************************************************");
//        }
    }
Example 16
Source File: Abalone.java    From NeurophFramework with Apache License 2.0 4 votes vote down vote up 
public void run() {
    System.out.println("Creating training set...");
    // get path to training set
    String trainingSetFileName = "data_sets/abalonerings.txt";
    int inputsCount = 8;
    int outputsCount = 29;

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

    // split data into train 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(inputsCount, 15, 10, outputsCount);

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

    // set learning rate and max error
    learningRule.setLearningRate(0.1);
    learningRule.setMaxIterations(5000);

    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);
}
Example 17
Source File: WineQualityClassification.java    From NeurophFramework with Apache License 2.0 4 votes vote down vote up 
public void run() {
    System.out.println("Creating training set...");
    // get path to training set
    String dataSetFile = "data_sets/wine.txt";
    int inputsCount = 11;
    int outputsCount = 10;

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

    // split data into train and test set
    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(inputsCount, 20, 15, outputsCount);

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

    // set learning rate and max error
    learningRule.setLearningRate(0.1);
    learningRule.setMaxIterations(5000);

    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);
}
Example 18
Source File: WheatSeeds.java    From NeurophFramework with Apache License 2.0 4 votes vote down vote up 
public void run() {
    System.out.println("Creating training set...");
    // get path to training set
    String trainingSetFileName = "data_sets/seeds.txt";
    int inputsCount = 7;
    int outputsCount = 3;

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

    // split data into train 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(inputsCount, 15, 2, 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);
    learningRule.setMaxIterations(5000);
    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);
}