diff --git a/tensorflow/python/keras/applications/densenet.py b/tensorflow/python/keras/applications/densenet.py
index 1302598f8e9..03f5754bd61 100644
--- a/tensorflow/python/keras/applications/densenet.py
+++ b/tensorflow/python/keras/applications/densenet.py
@@ -192,7 +192,7 @@ def DenseNet(
     ValueError: if `classifier_activation` is not `softmax` or `None` when
       using a pretrained top layer.
   """
-  if not (weights in {'imagenet', None} or file_io.file_exists(weights)):
+  if not (weights in {'imagenet', None} or file_io.file_exists_v2(weights)):
     raise ValueError('The `weights` argument should be either '
                      '`None` (random initialization), `imagenet` '
                      '(pre-training on ImageNet), '
diff --git a/tensorflow/python/keras/applications/efficientnet.py b/tensorflow/python/keras/applications/efficientnet.py
index b791bbc2bc1..1e75d32faa7 100644
--- a/tensorflow/python/keras/applications/efficientnet.py
+++ b/tensorflow/python/keras/applications/efficientnet.py
@@ -269,7 +269,7 @@ def EfficientNet(
   if blocks_args == 'default':
     blocks_args = DEFAULT_BLOCKS_ARGS
 
-  if not (weights in {'imagenet', None} or file_io.file_exists(weights)):
+  if not (weights in {'imagenet', None} or file_io.file_exists_v2(weights)):
     raise ValueError('The `weights` argument should be either '
                      '`None` (random initialization), `imagenet` '
                      '(pre-training on ImageNet), '
diff --git a/tensorflow/python/keras/applications/inception_resnet_v2.py b/tensorflow/python/keras/applications/inception_resnet_v2.py
index 3bf296967f6..2ac213861b9 100644
--- a/tensorflow/python/keras/applications/inception_resnet_v2.py
+++ b/tensorflow/python/keras/applications/inception_resnet_v2.py
@@ -112,7 +112,7 @@ def InceptionResNetV2(include_top=True,
     layers = VersionAwareLayers()
   if kwargs:
     raise ValueError('Unknown argument(s): %s' % (kwargs,))
-  if not (weights in {'imagenet', None} or file_io.file_exists(weights)):
+  if not (weights in {'imagenet', None} or file_io.file_exists_v2(weights)):
     raise ValueError('The `weights` argument should be either '
                      '`None` (random initialization), `imagenet` '
                      '(pre-training on ImageNet), '
diff --git a/tensorflow/python/keras/applications/inception_v3.py b/tensorflow/python/keras/applications/inception_v3.py
index 7237cf558e1..59a869840b4 100644
--- a/tensorflow/python/keras/applications/inception_v3.py
+++ b/tensorflow/python/keras/applications/inception_v3.py
@@ -108,7 +108,7 @@ def InceptionV3(
     ValueError: if `classifier_activation` is not `softmax` or `None` when
       using a pretrained top layer.
   """
-  if not (weights in {'imagenet', None} or file_io.file_exists(weights)):
+  if not (weights in {'imagenet', None} or file_io.file_exists_v2(weights)):
     raise ValueError('The `weights` argument should be either '
                      '`None` (random initialization), `imagenet` '
                      '(pre-training on ImageNet), '
diff --git a/tensorflow/python/keras/applications/mobilenet.py b/tensorflow/python/keras/applications/mobilenet.py
index f2949170077..b628f2ccc6a 100644
--- a/tensorflow/python/keras/applications/mobilenet.py
+++ b/tensorflow/python/keras/applications/mobilenet.py
@@ -164,7 +164,7 @@ def MobileNet(input_shape=None,
     layers = VersionAwareLayers()
   if kwargs:
     raise ValueError('Unknown argument(s): %s' % (kwargs,))
-  if not (weights in {'imagenet', None} or file_io.file_exists(weights)):
+  if not (weights in {'imagenet', None} or file_io.file_exists_v2(weights)):
     raise ValueError('The `weights` argument should be either '
                      '`None` (random initialization), `imagenet` '
                      '(pre-training on ImageNet), '
diff --git a/tensorflow/python/keras/applications/mobilenet_v2.py b/tensorflow/python/keras/applications/mobilenet_v2.py
index 77392d487bd..13867a40569 100644
--- a/tensorflow/python/keras/applications/mobilenet_v2.py
+++ b/tensorflow/python/keras/applications/mobilenet_v2.py
@@ -180,7 +180,7 @@ def MobileNetV2(input_shape=None,
     layers = VersionAwareLayers()
   if kwargs:
     raise ValueError('Unknown argument(s): %s' % (kwargs,))
-  if not (weights in {'imagenet', None} or file_io.file_exists(weights)):
+  if not (weights in {'imagenet', None} or file_io.file_exists_v2(weights)):
     raise ValueError('The `weights` argument should be either '
                      '`None` (random initialization), `imagenet` '
                      '(pre-training on ImageNet), '
diff --git a/tensorflow/python/keras/applications/mobilenet_v3.py b/tensorflow/python/keras/applications/mobilenet_v3.py
index 44ba6fd3a39..a83716e4893 100644
--- a/tensorflow/python/keras/applications/mobilenet_v3.py
+++ b/tensorflow/python/keras/applications/mobilenet_v3.py
@@ -158,7 +158,7 @@ def MobileNetV3(stack_fn,
                 pooling=None,
                 dropout_rate=0.2,
