diff --git a/Makefile b/Makefile
index 2e01f9f..79a8140 100644
--- a/Makefile
+++ b/Makefile
@@ -65,10 +65,10 @@ $(BUILDDIR)/mnist_%.o: $(MNIST_SRCDIR)/%.c $(MNIST_SRCDIR)/include/%.h
 #
 cnn: $(BUILDDIR)/cnn-main $(BUILDDIR)/cnn-main-cuda $(BUILDDIR)/cnn-preview;
 
-$(BUILDDIR)/cnn-main: $(CNN_SRCDIR)/main.c $(BUILDDIR)/cnn_train.o $(BUILDDIR)/cnn_cnn.o $(BUILDDIR)/cnn_creation.o $(BUILDDIR)/cnn_initialisation.o $(BUILDDIR)/cnn_make.o $(BUILDDIR)/cnn_neuron_io.o $(BUILDDIR)/cnn_function.o  $(BUILDDIR)/cnn_utils.o $(BUILDDIR)/cnn_update.o $(BUILDDIR)/cnn_free.o  $(BUILDDIR)/cnn_jpeg.o $(BUILDDIR)/cnn_convolution.o $(BUILDDIR)/cnn_backpropagation.o $(BUILDDIR)/colors.o $(BUILDDIR)/mnist.o
+$(BUILDDIR)/cnn-main: $(CNN_SRCDIR)/main.c $(BUILDDIR)/cnn_train.o $(BUILDDIR)/cnn_test_network.o $(BUILDDIR)/cnn_cnn.o $(BUILDDIR)/cnn_creation.o $(BUILDDIR)/cnn_initialisation.o $(BUILDDIR)/cnn_make.o $(BUILDDIR)/cnn_neuron_io.o $(BUILDDIR)/cnn_function.o  $(BUILDDIR)/cnn_utils.o $(BUILDDIR)/cnn_update.o $(BUILDDIR)/cnn_free.o  $(BUILDDIR)/cnn_jpeg.o $(BUILDDIR)/cnn_convolution.o $(BUILDDIR)/cnn_backpropagation.o $(BUILDDIR)/colors.o $(BUILDDIR)/mnist.o
 	$(CC)     $^ -o $@  $(CFLAGS)
 
-$(BUILDDIR)/cnn-main-cuda: $(BUILDDIR)/cnn_main.o $(BUILDDIR)/cnn_train.o $(BUILDDIR)/cnn_cnn.o $(BUILDDIR)/cnn_creation.o $(BUILDDIR)/cnn_initialisation.o $(BUILDDIR)/cnn_make.o $(BUILDDIR)/cnn_neuron_io.o $(BUILDDIR)/cnn_function.o  $(BUILDDIR)/cnn_utils.o $(BUILDDIR)/cnn_update.o $(BUILDDIR)/cnn_free.o $(BUILDDIR)/cnn_jpeg.o $(BUILDDIR)/cnn_cuda_convolution.o $(BUILDDIR)/cnn_backpropagation.o $(BUILDDIR)/cuda_utils.o $(BUILDDIR)/colors.o $(BUILDDIR)/mnist.o
+$(BUILDDIR)/cnn-main-cuda: $(BUILDDIR)/cnn_main.o $(BUILDDIR)/cnn_train.o $(BUILDDIR)/cnn_test_network.o $(BUILDDIR)/cnn_cnn.o $(BUILDDIR)/cnn_creation.o $(BUILDDIR)/cnn_initialisation.o $(BUILDDIR)/cnn_make.o $(BUILDDIR)/cnn_neuron_io.o $(BUILDDIR)/cnn_function.o  $(BUILDDIR)/cnn_utils.o $(BUILDDIR)/cnn_update.o $(BUILDDIR)/cnn_free.o $(BUILDDIR)/cnn_jpeg.o $(BUILDDIR)/cnn_cuda_convolution.o $(BUILDDIR)/cnn_backpropagation.o $(BUILDDIR)/cuda_utils.o $(BUILDDIR)/colors.o $(BUILDDIR)/mnist.o
 ifndef NVCC_INSTALLED
 	@echo "$(NVCC) not found, skipping"
 else
@@ -105,7 +105,7 @@ endif
 #
 run-tests: build-tests
 	$(foreach file, $(wildcard $(TEST_SRCDIR)/*.sh), $(file);)
-	@echo "$$(for file in build/test-*; do echo -e \\033[33m#####\\033[0m $$file \\033[33m#####\\033[0m; $$file; done)"
+	@echo "$$(for file in build/test-*; do echo -e \\033[33m#####\\033[0m $$file \\033[33m#####\\033[0m; $$file || echo "Erreur sur $$file"; done)"
 
