diff --git a/src/mnist/include/neuron.h b/src/mnist/include/neuron.h
index 92556a9..5c2691d 100644
--- a/src/mnist/include/neuron.h
+++ b/src/mnist/include/neuron.h
@@ -4,7 +4,7 @@
 typedef struct Neuron {
     float* weights; // Liste de tous les poids des arêtes sortants du neurone
     float bias; // Caractérise le bias du neurone
-    float z; // Sauvegarde des calculs faits sur le neurone (programmation dynamique)
+    float z; // Sauvegarde des calculs faits sur le neurone
 
     float *back_weights; // Changement des poids sortants lors de la backpropagation
     float *last_back_weights; // Dernier changement de d_poid_sortants
diff --git a/src/mnist_cnn/cnn.c b/src/mnist_cnn/cnn.c
index afbc8fb..9e51997 100644
--- a/src/mnist_cnn/cnn.c
+++ b/src/mnist_cnn/cnn.c
@@ -1,16 +1,638 @@
-#define PADING_INPUT 2
+#include <stdlib.h>
+#include <stdio.h>
+#include <time.h>
+#include <math.h>
+#include <float.h>
 
-void write_image_in_newtork_32(int** image, int height, int width, float** network) {
-    /* Ecrit une image 28*28 au centre d'un tableau 32*32 et met à 0 le reste */
+#include "cnn.h"
 
-    for (int i=0; i < height+2*PADING_INPUT; i++) {
-        for (int j=PADING_INPUT; j < width+2*PADING_INPUT; j++) {
-            if (i<PADING_INPUT || i>height+PADING_INPUT || j<PADING_INPUT || j>width+PADING_INPUT){
-                network[i][j] = 0.;
+#define PADING_INPUT 2 // Augmente les dimensions de l'image d'entrée
+#define RAND_FLT() ((float)rand())/((float)RAND_MAX) // Génère un flotant entre 0 et 1
+
+// Les dérivées sont l'opposé
+#define TANH 1
+#define SIGMOID 2
+#define RELU 3
+#define SOFTMAX 4
+
+#define ZERO 0
+#define GLOROT_NORMAL 1
+#define GLOROT_UNIFROM 2
+#define HE_NORMAL 3
+#define HE_UNIFORM 4
+
+// Penser à mettre srand(time(NULL)); (pour les proba de dropout)
+
+
+float max(float a, float b) {
+    return a<b?b:a;
+}
+
+float sigmoid(float x) {
+    return 1/(1 + exp(-x));
+}
+
+float sigmoid_derivative(float x) {
+    float tmp = exp(-x);
+    return tmp/((1+tmp)*(1+tmp));
+}
+
+float relu(float x) {
+    return max(0, x);
+}
+
+float relu_derivative(float x) {
+    if (x > 0)
+        return 1;
+    return 0;
+}
+
+float tanh_(float x) {
+    return tanh(x);
+}
+
+float tanh_derivative(float x) {
+    float a = tanh(x);
+    return 1 - a*a;
+}
+
+void apply_softmax_input(float ***input, int depth, int rows, int columns) {
+    int i, j, k;
+    float m = FLT_MIN;
+    float sum=0;
+    for (i=0; i<depth; i++) {
+        for (j=0; j<rows; j++) {
+            for (k=0; k<columns; k++) {
+                m = max(m, input[i][j][k]);
             }
-            else {
-                network[i][j] = (float)image[i][j] / 255.0f;
+        }
+    }
+    for (i=0; i<depth; i++) {
+        for (j=0; j<rows; j++) {
+            for (k=0; k<columns; k++) {
+                input[i][j][k] = exp(m-input[i][j][k]);
+                sum += input[i][j][k];
+            }
+        }
+    }
+    for (i=0; i<depth; i++) {
+        for (j=0; j<rows; j++) {
+            for (k=0; k<columns; k++) {
+                input[i][j][k] = input[i][j][k]/sum;
             }
         }
     }
 }
+
+void apply_function_input(float (*f)(float), float*** input, int depth, int rows, int columns) {
+    int i, j ,k;
+    for (i=0; i<depth; i++) {
+        for (j=0; j<rows; j++) {
+            for (k=0; k<columns; k++) {
+                input[i][j][k] = (*f)(input[i][j][k]);
+            }
+        }
+    }
+}
+
+void choose_apply_function_input(int activation, float*** input, int depth, int rows, int columns) {
+    if (activation == RELU) {
+        apply_function_input(relu, input, depth, rows, columns);
+    }
+    else if (activation == SIGMOID) {
+        apply_function_input(sigmoid, input, depth, rows, columns);
+    }
+    else if (activation == SOFTMAX) {
+        apply_softmax_input(input, depth, rows, columns);
+    }
+    else if (activation == TANH) {
+        apply_function_input(tanh_, input, depth, rows, columns);
+    }
+    else {
+        printf("Erreur, fonction d'activation inconnue");
+    }
+}
+
+int will_be_drop(int dropout_prob) {
+    /* Renvoie si oui ou non le neurone va être abandonné */
+    return (rand() % 100)<dropout_prob;
+}
+
+Network* create_network(int max_size, int dropout, int initialisation, int input_dim, int input_depth) {