                 classifier_activation='softmax'):
-  if not (weights in {'imagenet', None} or file_io.file_exists(weights)):
+  if not (weights in {'imagenet', None} or file_io.file_exists_v2(weights)):
     raise ValueError('The `weights` argument should be either '
                      '`None` (random initialization), `imagenet` '
                      '(pre-training on ImageNet), '
diff --git a/tensorflow/python/keras/applications/nasnet.py b/tensorflow/python/keras/applications/nasnet.py
index 4f71165f2e9..3f14646a8e2 100644
--- a/tensorflow/python/keras/applications/nasnet.py
+++ b/tensorflow/python/keras/applications/nasnet.py
@@ -150,7 +150,7 @@ def NASNet(
     ValueError: if `classifier_activation` is not `softmax` or `None` when
       using a pretrained top layer.
   """
-  if not (weights in {'imagenet', None} or file_io.file_exists(weights)):
+  if not (weights in {'imagenet', None} or file_io.file_exists_v2(weights)):
     raise ValueError('The `weights` argument should be either '
                      '`None` (random initialization), `imagenet` '
                      '(pre-training on ImageNet), '
diff --git a/tensorflow/python/keras/applications/resnet.py b/tensorflow/python/keras/applications/resnet.py
index 61310399180..720704f20df 100644
--- a/tensorflow/python/keras/applications/resnet.py
+++ b/tensorflow/python/keras/applications/resnet.py
@@ -137,7 +137,7 @@ def ResNet(stack_fn,
     layers = VersionAwareLayers()
   if kwargs:
     raise ValueError('Unknown argument(s): %s' % (kwargs,))
-  if not (weights in {'imagenet', None} or file_io.file_exists(weights)):
+  if not (weights in {'imagenet', None} or file_io.file_exists_v2(weights)):
     raise ValueError('The `weights` argument should be either '
                      '`None` (random initialization), `imagenet` '
                      '(pre-training on ImageNet), '
diff --git a/tensorflow/python/keras/applications/vgg16.py b/tensorflow/python/keras/applications/vgg16.py
index 0d508997d0f..9b46ca397af 100644
--- a/tensorflow/python/keras/applications/vgg16.py
+++ b/tensorflow/python/keras/applications/vgg16.py
@@ -113,7 +113,7 @@ def VGG16(
     ValueError: if `classifier_activation` is not `softmax` or `None` when
       using a pretrained top layer.
   """
-  if not (weights in {'imagenet', None} or file_io.file_exists(weights)):
+  if not (weights in {'imagenet', None} or file_io.file_exists_v2(weights)):
     raise ValueError('The `weights` argument should be either '
                      '`None` (random initialization), `imagenet` '
                      '(pre-training on ImageNet), '
diff --git a/tensorflow/python/keras/applications/vgg19.py b/tensorflow/python/keras/applications/vgg19.py
index c033f57338a..54dc62cf20b 100644
--- a/tensorflow/python/keras/applications/vgg19.py
+++ b/tensorflow/python/keras/applications/vgg19.py
@@ -113,7 +113,7 @@ def VGG19(
     ValueError: if `classifier_activation` is not `softmax` or `None` when
       using a pretrained top layer.
   """
-  if not (weights in {'imagenet', None} or file_io.file_exists(weights)):
+  if not (weights in {'imagenet', None} or file_io.file_exists_v2(weights)):
     raise ValueError('The `weights` argument should be either '
                      '`None` (random initialization), `imagenet` '
                      '(pre-training on ImageNet), '
diff --git a/tensorflow/python/keras/applications/xception.py b/tensorflow/python/keras/applications/xception.py
index b954f7735ed..80027b2164b 100644
--- a/tensorflow/python/keras/applications/xception.py
+++ b/tensorflow/python/keras/applications/xception.py
@@ -113,7 +113,7 @@ def Xception(
     ValueError: if `classifier_activation` is not `softmax` or `None` when
       using a pretrained top layer.
   """
-  if not (weights in {'imagenet', None} or file_io.file_exists(weights)):
+  if not (weights in {'imagenet', None} or file_io.file_exists_v2(weights)):
     raise ValueError('The `weights` argument should be either '
                      '`None` (random initialization), `imagenet` '
                      '(pre-training on ImageNet), '
diff --git a/tensorflow/python/keras/callbacks.py b/tensorflow/python/keras/callbacks.py
index 3469ccb68ef..21215db2c6f 100644
--- a/tensorflow/python/keras/callbacks.py
+++ b/tensorflow/python/keras/callbacks.py
@@ -1399,9 +1399,10 @@ class ModelCheckpoint(Callback):
   def _checkpoint_exists(self, filepath):
     """Returns whether the checkpoint `filepath` refers to exists."""
     if filepath.endswith('.h5'):
-      return file_io.file_exists(filepath)
-    tf_saved_model_exists = file_io.file_exists(filepath)
-    tf_weights_only_checkpoint_exists = file_io.file_exists(filepath + '.index')
+      return file_io.file_exists_v2(filepath)
+    tf_saved_model_exists = file_io.file_exists_v2(filepath)
+    tf_weights_only_checkpoint_exists = file_io.file_exists_v2(
+        filepath + '.index')
     return tf_saved_model_exists or tf_weights_only_checkpoint_exists
 
