Upgrade Keras applications and Keras preprocessing.

PiperOrigin-RevId: 210174523

34 files changed, 851 insertions, 149 deletions

diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md
index e8e13142e9..44ea18fa7b 100644
--- a/tensorflow/docs_src/install/install_sources.md
+++ b/tensorflow/docs_src/install/install_sources.md
@@ -180,9 +180,9 @@ If you follow these instructions, you will not need to disable SIP.
 
 After installing pip, invoke the following commands:
 
-<pre> $ <b>sudo pip install six numpy wheel mock h5py</b>
- $ <b>sudo pip install keras_applications==1.0.4 --no-deps</b>
- $ <b>sudo pip install keras_preprocessing==1.0.2 --no-deps</b>
+<pre> $ <b>pip install six numpy wheel mock h5py</b>
+ $ <b>pip install keras_applications==1.0.5 --no-deps</b>
+ $ <b>pip install keras_preprocessing==1.0.3 --no-deps</b>
 </pre>
 
 Note: These are just the minimum requirements to _build_ tensorflow. Installing
diff --git a/tensorflow/docs_src/install/install_sources_windows.md b/tensorflow/docs_src/install/install_sources_windows.md
index a1da122317..40dce106d6 100644
--- a/tensorflow/docs_src/install/install_sources_windows.md
+++ b/tensorflow/docs_src/install/install_sources_windows.md
@@ -94,8 +94,8 @@ Assume you already have `pip3` in `%PATH%`, issue the following command:
 
 <pre>
 C:\> <b>pip3 install six numpy wheel</b>
-C:\> <b>pip3 install keras_applications==1.0.4 --no-deps</b>
-C:\> <b>pip3 install keras_preprocessing==1.0.2 --no-deps</b>
+C:\> <b>pip3 install keras_applications==1.0.5 --no-deps</b>
+C:\> <b>pip3 install keras_preprocessing==1.0.3 --no-deps</b>
 </pre>
 
 <a name="InstallCUDA"></a>
diff --git a/tensorflow/python/keras/applications/__init__.py b/tensorflow/python/keras/applications/__init__.py
index cd9462d6b5..a8b6d55e41 100644
--- a/tensorflow/python/keras/applications/__init__.py
+++ b/tensorflow/python/keras/applications/__init__.py
@@ -14,6 +14,7 @@
 # ==============================================================================
 """Keras Applications are canned architectures with pre-trained weights."""
 # pylint: disable=g-import-not-at-top
+# pylint: disable=g-bad-import-order
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
@@ -25,13 +26,49 @@ from tensorflow.python.keras import engine
 from tensorflow.python.keras import layers
 from tensorflow.python.keras import models
 from tensorflow.python.keras import utils
+from tensorflow.python.util import tf_inspect
+
+# `get_submodules_from_kwargs` has been introduced in 1.0.5, but we would
+# like to be able to handle prior versions. Note that prior to 1.0.5,
+# `keras_applications` did not expose a `__version__` attribute.
+if not hasattr(keras_applications, 'get_submodules_from_kwargs'):
+
+  if 'engine' in tf_inspect.getfullargspec(
+      keras_applications.set_keras_submodules)[0]:
+    keras_applications.set_keras_submodules(
+        backend=backend,
+        layers=layers,
+        models=models,
+        utils=utils,
+        engine=engine)
+  else:
+    keras_applications.set_keras_submodules(
+        backend=backend,
+        layers=layers,
+        models=models,
+        utils=utils)
+
+
+def keras_modules_injection(base_fun):
+  """Decorator injecting tf.keras replacements for Keras modules.
+
+  Arguments:
+      base_fun: Application function to decorate (e.g. `MobileNet`).
+
+  Returns:
+      Decorated function that injects keyword argument for the tf.keras
+      modules required by the Applications.
+  """
+
+  def wrapper(*args, **kwargs):
+    if hasattr(keras_applications, 'get_submodules_from_kwargs'):
+      kwargs['backend'] = backend
+      kwargs['layers'] = layers
+      kwargs['models'] = models
+      kwargs['utils'] = utils
+    return base_fun(*args, **kwargs)
+  return wrapper
 
-keras_applications.set_keras_submodules(
-    backend=backend,
-    engine=engine,
-    layers=layers,
-    models=models,
-    utils=utils)
 
 from tensorflow.python.keras.applications.densenet import DenseNet121
 from tensorflow.python.keras.applications.densenet import DenseNet169
@@ -39,7 +76,7 @@ from tensorflow.python.keras.applications.densenet import DenseNet201
 from tensorflow.python.keras.applications.inception_resnet_v2 import InceptionResNetV2
 from tensorflow.python.keras.applications.inception_v3 import InceptionV3
 from tensorflow.python.keras.applications.mobilenet import MobileNet
-# TODO(fchollet): enable MobileNetV2 in next version.
+from tensorflow.python.keras.applications.mobilenet_v2 import MobileNetV2
 from tensorflow.python.keras.applications.nasnet import NASNetLarge
 from tensorflow.python.keras.applications.nasnet import NASNetMobile
 from tensorflow.python.keras.applications.resnet50 import ResNet50
diff --git a/tensorflow/python/keras/applications/applications_test.py b/tensorflow/python/keras/applications/applications_test.py
index ef3198a937..b15ca5990a 100644
--- a/tensorflow/python/keras/applications/applications_test.py
+++ b/tensorflow/python/keras/applications/applications_test.py
@@ -32,7 +32,8 @@ MODEL_LIST = [
     (applications.InceptionV3, 2048),
     (applications.InceptionResNetV2, 1536),
     (applications.MobileNet, 1024),
-    # TODO(fchollet): enable MobileNetV2 in next version.
+    # TODO(fchollet): enable MobileNetV2 tests when a new TensorFlow test image
+    # is released with keras_applications upgraded to 1.0.5 or above.
     (applications.DenseNet121, 1024),
     (applications.DenseNet169, 1664),
     (applications.DenseNet201, 1920),
@@ -44,11 +45,6 @@ MODEL_LIST = [
 class ApplicationsTest(test.TestCase, parameterized.TestCase):
 
   @parameterized.parameters(*MODEL_LIST)
-  def test_classification_model(self, model_fn, _):
-    model = model_fn(classes=1000, weights=None)
-    self.assertEqual(model.output_shape[-1], 1000)
-
-  @parameterized.parameters(*MODEL_LIST)
   def test_feature_extration_model(self, model_fn, output_dim):
     model = model_fn(include_top=False, weights=None)
     self.assertEqual(model.output_shape, (None, None, None, output_dim))
diff --git a/tensorflow/python/keras/applications/densenet.py b/tensorflow/python/keras/applications/densenet.py
index fbdcc66d2d..172848bbdb 100644
--- a/tensorflow/python/keras/applications/densenet.py
+++ b/tensorflow/python/keras/applications/densenet.py
@@ -20,18 +20,39 @@ from __future__ import division
 from __future__ import print_function
 
 from keras_applications import densenet
+
+from tensorflow.python.keras.applications import keras_modules_injection
 from tensorflow.python.util.tf_export import tf_export
 
