Preparation
Follow the instructions from the assignment Creative Programming to install Processing and if needed, Arduino software environment.
Install Neuroph, a lightweight Java neural network framework.
Download neurophstudio 2.8 installer for your platform (for Windows: neurophstudio-windows-2.8.exe). We have difficulties in getting the latest version to work with Processing, and all the tutorials and documentation are not yet updated for the latest version.
- 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.ph.
References
Examples
Try the Perceptron example first with Neuroph Studio. ![/!\](/moin_static1911/modern/img/alert.png "/!\"){kind=small}
For Windows 7 and up, run Neuroph Studio as an administrator.
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 }