   def _get_most_recently_modified_file_matching_pattern(self, pattern):
@@ -1466,7 +1467,7 @@ class ModelCheckpoint(Callback):
     n_file_with_latest_mod_time = 0
     file_path_with_largest_file_name = None
 
-    if file_io.file_exists(dir_name):
+    if file_io.file_exists_v2(dir_name):
       for file_name in os.listdir(dir_name):
         # Only consider if `file_name` matches the pattern.
         if re.match(base_name_regex, file_name):
@@ -2505,7 +2506,7 @@ class CSVLogger(Callback):
 
   def on_train_begin(self, logs=None):
     if self.append:
-      if file_io.file_exists(self.filename):
+      if file_io.file_exists_v2(self.filename):
         with open(self.filename, 'r' + self.file_flags) as f:
           self.append_header = not bool(len(f.readline()))
       mode = 'a'
diff --git a/tensorflow/python/keras/distribute/multi_worker_callback_tf2_test.py b/tensorflow/python/keras/distribute/multi_worker_callback_tf2_test.py
index abed53b0cd0..0b98e85e26d 100644
--- a/tensorflow/python/keras/distribute/multi_worker_callback_tf2_test.py
+++ b/tensorflow/python/keras/distribute/multi_worker_callback_tf2_test.py
@@ -37,9 +37,10 @@ from tensorflow.python.platform import test
 def checkpoint_exists(filepath):
   """Returns whether the checkpoint `filepath` refers to exists."""
   if filepath.endswith('.h5'):
-    return file_io.file_exists(filepath)
-  tf_saved_model_exists = file_io.file_exists(filepath)
-  tf_weights_only_checkpoint_exists = file_io.file_exists(filepath + '.index')
+    return file_io.file_exists_v2(filepath)
+  tf_saved_model_exists = file_io.file_exists_v2(filepath)
+  tf_weights_only_checkpoint_exists = file_io.file_exists_v2(
+      filepath + '.index')
   return tf_saved_model_exists or tf_weights_only_checkpoint_exists
 
