def train(args, model, trainloader, optimizer, epoch):
model.train()
for batch_idx, (data, target)in enumerate(trainloader):
data = data.cuda()
target = target.cuda()
optimizer.zero_grad()
#epsilon = np.random.uniform(0.01,0.04),
epsilon = 4.0/255
perturb_steps = 4
# print(epsilon[0])
# print(type(epsilon[0]))
perturb_steps = np.random.randint(1,10)
# calculate robust loss
loss = mart_loss(model=model,
x_natural=data,
y=target,
optimizer=optimizer,
step_size=args.step_size,
epsilon= epsilon,
perturb_steps= perturb_steps,
beta=args.beta)
loss.backward()
optimizer.step()
# print progress
if batch_idx % args.log_interval == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(trainloader.dataset),
100. * batch_idx / len(trainloader), loss.item()))
def train(args, model, device, train_loader, optimizer, epoch):
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
# calculate robust loss
loss = mart_loss(model=model,
x_natural=data,
y=target,
optimizer=optimizer,
step_size=args.step_size,
epsilon=args.epsilon,
perturb_steps=args.num_steps,
beta=args.beta)
loss.backward()
optimizer.step()
# print progress
if batch_idx % args.log_interval == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.item()))