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diff --git a/tensorflow/contrib/saved_model/python/saved_model/keras_saved_model_test.py b/tensorflow/contrib/saved_model/python/saved_model/keras_saved_model_test.py
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+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+# pylint: disable=protected-access
+"""Tests for saving/loading function for keras Model."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import shutil
+import numpy as np
+
+from tensorflow.contrib.saved_model.python.saved_model import keras_saved_model
+from tensorflow.python import keras
+from tensorflow.python.framework import test_util
+from tensorflow.python.keras.engine import training
+from tensorflow.python.platform import test
+from tensorflow.python.training import training as training_module
+
+
+class TestModelSavingandLoading(test.TestCase):
+
+  def test_saving_sequential_model(self):
+    with self.test_session():
+      model = keras.models.Sequential()
+      model.add(keras.layers.Dense(2, input_shape=(3,)))
+      model.add(keras.layers.RepeatVector(3))
+      model.add(keras.layers.TimeDistributed(keras.layers.Dense(3)))
+      model.compile(
+          loss=keras.losses.MSE,
+          optimizer=keras.optimizers.RMSprop(lr=0.0001),
+          metrics=[keras.metrics.categorical_accuracy],
+          sample_weight_mode='temporal')
+      x = np.random.random((1, 3))
+      y = np.random.random((1, 3, 3))
+      model.train_on_batch(x, y)
+
+      ref_y = model.predict(x)
+      temp_dir = self.get_temp_dir()
+      self.addCleanup(shutil.rmtree, temp_dir)
+
+      temp_saved_model = os.path.join(temp_dir, 'saved_model')
+      keras_saved_model.save_model(model, temp_saved_model)
+
+      loaded_model = keras_saved_model.load_model(temp_saved_model)
+      y = loaded_model.predict(x)
+      self.assertAllClose(ref_y, y, atol=1e-05)
+
+  @test_util.run_in_graph_and_eager_modes
+  def test_saving_sequential_model_without_compile(self):
+    with self.test_session():
+      model = keras.models.Sequential()
+      model.add(keras.layers.Dense(2, input_shape=(3,)))
+      model.add(keras.layers.RepeatVector(3))
+      model.add(keras.layers.TimeDistributed(keras.layers.Dense(3)))
+
+      x = np.random.random((1, 3))
+      ref_y = model.predict(x)
+
+      temp_dir = self.get_temp_dir()
+      self.addCleanup(shutil.rmtree, temp_dir)
+
+      temp_saved_model = os.path.join(temp_dir, 'saved_model')
+      keras_saved_model.save_model(model, temp_saved_model)
+      loaded_model = keras_saved_model.load_model(temp_saved_model)
+
+      y = loaded_model.predict(x)
+      self.assertAllClose(ref_y, y, atol=1e-05)
+
+  def test_saving_functional_model(self):
+    with self.test_session():
+      inputs = keras.layers.Input(shape=(3,))
+      x = keras.layers.Dense(2)(inputs)
+      output = keras.layers.Dense(3)(x)
+
+      model = keras.models.Model(inputs, output)
+      model.compile(
+          loss=keras.losses.MSE,
+          optimizer=keras.optimizers.RMSprop(lr=0.0001),
+          metrics=[keras.metrics.categorical_accuracy])
+      x = np.random.random((1, 3))
+      y = np.random.random((1, 3))
+      model.train_on_batch(x, y)
+
+      ref_y = model.predict(x)
+      temp_dir = self.get_temp_dir()
+      self.addCleanup(shutil.rmtree, temp_dir)
+
+      temp_saved_model = os.path.join(temp_dir, 'saved_model')
+      keras_saved_model.save_model(model, temp_saved_model)
+      loaded_model = keras_saved_model.load_model(temp_saved_model)
+
+      y = loaded_model.predict(x)
+      self.assertAllClose(ref_y, y, atol=1e-05)
+
+  @test_util.run_in_graph_and_eager_modes
+  def test_saving_functional_model_without_compile(self):
+    with self.test_session():
+      inputs = keras.layers.Input(shape=(3,))
+      x = keras.layers.Dense(2)(inputs)
+      output = keras.layers.Dense(3)(x)
+
+      model = keras.models.Model(inputs, output)
+
+      x = np.random.random((1, 3))
+      y = np.random.random((1, 3))
+
+      ref_y = model.predict(x)
+      temp_dir = self.get_temp_dir()
+      self.addCleanup(shutil.rmtree, temp_dir)
+
+      temp_saved_model = os.path.join(temp_dir, 'saved_model')
+      keras_saved_model.save_model(model, temp_saved_model)
+      loaded_model = keras_saved_model.load_model(temp_saved_model)
+
+      y = loaded_model.predict(x)
+      self.assertAllClose(ref_y, y, atol=1e-05)
+
+  @test_util.run_in_graph_and_eager_modes
+  def test_saving_with_tf_optimizer(self):
+    with self.test_session():
+      model = keras.models.Sequential()
+      model.add(keras.layers.Dense(2, input_shape=(3,)))
+      model.add(keras.layers.Dense(3))
+      model.compile(
+          loss='mse',
+          optimizer=training_module.RMSPropOptimizer(0.1),
+          metrics=['acc'])
+
+      x = np.random.random((1, 3))
+      y = np.random.random((1, 3))
+      model.train_on_batch(x, y)
+
+      ref_y = model.predict(x)
+      temp_dir = self.get_temp_dir()
+      self.addCleanup(shutil.rmtree, temp_dir)
+
+      temp_saved_model = os.path.join(temp_dir, 'saved_model')
+      keras_saved_model.save_model(model, temp_saved_model)
+      loaded_model = keras_saved_model.load_model(temp_saved_model)
+      loaded_model.compile(
+          loss='mse',
+          optimizer=training_module.RMSPropOptimizer(0.1),
+          metrics=['acc'])
+      y = loaded_model.predict(x)
+      self.assertAllClose(ref_y, y, atol=1e-05)
+
+      # test that new updates are the same with both models
+      x = np.random.random((1, 3))
+      y = np.random.random((1, 3))
+
+      ref_loss = model.train_on_batch(x, y)
+      loss = loaded_model.train_on_batch(x, y)
+      self.assertAllClose(ref_loss, loss, atol=1e-05)
+
+      ref_y = model.predict(x)
+      y = loaded_model.predict(x)
+      self.assertAllClose(ref_y, y, atol=1e-05)
+
+      # test saving/loading again
+      keras_saved_model.save_model(loaded_model, temp_saved_model)
+      loaded_model = keras_saved_model.load_model(temp_saved_model)
+      y = loaded_model.predict(x)
+      self.assertAllClose(ref_y, y, atol=1e-05)
+
+  def test_saving_subclassed_model_raise_error(self):
+    # For now, saving subclassed model should raise an error. It should be
+    # avoided later with loading from SavedModel.pb.
+
+    class SubclassedModel(training.Model):
+
+      def __init__(self):
+        super(SubclassedModel, self).__init__()
+        self.layer1 = keras.layers.Dense(3)
+        self.layer2 = keras.layers.Dense(1)
+
+      def call(self, inp):
+        return self.layer2(self.layer1(inp))
+
+    model = SubclassedModel()
+    temp_dir = self.get_temp_dir()
+    self.addCleanup(shutil.rmtree, temp_dir)
+    temp_saved_model = os.path.join(temp_dir, 'saved_model')
+    with self.assertRaises(NotImplementedError):
+      keras_saved_model.save_model(model, temp_saved_model)
+
+
+if __name__ == '__main__':
+  test.main()