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diff --git a/tensorflow/models/image/cifar10/cifar10_eval.py b/tensorflow/models/image/cifar10/cifar10_eval.py
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+"""Evaluation for CIFAR-10.
+
+Accuracy:
+cifar10_train.py achieves 83.0% accuracy after 100K steps (256 epochs
+of data) as judged by cifar10_eval.py.
+
+Speed:
+On a single Tesla K40, cifar10_train.py processes a single batch of 128 images
+in 0.25-0.35 sec (i.e. 350 - 600 images /sec). The model reaches ~86%
+accuracy after 100K steps in 8 hours of training time.
+
+Usage:
+Please see the tutorial and website for how to download the CIFAR-10
+data set, compile the program and train the model.
+
+http://tensorflow.org/tutorials/deep_cnn/
+"""
+from datetime import datetime
+import math
+import time
+
+import tensorflow.python.platform
+from tensorflow.python.platform import gfile
+import numpy as np
+import tensorflow as tf
+
+from tensorflow.models.image.cifar10 import cifar10
+
+FLAGS = tf.app.flags.FLAGS
+
+tf.app.flags.DEFINE_string('eval_dir', '/tmp/cifar10_eval',
+                           """Directory where to write event logs.""")
+tf.app.flags.DEFINE_string('eval_data', 'test',
+                           """Either 'test' or 'train_eval'.""")
+tf.app.flags.DEFINE_string('checkpoint_dir', '/tmp/cifar10_train',
+                           """Directory where to read model checkpoints.""")
+tf.app.flags.DEFINE_integer('eval_interval_secs', 60 * 5,
+                            """How often to run the eval.""")
+tf.app.flags.DEFINE_integer('num_examples', 10000,
+                            """Number of examples to run.""")
+tf.app.flags.DEFINE_boolean('run_once', False,
+                         """Whether to run eval only once.""")
+
+
+def eval_once(saver, summary_writer, top_k_op, summary_op):
+  """Run Eval once.
+
+  Args:
+    saver: Saver.
+    summary_writer: Summary writer.
+    top_k_op: Top K op.
+    summary_op: Summary op.
+  """
+  with tf.Session() as sess:
+    ckpt = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir)
+    if ckpt and ckpt.model_checkpoint_path:
+      # Restores from checkpoint
+      saver.restore(sess, ckpt.model_checkpoint_path)
+      # Assuming model_checkpoint_path looks something like:
+      #   /my-favorite-path/cifar10_train/model.ckpt-0,
+      # extract global_step from it.
+      global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
+    else:
+      print 'No checkpoint file found'
+      return
+
+    # Start the queue runners.
+    coord = tf.train.Coordinator()
+    try:
+      threads = []
+      for qr in tf.get_collection(tf.GraphKeys.QUEUE_RUNNERS):
+        threads.extend(qr.create_threads(sess, coord=coord, daemon=True,
+                                         start=True))
+
+      num_iter = int(math.ceil(FLAGS.num_examples / FLAGS.batch_size))
+      true_count = 0  # Counts the number of correct predictions.
+      total_sample_count = num_iter * FLAGS.batch_size
+      step = 0
+      while step < num_iter and not coord.should_stop():
+        predictions = sess.run([top_k_op])
+        true_count += np.sum(predictions)
+        step += 1
+
+      # Compute precision @ 1.
+      precision = float(true_count) / float(total_sample_count)
+      print '%s: precision @ 1 = %.3f' % (datetime.now(), precision)
+
+      summary = tf.Summary()
+      summary.ParseFromString(sess.run(summary_op))
+      summary.value.add(tag='Precision @ 1', simple_value=precision)
+      summary_writer.add_summary(summary, global_step)
+    except Exception, e:  # pylint: disable=broad-except
+      coord.request_stop(e)
+
+    coord.request_stop()
+    coord.join(threads, stop_grace_period_secs=10)
+
+
+def evaluate():
+  """Eval CIFAR-10 for a number of steps."""
+  with tf.Graph().as_default():
+    # Get images and labels for CIFAR-10.
+    eval_data = FLAGS.eval_data == 'test'
+    images, labels = cifar10.inputs(eval_data=eval_data)
+
+    # Build a Graph that computes the logits predictions from the
+    # inference model.
+    logits = cifar10.inference(images)
+
+    # Calculate predictions.
+    top_k_op = tf.nn.in_top_k(logits, labels, 1)
+
+    # Restore the moving average version of the learned variables for eval.
+    variable_averages = tf.train.ExponentialMovingAverage(
+        cifar10.MOVING_AVERAGE_DECAY)
+    variables_to_restore = {}
+    for v in tf.all_variables():
+      if v in tf.trainable_variables():
+        restore_name = variable_averages.average_name(v)
+      else:
+        restore_name = v.op.name
+      variables_to_restore[restore_name] = v
+    saver = tf.train.Saver(variables_to_restore)
+
+    # Build the summary operation based on the TF collection of Summaries.
+    summary_op = tf.merge_all_summaries()
+
+    graph_def = tf.get_default_graph().as_graph_def()
+    summary_writer = tf.train.SummaryWriter(FLAGS.eval_dir,
+                                            graph_def=graph_def)
+
+    while True:
+      eval_once(saver, summary_writer, top_k_op, summary_op)
+      if FLAGS.run_once:
+        break
+      time.sleep(FLAGS.eval_interval_secs)
+
+
+def main(argv=None):  # pylint: disable=unused-argument
+  cifar10.maybe_download_and_extract()
+  if gfile.Exists(FLAGS.eval_dir):
+    gfile.DeleteRecursively(FLAGS.eval_dir)
+  gfile.MakeDirs(FLAGS.eval_dir)
+  evaluate()
+
+
+if __name__ == '__main__':
+  tf.app.run()