update exception message

pytorch_lightning/trainer/trainer.py

@@ -573,11 +573,11 @@ def __init__(
             )
             limit_train_batches = train_percent_check
 
-        self.limit_test_batches = _determine_limit_batches(limit_test_batches)
-        self.limit_val_batches = _determine_limit_batches(limit_val_batches)
-        self.limit_train_batches = _determine_limit_batches(limit_train_batches)
-        self.val_check_interval = _determine_limit_batches(val_check_interval)
-        self.overfit_batches = _determine_limit_batches(overfit_batches)
+        self.limit_train_batches = _determine_batch_limits(limit_train_batches, 'limit_train_batches')
+        self.limit_val_batches = _determine_batch_limits(limit_val_batches, 'limit_val_batches')
+        self.limit_test_batches = _determine_batch_limits(limit_test_batches, 'limit_test_batches')
+        self.val_check_interval = _determine_batch_limits(val_check_interval, 'val_check_interval')
+        self.overfit_batches = _determine_batch_limits(overfit_batches, 'overfit_batches')
         self.determine_data_use_amount(self.overfit_batches)
 
         # AMP init
@@ -1428,12 +1428,12 @@ def __call__(self) -> Union[List[DataLoader], DataLoader]:
         return self.dataloader
 
 
-def _determine_limit_batches(batches: Union[int, float]) -> Union[int, float]:
+def _determine_batch_limits(batches: Union[int, float], name: str) -> Union[int, float]:
     if 0 <= batches <= 1:
         return batches
     elif batches > 1 and batches % 1.0 == 0:
         return int(batches)
     else:
         raise MisconfigurationException(
-            f'You have passed invalid value {batches}, it has to be in (0, 1) or an int.'
+            f'You have passed invalid value {batches} for {name}, it has to be in [0.0, 1.0] or an int.'
         )