+    /* Créé un réseau qui peut contenir max_size couche (dont celle d'input et d'output)  */
+    if (dropout<0 || dropout>100) {
+        printf("Erreur, la probabilité de dropout n'est pas respecté, elle doit être comprise entre 0 et 100\n");
+    }
+    Network* network = malloc(sizeof(Network));
+    network->max_size = max_size;
+    network->dropout = dropout;
+    network->initialisation = initialisation;
+    network->size = 1;
+    network->input = malloc(sizeof(float***)*max_size);
+    network->kernel = malloc(sizeof(Kernel)*(max_size-1));
+    create_a_cube_input_layer(network, 0, input_depth, input_dim);
+    int i, j;
+    network->dim = malloc(sizeof(int*)*max_size);
+    for (i=0; i<max_size; i++) {
+        network->dim[i] = malloc(sizeof(int)*2);
+    }
+    network->dim[0][0] = input_dim;
+    network->dim[0][1] = input_depth;
+    return network;
+}
+
+Network* create_network_lenet5(int dropout, int activation, int initialisation) {
+    /* Renvoie un réseau suivant l'architecture LeNet5 */
+    Network* network;
+    network = create_network(8, dropout, initialisation, 32, 1);
+    add_convolution(network, 6, 5, activation);
+    add_average_pooling(network, 2, activation);
+    add_convolution(network, 16, 5, activation);
+    add_average_pooling_flatten(network, 2, activation);
+    add_dense(network, 120, 84, activation);
+    add_dense(network, 84, 10, activation);
+    add_dense(network, 10, 10, SOFTMAX);
+    return network;
+}
+
+void create_a_cube_input_layer(Network* network, int pos, int depth, int dim) {
+    /* Créé et alloue de la mémoire à une couche de type input cube */
+    int i, j;
+    network->input[pos] = malloc(sizeof(float**)*depth);
+    for (i=0; i<depth; i++) {
+        network->input[pos][i] = malloc(sizeof(float*)*dim);
+        for (j=0; j<dim; j++) {
+            network->input[pos][i][j] = malloc(sizeof(float)*dim);
+        }
+    }
+    network->dim[pos][0] = dim;
+    network->dim[pos][1] = depth;
+}
+
+void create_a_line_input_layer(Network* network, int pos, int dim) {
+    /* Créé et alloue de la mémoire à une couche de type ligne */
+    int i;
+    network->input[pos] = malloc(sizeof(float**));
+    network->input[pos][0] = malloc(sizeof(float*));
+    network->input[pos][0][0] = malloc(sizeof(float)*dim);
+}
+
+void initialisation_1d_matrix(int initialisation, float* matrix, int rows, int n) { //NOT FINISHED
+    /* Initialise une matrice 1d rows de float en fonction du type d'initialisation */
+    int i;
+    float lower_bound = -6/sqrt((double)n);
+    float distance = -lower_bound-lower_bound;
+    for (i=0; i<rows; i++) {
+        matrix[i] = lower_bound + RAND_FLT()*distance;
+    }
+}
+void initialisation_2d_matrix(int initialisation, float** matrix, int rows, int columns, int n) { //NOT FINISHED
+    /* Initialise une matrice 2d rows*columns de float en fonction du type d'initialisation */
+    int i, j;
+    float lower_bound = -6/sqrt((double)n);
+    float distance = -lower_bound-lower_bound;
+    for (i=0; i<rows; i++) {
+        for (j=0; j<columns; j++) {
+            matrix[i][j] = lower_bound + RAND_FLT()*distance;
+        }
+    }
+}
+
+void initialisation_3d_matrix(int initialisation, float*** matrix, int depth, int rows, int columns, int n) { //NOT FINISHED
+    /* Initialise une matrice 3d depth*dim*columns de float en fonction du type d'initialisation */
+    int i, j, k;
+    float lower_bound = -6/sqrt((double)n);
+    float distance = -lower_bound-lower_bound;
+    for (i=0; i<depth; i++) {
+        for (j=0; j<rows; j++) {
+            for (k=0; k<columns; k++) {
+                matrix[i][j][k] = lower_bound + RAND_FLT()*distance;
+            }
+        }
+    }
+}
+
+void initialisation_4d_matrix(int initialisation, float**** matrix, int rows, int columns, int rows1, int columns1, int n) { //NOT FINISHED
+    /* Initialise une matrice 4d rows*columns*rows1*columns1 de float en fonction du type d'initialisation */
+    int i, j, k, l;
+    float lower_bound = -6/sqrt((double)n);
+    float distance = -lower_bound-lower_bound;
+    for (i=0; i<rows; i++) {
+        for (j=0; j<columns; j++) {
+            for (k=0; k<rows1; k++) {
+                for (l=0; l<columns1; l++) {
+                    matrix[i][j][k][l] = lower_bound + RAND_FLT()*distance;
+                }
+            }
+        }
