text: import os
  import tensorflow as tf
  
  NUM_CLASSES = 10
  NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = 50000
  NUM_EXAMPLES_PER_EPOCH_FOR_EVAL = 10000
  IMAGE_SIZE = 24
  height = IMAGE_SIZE
  width = IMAGE_SIZE
  batch_size = 10
  
  data_dir = os.path.join('/tmp/cifar10_data', 'cifar-10-batches-bin')
  filenames = [os.path.join(data_dir, 'data_batch_%d.bin' % i)
               for i in xrange(1, 6)]
  for f in filenames:
      if not tf.gfile.Exists(f):
          raise ValueError('Failed to find file: ' + f)
  
  
  def read_cifar10(filename_queue):
      """Reads and parses examples from CIFAR10 data files.
  
      Recommendation: if you want N-way read parallelism, call this function
      N times.  This will give you N independent Readers reading different
      files & positions within those files, which will give better mixing of
      examples.
  
      Args:
        filename_queue: A queue of strings with the filenames to read from.
  
      Returns:
        An object representing a single example, with the following fields:
          height: number of rows in the result (32)
          width: number of columns in the result (32)
          depth: number of color channels in the result (3)
          key: a scalar string Tensor describing the filename & record number
            for this example.
          label: an int32 Tensor with the label in the range 0..9.
          uint8image: a [height, width, depth] uint8 Tensor with the image data
      """
  
      class CIFAR10Record(object):
          pass
  
      result = CIFAR10Record()
  
      # Dimensions of the images in the CIFAR-10 dataset.
      # See http://www.cs.toronto.edu/~kriz/cifar.html for a description of the
      # input format.
      label_bytes = 1  # 2 for CIFAR-100
      result.height = 32
      result.width = 32
      result.depth = 3
      image_bytes = result.height * result.width * result.depth
      # Every record consists of a label followed by the image, with a
      # fixed number of bytes for each.
      record_bytes = label_bytes + image_bytes
  
      # Read a record, getting filenames from the filename_queue.  No
      # header or footer in the CIFAR-10 format, so we leave header_bytes
      # and footer_bytes at their default of 0.
      reader = tf.FixedLengthRecordReader(record_bytes=record_bytes)
      result.key, value = reader.read(filename_queue)
  
      # Convert from a string to a vector of uint8 that is record_bytes long.
      record_bytes = tf.decode_raw(value, tf.uint8)
  
      # The first bytes represent the label, which we convert from uint8->int32.
      result.label = tf.cast(
          tf.strided_slice(record_bytes, [0], [label_bytes]), tf.int32)
  
      # The remaining bytes after the label represent the image, which we reshape
      # from [depth * height * width] to [depth, height, width].
      depth_major = tf.reshape(
          tf.strided_slice(record_bytes, [label_bytes],
                           [label_bytes + image_bytes]),
          [result.depth, result.height, result.width])
      # Convert from [depth, height, width] to [height, width, depth].
      result.uint8image = tf.transpose(depth_major, [1, 2, 0])
  
      return result
  
  
  def generate_image_and_label_batch(image, label, min_queue_examples,
                                      batch_size, shuffle):
      """Construct a queued batch of images and labels.
  
      Args:
        image: 3-D Tensor of [height, width, 3] of type.float32.
        label: 1-D Tensor of type.int32
        min_queue_examples: int32, minimum number of samples to retain
          in the queue that provides of batches of examples.
        batch_size: Number of images per batch.
        shuffle: boolean indicating whether to use a shuffling queue.
  
      Returns:
        images: Images. 4D tensor of [batch_size, height, width, 3] size.
        labels: Labels. 1D tensor of [batch_size] size.
      """
      # Create a queue that shuffles the examples, and then
      # read 'batch_size' images + labels from the example queue.
      num_preprocess_threads = 16
      if shuffle:
          images, label_batch = tf.train.shuffle_batch(
              [image, label],
              batch_size=batch_size,
              num_threads=num_preprocess_threads,
              capacity=min_queue_examples + 3 * batch_size,
              min_after_dequeue=min_queue_examples)
      else:
          images, label_batch = tf.train.batch(
              [image, label],
              batch_size=batch_size,
              num_threads=num_preprocess_threads,
              capacity=min_queue_examples + 3 * batch_size)
  
      # Display the training images in the visualizer.
      tf.summary.image('images', images)
  
      return images, tf.reshape(label_batch, [batch_size])
  
  
  # Create a queue that produces the filenames to read.
  filename_queue = tf.train.string_input_producer(filenames)
  read_input = read_cifar10(filename_queue)
  reshaped_image = tf.cast(read_input.uint8image, tf.float32)
  
  
  # Image processing for training the network. Note the many random
  # distortions applied to the image.
  
  # Randomly crop a [height, width] section of the image.
  distorted_image = tf.random_crop(reshaped_image, [height, width, 3])
  
  # Randomly flip the image horizontally.
  distorted_image = tf.image.random_flip_left_right(distorted_image)
  
  # Because these operations are not commutative, consider randomizing
  # the order their operation.
  distorted_image = tf.image.random_brightness(distorted_image,
                                             max_delta=63)
  distorted_image = tf.image.random_contrast(distorted_image,
                                           lower=0.2, upper=1.8)
  
  # Subtract off the mean and divide by the variance of the pixels.
  float_image = tf.image.per_image_standardization(distorted_image)
  
  # Set the shapes of tensors.
  float_image.set_shape([height, width, 3])
  read_input.label.set_shape([1])
  
  # Ensure that the random shuffling has good mixing properties.
  min_fraction_of_examples_in_queue = 0.4
  min_queue_examples = int(NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN *
                         min_fraction_of_examples_in_queue)
  print ('Filling queue with %d CIFAR images before starting to train. '
       'This will take a few minutes.' % min_queue_examples)
  
  # Generate a batch of images and labels by building up a queue of examples.
  final = generate_image_and_label_batch(float_image, read_input.label,
                                       min_queue_examples, batch_size,
                                       shuffle=True)
  
  print 'd'