mirror of
https://github.com/augustin64/projet-tipe
synced 2025-01-24 23:46:25 +01:00
356 lines
12 KiB
C
356 lines
12 KiB
C
#include <stdlib.h>
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#include <stdio.h>
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#include <string.h>
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#include <float.h>
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#include "neural_network.c"
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#include "neuron_io.c"
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#include "mnist.c"
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void print_image(unsigned int width, unsigned int height, int** image, float* previsions) {
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char tab[] = {' ', '.', ':', '%', '#', '\0'};
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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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printf("%c", tab[image[i][j]/52]);
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}
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if (i < 10) {
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printf("\t%d : %f", i, previsions[i]);
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}
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printf("\n");
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}
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}
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int indice_max(float* tab, int n) {
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int indice = -1;
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float maxi = FLT_MIN;
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for (int i=0; i < n; i++) {
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if (tab[i] > maxi) {
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maxi = tab[i];
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indice = i;
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}
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}
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return indice;
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}
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void help(char* call) {
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printf("Usage: %s ( train | recognize ) [OPTIONS]\n\n", call);
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printf("OPTIONS:\n");
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printf("\ttrain:\n");
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printf("\t\t--batches | -b [int]\tNombre de batches.\n");
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printf("\t\t--layers | -c [int]\tNombres de layers.\n");
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printf("\t\t--neurons | -n [int]\tNombre de neurons sur la première layer.\n");
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printf("\t\t--recover | -r [FILENAME]\tRécupérer depuis un modèle existant.\n");
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printf("\t\t--images | -i [FILENAME]\tFichier contenant les images.\n");
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printf("\t\t--labels | -l [FILENAME]\tFichier contenant les labels.\n");
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printf("\t\t--out | -o [FILENAME]\tFichier où écrire le réseau de neurons.\n");
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printf("\trecognize:\n");
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printf("\t\t--modele | -m [FILENAME]\tFichier contenant le réseau de neurons.\n");
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printf("\t\t--in | -i [FILENAME]\tFichier contenant les images à reconnaître.\n");
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printf("\t\t--out | -o (text|json)\tFormat de sortie.\n");
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printf("\ttest:\n");
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printf("\t\t--images | -i [FILENAME]\tFichier contenant les images.\n");
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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 neurons.\n");
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printf("\t\t--preview-fails | -p\tAfficher les images ayant échoué.\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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network->layers[0]->neurons[i*height+j]->z = (float)image[i][j] / 255.0f;
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}
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}
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}
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void train(int batches, int layers, int neurons, char* recovery, char* image_file, char* label_file, char* out) {
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// Entraînement du réseau sur le set de données MNIST
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Network* network;
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//int* repartition = malloc(sizeof(int)*layers);
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int nb_neurons_der = 10;
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int repartition[2] = {784, nb_neurons_der};
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float* sortie = malloc(sizeof(float)*nb_neurons_der);
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int* desired_output;
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float accuracy;
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float loss;
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//generer_repartition(layers, repartition);
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/*
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* On repart d'un réseau déjà créée stocké dans un fichier
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* ou on repart de zéro si aucune backup n'est fournie
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* */
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if (! recovery) {
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network = malloc(sizeof(Network));
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network_creation(network, repartition, layers);
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network_initialisation(network);
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} else {
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network = read_network(recovery);
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printf("Backup restaurée.\n");
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}
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Layer* der_layer = network->layers[network->nb_layers-1];
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// Chargement des images du set de données MNIST
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int* parameters = read_mnist_images_parameters(image_file);
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int nb_images = parameters[0];
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int height = parameters[1];
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int width = parameters[2];
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int*** images = read_mnist_images(image_file);
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unsigned int* labels = read_mnist_labels(label_file);
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for (int i=0; i < batches; i++) {
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printf("Batch [%d/%d]", i, batches);
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accuracy = 0.;
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loss = 0.;
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for (int j=0; j < nb_images; j++) {
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printf("\rBatch [%d/%d]\tImage [%d/%d]",i, batches, j, nb_images);
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write_image_in_network(images[j], network, height, width);
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desired_output = desired_output_creation(network, labels[j]);
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forward_propagation(network);
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backward_propagation(network, desired_output);
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for (int k=0; k < nb_neurons_der; k++) {
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sortie[k] = der_layer->neurons[k]->z;
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}
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if (indice_max(sortie, nb_neurons_der) == labels[j]) {
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accuracy += 1. / (float)nb_images;
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}
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loss += loss_computing(network, labels[j]) / (float)nb_images;
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free(desired_output);
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}
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network_modification(network, nb_images);
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printf("\rBatch [%d/%d]\tImage [%d/%d]\tAccuracy: %0.1f%%\tLoss: %f\n",i, batches, nb_images, nb_images, accuracy*100, loss);
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write_network(out, network);
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}
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deletion_of_network(network);
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}
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float** recognize(char* modele, char* entree) {
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Network* network = read_network(modele);
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Layer* derniere_layer = network->layers[network->nb_layers-1];
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int* parameters = read_mnist_images_parameters(entree);
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int nb_images = parameters[0];
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int height = parameters[1];
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int width = parameters[2];
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int*** images = read_mnist_images(entree);
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float** results = malloc(sizeof(float*)*nb_images);
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for (int i=0; i < nb_images; i++) {
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results[i] = malloc(sizeof(float)*derniere_layer->nb_neurons);
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write_image_in_network(images[i], network, height, width);
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forward_propagation(network);
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for (int j=0; j < derniere_layer->nb_neurons; j++) {
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results[i][j] = derniere_layer->neurons[j]->z;
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}
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}
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deletion_of_network(network);
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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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Network* network = read_network(modele);
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int nb_der_layer = network->layers[network->nb_layers-1]->nb_neurons;
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deletion_of_network(network);
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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** resultats = recognize(modele, entree);
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if (! strcmp(sortie, "json")) {