+    }
+}
+
+void add_average_pooling(Network* network, int kernel_size, int activation) {
+    /* Ajoute au réseau une couche d'average pooling valide de dimension dim*dim */
+    int n = network->size;
+    if (network->max_size == n) {
+        printf("Impossible de rajouter une couche d'average pooling, le réseau est déjà plein\n");
+        return;
+    }
+    network->kernel[n].cnn = NULL;
+    network->kernel[n].nn = NULL;
+    network->kernel[n].activation = activation + 100*kernel_size;
+    create_a_cube_input_layer(network, n, network->dim[n-1][1], network->dim[n-1][0]/2);
+    network->size++;
+}
+
+void add_average_pooling_flatten(Network* network, int kernel_size, int activation) {
+    /* Ajoute au réseau une couche d'average pooling valide de dimension dim*dim qui aplatit */
+    int n = network->size;
+    if (network->max_size == n) {
+        printf("Impossible de rajouter une couche d'average pooling, le réseau est déjà plein\n");
+        return;
+    }
+    network->kernel[n].cnn = NULL;
+    network->kernel[n].nn = NULL;
+    network->kernel[n].activation = activation + 100*kernel_size;
+    int dim = (network->dim[n-1][0]*network->dim[n-1][0]*network->dim[n-1][1])/(kernel_size*kernel_size);
+    create_a_line_input_layer(network, n, dim);
+    network->size++;
+}
+
+void add_convolution(Network* network, int nb_filter, int kernel_size, int activation) {
+    /* Ajoute une couche de convolution dim*dim au réseau et initialise les kernels */
+    int n = network->size, i, j, k;
+    if (network->max_size == n) {
+        printf("Impossible de rajouter une couche de convolution, le réseau est déjà plein\n");
+        return;
+    }
+    int r = network->dim[n-1][1];
+    int c = nb_filter;
+    network->kernel[n].nn = NULL;
+    network->kernel[n].cnn = malloc(sizeof(Kernel_cnn));
+    network->kernel[n].activation = activation;
+    network->kernel[n].cnn->k_size = kernel_size;
+    network->kernel[n].cnn->rows = r;
+    network->kernel[n].cnn->columns = c;
+    network->kernel[n].cnn->w = malloc(sizeof(float***)*r);
+    network->kernel[n].cnn->d_w = malloc(sizeof(float***)*r);
+    for (i=0; i<r; i++) {
+        network->kernel[n].cnn->w[i] = malloc(sizeof(float**)*c);
+        network->kernel[n].cnn->d_w[i] = malloc(sizeof(float**)*c);
+        for (j=0; j<c; j++) {
+            network->kernel[n].cnn->w[i][j] = malloc(sizeof(float*)*kernel_size);
+            network->kernel[n].cnn->d_w[i][j] = malloc(sizeof(float*)*kernel_size);
+            for (k=0; k<kernel_size; k++) {
+                network->kernel[n].cnn->w[i][j][k] = malloc(sizeof(float)*kernel_size);
+                network->kernel[n].cnn->d_w[i][j][k] = malloc(sizeof(float)*kernel_size);
+            }
+        }
+    }
+    network->kernel[n].cnn->bias = malloc(sizeof(float**)*c);
+    network->kernel[n].cnn->d_bias = malloc(sizeof(float**)*c);
+    for (i=0; i<c; i++) {
+        network->kernel[n].cnn->bias[i] = malloc(sizeof(float*)*kernel_size);
+        network->kernel[n].cnn->d_bias[i] = malloc(sizeof(float*)*kernel_size);
+        for (j=0; j<kernel_size; j++) {
+            network->kernel[n].cnn->bias[i][j] = malloc(sizeof(float)*kernel_size);
+            network->kernel[n].cnn->d_bias[i][j] = malloc(sizeof(float)*kernel_size);
+        }
+    }
+    create_a_cube_input_layer(network, n, c, network->dim[n-1][0] - 2*(kernel_size/2));
+    int n_int = network->dim[n-1][0]*network->dim[n-1][0]*network->dim[n-1][1];
+    int n_out = network->dim[n][0]*network->dim[n][0]*network->dim[n][1];
+    initialisation_3d_matrix(network->initialisation, network->kernel[n].cnn->bias, c, kernel_size, kernel_size, n_int+n_out);
+    initialisation_3d_matrix(ZERO, network->kernel[n].cnn->d_bias, c, kernel_size, kernel_size, n_int+n_out);
+    initialisation_4d_matrix(network->initialisation, network->kernel[n].cnn->w, r, c, kernel_size, kernel_size, n_int+n_out);
+    initialisation_4d_matrix(ZERO, network->kernel[n].cnn->d_w, r, c, kernel_size, kernel_size, n_int+n_out);
+    network->size++;
+}
+
+void add_dense(Network* network, int input_units, int output_units, int activation) {
+    /* Ajoute une couche dense au réseau et initialise les poids et les biais*/
+    int n = network->size;
+    if (network->max_size == n) {
+        printf("Impossible de rajouter une couche dense, le réseau est déjà plein\n");
