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https://github.com/augustin64/projet-tipe
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dense: Add random offset option
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@ -28,7 +28,7 @@ void help(char* call);
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* network: réseau neuronal
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* height, width: dimensions de l'image
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*/
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void write_image_in_network(int** image, Network* network, int height, int width);
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void write_image_in_network(int** image, Network* network, int height, int width, bool random_offset);
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/*
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* Sous fonction de 'train' assignée à un thread
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@ -49,7 +49,7 @@ void* train_thread(void* parameters);
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* nb_images_to_process: nombre d'images sur lesquelles entraîner le réseau (-1 si non utilisé)
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* start: index auquel démarrer si nb_images_to_process est utilisé (0 si non utilisé)
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*/
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void train(int epochs, char* recovery, char* image_file, char* label_file, char* out, char* delta, int nb_images_to_process, int start);
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void train(int epochs, char* recovery, char* image_file, char* label_file, char* out, char* delta, int nb_images_to_process, int start, bool random_offset);
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/*
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* Échange deux éléments d'un tableau
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@ -66,7 +66,7 @@ void knuth_shuffle(int* tab, int n);
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* modele: nom du fichier contenant le réseau neuronal
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* entree: nom du fichier contenant les images à reconnaître
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*/
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float** recognize(char* modele, char* entree);
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float** recognize(char* modele, char* entree, bool random_offset);
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/*
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* Renvoie les prédictions d'images sur stdout
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@ -74,7 +74,7 @@ float** recognize(char* modele, char* entree);
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* entree: fichier contenant les images
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* sortie: vaut 'text' ou 'json', spécifie le format auquel afficher les prédictions
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*/
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void print_recognize(char* modele, char* entree, char* sortie);
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void print_recognize(char* modele, char* entree, char* sortie, bool random_offset);
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/*
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* Teste un réseau neuronal avec un fichier d'images ainsi que leurs propriétés
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@ -83,7 +83,7 @@ void print_recognize(char* modele, char* entree, char* sortie);
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* fichier_labels: nom du fichier contenant les labels
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* preview_fails: faut-il afficher les images qui ne sont pas correctement reconnues ?
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*/
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void test(char* modele, char* fichier_images, char* fichier_labels, bool preview_fails);
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void test(char* modele, char* fichier_images, char* fichier_labels, bool preview_fails, bool random_offset);
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int main(int argc, char* argv[]);
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@ -22,8 +22,8 @@
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#define PRINT_BIAIS false
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// Mettre à 1 pour désactiver
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#define DROPOUT 0.7
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#define ENTRY_DROPOUT 0.85
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#define DROPOUT 1
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#define ENTRY_DROPOUT 1
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bool drop(float prob);
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104
src/dense/main.c
104
src/dense/main.c
@ -29,6 +29,7 @@ typedef struct TrainParameters {
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int height;
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int width;
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float accuracy;
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bool offset;
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} TrainParameters;
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@ -71,6 +72,7 @@ void help(char* call) {
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printf("\t\t--delta | -d [FILENAME]\tFichier où écrire le réseau différentiel.\n");
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printf("\t\t--nb-images | -N [int]\tNombres d'images à traiter.\n");
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printf("\t\t--start | -s [int]\tPremière image à traiter.\n");
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printf("\t\t--offset \tActiver le décalage aléatoire.\n");
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printf("\trecognize:\n");
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printf("\t\t--modele | -m [FILENAME]\tFichier contenant le réseau de neurones.\n");
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printf("\t\t--in | -i [FILENAME]\tFichier contenant les images à reconnaître.\n");
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@ -80,16 +82,67 @@ void help(char* call) {
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printf("\t\t--labels | -l [FILENAME]\tFichier contenant les labels.\n");
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printf("\t\t--modele | -m [FILENAME]\tFichier contenant le réseau de neurones.\n");
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printf("\t\t--preview-fails | -p\tAfficher les images ayant échoué.\n");
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printf("\t\t--offset \tActiver le décalage aléatoire.\n");
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}
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void write_image_in_network(int** image, Network* network, int height, int width) {
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for (int i=0; i < height; i++) {
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for (int j=0; j < width; j++) {
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if (!drop(ENTRY_DROPOUT)) {
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network->layers[0]->neurons[i*height+j]->z = (float)image[i][j] / 255.0f;
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void write_image_in_network(int** image, Network* network, int height, int width, bool random_offset) {
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int i_offset = 0;
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int j_offset = 0;
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int min_col = 0;
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int min_ligne = 0;
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if (random_offset) {
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int sum_colonne[width];