 build-tests: prepare-tests $(TESTS_OBJ) $(BUILDDIR)/test-cnn_matrix_multiplication $(BUILDDIR)/test-cnn_convolution
 
diff --git a/src/cnn/cnn.c b/src/cnn/cnn.c
index 5feb795..9ebfbe9 100644
--- a/src/cnn/cnn.c
+++ b/src/cnn/cnn.c
@@ -16,6 +16,19 @@
 // Augmente les dimensions de l'image d'entrée
 #define PADDING_INPUT 2
 
+int indice_max(float* tab, int n) {
+    int indice = -1;
+    float maxi = FLT_MIN;
+    
+    for (int i=0; i < n; i++) {
+        if (tab[i] > maxi) {
+            maxi = tab[i];
+            indice = i;
+        }
+    }
+    return indice;
+}
+
 int will_be_drop(int dropout_prob) {
     return (rand() % 100) < dropout_prob;
 }
diff --git a/src/cnn/include/cnn.h b/src/cnn/include/cnn.h
index 0764257..884d13a 100644
--- a/src/cnn/include/cnn.h
+++ b/src/cnn/include/cnn.h
@@ -3,6 +3,11 @@
 #ifndef DEF_MAIN_H
 #define DEF_MAIN_H
 
+/*
+* Renvoie l'indice de l'élément de valeur maximale dans un tableau de flottants
+* Utilisé pour trouver le neurone le plus activé de la dernière couche (résultat de la classification)
+*/
+int indice_max(float* tab, int n);
 
 /*
 * Renvoie si oui ou non (1 ou 0) le neurone va être abandonné
diff --git a/src/cnn/include/test_network.h b/src/cnn/include/test_network.h
new file mode 100644
index 0000000..1c924e9
--- /dev/null
+++ b/src/cnn/include/test_network.h
@@ -0,0 +1,25 @@
+#include "struct.h"
+
+#ifndef DEF_TEST_NETWORK_H
+#define DEF_TEST_NETWORK_H
+
+/*
+* 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);
+
+/*
+* Classifie un fichier d'images sous le format MNIST à partir d'un réseau préalablement entraîné
+*/
+void recognize_mnist(Network* network, char* input_file);
+
+/*
+* Classifie une image jpg à partir d'un réseau préalablement entraîné
+*/
+void recognize_jpg(Network* network, char* input_file);
+
+/*
+* Classifie une image à partir d'un réseau préalablement entraîné
+*/
+void recognize(int dataset_type, char* modele, char* input_file);
+#endif
\ No newline at end of file
diff --git a/src/cnn/include/train.h b/src/cnn/include/train.h
index 2afc718..2cfe564 100644
--- a/src/cnn/include/train.h
+++ b/src/cnn/include/train.h
@@ -27,11 +27,6 @@ typedef struct TrainParameters {
 } TrainParameters;
 
 
-/*
-* Renvoie l'indice maximal d'un tableau tab de taille n
-*/
-int indice_max(float* tab, int n);
-
 /*
  * Fonction auxiliaire d'entraînement destinée à être exécutée sur plusieurs threads à la fois
 */
diff --git a/src/cnn/main.c b/src/cnn/main.c
index e9cd7f4..07915df 100644
--- a/src/cnn/main.c
+++ b/src/cnn/main.c
@@ -2,9 +2,11 @@
 #include <stdio.h>
 #include <string.h>
 #include <math.h>
+#include <stdbool.h>
 #include <float.h>
 