+        return;
+    }
+    network->kernel[n].cnn = NULL;
+    network->kernel[n].nn = malloc(sizeof(Kernel_nn));
+    network->kernel[n].activation = activation;
+    network->kernel[n].nn->input_units = input_units;
+    network->kernel[n].nn->output_units = output_units;
+    network->kernel[n].nn->bias = malloc(sizeof(float)*output_units);
+    network->kernel[n].nn->d_bias = malloc(sizeof(float)*output_units);
+    network->kernel[n].nn->weights = malloc(sizeof(float*)*input_units);
+    network->kernel[n].nn->d_weights = malloc(sizeof(float*)*input_units);
+    for (int i=0; i<input_units; i++) {
+        network->kernel[n].nn->weights[i] = malloc(sizeof(float)*output_units);
+        network->kernel[n].nn->d_weights[i] = malloc(sizeof(float)*output_units);
+    }
+    initialisation_1d_matrix(network->initialisation, network->kernel[n].nn->bias, output_units, output_units+input_units);
+    initialisation_1d_matrix(ZERO, network->kernel[n].nn->d_bias, output_units, output_units+input_units);
+    initialisation_2d_matrix(network->initialisation, network->kernel[n].nn->weights, input_units, output_units, output_units+input_units);
+    initialisation_2d_matrix(ZERO, network->kernel[n].nn->d_weights, input_units, output_units, output_units+input_units);
+    create_a_line_input_layer(network, n, output_units);
+    network->size++;
+}
+
+void write_image_in_newtork_32(int** image, int height, int width, float** input) {
+    /* Ecrit une image 28*28 au centre d'un tableau 32*32 et met à 0 le reste */
+
+    for (int i=0; i < height+2*PADING_INPUT; i++) {
+        for (int j=PADING_INPUT; j < width+2*PADING_INPUT; j++) {
+            if (i<PADING_INPUT || i>height+PADING_INPUT || j<PADING_INPUT || j>width+PADING_INPUT) {
+                input[i][j] = 0.;
+            }
+            else {
+                input[i][j] = (float)image[i][j] / 255.0f;
+            }
+        }
+    }
+}
+
+void make_convolution(float*** input, Kernel_cnn* kernel, float*** output, int output_dim) {
+    /* Effectue une convolution sans stride */
+    //NOT FINISHED, MISS CONDITIONS ON THE CONVOLUTION
+    float f;
+    int i, j, k, a, b, c, n=kernel->k_size;
+    for (i=0; i<kernel->columns; i++) {
+        for (j=0; j<output_dim; j++) {
+            for (k=0; k<output_dim; k++) {
+                f = kernel->bias[i][j][k];
+                for (a=0; a<kernel->rows; a++) {
+                    for (b=0; b<n; b++) {
+                        for (c=0; c<n; c++) {
+                            f += kernel->w[a][i][b][c]*input[a][j+a][k+b];
+                        }
+                    }
+                }
+                output[i][j][k] = f;
+            }
+        }
+    }
+}
+
+void make_average_pooling(float*** input, float*** output, int size, int output_depth, int output_dim) {
+    /* Effecute un average pooling avec stride=size */
+    //NOT FINISHED, MISS CONDITIONS ON THE POOLING
+    float average;
+    int i, j, k, a, b, n=size*size;
+    for (i=0; i<output_depth; i++) {
+        for (j=0; j<output_dim; j++) {
+            for (k=0; k<output_dim; k++) {
+                average = 0.;
+                for (a=0; a<size; a++) {
+                    for (b=0; b<size; b++) {
+                        average += input[i][2*j +a][2*k +b];
+                    }
+                }
+                output[i][j][k] = average;
+            }
+        }
+    }
+}
+
+void make_average_pooling_flattened(float*** input, float* output, int size, int input_depth, int input_dim) {
+    /* Effectue un average pooling avec stride=size et aplatissement */
+    if ((input_depth*input_dim*input_dim) % (size*size) != 0) {
+        printf("Erreur, deux layers non compatibles avec un average pooling flattened");
+        return;
+    }
+    float average;
+    int i, j, k, a, b, n=size*size, cpt=0;
+    int output_dim = input_dim - 2*(size/2);
+    for (i=0; i<input_depth; i++) {
+        for (j=0; j<output_dim; j++) {
+            for (k=0; k<output_dim; k++) {
+                average = 0.;
+                for (a=0; a<size; a++) {
+                    for (b=0; b<size; b++) {
+                        average += input[i][2*j +a][2*k +b];
+                    }
+                }
+                output[cpt] = average;
+                cpt++;
+            }
+        }
+    }
+}
+
