_args = [net, images, labels, NUM_STEPS, LR, EPS, args.random_step]
new_ims = attack(*_args).detach()
pred_probs = net(new_ims)
loss = loss_fn(pred_probs, labels)
pred_classes = pred_probs.argmax(1) # Shape: (BATCH_SIZE)
val_num_correct += (pred_classes == labels).float().sum()
val_num_total += labels.shape[0]
print("###### EPOCH {0} COMPLETE ######".format(ep))
print("Adversarial Validation Accuracy: %f" % (val_num_correct/val_num_total).cpu().item() )
print("############################")
if val_num_correct/val_num_total > best_adv_acc:
ch.save(net.state_dict(), "results/%s_%s_%s_best" % (args.dataset, MODE, args.save_str))
print("Saved model...")
best_adv_acc = val_num_correct/val_num_total
scheduler.step()
net.eval()
with ch.no_grad():
num_correct = 0
num_total = 0
for (images, labels) in testloader:
images, labels = images.cuda(), labels.cuda()
pred_probs = net(images) # Shape: (BATCH_SIZE x 10)
pred_classes = pred_probs.argmax(1) # Shape: (BATCH_SIZE)
num_correct += (pred_classes == labels).float().sum()
num_total += labels.shape[0]
print("###### EPOCH {0} COMPLETE ######".format(ep))
new_ims = encode(images.clone(), only_decode=args.attack_latent)
pred_probs = net(new_ims)
loss = loss_fn(pred_probs, labels)
pred_classes = pred_probs.argmax(1) # Shape: (BATCH_SIZE)
val_num_correct += (pred_classes == labels).float().sum()
val_num_total += labels.shape[0]
print("###### EPOCH {0} COMPLETE ######".format(ep))
print("Adversarial Validation Accuracy: %f" %
(val_num_correct / val_num_total).cpu().item())
print("############################")
if val_num_correct / val_num_total > best_adv_acc:
ch.save(ae.state_dict(),
"results/retrained_enc_%s_%s" % (MODE, args.save_str))
ch.save(net.state_dict(),
"results/trained_net_%s_%s" % (MODE, args.save_str))
print("Saved model...")
best_adv_acc = val_num_correct / val_num_total
ae.eval()
net.eval()
with ch.no_grad():
num_correct = 0
num_total = 0
for (images, labels) in testloader:
images, labels = images.cuda(), labels.cuda()
new_ims = encode(images)
pred_probs = net(new_ims) # Shape: (BATCH_SIZE x 10)
pred_classes = pred_probs.argmax(1) # Shape: (BATCH_SIZE)
num_correct += (pred_classes == labels).float().sum()