 #include "include/initialisation.h"
+#include "include/test_network.h"
 #include "../include/colors.h"
 #include "include/function.h"
 #include "include/creation.h"
@@ -15,10 +17,8 @@
 
 
 void help(char* call) {
-    printf("Usage: %s ( train | dev ) [OPTIONS]\n\n", call);
+    printf("Usage: %s ( train | recognize | test ) [OPTIONS]\n\n", call);
     printf("OPTIONS:\n");
-    printf("\tdev:\n");
-    printf("\t\t--conv | -c\tTester la fonction dev_conv().\n");
     printf("\ttrain:\n");
     printf("\t\t--dataset | -d (mnist|jpg)\tFormat du set de données.\n");
     printf("\t(mnist)\t--images  | -i [FILENAME]\tFichier contenant les images.\n");
@@ -26,12 +26,17 @@ void help(char* call) {
     printf("\t (jpg) \t--datadir | -dd [FOLDER]\tDossier contenant les images.\n");
     printf("\t\t--epochs  | -e [int]\t\tNombre d'époques.\n");
     printf("\t\t--out     | -o [FILENAME]\tFichier où écrire le réseau de neurones.\n");
-}
-
-
-void dev_conv() {
-    Network* network = create_network_lenet5(0, 0, TANH, GLOROT, 32, 1);
-    forward_propagation(network);
+    printf("\trecognize:\n");
+    printf("\t\t--dataset | -d (mnist|jpg)\tFormat de l'image à reconnaître.\n");
+    printf("\t\t--modele  | -m [FILENAME]\tFichier contenant le réseau entraîné.\n");
+    printf("\t\t--input   | -i [FILENAME]\tImage jpeg ou fichier binaire à reconnaître.\n");
+    printf("\ttest:\n");
+    printf("\t\t--modele  | -m [FILENAME]\tFichier contenant le réseau entraîné.\n");
+    printf("\t\t--dataset | -d (mnist|jpg)\tFormat du set de données.\n");
+    printf("\t(mnist)\t--images  | -i [FILENAME]\tFichier contenant les images.\n");
+    printf("\t(mnist)\t--labels  | -l [FILENAME]\tFichier contenant les labels.\n");
+    printf("\t (jpg) \t--datadir | -dd [FOLDER]\tDossier contenant les images.\n");
+    printf("\t\t--preview-fails | -p\t\tAfficher les images ayant échoué.\n");
 }
 
