diff --git a/src/cnn/backpropagation.c b/src/cnn/backpropagation.c
index de0b4b5..ce68c1d 100644
--- a/src/cnn/backpropagation.c
+++ b/src/cnn/backpropagation.c
@@ -74,7 +74,7 @@ void backward_fully_connected(Kernel_nn* ker, float* input, float* input_z, floa
         for (int j=0; j < size_output; j++) {
             tmp += output[j]*ker->weights[i][j];
         }
-        input[i] = tmp*derivative_function(input_z[i]);
+        input[i] = tmp*d_function(input_z[i]);
     }
 }
 
@@ -106,7 +106,7 @@ void backward_linearisation(Kernel_nn* ker, float*** input, float*** input_z, fl
                 for (int j=0; j < size_output; j++) {
                     tmp += output[j]*ker->weights[cpt][j];
                 }
-                input[i][k][l] = tmp*derivative_function(input_z[i][k][l]);
+                input[i][k][l] = tmp*d_function(input_z[i][k][l]);
                 cpt++;
             }
         }
@@ -125,7 +125,7 @@ void backward_convolution(Kernel_cnn* ker, float*** input, float*** input_z, flo
 
     // Weights
     int k_size = dim_input - dim_output +1;
-    int var = dim_input - k_size +1;
+
     for (int h=0; h < depth_input; h++) {
         for (int i=0; i < depth_output; i++) {
             for (int j=0; j < k_size; j++) {
@@ -161,7 +161,7 @@ void backward_convolution(Kernel_cnn* ker, float*** input, float*** input_z, flo
                         }
                     }
                 }
-                input[i][j][k] = tmp*derivative_function(input_z[i][j][k]);
+                input[i][j][k] = tmp*d_function(input_z[i][j][k]);
             }
         }
     }
diff --git a/src/cnn/cnn.c b/src/cnn/cnn.c
index f7eb4e0..19f7b35 100644
--- a/src/cnn/cnn.c
+++ b/src/cnn/cnn.c
@@ -3,6 +3,7 @@
 #include <math.h>
 #include <float.h> // Is it used ?
 
+#include "include/backpropagation.h"
 #include "include/initialisation.h"
 #include "include/function.h"
 #include "include/creation.h"
@@ -91,13 +92,12 @@ void backward_propagation(Network* network, float wanted_number) {
     float*** input_z;
     float*** output;
     Kernel* k_i;
-    Kernel* k_i_1;
+    
     rms_backward(network->input[n-1][0][0], network->input_z[n-1][0][0], wanted_output, network->width[n-1]); // Backward sur la dernière colonne
 
     for (int i=n-2; i >= 0; i--) {
         // Modifie 'k_i' à partir d'une comparaison d'informations entre 'input' et 'output'
         k_i = network->kernel[i];
-        k_i_1 = network->kernel[i+1];
         input = network->input[i];
         input_z = network->input_z[i];
         input_depth = network->depth[i];
diff --git a/src/cnn/creation.c b/src/cnn/creation.c
index 5b8ec21..5228ad0 100644
--- a/src/cnn/creation.c
+++ b/src/cnn/creation.c
@@ -156,8 +156,8 @@ void add_convolution(Network* network, int depth_output, int dim_output, int act
     }
     create_a_cube_input_layer(network, n, depth_output, bias_size);
     create_a_cube_input_z_layer(network, n, depth_output, bias_size);
-    int n_int = network->width[n-1]*network->width[n-1]*network->depth[n-1];
-    int n_out = network->width[n]*network->width[n]*network->depth[n];
+    // int n_int = network->width[n-1]*network->width[n-1]*network->depth[n-1];
+    // int n_out = network->width[n]*network->width[n]*network->depth[n];
     /* Not currently used 
     initialisation_3d_matrix(network->initialisation, cnn->bias, depth_output, kernel_size, kernel_size, n_int+n_out);
     initialisation_3d_matrix(ZERO, cnn->d_bias, depth_output, kernel_size, kernel_size, n_int+n_out);