def make_confidence_report(filepath, train_start=TRAIN_START,
                           train_end=TRAIN_END,
                           test_start=TEST_START, test_end=TEST_END,
                           batch_size=BATCH_SIZE, which_set=WHICH_SET,
                           mc_batch_size=MC_BATCH_SIZE,
                           report_path=REPORT_PATH,
                           base_eps_iter=BASE_EPS_ITER,
                           nb_iter=NB_ITER, save_advx=SAVE_ADVX):
  """
  Load a saved model, gather its predictions, and save a confidence report.


  This function works by running a single MaxConfidence attack on each example.
  This provides a reasonable estimate of the true failure rate quickly, so
  long as the model does not suffer from gradient masking.
  However, this estimate is mostly intended for development work and not
  for publication. A more accurate estimate may be obtained by running
  make_confidence_report_bundled.py instead.

  :param filepath: path to model to evaluate
  :param train_start: index of first training set example to use
  :param train_end: index of last training set example to use
  :param test_start: index of first test set example to use
  :param test_end: index of last test set example to use
  :param batch_size: size of evaluation batches
  :param which_set: 'train' or 'test'
  :param mc_batch_size: batch size for MaxConfidence attack
  :param base_eps_iter: step size if the data were in [0,1]
    (Step size will be rescaled proportional to the actual data range)
  :param nb_iter: Number of iterations of PGD to run per class
  :param save_advx: bool. If True, saves the adversarial examples to disk.
    On by default, but can be turned off to save memory, etc.
  """

  # Set TF random seed to improve reproducibility
  tf.set_random_seed(1234)

  # Set logging level to see debug information
  set_log_level(logging.INFO)

  # Create TF session
  sess = tf.Session()

  if report_path is None:
    assert filepath.endswith('.joblib')
    report_path = filepath[:-len('.joblib')] + "_report.joblib"

  with sess.as_default():
    model = load(filepath)
  assert len(model.get_params()) > 0
  factory = model.dataset_factory
  factory.kwargs['train_start'] = train_start
  factory.kwargs['train_end'] = train_end
  factory.kwargs['test_start'] = test_start
  factory.kwargs['test_end'] = test_end
  dataset = factory()

  center = dataset.kwargs['center']
  max_val = dataset.kwargs['max_val']
  value_range = max_val * (1. + center)
  min_value = 0. - center * max_val

  if 'CIFAR' in str(factory.cls):
    base_eps = 8. / 255.
    if base_eps_iter is None:
      base_eps_iter = 2. / 255.
  elif 'MNIST' in str(factory.cls):
    base_eps = .3
    if base_eps_iter is None:
      base_eps_iter = .1
  else:
    raise NotImplementedError(str(factory.cls))

  mc_params = {'eps': base_eps * value_range,
               'eps_iter': base_eps_iter * value_range,
               'nb_iter': nb_iter,
               'clip_min': min_value,
               'clip_max': max_val}

  x_data, y_data = dataset.get_set(which_set)

  report = ConfidenceReport()

  semantic = Semantic(model, center, max_val, sess)
  mc = MaxConfidence(model, sess=sess)

  jobs = [('clean', None, None, None, False),
          ('Semantic', semantic, None, None, False),
          ('mc', mc, mc_params, mc_batch_size, True)]

  for job in jobs:
    name, attack, attack_params, job_batch_size, save_this_job = job
    if job_batch_size is None:
      job_batch_size = batch_size
    t1 = time.time()
    if save_advx and save_this_job:
      # If we want to save the adversarial examples to the filesystem, we need
      # to fetch all of them. Otherwise they're just computed one batch at a
      # time and discarded

      # The path to save to
      assert report_path.endswith('.joblib')
      advx_path = report_path[:-len('.joblib')] + '_advx_' + name + '.npy'

      # Fetch the adversarial examples
      x_data = run_attack(sess, model, x_data, y_data, attack, attack_params,
                          batch_size=job_batch_size, devices=devices)

      # Turn off the attack so `correctness_and_confidence` won't run it a
      # second time.
      attack = None
      attack_params = None

      # Save the adversarial examples
      np.save(advx_path, x_data)

    # Run correctness and confidence evaluation on adversarial examples
    packed = correctness_and_confidence(sess, model, x_data, y_data,
                                        batch_size=job_batch_size,
                                        devices=devices,
                                        attack=attack,
                                        attack_params=attack_params)
    t2 = time.time()
    print("Evaluation took", t2 - t1, "seconds")
    correctness, confidence = packed

    report[name] = ConfidenceReportEntry(correctness=correctness,
                                         confidence=confidence)

    print_stats(correctness, confidence, name)

  save(report_path, report)
Load a saved model, gather its predictions, and save a confidence report.


  This function works by running a single MaxConfidence attack on each example.
  This provides a reasonable estimate of the true failure rate quickly, so
  long as the model does not suffer from gradient masking.
  However, this estimate is mostly intended for development work and not
  for publication. A more accurate estimate may be obtained by running
  make_confidence_report_bundled.py instead.

  :param filepath: path to model to evaluate
  :param train_start: index of first training set example to use
  :param train_end: index of last training set example to use
  :param test_start: index of first test set example to use
  :param test_end: index of last test set example to use
  :param batch_size: size of evaluation batches
  :param which_set: 'train' or 'test'
  :param mc_batch_size: batch size for MaxConfidence attack
  :param base_eps_iter: step size if the data were in [0,1]
    (Step size will be rescaled proportional to the actual data range)
  :param nb_iter: Number of iterations of PGD to run per class
  :param save_advx: bool. If True, saves the adversarial examples to disk.
    On by default, but can be turned off to save memory, etc.