 
@@ -145,7 +146,7 @@ class KerasCallbackMultiProcessTest(parameterized.TestCase, test.TestCase):
       num_epoch = 2
 
       # The saving_filepath shouldn't exist at the beginning (as it's unique).
-      test_obj.assertFalse(file_io.file_exists(saving_filepath))
+      test_obj.assertFalse(file_io.file_exists_v2(saving_filepath))
 
       model.fit(
           x=train_ds,
@@ -153,7 +154,7 @@ class KerasCallbackMultiProcessTest(parameterized.TestCase, test.TestCase):
           steps_per_epoch=steps,
           callbacks=[callbacks.ModelCheckpoint(filepath=saving_filepath)])
 
-      test_obj.assertTrue(file_io.file_exists(saving_filepath))
+      test_obj.assertTrue(file_io.file_exists_v2(saving_filepath))
 
     saving_filepath = os.path.join(self.get_temp_dir(), 'checkpoint')
 
@@ -185,7 +186,7 @@ class KerasCallbackMultiProcessTest(parameterized.TestCase, test.TestCase):
       num_epoch = 4
 
       # The saving_filepath shouldn't exist at the beginning (as it's unique).
-      test_obj.assertFalse(file_io.file_exists(saving_filepath))
+      test_obj.assertFalse(file_io.file_exists_v2(saving_filepath))
       bar_dir = os.path.join(os.path.dirname(saving_filepath), 'backup')
 
       try:
@@ -204,8 +205,8 @@ class KerasCallbackMultiProcessTest(parameterized.TestCase, test.TestCase):
 
       multi_process_runner.barrier().wait()
       backup_filepath = os.path.join(bar_dir, 'checkpoint')
-      test_obj.assertTrue(file_io.file_exists(backup_filepath))
-      test_obj.assertTrue(file_io.file_exists(saving_filepath))
+      test_obj.assertTrue(file_io.file_exists_v2(backup_filepath))
+      test_obj.assertTrue(file_io.file_exists_v2(saving_filepath))
 
       model.fit(
           x=train_ds,
@@ -217,8 +218,8 @@ class KerasCallbackMultiProcessTest(parameterized.TestCase, test.TestCase):
               AssertCallback()
           ])
       multi_process_runner.barrier().wait()
-      test_obj.assertFalse(file_io.file_exists(backup_filepath))
-      test_obj.assertTrue(file_io.file_exists(saving_filepath))
+      test_obj.assertFalse(file_io.file_exists_v2(backup_filepath))
+      test_obj.assertTrue(file_io.file_exists_v2(saving_filepath))
 
     saving_filepath = os.path.join(self.get_temp_dir(), 'checkpoint')
 
@@ -244,7 +245,7 @@ class KerasCallbackMultiProcessTest(parameterized.TestCase, test.TestCase):
           'logfile_%s_%d' % (task_config['type'], task_config['index']))
 
       # The saving_filepath shouldn't exist at the beginning (as it's unique).
-      test_obj.assertFalse(file_io.file_exists(saving_filepath))
+      test_obj.assertFalse(file_io.file_exists_v2(saving_filepath))
 
       model.fit(
           x=train_ds,
@@ -257,7 +258,8 @@ class KerasCallbackMultiProcessTest(parameterized.TestCase, test.TestCase):
       # `file_io.list_directory()` since the directory may be created at this
       # point.
       test_obj.assertEqual(
-          bool(file_io.list_directory(saving_filepath)), test_base.is_chief())
+          bool(file_io.list_directory_v2(saving_filepath)),
+          test_base.is_chief())
 
     multi_process_runner.run(
         proc_tensorboard_saves_on_chief_but_not_otherwise,
@@ -280,7 +282,7 @@ class KerasCallbackMultiProcessTest(parameterized.TestCase, test.TestCase):
 
