Runs a final evaluation on an eval graph using the test data set.

  Args:
    train_session: Session for the train graph with the tensors below.
    module_spec: The hub.ModuleSpec for the image module being used.
    class_count: Number of classes
    image_lists: OrderedDict of training images for each label.
    jpeg_data_tensor: The layer to feed jpeg image data into.
    decoded_image_tensor: The output of decoding and resizing the image.
    resized_image_tensor: The input node of the recognition graph.
    bottleneck_tensor: The bottleneck output layer of the CNN graph.

def run_final_eval(train_session, module_spec, class_count, image_lists,
                   jpeg_data_tensor, decoded_image_tensor,
                   resized_image_tensor, bottleneck_tensor):
  """Runs a final evaluation on an eval graph using the test data set.

  Args:
    train_session: Session for the train graph with the tensors below.
    module_spec: The hub.ModuleSpec for the image module being used.
    class_count: Number of classes
    image_lists: OrderedDict of training images for each label.
    jpeg_data_tensor: The layer to feed jpeg image data into.
    decoded_image_tensor: The output of decoding and resizing the image.
    resized_image_tensor: The input node of the recognition graph.
    bottleneck_tensor: The bottleneck output layer of the CNN graph.
  """
  test_bottlenecks, test_ground_truth, test_filenames = (
      get_random_cached_bottlenecks(train_session, image_lists,
                                    FLAGS.test_batch_size,
                                    'testing', FLAGS.bottleneck_dir,
                                    FLAGS.image_dir, jpeg_data_tensor,
                                    decoded_image_tensor, resized_image_tensor,
                                    bottleneck_tensor, FLAGS.tfhub_module))

  (eval_session, _, bottleneck_input, ground_truth_input, evaluation_step,
   prediction) = build_eval_session(module_spec, class_count)
  test_accuracy, predictions = eval_session.run(
      [evaluation_step, prediction],
      feed_dict={
          bottleneck_input: test_bottlenecks,
          ground_truth_input: test_ground_truth
      })
  tf.logging.info('Final test accuracy = %.1f%% (N=%d)' %
                  (test_accuracy * 100, len(test_bottlenecks)))

  if FLAGS.print_misclassified_test_images:
    tf.logging.info('=== MISCLASSIFIED TEST IMAGES ===')
    for i, test_filename in enumerate(test_filenames):
      if predictions[i] != test_ground_truth[i]:
        tf.logging.info('%70s  %s' % (test_filename,
                                      list(image_lists.keys())[predictions[i]]))