JOONE(Java Object-Oriented Network Engine)使用初探
1
/**/
/*
2
* JOONE - Java Object Oriented Neural Engine
3 * http://joone.sourceforge.net
4 *
5 * XOR_using_NeuralNet.java
6 *
7
*/
8
package
study;
9
10
import
org.joone.engine.
*
;
11
import
org.joone.engine.learning.
*
;
12
import
org.joone.io.
*
;
13
import
org.joone.net.
*
;
14
import
java.util.Vector;
15
16
public
class
XOR_using_NeuralNet
implements
NeuralNetListener
{
17 private NeuralNet nnet = null ;
18 private MemoryInputSynapse inputSynapse, desiredOutputSynapse;
19 private MemoryOutputSynapse outputSynapse;
20 LinearLayer input;
21 SigmoidLayer hidden, output;
22 boolean singleThreadMode = true ;
23
24 // XOR input
25
private double [][] inputArray = new double [][] {
26 { 0.0 , 0.0 } ,
27 { 0.0 , 1.0 } ,
28 { 1.0 , 0.0 } ,
29 { 1.0 , 1.0 }
30
} ;
31
32 // XOR desired output
33 private double [][] desiredOutputArray = new double [][] {
34 { 0.0 } ,
35 { 1.0 } ,
36 { 1.0 } ,
37 { 0.0 }
38 } ;
39
40 /** */ /**
41 * @param args the command line arguments
42 */
43 public static void main(String args[]) {
44 XOR_using_NeuralNet xor = new XOR_using_NeuralNet();
45
46 xor.initNeuralNet();
47 xor.train();
48 xor.interrogate();
49 }
50
51 /** */ /**
52 * Method declaration
53 */
54 public void train() {
55
56 // set the inputs
57 inputSynapse.setInputArray(inputArray);
58 inputSynapse.setAdvancedColumnSelector( " 1,2 " );
59 // set the desired outputs
60 desiredOutputSynapse.setInputArray(desiredOutputArray);
61 desiredOutputSynapse.setAdvancedColumnSelector( " 1 " );
62
63 // get the monitor object to train or feed forward
64 Monitor monitor = nnet.getMonitor();
65
66 // set the monitor parameters
67 monitor.setLearningRate( 0.8 );
68 monitor.setMomentum( 0.3 );
69 monitor.setTrainingPatterns(inputArray.length);
70 monitor.setTotCicles( 5000 );
71 monitor.setLearning( true );
72
73 long initms = System.currentTimeMillis();
74 // Run the network in single-thread, synchronized mode
75 nnet.getMonitor().setSingleThreadMode(singleThreadMode);
76 nnet.go( true );
77 System.out.println( " Total time= " + (System.currentTimeMillis() - initms) + " ms " );
78 }
79
80 private void interrogate() {
81 // set the inputs
82 inputSynapse.setInputArray(inputArray);
83 inputSynapse.setAdvancedColumnSelector( " 1,2 " );
84 Monitor monitor = nnet.getMonitor();
85 monitor.setTrainingPatterns( 4 );
86 monitor.setTotCicles( 1 );
87 monitor.setLearning( false );
88 MemoryOutputSynapse memOut = new MemoryOutputSynapse();
89 // set the output synapse to write the output of the net
90
91 if (nnet != null ) {
92 nnet.addOutputSynapse(memOut);
93 System.out.println(nnet.check());
94 nnet.getMonitor().setSingleThreadMode(singleThreadMode);
95 nnet.go();
96
97 for ( int i = 0 ; i < 4 ; i ++ ) {
98 double [] pattern = memOut.getNextPattern();
99 System.out.println( " Output pattern # " + (i + 1 ) + " = " + pattern[ 0 ]);
100 }
101 System.out.println( " Interrogating Finished " );
102 }
103 }
104
105 /** */ /**
106 * Method declaration
107 */
108 protected void initNeuralNet() {
109
110 // First create the three layers
111 input = new LinearLayer();
112 hidden = new SigmoidLayer();
113 output = new SigmoidLayer();
114
115 // set the dimensions of the layers
116 input.setRows( 2 );
117 hidden.setRows( 3 );
118 output.setRows( 1 );
119
120 input.setLayerName( " L.input " );
121 hidden.setLayerName(</s
/**/
/* 2
* JOONE - Java Object Oriented Neural Engine3
* http://joone.sourceforge.net 4
*5
* XOR_using_NeuralNet.java6
*7
*/
8
package
study;9
10
import
org.joone.engine.
