diff --git a/src/cnn/cnn.c b/src/cnn/cnn.c
index c18a37b..abc8357 100644
--- a/src/cnn/cnn.c
+++ b/src/cnn/cnn.c
@@ -1,7 +1,8 @@
+#include <stdbool.h>
#include <stdlib.h>
#include <stdio.h>
-#include <math.h>
#include <float.h> // Is it used ?
+#include <math.h>
#include "include/backpropagation.h"
#include "include/initialisation.h"
@@ -33,7 +34,80 @@ int will_be_drop(int dropout_prob) {
return (rand() % 100) < dropout_prob;
}
-void write_image_in_network_32(int** image, int height, int width, float** input) {
+void write_image_in_network_32(int** image, int height, int width, float** input, bool random_offset) {
+ int i_offset = 0;
+ int j_offset = 0;
+ int min_col = 0;
+ int min_ligne = 0;
+
+ if (random_offset) {
+ /*
+ <-- min_ligne
+ .%%:.
+ ######%%%%%%%%%.
+ .:.:%##########:
+ . .... ##:
+ .##
+ ##.
+ :##
+ .##.
+ :#%
+ %#.
+ :#%
+ .##.
+ ##%
+ %##
+ ##.
+ ##:
+ :##.
+ .###.
+ :###
+ :#%
+ <-- max_ligne
+ ^-- min_col
+ ^-- max_col
+ */
+ int sum_colonne[width];
+ int sum_ligne[height];
+
+ for (int i=0; i < width; i++) {
+ sum_colonne[i] = 0;
+ }
+ for (int j=0; j < height; j++) {
+ sum_ligne[j] = 0;
+ }
+
+ for (int i=0; i < width; i++) {
+ for (int j=0; j < height; j++) {
+ sum_ligne[i] += image[i][j];
+ sum_colonne[j] += image[i][j];
+ }
+ }
+
+ min_ligne = -1;
+ while (sum_ligne[min_ligne+1] == 0 && min_ligne < width+1) {
+ min_ligne++;
+ }
+
+ int max_ligne = width;
+ while (sum_ligne[max_ligne-1] == 0 && max_ligne > 0) {
+ max_ligne--;
+ }
+
+ min_col = -1;
+ while (sum_colonne[min_col+1] == 0 && min_col < height+1) {
+ min_col++;
+ }
+
+ int max_col = height;
+ while (sum_colonne[max_col-1] == 0 && max_col > 0) {
+ max_col--;
+ }
+
+ i_offset = rand()%(27-max_ligne+min_ligne);
+ j_offset = rand()%(27-max_col+min_col);
+ }
+
int padding = (32 - height)/2;
for (int i=0; i < padding; i++) {
for (int j=0; j < 32; j++) {
@@ -46,7 +120,10 @@ void write_image_in_network_32(int** image, int height, int width, float** input
for (int i=0; i < width; i++) {
for (int j=0; j < height; j++) {
- input[i+2][j+2] = (float)image[i][j] / 255.0f;
+ int adjusted_i = i + min_ligne - i_offset;
+ int adjusted_j = j + min_col - j_offset;
+ // Make sure not to be out of the image
+ 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.;
}
}
}
diff --git a/src/cnn/include/cnn.h b/src/cnn/include/cnn.h
index 5f117d0..150f9b8 100644
--- a/src/cnn/include/cnn.h
+++ b/src/cnn/include/cnn.h
@@ -17,7 +17,7 @@ int will_be_drop(int dropout_prob);
/*
* Écrit une image 28*28 au centre d'un tableau 32*32 et met à 0 le reste
*/
-void write_image_in_network_32(int** image, int height, int width, float** input);
+void write_image_in_network_32(int** image, int height, int width, float** input, bool random_offset);
/*
* Écrit une image linéarisée de 256*256*3 pixels dans un tableau de taille 260*260*3
diff --git a/src/cnn/include/test_network.h b/src/cnn/include/test_network.h
index de7f62b..109229e 100644
--- a/src/cnn/include/test_network.h
+++ b/src/cnn/include/test_network.h
@@ -6,7 +6,7 @@
/*
* Renvoie le taux de réussite d'un réseau sur des données de test
*/
-void test_network(int dataset_type, char* modele, char* images_file, char* labels_file, char* data_dir, bool preview_fails);
+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);
/*
* Classifie un fichier d'images sous le format MNIST à partir d'un réseau préalablement entraîné
diff --git a/src/cnn/main.c b/src/cnn/main.c
index 2fe2d46..a641b68 100644
--- a/src/cnn/main.c
+++ b/src/cnn/main.c
@@ -188,7 +188,7 @@ int main(int argc, char* argv[]) {
printf_error("Pas de modèle à utiliser spécifié.\n");
