1ff8c3da9838d71d3c5fbe91dcef50b160f5660c,cleverhans/future/jax/attacks/fast_gradient_method.py,,fast_gradient_method,#Any#Any#Any#Any#Any#Any#Any#Any#,8

Before Change

  if ord == np.inf:
    perturbation = eps * np.sign(grads)
  elif ord == 1:
    abs_grads = np.abs(grads)
    sign = np.sign(grads)
    max_abs_grads = np.amax(abs_grads, axis=axis, keepdims=True)
    tied_for_max = np.asarray(np.equal(abs_grads, max_abs_grads), dtype=np.float32)
    num_ties = np.sum(tied_for_max, axis=axis, keepdims=True)
    perturbation = sign * tied_for_max / num_ties
  elif ord == 2:
    square = np.maximum(avoid_zero_div, np.sum(np.square(grads), axis=axis, keepdims=True))
    perturbation = grads / np.sqrt(square)

After Change

  if ord == np.inf:
    perturbation = eps * np.sign(grads)
  elif ord == 1:
    raise NotImplementedError("L_1 norm has not been implemented yet.")
  elif ord == 2:
    square = np.maximum(avoid_zero_div, np.sum(np.square(grads), axis=axis, keepdims=True))
    perturbation = grads / np.sqrt(square)

Instances