 
@@ -42,27 +47,6 @@ int main(int argc, char* argv[]) {
         help(argv[0]);
         return 1;
     }
-    if (! strcmp(argv[1], "dev")) {
-        int option = 0;
-        // 0 pour la fonction dev_conv()
-        int i = 2;
-        while (i < argc) {
-            // Utiliser un switch serait sans doute plus élégant
-            if ((! strcmp(argv[i], "--conv"))||(! strcmp(argv[i], "-c"))) {
-                option = 0;
-                i++;
-            } else {
-                printf("Option choisie inconnue: %s\n", argv[i]);
-                i++;
-            }
-        }
-        if (option == 0) {
-            dev_conv();
-            return 0;
-        }
-        printf("Option choisie inconnue: dev %d\n", option);
-        return 1;
-    }
     if (! strcmp(argv[1], "train")) {
         char* dataset = NULL;
         char* images_file = NULL;
@@ -130,6 +114,118 @@ int main(int argc, char* argv[]) {
         train(dataset_type, images_file, labels_file, data_dir, epochs, out);
         return 0;
     }
+    if (! strcmp(argv[1], "test")) {
+        char* dataset = NULL; // mnist ou jpg
+        char* modele = NULL; // Fichier contenant le modèle
+        char* images_file = NULL; // Fichier d'images (mnist)
+        char* labels_file = NULL; // Fichier de labels (mnist)
+        char* data_dir = NULL; // Dossier d'images (jpg)
+        int dataset_type; // Type de dataset (0 pour mnist, 1 pour jpg)
+        bool preview_fails = false;
+        int i = 2;
+        while (i < argc) {
+            if ((! strcmp(argv[i], "--dataset"))||(! strcmp(argv[i], "-d"))) {
+                dataset = argv[i+1];
+                i += 2;
+            }
+            else if ((! strcmp(argv[i], "--modele"))||(! strcmp(argv[i], "-m"))) {
+                modele = argv[i+1];
+                i += 2;
+            }
+            else if ((! strcmp(argv[i], "--images"))||(! strcmp(argv[i], "-i"))) {
+                images_file = argv[i+1];
+                i += 2;
+            }
+            else if ((! strcmp(argv[i], "--labels"))||(! strcmp(argv[i], "-l"))) {
+                labels_file = argv[i+1];
+                i += 2;
+            }
+            else if ((! strcmp(argv[i], "--datadir"))||(! strcmp(argv[i], "-dd"))) {
+                data_dir = argv[i+1];
+                i += 2;
+            }
+            else if ((! strcmp(argv[i], "--preview-fails"))||(! strcmp(argv[i], "-p"))) {
+                preview_fails = true;
+                i++;
+            }
+            else {
+                printf("Option choisie inconnue: %s\n", argv[i]);
+                i++;
+            }
+        }
+        if ((dataset!=NULL) && !strcmp(dataset, "mnist")) {
+            dataset_type = 0;
+            if (!images_file) {
+                printf("Pas de fichier d'images spécifié\n");
+                return 1;
+            }
+            if (!labels_file) {
+                printf("Pas de fichier de labels spécifié\n");
+                return 1;
+            }
+        }
+        else if ((dataset!=NULL) && !strcmp(dataset, "jpg")) {
