code: def main(_):
    
    # Images for inception classifier are normalized to be in [-1, 1] interval,
    # eps is a difference between pixels so it should be in [0, 2] interval.
    # Renormalizing epsilon from [0, 255] to [0, 2].
    eps = 2.0 * FLAGS.max_epsilon / 255.0
    batch_shape = [FLAGS.batch_size, FLAGS.image_height, FLAGS.image_width, 3]
    nb_classes = 1001
  
    tf.logging.set_verbosity(tf.logging.INFO)
  
    with tf.Graph().as_default():
      # Prepare graph
      x_input = tf.placeholder(tf.float32, shape=batch_shape)
  
      model = InceptionModel(nb_classes)
  
      fgsm = FastGradientMethod(model)
      x_adv = fgsm.generate(x_input, eps=eps, clip_min=-1., clip_max=1.)
  
      # Run computation
      saver = tf.train.Saver(slim.get_model_variables())
      session_creator = tf.train.ChiefSessionCreator(
          scaffold=tf.train.Scaffold(saver=saver),
          checkpoint_filename_with_path=FLAGS.checkpoint_path,
          master=FLAGS.master)
  
      with tf.train.MonitoredSession(session_creator=session_creator) as sess:
        for filenames, images in load_images(FLAGS.input_dir, batch_shape):
          adv_images = sess.run(x_adv, feed_dict={x_input: images})
          save_images(adv_images, filenames, FLAGS.output_dir)
docs: Run the sample attack