diff --git a/mupixutils.py b/mupixutils.py
index 60a3d592e263ef36adb773bd5acdaf110c1c8d12..1192292f31c13438b292d0a370aab5a4f4fd05e5 100644
--- a/mupixutils.py
+++ b/mupixutils.py
@@ -184,11 +184,13 @@ def train(d_model, g_model, gan_model, dataset, output_path, val_dataset = None,
                 history[1].append(mse)
                 history[2].append(ssim)
                 print("Epoch", current_epoch)
-                print('Val> mse[%.3e], ssim[%.3e]' % (mse, ssim))
+                # print('Val> mse[%.3e], ssim[%.3e]' % (mse, ssim))
+                print("Val> mse[",str(mse),"], ssim[",str(ssim),"]" % (mse, ssim))
                 cur_patience-=1
                 val_loss = mse/((ssim+1)/2) # scaling ssim on 0,1 instead of -1,1 : trouble when ssim is <0
                 with open(output_path+"/log.txt", 'a') as file:
-                        file.write("[Loss] Val loss at epoch %d : MSE[%.3e], SSIM[%.3e], Validation_Loss[%.3e]\n"%(current_epoch,mse,ssim,val_loss))
+                        # file.write("[Loss] Val loss at epoch %d : MSE[%.3e], SSIM[%.3e], Validation_Loss[%.3e]\n"%(current_epoch,mse,ssim,val_loss))
+                        file.write("[Loss] Val loss at epoch ",str(current_epoch)," : MSE[",str(mse),"], SSIM[",str(ssim),"], Validation_Loss[",str(val_loss),"]\n")
                 if val_loss < best_val_loss:
                     best_val_loss = np.copy(val_loss)
                     g_model.save(output_path+"/networks/Generator")
@@ -211,7 +213,8 @@ def train(d_model, g_model, gan_model, dataset, output_path, val_dataset = None,
                         cur_patience=patience
                     else :
                         with open(output_path+"/log.txt", 'a') as file:
-                            file.write("[Patience]: Reached at epoch %d with best validation loss :[%.3e]\n"%(current_epoch,val_loss))
+                            # file.write("[Patience]: Reached at epoch %d with best validation loss :[%.3e]\n"%(current_epoch,val_loss))
+                            file.write("[Patience]: Reached at epoch ",str(current_epoch),"with best validation loss :[",str(val_loss),"]\n")
                         break
 
             gc.collect()
@@ -232,7 +235,8 @@ def train(d_model, g_model, gan_model, dataset, output_path, val_dataset = None,
         # update the generator
         g_loss1,_ ,_ = gan_model.train_on_batch(X_realA, [y_real, X_realB])
         # summarize performance
-        print('Step>%d, Generator loss : %.3e' % (j, g_loss1))
+        # print('Step>%d, Generator loss : %.3e' % (j, g_loss1))
+        print('Step>',str(j),', Generator loss : ', str(g_loss1))
         # Saving the network every time it become better and when the network already did at least 500 batches.
         history[0].append(g_loss1)
         del _,g_loss1