*
;11
import
org.joone.engine.learning.
*
;12
import
org.joone.io.
*
;13
import
org.joone.net.
*
;14
import
java.util.Vector;15
16
public
class
XOR_using_NeuralNet
implements
NeuralNetListener
{17
private NeuralNet nnet = null ;18
private MemoryInputSynapse inputSynapse, desiredOutputSynapse;19
private MemoryOutputSynapse outputSynapse;20
LinearLayer input;21
SigmoidLayer hidden, output;22
boolean singleThreadMode = true ;23
24
// XOR input 25
private double [][] inputArray = new double [][] {26
{ 0.0 , 0.0 } ,27
{ 0.0 , 1.0 } ,28
{ 1.0 , 0.0 } ,29
{ 1.0 , 1.0 } 30
} ;31
32
// XOR desired output 33
private double [][] desiredOutputArray = new double [][] {34
{ 0.0 } ,35
{ 1.0 } ,36
{ 1.0 } ,37
{ 0.0 } 38
} ;39
40
/** */ /** 41
* @param args the command line arguments42
*/ 43
public static void main(String args[]) {44
XOR_using_NeuralNet xor = new XOR_using_NeuralNet();45
46
xor.initNeuralNet();47
xor.train();48
xor.interrogate();49
} 50
51
/** */ /** 52
* Method declaration53
*/ 54
public void train() {55
56
// set the inputs 57
inputSynapse.setInputArray(inputArray);58
inputSynapse.setAdvancedColumnSelector( " 1,2 " );59
// set the desired outputs 60
desiredOutputSynapse.setInputArray(desiredOutputArray);61
desiredOutputSynapse.setAdvancedColumnSelector( " 1 " );62
63
// get the monitor object to train or feed forward 64
Monitor monitor = nnet.getMonitor();65
66
// set the monitor parameters 67
monitor.setLearningRate( 0.8 );68
monitor.setMomentum( 0.3 );69
monitor.setTrainingPatterns(inputArray.length);70
monitor.setTotCicles( 5000 );71
monitor.setLearning( true );72
73
long initms = System.currentTimeMillis();74
// Run the network in single-thread, synchronized mode 75
nnet.getMonitor().setSingleThreadMode(singleThreadMode);76
nnet.go( true );77
System.out.println( " Total time= " + (System.currentTimeMillis() - initms) + " ms " );78
} 79
80
private void interrogate() {81
// set the inputs 82
inputSynapse.setInputArray(inputArray);83
inputSynapse.setAdvancedColumnSelector( " 1,2 " );84
Monitor monitor = nnet.getMonitor();85
monitor.setTrainingPatterns( 4 );86
monitor.setTotCicles( 1 );87
monitor.setLearning( false );88
MemoryOutputSynapse memOut = new MemoryOutputSynapse();89
// set the output synapse to write the output of the net 90
91
if (nnet != null ) {92
nnet.addOutputSynapse(memOut);93
System.out.println(nnet.check());94
nnet.getMonitor().setSingleThreadMode(singleThreadMode);95
nnet.go();96
97
for ( int i = 0 ; i < 4 ; i ++ ) {98
double [] pattern = memOut.getNextPattern();99
System.out.println( " Output pattern # " + (i + 1 ) + " = " + pattern[ 0 ]);100
} 101
System.out.println( " Interrogating Finished " );102
} 103
} 104
105
/** */ /** 106
* Method declaration107
*/ 108
protected void initNeuralNet() {109
110
// First create the three layers 111
input = new LinearLayer();112
hidden = new SigmoidLayer();113
output = new SigmoidLayer();114
115
// set the dimensions of the layers 116
input.setRows( 2 );117
hidden.setRows( 3 );118
output.setRows( 1 );119
120
input.setLayerName( " L.input " );121
hidden.setLayerName(</s
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