def setUpClass(cls):
master_seed(seed=1234, set_mxnet=True)
super().setUpClass()
cls.x_train_mnist = np.swapaxes(cls.x_train_mnist, 1, 3)
cls.x_test_mnist = np.swapaxes(cls.x_test_mnist, 1, 3)
# Create a simple CNN - this one comes from the Gluon tutorial
net = nn.Sequential()
with net.name_scope():
net.add(
nn.Conv2D(channels=6, kernel_size=5, activation="relu"),
nn.MaxPool2D(pool_size=2, strides=2),
nn.Conv2D(channels=16, kernel_size=3, activation="relu"),
nn.MaxPool2D(pool_size=2, strides=2),
nn.Flatten(),
nn.Dense(120, activation="relu"),
nn.Dense(84, activation="relu"),
nn.Dense(10),
)
net.initialize(init=init.Xavier())
# Create optimizer
loss = gluon.loss.SoftmaxCrossEntropyLoss()
trainer = gluon.Trainer(net.collect_params(), "sgd", {"learning_rate": 0.1})
# Fit classifier
classifier = MXClassifier(
model=net, loss=loss, clip_values=(0, 1), input_shape=(1, 28, 28), nb_classes=10, optimizer=trainer
)
classifier.fit(cls.x_train_mnist, cls.y_train_mnist, batch_size=128, nb_epochs=2)
cls.classifier = classifier
cls.x_train_mnist = np.swapaxes(cls.x_train_mnist, 1, 3)
cls.x_test_mnist = np.swapaxes(cls.x_test_mnist, 1, 3)
def setUpClass(cls):
# Get MNIST
(x_train, y_train), (x_test, y_test), _, _ = load_mnist()
x_train, y_train = x_train[:NB_TRAIN], y_train[:NB_TRAIN]
x_test, y_test = x_test[:NB_TEST], y_test[:NB_TEST]
x_train = np.swapaxes(x_train, 1, 3)
x_test = np.swapaxes(x_test, 1, 3)
cls.mnist = (x_train, y_train), (x_test, y_test)
# Create a simple CNN - this one comes from the Gluon tutorial
net = nn.Sequential()
with net.name_scope():
net.add(nn.Conv2D(channels=6, kernel_size=5, activation='relu'),
nn.MaxPool2D(pool_size=2, strides=2),
nn.Conv2D(channels=16, kernel_size=3, activation='relu'),
nn.MaxPool2D(pool_size=2, strides=2), nn.Flatten(),
nn.Dense(120, activation="relu"),
nn.Dense(84, activation="relu"), nn.Dense(10))
net.initialize(init=init.Xavier())
# Create optimizer
loss = gluon.loss.SoftmaxCrossEntropyLoss()
trainer = gluon.Trainer(net.collect_params(), 'sgd',
{'learning_rate': 0.1})
# Fit classifier
classifier = MXClassifier(model=net,
loss=loss,
clip_values=(0, 1),
input_shape=(1, 28, 28),
nb_classes=10,
optimizer=trainer)
classifier.fit(x_train, y_train, batch_size=128, nb_epochs=2)
cls.classifier = classifier
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)
# Step 3: 创建ART分类器
classifier = MXClassifier(model=model,
clip_values=(min_pixel_value, max_pixel_value),
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)