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Backprop and backprop_test added (no testing).
author revcompgeek
date Fri, 11 Apr 2008 18:12:55 -0600
parents
children 73beed484455
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2:9655c8362b25 3:314d68bafeff
1 module backprop_test;
2
3 import aid.nn.multilayer.backprop;
4 import aid.nn.outputFunctions;
5 import std.stdio;
6
7 /+float[][] trainingInputs = [
8 [0,0,0],
9 [0,0,1],
10 [0,1,0],
11 [0,1,1],
12 [1,0,0],
13 [1,0,1],
14 [1,1,0],
15 [1,1,1]];
16
17 float[][] trainingOutputs = [
18 [0.1],
19 [0.9],
20 [0.9],
21 [0.1],
22 [0.9],
23 [0.1],
24 [0.1],
25 [0.9]];+/
26
27 float[][] trainingInputs = [
28 [0,0],
29 [0,1],
30 [1,0],
31 [1,1]];
32
33 float[][] trainingOutputs = [
34 [0.1],
35 [0.9],
36 [0.9],
37 [0.1]];
38
39 void main(){
40 Backprop nn = new Backprop(2,[4,1],[&sigmoid,&sigmoid]);
41
42 float error = 10.0;
43 float[] output;
44 int iter = 0;
45 while(error >= 0.5){
46 error = nn.calculateError(trainingInputs,trainingOutputs);
47 if(iter % 100 == 0){
48 writefln("Iter: %d",iter);
49 for(int i=0; i<trainingInputs.length; i++){
50 output = nn.evaluate(trainingInputs[i]);
51 writef(" %d:", i); printArray(output);
52 }
53 writefln(" Error: %f", error);
54 }
55 nn.train(trainingInputs,trainingOutputs);
56 }
57 writefln("Total Iters: %d",iter);
58 for(int i=0; i<trainingInputs.length; i++){
59 output = nn.evaluate(trainingInputs[i]);
60 writef(" %d:", i); printArray(output);
61 }
62 writefln(" Error: %f", error);
63 }
64
65 void printArray(float[] array){
66 writef("[");
67 for(int i=0; i<array.length-1; i++){
68 writef("%f, ",array[i]);
69 }
70 writefln("%f]",array[$]);
71 }