How to use tcav - 6 common examples

###### related DIRs on CNS to store results #######
  discovered_concepts_dir = os.path.join(args.working_dir, 'concepts/')
  results_dir = os.path.join(args.working_dir, 'results/')
  cavs_dir = os.path.join(args.working_dir, 'cavs/')
  activations_dir = os.path.join(args.working_dir, 'acts/')
  results_summaries_dir = os.path.join(args.working_dir, 'results_summaries/')
  if tf.gfile.Exists(args.working_dir):
    tf.gfile.DeleteRecursively(args.working_dir)
  tf.gfile.MakeDirs(args.working_dir)
  tf.gfile.MakeDirs(discovered_concepts_dir)
  tf.gfile.MakeDirs(results_dir)
  tf.gfile.MakeDirs(cavs_dir)
  tf.gfile.MakeDirs(activations_dir)
  tf.gfile.MakeDirs(results_summaries_dir)
  random_concept = 'random_discovery'  # Random concept for statistical testing
  sess = utils.create_session()
  mymodel = ace_helpers.make_model(
      sess, args.model_to_run, args.model_path, args.labels_path)
  # Creating the ConceptDiscovery class instance
  cd = ConceptDiscovery(
      mymodel,
      args.target_class,
      random_concept,
      args.bottlenecks.split(','),
      sess,
      args.source_dir,
      activations_dir,
      cavs_dir,
      num_random_exp=args.num_random_exp,
      channel_mean=True,
      max_imgs=args.max_imgs,
      min_imgs=args.min_imgs,

Args:
      c: conept name
      r: random concept name
      bn: the layer name
      act_c: activation matrix of the concept in the 'bn' layer
      ow: overwrite if CAV already exists
      directory: to save the generated CAV

    Returns:
      The accuracy of the CAV
    """
    if directory is None:
      directory = self.cav_dir
    act_r = self._random_concept_activations(bn, r)
    cav_instance = cav.get_or_train_cav([c, r],
                                        bn, {
                                            c: {
                                                bn: act_c
                                            },
                                            r: {
                                                bn: act_r
                                            }
                                        },
                                        cav_dir=directory,
                                        overwrite=ow)
    return cav_instance.accuracies['overall']

Args:
      c: concept name
      r: random concept name
      bn: bottleneck layer
      directory: where CAV is saved

    Returns:
      The cav instance
    """
    if directory is None:
      directory = self.cav_dir
    params = tf.contrib.training.HParams(model_type='linear', alpha=.01)
    cav_key = cav.CAV.cav_key([c, r], bn, params.model_type, params.alpha)
    cav_path = os.path.join(self.cav_dir, cav_key.replace('/', '.') + '.pkl')
    vector = cav.CAV.load_cav(cav_path).cavs[0]
    return np.expand_dims(vector, 0) / np.linalg.norm(vector, ord=2)

def load_cav_direction(self, c, r, bn, directory=None):
    """Loads an already computed cav.

    Args:
      c: concept name
      r: random concept name
      bn: bottleneck layer
      directory: where CAV is saved

    Returns:
      The cav instance
    """
    if directory is None:
      directory = self.cav_dir
    params = tf.contrib.training.HParams(model_type='linear', alpha=.01)
    cav_key = cav.CAV.cav_key([c, r], bn, params.model_type, params.alpha)
    cav_path = os.path.join(self.cav_dir, cav_key.replace('/', '.') + '.pkl')
    vector = cav.CAV.load_cav(cav_path).cavs[0]
    return np.expand_dims(vector, 0) / np.linalg.norm(vector, ord=2)

model_path: Path to models saved graph.
    randomize: Start with random weights
    labels_path: Path to models line separated class names text file.

  Returns:
    a model instance.

  Raises:
    ValueError: If model name is not valid.
  """
  if model_to_run == 'InceptionV3':
    mymodel = model.InceptionV3Wrapper_public(
        sess, model_saved_path=model_path, labels_path=labels_path)
  elif model_to_run == 'GoogleNet':
    # common_typos_disable
    mymodel = model.GoolgeNetWrapper_public(
        sess, model_saved_path=model_path, labels_path=labels_path)
  else:
    raise ValueError('Invalid model name')
  if randomize:  # randomize the network!
    sess.run(tf.global_variables_initializer())
  return mymodel

Args:
    sess: tf session instance.
    model_to_run: a string that describes which model to make.
    model_path: Path to models saved graph.
    randomize: Start with random weights
    labels_path: Path to models line separated class names text file.

  Returns:
    a model instance.

  Raises:
    ValueError: If model name is not valid.
  """
  if model_to_run == 'InceptionV3':
    mymodel = model.InceptionV3Wrapper_public(
        sess, model_saved_path=model_path, labels_path=labels_path)
  elif model_to_run == 'GoogleNet':
    # common_typos_disable
    mymodel = model.GoolgeNetWrapper_public(
        sess, model_saved_path=model_path, labels_path=labels_path)
  else:
    raise ValueError('Invalid model name')
  if randomize:  # randomize the network!
    sess.run(tf.global_variables_initializer())
  return mymodel