-DenseNet121 = densenet.DenseNet121
-DenseNet169 = densenet.DenseNet169
-DenseNet201 = densenet.DenseNet201
-decode_predictions = densenet.decode_predictions
-preprocess_input = densenet.preprocess_input
-
-tf_export('keras.applications.densenet.DenseNet121',
-          'keras.applications.DenseNet121')(DenseNet121)
-tf_export('keras.applications.densenet.DenseNet169',
-          'keras.applications.DenseNet169')(DenseNet169)
-tf_export('keras.applications.densenet.DenseNet201',
-          'keras.applications.DenseNet201')(DenseNet201)
-tf_export('keras.applications.densenet.preprocess_input')(preprocess_input)
+
+@tf_export('keras.applications.densenet.DenseNet121',
+           'keras.applications.DenseNet121')
+@keras_modules_injection
+def DenseNet121(*args, **kwargs):
+  return densenet.DenseNet121(*args, **kwargs)
+
+
+@tf_export('keras.applications.densenet.DenseNet169',
+           'keras.applications.DenseNet169')
+@keras_modules_injection
+def DenseNet169(*args, **kwargs):
+  return densenet.DenseNet169(*args, **kwargs)
+
+
+@tf_export('keras.applications.densenet.DenseNet201',
+           'keras.applications.DenseNet201')
+@keras_modules_injection
+def DenseNet201(*args, **kwargs):
+  return densenet.DenseNet201(*args, **kwargs)
+
+
+@tf_export('keras.applications.densenet.decode_predictions')
+@keras_modules_injection
+def decode_predictions(*args, **kwargs):
+  return densenet.decode_predictions(*args, **kwargs)
+
+
+@tf_export('keras.applications.densenet.preprocess_input')
+@keras_modules_injection
+def preprocess_input(*args, **kwargs):
+  return densenet.preprocess_input(*args, **kwargs)
diff --git a/tensorflow/python/keras/applications/imagenet_utils.py b/tensorflow/python/keras/applications/imagenet_utils.py
index 70f8f6fb32..c25b5c2bdd 100644
--- a/tensorflow/python/keras/applications/imagenet_utils.py
+++ b/tensorflow/python/keras/applications/imagenet_utils.py
@@ -19,27 +19,18 @@ from __future__ import division
 from __future__ import print_function
 
 from keras_applications import imagenet_utils
+
+from tensorflow.python.keras.applications import keras_modules_injection
 from tensorflow.python.util.tf_export import tf_export
 
-decode_predictions = imagenet_utils.decode_predictions
-preprocess_input = imagenet_utils.preprocess_input
 
-tf_export(
-    'keras.applications.imagenet_utils.decode_predictions',
-    'keras.applications.densenet.decode_predictions',
-    'keras.applications.inception_resnet_v2.decode_predictions',
-    'keras.applications.inception_v3.decode_predictions',
-    'keras.applications.mobilenet.decode_predictions',
-    'keras.applications.mobilenet_v2.decode_predictions',
-    'keras.applications.nasnet.decode_predictions',
-    'keras.applications.resnet50.decode_predictions',
-    'keras.applications.vgg16.decode_predictions',
-    'keras.applications.vgg19.decode_predictions',
-    'keras.applications.xception.decode_predictions',
-)(decode_predictions)
-tf_export(
-    'keras.applications.imagenet_utils.preprocess_input',
-    'keras.applications.resnet50.preprocess_input',
-    'keras.applications.vgg16.preprocess_input',
-    'keras.applications.vgg19.preprocess_input',
-)(preprocess_input)
+@tf_export('keras.applications.imagenet_utils.preprocess_input')
+@keras_modules_injection
+def decode_predictions(*args, **kwargs):
+  return imagenet_utils.decode_predictions(*args, **kwargs)
+
+
+@tf_export('keras.applications.imagenet_utils.preprocess_input')
+@keras_modules_injection
+def preprocess_input(*args, **kwargs):
+  return imagenet_utils.preprocess_input(*args, **kwargs)
diff --git a/tensorflow/python/keras/applications/inception_resnet_v2.py b/tensorflow/python/keras/applications/inception_resnet_v2.py
index 63debb4e0d..0b9ef371fa 100644
--- a/tensorflow/python/keras/applications/inception_resnet_v2.py
+++ b/tensorflow/python/keras/applications/inception_resnet_v2.py
@@ -20,13 +20,25 @@ from __future__ import division
 from __future__ import print_function
 
 from keras_applications import inception_resnet_v2
+
+from tensorflow.python.keras.applications import keras_modules_injection
 from tensorflow.python.util.tf_export import tf_export
 
-InceptionResNetV2 = inception_resnet_v2.InceptionResNetV2
-decode_predictions = inception_resnet_v2.decode_predictions
-preprocess_input = inception_resnet_v2.preprocess_input
 
-tf_export('keras.applications.inception_resnet_v2.InceptionResNetV2',
-          'keras.applications.InceptionResNetV2')(InceptionResNetV2)
-tf_export(
-    'keras.applications.inception_resnet_v2.preprocess_input')(preprocess_input)
+@tf_export('keras.applications.inception_resnet_v2.InceptionResNetV2',
+           'keras.applications.InceptionResNetV2')
+@keras_modules_injection
+def InceptionResNetV2(*args, **kwargs):
+  return inception_resnet_v2.InceptionResNetV2(*args, **kwargs)
+
+
+@tf_export('keras.applications.inception_resnet_v2.decode_predictions')
+@keras_modules_injection
+def decode_predictions(*args, **kwargs):
+  return inception_resnet_v2.decode_predictions(*args, **kwargs)
+
+
+@tf_export('keras.applications.inception_resnet_v2.preprocess_input')
+@keras_modules_injection
+def preprocess_input(*args, **kwargs):
+  return inception_resnet_v2.preprocess_input(*args, **kwargs)
diff --git a/tensorflow/python/keras/applications/inception_v3.py b/tensorflow/python/keras/applications/inception_v3.py
index 87534086c8..ab76826e17 100644
--- a/tensorflow/python/keras/applications/inception_v3.py
+++ b/tensorflow/python/keras/applications/inception_v3.py
@@ -20,12 +20,25 @@ from __future__ import division
 from __future__ import print_function
 
 from keras_applications import inception_v3
+
+from tensorflow.python.keras.applications import keras_modules_injection
 from tensorflow.python.util.tf_export import tf_export
 
-InceptionV3 = inception_v3.InceptionV3
-decode_predictions = inception_v3.decode_predictions
-preprocess_input = inception_v3.preprocess_input
 
-tf_export('keras.applications.inception_v3.InceptionV3',
-          'keras.applications.InceptionV3')(InceptionV3)
-tf_export('keras.applications.inception_v3.preprocess_input')(preprocess_input)
+@tf_export('keras.applications.inception_v3.InceptionV3',
+           'keras.applications.InceptionV3')
+@keras_modules_injection
+def InceptionV3(*args, **kwargs):
+  return inception_v3.InceptionV3(*args, **kwargs)
+
+
+@tf_export('keras.applications.inception_v3.decode_predictions')
+@keras_modules_injection
+def decode_predictions(*args, **kwargs):
+  return inception_v3.decode_predictions(*args, **kwargs)
+
+
+@tf_export('keras.applications.inception_v3.preprocess_input')
+@keras_modules_injection
+def preprocess_input(*args, **kwargs):
+  return inception_v3.preprocess_input(*args, **kwargs)
diff --git a/tensorflow/python/keras/applications/mobilenet.py b/tensorflow/python/keras/applications/mobilenet.py
index 3528f027b3..1f71a5ae99 100644
--- a/tensorflow/python/keras/applications/mobilenet.py
+++ b/tensorflow/python/keras/applications/mobilenet.py
@@ -20,12 +20,25 @@ from __future__ import division
 from __future__ import print_function
 
 from keras_applications import mobilenet
+
+from tensorflow.python.keras.applications import keras_modules_injection
 from tensorflow.python.util.tf_export import tf_export
 
-MobileNet = mobilenet.MobileNet
-decode_predictions = mobilenet.decode_predictions
-preprocess_input = mobilenet.preprocess_input
 
