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https://github.com/augustin64/projet-tipe
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Add random offset to mnist images during learning
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@ -1,7 +1,8 @@
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#include <stdbool.h>
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#include <stdlib.h>
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#include <stdio.h>
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#include <math.h>
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#include <float.h> // Is it used ?
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#include <math.h>
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#include "include/backpropagation.h"
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#include "include/initialisation.h"
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@ -33,7 +34,80 @@ int will_be_drop(int dropout_prob) {
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return (rand() % 100) < dropout_prob;
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}
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void write_image_in_network_32(int** image, int height, int width, float** input) {
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void write_image_in_network_32(int** image, int height, int width, float** input, 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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/*
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<-- min_ligne
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.%%:.
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######%%%%%%%%%.
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.:.:%##########:
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. .... ##:
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.##
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##.
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:##
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.##.
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:#%
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%#.
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:#%
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.##.
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##%
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%##
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##.
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##:
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:##.
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.###.
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:###
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:#%
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<-- max_ligne
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^-- min_col
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^-- max_col
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*/
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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 = rand()%(27-max_ligne+min_ligne);
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j_offset = rand()%(27-max_col+min_col);
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}
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int padding = (32 - height)/2;
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for (int i=0; i < padding; i++) {
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for (int j=0; j < 32; j++) {
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@ -46,7 +120,10 @@ void write_image_in_network_32(int** image, int height, int width, float** input
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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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input[i+2][j+2] = (float)image[i][j] / 255.0f;
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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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input[i+2][j+2] = adjusted_i < height && adjusted_j < width && adjusted_i >= 0 && adjusted_j >= 0 ? (float)image[adjusted_i][adjusted_j] / 255.0f : 0.;
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}
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}
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}
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@ -17,7 +17,7 @@ int will_be_drop(int dropout_prob);
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/*
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* Écrit une image 28*28 au centre d'un tableau 32*32 et met à 0 le reste
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*/
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void write_image_in_network_32(int** image, int height, int width, float** input);
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void write_image_in_network_32(int** image, int height, int width, float** input, bool random_offset);
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/*
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* Écrit une image linéarisée de 256*256*3 pixels dans un tableau de taille 260*260*3
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@ -6,7 +6,7 @@
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/*
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* Renvoie le taux de réussite d'un réseau sur des données de test
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*/
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void test_network(int dataset_type, char* modele, char* images_file, char* labels_file, char* data_dir, bool preview_fails);
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float* test_network(int dataset_type, char* modele, char* images_file, char* labels_file, char* data_dir, bool preview_fails, bool to_stdout, bool with_offset);
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/*
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* Classifie un fichier d'images sous le format MNIST à partir d'un réseau préalablement entraîné
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@ -188,7 +188,7 @@ int main(int argc, char* argv[]) {
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printf_error("Pas de modèle à utiliser spécifié.\n");
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return 1;
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}
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test_network(dataset_type, modele, images_file, labels_file, data_dir, preview_fails);
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(void)test_network(dataset_type, modele, images_file, labels_file, data_dir, preview_fails, true, false);
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return 0;
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}
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if (! strcmp(argv[1], "recognize")) {
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@ -12,7 +12,7 @@
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#include "include/cnn.h"
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void test_network_mnist(Network* network, char* images_file, char* labels_file, bool preview_fails) {
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float* test_network_mnist(Network* network, char* images_file, char* labels_file, bool preview_fails, bool to_stdout, bool with_offset) {
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(void)preview_fails; // Inutilisé pour le moment
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int width, height; // Dimensions des images
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int nb_elem; // Nombre d'éléments
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@ -36,11 +36,11 @@ void test_network_mnist(Network* network, char* images_file, char* labels_file,
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// Load image in the first layer of the Network
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for (int i=0; i < nb_elem; i++) {
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if(i %(nb_elem/100) == 0) {
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if(i %(nb_elem/100) == 0 && to_stdout) {
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printf("Avancement: %.0f%%\r", 100*i/(float)nb_elem);
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fflush(stdout);
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}
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write_image_in_network_32(images[i], height, width, network->input[0][0]);
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write_image_in_network_32(images[i], height, width, network->input[0][0], with_offset);
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forward_propagation(network);
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maxi = indice_max(network->input[network->size-1][0][0], 10);
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@ -59,11 +59,15 @@ void test_network_mnist(Network* network, char* images_file, char* labels_file,
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free(images[i]);
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}
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free(images);
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printf("%d Images. Taux de réussite: %.2f%%\tLoss: %lf\n", nb_elem, 100*accuracy/(float)nb_elem, loss/nb_elem);
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float* results = malloc(sizeof(float)*2);
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results[0] = 100*accuracy/(float)nb_elem;
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results[1] = loss/(float)nb_elem;
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return results;
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}
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void test_network_jpg(Network* network, char* data_dir, bool preview_fails) {
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float* test_network_jpg(Network* network, char* data_dir, bool preview_fails, bool to_stdout) {
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(void)preview_fails; // Inutilisé pour le moment
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jpegDataset* dataset = loadJpegDataset(data_dir);
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@ -71,7 +75,7 @@ void test_network_jpg(Network* network, char* data_dir, bool preview_fails) {
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int maxi;
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for (int i=0; i < (int)dataset->numImages; i++) {
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if(i %(dataset->numImages/100) == 0) {
