augustin64/tipe
1
1
Fork 0
mirror of https://github.com/augustin64/projet-tipe synced 2025-01-23 23:26:25 +01:00
Code Issues Packages Projects Releases Wiki Activity

Add preview fails option & loss stat

Browse Source
This commit is contained in:
augustin64 2022-04-26 16:47:43 +02:00
parent 34cb91c68a
commit 95222fdf81
1 changed files with 32 additions and 7 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

39
src/mnist/main.c
View File

@ -7,6 +7,19 @@
#include "neuron_io.c"
#include "mnist.c"
void print_image(unsigned int width, unsigned int height, int** image, float* previsions) {
char tab[] = {' ', '.', ':', '%', '#', '\0'};
for (int i=0; i < height; i++) {
for (int j=0; j < width; j++) {
printf("%c", tab[image[i][j]/52]);
}
if (i < 10) {
printf("\t%d : %f", i, previsions[i]);
}
printf("\n");
}
}
int indice_max(float* tab, int n) {
int indice = -1;
@ -26,7 +39,7 @@ void help(char* call) {
printf("OPTIONS:\n");
printf("\ttrain:\n");
printf("\t\t--batches | -b [int]\tNombre de batches.\n");
printf("\t\t--layers | -c [int]\tNombres de layers.\n");
printf("\t\t--layers | -c [int]\tNombres de layers.\n");
printf("\t\t--neurons | -n [int]\tNombre de neurons sur la première layer.\n");
printf("\t\t--recover | -r [FILENAME]\tRécupérer depuis un modèle existant.\n");
printf("\t\t--images | -i [FILENAME]\tFichier contenant les images.\n");
@ -40,6 +53,7 @@ void help(char* call) {
printf("\t\t--images | -i [FILENAME]\tFichier contenant les images.\n");
printf("\t\t--labels | -l [FILENAME]\tFichier contenant les labels.\n");
printf("\t\t--modele | -m [FILENAME]\tFichier contenant le réseau de neurons.\n");
printf("\t\t--preview-fails | -p\tAfficher les images ayant échoué.\n");
}
@ -58,11 +72,12 @@ void train(int batches, int layers, int neurons, char* recovery, char* image_fil
//int* repartition = malloc(sizeof(int)*layers);
int nb_neurons_der = 10;
int repartition[3] = {784, 32, nb_neurons_der};
int repartition[2] = {784, nb_neurons_der};
float* sortie = malloc(sizeof(float)*nb_neurons_der);
int* desired_output;
float accuracy;
float loss;
//generer_repartition(layers, repartition);
/*
@ -92,6 +107,7 @@ void train(int batches, int layers, int neurons, char* recovery, char* image_fil
for (int i=0; i < batches; i++) {
printf("Batch [%d/%d]", i, batches);
accuracy = 0.;
loss = 0.;
for (int j=0; j < nb_images; j++) {
printf("\rBatch [%d/%d]\tImage [%d/%d]",i, batches, j, nb_images);
@ -107,10 +123,11 @@ void train(int batches, int layers, int neurons, char* recovery, char* image_fil
if (indice_max(sortie, nb_neurons_der) == labels[j]) {
accuracy += 1. / (float)nb_images;
}
loss += loss_computing(network, labels[j]) / (float)nb_images;
free(desired_output);
}
network_modification(network, nb_images);
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: %0.1f%%\tLoss: %f\n",i, batches, nb_images, nb_images, accuracy*100, loss);
write_network(out, network);
}
deletion_of_network(network);
@ -186,7 +203,7 @@ void print_recognize(char* modele, char* entree, char* sortie) {
}
void test(char* modele, char* fichier_images, char* fichier_labels) {
void test(char* modele, char* fichier_images, char* fichier_labels, bool preview_fails) {
Network* network = read_network(modele);
int nb_der_layer = network->layers[network->nb_layers-1]->nb_neurons;
@ -194,6 +211,7 @@ void test(char* modele, char* fichier_images, char* fichier_labels) {
int* parameters = read_mnist_images_parameters(fichier_images);
int nb_images = parameters[0];
int*** images = read_mnist_images(fichier_images);
float** resultats = recognize(modele, fichier_images);
unsigned int* labels = read_mnist_labels(fichier_labels);
@ -201,7 +219,10 @@ void test(char* modele, char* fichier_images, char* fichier_labels) {
for (int i=0; i < nb_images; i++) {
if (indice_max(resultats[i], nb_der_layer) == labels[i]) {
accuracy += 1. / (float)nb_images;
accuracy += 1. / (float)nb_images;
} else {
printf("--- Image %d, %d --- Prévision: %d ---\n", i, labels[i], indice_max(resultats[i], nb_der_layer));
print_image(28, 28, images[i], resultats[i]);
}
}
printf("%d Images\tAccuracy: %0.1f%%\n", nb_images, accuracy*100);
@ -216,7 +237,7 @@ int main(int argc, char* argv[]) {
}
if (! strcmp(argv[1], "train")) {
int batches = 100;
int layers = 3;
int layers = 2;
int neurons = 784;
char* images = NULL;
char* labels = NULL;
@ -307,6 +328,7 @@ int main(int argc, char* argv[]) {
char* modele = NULL;
char* images = NULL;
char* labels = NULL;
bool preview_fails = false;
int i = 2;
while (i < argc) {
if ((! strcmp(argv[i], "--images"))||(! strcmp(argv[i], "-i"))) {
@ -318,9 +340,12 @@ int main(int argc, char* argv[]) {
} else if ((! strcmp(argv[i], "--modele"))||(! strcmp(argv[i], "-m"))) {
modele = argv[i+1];
i += 2;
} else if ((! strcmp(argv[i], "--preview-fails"))||(! strcmp(argv[i], "-p"))) {
preview_fails = true;
i++;
}
}
test(modele, images, labels);
test(modele, images, labels, preview_fails);
exit(0);
}
printf("Option choisie non reconnue: %s\n", argv[1]);