+            dataset_type = 1;
+            if (!data_dir) {
+                printf("Pas de dossier de données spécifié.\n");
+                return 1;
+            }
+        }
+        else {
+            printf("Pas de type de dataset spécifié.\n");
+            return 1;
+        }
+
+        if (!modele) {
+            printf("Pas de modèle à utiliser spécifié.\n");
+            return 1;
+        }
+        test_network(dataset_type, modele, images_file, labels_file, data_dir, preview_fails);
+        return 0;
+    }
+    if (! strcmp(argv[1], "recognize")) {
+        char* dataset = NULL; // mnist ou jpg
+        char* modele = NULL; // Fichier contenant le modèle
+        char* input_file = NULL; // Image à reconnaître
+        int dataset_type;
+        int i = 2;
+        while (i < argc) {
+            if ((! strcmp(argv[i], "--dataset"))||(! strcmp(argv[i], "-d"))) {
+                dataset = argv[i+1];
+                i += 2;
+            }
+            else if ((! strcmp(argv[i], "--modele"))||(! strcmp(argv[i], "-m"))) {
+                modele = argv[i+1];
+                i += 2;
+            }
+            else if ((! strcmp(argv[i], "--input"))||(! strcmp(argv[i], "-i"))) {
+                input_file = argv[i+1];
+                i += 2;
+            } else {
+                printf("Option choisie inconnue: %s\n", argv[i]);
+                i++;
+            }
+        }
+        if ((dataset!=NULL) && !strcmp(dataset, "mnist")) {
+            dataset_type = 0;
+        } else if ((dataset!=NULL) && !strcmp(dataset, "jpg")) {
+            dataset_type = 1;
+        }
+        else {
+            printf("Pas de type de dataset spécifié.\n");
+            return 1;
+        }
+        if (!input_file) {
+            printf("Pas de fichier d'entrée spécifié, rien à faire.\n");
+            return 1;
+        }
+        if (!modele) {
+            printf("Pas de modèle à utiliser spécifié.\n");
+            return 1;
+        }
+        recognize(dataset_type, modele, input_file);
+        return 0;
+    }
     printf("Option choisie non reconnue: %s\n", argv[1]);
     help(argv[0]);
     return 1;
diff --git a/src/cnn/neuron_io.c b/src/cnn/neuron_io.c
index 3b65560..b25865b 100644
--- a/src/cnn/neuron_io.c
+++ b/src/cnn/neuron_io.c
@@ -184,6 +184,20 @@ Network* read_network(char* filename) {
             }
         }
     }
+
+    network->input_z = (float****)malloc(sizeof(float***)*size);
+    for (int i=0; i < (int)size; i++) { // input[size][couche->depth][couche->dim][couche->dim]
+        network->input_z[i] = (float***)malloc(sizeof(float**)*network->depth[i]);
+        for (int j=0; j < network->depth[i]; j++) {
+            network->input_z[i][j] = (float**)malloc(sizeof(float*)*network->width[i]);
+            for (int k=0; k < network->width[i]; k++) {
+                network->input_z[i][j][k] = (float*)malloc(sizeof(float)*network->width[i]);
+                for (int l=0; l < network->width[i]; l++) {
+                    network->input_z[i][j][k][l] = 0.;
+                }
+            }
+        }
+    }
     