-tf_export('keras.applications.mobilenet.MobileNet',
-          'keras.applications.MobileNet')(MobileNet)
-tf_export('keras.applications.mobilenet.preprocess_input')(preprocess_input)
+@tf_export('keras.applications.mobilenet.MobileNet',
+           'keras.applications.MobileNet')
+@keras_modules_injection
+def MobileNet(*args, **kwargs):
+  return mobilenet.MobileNet(*args, **kwargs)
+
+
+@tf_export('keras.applications.mobilenet.decode_predictions')
+@keras_modules_injection
+def decode_predictions(*args, **kwargs):
+  return mobilenet.decode_predictions(*args, **kwargs)
+
+
+@tf_export('keras.applications.mobilenet.preprocess_input')
+@keras_modules_injection
+def preprocess_input(*args, **kwargs):
+  return mobilenet.preprocess_input(*args, **kwargs)
diff --git a/tensorflow/python/keras/applications/mobilenet_v2.py b/tensorflow/python/keras/applications/mobilenet_v2.py
index 9194c3ee14..52ac5959ad 100644
--- a/tensorflow/python/keras/applications/mobilenet_v2.py
+++ b/tensorflow/python/keras/applications/mobilenet_v2.py
@@ -19,4 +19,26 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-# TODO(fchollet): export MobileNetV2 as part of the public API in next version.
+from keras_applications import mobilenet_v2
+
+from tensorflow.python.keras.applications import keras_modules_injection
+from tensorflow.python.util.tf_export import tf_export
+
+
+@tf_export('keras.applications.mobilenet_v2.MobileNetV2',
+           'keras.applications.MobileNetV2')
+@keras_modules_injection
+def MobileNetV2(*args, **kwargs):
+  return mobilenet_v2.MobileNetV2(*args, **kwargs)
+
+
+@tf_export('keras.applications.mobilenet_v2.decode_predictions')
+@keras_modules_injection
+def decode_predictions(*args, **kwargs):
+  return mobilenet_v2.decode_predictions(*args, **kwargs)
+
+
+@tf_export('keras.applications.mobilenet_v2.preprocess_input')
+@keras_modules_injection
+def preprocess_input(*args, **kwargs):
+  return mobilenet_v2.preprocess_input(*args, **kwargs)
diff --git a/tensorflow/python/keras/applications/nasnet.py b/tensorflow/python/keras/applications/nasnet.py
index 26ff5db53f..44fc329d57 100644
--- a/tensorflow/python/keras/applications/nasnet.py
+++ b/tensorflow/python/keras/applications/nasnet.py
@@ -20,15 +20,32 @@ from __future__ import division
 from __future__ import print_function
 
 from keras_applications import nasnet
+
+from tensorflow.python.keras.applications import keras_modules_injection
 from tensorflow.python.util.tf_export import tf_export
 
-NASNetMobile = nasnet.NASNetMobile
-NASNetLarge = nasnet.NASNetLarge
-decode_predictions = nasnet.decode_predictions
-preprocess_input = nasnet.preprocess_input
 
-tf_export('keras.applications.nasnet.NASNetMobile',
-          'keras.applications.NASNetMobile')(NASNetMobile)
-tf_export('keras.applications.nasnet.NASNetLarge',
-          'keras.applications.NASNetLarge')(NASNetLarge)
-tf_export('keras.applications.nasnet.preprocess_input')(preprocess_input)
+@tf_export('keras.applications.nasnet.NASNetMobile',
+           'keras.applications.NASNetMobile')
+@keras_modules_injection
+def NASNetMobile(*args, **kwargs):
+  return nasnet.NASNetMobile(*args, **kwargs)
+
+
+@tf_export('keras.applications.nasnet.NASNetLarge',
+           'keras.applications.NASNetLarge')
+@keras_modules_injection
+def NASNetLarge(*args, **kwargs):
+  return nasnet.NASNetLarge(*args, **kwargs)
+
+
+@tf_export('keras.applications.nasnet.decode_predictions')
+@keras_modules_injection
+def decode_predictions(*args, **kwargs):
+  return nasnet.decode_predictions(*args, **kwargs)
+
+
+@tf_export('keras.applications.nasnet.preprocess_input')
+@keras_modules_injection
+def preprocess_input(*args, **kwargs):
+  return nasnet.preprocess_input(*args, **kwargs)
diff --git a/tensorflow/python/keras/applications/resnet50.py b/tensorflow/python/keras/applications/resnet50.py
index 4d804a3c44..80d3f9044f 100644
--- a/tensorflow/python/keras/applications/resnet50.py
+++ b/tensorflow/python/keras/applications/resnet50.py
@@ -20,11 +20,25 @@ from __future__ import division
 from __future__ import print_function
 
 from keras_applications import resnet50
+
+from tensorflow.python.keras.applications import keras_modules_injection
 from tensorflow.python.util.tf_export import tf_export
 
-ResNet50 = resnet50.ResNet50
-decode_predictions = resnet50.decode_predictions
-preprocess_input = resnet50.preprocess_input
 
-tf_export('keras.applications.resnet50.ResNet50',
-          'keras.applications.ResNet50')(ResNet50)
+@tf_export('keras.applications.resnet50.ResNet50',
+           'keras.applications.ResNet50')
+@keras_modules_injection
+def ResNet50(*args, **kwargs):
+  return resnet50.ResNet50(*args, **kwargs)
+
+
+@tf_export('keras.applications.resnet50.decode_predictions')
+@keras_modules_injection
+def decode_predictions(*args, **kwargs):
+  return resnet50.decode_predictions(*args, **kwargs)
+
+
+@tf_export('keras.applications.resnet50.preprocess_input')
+@keras_modules_injection
+def preprocess_input(*args, **kwargs):
+  return resnet50.preprocess_input(*args, **kwargs)
diff --git a/tensorflow/python/keras/applications/vgg16.py b/tensorflow/python/keras/applications/vgg16.py
index c420d9b81e..8557d26931 100644
--- a/tensorflow/python/keras/applications/vgg16.py
+++ b/tensorflow/python/keras/applications/vgg16.py
@@ -20,11 +20,25 @@ from __future__ import division
 from __future__ import print_function
 
 from keras_applications import vgg16
+
+from tensorflow.python.keras.applications import keras_modules_injection
 from tensorflow.python.util.tf_export import tf_export
 
-VGG16 = vgg16.VGG16
-decode_predictions = vgg16.decode_predictions
-preprocess_input = vgg16.preprocess_input
 
-tf_export('keras.applications.vgg16.VGG16',
-          'keras.applications.VGG16')(VGG16)
+@tf_export('keras.applications.vgg16.VGG16',
+           'keras.applications.VGG16')
+@keras_modules_injection
+def VGG16(*args, **kwargs):
+  return vgg16.VGG16(*args, **kwargs)
+
+
+@tf_export('keras.applications.vgg16.decode_predictions')
+@keras_modules_injection
+def decode_predictions(*args, **kwargs):
+  return vgg16.decode_predictions(*args, **kwargs)
+
+
+@tf_export('keras.applications.vgg16.preprocess_input')
+@keras_modules_injection
+def preprocess_input(*args, **kwargs):
+  return vgg16.preprocess_input(*args, **kwargs)
diff --git a/tensorflow/python/keras/applications/vgg19.py b/tensorflow/python/keras/applications/vgg19.py
index 73d3d1d1c3..8fc04413a0 100644
--- a/tensorflow/python/keras/applications/vgg19.py
+++ b/tensorflow/python/keras/applications/vgg19.py
@@ -20,11 +20,25 @@ from __future__ import division
 from __future__ import print_function
 
 from keras_applications import vgg19
+
+from tensorflow.python.keras.applications import keras_modules_injection
 from tensorflow.python.util.tf_export import tf_export
 
-VGG19 = vgg19.VGG19
-decode_predictions = vgg19.decode_predictions
-preprocess_input = vgg19.preprocess_input
 
