def test_preprocessing(self):
# Get MNIST
(_, _), (x_test, _) = self._mnist
# Create classifier
classifier = MXClassifier((0, 1), self._model, (1, 28, 28), 10,
self._trainer)
classifier_preproc = MXClassifier((0, 1),
self._model, (1, 28, 28),
10,
self._trainer,
preprocessing=(1, 2))
preds = classifier.predict((x_test - 1.) / 2)
preds_preproc = classifier_preproc.predict(x_test)
self.assertTrue(np.sum(preds - preds_preproc) == 0)
def test_preprocessing(self):
# Get MNIST
(_, _), (x_test, _) = self.mnist
# Create classifier
classifier_preproc = MXClassifier(model=self.classifier._model, clip_values=(0, 1), input_shape=(1, 28, 28),
nb_classes=10, optimizer=self.classifier._optimizer, preprocessing=(1, 2))
preds = self.classifier.predict((x_test - 1.) / 2)
preds_preproc = classifier_preproc.predict(x_test)
self.assertEqual(np.sum(preds - preds_preproc), 0)
def test_fit_predict(self):
(x_train, y_train), (x_test, y_test) = self._mnist
# Fit classifier
classifier = MXClassifier((0, 1), self._model, (1, 28, 28), 10,
self._trainer)
classifier.fit(x_train, y_train, batch_size=128, nb_epochs=2)
preds = classifier.predict(x_test)
acc = np.sum(np.argmax(preds, axis=1) == np.argmax(
y_test, axis=1)) / len(y_test)
print("\nAccuracy: %.2f%%" % (acc * 100))
self.assertGreater(acc, 0.1)
def test_preprocessing(self):
# Create classifier
loss = gluon.loss.SoftmaxCrossEntropyLoss()
classifier_preproc = MXClassifier(
model=self.classifier._model,
loss=loss,
clip_values=(0, 1),
input_shape=(1, 28, 28),
nb_classes=10,
optimizer=self.classifier._optimizer,
preprocessing=(1, 2),
)
preds = self.classifier.predict((self.x_test_mnist - 1.0) / 2)
preds_preproc = classifier_preproc.predict(self.x_test_mnist)
self.assertEqual(np.sum(preds - preds_preproc), 0)
loss=loss,
input_shape=(28, 28, 1),
nb_classes=10,
optimizer=trainer,
ctx=None,
channel_index=1,
defences=None,
preprocessing=(0, 1))
# Step 4: 训练ART分类器
classifier.fit(x_train, y_train, batch_size=64, nb_epochs=3)
# Step 5: 在良性的测试实例上评价ART分类器
predictions = classifier.predict(x_test)
accuracy = np.sum(
np.argmax(predictions, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)
print('Accuracy on benign test examples: {}%'.format(accuracy * 100))
# Step 6: 生成对抗性测试示例
attack = FastGradientMethod(classifier=classifier, eps=0.2)
x_test_adv = attack.generate(x=x_test)
# Step 7: 通过对抗性测试实例对ART分类器进行评价
predictions = classifier.predict(x_test_adv)
accuracy = np.sum(
np.argmax(predictions, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)
print('Accuracy on adversarial test examples: {}%'.format(accuracy * 100))