     fclose(ptr);
     return network;
diff --git a/src/cnn/test_network.c b/src/cnn/test_network.c
new file mode 100644
index 0000000..70b778b
--- /dev/null
+++ b/src/cnn/test_network.c
@@ -0,0 +1,158 @@
+#include <stdlib.h>
+#include <stdio.h>
+#include <stdbool.h>
+
+#include "../mnist/include/mnist.h"
+#include "include/neuron_io.h"
+#include "include/struct.h"
+#include "include/jpeg.h"
+#include "include/free.h"
+#include "include/cnn.h"
+
+
+void test_network_mnist(Network* network, char* images_file, char* labels_file, bool preview_fails) {
+    (void)preview_fails; // Inutilisé pour le moment
+    int width, height; // Dimensions des images
+    int nb_elem; // Nombre d'éléments
+    int maxi; // Catégorie reconnue
+
+    int accuracy = 0; // Nombre d'images reconnues
+
+    // Load image
+    int* mnist_parameters = read_mnist_images_parameters(images_file);
+    
+    int*** images = read_mnist_images(images_file);
+    unsigned int* labels = read_mnist_labels(labels_file);
+    
+    nb_elem = mnist_parameters[0];
+
+    width = mnist_parameters[1];
+    height = mnist_parameters[2];
+    free(mnist_parameters);
+
+    // Load image in the first layer of the Network
+    for (int i=0; i < nb_elem; i++) {
+        if(i %(nb_elem/100) == 0) {
+            printf("Avancement: %.0f%%\r",  100*i/(float)nb_elem);
+            fflush(stdout);
+        }
+        write_image_in_network_32(images[i], height, width, network->input[0][0]);
+        forward_propagation(network);
+        maxi = indice_max(network->input[network->size-1][0][0], 10);
+
+        if (maxi == (int)labels[i]) {
+            accuracy++;
+        }
+
+        for (int j=0; j < height; j++) {
+            free(images[i][j]);
+        }
+        free(images[i]);
+    }
+    free(images);
+    printf("%d Images. Taux de réussite: %.2f%%\n", nb_elem, 100*accuracy/(float)nb_elem);
+}
+
+
+void test_network_jpg(Network* network, char* data_dir, bool preview_fails) {
+    (void)preview_fails; // Inutilisé pour le moment
+    jpegDataset* dataset = loadJpegDataset(data_dir);
+
+    int accuracy = 0;
+    int maxi;
+
+    for (int i=0; i < (int)dataset->numImages; i++) {
+        if(i %(dataset->numImages/100) == 0) {
+            printf("Avancement: %.1f%%\r",  1000*i/(float)dataset->numImages);
+            fflush(stdout);
+        }
+        write_image_in_network_260(dataset->images[i], dataset->height, dataset->height, network->input[0]);
+        forward_propagation(network);
+        maxi = indice_max(network->input[network->size-1][0][0], 50);
+
+        if (maxi == (int)dataset->labels[i]) {
+            accuracy++;
+        }
+
+        free(dataset->images[i]);
+    }
+
+    printf("%d Images. Taux de réussite: %.2f%%\n", dataset->numImages, 100*accuracy/(float)dataset->numImages);
+    free(dataset->images);
+    free(dataset->labels);
+    free(dataset);
+}
+
+
+void test_network(int dataset_type, char* modele, char* images_file, char* labels_file, char* data_dir, bool preview_fails) {
+    Network* network = read_network(modele);
+
+    if (dataset_type == 0) {
+        test_network_mnist(network, images_file, labels_file, preview_fails);
+    } else {
+        test_network_jpg(network, data_dir, preview_fails);
+    }
+
+    free_network(network);
+}
+
+
+void recognize_mnist(Network* network, char* input_file) {
+    int width, height; // Dimensions de l'image
+    int nb_elem; // Nombre d'éléments
+    int maxi; // Catégorie reconnue
+
+    // Load image
+    int* mnist_parameters = read_mnist_images_parameters(input_file);
+    int*** images = read_mnist_images(input_file);
+    nb_elem = mnist_parameters[0];
+
+    width = mnist_parameters[1];
+    height = mnist_parameters[2];
+    free(mnist_parameters);
+
+    printf("Image\tCatégorie détectée\n");
+    // Load image in the first layer of the Network
+    for (int i=0; i < nb_elem; i++) {
+        write_image_in_network_32(images[i], height, width, network->input[0][0]);
+        forward_propagation(network);
+        maxi = indice_max(network->input[network->size-1][0][0], 10);
+
+        printf("%d\t%d\n", i, maxi);
+
+        for (int j=0; j < height; j++) {
+            free(images[i][j]);
+        }
+        free(images[i]);
+    }
+    free(images);
+}
+
+void recognize_jpg(Network* network, char* input_file) {
+    int width, height; // Dimensions de l'image
+    int maxi;
+
+    imgRawImage* image = loadJpegImageFile(input_file);
+    width = image->width;
+    height = image->height;
+
+    write_image_in_network_260(image->lpData, height, width, network->input[0]);
+    forward_propagation(network);
+    maxi = indice_max(network->input[network->size-1][0][0], 50);
+
+    printf("Catégorie reconnue: %d\n", maxi);
+    free(image->lpData);
+    free(image);
+}
+
+void recognize(int dataset_type, char* modele, char* input_file) {
+    Network* network = read_network(modele);
+
+    if (dataset_type == 0) {
+        recognize_mnist(network, input_file);
+    } else {
+        recognize_jpg(network, input_file);
+    }
+
+    free_network(network);
+}
\ No newline at end of file
diff --git a/src/cnn/train.c b/src/cnn/train.c
index a5bcf02..b7761b0 100644
--- a/src/cnn/train.c
+++ b/src/cnn/train.c
@@ -19,18 +19,8 @@
 
