diff --git a/src/cnn/cnn.c b/src/cnn/cnn.c
index 17a8601..8113d25 100644
--- a/src/cnn/cnn.c
+++ b/src/cnn/cnn.c
@@ -52,16 +52,16 @@ void write_image_in_network_32(int** image, int height, int width, float** input
 }
 
 void write_image_in_network_260(unsigned char* image, int height, int width, float*** input) {
-    int input_size = 260;
-    int padding = (input_size - height)/2;
+    int size_input = 260;
+    int padding = (size_input - height)/2;
 
     for (int i=0; i < padding; i++) {
-        for (int j=0; j < input_size; j++) {
+        for (int j=0; j < size_input; j++) {
             for (int composante=0; composante < 3; composante++) {
                 input[composante][i][j] = 0.;
-                input[composante][input_size-1-i][j] = 0.;
+                input[composante][size_input-1-i][j] = 0.;
                 input[composante][j][i] = 0.;
-                input[composante][j][input_size-1-i] = 0.;
+                input[composante][j][size_input-1-i] = 0.;
             }
         }
     }
diff --git a/src/cnn/update.c b/src/cnn/update.c
index 0873be6..e2501a4 100644
--- a/src/cnn/update.c
+++ b/src/cnn/update.c
@@ -48,8 +48,8 @@ void update_weights(Network* network, Network* d_network) {
             } else { // Matrice -> vecteur
                 Kernel_nn* nn = k_i->nn;
                 Kernel_nn* d_nn = dk_i->nn;
-                int input_size = input_width*input_width*input_depth;
-                for (int a=0; a<input_size; a++) {
+                int size_input = input_width*input_width*input_depth;
+                for (int a=0; a<size_input; a++) {
                     for (int b=0; b<output_width; b++) {
                         nn->weights[a][b] -= network->learning_rate * d_nn->d_weights[a][b];
                         d_nn->d_weights[a][b] = 0;
@@ -148,8 +148,8 @@ void reset_d_weights(Network* network) {
                 }
             } else { // Matrice -> vecteur
                 Kernel_nn* nn = k_i_1->nn;
-                int input_size = input_width*input_width*input_depth;
-                for (int a=0; a<input_size; a++) {
+                int size_input = input_width*input_width*input_depth;
+                for (int a=0; a<size_input; a++) {
                     for (int b=0; b<output_width; b++) {
                         nn->d_weights[a][b] = 0;
                     }