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diff --git a/tensorflow/contrib/lite/tutorials/mnist_tflite.py b/tensorflow/contrib/lite/tutorials/mnist_tflite.py
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+++ b/tensorflow/contrib/lite/tutorials/mnist_tflite.py
@@ -0,0 +1,87 @@
+# 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.
+# ==============================================================================
+"""Script to evaluate accuracy of TFLite flatbuffer model on mnist dataset."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import numpy as np
+import tensorflow as tf  # pylint: disable=g-bad-import-order
+from tensorflow.contrib.lite.tutorials import dataset
+flags = tf.app.flags
+
+flags.DEFINE_string('data_dir', '/tmp/data_dir',
+                    'Directory where data is stored.')
+flags.DEFINE_string('model_file', '',
+                    'The path to the TFLite flatbuffer model file.')
+
+
+flags = flags.FLAGS
+
+
+def test_image_generator():
+  # Generates an iterator over images
+  with tf.Session() as sess:
+    input_data = dataset.test(
+        flags.data_dir).make_one_shot_iterator().get_next()
+    try:
+      while True:
+        yield sess.run(input_data)
+    except tf.errors.OutOfRangeError:
+      pass
+
+
+def run_eval(interpreter, input_image):
+  """Performs evaluation for input image over specified model.
+
+  Args:
+      interpreter: TFLite interpreter initialized with model to execute.
+      input_image: Image input to the model.
+
+  Returns:
+      output: output tensor of model being executed.
+  """
+
+  # Get input and output tensors.
+  input_details = interpreter.get_input_details()
+  output_details = interpreter.get_output_details()
+
+  # Test model on the input images.
+  input_image = np.reshape(input_image, input_details[0]['shape'])
+  interpreter.set_tensor(input_details[0]['index'], input_image)
+
+  interpreter.invoke()
+  output_data = interpreter.get_tensor(output_details[0]['index'])
+  output = np.squeeze(output_data)
+  return output
+
+
+def main(_):
+  interpreter = tf.contrib.lite.Interpreter(model_path=flags.model_file)
+  interpreter.allocate_tensors()
+  num_correct, total = 0, 0
+  for input_data in test_image_generator():
+    output = run_eval(interpreter, input_data[0])
+    total += 1
+    if output == input_data[1]:
+      num_correct += 1
+    if total % 500 == 0:
+      print('Accuracy after %i images: %f' %
+            (total, float(num_correct) / float(total)))
+
+
+if __name__ == '__main__':
+  tf.logging.set_verbosity(tf.logging.INFO)
+  tf.app.run(main)