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

/**
 * Runs this sample
 */
public static void main(String args[]) {

        // create training set (logical AND 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[]{0}));
        trainingSet.add(new DataSetRow(new double[]{1, 0}, new double[]{0}));
        trainingSet.add(new DataSetRow(new double[]{1, 1}, new double[]{1}));

        // create perceptron neural network
        NeuralNetwork myPerceptron = new Perceptron(2, 1);
        // learn the training set
        myPerceptron.learn(trainingSet);
        // test perceptron
        System.out.println("Testing trained perceptron");
        testNeuralNetwork(myPerceptron, trainingSet);
        // save trained perceptron
        myPerceptron.save("mySamplePerceptron.nnet");
        // load saved neural network
        NeuralNetwork loadedPerceptron = NeuralNetwork.load("mySamplePerceptron.nnet");
        // test loaded neural network
        System.out.println("Testing loaded perceptron");
        testNeuralNetwork(loadedPerceptron, trainingSet);
}
 

@FXML
private void startCheck(ActionEvent event) throws IOException {
    if (nnetSrc == null || imgSrc == null) {
        Calert.showAlert("Invalid Data", "Select Required Files", Alert.AlertType.ERROR);
        return;
    }
    try {
        nnet = NeuralNetwork.load(new FileInputStream(nnetSrc)); // load trained neural network saved with Neuroph Studio
        System.out.println("Learning Rule = " + nnet.getLearningRule());
        ImageRecognitionPlugin imageRecognition = (ImageRecognitionPlugin) nnet.getPlugin(ImageRecognitionPlugin.class); // get the 
        HashMap<String, Double> output = imageRecognition.recognizeImage(ImageIO.read(imgSrc));
        if (output == null) {
            System.err.println("Image Recognition Failed");
        }
        double real = output.get("real");
        double fake = output.get("faked");
        System.out.println(output.toString());
        Calert.showAlert("Result", "Real = " + real + "\nFake = " + fake, Alert.AlertType.INFORMATION);
    } catch (FileNotFoundException ex) {
        Logger.getLogger(SingleImageAnalyzerController.class.getName()).log(Level.SEVERE, null, ex);
    }
}
 

public static void main(String[] args) {
    try {
        System.out.println("usage java -jar nn.jar image_to_be_processed file_of_neural_network");
        System.out.println("Loading Image....");
        image = ImageIO.read(new File(args[0]));
        System.out.println("Loading NN....");
        File NNetwork = new File(args[1]);
        if (!NNetwork.exists()) {
            System.err.println("Cant Find NN");
            return;
        }
        nnet = NeuralNetwork.load(new FileInputStream(NNetwork)); // load trained neural network saved with Neuroph Studio
        System.out.println("Load Image Recog Plugin....");
        imageRecognition = (ImageRecognitionPlugin) nnet.getPlugin(ImageRecognitionPlugin.class); // get the image recognition plugin from neural network
        System.out.println("Recognize Image....");
        HashMap<String, Double> output = imageRecognition.recognizeImage(image);
        System.out.println("Output is....");
        System.out.println(output.toString());
    } catch (IOException ex) {
        Logger.getLogger(NeuralNetProcessor.class.getName()).log(Level.SEVERE, null, ex);
    }
}
 

public static void main(String[]args) {
    NeuralNetwork nnet = NeuralNetwork.load("C:\\Users\\zoran\\Desktop\\nn.nnet");
    OcrPlugin ocrPlugin = (OcrPlugin) nnet.getPlugin(OcrPlugin.class);
    
    // load letter images
    Image charImage = ImageFactory.getImage("C:\\Users\\zoran\\Desktop\\Letters\\A.png");
    Character ch = ocrPlugin.recognizeCharacter(charImage);
    System.out.println(ch);
}
 

/**
 * @param args command line arguments which represent paths to persisted neural network
 *             [0] - location of  neural network
 */
public static void main(String[] args) throws IOException {

    DataSet testSet = MNISTDataSet.createFromFile(MNISTDataSet.TEST_LABEL_NAME, MNISTDataSet.TEST_IMAGE_NAME, 10000);
    NeuralNetwork nn = NeuralNetwork.load(new FileInputStream(args[0]));

