Package org.neuroph.core

Examples of org.neuroph.core.NeuralNetwork

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            trainingSet.addElement(new SupervisedTrainingElement(new double[]{0, 1}, new double[]{0}));
            trainingSet.addElement(new SupervisedTrainingElement(new double[]{1, 0}, new double[]{0}));
            trainingSet.addElement(new SupervisedTrainingElement(new double[]{1, 1}, new double[]{1}));

            // create perceptron neural network
            NeuralNetwork myPerceptron = new Perceptron(2, 1);
            // learn the training set
            myPerceptron.learnInSameThread(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);

    }
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  public void run() {
   
    // uncomment the following line to use regular Neuroph (non-flat) processing
    Neuroph.getInstance().setFlattenNetworks(false);
   
    NeuralNetwork network = new MultiLayerPerceptron(TransferFunctionType.SIGMOID, WINDOW_SIZE, 10, 1);
     
    normalizeSunspots(0.1, 0.9);
   
    network.getLearningRule().addObserver(this);
   
    TrainingSet training = generateTraining();
    network.learnInSameThread(training);
    predict(network);
   
    Neuroph.getInstance().shutdown();
  }
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        // save trained neural network
        myMlPerceptron.save("myMlPerceptron.nnet");

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

        // test loaded neural network
        System.out.println("Testing loaded neural network");
        testNeuralNetwork(loadedMlPerceptron, trainingSet);
    }
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     * @param layersNeuronsCount number of neurons ih hidden layers
     * @param transferFunctionType neurons transfer function type
     * @return returns NeuralNetwork with the OCR plugin
     */
    public static NeuralNetwork createNewNeuralNetwork(String label, Dimension samplingResolution, ColorMode colorMode, List<String> characterLabels,  List<Integer> layersNeuronsCount, TransferFunctionType transferFunctionType) {
        NeuralNetwork neuralNetwork = ImageRecognitionHelper.createNewNeuralNetwork(label, samplingResolution, colorMode, characterLabels, layersNeuronsCount, transferFunctionType);
        neuralNetwork.addPlugin(new OcrPlugin(samplingResolution, colorMode));

        return neuralNetwork;
    }
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    public static void main(String[] args) {
          // load trained neural network saved with easyNeurons (specify existing neural network file here)
          NeuralNetwork nnet = NeuralNetwork.load("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());
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    public static void main(String[] args) {
        System.out.println("Time stamp N1:" + new SimpleDateFormat("dd-MMM-yyyy HH:mm:ss:MM").format(new Date()));

        int maxIterations = 10000;
        NeuralNetwork neuralNet = new MultiLayerPerceptron(4, 9, 1);
        ((LMS) neuralNet.getLearningRule()).setMaxError(0.001);//0-1
        ((LMS) neuralNet.getLearningRule()).setLearningRate(0.7);//0-1
        ((LMS) neuralNet.getLearningRule()).setMaxIterations(maxIterations);//0-1
        TrainingSet trainingSet = new TrainingSet();

        double daxmax = 10000.0D;
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{3710.0D / daxmax, 3690.0D / daxmax, 3890.0D / daxmax, 3695.0D / daxmax}, new double[]{3666.0D / daxmax}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{3690.0D / daxmax, 3890.0D / daxmax, 3695.0D / daxmax, 3666.0D / daxmax}, new double[]{3692.0D / daxmax}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{3890.0D / daxmax, 3695.0D / daxmax, 3666.0D / daxmax, 3692.0D / daxmax}, new double[]{3886.0D / daxmax}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{3695.0D / daxmax, 3666.0D / daxmax, 3692.0D / daxmax, 3886.0D / daxmax}, new double[]{3914.0D / daxmax}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{3666.0D / daxmax, 3692.0D / daxmax, 3886.0D / daxmax, 3914.0D / daxmax}, new double[]{3956.0D / daxmax}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{3692.0D / daxmax, 3886.0D / daxmax, 3914.0D / daxmax, 3956.0D / daxmax}, new double[]{3953.0D / daxmax}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{3886.0D / daxmax, 3914.0D / daxmax, 3956.0D / daxmax, 3953.0D / daxmax}, new double[]{4044.0D / daxmax}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{3914.0D / daxmax, 3956.0D / daxmax, 3953.0D / daxmax, 4044.0D / daxmax}, new double[]{3987.0D / daxmax}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{3956.0D / daxmax, 3953.0D / daxmax, 4044.0D / daxmax, 3987.0D / daxmax}, new double[]{3996.0D / daxmax}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{3953.0D / daxmax, 4044.0D / daxmax, 3987.0D / daxmax, 3996.0D / daxmax}, new double[]{4043.0D / daxmax}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{4044.0D / daxmax, 3987.0D / daxmax, 3996.0D / daxmax, 4043.0D / daxmax}, new double[]{4068.0D / daxmax}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{3987.0D / daxmax, 3996.0D / daxmax, 4043.0D / daxmax, 4068.0D / daxmax}, new double[]{4176.0D / daxmax}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{3996.0D / daxmax, 4043.0D / daxmax, 4068.0D / daxmax, 4176.0D / daxmax}, new double[]{4187.0D / daxmax}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{4043.0D / daxmax, 4068.0D / daxmax, 4176.0D / daxmax, 4187.0D / daxmax}, new double[]{4223.0D / daxmax}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{4068.0D / daxmax, 4176.0D / daxmax, 4187.0D / daxmax, 4223.0D / daxmax}, new double[]{4259.0D / daxmax}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{4176.0D / daxmax, 4187.0D / daxmax, 4223.0D / daxmax, 4259.0D / daxmax}, new double[]{4203.0D / daxmax}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{4187.0D / daxmax, 4223.0D / daxmax, 4259.0D / daxmax, 4203.0D / daxmax}, new double[]{3989.0D / daxmax}));
        neuralNet.learnInSameThread(trainingSet);
        System.out.println("Time stamp N2:" + new SimpleDateFormat("dd-MMM-yyyy HH:mm:ss:MM").format(new Date()));

