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This is the Perceptron example from http://neuroph.sourceforge.net/tutorials/Perceptron.html:
This is the Perceptron example from http://neuroph.sourceforge.net/tutorials/Perceptron.html.

If you have difficulties with the terminology, read https
://en.wikibooks.org/wiki/Artificial_Neural_Networks/Neural_Network_Basics.

Preparation

  • Follow the instructions from the assignment Creative Programming to prepare the software environment, including Processing and Arduino. If you are not planning to use the GUI components from the ControlP5 library, you can skip the ControlP5 part.

  • Install Neuroph, a lightweight Java neural network framework.

    • Download proper neurophstudio installer for your platform. I will use Windows platform as examples here
    • In "Processing Sketchbook location"\libraries, create a sub-directory "neuroph".
    • In "Processing Sketchbook location"\libraries\neuroph, create a sub-directory "library".
    • From <neurophstudio directory>\neurophstudio\modules\ext copy neuroph-core-<version>.jar to <Processing Sketchbook location>\libraries\neuroph\library. Rename neuroph-core-<version>.jar to neuroph.jar.

  • If you are confident enough, you can also try to use Eclipse to program in Java. Then try to follow the instructions from the assignment Processing2Java. Remember you shall include the aforementioned jar files in the projects if you want to use neuroph.

References

Examples

Try the Perceptron example first with Neuroph Studio:

This is the Perceptron example from http://neuroph.sourceforge.net/tutorials/Perceptron.html.

If you have difficulties with the terminology, read https://en.wikibooks.org/wiki/Artificial_Neural_Networks/Neural_Network_Basics.

Java code

   1 import java.util.Arrays;
   2 import org.neuroph.core.NeuralNetwork;
   3 import org.neuroph.nnet.Perceptron;
   4 import org.neuroph.core.data.DataSet;
   5 import org.neuroph.core.data.DataSetRow;
   6 
   7 /**
   8  * This sample shows how to create, train, save and load simple Perceptron
   9  * neural network
  10  */
  11 public class PerceptronSample {
  12 
  13         public static void main(String args[]) {
  14 
  15                 // create training set (logical AND function)
  16                 DataSet trainingSet = new DataSet(2, 1);
  17                 trainingSet.addRow(new DataSetRow(new double[] { 0, 0 }, new double[] { 0 }));
  18                 trainingSet.addRow(new DataSetRow(new double[] { 0, 1 }, new double[] { 0 }));
  19                 trainingSet.addRow(new DataSetRow(new double[] { 1, 0 }, new double[] { 0 }));
  20                 trainingSet.addRow(new DataSetRow(new double[] { 1, 1 }, new double[] { 1 }));
  21 
  22                 // create perceptron neural network
  23                 NeuralNetwork myPerceptron = new Perceptron(2, 1);
  24 
  25                 // learn the training set
  26                 myPerceptron.learn(trainingSet);
  27 
  28                 // test perceptron
  29                 System.out.println("Testing trained perceptron");
  30                 testNeuralNetwork(myPerceptron, trainingSet);
  31 
  32                 // save trained perceptron
  33                 myPerceptron.save("mySamplePerceptron.nnet");
  34 
  35                 // load saved neural network
  36                 NeuralNetwork loadedPerceptron = NeuralNetwork.load("mySamplePerceptron.nnet");
  37 
  38                 // test loaded neural network
  39                 System.out.println("Testing loaded perceptron");
  40                 testNeuralNetwork(loadedPerceptron, trainingSet);
  41         }
  42 
  43         public static void testNeuralNetwork(NeuralNetwork nnet, DataSet tset) {
  44 
  45                 for (DataSetRow dataRow : tset.getRows()) {
  46 
  47                         nnet.setInput(dataRow.getInput());
  48                         nnet.calculate();
  49                         double[] networkOutput = nnet.getOutput();
  50                         System.out.print("Input: " + Arrays.toString(dataRow.getInput()));
  51                         System.out.println(" Output: " + Arrays.toString(networkOutput));
  52                 }
  53         }
  54 }

Processing code

  • /!\ Notice the use of the sketchPath() function for saving the data file next to your Processing sketch.

   1 import org.neuroph.core.*;
   2 import org.neuroph.core.data.*;
   3 import org.neuroph.core.data.norm.*;
   4 import org.neuroph.core.data.sample.*;
   5 import org.neuroph.core.events.*;
   6 import org.neuroph.core.exceptions.*;
   7 import org.neuroph.core.input.*;
   8 import org.neuroph.core.learning.error.*;
   9 import org.neuroph.core.learning.*;
  10 import org.neuroph.core.learning.stop.*;
  11 import org.neuroph.core.transfer.*;
  12 import org.neuroph.nnet.*;
  13 import org.neuroph.nnet.comp.*;
  14 import org.neuroph.nnet.comp.layer.*;
  15 import org.neuroph.nnet.comp.neuron.*;
  16 import org.neuroph.nnet.learning.*;
  17 import org.neuroph.util.benchmark.*;
  18 import org.neuroph.util.*;
  19 import org.neuroph.util.io.*;
  20 import org.neuroph.util.plugins.*;
  21 import org.neuroph.util.random.*;
  22 
  23 import java.util.*;
  24 
  25 /**
  26  * This sample shows how to create, train, save and load simple Perceptron neural network 
  27  */
  28 void setup() {
  29 
  30   // create training set (logical AND function)
  31   DataSet trainingSet = new DataSet(2, 1);
  32   trainingSet.addRow(new DataSetRow(new double[] { 0, 0 }, new double[] { 0 }));
  33   trainingSet.addRow(new DataSetRow(new double[] { 0, 1 }, new double[] { 0 }));
  34   trainingSet.addRow(new DataSetRow(new double[] { 1, 0 }, new double[] { 0 }));
  35   trainingSet.addRow(new DataSetRow(new double[] { 1, 1 }, new double[] { 1 }));
  36 
  37   // create perceptron neural network
  38   NeuralNetwork myPerceptron = new Perceptron(2, 1);
  39 
  40   // learn the training set
  41   myPerceptron.learn(trainingSet);
  42 
  43   // test perceptron
  44   println("Testing trained perceptron");
  45   testNeuralNetwork(myPerceptron, trainingSet);
  46   
  47   println(dataPath(""));
  48 
  49   // save trained perceptron
  50   myPerceptron.save(sketchPath("mySamplePerceptron.nnet"));
  51 
  52   // load saved neural network
  53   NeuralNetwork loadedPerceptron = NeuralNetwork.load(sketchPath("mySamplePerceptron.nnet"));
  54 
  55   // test loaded neural network
  56   println("Testing loaded perceptron");
  57   testNeuralNetwork(loadedPerceptron, trainingSet);
  58 }
  59 
  60 void testNeuralNetwork(NeuralNetwork nnet, DataSet tset) {
  61 
  62   for (DataSetRow dataRow : tset.getRows()) {
  63 
  64     nnet.setInput(dataRow.getInput());
  65     nnet.calculate();
  66     double[ ] networkOutput = nnet.getOutput();
  67     print("Input: " + Arrays.toString(dataRow.getInput()) );
  68     println(" Output: " + Arrays.toString(networkOutput) );
  69   }
  70 }

s4ph: NeurophNeuralNetwork (last edited 2016-12-12 22:46:15 by JunHu)