def _get_image_classifier_mx_instance(from_logits=True):
if from_logits is False:
# due to the fact that only 1 instance of get_image_classifier_mx_model can be created in one session
# this will be resolved once Mxnet allows for 2 models with identical weights to be created in 1 session
raise ARTTestFixtureNotImplemented(
"Currently only supporting Mxnet classifier with from_logit set to True",
get_image_classifier_mx_instance.__name__,
framework,
)
loss = mxnet.gluon.loss.SoftmaxCrossEntropyLoss(from_logits=from_logits)
trainer = mxnet.gluon.Trainer(model.collect_params(), "sgd", {"learning_rate": 0.1})
# Get classifier
mxc = MXClassifier(
model=model,
loss=loss,
input_shape=mnist_shape,
# input_shape=(28, 28, 1),
nb_classes=10,
optimizer=trainer,
ctx=None,
channels_first=True,
clip_values=(0, 1),
preprocessing_defences=None,
postprocessing_defences=None,
preprocessing=(0.0, 1.0),
)
return mxc
model.add(Flatten())
model.add(Dense(100, activation="relu"))
model.add(Dense(10))
model.initialize()
loss = mxnet.gluon.loss.SoftmaxCrossEntropyLoss()
trainer = mxnet.gluon.Trainer(model.collect_params(), "adam", {"learning_rate": 0.01})
# Step 3: Create the ART classifier
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,
channels_first=True,
preprocessing_defences=None,
preprocessing=(0, 1),
)
# Step 4: Train the ART classifier
classifier.fit(x_train, y_train, batch_size=64, nb_epochs=3)
# Step 5: Evaluate the ART classifier on benign test examples
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))