       # Verifies that even if `saving_filepath_for_temp` exists, tensorboard
       # can still save to temporary directory.
-      test_obj.assertTrue(file_io.file_exists(saving_filepath_for_temp))
+      test_obj.assertTrue(file_io.file_exists_v2(saving_filepath_for_temp))
 
       model.fit(
           x=train_ds,
@@ -301,7 +303,7 @@ class KerasCallbackMultiProcessTest(parameterized.TestCase, test.TestCase):
       num_epoch = 2
 
       # The saving_filepath shouldn't exist at the beginning (as it's unique).
-      test_obj.assertFalse(file_io.file_exists(saving_filepath))
+      test_obj.assertFalse(file_io.file_exists_v2(saving_filepath))
 
       multi_process_runner.barrier().wait()
 
@@ -313,7 +315,7 @@ class KerasCallbackMultiProcessTest(parameterized.TestCase, test.TestCase):
 
       multi_process_runner.barrier().wait()
 
-      test_obj.assertTrue(file_io.list_directory(saving_filepath))
+      test_obj.assertTrue(file_io.list_directory_v2(saving_filepath))
 
     saving_filepath = os.path.join(self.get_temp_dir(), 'logfile')
 
diff --git a/tensorflow/python/keras/distribute/multi_worker_tutorial_test.py b/tensorflow/python/keras/distribute/multi_worker_tutorial_test.py
index f7d64c2fc23..ee2f64d5338 100644
--- a/tensorflow/python/keras/distribute/multi_worker_tutorial_test.py
+++ b/tensorflow/python/keras/distribute/multi_worker_tutorial_test.py
@@ -159,9 +159,10 @@ class MultiWorkerTutorialTest(parameterized.TestCase, test.TestCase):
       # Make sure chief finishes saving before non-chief's assertions.
       multi_process_runner.barrier().wait()
 
-      if not file_io.file_exists(model_path):
+      if not file_io.file_exists_v2(model_path):
         raise RuntimeError()
-      if file_io.file_exists(write_model_path) != _is_chief(task_type, task_id):
+      if file_io.file_exists_v2(write_model_path) != _is_chief(
+          task_type, task_id):
         raise RuntimeError()
 
       loaded_model = keras.saving.save.load_model(model_path)
@@ -179,9 +180,9 @@ class MultiWorkerTutorialTest(parameterized.TestCase, test.TestCase):
       # Make sure chief finishes saving before non-chief's assertions.
       multi_process_runner.barrier().wait()
 
-      if not file_io.file_exists(checkpoint_dir):
+      if not file_io.file_exists_v2(checkpoint_dir):
         raise RuntimeError()
-      if file_io.file_exists(write_checkpoint_dir) != _is_chief(
+      if file_io.file_exists_v2(write_checkpoint_dir) != _is_chief(
           task_type, task_id):
         raise RuntimeError()
 
diff --git a/tensorflow/python/keras/distribute/worker_training_state.py b/tensorflow/python/keras/distribute/worker_training_state.py
index 29939edc8f9..6385594e0c0 100644
--- a/tensorflow/python/keras/distribute/worker_training_state.py
+++ b/tensorflow/python/keras/distribute/worker_training_state.py
@@ -112,12 +112,12 @@ class WorkerTrainingState(object):
     successfully finishes.
     """
     # pylint: disable=protected-access
-    for pathname in file_io.get_matching_files(
+    for pathname in file_io.get_matching_files_v2(
         self.write_checkpoint_manager._prefix + '*'):
-      file_io.delete_recursively(pathname)
-    for pathname in file_io.get_matching_files(
+      file_io.delete_recursively_v2(pathname)
+    for pathname in file_io.get_matching_files_v2(
         os.path.join(self.write_checkpoint_manager.directory, 'checkpoint')):
-      file_io.delete_recursively(pathname)
+      file_io.delete_recursively_v2(pathname)
 