+void make_fully_connected(float* input, Kernel_nn* kernel, float* output, int size_input, int size_output) {
+    /* Effecute une full connection */
+    int i, j, k;
+    float f;
+    for (i=0; i<size_output; i++) {
+        f = kernel->bias[i];
+        for (j=0; j<size_input; j++) {
+            f += kernel->weights[i][j]*input[j];
+        }
+        output[i] = f;
+    }
+}
+
+void free_a_cube_input_layer(Network* network, int pos, int depth, int dim) {
+    /* Libère la mémoire allouée à une couche de type input cube */
+    int i, j, k;
+    for (i=0; i<depth; i++) {
+        for (j=0; j<dim; j++) {
+            free(network->input[pos][i][j]);
+        }
+        free(network->input[pos][i]);
+    }
+    free(network->input[pos]);
+}
+
+void free_a_line_input_layer(Network* network, int pos) {
+    /* Libère la mémoire allouée à une couche de type input line */
+    free(network->input[pos][0][0]);
+    free(network->input[pos][0]);
+    free(network->input[pos]);
+}
+
+void free_average_pooling(Network* network, int pos) {
+    /* Libère l'espace mémoie et supprime une couche d'average pooling classique */
+    free_a_cube_input_layer(network, pos, network->dim[pos-1][1], network->dim[pos-1][0]/2);
+}
+
+void free_average_pooling_flatten(Network* network, int pos) {
+    /* Libère l'espace mémoie et supprime une couche d'average pooling flatten */
+    free_a_line_input_layer(network, pos);
+}
+
+void free_convolution(Network* network, int pos) {
+    /* Libère l'espace mémoire et supprime une couche de convolution */
+    int i, j, k, c = network->kernel[pos].cnn->columns;
+    int k_size = network->kernel[pos].cnn->k_size;
+    int r = network->kernel[pos].cnn->rows;
+    free_a_cube_input_layer(network, pos, c, network->dim[pos-1][0] - 2*(k_size/2));
+    for (i=0; i<c; i++) {
+        for (j=0; j<k_size; j++) {
+            free(network->kernel[pos].cnn->bias[i][j]);
+            free(network->kernel[pos].cnn->d_bias[i][j]);
+        }
+        free(network->kernel[pos].cnn->bias[i]);
+        free(network->kernel[pos].cnn->d_bias[i]);
+    }
+    free(network->kernel[pos].cnn->bias);
+    free(network->kernel[pos].cnn->d_bias);
+
+    for (i=0; i<r; i++) {
+        for (j=0; j<c; j++) {
+            for (k=0; k<k_size; k++) {
+                free(network->kernel[pos].cnn->w[i][j][k]);
+                free(network->kernel[pos].cnn->d_w[i][j][k]);
+            }
+            free(network->kernel[pos].cnn->w[i][j]);
+            free(network->kernel[pos].cnn->d_w[i][j]);
+        }
+        free(network->kernel[pos].cnn->w[i]);
+        free(network->kernel[pos].cnn->d_w[i]);
+    }
+    free(network->kernel[pos].cnn->w);
+    free(network->kernel[pos].cnn->d_w);
+
+    free(network->kernel[pos].cnn);
+}
+
+void free_dense(Network* network, int pos) {
+    /* Libère l'espace mémoire et supprime une couche dense */
+    free_a_line_input_layer(network, pos);
+    int i, dim = network->kernel[pos].nn->output_units;
+    for (int i=0; i<dim; i++) {
+        free(network->kernel[pos].nn->weights[i]);
+        free(network->kernel[pos].nn->d_weights[i]);
+    }
+    free(network->kernel[pos].nn->weights);
+    free(network->kernel[pos].nn->d_weights);
+
+    free(network->kernel[pos].nn->bias);
+    free(network->kernel[pos].nn->d_bias);
+
+    free(network->kernel[pos].nn);
+}
+
+void free_network_creation(Network* network) {
+    /* Libère l'espace alloué dans la fonction 'create_network' */
+    free_a_cube_input_layer(network, 0, network->dim[0][1], network->dim[0][0]);
+
+    for (int i=0; i<network->max_size; i++) {
+        free(network->dim[i]);
+    }
+    free(network->dim);
+
+    free(network->kernel);
+    free(network->input);
+
+    free(network);
+}
+
+void free_network_lenet5(Network* network) {
+    /* Libère l'espace alloué dans la fonction 'create_network_lenet5' */
+    free_dense(network, 6);
+    free_dense(network, 5);
+    free_dense(network, 4);
+    free_average_pooling_flatten(network, 3);
+    free_convolution(network, 2);
+    free_average_pooling(network, 1);
+    free_convolution(network, 0);
+    free_network_creation(network);
+    if (network->size != network->max_size) {
+        printf("Attention, le réseau LeNet5 n'est pas complet");
+    }
+}
+
+void forward_propagation(Network* network) {
+    /* Propage en avant le cnn */