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int sum_ligne[height];
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for (int i=0; i < width; i++) {
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sum_colonne[i] = 0;
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}
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for (int j=0; j < height; j++) {
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sum_ligne[j] = 0;
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}
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for (int i=0; i < width; i++) {
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for (int j=0; j < height; j++) {
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sum_ligne[i] += image[i][j];
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sum_colonne[j] += image[i][j];
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}
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}
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min_ligne = -1;
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while (sum_ligne[min_ligne+1] == 0 && min_ligne < width+1) {
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min_ligne++;
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}
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int max_ligne = width;
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while (sum_ligne[max_ligne-1] == 0 && max_ligne > 0) {
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max_ligne--;
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}
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min_col = -1;
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while (sum_colonne[min_col+1] == 0 && min_col < height+1) {
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min_col++;
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}
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int max_col = height;
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while (sum_colonne[max_col-1] == 0 && max_col > 0) {
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max_col--;
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}
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i_offset = 27-max_ligne+min_ligne == 0 ? 0 : rand()%(27-max_ligne+min_ligne);
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j_offset = 27 - max_col + min_col == 0 ? 0 : rand()%(27-max_col+min_col);
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}
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for (int i=0; i < width; i++) {
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for (int j=0; j < height; j++) {
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int adjusted_i = i + min_ligne - i_offset;
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int adjusted_j = j + min_col - j_offset;
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// Make sure not to be out of the image
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if (!drop(ENTRY_DROPOUT) && adjusted_i < height && adjusted_j < width && adjusted_i >= 0 && adjusted_j >= 0) {
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network->layers[0]->neurons[i*height+j]->z = (float)image[adjusted_i][adjusted_j] / 255.0f;
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} else {
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network->layers[0]->neurons[i*height+j]->z = 0;
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network->layers[0]->neurons[i*height+j]->z = 0.;
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}
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}
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}
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@ -114,7 +167,7 @@ void* train_thread(void* parameters) {
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int* desired_output;
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for (int i=start; i < start+nb_images; i++) {
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write_image_in_network(images[shuffle[i]], network, height, width);
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write_image_in_network(images[shuffle[i]], network, height, width, param->offset);
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desired_output = desired_output_creation(network, labels[shuffle[i]]);
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forward_propagation(network, true);
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backward_propagation(network, desired_output);
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@ -134,7 +187,7 @@ void* train_thread(void* parameters) {
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}
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void train(int epochs, char* recovery, char* image_file, char* label_file, char* out, char* delta, int nb_images_to_process, int start) {
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void train(int epochs, char* recovery, char* image_file, char* label_file, char* out, char* delta, int nb_images_to_process, int start, bool offset) {
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// Entraînement du réseau sur le set de données MNIST
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Network* network;
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Network* delta_network;
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@ -207,6 +260,7 @@ void train(int epochs, char* recovery, char* image_file, char* label_file, char*
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train_parameters[j]->width = width;
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train_parameters[j]->nb_images = BATCHES / nb_threads;
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train_parameters[j]->shuffle_indices = shuffle_indices;
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train_parameters[j]->offset = offset;
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}
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for (int i=0; i < epochs; i++) {
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@ -245,7 +299,7 @@ void train(int epochs, char* recovery, char* image_file, char* label_file, char*
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if (delta != NULL)
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write_delta_network(delta, delta_network);
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test(out, "data/mnist/t10k-images-idx3-ubyte", "data/mnist/t10k-labels-idx1-ubyte", false);
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test(out, "data/mnist/t10k-images-idx3-ubyte", "data/mnist/t10k-labels-idx1-ubyte", false, offset);
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}
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write_network(out, network);
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if (delta != NULL) {
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@ -283,7 +337,7 @@ void knuth_shuffle(int* tab, int n) {
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}
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}
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float** recognize(char* modele, char* entree) {
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float** recognize(char* modele, char* entree, bool offset) {
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Network* network = read_network(modele);
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Layer* last_layer = network->layers[network->nb_layers-1];
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@ -299,7 +353,7 @@ float** recognize(char* modele, char* entree) {
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for (int i=0; i < nb_images; i++) {
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results[i] = (float*)malloc(sizeof(float)*last_layer->nb_neurons);
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write_image_in_network(images[i], network, height, width);
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write_image_in_network(images[i], network, height, width, offset);
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forward_propagation(network, false);
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for (int j=0; j < last_layer->nb_neurons; j++) {