return 1;
}
- test_network(dataset_type, modele, images_file, labels_file, data_dir, preview_fails);
+ (void)test_network(dataset_type, modele, images_file, labels_file, data_dir, preview_fails, true, false);
return 0;
}
if (! strcmp(argv[1], "recognize")) {
diff --git a/src/cnn/test_network.c b/src/cnn/test_network.c
index 316aa25..6b83c40 100644
--- a/src/cnn/test_network.c
+++ b/src/cnn/test_network.c
@@ -12,7 +12,7 @@
#include "include/cnn.h"
-void test_network_mnist(Network* network, char* images_file, char* labels_file, bool preview_fails) {
+float* test_network_mnist(Network* network, char* images_file, char* labels_file, bool preview_fails, bool to_stdout, bool with_offset) {
(void)preview_fails; // Inutilisé pour le moment
int width, height; // Dimensions des images
int nb_elem; // Nombre d'éléments
@@ -36,11 +36,11 @@ void test_network_mnist(Network* network, char* images_file, char* labels_file,
// Load image in the first layer of the Network
for (int i=0; i < nb_elem; i++) {
- if(i %(nb_elem/100) == 0) {
+ if(i %(nb_elem/100) == 0 && to_stdout) {
printf("Avancement: %.0f%%\r", 100*i/(float)nb_elem);
fflush(stdout);
}
- write_image_in_network_32(images[i], height, width, network->input[0][0]);
+ write_image_in_network_32(images[i], height, width, network->input[0][0], with_offset);
forward_propagation(network);
maxi = indice_max(network->input[network->size-1][0][0], 10);
@@ -59,11 +59,15 @@ void test_network_mnist(Network* network, char* images_file, char* labels_file,
free(images[i]);
}
free(images);
- printf("%d Images. Taux de réussite: %.2f%%\tLoss: %lf\n", nb_elem, 100*accuracy/(float)nb_elem, loss/nb_elem);
+
+ float* results = malloc(sizeof(float)*2);
+ results[0] = 100*accuracy/(float)nb_elem;
+ results[1] = loss/(float)nb_elem;
+ return results;
}
-void test_network_jpg(Network* network, char* data_dir, bool preview_fails) {
+float* test_network_jpg(Network* network, char* data_dir, bool preview_fails, bool to_stdout) {
(void)preview_fails; // Inutilisé pour le moment
jpegDataset* dataset = loadJpegDataset(data_dir);
@@ -71,7 +75,7 @@ void test_network_jpg(Network* network, char* data_dir, bool preview_fails) {
int maxi;
for (int i=0; i < (int)dataset->numImages; i++) {
- if(i %(dataset->numImages/100) == 0) {
+ if(i %(dataset->numImages/100) == 0 && to_stdout) {
printf("Avancement: %.1f%%\r", 1000*i/(float)dataset->numImages);
fflush(stdout);
}
@@ -86,23 +90,35 @@ void test_network_jpg(Network* network, char* data_dir, bool preview_fails) {
free(dataset->images[i]);
}
- printf("%d Images. Taux de réussite: %.2f%%\n", dataset->numImages, 100*accuracy/(float)dataset->numImages);
+ float* results = malloc(sizeof(float)*2);
+ results[0] = 100*accuracy/(float)dataset->numImages;
+ results[1] = 0;
+
free(dataset->images);
free(dataset->labels);
free(dataset);
+
+ return results;
}
-void test_network(int dataset_type, char* modele, char* images_file, char* labels_file, char* data_dir, bool preview_fails) {
+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) {
Network* network = read_network(modele);
+ float* results;
if (dataset_type == 0) {
- test_network_mnist(network, images_file, labels_file, preview_fails);
+ results = test_network_mnist(network, images_file, labels_file, preview_fails, to_stdout, with_offset);
} else {
- test_network_jpg(network, data_dir, preview_fails);
+ results = test_network_jpg(network, data_dir, preview_fails, to_stdout);
}
free_network(network);
+
+ if (to_stdout) {
+ printf("Accuracy: %lf\tLoss: %lf\n", results[0], results[1]);
+ free(results); // Will not be used if to_stdout is used