-tf_export('keras.applications.vgg19.VGG19',
-          'keras.applications.VGG19')(VGG19)
+@tf_export('keras.applications.vgg19.VGG19',
+           'keras.applications.VGG19')
+@keras_modules_injection
+def VGG19(*args, **kwargs):
+  return vgg19.VGG19(*args, **kwargs)
+
+
+@tf_export('keras.applications.vgg19.decode_predictions')
+@keras_modules_injection
+def decode_predictions(*args, **kwargs):
+  return vgg19.decode_predictions(*args, **kwargs)
+
+
+@tf_export('keras.applications.vgg19.preprocess_input')
+@keras_modules_injection
+def preprocess_input(*args, **kwargs):
+  return vgg19.preprocess_input(*args, **kwargs)
diff --git a/tensorflow/python/keras/applications/xception.py b/tensorflow/python/keras/applications/xception.py
index 5b221ac8e0..960e6dec69 100644
--- a/tensorflow/python/keras/applications/xception.py
+++ b/tensorflow/python/keras/applications/xception.py
@@ -20,12 +20,25 @@ from __future__ import division
 from __future__ import print_function
 
 from keras_applications import xception
+
+from tensorflow.python.keras.applications import keras_modules_injection
 from tensorflow.python.util.tf_export import tf_export
 
-Xception = xception.Xception
-decode_predictions = xception.decode_predictions
-preprocess_input = xception.preprocess_input
 
-tf_export('keras.applications.xception.Xception',
-          'keras.applications.Xception')(Xception)
-tf_export('keras.applications.xception.preprocess_input')(preprocess_input)
+@tf_export('keras.applications.xception.Xception',
+           'keras.applications.Xception')
+@keras_modules_injection
+def Xception(*args, **kwargs):
+  return xception.Xception(*args, **kwargs)
+
+
+@tf_export('keras.applications.xception.decode_predictions')
+@keras_modules_injection
+def decode_predictions(*args, **kwargs):
+  return xception.decode_predictions(*args, **kwargs)
+
+
+@tf_export('keras.applications.xception.preprocess_input')
+@keras_modules_injection
+def preprocess_input(*args, **kwargs):
+  return xception.preprocess_input(*args, **kwargs)
diff --git a/tensorflow/python/keras/preprocessing/__init__.py b/tensorflow/python/keras/preprocessing/__init__.py
index 2f08f88600..0860eed3cf 100644
--- a/tensorflow/python/keras/preprocessing/__init__.py
+++ b/tensorflow/python/keras/preprocessing/__init__.py
@@ -23,6 +23,8 @@ import keras_preprocessing
 from tensorflow.python.keras import backend
 from tensorflow.python.keras import utils
 
+# This exists for compatibility with prior version of keras_preprocessing.
+# TODO(fchollet): remove in the future.
 keras_preprocessing.set_keras_submodules(backend=backend, utils=utils)
 
 from tensorflow.python.keras.preprocessing import image
diff --git a/tensorflow/python/keras/preprocessing/image.py b/tensorflow/python/keras/preprocessing/image.py
index ba227385ef..e33993950d 100644
--- a/tensorflow/python/keras/preprocessing/image.py
+++ b/tensorflow/python/keras/preprocessing/image.py
@@ -27,6 +27,9 @@ try:
 except ImportError:
   pass
 
+from tensorflow.python.keras import backend
+from tensorflow.python.keras import utils
+from tensorflow.python.util import tf_inspect
 from tensorflow.python.util.tf_export import tf_export
 
