diff --git a/deeprank2/trainer.py b/deeprank2/trainer.py
index 1240b8694..fe73c7a45 100644
--- a/deeprank2/trainer.py
+++ b/deeprank2/trainer.py
@@ -577,7 +577,7 @@ def train( # pylint: disable=too-many-arguments, too-many-branches, too-many-loc
 
         train_losses = []
         valid_losses = []
-        checkpoint_model = None
+        saved_model = False
 
         if earlystop_patience or earlystop_maxgap:
             early_stopping = EarlyStopping(patience=earlystop_patience, maxgap=earlystop_maxgap, min_epoch=min_epoch, trace_func=_log.info)
@@ -610,6 +610,7 @@ def train( # pylint: disable=too-many-arguments, too-many-branches, too-many-loc
                     if best_model:
                         if min(valid_losses) == loss_:
                             checkpoint_model = self._save_model()
+                            saved_model = True
                             self.epoch_saved_model = epoch
                             _log.info(f'Best model saved at epoch # {self.epoch_saved_model}.')
                     # check early stopping criteria (in validation case only)
@@ -627,17 +628,19 @@ def train( # pylint: disable=too-many-arguments, too-many-branches, too-many-loc
                                 "Training data is used both for learning and model selection, which will to overfitting." +
                                 "\n\tIt is preferable to use an independent training and validation data sets.")
                             checkpoint_model = self._save_model()
+                            saved_model = True
                             self.epoch_saved_model = epoch
                             _log.info(f'Best model saved at epoch # {self.epoch_saved_model}.')
 
             # Save the last model
-            if best_model is False or checkpoint_model is None:
+            if best_model is False or not saved_model:
                 checkpoint_model = self._save_model()
                 self.epoch_saved_model = epoch
                 _log.info(f'Last model saved at epoch # {self.epoch_saved_model}.')
-                if checkpoint_model is None:
-                    _log.warning("A model has been saved but the training and validation losses were NaN;" +
-                              "make sure that you are using enough data points during the trainig.")
+                if not saved_model:
+                    _log.warning("A model has been saved but the validation and/or the training losses were NaN;" +
+                              "try to increase the cutoff distance during the data processing or the number of data points" +
+                              "during the training.")
 
         # Now that the training loop is over, save the model
         if filename: