88 lines
2.2 KiB
C
88 lines
2.2 KiB
C
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#include "training.h"
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#include <math.h>
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#include <stdio.h>
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#include <stdlib.h>
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#include <string.h>
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#include <time.h>
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#include "structure.h"
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#include "toolsnetworks.h"
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#include "traitement.h"
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#define NB_OUTPUT 65
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void training(Neural_Network *network, int nb_repetition)
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{
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int nbchar = 65;
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int nbimageschar = 53;
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int datasetsize = nbchar * nbimageschar;
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// On construit les tableaux input de taille nbrepetition, et on initialise
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// en même temps le coût attendu à 0 pour toutres les répétitions (on a
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// nbrépétition tableaux de taille 63 initialisés à 0).
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double **input = (double **)malloc(datasetsize * sizeof(double *));
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double **cost = (double **)malloc(datasetsize * sizeof(double));
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char str[100];
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for (int i = 0; i < datasetsize; i++)
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{
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cost[i] = (double *)calloc(NB_OUTPUT, sizeof(double));
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}
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for (int i = 0; i < nbimageschar; i++)
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{
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for (int j = 0; j < nbchar; j++)
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{
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sprintf(str, "src/Dataset/image-%d-%d.bmp", j, i + 1);
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input[i * nbchar + j] = imagetomatrix(str, 20);
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cost[i * nbchar + j][j] = 1;
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}
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}
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double err = 1.0;
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for (int i = 0; i < nb_repetition; i++)
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{
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err = 0;
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for (int j = 0; j < datasetsize; j++)
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{
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// On fait ensuite appel au ForwardPass sur cette itération <=> on
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// voit ce que renvoie notre réseau pour la matrice de pixel
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// correspondant à l'image récupérée.
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ForwardPass(input[j], network);
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double tmp_err = 0.0;
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for (int k = 0; k < NB_OUTPUT; k++)
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{
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tmp_err += (cost[j][k] - network->layers[1].cells[k].output)
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* (cost[j][k] - network->layers[1].cells[k].output);
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}
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tmp_err /= NB_OUTPUT;
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err += tmp_err;
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BackwardPass(cost[j], input[j], network);
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}
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err /= datasetsize;
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printf("Erreur : %f\n", err);
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}
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for (int i = 0; i < datasetsize; i++)
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{
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free(input[i]);
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}
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for (int i = 0; i < datasetsize; i++)
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{
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free(cost[i]);
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}
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free(input);
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free(cost);
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}
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