mirror of
https://github.com/codeplea/genann
synced 2024-11-21 22:11:34 +03:00
68 lines
2.0 KiB
C
68 lines
2.0 KiB
C
#include <stdio.h>
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#include <stdlib.h>
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#include <math.h>
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#include "genann.h"
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int main(int argc, char *argv[])
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{
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printf("GENANN example 2.\n");
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printf("Train a small ANN to the XOR function using random search.\n");
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/* Input and expected out data for the XOR function. */
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const double input[4][2] = {{0, 0}, {0, 1}, {1, 0}, {1, 1}};
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const double output[4] = {0, 1, 1, 0};
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int i;
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/* New network with 2 inputs,
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* 1 hidden layer of 2 neurons,
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* and 1 output. */
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genann *ann = genann_init(2, 1, 2, 1);
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double err;
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double last_err = 1000;
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int count = 0;
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do {
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++count;
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if (count % 1000 == 0) {
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/* We're stuck, start over. */
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genann_randomize(ann);
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}
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genann *save = genann_copy(ann);
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/* Take a random guess at the ANN weights. */
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for (i = 0; i < ann->total_weights; ++i) {
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ann->weight[i] += ((double)rand())/RAND_MAX-0.5;
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}
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/* See how we did. */
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err = 0;
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err += pow(*genann_run(ann, input[0]) - output[0], 2.0);
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err += pow(*genann_run(ann, input[1]) - output[1], 2.0);
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err += pow(*genann_run(ann, input[2]) - output[2], 2.0);
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err += pow(*genann_run(ann, input[3]) - output[3], 2.0);
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/* Keep these weights if they're an improvement. */
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if (err < last_err) {
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genann_free(save);
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last_err = err;
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} else {
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genann_free(ann);
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ann = save;
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}
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} while (err > 0.01);
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printf("Finished in %d loops.\n", count);
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/* Run the network and see what it predicts. */
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printf("Output for [%1.f, %1.f] is %1.f.\n", input[0][0], input[0][1], *genann_run(ann, input[0]));
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printf("Output for [%1.f, %1.f] is %1.f.\n", input[1][0], input[1][1], *genann_run(ann, input[1]));
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printf("Output for [%1.f, %1.f] is %1.f.\n", input[2][0], input[2][1], *genann_run(ann, input[2]));
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printf("Output for [%1.f, %1.f] is %1.f.\n", input[3][0], input[3][1], *genann_run(ann, input[3]));
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genann_free(ann);
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return 0;
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}
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