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if(i %(dataset->numImages/100) == 0 && to_stdout) {
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printf("Avancement: %.1f%%\r", 1000*i/(float)dataset->numImages);
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fflush(stdout);
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}
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@ -86,23 +90,35 @@ void test_network_jpg(Network* network, char* data_dir, bool preview_fails) {
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free(dataset->images[i]);
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}
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printf("%d Images. Taux de réussite: %.2f%%\n", dataset->numImages, 100*accuracy/(float)dataset->numImages);
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float* results = malloc(sizeof(float)*2);
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results[0] = 100*accuracy/(float)dataset->numImages;
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results[1] = 0;
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free(dataset->images);
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free(dataset->labels);
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free(dataset);
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return results;
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}
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void test_network(int dataset_type, char* modele, char* images_file, char* labels_file, char* data_dir, bool preview_fails) {
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float* test_network(int dataset_type, char* modele, char* images_file, char* labels_file, char* data_dir, bool preview_fails, bool to_stdout, bool with_offset) {
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Network* network = read_network(modele);
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float* results;
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if (dataset_type == 0) {
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test_network_mnist(network, images_file, labels_file, preview_fails);
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results = test_network_mnist(network, images_file, labels_file, preview_fails, to_stdout, with_offset);
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} else {
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test_network_jpg(network, data_dir, preview_fails);
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results = test_network_jpg(network, data_dir, preview_fails, to_stdout);
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}
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free_network(network);
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if (to_stdout) {
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printf("Accuracy: %lf\tLoss: %lf\n", results[0], results[1]);
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free(results); // Will not be used if to_stdout is used
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}
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return results;
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}
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@ -130,7 +146,7 @@ void recognize_mnist(Network* network, char* input_file, char* out) {
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printf("\"%d\" : [", i);
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}
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write_image_in_network_32(images[i], height, width, network->input[0][0]);
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write_image_in_network_32(images[i], height, width, network->input[0][0], false);
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forward_propagation(network);
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@ -51,7 +51,7 @@ void* train_thread(void* parameters) {
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for (int i=start; i < start+nb_images; i++) {
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if (dataset_type == 0) {
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write_image_in_network_32(images[index[i]], height, width, network->input[0][0]);
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write_image_in_network_32(images[index[i]], height, width, network->input[0][0], true);
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forward_propagation(network);
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maxi = indice_max(network->input[network->size-1][0][0], 10);
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if (maxi == -1) {
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@ -106,6 +106,7 @@ void train(int dataset_type, char* images_file, char* labels_file, char* data_di
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srand(time(NULL));
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float loss;
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float batch_loss; // May be redundant with loss, but gives more informations
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float test_accuracy = 0.; // Used to decrease Learning rate
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float accuracy;
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float batch_accuracy;
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float current_accuracy;
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@ -233,7 +234,7 @@ void train(int dataset_type, char* images_file, char* labels_file, char* data_di
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end_time = omp_get_wtime();
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elapsed_time = end_time - start_time;
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printf("Taux d'apprentissage initial: %lf\n", network->learning_rate);
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printf("Taux d'apprentissage initial: %0.2e\n", network->learning_rate);
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printf("Initialisation: ");
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printf_time(elapsed_time);
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printf("\n\n");
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@ -308,7 +309,6 @@ void train(int dataset_type, char* images_file, char* labels_file, char* data_di
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}
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current_accuracy = accuracy * nb_images_total/((j+1)*BATCHES);
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printf("\rThreads [%d]\tÉpoque [%d/%d]\tImage [%d/%d]\tAccuracy: " YELLOW "%0.2f%%" RESET " \tBatch Accuracy: " YELLOW "%0.2f%%" RESET, nb_threads, i, epochs, BATCHES*(j+1), nb_images_total, current_accuracy*100, batch_accuracy*100);
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fflush(stdout);
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#else
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(void)nb_images_total_remaining; // Juste pour enlever un warning
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@ -331,7 +331,6 @@ void train(int dataset_type, char* images_file, char* labels_file, char* data_di
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update_bias(network, network);
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printf("\rÉpoque [%d/%d]\tImage [%d/%d]\tAccuracy: " YELLOW "%0.4f%%" RESET "\tBatch Accuracy: " YELLOW "%0.2f%%" RESET, i, epochs, BATCHES*(j+1), nb_images_total, current_accuracy*100, batch_accuracy*100);
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fflush(stdout);
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#endif
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}
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//* Fin d'une époque: affichage des résultats et sauvegarde du réseau
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@ -347,11 +346,25 @@ void train(int dataset_type, char* images_file, char* labels_file, char* data_di
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printf("\n");
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#endif
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write_network(out, network);
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// If you want to test the network between each epoch, uncomment the following line:
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test_network(0, out, "data/mnist/t10k-images-idx3-ubyte", "data/mnist/t10k-labels-idx1-ubyte", NULL, false);
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// If you want to test the network between each epoch, uncomment the following lines:
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/*
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float* test_results = test_network(0, out, "data/mnist/t10k-images-idx3-ubyte", "data/mnist/t10k-labels-idx1-ubyte", NULL, false, false, true);
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printf("Tests: Accuracy: %0.2lf%%\tLoss: %lf\n", test_results[0], test_results[1]);
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if (test_results[0] < test_accuracy) {
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network->learning_rate *= 0.1;
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printf("Decreased learning rate to %0.2e\n", network->learning_rate);
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}
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if (test_results[0] == test_accuracy) {
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network->learning_rate *= 2;
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printf("Increased learning rate to %0.2e\n", network->learning_rate);
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}
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test_accuracy = test_results[0];
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free(test_results);
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// Learning Rate decay
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network->learning_rate -= LEARNING_RATE*(1./(float)(epochs+1));
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test_results = test_network(0, out, "data/mnist/t10k-images-idx3-ubyte", "data/mnist/t10k-labels-idx1-ubyte", NULL, false, false, false);
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printf("Tests sans offset: Accuracy: %0.2lf%%\tLoss: %lf\n", test_results[0], test_results[1]);
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free(test_results);
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*/
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}
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//* Fin de l'algo
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