diff --git a/src/cnn/test_network.c b/src/cnn/test_network.c
index 1b65958..ddb9dde 100644
--- a/src/cnn/test_network.c
+++ b/src/cnn/test_network.c
@@ -19,6 +19,8 @@ void test_network_mnist(Network* network, char* images_file, char* labels_file,
     int maxi; // Catégorie reconnue
 
     int accuracy = 0; // Nombre d'images reconnues
+    float loss = 0.;
+    float* wanted_output;
 
     // Load image
     int* mnist_parameters = read_mnist_images_parameters(images_file);
@@ -46,13 +48,18 @@ void test_network_mnist(Network* network, char* images_file, char* labels_file,
             accuracy++;
         }
 
+        // Compute loss
+        wanted_output = generate_wanted_output(labels[i], 10);
+        loss += compute_mean_squared_error(network->input[network->size-1][0][0], wanted_output, 10);
+        free(wanted_output);
+
         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);
+    printf("%d Images. Taux de réussite: %.2f%%\tLoss: %lf\n", nb_elem, 100*accuracy/(float)nb_elem, loss/nb_elem);
 }
 
 
diff --git a/src/cnn/train.c b/src/cnn/train.c
index fdd74f0..2233faa 100644
--- a/src/cnn/train.c
+++ b/src/cnn/train.c
@@ -10,6 +10,7 @@
 #include "../include/memory_management.h"
 #include "../mnist/include/mnist.h"
 #include "include/initialisation.h"
+#include "include/test_network.h"
 #include "include/neuron_io.h"
 #include "../include/colors.h"
 #include "../include/utils.h"
@@ -326,15 +327,17 @@ void train(int dataset_type, char* images_file, char* labels_file, char* data_di
         end_time = omp_get_wtime();
         elapsed_time = end_time - start_time;
         #ifdef USE_MULTITHREADING
-        printf("\rThreads [%d]\tÉpoque [%d/%d]\tImage [%d/%d]\tAccuracy: " GREEN "%0.4f%%" RESET " \tTemps: ", nb_threads, i, epochs, nb_images_total, nb_images_total, accuracy*100);
+        printf("\rThreads [%d]\tÉpoque [%d/%d]\tImage [%d/%d]\tAccuracy: " GREEN "%0.4f%%" RESET " \tLoss: %lf\tTemps: ", nb_threads, i, epochs, nb_images_total, nb_images_total, accuracy*100, loss);
         printf_time(elapsed_time);
         printf("\n");
         #else
-        printf("\rÉpoque [%d/%d]\tImage [%d/%d]\tAccuracy: " GREEN "%0.4f%%" RESET " \tTemps: ", i, epochs, nb_images_total, nb_images_total, accuracy*100);
+        printf("\rÉpoque [%d/%d]\tImage [%d/%d]\tAccuracy: " GREEN "%0.4f%%" RESET " \tLoss: %lf\tTemps: ", i, epochs, nb_images_total, nb_images_total, accuracy*100, loss);
         printf_time(elapsed_time);
         printf("\n");
         #endif
         write_network(out, network);
+        // If you want to test the network between each epoch, uncomment the following line:
+        //test_network(0, out, "data/mnist/t10k-images-idx3-ubyte", "data/mnist/t10k-labels-idx1-ubyte", NULL, false);
     }
 
     // To generate a new neural and compare performances with scripts/benchmark_binary.py