def main():
print("Loading data")
train, valid, test, vocab = prepare_data() # Load dataset
model = make_model(len(vocab)) # Design a victim model
print("Training")
trained_model = train_model(model, train, valid,
vocab) # Train the victim model
print(
"Generating adversarial samples (this step will take dozens of minutes)"
)
clsf = OpenAttack.PytorchClassifier(trained_model,
word2id=vocab) # Wrap the victim model
adversarial_samples = attack(
clsf,
train) # Conduct adversarial attacks and generate adversarial examples
print("Adversarially training classifier")
finetune_model = train_model(
trained_model, train + adversarial_samples, valid,
vocab) # Retrain the classifier with additional adversarial examples
print("Testing enhanced model (this step will take dozens of minutes)")
attack(
clsf, train
) # Re-attack the victim model to measure the effect of adversarial training
def train_model(model, data_train, data_valid, vocab, num_epoch=10):
mx_acc = None
mx_model = None
for i in range(num_epoch):
loss = train_epoch(model, data_train, vocab)
clsf = OpenAttack.PytorchClassifier(model, word2id=vocab)
accuracy = len(data_valid.eval(clsf).correct()) / len(data_valid)
print("Epoch %d: loss: %lf, accuracy %lf" % (i, loss, accuracy))
if mx_acc is None or mx_acc < accuracy:
mx_model = model.state_dict()
model.load_state_dict(mx_model)
return model
def main():
print("Loading data")
train, valid, test, vocab = prepare_data() # Load dataset
model = make_model(len(vocab)) # Design a victim model
print("Training")
trained_model = train_model(model, train, valid,
vocab) # Train the victim model
print(
"Generating adversarial samples (this step will take dozens of minutes)"
)
clsf = OpenAttack.PytorchClassifier(trained_model,
word2id=vocab) # Wrap the victim model
adversarial_samples = attack(
clsf,
train) # Conduct adversarial attacks and generate adversarial examples
print("Adversarially training classifier")
print(train.features)
print(adversarial_samples.features)
new_dataset = {"x": [], "y": [], "tokens": []}
for it in train:
new_dataset["x"].append(it["x"])
new_dataset["y"].append(it["y"])
new_dataset["tokens"].append(it["tokens"])
for it in adversarial_samples:
new_dataset["x"].append(it["x"])
new_dataset["y"].append(it["y"])
new_dataset["tokens"].append(it["tokens"])
finetune_model = train_model(
trained_model, datasets.Dataset.from_dict(new_dataset), valid,
vocab) # Retrain the classifier with additional adversarial examples
print("Testing enhanced model (this step will take dozens of minutes)")
attack(
clsf, train
) # Re-attack the victim model to measure the effect of adversarial training