   def maybe_load_initial_epoch_from_ckpt(self, initial_epoch, mode):
     """Maybe load initial epoch from ckpt considering possible worker recovery.
diff --git a/tensorflow/python/keras/distribute/worker_training_state_test.py b/tensorflow/python/keras/distribute/worker_training_state_test.py
index 80a3deaa914..0411aedc3c1 100644
--- a/tensorflow/python/keras/distribute/worker_training_state_test.py
+++ b/tensorflow/python/keras/distribute/worker_training_state_test.py
@@ -48,7 +48,7 @@ class ModelCheckpointTest(test_base.IndependentWorkerTestBase,
         callbacks.ModelCheckpoint(
             filepath=saving_filepath, save_weights_only=save_weights_only)
     ]
-    self.assertFalse(file_io.file_exists(saving_filepath))
+    self.assertFalse(file_io.file_exists_v2(saving_filepath))
 
     try:
       model.fit(
@@ -56,9 +56,9 @@ class ModelCheckpointTest(test_base.IndependentWorkerTestBase,
     except NotFoundError as e:
       if 'Failed to create a NewWriteableFile' in e.message:
         self.skipTest('b/138941852, path not found error in Windows py35.')
-    tf_saved_model_exists = file_io.file_exists(saving_filepath)
-    tf_weights_only_checkpoint_exists = file_io.file_exists(saving_filepath +
-                                                            '.index')
+    tf_saved_model_exists = file_io.file_exists_v2(saving_filepath)
+    tf_weights_only_checkpoint_exists = file_io.file_exists_v2(
+        saving_filepath + '.index')
     self.assertTrue(tf_saved_model_exists or tf_weights_only_checkpoint_exists)
 
 
diff --git a/tensorflow/python/keras/saving/hdf5_format_test.py b/tensorflow/python/keras/saving/hdf5_format_test.py
index 92296b58023..1817bfc9263 100644
--- a/tensorflow/python/keras/saving/hdf5_format_test.py
+++ b/tensorflow/python/keras/saving/hdf5_format_test.py
@@ -1276,7 +1276,7 @@ class TestWeightSavingAndLoadingTFFormat(test.TestCase, parameterized.TestCase):
       prefix = 'ackpt'
       self.evaluate(v.assign(42.))
       m.save_weights(prefix)
-      self.assertTrue(file_io.file_exists('ackpt.index'))
+      self.assertTrue(file_io.file_exists_v2('ackpt.index'))
       self.evaluate(v.assign(1.))
       m.load_weights(prefix)
       self.assertEqual(42., self.evaluate(v))
@@ -1284,7 +1284,7 @@ class TestWeightSavingAndLoadingTFFormat(test.TestCase, parameterized.TestCase):
       prefix = 'subdir/ackpt'
       self.evaluate(v.assign(43.))
       m.save_weights(prefix)
-      self.assertTrue(file_io.file_exists('subdir/ackpt.index'))
+      self.assertTrue(file_io.file_exists_v2('subdir/ackpt.index'))
       self.evaluate(v.assign(2.))
       m.load_weights(prefix)
       self.assertEqual(43., self.evaluate(v))
@@ -1292,7 +1292,7 @@ class TestWeightSavingAndLoadingTFFormat(test.TestCase, parameterized.TestCase):
       prefix = 'ackpt/'
       self.evaluate(v.assign(44.))
       m.save_weights(prefix)
-      self.assertTrue(file_io.file_exists('ackpt/.index'))
+      self.assertTrue(file_io.file_exists_v2('ackpt/.index'))
       self.evaluate(v.assign(3.))
       m.load_weights(prefix)
       self.assertEqual(44., self.evaluate(v))
diff --git a/tensorflow/python/keras/saving/saved_model_experimental.py b/tensorflow/python/keras/saving/saved_model_experimental.py
index 25628cd1ba3..64765185986 100644
--- a/tensorflow/python/keras/saving/saved_model_experimental.py
+++ b/tensorflow/python/keras/saving/saved_model_experimental.py
@@ -30,8 +30,8 @@ from tensorflow.python.keras.saving import model_config
 from tensorflow.python.keras.saving import saving_utils
 from tensorflow.python.keras.utils import mode_keys
 from tensorflow.python.keras.utils.generic_utils import LazyLoader
-from tensorflow.python.lib.io import file_io
 from tensorflow.python.ops import variables
+from tensorflow.python.platform import gfile
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.saved_model import builder as saved_model_builder
 from tensorflow.python.saved_model import constants
@@ -152,7 +152,8 @@ def _export_model_json(model, saved_model_path):
   model_json_filepath = os.path.join(
       saved_model_utils.get_or_create_assets_dir(saved_model_path),
       compat.as_text(constants.SAVED_MODEL_FILENAME_JSON))
-  file_io.write_string_to_file(model_json_filepath, model_json)
+  with gfile.Open(model_json_filepath, 'w') as f:
+    f.write(model_json)
 
