text: # 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.
  # ==============================================================================
  
  """Converts MNIST data to TFRecords file format with Example protos."""
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import os
  import tensorflow as tf
  from tensorflow.contrib.learn.python.learn.datasets import mnist
  
  
  SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/'
  
  TRAIN_IMAGES = 'train-images-idx3-ubyte.gz'  # MNIST filenames
  TRAIN_LABELS = 'train-labels-idx1-ubyte.gz'
  TEST_IMAGES = 't10k-images-idx3-ubyte.gz'
  TEST_LABELS = 't10k-labels-idx1-ubyte.gz'
  
  
  tf.app.flags.DEFINE_string('directory', '/tmp/data',
                             'Directory to download data files and write the '
                             'converted result')
  tf.app.flags.DEFINE_integer('validation_size', 5000,
                              'Number of examples to separate from the training '
                              'data for the validation set.')
  FLAGS = tf.app.flags.FLAGS
  
  
  def _int64_feature(value):
    return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
  
  
  def _bytes_feature(value):
    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
  
  
  def convert_to(data_set, name):
    images = data_set.images
    labels = data_set.labels
    num_examples = data_set.num_examples
  
    if images.shape[0] != num_examples:
      raise ValueError('Images size %d does not match label size %d.' %
                       (images.shape[0], num_examples))
    rows = images.shape[1]
    cols = images.shape[2]
    depth = images.shape[3]
  
    filename = os.path.join(FLAGS.directory, name + '.tfrecords')
    print('Writing', filename)
    writer = tf.python_io.TFRecordWriter(filename)
    for index in range(num_examples):
      image_raw = images[index].tostring()
      example = tf.train.Example(features=tf.train.Features(feature={
          'height': _int64_feature(rows),
          'width': _int64_feature(cols),
          'depth': _int64_feature(depth),
          'label': _int64_feature(int(labels[index])),
          'image_raw': _bytes_feature(image_raw)}))
      writer.write(example.SerializeToString())
    writer.close()
  
  
  def main(argv):
    # Get the data.
    data_sets = mnist.read_data_sets(FLAGS.directory,
                                     dtype=tf.uint8,
                                     reshape=False)
  
    # Convert to Examples and write the result to TFRecords.
    convert_to(data_sets.train, 'train')
    convert_to(data_sets.validation, 'validation')
    convert_to(data_sets.test, 'test')
  
  
  if __name__ == '__main__':
    tf.app.run()
  