fix Trainer _eval method for cases in which there is a target attribu…

…te but no target values are present in the hdf5 file/s

deeprank2/trainer.py

@@ -748,6 +748,9 @@ def _eval( # pylint: disable=too-many-locals
                 loss_ = loss_func(pred, y)
                 count_predictions += pred.shape[0]
                 sum_of_losses += loss_.detach().item() * pred.shape[0]
+            else:
+                target_vals = ['None'] * pred.shape[0]
+                eval_loss = 'None'
 
             # Get the outputs for export
             # Remember that non-linear activation is automatically applied in CrossEntropyLoss
@@ -764,7 +767,7 @@ def _eval( # pylint: disable=too-many-locals
         if count_predictions > 0:
             eval_loss = sum_of_losses / count_predictions
         else:
-            eval_loss = 0.0
+            eval_loss = 'None'
 
         self._output_exporters.process(
             pass_name, epoch_number, entry_names, outputs, target_vals, eval_loss)