text: # Copyright 2015 Google Inc. 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.
  # ==============================================================================
  
  """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 __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from datetime import datetime
  import math
  import time
  
  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 = true_count / 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 as 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 = variable_averages.variables_to_restore()
      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(add_shapes=True)
      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 tf.gfile.Exists(FLAGS.eval_dir):
      tf.gfile.DeleteRecursively(FLAGS.eval_dir)
    tf.gfile.MakeDirs(FLAGS.eval_dir)
    evaluate()
  
  
  if __name__ == '__main__':
    tf.app.run()
  