+ }
+ return results;
}
@@ -130,7 +146,7 @@ void recognize_mnist(Network* network, char* input_file, char* out) {
printf("\"%d\" : [", i);
}
- write_image_in_network_32(images[i], height, width, network->input[0][0]);
+ write_image_in_network_32(images[i], height, width, network->input[0][0], false);
forward_propagation(network);
diff --git a/src/cnn/train.c b/src/cnn/train.c
index 3855a56..42033ff 100644
--- a/src/cnn/train.c
+++ b/src/cnn/train.c
@@ -51,7 +51,7 @@ void* train_thread(void* parameters) {
for (int i=start; i < start+nb_images; i++) {
if (dataset_type == 0) {
- write_image_in_network_32(images[index[i]], height, width, network->input[0][0]);
+ write_image_in_network_32(images[index[i]], height, width, network->input[0][0], true);
forward_propagation(network);
maxi = indice_max(network->input[network->size-1][0][0], 10);
if (maxi == -1) {
@@ -106,6 +106,7 @@ void train(int dataset_type, char* images_file, char* labels_file, char* data_di
srand(time(NULL));
float loss;
float batch_loss; // May be redundant with loss, but gives more informations
+ float test_accuracy = 0.; // Used to decrease Learning rate
float accuracy;
float batch_accuracy;
float current_accuracy;
@@ -233,7 +234,7 @@ void train(int dataset_type, char* images_file, char* labels_file, char* data_di
end_time = omp_get_wtime();
elapsed_time = end_time - start_time;
- printf("Taux d'apprentissage initial: %lf\n", network->learning_rate);
+ printf("Taux d'apprentissage initial: %0.2e\n", network->learning_rate);
printf("Initialisation: ");
printf_time(elapsed_time);
printf("\n\n");
@@ -308,7 +309,6 @@ void train(int dataset_type, char* images_file, char* labels_file, char* data_di
}
current_accuracy = accuracy * nb_images_total/((j+1)*BATCHES);
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);
- fflush(stdout);
#else
(void)nb_images_total_remaining; // Juste pour enlever un warning
@@ -331,7 +331,6 @@ void train(int dataset_type, char* images_file, char* labels_file, char* data_di
update_bias(network, network);
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);
- fflush(stdout);
#endif
}
//* Fin d'une époque: affichage des résultats et sauvegarde du réseau
@@ -347,11 +346,25 @@ void train(int dataset_type, char* images_file, char* labels_file, char* data_di
printf("\n");
#endif
write_network(out, network);
- // If you want to test the network between each epoch, uncomment the following line:
- test_network(0, out, "data/mnist/t10k-images-idx3-ubyte", "data/mnist/t10k-labels-idx1-ubyte", NULL, false);
+ // If you want to test the network between each epoch, uncomment the following lines:
+ /*
+ float* test_results = test_network(0, out, "data/mnist/t10k-images-idx3-ubyte", "data/mnist/t10k-labels-idx1-ubyte", NULL, false, false, true);
+ printf("Tests: Accuracy: %0.2lf%%\tLoss: %lf\n", test_results[0], test_results[1]);
+ if (test_results[0] < test_accuracy) {
+ network->learning_rate *= 0.1;
+ printf("Decreased learning rate to %0.2e\n", network->learning_rate);
+ }
+ if (test_results[0] == test_accuracy) {
+ network->learning_rate *= 2;
+ printf("Increased learning rate to %0.2e\n", network->learning_rate);
+ }
+ test_accuracy = test_results[0];
+ free(test_results);
- // Learning Rate decay
- network->learning_rate -= LEARNING_RATE*(1./(float)(epochs+1));
+ test_results = test_network(0, out, "data/mnist/t10k-images-idx3-ubyte", "data/mnist/t10k-labels-idx1-ubyte", NULL, false, false, false);
+ printf("Tests sans offset: Accuracy: %0.2lf%%\tLoss: %lf\n", test_results[0], test_results[1]);
+ free(test_results);
+ */
}
//* Fin de l'algo