def __init__(self, model, dtypestr="float32"):
"""
Creates a BasicIterativeMethod instance in eager execution.
:param model: cleverhans.model.Model
:param dtypestr: datatype in the string format.
"""
if not isinstance(model, Model):
wrapper_warning()
model = CallableModelWrapper(model, "probs")
super(BasicIterativeMethod, self).__init__(model, dtypestr)
def __init__(self, model, dtypestr="float32", **kwargs):
"""
Creates a FastGradientMethod instance in eager execution.
:model: cleverhans.model.Model
:dtypestr: datatype in the string format.
"""
del kwargs
if not isinstance(model, Model):
wrapper_warning()
model = CallableModelWrapper(model, "probs")
super(FastGradientMethod, self).__init__(model, dtypestr)
def __init__(self, model, sess, dtypestr='float32', **kwargs):
if not isinstance(model, Model):
wrapper_warning()
model = CallableModelWrapper(model, 'probs')
super(LBFGS, self).__init__(model, sess, dtypestr, **kwargs)
self.feedable_kwargs = ('y_target', )
self.structural_kwargs = [
'batch_size', 'binary_search_steps', 'max_iterations',
'initial_const', 'clip_min', 'clip_max'
]
def __init__(self, model, sess, dtypestr='float32', **kwargs):
"""
Note: the model parameter should be an instance of the
cleverhans.model.Model abstraction provided by CleverHans.
"""
if not isinstance(model, Model):
wrapper_warning()
model = CallableModelWrapper(model, 'probs')
super(LBFGS, self).__init__(model, sess, dtypestr, **kwargs)
self.feedable_kwargs = ('y_target',)
self.structural_kwargs = [
'batch_size', 'binary_search_steps', 'max_iterations',
'initial_const', 'clip_min', 'clip_max'
]
def __init__(self, model, sess, dtypestr="float32", **kwargs):
if not isinstance(model, Model):
wrapper_warning()
model = CallableModelWrapper(model, "probs")
super(LBFGS, self).__init__(model, sess, dtypestr, **kwargs)
self.feedable_kwargs = ("y_target",)
self.structural_kwargs = [
"batch_size",
"binary_search_steps",
"max_iterations",
"initial_const",
"clip_min",
"clip_max",
]