 #include "include/train.h"
 
-
-int indice_max(float* tab, int n) {
-    int indice = -1;
-    float maxi = FLT_MIN;
-    
-    for (int i=0; i < n; i++) {
-        if (tab[i] > maxi) {
-            maxi = tab[i];
-            indice = i;
-        }
-    }
-    return indice;
+int div_up(int a, int b) { // Partie entière supérieure de a/b
+    return ((a % b) != 0) ? (a / b + 1) : (a / b);
 }
 
 
@@ -91,7 +81,9 @@ void train(int dataset_type, char* images_file, char* labels_file, char* data_di
     float accuracy;
     float current_accuracy;
 
-    int nb_images_total;
+    int nb_images_total; // Images au total
+    int nb_images_total_remaining; // Images restantes dans un batch
+    int batches_epoques; // Batches par époque
 
     int*** images;
     unsigned int* labels;
@@ -180,9 +172,17 @@ void train(int dataset_type, char* images_file, char* labels_file, char* data_di
         // du multi-threading car chaque copie du réseau initiale sera légèrement différente
         // et donnera donc des résultats différents sur les mêmes images.
         accuracy = 0.;
-        for (int j=0; j < nb_images_total / BATCHES; j++) {
+        batches_epoques = div_up(nb_images_total, BATCHES);
+        nb_images_total_remaining = nb_images_total;
+        for (int j=0; j < batches_epoques; j++) {
             #ifdef USE_MULTITHREADING
-            nb_remaining_images = BATCHES;
+            if (j == batches_epoques-1) {
+                nb_remaining_images = nb_images_total_remaining;
+                nb_images_total_remaining = 0;
+            } else {
+                nb_images_total_remaining -= BATCHES;
+                nb_remaining_images = BATCHES;
+            }
 
             for (int k=0; k < nb_threads; k++) {
                 if (k == nb_threads-1) {
@@ -191,8 +191,9 @@ void train(int dataset_type, char* images_file, char* labels_file, char* data_di
                 } else {
                     nb_remaining_images -= BATCHES / nb_threads;
                 }
+                train_parameters[k]->start = BATCHES*j + (BATCHES/nb_threads)*k;
                 train_parameters[k]->network = copy_network(network);
-                train_parameters[k]->start = BATCHES*j + (nb_images_total/BATCHES)*k;
+
                 pthread_create( &tid[k], NULL, train_thread, (void*) train_parameters[k]);
             }
             for (int k=0; k < nb_threads; k++) {
@@ -206,6 +207,7 @@ 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"%0.1f%%"RESET" ", nb_threads, i, epochs, BATCHES*(j+1), nb_images_total, current_accuracy*100);
+            fflush(stdout);
             #else
             train_params->start = j*BATCHES;
             
diff --git a/src/mnist/include/main.h b/src/mnist/include/main.h
index fb69e8c..57718e8 100644
--- a/src/mnist/include/main.h
+++ b/src/mnist/include/main.h
@@ -1,3 +1,5 @@
+#include "neural_network.h"
+
 #ifndef DEF_MAIN_H
 #define DEF_MAIN_H
 
@@ -49,6 +51,16 @@ void* train_thread(void* parameters);
 */
 void train(int epochs, int layers, int neurons, char* recovery, char* image_file, char* label_file, char* out, char* delta, int nb_images_to_process, int start);
 
+/*
+* Échange deux éléments d'un tableau
+*/
+void swap(int* tab, int i, int j);
+
+/*
+* Mélange un tableau avec le mélange de Knuth
+*/
+void knuth_shuffle(int* tab, int n);
+
 /*
 * Reconnaissance d'un set d'images, renvoie un tableau de float contentant les prédictions
 * modele: nom du fichier contenant le réseau neuronal
diff --git a/src/mnist/main.c b/src/mnist/main.c
index 9d4cb57..3a199dc 100644
--- a/src/mnist/main.c
+++ b/src/mnist/main.c
@@ -6,6 +6,7 @@
 #include <pthread.h>
 #include <sys/sysinfo.h>
 
+#include "include/main.h"
 #include "include/mnist.h"
 #include "include/neuron_io.h"
 #include "include/neural_network.h"
@@ -20,6 +21,7 @@ typedef struct TrainParameters {
     Network* network;
     int*** images;
     int* labels;
+    int* shuffle_indices;
     int start;
     int nb_images;
     int height;
@@ -97,6 +99,7 @@ void* train_thread(void* parameters) {
 
     int*** images = param->images;
     int* labels = param->labels;
+    int* shuffle = param->shuffle_indices;
 
     int start = param->start;
     int nb_images = param->nb_images;
@@ -107,15 +110,15 @@ void* train_thread(void* parameters) {
     int* desired_output;
 
     for (int i=start; i < start+nb_images; i++) {
-        write_image_in_network(images[i], network, height, width);
-        desired_output = desired_output_creation(network, labels[i]);
+        write_image_in_network(images[shuffle[i]], network, height, width);
+        desired_output = desired_output_creation(network, labels[shuffle[i]]);
         forward_propagation(network);
         backward_propagation(network, desired_output);
 
