Preparation

References

Examples

   1 import org.neuroph.core.NeuralNetwork;
   2 import org.neuroph.nnet.MultiLayerPerceptron;
   3 import org.neuroph.core.learning.TrainingSet;
   4 import org.neuroph.core.learning.TrainingElement;
   5 import org.neuroph.core.learning.SupervisedTrainingElement;
   6 import java.util.Vector;
   7 import org.neuroph.util.TransferFunctionType;
   8 
   9 /**
  10  * This sample shows how to create, train, save and load simple Multi Layer Perceptron
  11  */
  12 
  13 void setup() {
  14 
  15     // create training set (logical XOR function)
  16     TrainingSet trainingSet = new TrainingSet();
  17     trainingSet.addElement(new SupervisedTrainingElement(new double[] {
  18       0, 0
  19     }
  20     , new double[] {
  21       0
  22     }
  23     ));
  24     trainingSet.addElement(new SupervisedTrainingElement(new double[] {
  25       0, 1
  26     }
  27     , new double[] {
  28       1
  29     }
  30     ));
  31     trainingSet.addElement(new SupervisedTrainingElement(new double[] {
  32       1, 0
  33     }
  34     , new double[] {
  35       1
  36     }
  37     ));
  38     trainingSet.addElement(new SupervisedTrainingElement(new double[] {
  39       1, 1
  40     }
  41     , new double[] {
  42       0
  43     }
  44     ));
  45 
  46     // create multi layer perceptron
  47     MultiLayerPerceptron myMlPerceptron = new MultiLayerPerceptron(TransferFunctionType.TANH, 2, 3, 1);
  48     // learn the training set
  49     myMlPerceptron.learnInSameThread(trainingSet);
  50 
  51     // test perceptron
  52     System.out.println("Testing trained neural network");
  53     testNeuralNetwork(myMlPerceptron, trainingSet);
  54 
  55     // save trained neural network
  56     myMlPerceptron.save("myMlPerceptron.nnet");
  57 
  58     // load saved neural network
  59     NeuralNetwork loadedMlPerceptron = NeuralNetwork.load("myMlPerceptron.nnet");
  60 
  61     // test loaded neural network
  62     System.out.println("Testing loaded neural network");
  63     testNeuralNetwork(loadedMlPerceptron, trainingSet);
  64     
  65     noLoop();
  66   }
  67 
  68 void testNeuralNetwork(NeuralNetwork nnet, TrainingSet tset) {
  69   
  70     nnet.setInput(new double[]{1, 0});
  71     nnet.calculate();
  72     double networkOutput = nnet.getOutput()[0];
  73     System.out.println(" Output: " + networkOutput);
  74   }


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