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Add accuracy

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augustin64 2022-04-10 21:28:54 +02:00
parent 6b32d2ba3e
commit 9b60e068ed
1 changed files with 36 additions and 3 deletions
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39
src/mnist/main.c
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@ -7,6 +7,19 @@
#include "mnist.c" #include "mnist.c"
int indice_max(float* tab, int n) {
int indice = -1;
float maxi = 0.;
for (int i=0; i < n; i++) {
if (tab[i] > maxi) {
maxi = tab[i];
indice = i;
}
}
return indice;
}
void help(char* call) { void help(char* call) {
printf("Usage: %s ( train | recognize ) [OPTIONS]\n\n", call); printf("Usage: %s ( train | recognize ) [OPTIONS]\n\n", call);
printf("OPTIONS:\n"); printf("OPTIONS:\n");
@ -39,8 +52,12 @@ void train(int batches, int couches, int neurons, char* recovery, char* image_fi
Reseau* reseau; Reseau* reseau;
//int* repartition = malloc(sizeof(int)*couches); //int* repartition = malloc(sizeof(int)*couches);
int nb_neurones_der = 10;
int repartition[5] = {784, 100, 75, 40, nb_neurones_der};
float* sortie = malloc(sizeof(float)*nb_neurones_der);
int* sortie_voulue; int* sortie_voulue;
int repartition[5] = {784, 100, 75, 40, 10}; float accuracy;
//generer_repartition(couches, repartition); //generer_repartition(couches, repartition);
/* /*
@ -56,6 +73,8 @@ void train(int batches, int couches, int neurons, char* recovery, char* image_fi
printf("Backup restaurée.\n"); printf("Backup restaurée.\n");
} }
Couche* der_couche = reseau->couches[reseau->nb_couches-1];
// Chargement des images du set de données MNIST // Chargement des images du set de données MNIST
int* parameters = read_mnist_images_parameters(image_file); int* parameters = read_mnist_images_parameters(image_file);
int nb_images = parameters[0]; int nb_images = parameters[0];
@ -67,15 +86,27 @@ void train(int batches, int couches, int neurons, char* recovery, char* image_fi
for (int i=0; i < batches; i++) { for (int i=0; i < batches; i++) {
printf("Batch [%d/%d]", i, batches); printf("Batch [%d/%d]", i, batches);
accuracy = 0.;
for (int j=0; j < nb_images; j++) { for (int j=0; j < nb_images; j++) {
printf("\rBatch [%d/%d]\tImage [%d/%d]",i, batches, j, nb_images); printf("\rBatch [%d/%d]\tImage [%d/%d]",i, batches, j, nb_images);
ecrire_image_dans_reseau(images[j], reseau, height, width); ecrire_image_dans_reseau(images[j], reseau, height, width);
sortie_voulue = creation_de_la_sortie_voulue(reseau, labels[j]); sortie_voulue = creation_de_la_sortie_voulue(reseau, labels[j]);
forward_propagation(reseau); forward_propagation(reseau);
for (int k=0; k < nb_neurones_der; k++) {
sortie[k] = der_couche->neurones[k]->activation;
}
if (indice_max(sortie, nb_neurones_der) == labels[j]) {
accuracy += 1. / (float)nb_images;
}
backward_propagation(reseau, sortie_voulue); backward_propagation(reseau, sortie_voulue);
} }
// TODO: récupération accuracy printf("\rBatch [%d/%d]\tImage [%d/%d]\tAccuracy: %0.1f%%\n",i, batches, nb_images, nb_images, accuracy*100);
printf("\rBatch [%d/%d]\tImage [%d/%d]\tAccuracy: %d%%\n",i, batches, nb_images, nb_images, 1);
modification_du_reseau_neuronal(reseau); modification_du_reseau_neuronal(reseau);
ecrire_reseau(out, reseau); ecrire_reseau(out, reseau);
} }
@ -103,9 +134,11 @@ void recognize(char* modele, char* entree, char* sortie) {
ecrire_image_dans_reseau(images[i], reseau, height, width); ecrire_image_dans_reseau(images[i], reseau, height, width);
forward_propagation(reseau); forward_propagation(reseau);
for (int j=0; j < derniere_couche->nb_neurones; j++) { for (int j=0; j < derniere_couche->nb_neurones; j++) {
if (! strcmp(sortie, "json")) { if (! strcmp(sortie, "json")) {
printf("%f", derniere_couche->neurones[j]->activation); // CHECK: ->activation ou ->z printf("%f", derniere_couche->neurones[j]->activation); // CHECK: ->activation ou ->z
if (j+1 < derniere_couche->nb_neurones) { if (j+1 < derniere_couche->nb_neurones) {
printf(", "); printf(", ");
} }