+    for (int i=0; i < network->size-1; i++) {
+        if (network->kernel[i].nn==NULL && network->kernel[i].cnn!=NULL) {
+            make_convolution(network->input[i], network->kernel[i].cnn, network->input[i+1], network->dim[i+1][0]);
+            choose_apply_function_input(network->kernel[i].activation, network->input[i+1], network->dim[i+1][1], network->dim[i+1][0], network->dim[i+1][0]);
+        }
+        else if (network->kernel[i].nn!=NULL && network->kernel[i].cnn==NULL) {
+            make_fully_connected(network->input[i][0][0], network->kernel[i].nn, network->input[i+1][0][0], network->dim[i][0], network->dim[i+1][0]);
+            choose_apply_function_input(network->kernel[i].activation, network->input[i+1], 1, 1, network->dim[i+1][0]);
+        }
+        else {
+            if (network->size-2==i) {
+                printf("Le réseau ne peut pas finir par une pooling layer");
+                return;
+            }
+            if (network->kernel[i+1].nn!=NULL && network->kernel[i+1].cnn==NULL) {
+                make_average_pooling_flattened(network->input[i], network->input[i+1][0][0], network->kernel[i].activation/100, network->dim[i][1], network->dim[i][0]);
+                choose_apply_function_input(network->kernel[i].activation%100, network->input[i+1], 1, 1, network->dim[i+1][0]);
+            }
+            else if (network->kernel[i+1].nn==NULL && network->kernel[i+1].cnn!=NULL) {
+                make_average_pooling(network->input[i], network->input[i+1], network->kernel[i].activation/100, network->dim[i+1][1], network->dim[i+1][0]);
+                choose_apply_function_input(network->kernel[i].activation%100, network->input[i+1], network->dim[i+1][1], network->dim[i+1][0], network->dim[i+1][0]);
+            }
+            else {
+                printf("Le réseau ne peut pas contenir deux poolings layers collées");
+                return;
+            }
+        }
+    }
+}
+
+void backward_propagation(Network* network, float wanted_number) {
+    /* Propage en arrière le cnn */
+    float* wanted_output = generate_wanted_output(wanted_number);
+    int n = network->size-1;
+    float loss = compute_cross_entropy_loss(network->input[n][0][0], wanted_output, network->dim[n][0]);
+    int i, j;
+    for (i=n; i>=0; i--) {
+        if (i==n) {
+            if (network->kernel[i].activation == SOFTMAX) {
+                int l2 = network->dim[i][0]; // Taille de la dernière couche
+                int l1 = network->dim[i-1][0];
+                for (j=0; j<l2; j++) {
+
+                }
+            }
+            else {
+                printf("Erreur, seule la fonction softmax est implémentée pour la dernière couche");
+                return;
+            }
+        }
+        else {
+            if (network->kernel[i].activation == SIGMOID) {
+
+            }
+            else if (network->kernel[i].activation == TANH) {
+
+            }
+            else if (network->kernel[i].activation == RELU) {
+                
+            }
+        }
+    }
+    free(wanted_output);
+}
+
+float compute_cross_entropy_loss(float* output, float* wanted_output, int len) {
+    /* Renvoie l'erreur du réseau neuronal pour une sortie */
+    float loss=0.;
+    for (int i=0; i<len ; i++) {
+        if (wanted_output[i]==1) {
+            if (output[i]==0.) {
+                loss -= log(FLT_EPSILON);
+            }
+            else {
+                loss -= log(output[i]);
+            }
+        }
+    }
+    return loss;
+}
+                
+float* generate_wanted_output(float wanted_number) {
+    /* On considère que la sortie voulue comporte 10 éléments */
+    float* wanted_output = malloc(sizeof(float)*10);
+    for (int i=0; i<10; i++) {
+        if (i==wanted_number) {
+            wanted_output[i]=1;
+        }
+        else {
+            wanted_output[i]=0;
+        }
+    }
+    return wanted_output;
+}
diff --git a/src/mnist_cnn/cnn.h b/src/mnist_cnn/cnn.h
new file mode 100644
index 0000000..6a3483a
--- /dev/null
+++ b/src/mnist_cnn/cnn.h
@@ -0,0 +1,94 @@
+#include <stdlib.h>
+#include <stdio.h>
+#include <time.h>
+#include <math.h>
+#include <float.h>
+
+#ifndef DEF_CNN_H
+#define DEF_CNN_H
+
+
+typedef struct Kernel_cnn {
+    int k_size;
+    int rows;
+    int columns;
+    int b;
+    float*** bias; // De dimension columns*k_size*k_size
