class_docstr: Uses L-BFGS-B to minimize the cross-entropy and the distance between the
  original and the adversary.
  
  Paper link: https://arxiv.org/abs/1510.05328
class_docstr_tok: ["Uses","L-BFGS-B","to","minimize","the","cross-entropy","and","the","distance","between","the","original","and","the","adversary",".","Paper","link",":","https",":","\/\/arxiv.org\/abs\/1510.05328"]
docstr: To determine the predicts is normalized.
  :param predicts(np.array): the output of the model.
  :return: bool
docstr_tok: ["To","determine","the","predicts","is","normalized",".",":","param","predicts","(","np.array",")",":","the","output","of","the","model",".",":","return",":","bool"]
code: def _is_predicts_normalized(self, predicts):
      """
      To determine the predicts is normalized.
      :param predicts(np.array): the output of the model.
      :return: bool
      """
      if self._predicts_normalized is None:
          if self.model.predict_name().lower() in [
              "softmax",
              "probabilities",
              "probs",
          ]:
              self._predicts_normalized = True
          else:
              if np.any(predicts < 0.0):
                  self._predicts_normalized = False
              else:
                  s = np.sum(predicts.flatten())
                  if 0.999 <= s <= 1.001:
                      self._predicts_normalized = True
                  else:
                      self._predicts_normalized = False
      assert self._predicts_normalized is not None
      return self._predicts_normalized
  
file_path: AdvBox/adversarialbox/attacks/lbfgs.py
imports: from __future__ import division
  from builtins import range
  import logging
  import numpy
  from scipy.optimize import fmin_l_bfgs_b
  from .base import Attack
label_desc: Use image node_id 3 for calling the LBFGSAttack obj's underlying member method code with example usage: obj._is_predicts_normalized(predicts) and returns: self