title: How is data augmentation implemented in Tensorflow?
body: <p>Based on the <a href="https://www.tensorflow.org/versions/r0.8/tutorials/deep_cnn/index.html#convolutional-neural-networks" rel="nofollow">Tensorflow tutorial for ConvNet</a>, some points are not readily apparent to me:</p>
  
  <ul>
  <li>are the images being distorted actually <strong>added</strong> to the pool of original images?</li>
  <li>or are the distorted images used <strong>instead</strong> of the originals?</li>
  <li>how many distorted images are being produced? (i.e. what augmentation factor was defined?)</li>
  </ul>
  
  <p>The flow of functions for the tutorial seems to be as follows:</p>
  
  <p><strong>cifar_10_train.py</strong></p>
  
  <pre><code>def train
      """Train CIFAR-10 for a number of steps."""
      with tf.Graph().as_default():
          [...]
          # Get images and labels for CIFAR-10.
          images, labels = cifar10.distorted_inputs()
          [...]
  </code></pre>
  
  <p><strong>cifar10.py</strong></p>
  
  <pre><code>def distorted_inputs():
      """Construct distorted input for CIFAR training using the Reader ops.
  
      Returns:
        images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
        labels: Labels. 1D tensor of [batch_size] size.
  
      Raises:
        ValueError: If no data_dir
      """
      if not FLAGS.data_dir:
          raise ValueError('Please supply a data_dir')
      data_dir = os.path.join(FLAGS.data_dir, 'cifar-10-batches-bin')
      return cifar10_input.distorted_inputs(data_dir=data_dir,
                                            batch_size=FLAGS.batch_size)
  </code></pre>
  
  <p>and finally <strong>cifar10_input.py</strong></p>
  
  <pre><code>def distorted_inputs(data_dir, batch_size):
      """Construct distorted input for CIFAR training using the Reader ops.
  
      Args:
      data_dir: Path to the CIFAR-10 data directory.
      batch_size: Number of images per batch.
  
      Returns:
      images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
      labels: Labels. 1D tensor of [batch_size] size.
      """
      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)
  
      # Create a queue that produces the filenames to read.
      filename_queue = tf.train.string_input_producer(filenames)
  
      # Read examples from files in the filename queue.
      read_input = read_cifar10(filename_queue)
      reshaped_image = tf.cast(read_input.uint8image, tf.float32)
  
      height = IMAGE_SIZE
      width = IMAGE_SIZE
  
      # 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_whitening(distorted_image)
  
      # 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.
      return _generate_image_and_label_batch(float_image, read_input.label,
                                             min_queue_examples, batch_size,
                                             shuffle=True)
  </code></pre>