def config(self, **kwargs):
''' (Re)config the attack.
:param magnitude: Max distortion, could be either a float number or a numpy float number array with shape of
(batch_size,).
:param alpha: Step size for each iteration, could be either a float number or a numpy float number array with
shape of (batch_size,).
:param iteration: Iteration count. An integer.
'''
if 'magnitude' in kwargs:
eps = maybe_to_array(kwargs['magnitude'], self.batch_size)
self._session.run(self.config_eps_step, feed_dict={self.eps_ph: eps})
if 'alpha' in kwargs:
alpha = maybe_to_array(kwargs['alpha'], self.batch_size)
self._session.run(self.config_alpha_step, feed_dict={self.alpha_ph: alpha})
if 'iteration' in kwargs:
self.iteration = kwargs['iteration']
def config(self, **kwargs):
''' (Re)config the attack.
:param magnitude: Max distortion, could be either a float number or a numpy float number array with shape of
`(self.batch_size,)`.
'''
if 'magnitude' in kwargs:
eps = maybe_to_array(kwargs['magnitude'], self.batch_size)
self._session.run(self.config_eps_step,
feed_dict={self.eps_ph: eps})
def config(self, **kwargs):
''' (Re)config the attack.
:param magnitude: Max distortion, could be either a float number or a numpy float number array with shape of
``(self.batch_size,)``.
:param alpha: Step size for each iteration, could be either a float number or a numpy float number array with
shape of ``(self.batch_size,)``.
:param decay_factor: A float number, the decay factor for momentum.
:param iteration: An integer, the iteration count.
'''
if 'magnitude' in kwargs:
eps = maybe_to_array(kwargs['magnitude'], self.batch_size)
self._session.run(self.config_eps_step, feed_dict={self.eps_ph: eps})
if 'alpha' in kwargs:
alpha = maybe_to_array(kwargs['alpha'], self.batch_size)
self._session.run(self.config_alpha_step, feed_dict={self.alpha_ph: alpha})
if 'decay_factor' in kwargs:
decay_factor = kwargs['decay_factor']
self._session.run(self.config_decay_factor_step, feed_dict={self.decay_factor_ph: decay_factor})
if 'iteration' in kwargs:
self.iteration = kwargs['iteration']
def config(self, **kwargs):
''' (Re)config the attack.
:param rand_init_magnitude: Random init max distortion, could be either a float number or a numpy float number
array with shape of `(self.batch_size,)`.
:param magnitude: Max distortion, could be either a float number or a numpy float number array with shape of
`(self.batch_size,)`.
:param alpha: Step size for each iteration, could be either a float number or a numpy float number array with
shape of `(self.batch_size,)`.
:param iteration: Iteration count. An integer.
'''
super().config(**kwargs)
if 'rand_init_magnitude' in kwargs:
rand_init_eps = maybe_to_array(kwargs['rand_init_magnitude'],
self.batch_size)
self._session.run(self.config_rand_init_eps,
feed_dict={self.rand_init_eps_ph: rand_init_eps})
def config(self, **kwargs):
''' (Re)config the attack.
:param cs: Initial c, could be either a float number or a numpy float number array with shape of
`(self.batch_size,)`.
:param iteration: An integer, represent iteration count for each search step and binary search step.
:param search_steps: An integer, the number of times for running initial search for c before binary search.
:param binsearch_steps: An integer, the number of times for running binary search on c.
:param logger: A standard logger for logging verbose information during attacking.
'''
if 'cs' in kwargs:
self.cs = maybe_to_array(kwargs['cs'],
target_len=self.batch_size).astype(
self.model.x_dtype.as_numpy_dtype)
if 'iteration' in kwargs:
self.iteration = kwargs['iteration']
if 'search_steps' in kwargs:
self.search_steps = kwargs['search_steps']
if 'binsearch_steps' in kwargs:
self.binsearch_steps = kwargs['binsearch_steps']
if 'logger' in kwargs:
self.logger = kwargs['logger']