 
 def _export_model_variables(model, saved_model_path):
@@ -417,7 +418,8 @@ def load_from_saved_model(saved_model_path, custom_objects=None):
       compat.as_bytes(saved_model_path),
       compat.as_bytes(constants.ASSETS_DIRECTORY),
       compat.as_bytes(constants.SAVED_MODEL_FILENAME_JSON))
-  model_json = file_io.read_file_to_string(model_json_filepath)
+  with gfile.Open(model_json_filepath, 'r') as f:
+    model_json = f.read()
   model = model_config.model_from_json(
       model_json, custom_objects=custom_objects)
 
diff --git a/tensorflow/python/keras/utils/vis_utils_test.py b/tensorflow/python/keras/utils/vis_utils_test.py
index 8b401c3d21b..ccdde30446d 100644
--- a/tensorflow/python/keras/utils/vis_utils_test.py
+++ b/tensorflow/python/keras/utils/vis_utils_test.py
@@ -38,8 +38,8 @@ class ModelToDotFormatTest(test.TestCase):
     try:
       vis_utils.plot_model(
           model, to_file=dot_img_file, show_shapes=True, show_dtype=True)
-      self.assertTrue(file_io.file_exists(dot_img_file))
-      file_io.delete_file(dot_img_file)
+      self.assertTrue(file_io.file_exists_v2(dot_img_file))
+      file_io.delete_file_v2(dot_img_file)
     except ImportError:
       pass
 
@@ -68,8 +68,8 @@ class ModelToDotFormatTest(test.TestCase):
           show_shapes=True,
           show_dtype=True,
           expand_nested=True)
-      self.assertTrue(file_io.file_exists(dot_img_file))
-      file_io.delete_file(dot_img_file)
+      self.assertTrue(file_io.file_exists_v2(dot_img_file))
+      file_io.delete_file_v2(dot_img_file)
     except ImportError:
       pass
 
@@ -86,8 +86,8 @@ class ModelToDotFormatTest(test.TestCase):
           show_shapes=True,
           show_dtype=True,
           expand_nested=True)
-      self.assertTrue(file_io.file_exists(dot_img_file))
-      file_io.delete_file(dot_img_file)
+      self.assertTrue(file_io.file_exists_v2(dot_img_file))
+      file_io.delete_file_v2(dot_img_file)
     except ImportError:
       pass
 
@@ -102,8 +102,8 @@ class ModelToDotFormatTest(test.TestCase):
           show_shapes=True,
           show_dtype=True,
           expand_nested=True)
-      self.assertTrue(file_io.file_exists(dot_img_file))
-      file_io.delete_file(dot_img_file)
+      self.assertTrue(file_io.file_exists_v2(dot_img_file))
+      file_io.delete_file_v2(dot_img_file)
     except ImportError:
       pass