 random_rotation = image.random_rotation
@@ -38,14 +41,482 @@ random_channel_shift = image.random_channel_shift
 apply_brightness_shift = image.apply_brightness_shift
 random_brightness = image.random_brightness
 apply_affine_transform = image.apply_affine_transform
-array_to_img = image.array_to_img
-img_to_array = image.img_to_array
-save_img = image.save_img
 load_img = image.load_img
-ImageDataGenerator = image.ImageDataGenerator
-Iterator = image.Iterator
-NumpyArrayIterator = image.NumpyArrayIterator
-DirectoryIterator = image.DirectoryIterator
+
+
+@tf_export('keras.preprocessing.image.array_to_img')
+def array_to_img(x, data_format=None, scale=True, dtype=None):
+  """Converts a 3D Numpy array to a PIL Image instance.
+
+  Arguments:
+      x: Input Numpy array.
+      data_format: Image data format.
+          either "channels_first" or "channels_last".
+      scale: Whether to rescale image values
+          to be within `[0, 255]`.
+      dtype: Dtype to use.
+
+  Returns:
+      A PIL Image instance.
+
+  Raises:
+      ImportError: if PIL is not available.
+      ValueError: if invalid `x` or `data_format` is passed.
+  """
+
+  if data_format is None:
+    data_format = backend.image_data_format()
+  kwargs = {}
+  if 'dtype' in tf_inspect.getfullargspec(image.array_to_img)[0]:
+    if dtype is None:
+      dtype = backend.floatx()
+    kwargs['dtype'] = dtype
+  return image.array_to_img(x, data_format=data_format, scale=scale, **kwargs)
+
+
+@tf_export('keras.preprocessing.image.img_to_array')
+def img_to_array(img, data_format=None, dtype=None):
+  """Converts a PIL Image instance to a Numpy array.
+
+  Arguments:
+      img: PIL Image instance.
+      data_format: Image data format,
+          either "channels_first" or "channels_last".
+      dtype: Dtype to use for the returned array.
+
+  Returns:
+      A 3D Numpy array.
+
+  Raises:
+      ValueError: if invalid `img` or `data_format` is passed.
+  """
+
+  if data_format is None:
+    data_format = backend.image_data_format()
+  kwargs = {}
+  if 'dtype' in tf_inspect.getfullargspec(image.img_to_array)[0]:
+    if dtype is None:
+      dtype = backend.floatx()
+    kwargs['dtype'] = dtype
+  return image.img_to_array(img, data_format=data_format, **kwargs)
+
+
+@tf_export('keras.preprocessing.image.save_img')
+def save_img(path,
+             x,
+             data_format=None,
+             file_format=None,
+             scale=True,
+             **kwargs):
+  """Saves an image stored as a Numpy array to a path or file object.
+
+  Arguments:
+      path: Path or file object.
+      x: Numpy array.
+      data_format: Image data format,
+          either "channels_first" or "channels_last".
+      file_format: Optional file format override. If omitted, the
+          format to use is determined from the filename extension.
+          If a file object was used instead of a filename, this
+          parameter should always be used.
+      scale: Whether to rescale image values to be within `[0, 255]`.
+      **kwargs: Additional keyword arguments passed to `PIL.Image.save()`.
+  """
+  if data_format is None:
+    data_format = backend.image_data_format()
+  image.save_img(path,
+                 x,
+                 data_format=data_format,
+                 file_format=file_format,
+                 scale=scale, **kwargs)
+
+
+@tf_export('keras.preprocessing.image.Iterator')
+class Iterator(image.Iterator, utils.Sequence):
+  pass
+
+
+@tf_export('keras.preprocessing.image.DirectoryIterator')
+class DirectoryIterator(image.DirectoryIterator, Iterator):
+  """Iterator capable of reading images from a directory on disk.
+
+  Arguments:
+      directory: Path to the directory to read images from.
+          Each subdirectory in this directory will be
+          considered to contain images from one class,
+          or alternatively you could specify class subdirectories
+          via the `classes` argument.
+      image_data_generator: Instance of `ImageDataGenerator`
+          to use for random transformations and normalization.
+      target_size: tuple of integers, dimensions to resize input images to.
+      color_mode: One of `"rgb"`, `"rgba"`, `"grayscale"`.
+          Color mode to read images.
+      classes: Optional list of strings, names of subdirectories
+          containing images from each class (e.g. `["dogs", "cats"]`).
+          It will be computed automatically if not set.
+      class_mode: Mode for yielding the targets:
+          `"binary"`: binary targets (if there are only two classes),
+          `"categorical"`: categorical targets,
+          `"sparse"`: integer targets,
+          `"input"`: targets are images identical to input images (mainly
+              used to work with autoencoders),
+          `None`: no targets get yielded (only input images are yielded).
+      batch_size: Integer, size of a batch.
+      shuffle: Boolean, whether to shuffle the data between epochs.
+      seed: Random seed for data shuffling.
+      data_format: String, one of `channels_first`, `channels_last`.
+      save_to_dir: Optional directory where to save the pictures
+          being yielded, in a viewable format. This is useful
+          for visualizing the random transformations being
+          applied, for debugging purposes.
+      save_prefix: String prefix to use for saving sample
+          images (if `save_to_dir` is set).
+      save_format: Format to use for saving sample images
+          (if `save_to_dir` is set).
+      subset: Subset of data (`"training"` or `"validation"`) if
+          validation_split is set in ImageDataGenerator.
+      interpolation: Interpolation method used to resample the image if the
+          target size is different from that of the loaded image.
+          Supported methods are "nearest", "bilinear", and "bicubic".
+          If PIL version 1.1.3 or newer is installed, "lanczos" is also
+          supported. If PIL version 3.4.0 or newer is installed, "box" and
+          "hamming" are also supported. By default, "nearest" is used.
+      dtype: Dtype to use for generated arrays.
+  """
+
+  def __init__(self, directory, image_data_generator,
+               target_size=(256, 256),
+               color_mode='rgb',
+               classes=None,
+               class_mode='categorical',
+               batch_size=32,
+               shuffle=True,
+               seed=None,
+               data_format=None,
+               save_to_dir=None,
+               save_prefix='',
+               save_format='png',
+               follow_links=False,
+               subset=None,
+               interpolation='nearest',
+               dtype=None):
+    if data_format is None:
+      data_format = backend.image_data_format()
+    kwargs = {}
+    if 'dtype' in tf_inspect.getfullargspec(
+        image.ImageDataGenerator.__init__)[0]:
+      if dtype is None:
+        dtype = backend.floatx()
+      kwargs['dtype'] = dtype
+    super(DirectoryIterator, self).__init__(
+        directory, image_data_generator,
+        target_size=target_size,
+        color_mode=color_mode,
+        classes=classes,
+        class_mode=class_mode,
+        batch_size=batch_size,
+        shuffle=shuffle,
+        seed=seed,
+        data_format=data_format,
+        save_to_dir=save_to_dir,
+        save_prefix=save_prefix,
+        save_format=save_format,
+        follow_links=follow_links,
+        subset=subset,
+        interpolation=interpolation,
+        **kwargs)
+
+
+@tf_export('keras.preprocessing.image.NumpyArrayIterator')
+class NumpyArrayIterator(image.NumpyArrayIterator, Iterator):
+  """Iterator yielding data from a Numpy array.
+
+  Arguments:
+      x: Numpy array of input data or tuple.
+          If tuple, the second elements is either
+          another numpy array or a list of numpy arrays,
+          each of which gets passed
+          through as an output without any modifications.
+      y: Numpy array of targets data.
+      image_data_generator: Instance of `ImageDataGenerator`
+          to use for random transformations and normalization.
+      batch_size: Integer, size of a batch.
+      shuffle: Boolean, whether to shuffle the data between epochs.
+      sample_weight: Numpy array of sample weights.
+      seed: Random seed for data shuffling.
+      data_format: String, one of `channels_first`, `channels_last`.
+      save_to_dir: Optional directory where to save the pictures
+          being yielded, in a viewable format. This is useful
+          for visualizing the random transformations being
+          applied, for debugging purposes.
+      save_prefix: String prefix to use for saving sample
+          images (if `save_to_dir` is set).
+      save_format: Format to use for saving sample images
+          (if `save_to_dir` is set).
+      subset: Subset of data (`"training"` or `"validation"`) if
+          validation_split is set in ImageDataGenerator.
+      dtype: Dtype to use for the generated arrays.
+  """
+
+  def __init__(self, x, y, image_data_generator,
+               batch_size=32,
+               shuffle=False,
+               sample_weight=None,
+               seed=None,
+               data_format=None,
+               save_to_dir=None,
+               save_prefix='',
+               save_format='png',
+               subset=None,
+               dtype=None):
+    if data_format is None:
+      data_format = backend.image_data_format()
+    kwargs = {}
+    if 'dtype' in tf_inspect.getfullargspec(
+        image.NumpyArrayIterator.__init__)[0]:
+      if dtype is None:
+        dtype = backend.floatx()
+      kwargs['dtype'] = dtype
+    super(NumpyArrayIterator, self).__init__(
+        x, y, image_data_generator,
+        batch_size=batch_size,
+        shuffle=shuffle,
+        sample_weight=sample_weight,
+        seed=seed,
+        data_format=data_format,
+        save_to_dir=save_to_dir,
+        save_prefix=save_prefix,
+        save_format=save_format,
+        subset=subset,
+        **kwargs)
+
+
+@tf_export('keras.preprocessing.image.ImageDataGenerator')
+class ImageDataGenerator(image.ImageDataGenerator):
+  """Generate batches of tensor image data with real-time data augmentation.
+
+   The data will be looped over (in batches).
+
+  Arguments:
+      featurewise_center: Boolean.
+          Set input mean to 0 over the dataset, feature-wise.
+      samplewise_center: Boolean. Set each sample mean to 0.
+      featurewise_std_normalization: Boolean.
+          Divide inputs by std of the dataset, feature-wise.
+      samplewise_std_normalization: Boolean. Divide each input by its std.
+      zca_epsilon: epsilon for ZCA whitening. Default is 1e-6.
+      zca_whitening: Boolean. Apply ZCA whitening.
+      rotation_range: Int. Degree range for random rotations.
+      width_shift_range: Float, 1-D array-like or int
+          - float: fraction of total width, if < 1, or pixels if >= 1.
+          - 1-D array-like: random elements from the array.
+          - int: integer number of pixels from interval
+              `(-width_shift_range, +width_shift_range)`
+          - With `width_shift_range=2` possible values
+              are integers `[-1, 0, +1]`,
+              same as with `width_shift_range=[-1, 0, +1]`,
+              while with `width_shift_range=1.0` possible values are floats
+              in the interval [-1.0, +1.0).
+      height_shift_range: Float, 1-D array-like or int
+          - float: fraction of total height, if < 1, or pixels if >= 1.
+          - 1-D array-like: random elements from the array.
+          - int: integer number of pixels from interval
+              `(-height_shift_range, +height_shift_range)`
+          - With `height_shift_range=2` possible values
+              are integers `[-1, 0, +1]`,
+              same as with `height_shift_range=[-1, 0, +1]`,
+              while with `height_shift_range=1.0` possible values are floats
+              in the interval [-1.0, +1.0).
+      brightness_range: Tuple or list of two floats. Range for picking
+          a brightness shift value from.
+      shear_range: Float. Shear Intensity
+          (Shear angle in counter-clockwise direction in degrees)
+      zoom_range: Float or [lower, upper]. Range for random zoom.
+          If a float, `[lower, upper] = [1-zoom_range, 1+zoom_range]`.
+      channel_shift_range: Float. Range for random channel shifts.
+      fill_mode: One of {"constant", "nearest", "reflect" or "wrap"}.
+          Default is 'nearest'.
+          Points outside the boundaries of the input are filled
+          according to the given mode:
+          - 'constant': kkkkkkkk|abcd|kkkkkkkk (cval=k)
+          - 'nearest':  aaaaaaaa|abcd|dddddddd
+          - 'reflect':  abcddcba|abcd|dcbaabcd
+          - 'wrap':  abcdabcd|abcd|abcdabcd
+      cval: Float or Int.
+          Value used for points outside the boundaries
+          when `fill_mode = "constant"`.
+      horizontal_flip: Boolean. Randomly flip inputs horizontally.
+      vertical_flip: Boolean. Randomly flip inputs vertically.
+      rescale: rescaling factor. Defaults to None.
+          If None or 0, no rescaling is applied,
+          otherwise we multiply the data by the value provided
+          (after applying all other transformations).
+      preprocessing_function: function that will be implied on each input.
+          The function will run after the image is resized and augmented.
+          The function should take one argument:
+          one image (Numpy tensor with rank 3),
+          and should output a Numpy tensor with the same shape.
+      data_format: Image data format,
+          either "channels_first" or "channels_last".
+          "channels_last" mode means that the images should have shape
+          `(samples, height, width, channels)`,
+          "channels_first" mode means that the images should have shape
+          `(samples, channels, height, width)`.
+          It defaults to the `image_data_format` value found in your
+          Keras config file at `~/.keras/keras.json`.
+          If you never set it, then it will be "channels_last".
+      validation_split: Float. Fraction of images reserved for validation
+          (strictly between 0 and 1).
+      dtype: Dtype to use for the generated arrays.
+
+  Examples:
+
+  Example of using `.flow(x, y)`:
+
+  ```python
+  (x_train, y_train), (x_test, y_test) = cifar10.load_data()
+  y_train = np_utils.to_categorical(y_train, num_classes)
+  y_test = np_utils.to_categorical(y_test, num_classes)
+  datagen = ImageDataGenerator(
+      featurewise_center=True,
+      featurewise_std_normalization=True,
+      rotation_range=20,
+      width_shift_range=0.2,
+      height_shift_range=0.2,
+      horizontal_flip=True)
+  # compute quantities required for featurewise normalization
+  # (std, mean, and principal components if ZCA whitening is applied)
+  datagen.fit(x_train)
+  # fits the model on batches with real-time data augmentation:
+  model.fit_generator(datagen.flow(x_train, y_train, batch_size=32),
+                      steps_per_epoch=len(x_train) / 32, epochs=epochs)
+  # here's a more "manual" example
+  for e in range(epochs):
+      print('Epoch', e)
+      batches = 0
+      for x_batch, y_batch in datagen.flow(x_train, y_train, batch_size=32):
+          model.fit(x_batch, y_batch)
+          batches += 1
+          if batches >= len(x_train) / 32:
+              # we need to break the loop by hand because
+              # the generator loops indefinitely
+              break
+  ```
+
+  Example of using `.flow_from_directory(directory)`:
+
+  ```python
+  train_datagen = ImageDataGenerator(
+          rescale=1./255,
+          shear_range=0.2,
+          zoom_range=0.2,
+          horizontal_flip=True)
+  test_datagen = ImageDataGenerator(rescale=1./255)
+  train_generator = train_datagen.flow_from_directory(
+          'data/train',
+          target_size=(150, 150),
+          batch_size=32,
+          class_mode='binary')
+  validation_generator = test_datagen.flow_from_directory(
+          'data/validation',
+          target_size=(150, 150),
+          batch_size=32,
+          class_mode='binary')
+  model.fit_generator(
+          train_generator,
+          steps_per_epoch=2000,
+          epochs=50,
+          validation_data=validation_generator,
+          validation_steps=800)
+  ```
+
+  Example of transforming images and masks together.
+
+  ```python
+  # we create two instances with the same arguments
+  data_gen_args = dict(featurewise_center=True,
+                       featurewise_std_normalization=True,
+                       rotation_range=90,
+                       width_shift_range=0.1,
+                       height_shift_range=0.1,
+                       zoom_range=0.2)
+  image_datagen = ImageDataGenerator(**data_gen_args)
+  mask_datagen = ImageDataGenerator(**data_gen_args)
+  # Provide the same seed and keyword arguments to the fit and flow methods
+  seed = 1
+  image_datagen.fit(images, augment=True, seed=seed)
+  mask_datagen.fit(masks, augment=True, seed=seed)
+  image_generator = image_datagen.flow_from_directory(
+      'data/images',
+      class_mode=None,
+      seed=seed)
+  mask_generator = mask_datagen.flow_from_directory(
+      'data/masks',
+      class_mode=None,
+      seed=seed)
+  # combine generators into one which yields image and masks
+  train_generator = zip(image_generator, mask_generator)
+  model.fit_generator(
+      train_generator,
+      steps_per_epoch=2000,
+      epochs=50)
+  ```
+  """
+
+  def __init__(self,
+               featurewise_center=False,
+               samplewise_center=False,
+               featurewise_std_normalization=False,
+               samplewise_std_normalization=False,
+               zca_whitening=False,
+               zca_epsilon=1e-6,
+               rotation_range=0,
+               width_shift_range=0.,
+               height_shift_range=0.,
+               brightness_range=None,
+               shear_range=0.,
+               zoom_range=0.,
+               channel_shift_range=0.,
+               fill_mode='nearest',
+               cval=0.,
+               horizontal_flip=False,
+               vertical_flip=False,
+               rescale=None,
+               preprocessing_function=None,
+               data_format=None,
+               validation_split=0.0,
+               dtype=None):
+    if data_format is None:
+      data_format = backend.image_data_format()
+    kwargs = {}
+    if 'dtype' in tf_inspect.getfullargspec(
+        image.ImageDataGenerator.__init__)[0]:
+      if dtype is None:
+        dtype = backend.floatx()
+      kwargs['dtype'] = dtype
+    super(ImageDataGenerator, self).__init__(
+        featurewise_center=featurewise_center,
+        samplewise_center=samplewise_center,
+        featurewise_std_normalization=featurewise_std_normalization,
+        samplewise_std_normalization=samplewise_std_normalization,
+        zca_whitening=zca_whitening,
+        zca_epsilon=zca_epsilon,
+        rotation_range=rotation_range,
+        width_shift_range=width_shift_range,
+        height_shift_range=height_shift_range,
+        brightness_range=brightness_range,
+        shear_range=shear_range,
+        zoom_range=zoom_range,
+        channel_shift_range=channel_shift_range,
+        fill_mode=fill_mode,
+        cval=cval,
+        horizontal_flip=horizontal_flip,
+        vertical_flip=vertical_flip,
+        rescale=rescale,
+        preprocessing_function=preprocessing_function,
+        data_format=data_format,
+        validation_split=validation_split,
+        **kwargs)
 