    Evaluation.runFullEvaluation(nn, testSet);
}
 

@Override
public void doRun() {
    try {
        //Bypass network reload during comeback through home button
        if (nnet == null) {
            File NNetwork = new File(ConstantObjects.neuralNetworkPath);
            System.out.println("Nueral network loaded = " + NNetwork.getAbsolutePath());
            if (!NNetwork.exists()) {
                notifyUser();
                return;
            }
            nnet = NeuralNetwork.load(new FileInputStream(NNetwork)); // load trained neural network saved with Neuroph Studio
            System.out.println("Learning Rule = " + nnet.getLearningRule());
            imageRecognition = (ImageRecognitionPlugin) nnet.getPlugin(ImageRecognitionPlugin.class); // get the image recognition plugin from neural network
        }
        HashMap<String, Double> output = imageRecognition.recognizeImage(image);
        if (output == null) {
            System.err.println("Image Recognition Failed");
        }
        System.out.println(output.toString());
        listener.neuralnetProcessCompleted(output);

    } catch (Exception ex) {
        Logger.getLogger(NeuralNetProcessor.class.getName()).log(Level.SEVERE, null, ex);
        listener.neuralnetProcessCompleted(null);
    }
}
 

@Override
public void doRun() {
    try {
        System.out.println("Starting training thread....." + sampleDimension.toString() + " and " + imageLabels.toString());

        HashMap<String, BufferedImage> imagesMap = new HashMap<String, BufferedImage>();
        for (File file : srcDirectory.listFiles()) {
            imageLabels.add(FilenameUtils.removeExtension(file.getName()));
            if (sampleDimension.getWidth() > 0 && sampleDimension.getHeight() > 0) {
                Double w = sampleDimension.getWidth();
                Double h = sampleDimension.getHeight();
                imagesMap.put(file.getName(), ImageUtilities.resizeImage(ImageUtilities.loadImage(file), w.intValue(), h.intValue()));
            }
        }
        Map<String, FractionRgbData> imageRgbData = ImageUtilities.getFractionRgbDataForImages(imagesMap);
        DataSet learningData = ImageRecognitionHelper.createRGBTrainingSet(imageLabels, imageRgbData);

        nnet = NeuralNetwork.load(new FileInputStream(nnFile)); //Load NNetwork
        MomentumBackpropagation mBackpropagation = (MomentumBackpropagation) nnet.getLearningRule();
        mBackpropagation.setLearningRate(learningRate);
        mBackpropagation.setMaxError(maxError);
        mBackpropagation.setMomentum(momentum);

        System.out.println("Network Information\nLabel = " + nnet.getLabel()
                + "\n Input Neurons = " + nnet.getInputsCount()
                + "\n Number of layers = " + nnet.getLayersCount()
        );

        mBackpropagation.addListener(this);
        System.out.println("Starting training......");
        nnet.learn(learningData, mBackpropagation);
        //Training Completed
        listener.batchImageTrainingCompleted();
    } catch (FileNotFoundException ex) {
        System.out.println(ex.getMessage() + "\n" + ex.getLocalizedMessage());
    }

}
 

public FIDNetworkAnalyser(String nSourceFile) throws FileNotFoundException {
    nnet = NeuralNetwork.load(new FileInputStream(nSourceFile)); // load trained neural network saved with Neuroph Studio
}
 

/**
 * @param args command line arguments which represent paths to persisted neural networks
 *             [0] - location of first neural network
 *             [1] - location of second neural network
 */
public static void main(String[] args) throws IOException {

    DataSet testSet = MNISTDataSet.createFromFile(MNISTDataSet.TEST_LABEL_NAME, MNISTDataSet.TEST_IMAGE_NAME, 10000);

    NeuralNetwork nn1 = NeuralNetwork.load(new FileInputStream(args[0]));
    NeuralNetwork nn2 = NeuralNetwork.load(new FileInputStream(args[1]));

    new McNemarTest().evaluateNetworks(nn1, nn2, testSet);
}