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@ -310,7 +364,7 @@ float** recognize(char* modele, char* entree) {
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return results;
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}
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void print_recognize(char* modele, char* entree, char* sortie) {
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void print_recognize(char* modele, char* entree, char* sortie, bool offset) {
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Network* network = read_network(modele);
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int nb_last_layer = network->layers[network->nb_layers-1]->nb_neurons;
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@ -319,7 +373,7 @@ void print_recognize(char* modele, char* entree, char* sortie) {
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int* parameters = read_mnist_images_parameters(entree);
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int nb_images = parameters[0];
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float** results = recognize(modele, entree);
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float** results = recognize(modele, entree, offset);
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if (! strcmp(sortie, "json")) {
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printf("{\n");
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@ -356,7 +410,7 @@ void print_recognize(char* modele, char* entree, char* sortie) {
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}
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}
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void test(char* modele, char* fichier_images, char* fichier_labels, bool preview_fails) {
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void test(char* modele, char* fichier_images, char* fichier_labels, bool preview_fails, bool offset) {
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Network* network = read_network(modele);
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int nb_last_layer = network->layers[network->nb_layers-1]->nb_neurons;
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@ -368,7 +422,7 @@ void test(char* modele, char* fichier_images, char* fichier_labels, bool preview
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int height = parameters[2];
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int*** images = read_mnist_images(fichier_images);
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float** results = recognize(modele, fichier_images);
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float** results = recognize(modele, fichier_images, offset);
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unsigned int* labels = read_mnist_labels(fichier_labels);
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float accuracy = 0.;
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@ -402,6 +456,8 @@ int main(int argc, char* argv[]) {
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char* recovery = NULL;
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char* out = NULL;
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char* delta = NULL;
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bool offset = false;
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int i = 2;
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while (i < argc) {
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// Utiliser un switch serait sans doute plus élégant
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@ -429,6 +485,9 @@ int main(int argc, char* argv[]) {
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} else if ((! strcmp(argv[i], "--start"))||(! strcmp(argv[i], "-s"))) {
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start = strtol(argv[i+1], NULL, 10);
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i += 2;
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} else if (! strcmp(argv[i], "--offset")) {
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offset = true;
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i++;
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} else {
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printf("%s : Argument non reconnu\n", argv[i]);
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i++;
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@ -446,8 +505,8 @@ int main(int argc, char* argv[]) {
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printf("Pas de fichier de sortie spécifié, default: out.bin\n");
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out = "out.bin";
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}
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// Entraînement en sourçant neural_network.c
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train(epochs, recovery, images, labels, out, delta, nb_images, start);
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// Entraînement (dans neural_network.c)
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train(epochs, recovery, images, labels, out, delta, nb_images, start, offset);
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return 0;
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}
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if (! strcmp(argv[1], "recognize")) {
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@ -481,7 +540,7 @@ int main(int argc, char* argv[]) {
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if (! out) {
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out = "text";
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}
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print_recognize(modele, in, out);
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print_recognize(modele, in, out, false);
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// Reconnaissance puis affichage des données sous le format spécifié
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return 0;
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}
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@ -490,6 +549,7 @@ int main(int argc, char* argv[]) {
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char* images = NULL;
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char* labels = NULL;
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bool preview_fails = false;
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bool offset = false;
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int i = 2;
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while (i < argc) {
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if ((! strcmp(argv[i], "--images"))||(! strcmp(argv[i], "-i"))) {
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@ -504,9 +564,15 @@ int main(int argc, char* argv[]) {
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} else if ((! strcmp(argv[i], "--preview-fails"))||(! strcmp(argv[i], "-p"))) {
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preview_fails = true;
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i++;
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} else if (! strcmp(argv[i], "--offset")) {
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offset = true;
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i++;
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} else {
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printf("%s : Argument non reconnu\n", argv[i]);
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i++;
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}
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}
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test(modele, images, labels, preview_fails);
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test(modele, images, labels, preview_fails, offset);
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return 0;
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}
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printf("Option choisie non reconnue: %s\n", argv[1]);
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