 tf_export('keras.preprocessing.image.random_rotation')(random_rotation)
 tf_export('keras.preprocessing.image.random_shift')(random_shift)
@@ -59,11 +530,4 @@ tf_export(
 tf_export('keras.preprocessing.image.random_brightness')(random_brightness)
 tf_export(
     'keras.preprocessing.image.apply_affine_transform')(apply_affine_transform)
-tf_export('keras.preprocessing.image.array_to_img')(array_to_img)
-tf_export('keras.preprocessing.image.img_to_array')(img_to_array)
-tf_export('keras.preprocessing.image.save_img')(save_img)
 tf_export('keras.preprocessing.image.load_img')(load_img)
-tf_export('keras.preprocessing.image.ImageDataGenerator')(ImageDataGenerator)
-tf_export('keras.preprocessing.image.Iterator')(Iterator)
-tf_export('keras.preprocessing.image.NumpyArrayIterator')(NumpyArrayIterator)
-tf_export('keras.preprocessing.image.DirectoryIterator')(DirectoryIterator)
diff --git a/tensorflow/python/keras/preprocessing/sequence.py b/tensorflow/python/keras/preprocessing/sequence.py
index 116d3108d9..f014668909 100644
--- a/tensorflow/python/keras/preprocessing/sequence.py
+++ b/tensorflow/python/keras/preprocessing/sequence.py
@@ -21,6 +21,7 @@ from __future__ import print_function
 
