Java Code Examples for org.neuroph.core.NeuralNetwork#createFromFile()

public static void main(String[] args) {
    Evaluation evaluation = new Evaluation();
    evaluation.addEvaluator(new ErrorEvaluator(new MeanSquaredError()));

    String[] classNames = {"Virginica", "Setosa", "Versicolor"};

    MultiLayerPerceptron neuralNet = (MultiLayerPerceptron) NeuralNetwork.createFromFile("irisNet.nnet");
    DataSet dataSet = DataSet.createFromFile("data_sets/iris_data_normalised.txt", 4, 3, ",");

    evaluation.addEvaluator(new ClassifierEvaluator.MultiClass(classNames));
    evaluation.evaluate(neuralNet, dataSet);

    ClassifierEvaluator evaluator = evaluation.getEvaluator(ClassifierEvaluator.MultiClass.class);
    ConfusionMatrix confusionMatrix = evaluator.getResult();
    System.out.println("Confusion matrrix:\r\n");
    System.out.println(confusionMatrix.toString() + "\r\n\r\n");
    System.out.println("Classification metrics\r\n");
    ClassificationMetrics[] metrics = ClassificationMetrics.createFromMatrix(confusionMatrix);
    ClassificationMetrics.Stats average = ClassificationMetrics.average(metrics);
    for (ClassificationMetrics cm : metrics) {
        System.out.println(cm.toString() + "\r\n");
    }
    System.out.println(average.toString());

}
 

public static void main(String[] args) {
      // load trained neural network saved with NeurophStudio (specify existing neural network file here)
      NeuralNetwork nnet = NeuralNetwork.createFromFile("MyImageRecognition.nnet");
      // get the image recognition plugin from neural network
      ImageRecognitionPlugin imageRecognition = (ImageRecognitionPlugin)nnet.getPlugin(ImageRecognitionPlugin.class);

      try {
            // image recognition is done here
            HashMap<String, Double> output = imageRecognition.recognizeImage(new File("someImage.jpg")); // specify some existing image file here
            System.out.println(output.toString());
      } catch(IOException ioe) {
          System.out.println("Error: could not read file!");
      } catch (VectorSizeMismatchException vsme) {
          System.out.println("Error: Image dimensions dont !");
      }
}
 

public static void main(String[] args) throws IOException {

        // User input parameters
//***********************************************************************************************************************************
        String networkPath = "C:/Users/Mihailo/Desktop/OCR/nnet/nnet-12-0.01.nnet"; // path to the trained network                *
        String letterPath = "C:/Users/Mihailo/Desktop/OCR/letters/259.png"; // path to the letter for recognition                   *
//***********************************************************************************************************************************
        
        NeuralNetwork nnet = NeuralNetwork.createFromFile(networkPath);
        ImageRecognitionPlugin imageRecognition = (ImageRecognitionPlugin) nnet.getPlugin(ImageRecognitionPlugin.class);
        Map<String, Double> output = imageRecognition.recognizeImage(new File(letterPath));
        System.out.println("Recognized letter: "+OCRUtilities.getCharacter(output));

    }
 

public void train() {
    System.out.println("Training neural network... ");
    MultiLayerPerceptron neuralNet = (MultiLayerPerceptron) NeuralNetwork.createFromFile(config.getTrainedNetworkFileName());

    DataSet dataSet = DataSet.load(config.getNormalizedBalancedFileName());
    neuralNet.getLearningRule().addListener(this);
    neuralNet.learn(dataSet);
    System.out.println("Saving trained neural network to file... ");
    neuralNet.save(config.getTrainedNetworkFileName());
    System.out.println("Neural network successfully saved!");
}
 

public void evaluate() {
    System.out.println("Evaluating neural network...");
    //Loading neural network from file
    MultiLayerPerceptron neuralNet = (MultiLayerPerceptron) NeuralNetwork.createFromFile(config.getTrainedNetworkFileName());

    //Load normalized balanced data set from file
    DataSet dataSet = DataSet.load(config.getTestFileName());

    //Testing neural network
    testNeuralNetwork(neuralNet, dataSet);

}
 

/**
 * @param args the command line arguments
 */
public static void main(String[] args) {
    NeuralNetwork nnet = NeuralNetwork.createFromFile("irisNet.nnet");
    DataSet dataSet =  DataSet.createFromFile("data_sets/iris_data_normalised.txt", 4, 3, ",");
    
    Evaluation.runFullEvaluation(nnet, dataSet);
     
}
 

/**
 * @param networkPath path of the trained neural network
 */
public void setNetworkPath(String networkPath) {
    nnet = NeuralNetwork.createFromFile(networkPath);
    plugin = (ImageRecognitionPlugin) nnet.getPlugin(ImageRecognitionPlugin.class);
}
 

/**
     * 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);
    }