+    float*** d_bias; // De dimension columns*k_size*k_size
+    float**** w; // De dimension rows*columns*k_size*k_size
+    float**** d_w; // De dimension rows*columns*k_size*k_size
+} Kernel_cnn;
+
+typedef struct Kernel_nn {
+    int input_units;
+    int output_units;
+    float* bias; // De dimension output_units
+    float* d_bias; // De dimension output_units
+    float** weights; // De dimension input_units*output_units
+    float** d_weights; // De dimension input_units*output_units
+} Kernel_nn;
+
+typedef struct Kernel {
+    Kernel_cnn* cnn;
+    Kernel_nn* nn;
+    int activation; // Vaut l'activation sauf pour un pooling où il: vaut kernel_size*100 + activation
+} Kernel;
+
+typedef struct Layer {
+
+} Layer;
+
+typedef struct Network{
+    int dropout; // Contient la probabilité d'abandon entre 0 et 100 (inclus)
+    int initialisation; // Contient le type d'initialisation
+    int max_size; // Taille maximale du réseau après initialisation
+    int size; // Taille actuelle du réseau
+    int** dim; // Contient les dimensions de l'input (width*depth)
+    Kernel* kernel;
+    float**** input;
+} Network;
+
+float max(float a, float b);
+float sigmoid(float x);
+float sigmoid_derivative(float x);
+float relu(float x);
+float relu_derivative(float x);
+float tanh_(float x);
+float tanh_derivative(float x);
+void apply_softmax_input(float ***input, int depth, int rows, int columns);
+void apply_function_input(float (*f)(float), float*** input, int depth, int rows, int columns);
+void choose_apply_function_input(int activation, float*** input, int depth, int rows, int columns);
+int will_be_drop(int dropout_prob);
+Network* create_network(int max_size, int dropout, int initialisation, int input_dim, int input_depth);
+Network* create_network_lenet5(int dropout, int activation, int initialisation);
+void create_a_cube_input_layer(Network* network, int pos, int depth, int dim);
+void create_a_line_input_layer(Network* network, int pos, int dim);
+void initialisation_1d_matrix(int initialisation, float* matrix, int rows, int n); //NOT FINISHED (UNIFORM AND VARIATIONS)
+void initialisation_2d_matrix(int initialisation, float** matrix, int rows, int columns, int n); //NOT FINISHED
+void initialisation_3d_matrix(int initialisation, float*** matrix, int depth, int rows, int columns, int n); //NOT FINISHED
+void initialisation_4d_matrix(int initialisation, float**** matrix, int rows, int columns, int rows1, int columns1, int n); //NOT FINISHED
+void add_average_pooling(Network* network, int kernel_size, int activation);
+void add_average_pooling_flatten(Network* network, int kernel_size, int activation);
+void add_convolution(Network* network, int nb_filter, int kernel_size, int activation);
+void add_dense(Network* network, int input_units, int output_units, int activation);
+void write_image_in_newtork_32(int** image, int height, int width, float** input);
+void make_convolution(float*** input, Kernel_cnn* kernel, float*** output, int output_dim);
+void make_average_pooling(float*** input, float*** output, int size, int output_depth, int output_dim);
+void make_average_pooling_flattened(float*** input, float* output, int size, int input_depth, int input_dim);
+void make_fully_connected(float* input, Kernel_nn* kernel, float* output, int size_input, int size_output);
+void free_a_cube_input_layer(Network* network, int pos, int depth, int dim);
+void free_a_line_input_layer(Network* network, int pos);
+void free_average_pooling(Network* network, int pos);
+void free_average_pooling_flatten(Network* network, int pos);
+void free_convolution(Network* network, int pos);
+void free_dense(Network* network, int pos);
+void free_network_creation(Network* network);
+void free_network_lenet5(Network* network);
+float compute_cross_entropy_loss(float* output, float* wanted_output, int len);
+void forward_propagation(Network* network);
+void backward_propagation(Network* network, float wanted_number); //NOT FINISHED
+float compute_cross_entropy_loss(float* output, float* wanted_output, int len);
+float* generate_wanted_output(float wanted_number);
+
+
+#endif
\ No newline at end of file
diff --git a/src/test.c b/src/test.c
new file mode 100644
index 0000000..0f25d30
--- /dev/null
+++ b/src/test.c
@@ -0,0 +1,127 @@
+#include <stdlib.h>
+#include <stdio.h>
