old_contents: # Copyright 2016 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.
  # ==============================================================================
  """A binary to evaluate Inception on the flowers data set.
  
  Note that using the supplied pre-trained inception checkpoint, the eval should
  achieve:
    precision @ 1 = 0.7874 recall @ 5 = 0.9436 [50000 examples]
  
  See the README.md for more details.
  """
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  
  import tensorflow as tf
  
  from inception import inception_eval
  from inception.imagenet_data import ImagenetData
  
  FLAGS = tf.app.flags.FLAGS
  
  
  def main(unused_argv=None):
    dataset = ImagenetData(subset=FLAGS.subset)
    assert dataset.data_files()
    if tf.gfile.Exists(FLAGS.eval_dir):
      tf.gfile.DeleteRecursively(FLAGS.eval_dir)
    tf.gfile.MakeDirs(FLAGS.eval_dir)
    inception_eval.evaluate(dataset)
  
  
  if __name__ == '__main__':
    tf.app.run()
  
new_contents: # Copyright 2016 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.
  # ==============================================================================
  """A binary to evaluate Inception on the ImageNet data set.
  
  Note that using the supplied pre-trained inception checkpoint, the eval should
  achieve:
    precision @ 1 = 0.7874 recall @ 5 = 0.9436 [50000 examples]
  
  See the README.md for more details.
  """
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  
  import tensorflow as tf
  
  from inception import inception_eval
  from inception.imagenet_data import ImagenetData
  
  FLAGS = tf.app.flags.FLAGS
  
  
  def main(unused_argv=None):
    dataset = ImagenetData(subset=FLAGS.subset)
    assert dataset.data_files()
    if tf.gfile.Exists(FLAGS.eval_dir):
      tf.gfile.DeleteRecursively(FLAGS.eval_dir)
    tf.gfile.MakeDirs(FLAGS.eval_dir)
    inception_eval.evaluate(dataset)
  
  
  if __name__ == '__main__':
    tf.app.run()
  
subject: Fix typo