 from keras_preprocessing import sequence
 
+from tensorflow.python.keras import utils
 from tensorflow.python.util.tf_export import tf_export
 
 pad_sequences = sequence.pad_sequences
@@ -28,11 +29,67 @@ make_sampling_table = sequence.make_sampling_table
 skipgrams = sequence.skipgrams
 # TODO(fchollet): consider making `_remove_long_seq` public.
 _remove_long_seq = sequence._remove_long_seq  # pylint: disable=protected-access
-TimeseriesGenerator = sequence.TimeseriesGenerator
+
+
+@tf_export('keras.preprocessing.sequence.TimeseriesGenerator')
+class TimeseriesGenerator(sequence.TimeseriesGenerator, utils.Sequence):
+  """Utility class for generating batches of temporal data.
+  This class takes in a sequence of data-points gathered at
+  equal intervals, along with time series parameters such as
+  stride, length of history, etc., to produce batches for
+  training/validation.
+  # Arguments
+      data: Indexable generator (such as list or Numpy array)
+          containing consecutive data points (timesteps).
+          The data should be at 2D, and axis 0 is expected
+          to be the time dimension.
+      targets: Targets corresponding to timesteps in `data`.
+          It should have same length as `data`.
+      length: Length of the output sequences (in number of timesteps).
+      sampling_rate: Period between successive individual timesteps
+          within sequences. For rate `r`, timesteps
+          `data[i]`, `data[i-r]`, ... `data[i - length]`
+          are used for create a sample sequence.
+      stride: Period between successive output sequences.
+          For stride `s`, consecutive output samples would
+          be centered around `data[i]`, `data[i+s]`, `data[i+2*s]`, etc.
+      start_index: Data points earlier than `start_index` will not be used
+          in the output sequences. This is useful to reserve part of the
+          data for test or validation.
+      end_index: Data points later than `end_index` will not be used
+          in the output sequences. This is useful to reserve part of the
+          data for test or validation.
+      shuffle: Whether to shuffle output samples,
+          or instead draw them in chronological order.
+      reverse: Boolean: if `true`, timesteps in each output sample will be
+          in reverse chronological order.
+      batch_size: Number of timeseries samples in each batch
+          (except maybe the last one).
+  # Returns
+      A [Sequence](/utils/#sequence) instance.
+  # Examples
+  ```python
+  from keras.preprocessing.sequence import TimeseriesGenerator
+  import numpy as np
+  data = np.array([[i] for i in range(50)])
+  targets = np.array([[i] for i in range(50)])
+  data_gen = TimeseriesGenerator(data, targets,
+                                 length=10, sampling_rate=2,
+                                 batch_size=2)
+  assert len(data_gen) == 20
+  batch_0 = data_gen[0]
+  x, y = batch_0
+  assert np.array_equal(x,
+                        np.array([[[0], [2], [4], [6], [8]],
+                                  [[1], [3], [5], [7], [9]]]))
+  assert np.array_equal(y,
+                        np.array([[10], [11]]))
+  ```
+  """
+  pass
+
 
 tf_export('keras.preprocessing.sequence.pad_sequences')(pad_sequences)
 tf_export(
     'keras.preprocessing.sequence.make_sampling_table')(make_sampling_table)
 tf_export('keras.preprocessing.sequence.skipgrams')(skipgrams)
-tf_export(
-    'keras.preprocessing.sequence.TimeseriesGenerator')(TimeseriesGenerator)
diff --git a/tensorflow/python/tools/api/generator/api_init_files.bzl b/tensorflow/python/tools/api/generator/api_init_files.bzl
index 7001e566ce..64f0469482 100644
--- a/tensorflow/python/tools/api/generator/api_init_files.bzl
+++ b/tensorflow/python/tools/api/generator/api_init_files.bzl
@@ -25,6 +25,7 @@ TENSORFLOW_API_INIT_FILES = [
     "keras/applications/inception_resnet_v2/__init__.py",
     "keras/applications/inception_v3/__init__.py",
     "keras/applications/mobilenet/__init__.py",
+    "keras/applications/mobilenet_v2/__init__.py",
     "keras/applications/nasnet/__init__.py",
     "keras/applications/resnet50/__init__.py",
     "keras/applications/vgg16/__init__.py",
diff --git a/tensorflow/python/tools/api/generator/api_init_files_v1.bzl b/tensorflow/python/tools/api/generator/api_init_files_v1.bzl
index 73d11199d9..bc2f3516d1 100644
--- a/tensorflow/python/tools/api/generator/api_init_files_v1.bzl
+++ b/tensorflow/python/tools/api/generator/api_init_files_v1.bzl
@@ -25,6 +25,7 @@ TENSORFLOW_API_INIT_FILES_V1 = [
     "keras/applications/inception_resnet_v2/__init__.py",
     "keras/applications/inception_v3/__init__.py",
     "keras/applications/mobilenet/__init__.py",
+    "keras/applications/mobilenet_v2/__init__.py",
     "keras/applications/nasnet/__init__.py",
     "keras/applications/resnet50/__init__.py",
     "keras/applications/vgg16/__init__.py",
diff --git a/tensorflow/tools/ci_build/Dockerfile.cmake b/tensorflow/tools/ci_build/Dockerfile.cmake
index 4587bcf891..b7450c83de 100644
--- a/tensorflow/tools/ci_build/Dockerfile.cmake
+++ b/tensorflow/tools/ci_build/Dockerfile.cmake
@@ -28,8 +28,8 @@ RUN pip install --upgrade astor
 RUN pip install --upgrade gast
 RUN pip install --upgrade numpy
 RUN pip install --upgrade termcolor
-RUN pip install keras_applications==1.0.4
-RUN pip install keras_preprocessing==1.0.2
+RUN pip install keras_applications==1.0.5
+RUN pip install keras_preprocessing==1.0.3
 
 # Install golang
 RUN apt-get install -t xenial-backports -y golang-1.9
diff --git a/tensorflow/tools/ci_build/install/install_pip_packages.sh b/tensorflow/tools/ci_build/install/install_pip_packages.sh
index bb316ecfc9..af478eded4 100755
--- a/tensorflow/tools/ci_build/install/install_pip_packages.sh
+++ b/tensorflow/tools/ci_build/install/install_pip_packages.sh
@@ -115,10 +115,10 @@ pip2 install --upgrade setuptools==39.1.0
 pip3 install --upgrade setuptools==39.1.0
 
 # Keras
-pip2 install keras_applications==1.0.4 --no-deps
-pip3 install keras_applications==1.0.4 --no-deps
-pip2 install keras_preprocessing==1.0.2 --no-deps
-pip3 install keras_preprocessing==1.0.2 --no-deps
+pip2 install keras_applications==1.0.5 --no-deps
+pip3 install keras_applications==1.0.5 --no-deps
+pip2 install keras_preprocessing==1.0.3 --no-deps
+pip3 install keras_preprocessing==1.0.3 --no-deps
 
 # Install last working version of setuptools.
 pip2 install --upgrade setuptools==39.1.0
diff --git a/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh b/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh
index 15e4396ce3..93ea0c3db6 100755
--- a/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh
+++ b/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh
@@ -85,8 +85,8 @@ pip3.5 install --upgrade termcolor
 pip3.5 install --upgrade setuptools==39.1.0
 
 # Keras
-pip3.5 install keras_applications==1.0.4
-pip3.5 install keras_preprocessing==1.0.2
+pip3.5 install keras_applications==1.0.5
+pip3.5 install keras_preprocessing==1.0.3
 