         for (int k=0; k < nb_neurons_last_layer; k++) {
             sortie[k] = last_layer->neurons[k]->z;
         }
-        if (indice_max(sortie, nb_neurons_last_layer) == labels[i]) {
+        if (indice_max(sortie, nb_neurons_last_layer) == labels[shuffle[i]]) {
             accuracy += 1.;
         }
         free(desired_output);
@@ -134,7 +137,7 @@ 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] = {neurons, nb_neurons_last_layer};
+    int repartition[3] = {neurons, 42, nb_neurons_last_layer};
 
     float accuracy;
 
@@ -178,6 +181,11 @@ 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);
+    
+    int* shuffle_indices = (int*)malloc(sizeof(int)*nb_images_total);
+    for (int i=0; i < nb_images_total; i++) {
+        shuffle_indices[i] = i;
+    }
 
     if (nb_images_to_process != -1) {
         nb_images_total = nb_images_to_process;
@@ -191,9 +199,11 @@ void train(int epochs, int layers, int neurons, char* recovery, char* image_file
         train_parameters[j]->height = height;
         train_parameters[j]->width = width;
         train_parameters[j]->nb_images = BATCHES / nb_threads;
+        train_parameters[j]->shuffle_indices = shuffle_indices;
     }
 
     for (int i=0; i < epochs; i++) {
+        knuth_shuffle(shuffle_indices, nb_images_total); // Shuffle images between each epoch
         accuracy = 0.;
         for (int k=0; k < nb_images_total / BATCHES; k++) {
             nb_remaining_images = BATCHES;
@@ -220,6 +230,7 @@ void train(int epochs, int layers, int neurons, char* recovery, char* image_file
                 deletion_of_network(train_parameters[j]->network);
             }
             printf("\rThreads [%d]\tÉpoque [%d/%d]\tImage [%d/%d]\tAccuracy: %0.1f%%", nb_threads, i, epochs, BATCHES*(k+1), nb_images_total, accuracy*100);
+            fflush(stdout);
         }
         printf("\rThreads [%d]\tÉpoque [%d/%d]\tImage [%d/%d]\tAccuracy: %0.1f%%\n", nb_threads, i, epochs, nb_images_total, nb_images_total, accuracy*100);
         write_network(out, network);
@@ -239,6 +250,18 @@ void train(int epochs, int layers, int neurons, char* recovery, char* image_file
     free(tid);
 }
 
+void swap(int* tab, int i, int j) {
+    int tmp = tab[i];
+    tab[i] = tab[j];
+    tab[j] = tmp;
+}
+
+void knuth_shuffle(int* tab, int n) {
+    for(int i=1; i < n; i++) {
+        swap(tab, i, rand() %i);
+    }
+}
+
 float** recognize(char* modele, char* entree) {
     Network* network = read_network(modele);
     Layer* last_layer = network->layers[network->nb_layers-1];
@@ -351,7 +374,7 @@ int main(int argc, char* argv[]) {
     }
     if (! strcmp(argv[1], "train")) {
         int epochs = EPOCHS;
-        int layers = 2;
+        int layers = 3;
         int neurons = 784;
         int nb_images = -1;
         int start = 0;
diff --git a/src/mnist/neural_network.c b/src/mnist/neural_network.c
index 652aa82..6e40ccd 100644
--- a/src/mnist/neural_network.c
+++ b/src/mnist/neural_network.c
@@ -10,7 +10,7 @@
 
 // Définit le taux d'apprentissage du réseau neuronal, donc la rapidité d'adaptation du modèle (compris entre 0 et 1)
 // Cette valeur peut évoluer au fur et à mesure des époques (linéaire c'est mieux)
-#define LEARNING_RATE 0.5
+#define LEARNING_RATE 0.1
 // Retourne un nombre aléatoire entre 0 et 1
 #define RAND_DOUBLE() ((double)rand())/((double)RAND_MAX)
 //Coefficient leaking ReLU