Delete "RuntimeWarning" it is not having the intended effect.

These `RuntimeWarning` are being interpreted as arguments to the string formatting, raising "TypeError: not all arguments converted during string formatting" errors. PiperOrigin-RevId: 199307228
STCYC · Jun 5, 2018 · 72f6b4d · 72f6b4d
1 parent 51445a7
commit 72f6b4d
Showing 1 changed file with 6 additions and 6 deletions.
diff --git a/tensorflow/python/keras/callbacks.py b/tensorflow/python/keras/callbacks.py
@@ -424,7 +424,7 @@ def __init__(self,
 
     if mode not in ['auto', 'min', 'max']:
       logging.warning('ModelCheckpoint mode %s is unknown, '
-                      'fallback to auto mode.', (mode), RuntimeWarning)
+                      'fallback to auto mode.', mode)
       mode = 'auto'
 
     if mode == 'min':
@@ -451,7 +451,7 @@ def on_epoch_end(self, epoch, logs=None):
         current = logs.get(self.monitor)
         if current is None:
           logging.warning('Can save best model only with %s available, '
-                          'skipping.', self.monitor, RuntimeWarning)
+                          'skipping.', self.monitor)
         else:
           if self.monitor_op(current, self.best):
             if self.verbose > 0:
@@ -515,7 +515,7 @@ def __init__(self,
 
     if mode not in ['auto', 'min', 'max']:
       logging.warning('EarlyStopping mode %s is unknown, '
-                      'fallback to auto mode.', mode, RuntimeWarning)
+                      'fallback to auto mode.', mode)
       mode = 'auto'
 
     if mode == 'min':
@@ -544,7 +544,7 @@ def on_epoch_end(self, epoch, logs=None):
     if current is None:
       logging.warning('Early stopping conditioned on metric `%s` '
                       'which is not available. Available metrics are: %s',
-                      self.monitor, ','.join(list(logs.keys())), RuntimeWarning)
+                      self.monitor, ','.join(list(logs.keys())))
       return
     if self.monitor_op(current - self.min_delta, self.best):
       self.best = current
@@ -898,7 +898,7 @@ def _reset(self):
     """
     if self.mode not in ['auto', 'min', 'max']:
       logging.warning('Learning Rate Plateau Reducing mode %s is unknown, '
-                      'fallback to auto mode.', self.mode, RuntimeWarning)
+                      'fallback to auto mode.', self.mode)
       self.mode = 'auto'
     if (self.mode == 'min' or
         (self.mode == 'auto' and 'acc' not in self.monitor)):
@@ -920,7 +920,7 @@ def on_epoch_end(self, epoch, logs=None):
     if current is None:
       logging.warning('Reduce LR on plateau conditioned on metric `%s` '
                       'which is not available. Available metrics are: %s',
-                      self.monitor, ','.join(list(logs.keys())), RuntimeWarning)
+                      self.monitor, ','.join(list(logs.keys())))
 
     else:
       if self.in_cooldown():