-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathexample2.c
More file actions
172 lines (166 loc) · 5.34 KB
/
example2.c
File metadata and controls
172 lines (166 loc) · 5.34 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
#include <example2.c>
#include <genann.h>
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
struct genann;
typedef double (*genann_actfun)(const struct genann *ann, double a);
typedef struct genann
{
int inputs;
int hidden_layers;
int hidden;
int outputs;
genann_actfun activation_hidden;
genann_actfun activation_output;
int total_weights;
int total_neurons;
double *weight;
double *output;
double *delta;
} genann;
genann *genann_init(int inputs, int hidden_layers, int hidden, int outputs);
genann *genann_read(FILE *in);
void genann_randomize(genann *ann);
genann *genann_copy(const genann *ann);
void genann_free(genann *ann);
const double *genann_run(const genann *ann, const double *inputs);
void genann_train(const genann *ann, const double *inputs, const double *desired_outputs, double learning_rate);
void genann_write(const genann *ann, FILE *out);
void genann_init_sigmoid_lookup(const genann *ann);
double genann_act_sigmoid(const genann *ann, double a);
double genann_act_sigmoid_cached(const genann *ann, double a);
double genann_act_threshold(const genann *ann, double a);
double genann_act_linear(const genann *ann, double a);
int main(int argc, char *argv[])
{
printf("GENANN example 2.\n");
printf("Train a small ANN to the XOR function using random search.\n");
srand(time(0));
const double input[4][2] = {{0, 0}, {0, 1}, {1, 0}, {1, 1}};
const double output[4] = {0, 1, 1, 0};
int i;
genann *ann = genann_init(2, 1, 2, 1);
unsigned int ann_idx = 0;
double err;
double last_err = 1000;
int count = 0;
do
{
count += 1;
if ((count % 1000) == 0)
{
genann_randomize(ann);
last_err = 1000;
}
genann *save = genann_copy(ann);
for (i = 0; i < ann->total_weights; i += 1)
{
ann->weight[i] += (((double) rand()) / 32767) - 0.5;
}
err = 0;
err += pow((*genann_run(ann, input[0])) - output[0], 2.0);
err += pow((*genann_run(ann, input[1])) - output[1], 2.0);
err += pow((*genann_run(ann, input[2])) - output[2], 2.0);
err += pow((*genann_run(ann, input[3])) - output[3], 2.0);
if (err < last_err)
{
genann_free(save);
last_err = err;
}
else
{
genann_free(ann);
ann_idx = save;
}
}
while (err > 0.01);
printf("Finished in %d loops.\n", count);
printf("Output for [%1.f, %1.f] is %1.f.\n", input[0][0], input[0][1], *genann_run(ann, input[0]));
printf("Output for [%1.f, %1.f] is %1.f.\n", input[1][0], input[1][1], *genann_run(ann, input[1]));
printf("Output for [%1.f, %1.f] is %1.f.\n", input[2][0], input[2][1], *genann_run(ann, input[2]));
printf("Output for [%1.f, %1.f] is %1.f.\n", input[3][0], input[3][1], *genann_run(ann, input[3]));
genann_free(ann);
return 0;
}
struct genann;
typedef double (*genann_actfun)(const struct genann *ann, double a);
typedef struct genann
{
int inputs;
int hidden_layers;
int hidden;
int outputs;
genann_actfun activation_hidden;
genann_actfun activation_output;
int total_weights;
int total_neurons;
double *weight;
double *output;
double *delta;
} genann;
genann *genann_init(int inputs, int hidden_layers, int hidden, int outputs);
genann *genann_read(FILE *in);
void genann_randomize(genann *ann);
genann *genann_copy(const genann *ann);
void genann_free(genann *ann);
const double *genann_run(const genann *ann, const double *inputs);
void genann_train(const genann *ann, const double *inputs, const double *desired_outputs, double learning_rate);
void genann_write(const genann *ann, FILE *out);
void genann_init_sigmoid_lookup(const genann *ann);
double genann_act_sigmoid(const genann *ann, double a);
double genann_act_sigmoid_cached(const genann *ann, double a);
double genann_act_threshold(const genann *ann, double a);
double genann_act_linear(const genann *ann, double a);
int main(int argc, char *argv[])
{
printf("GENANN example 2.\n");
printf("Train a small ANN to the XOR function using random search.\n");
srand(time(0));
const double input[4][2] = {{0, 0}, {0, 1}, {1, 0}, {1, 1}};
const double output[4] = {0, 1, 1, 0};
int i;
genann *ann = genann_init(2, 1, 2, 1);
unsigned int ann_idx = 0;
double err;
double last_err = 1000;
int count = 0;
do
{
count += 1;
if ((count % 1000) == 0)
{
genann_randomize(ann);
last_err = 1000;
}
genann *save = genann_copy(ann);
for (i = 0; i < ann->total_weights; i += 1)
{
ann->weight[i] += (((double) rand()) / 32767) - 0.5;
}
err = 0;
err += pow((*genann_run(ann, input[0])) - output[0], 2.0);
err += pow((*genann_run(ann, input[1])) - output[1], 2.0);
err += pow((*genann_run(ann, input[2])) - output[2], 2.0);
err += pow((*genann_run(ann, input[3])) - output[3], 2.0);
if (err < last_err)
{
genann_free(save);
last_err = err;
}
else
{
genann_free(ann);
ann_idx = save;
}
}
while (err > 0.01);
printf("Finished in %d loops.\n", count);
printf("Output for [%1.f, %1.f] is %1.f.\n", input[0][0], input[0][1], *genann_run(ann, input[0]));
printf("Output for [%1.f, %1.f] is %1.f.\n", input[1][0], input[1][1], *genann_run(ann, input[1]));
printf("Output for [%1.f, %1.f] is %1.f.\n", input[2][0], input[2][1], *genann_run(ann, input[2]));
printf("Output for [%1.f, %1.f] is %1.f.\n", input[3][0], input[3][1], *genann_run(ann, input[3]));
genann_free(ann);
return 0;
}