cifar10_eval.py:

# Copyright 2015 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.# =============================================================================="""Evaluation for CIFAR-10.Accuracy:cifar10_train.py achieves 83.0% accuracy after 100K steps (256 epochsof data) as judged by cifar10_eval.py.Speed:On a single Tesla K40, cifar10_train.py processes a single batch of 128 imagesin 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-10data set, compile the program and train the model.http://tensorflow.org/tutorials/deep_cnn/"""from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionfrom datetime import datetimeimport mathimport timeimport numpy as npimport tensorflow as tfimport cifar10FLAGS = tf.app.flags.FLAGStf.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() as g:    # 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.summary.merge_all()    summary_writer = tf.summary.FileWriter(FLAGS.eval_dir, g)    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()