+#include <time.h>
+typedef struct Neuron{
+    float bias; // Caractérise le bias du neurone
+    float z; // Sauvegarde des calculs faits sur le neurone (programmation dynamique)
+
+    float back_bias; // Changement du bias lors de la backpropagation
+    float last_back_bias; // Dernier changement de back_bias
+} Neuron;
+
+typedef struct Bias{
+    float bias; 
+    float back_bias;
+    float last_back_bias;
+} Bias;
+
+typedef struct Matrix {
+    int rows; // Nombre de lignes de la matrice
+    int columns; // Nombre de colonnes de la matrice
+    float** value; // Tableau 2d comportant les valeurs de matrice
+} Matrix;
+
+typedef struct Matrix_of_neurons {
+    int rows; // Nombre de lignes de la matrice
+    int columns; // Nombre de colonnes de la matrice
+    float** Neuron; // Tableau 2d comportant les valeurs de matrice
+} Matrix_of_neurons;
+
+typedef struct Matrix_of_bias {
+    int rows;
+    int columns;
+    float*** bias;
+} Matrix_of_bias;
+
+typedef struct Layer {
+    int rows; // Nombre de matrices du tableau de neurones
+    Matrix** conv; // Tableau de matrices des neurones dans la couche
+} Layer;
+
+typedef struct Filter {
+    int columns;
+    int rows;
+    int dim_bias;
+    Matrix_of_neurons*** kernel; // De dimension columns*rows
+    Matrix_of_bias** bias; // De dimension columns
+} Filter;
+
+typedef struct Network{
+    int dropout; // Contains the probability of dropout bewteen 0 and 100
+    int max_size;
+    int size; // Taille total du réseau
+    int size_cnn; // Nombre de couches dans le cnn
+    int* type_kernel; //De taille size -1
+
+    Layer** input; // Tableau des couches dans le réseau neuronal
+    Filter** kernel;
+} Network;
+
+void write_image_in_newtork_32(int** image, int height, int width, float** network) {
+    /* Ecrit une image 28*28 au centre d'un tableau 32*32 et met à 0 le reste */
+
+    for (int i=0; i < height+2*PADING_INPUT; i++) {
+        for (int j=PADING_INPUT; j < width+2*PADING_INPUT; j++) {
+            if (i<PADING_INPUT || i>height+PADING_INPUT || j<PADING_INPUT || j>width+PADING_INPUT){
+                network[i][j] = 0.;
+            }
+            else {
+                network[i][j] = (float)image[i][j] / 255.0f;
+            }
+        }
+    }
+}
+
+
+
+void make_convolution(Layer* input, Filter* filter, Layer* output){
+    /* Effectue une convolution sans stride */
+    if (filter->columns != output->rows) {
+        printf("Erreur, le filtre de la convolution et la sortie ne sont pas compatibles");
+        return;
+    }
+    if (filter->dim_bias != output->rows) {
+        printf("Erreur, le biais et la sortie de la convolution n'ont pas les mêmes dimensions");
+        return;
+    }
+
+    // MISS CONDITIONS ON THE CONVOLUTION
+    int i, j, k;
+    for (i=0; i < filter->rows; i++) {
+        for (j=0; j < filter->dim_bias; j++) {
+            for (int k=0; k < filter->dim_bias; k++) {
+                //output->conv[j][k] = filter->bias[i]->bias
+                // COPY BIAS OF FILTERS IN OUTPUT
+                // POUR CHAQUE COLONNE DANS LE KERNEL
+                    // ON APPLIQUE LE FILTRE SUR CHAQUE LIGNE DE L'INPUT ET LES SOMMES
+            }
+        }
+    }
+}
+
+void make_average_pooling(Layer* input, int dim_pooling, Layer* output){
+    /* Effectue un average pooling avec full strides */
+    
+    if (input->rows != output->rows || output->conv[0]->rows*dim_pooling != input->conv[0]->rows || input->rows != output->rows) {
+        printf("Erreur, dimension de la sortie et de l'entrée ne sont pas compatibles avec l'average pooling");
+        return;
+    }
+    int i, j, k, a, b, nb=dim_pooling*dim_pooling;
+    for (i=0; i < input->rows; i++) {
+        for (j=0; j < output->conv[i]->rows; j++) {
+            for (k=0; k < output->conv[i]->columns; k++) {
+                output->conv[i]->value[j][k] = 0;
+                for (a=0; a < dim_pooling; a++) {
+                    for (b=0; b < dim_pooling; b++) {
+                        output->conv[i]->value[j][k] += input->conv[i]->value[dim_pooling*j + a][dim_pooling*k + b];
+                    }
+                }
+                output->conv[i]->value[j][k] /= nb;
+            }
+        }
+    }
+}
+
+void forward_propagation_cnn() {
+    /* Effectue une forward propagation d'un cnn */
+}
\ No newline at end of file