        TrainingSet testSet = new TrainingSet();
        testSet.addElement(new TrainingElement(new double[]{4223.0D / daxmax, 4259.0D / daxmax, 4203.0D / daxmax, 3989.0D / daxmax}));

        for (TrainingElement testElement : testSet.trainingElements()) {
            neuralNet.setInput(testElement.getInput());
            neuralNet.calculate();
            double[] networkOutput = neuralNet.getOutput();
            System.out.print("Input: " + Arrays.toString(testElement.getInput()) );
            System.out.println(" Output: " + Arrays.toString(networkOutput) );
        }

        //Experiments:
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    layersNeuronsCount.add(0, numberOfInputNeurons);
    layersNeuronsCount.add(numberOfOuputNeurons);
   
    System.out.println("Neuron layer size counts vector = " + layersNeuronsCount);
   
    NeuralNetwork neuralNetwork = new MultiLayerPerceptron(layersNeuronsCount, transferFunctionType);

    neuralNetwork.setLabel(label);
    PluginBase imageRecognitionPlugin = new ImageRecognitionPlugin(samplingResolution, colorMode);
    neuralNetwork.addPlugin(imageRecognitionPlugin);

    assignLabelsToOutputNeurons(neuralNetwork, imageLabels);
                neuralNetwork.setLearningRule(new MomentumBackpropagation());

            return neuralNetwork;
  }
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* @author zoran
*/
public class IrisNeurophClassifierEvaluation {

    public static void main(String[] args) {          
        NeuralNetwork neuralNet = NeuralNetwork.load(IrisNeurophClassifierEvaluation.class.getResource("IrisClassifier.nnet").getFile());
        DataSet dataSet = DataSet.createFromFile(IrisNeurophClassifierEvaluation.class.getResource("iris_data_normalised.txt").getPath(), 4, 3, ",");
             
        NeurophClassifierEvaluation evaluation =
                new NeurophClassifierEvaluation(neuralNet, dataSet);
       
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    public void run() {
        // get path to neural network file for iris classification
        String nnFileName = IrisNeurophClassifierSample.class.getResource("IrisClassifier.nnet").getFile();
       
        // load neural network from file
        NeuralNetwork neuralNet = NeuralNetwork.load(nnFileName);
        // set labels manualy
//        neuralNet.getOutputNeurons()[0].setLabel("Setosa");
//        neuralNet.getOutputNeurons()[1].setLabel("Versicolor");
//        neuralNet.getOutputNeurons()[2].setLabel("Virginica");
//        neuralNet.save(nnFileName);
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    public static void main(String args[]) throws IOException {
        // get path to neural network file for iris classification
        String nnFileName = ImageRecognitionSample.class.getResource("resources/ImageRecNet.nnet").getFile();
       
        // load neural network from file
        NeuralNetwork neuralNet = NeuralNetwork.load(nnFileName);
       
        // create GOAI classifier with loaded Neuroph neural network
        ImageRecognizer imageRecognizer = new NeurophImageRecognizer(neuralNet);
       
        // test image recognition
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Related Classes of org.neuroph.core.NeuralNetwork

  • org.goai.classification.samples.IrisNeurophClassifierEvaluation
  • org.goai.classification.samples.IrisNeurophClassifierSample
  • org.goai.imagerec.samples.ImageRecognitionSample
  • org.neuroph.contrib.imgrec.ImageRecognitionHelper
  • org.neuroph.contrib.imgrec.ImageRecognitionSample
  • org.neuroph.contrib.ocr.OcrHelper
  • org.neuroph.samples.intronn.SunSpots
  • org.neuroph.samples.PerceptronSample
  • org.neuroph.samples.stockmarket.Main
  • org.neuroph.samples.XorMultiLayerPerceptronSample
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