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Update main.c

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augustin64 2022-06-01 17:14:41 +02:00
parent bea5eafab7
commit c39e9bab4f
1 changed files with 48 additions and 17 deletions
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65
src/mnist/main.c
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@ -12,6 +12,10 @@
#define EPOCHS 10
#define BATCHES 100
#ifdef __CUDACC__
# include "cuda_utils.cu"
# define MAX_CUDA_THREADS 1024 // from NVIDIA documentation
#endif
typedef struct TrainParameters {
Network* network;
@ -119,6 +123,8 @@ void* train_images(void* parameters) {
}
free(sortie);
param->accuracy = accuracy;
return NULL;
}
@ -129,13 +135,18 @@ void train(int epochs, int layers, int neurons, char* recovery, char* image_file
//int* repartition = malloc(sizeof(int)*layers);
int nb_neurons_last_layer = 10;
int repartition[2] = {784, nb_neurons_last_layer};
int repartition[2] = {neurons, nb_neurons_last_layer};
float accuracy;
#ifdef __CUDACC__
printf("Utilisation du GPU\n");
int nb_threads = MAX_CUDA_THREADS;
#else
printf("Pas d'utilisation du GPU\n");
int nb_threads = get_nprocs();
pthread_t *tid = (pthread_t *)malloc(nb_threads * sizeof(pthread_t));
//generer_repartition(layers, repartition);
#endif
/*
* On repart d'un réseau déjà créée stocké dans un fichier
@ -174,6 +185,11 @@ void train(int epochs, int layers, int neurons, char* recovery, char* image_file
int*** images = read_mnist_images(image_file);
unsigned int* labels = read_mnist_labels(label_file);
#ifdef __CUDACC__
int*** images_cuda = copy_images_cuda(images, nb_images_total, width, height);
unsigned int* labels_cuda = copy_labels_cuda(labels);
#endif
if (nb_images_to_process != -1) {
nb_images_total = nb_images_to_process;
}
@ -199,10 +215,20 @@ void train(int epochs, int layers, int neurons, char* recovery, char* image_file
}
nb_remaining_images -= train_parameters[j]->nb_images;
#ifdef __CUDACC__
// Création des threads sur le GPU
#else
// Création des threads sur le CPU
pthread_create( &tid[j], NULL, train_images, (void*) train_parameters[j]);
#endif
}
for(int j=0; j < nb_threads; j++ ) {
#ifdef __CUDACC__
// On join les threads créés sur le GPU
#else
// On join les threads créés sur le CPU
pthread_join( tid[j], NULL );
#endif
accuracy += train_parameters[j]->accuracy / (float) nb_images_total;
if (delta != NULL)
patch_delta(delta_network, train_parameters[j]->network, train_parameters[j]->nb_images);
@ -223,12 +249,17 @@ void train(int epochs, int layers, int neurons, char* recovery, char* image_file
}
deletion_of_network(network);
free(train_parameters);
#ifdef __CUDACC__
// On libère les espaces mémoires utilisés sur le GPU
#else
// On libère les espaces mémoire utilisés spécialement sur le CPU
free(tid);
#endif
}
float** recognize(char* modele, char* entree) {
Network* network = read_network(modele);
Layer* derniere_layer = network->layers[network->nb_layers-1];
Layer* last_layer = network->layers[network->nb_layers-1];
int* parameters = read_mnist_images_parameters(entree);
int nb_images = parameters[0];
@ -239,13 +270,13 @@ float** recognize(char* modele, char* entree) {
float** results = (float**)malloc(sizeof(float*)*nb_images);
for (int i=0; i < nb_images; i++) {
results[i] = (float*)malloc(sizeof(float)*derniere_layer->nb_neurons);
results[i] = (float*)malloc(sizeof(float)*last_layer->nb_neurons);
write_image_in_network(images[i], network, height, width);
forward_propagation(network);
for (int j=0; j < derniere_layer->nb_neurons; j++) {
results[i][j] = derniere_layer->neurons[j]->z;
for (int j=0; j < last_layer->nb_neurons; j++) {
results[i][j] = last_layer->neurons[j]->z;
}
}
deletion_of_network(network);
@ -262,7 +293,7 @@ void print_recognize(char* modele, char* entree, char* sortie) {
int* parameters = read_mnist_images_parameters(entree);
int nb_images = parameters[0];
float** resultats = recognize(modele, entree);
float** results = recognize(modele, entree);
if (! strcmp(sortie, "json")) {
printf("{\n");
@ -275,15 +306,15 @@ void print_recognize(char* modele, char* entree, char* sortie) {
for (int j=0; j < nb_last_layer; j++) {
if (! strcmp(sortie, "json")) {
printf("%f", resultats[i][j]);
printf("%f", results[i][j]);
if (j+1 < nb_last_layer) {
printf(", ");
}
} else
printf("Probabilité %d: %f\n", j, resultats[i][j]);
printf("Probabilité %d: %f\n", j, results[i][j]);
}
free(resultats[i]);
free(results[i]);
if (! strcmp(sortie, "json")) {
if (i+1 < nb_images) {
printf("],\n");
@ -292,7 +323,7 @@ void print_recognize(char* modele, char* entree, char* sortie) {
}
}
}
free(resultats);
free(results);
free(parameters);
if (! strcmp(sortie, "json")) {
printf("}\n");
@ -311,22 +342,22 @@ void test(char* modele, char* fichier_images, char* fichier_labels, bool preview
int height = parameters[2];
int*** images = read_mnist_images(fichier_images);
float** resultats = recognize(modele, fichier_images);
float** results = recognize(modele, fichier_images);
unsigned int* labels = read_mnist_labels(fichier_labels);
float accuracy = 0.;
for (int i=0; i < nb_images; i++) {
if (indice_max(resultats[i], nb_last_layer) == (int)labels[i]) {
if (indice_max(results[i], nb_last_layer) == (int)labels[i]) {
accuracy += 1. / (float)nb_images;
} else if (preview_fails) {
printf("--- Image %d, %d --- Prévision: %d ---\n", i, labels[i], indice_max(resultats[i], nb_last_layer));
print_image(width, height, images[i], resultats[i]);
printf("--- Image %d, %d --- Prévision: %d ---\n", i, labels[i], indice_max(results[i], nb_last_layer));
print_image(width, height, images[i], results[i]);
}
free(resultats[i]);
free(results[i]);
}
printf("%d Images\tAccuracy: %0.1f%%\n", nb_images, accuracy*100);
free(parameters);
free(resultats);
free(results);
}