mirror of
https://github.com/codeplea/genann
synced 2024-11-21 22:11:34 +03:00
42 lines
1.5 KiB
C
42 lines
1.5 KiB
C
#include <stdio.h>
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#include <stdlib.h>
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#include <time.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 1.\n");
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printf("Train a small ANN to the XOR function using backpropagation.\n");
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/* This will make the neural network initialize differently each run. */
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/* If you don't get a good result, try again for a different result. */
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srand(time(0));
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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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/* Train on the four labeled data points many times. */
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for (i = 0; i < 300; ++i) {
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genann_train(ann, input[0], output + 0, 3);
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genann_train(ann, input[1], output + 1, 3);
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genann_train(ann, input[2], output + 2, 3);
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genann_train(ann, input[3], output + 3, 3);
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}
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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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