 # Install last working version of setuptools.
 pip3.5 install --upgrade setuptools==39.1.0
diff --git a/tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh b/tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh
index 0fc3eee71c..7a9eef7c64 100755
--- a/tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh
+++ b/tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh
@@ -101,7 +101,7 @@ pip3 install --upgrade termcolor
 pip3 install --upgrade setuptools==39.1.0
 
 # Keras
-pip3 install keras_applications==1.0.4
-pip3 install keras_preprocessing==1.0.2
+pip3 install keras_applications==1.0.5
+pip3 install keras_preprocessing==1.0.3
 
 # LINT.ThenChange(//tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh)
diff --git a/tensorflow/tools/docker/Dockerfile b/tensorflow/tools/docker/Dockerfile
index 2c31d784e5..0114ef9dbf 100644
--- a/tensorflow/tools/docker/Dockerfile
+++ b/tensorflow/tools/docker/Dockerfile
@@ -29,8 +29,8 @@ RUN pip --no-cache-dir install \
         h5py \
         ipykernel \
         jupyter \
-        keras_applications==1.0.4 \
-        keras_preprocessing==1.0.2 \
+        keras_applications==1.0.5 \
+        keras_preprocessing==1.0.3 \
         matplotlib \
         numpy==1.14.5 \
         pandas \
diff --git a/tensorflow/tools/docker/Dockerfile.devel b/tensorflow/tools/docker/Dockerfile.devel
index bacdea72ce..aec5ca965e 100644
--- a/tensorflow/tools/docker/Dockerfile.devel
+++ b/tensorflow/tools/docker/Dockerfile.devel
@@ -33,8 +33,8 @@ RUN pip --no-cache-dir install \
         h5py \
         ipykernel \
         jupyter \
-        keras_applications==1.0.4 \
-        keras_preprocessing==1.0.2 \
+        keras_applications==1.0.5 \
+        keras_preprocessing==1.0.3 \
         matplotlib \
         mock \
         numpy==1.14.5 \
diff --git a/tensorflow/tools/docker/Dockerfile.devel-gpu b/tensorflow/tools/docker/Dockerfile.devel-gpu
index 4f89e3f701..ba421d9978 100644
--- a/tensorflow/tools/docker/Dockerfile.devel-gpu
+++ b/tensorflow/tools/docker/Dockerfile.devel-gpu
@@ -49,8 +49,8 @@ RUN pip --no-cache-dir install \
         h5py \
         ipykernel \
         jupyter \
-        keras_applications==1.0.4 \
-        keras_preprocessing==1.0.2 \
+        keras_applications==1.0.5 \
+        keras_preprocessing==1.0.3 \
         matplotlib \
         mock \
         numpy==1.14.5 \
diff --git a/tensorflow/tools/docker/Dockerfile.devel-gpu-cuda9-cudnn7 b/tensorflow/tools/docker/Dockerfile.devel-gpu-cuda9-cudnn7
index 056b4755f4..eb139ec5f8 100644
--- a/tensorflow/tools/docker/Dockerfile.devel-gpu-cuda9-cudnn7
+++ b/tensorflow/tools/docker/Dockerfile.devel-gpu-cuda9-cudnn7
@@ -37,8 +37,8 @@ RUN pip --no-cache-dir install --upgrade \
 RUN pip --no-cache-dir install \
         ipykernel \
         jupyter \
-        keras_applications==1.0.4 \
-        keras_preprocessing==1.0.2 \
+        keras_applications==1.0.5 \
+        keras_preprocessing==1.0.3 \
         matplotlib \
         numpy \
         scipy \
diff --git a/tensorflow/tools/docker/Dockerfile.devel-mkl b/tensorflow/tools/docker/Dockerfile.devel-mkl
index 2df770e525..371451d2aa 100755
--- a/tensorflow/tools/docker/Dockerfile.devel-mkl
+++ b/tensorflow/tools/docker/Dockerfile.devel-mkl
@@ -52,8 +52,8 @@ RUN ${PIP} --no-cache-dir install \
         h5py \
         ipykernel \
         jupyter \
-        keras_applications==1.0.4 \
-        keras_preprocessing==1.0.2 \
+        keras_applications==1.0.5 \
+        keras_preprocessing==1.0.3 \
         matplotlib \
         mock \
         numpy \
diff --git a/tensorflow/tools/docker/Dockerfile.devel-mkl-horovod b/tensorflow/tools/docker/Dockerfile.devel-mkl-horovod
index ab2eec1728..987b582d10 100755
--- a/tensorflow/tools/docker/Dockerfile.devel-mkl-horovod
+++ b/tensorflow/tools/docker/Dockerfile.devel-mkl-horovod
@@ -45,8 +45,8 @@ RUN ${PIP} --no-cache-dir install \
         h5py \
         ipykernel \
         jupyter \
-        keras_applications==1.0.4 \
-        keras_preprocessing==1.0.2 \
+        keras_applications==1.0.5 \
+        keras_preprocessing==1.0.3 \
         matplotlib \
         mock \
         numpy \
diff --git a/tensorflow/tools/docker/Dockerfile.gpu b/tensorflow/tools/docker/Dockerfile.gpu
index aa0e0face1..806b8836c7 100644
--- a/tensorflow/tools/docker/Dockerfile.gpu
+++ b/tensorflow/tools/docker/Dockerfile.gpu
@@ -37,8 +37,8 @@ RUN pip --no-cache-dir install \
         h5py \
         ipykernel \
         jupyter \
-        keras_applications==1.0.4 \
-        keras_preprocessing==1.0.2 \
+        keras_applications==1.0.5 \
+        keras_preprocessing==1.0.3 \
         matplotlib \
         numpy==1.14.5 \
         pandas \
diff --git a/tensorflow/tools/docker/Dockerfile.mkl b/tensorflow/tools/docker/Dockerfile.mkl
index 69553302d8..641c9e3b16 100755
--- a/tensorflow/tools/docker/Dockerfile.mkl
+++ b/tensorflow/tools/docker/Dockerfile.mkl
@@ -38,8 +38,8 @@ RUN ${PIP} --no-cache-dir install \
         h5py \
         ipykernel \
         jupyter \
-        keras_applications==1.0.4 \
-        keras_preprocessing==1.0.2 \
+        keras_applications==1.0.5 \
+        keras_preprocessing==1.0.3 \
         matplotlib \
         numpy \
         pandas \
diff --git a/tensorflow/tools/docker/Dockerfile.mkl-horovod b/tensorflow/tools/docker/Dockerfile.mkl-horovod
index 756716ee0e..2b11679f54 100755
--- a/tensorflow/tools/docker/Dockerfile.mkl-horovod
+++ b/tensorflow/tools/docker/Dockerfile.mkl-horovod
@@ -38,8 +38,8 @@ RUN ${PIP} --no-cache-dir install \
         h5py \
         ipykernel \
         jupyter \
-        keras_applications==1.0.4 \
-        keras_preprocessing==1.0.2 \
+        keras_applications==1.0.5 \
+        keras_preprocessing==1.0.3 \
         matplotlib \
         numpy \
         pandas \
diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py
index 5e179079c5..8cefbef82d 100644
--- a/tensorflow/tools/pip_package/setup.py
+++ b/tensorflow/tools/pip_package/setup.py
@@ -51,8 +51,8 @@ REQUIRED_PACKAGES = [
     'absl-py >= 0.1.6',
     'astor >= 0.6.0',
     'gast >= 0.2.0',
-    'keras_applications == 1.0.4',
-    'keras_preprocessing == 1.0.2',
+    'keras_applications >= 1.0.5',
+    'keras_preprocessing >= 1.0.3',
     'numpy >= 1.13.3, <= 1.14.5',
     'six >= 1.10.0',
     'protobuf >= 3.6.0',