def evaluate(self):
return Evaluator.evaluate(self.vector, self.str_branch,
self.path_branch)
## contained in the LICENCE file in this directory.
from setup_mnist import MNIST
from mn_utils import prepare_data
from worker import AEDetector, SimpleReformer, IdReformer, AttackData, Classifier, Operator, Evaluator
import mn_utils as utils
detector_I = AEDetector("./defensive_models/MNIST_I", p=2)
detector_II = AEDetector("./defensive_models/MNIST_II", p=1)
reformer = SimpleReformer("./defensive_models/MNIST_I")
id_reformer = IdReformer()
classifier = Classifier("./models/example_classifier")
detector_dict = dict()
detector_dict["I"] = detector_I
detector_dict["II"] = detector_II
operator = Operator(MNIST(), classifier, detector_dict, reformer)
idx = utils.load_obj("example_idx")
_, _, Y = prepare_data(MNIST(), idx)
f = "example_carlini_0.0"
testAttack = AttackData(f, Y, "Carlini L2 0.0")
evaluator = Evaluator(operator, testAttack)
evaluator.plot_various_confidences("defense_performance",
drop_rate={"I": 0.001, "II": 0.001})
print('without reformer')
print(np.mean(predicted == np.argmax(lab, axis=1)), predicted)
thrs = operator.get_thrs(dr)
print(thrs)
passes, _ = operator.filter(adv, thrs)
print('rate of passing fooling', float(len(passes)) / len(dat)) #, passes)
exit(0)
for e in adv:
show(e)
detector0 = detector_dict['I0']
detector1 = detector_dict['II0']
print(detector0.mark(dat))
print(detector1.mark(dat))
print(detector0.mark(adv))
print(detector1.mark(adv))
exit(0)
idx = utils.load_obj("example_idx")
_, _, Y = prepare_data(data, idx)
f = "example_carlini_0.0"
testAttack = AttackData(f, Y, "Carlini L2 0.0")
Attack(operator)
exit(0)
evaluator = Evaluator(operator, testAttack)
evaluator.plot_various_confidences("defense_performance", drop_rate=dr)