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printf("{\n");
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}
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for (int i=0; i < nb_images; i++) {
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if (! strcmp(sortie, "text"))
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printf("Image %d\n", i);
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else
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printf("\"%d\" : [", i);
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for (int j=0; j < nb_der_layer; j++) {
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if (! strcmp(sortie, "json")) {
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printf("%f", resultats[i][j]);
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if (j+1 < nb_der_layer) {
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printf(", ");
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}
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} else
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printf("Probabilité %d: %f\n", j, resultats[i][j]);
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}
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if (! strcmp(sortie, "json")) {
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if (i+1 < nb_images) {
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printf("],\n");
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} else {
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printf("]\n");
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}
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}
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}
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if (! strcmp(sortie, "json"))
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printf("}\n");
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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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Network* network = read_network(modele);
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int nb_der_layer = network->layers[network->nb_layers-1]->nb_neurons;
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deletion_of_network(network);
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int* parameters = read_mnist_images_parameters(fichier_images);
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int nb_images = parameters[0];
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int width = parameters[1];
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int height = parameters[2];
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int*** images = read_mnist_images(fichier_images);
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float** resultats = recognize(modele, fichier_images);
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unsigned int* labels = read_mnist_labels(fichier_labels);
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float accuracy;
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for (int i=0; i < nb_images; i++) {
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if (indice_max(resultats[i], nb_der_layer) == labels[i]) {
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accuracy += 1. / (float)nb_images;
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} else {
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printf("--- Image %d, %d --- Prévision: %d ---\n", i, labels[i], indice_max(resultats[i], nb_der_layer));
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print_image(width, height, images[i], resultats[i]);
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}
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}
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printf("%d Images\tAccuracy: %0.1f%%\n", nb_images, accuracy*100);
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}
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int main(int argc, char* argv[]) {
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if (argc < 2) {
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printf("Pas d'action spécifiée\n");
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help(argv[0]);
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exit(1);
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}
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if (! strcmp(argv[1], "train")) {
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int batches = 100;
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int layers = 2;
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int neurons = 784;
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char* images = NULL;
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char* labels = NULL;
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char* recovery = NULL;
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char* out = NULL;
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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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if ((! strcmp(argv[i], "--batches"))||(! strcmp(argv[i], "-b"))) {
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batches = strtol(argv[i+1], NULL, 10);
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i += 2;
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} else
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if ((! strcmp(argv[i], "--layers"))||(! strcmp(argv[i], "-c"))) {
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layers = strtol(argv[i+1], NULL, 10);
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i += 2;
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} else if ((! strcmp(argv[i], "--neurons"))||(! strcmp(argv[i], "-n"))) {
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neurons = strtol(argv[i+1], NULL, 10);
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i += 2;
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} else if ((! strcmp(argv[i], "--images"))||(! strcmp(argv[i], "-i"))) {
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images = argv[i+1];
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i += 2;
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} else if ((! strcmp(argv[i], "--labels"))||(! strcmp(argv[i], "-l"))) {
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labels = argv[i+1];
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i += 2;
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} else if ((! strcmp(argv[i], "--recover"))||(! strcmp(argv[i], "-r"))) {
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recovery = argv[i+1];
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i += 2;
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} else if ((! strcmp(argv[i], "--out"))||(! strcmp(argv[i], "-o"))) {
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out = argv[i+1];
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i += 2;
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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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if (! images) {
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printf("Pas de fichier d'images spécifié\n");
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exit(1);
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}
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if (! labels) {
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printf("Pas de fichier de labels spécifié\n");
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exit(1);
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}
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if (! out) {
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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(batches, layers, neurons, recovery, images, labels, out);
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exit(0);
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}
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if (! strcmp(argv[1], "recognize")) {
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char* in = NULL;
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char* modele = NULL;
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char* out = NULL;
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int i = 2;
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while(i < argc) {
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if ((! strcmp(argv[i], "--in"))||(! strcmp(argv[i], "-i"))) {
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in = argv[i+1];
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i += 2;
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} else if ((! strcmp(argv[i], "--modele"))||(! strcmp(argv[i], "-m"))) {
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modele = argv[i+1];
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i += 2;
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} else if ((! strcmp(argv[i], "--out"))||(! strcmp(argv[i], "-o"))) {
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out = argv[i+1];
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i += 2;
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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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if (! in) {
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printf("Pas d'entrée spécifiée\n");
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exit(1);
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}
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if (! modele) {
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printf("Pas de modèle spécifié\n");
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exit(1);
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}
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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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// Reconnaissance puis affichage des données sous le format spécifié
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exit(0);
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}
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if (! strcmp(argv[1], "test")) {
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char* modele = NULL;
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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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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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images = argv[i+1];
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i += 2;
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} else if ((! strcmp(argv[i], "--labels"))||(! strcmp(argv[i], "-l"))) {
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labels = argv[i+1];
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i += 2;
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} else if ((! strcmp(argv[i], "--modele"))||(! strcmp(argv[i], "-m"))) {
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modele = argv[i+1];
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i += 2;
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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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}
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}
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test(modele, images, labels, preview_fails);
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exit(0);
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}
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printf("Option choisie non reconnue: %s\n", argv[1]);
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help(argv[0]);
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return 1;
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} |