class_docstr: A simple and fast gradient-based adversarial attack.
  Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Pascal Frossard,
  "DeepFool: a simple and accurate method to fool deep neural
  networks", https://arxiv.org/abs/1511.04599
  
  :param predict: forward pass function.
  :param num_classes: number of classes considered
  :param nb_iter: number of iterations.
  :eps=0.1
  :param clip_min: mininum value per input dimension.
  :param clip_max: maximum value per input dimension.
class_docstr_tok: ["A","simple","and","fast","gradient-based","adversarial","attack",".","Seyed-Mohsen","Moosavi-Dezfooli",",","Alhussein","Fawzi",",","Pascal","Frossard",",","''","DeepFool",":","a","simple","and","accurate","method","to","fool","deep","neural","networks","''",",","https",":","\/\/arxiv.org\/abs\/1511.04599",":","param","predict",":","forward","pass","function",".",":","param","num_classes",":","number","of","classes","considered",":","param","nb_iter",":","number","of","iterations",".",":","eps=0.1",":","param","clip_min",":","mininum","value","per","input","dimension",".",":","param","clip_max",":","maximum","value","per","input","dimension","."]
docstr: Deepfool Linf Attack implementation in pytorch.
docstr_tok: ["Deepfool","Linf","Attack","implementation","in","pytorch","."]
code: def __init__(
      self,
      predict,
      num_classes=None,
      nb_iter=50,
      eps=0.1,
      overshoot=0.02,
      clip_min=0.0,
      clip_max=1.0,
      loss_fn=None,
      targeted=False,
  ):
      """
      Deepfool Linf Attack implementation in pytorch.
      """
  
      super(DeepfoolLinfAttack, self).__init__(
          predict, loss_fn=loss_fn, clip_min=clip_min, clip_max=clip_max
      )
  
      self.predict = predict
      self.num_classes = num_classes
      self.nb_iter = nb_iter
      self.eps = eps
      self.overshoot = overshoot
      self.targeted = targeted
  
file_path: advertorch/advertorch/attacks/deepfool.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  from advertorch.utils import batch_clamp, clamp
  from advertorch.utils import replicate_input, replicate_input_withgrad
  import torch
  from .base import Attack, LabelMixin
label_desc: Use image node_id 1 to create a new DeepfoolLinfAttack object from inherited base classes: Attack, LabelMixin with example: obj = DeepfoolLinfAttack(predict, num_classes, nb_iter, eps, overshoot, clip_min, clip_max, loss_fn, targeted)