message: Clean up callback warnings
diff: @@ -1264,16 +1264,6 @@ class ModelCheckpoint(Callback):
  self.save_weights_only = True
  def on_train_begin(self, logs=None):
  - # pylint: disable=protected-access
  - if self.model._in_multi_worker_mode:
  - logging.warning(
  - 'Automatic model reloading for interrupted job was removed from '
  - 'the `ModelCheckpoint` callback in multi-worker mode, please use the '
  - '`keras.callbacks.experimental.BackupAndRestore` callback instead. '
  - 'See this tutorial for details: '
  - 'https://www.tensorflow.org/tutorials/distribute/'
  - 'multi_worker_with_keras#backupandrestore_callback.'
  - )
  if self.load_weights_on_restart:
  filepath_to_load = (
  self._get_most_recently_modified_file_matching_pattern(self.filepath))
  @@ -2422,7 +2412,7 @@ class ReduceLROnPlateau(Callback):
  """Resets wait counter and cooldown counter.
  """
  if self.mode not in ['auto', 'min', 'max']:
  - logging.warning('Learning Rate Plateau Reducing mode %s is unknown, '
  + logging.warning('Learning rate reduction mode %s is unknown, '
  'fallback to auto mode.', self.mode)
  self.mode = 'auto'
  if (self.mode == 'min' or
  @@ -2443,7 +2433,7 @@ class ReduceLROnPlateau(Callback):
  logs['lr'] = K.get_value(self.model.optimizer.lr)
  current = logs.get(self.monitor)
  if current is None:
  - logging.warning('Reduce LR on plateau conditioned on metric `%s` '
  + logging.warning('Learning rate reduction is conditioned on metric `%s` '
  'which is not available. Available metrics are: %s',
  self.monitor, ','.join(list(logs.keys())))
  