read_and_preprocess_tpu.py

def read_and_preprocess(example_data):
    parsed = tf.parse_single_example(example_data, {
      'image/encoded': tf.FixedLenFeature((), tf.string, ''),
      'image/class/label': tf.FixedLenFeature([], tf.int64, 1),
    })
    image_bytes = tf.reshape(parsed['image/encoded'], shape=[])
    label = tf.cast(
      tf.reshape(parsed['image/class/label'], shape=[]), dtype=tf.int32) - 1

    # end up with pixel values that are in the -1, 1 range
    image = tf.image.decode_jpeg(image_bytes, channels=NUM_CHANNELS)
    image = tf.image.convert_image_dtype(image, dtype=tf.float32) # 0-1
    image = tf.expand_dims(image, 0) # resize_bilinear needs batches

    image = tf.image.resize_bilinear(
      image, [HEIGHT + 10, WIDTH + 10], align_corners=False)
    image = tf.squeeze(image)  # remove batch dimension
    image = tf.random_crop(image, [HEIGHT, WIDTH, NUM_CHANNELS])
    image = tf.image.random_flip_left_right(image)
    image = tf.image.random_brightness(image, max_delta=63.0 / 255.0)
    image = tf.image.random_contrast(image, lower=0.2, upper=1.8)

        
    #pixel values are in range [0,1], convert to [-1,1]
    image = tf.subtract(image, 0.5)
    image = tf.multiply(image, 2.0)
    return image, label