Remove irrelevant warnings.

- tf.train is not longer part of the API.
- `Sequence`-based generator training is a LTS API and should not print warning when used the way it's intended to be used.

PiperOrigin-RevId: 351188267
Change-Id: Ibf8e47f4cc475351df732f91a8cb2105fc42ea13

tensorflow/python/keras/engine/training.py

@@ -2160,16 +2160,6 @@ class Model(base_layer.Layer, version_utils.ModelVersionSelector):
         session = None
       else:
         session = backend.get_session()
-      optimizer = getattr(self, 'optimizer', None)
-      if (optimizer
-          and not isinstance(optimizer, trackable.Trackable)):
-        logging.warning(
-            ('This model was compiled with a Keras optimizer (%s) but is being '
-             'saved in TensorFlow format with `save_weights`. The model\'s '
-             'weights will be saved, but unlike with TensorFlow optimizers in '
-             'the TensorFlow format the optimizer\'s state will not be '
-             'saved.\n\nConsider using a TensorFlow optimizer from `tf.train`.')
-            % (optimizer,))
       self._trackable_saver.save(filepath, session=session, options=options)
       # Record this checkpoint so it's visible from tf.train.latest_checkpoint.
       checkpoint_management.update_checkpoint_state_internal(

tensorflow/python/keras/saving/save_weights_test.py

@@ -40,7 +40,6 @@ from tensorflow.python.lib.io import file_io
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import random_ops
 from tensorflow.python.platform import test
-from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.training import checkpoint_management
 from tensorflow.python.training import training as training_module
 from tensorflow.python.training.tracking import util as trackable
@@ -402,21 +401,6 @@ class SubclassedModel(training.Model):
 
 class TestWeightSavingAndLoadingTFFormat(test.TestCase, parameterized.TestCase):
 
-  def test_keras_optimizer_warning(self):
-    graph = ops.Graph()
-    with graph.as_default(), self.session(graph):
-      model = keras.models.Sequential()
-      model.add(keras.layers.Dense(2, input_shape=(3,)))
-      model.add(keras.layers.Dense(3))
-      model.compile(loss='mse', optimizer=optimizer_v1.Adam(), metrics=['acc'])
-      if not ops.executing_eagerly_outside_functions():
-        model._make_train_function()
-      temp_dir = self.get_temp_dir()
-      prefix = os.path.join(temp_dir, 'ckpt')
-      with test.mock.patch.object(logging, 'warning') as mock_log:
-        model.save_weights(prefix)
-        self.assertRegex(str(mock_log.call_args), 'Keras optimizer')
-
   @combinations.generate(combinations.combine(mode=['graph', 'eager']))
   def test_tensorflow_format_overwrite(self):
     with self.cached_session() as session:

tensorflow/python/keras/utils/data_utils.py

@@ -46,7 +46,6 @@ from six.moves.urllib.request import urlopen
 from tensorflow.python.keras.utils import tf_inspect
 from tensorflow.python.keras.utils.generic_utils import Progbar
 from tensorflow.python.keras.utils.io_utils import path_to_string
-from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.util.tf_export import keras_export
 
 
@@ -530,10 +529,6 @@ def get_pool_class(use_multiprocessing):
   global _FORCE_THREADPOOL
   if not use_multiprocessing or _FORCE_THREADPOOL:
     return multiprocessing.dummy.Pool  # ThreadPool
-  logging.warning(
-      'multiprocessing can interact badly with TensorFlow, causing '
-      'nondeterministic deadlocks. For high performance data pipelines tf.data '
-      'is recommended.')
   return multiprocessing.Pool