diff --git a/doc/cnn/backpropagation_explaination.md b/doc/cnn/backpropagation_explaination.md
index 102ac58..45587c7 100644
--- a/doc/cnn/backpropagation_explaination.md
+++ b/doc/cnn/backpropagation_explaination.md
@@ -25,7 +25,7 @@ $\dfrac{\partial E}{\partial b_i} = \dfrac{\partial E}{\partial a_i}$
 
 ## Backpropagation of a fully connected layer
 
-<img src="fully_connected.png" width="200">
+<img src="dense.png" width="200">
 
 Soit f la fonction d'activation de la première couche (qui transforme les $a_i$ en $l_i$) et g la fonction d'activation de la deuxième couche (qui transforme les $c_i$ en $d_i$).
 - $d_1 =g(c_1)$  
diff --git a/doc/cnn/fully_connected.png b/doc/cnn/dense.png
similarity index 100%
rename from doc/cnn/fully_connected.png
rename to doc/cnn/dense.png
diff --git a/src/cnn/backpropagation.c b/src/cnn/backpropagation.c
index ee46fd9..30d9cf1 100644
--- a/src/cnn/backpropagation.c
+++ b/src/cnn/backpropagation.c
@@ -46,7 +46,7 @@ void backward_2d_pooling(float*** input, float*** output, int input_width, int o
     }
 }
 
-void backward_fully_connected(Kernel_nn* ker, float* input, float* input_z, float* output, int size_input, int size_output, ptr d_function, int is_first) {
+void backward_dense(Kernel_nn* ker, float* input, float* input_z, float* output, int size_input, int size_output, ptr d_function, int is_first) {
     // Bias
     for (int j=0; j < size_output; j++) {
         ker->d_bias[j] += output[j];
diff --git a/src/cnn/cnn.c b/src/cnn/cnn.c
index eeb0f4a..97fe331 100644
--- a/src/cnn/cnn.c
+++ b/src/cnn/cnn.c
@@ -163,7 +163,7 @@ void backward_propagation(Network* network, int wanted_number) {
         } else if (k_i->nn) { // Full connection
             ptr d_f = get_function_activation(activation);
             if (k_i->linearisation == 0) { // Vecteur -> Vecteur
-                backward_fully_connected(k_i->nn, input[0][0], input_z[0][0], output[0][0], input_width, output_width, d_f, i==0);
+                backward_dense(k_i->nn, input[0][0], input_z[0][0], output[0][0], input_width, output_width, d_f, i==0);
             } else { // Matrice -> vecteur
                 backward_linearisation(k_i->nn, input, input_z, output[0][0], input_depth, input_width, output_width, d_f);
             }
diff --git a/src/cnn/include/backpropagation.h b/src/cnn/include/backpropagation.h
index 61b66ba..461c061 100644
--- a/src/cnn/include/backpropagation.h
+++ b/src/cnn/include/backpropagation.h
@@ -27,7 +27,7 @@ void backward_2d_pooling(float*** input, float*** output, int input_width, int o
 /*
 * Transfert les informations d'erreur à travers une couche fully connected
 */
-void backward_fully_connected(Kernel_nn* ker, float* input, float* input_z, float* output, int size_input, int size_output, ptr d_function, int is_first);
+void backward_dense(Kernel_nn* ker, float* input, float* input_z, float* output, int size_input, int size_output, ptr d_function, int is_first);
 
 /*
 * Transfert les informations d'erreur à travers une couche de linéarisation