mirror of
https://github.com/codeplea/genann
synced 2024-11-24 23:40:02 +03:00
36 lines
1.3 KiB
C
36 lines
1.3 KiB
C
#include <stdio.h>
|
|
#include "genann.h"
|
|
|
|
int main(int argc, char *argv[])
|
|
{
|
|
printf("GENANN example 1.\n");
|
|
printf("Train a small ANN to the XOR function using backpropagation.\n");
|
|
|
|
/* Input and expected out data for the XOR function. */
|
|
const double input[4][2] = {{0, 0}, {0, 1}, {1, 0}, {1, 1}};
|
|
const double output[4] = {0, 1, 1, 0};
|
|
int i;
|
|
|
|
/* New network with 2 inputs,
|
|
* 1 hidden layer of 2 neurons,
|
|
* and 1 output. */
|
|
genann *ann = genann_init(2, 1, 2, 1);
|
|
|
|
/* Train on the four labeled data points many times. */
|
|
for (i = 0; i < 300; ++i) {
|
|
genann_train(ann, input[0], output + 0, 3);
|
|
genann_train(ann, input[1], output + 1, 3);
|
|
genann_train(ann, input[2], output + 2, 3);
|
|
genann_train(ann, input[3], output + 3, 3);
|
|
}
|
|
|
|
/* Run the network and see what it predicts. */
|
|
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;
|
|
}
|