def train()

in train.py [0:0]

• 29 lines of code
• 3 McCabe index (conditional complexity)

def train(epoch):
    print('\nEpoch: %d' % epoch)
    net.train()
    train_loss = 0
    reg_loss = 0
    correct = 0
    total = 0
    for batch_idx, (inputs, targets) in enumerate(trainloader):
        if use_cuda:
            inputs, targets = inputs.cuda(), targets.cuda()

        inputs, targets_a, targets_b, lam = mixup_data(inputs, targets,
                                                       args.alpha, use_cuda)
        inputs, targets_a, targets_b = map(Variable, (inputs,
                                                      targets_a, targets_b))
        outputs = net(inputs)
        loss = mixup_criterion(criterion, outputs, targets_a, targets_b, lam)
        train_loss += loss.data[0]
        _, predicted = torch.max(outputs.data, 1)
        total += targets.size(0)
        correct += (lam * predicted.eq(targets_a.data).cpu().sum().float()
                    + (1 - lam) * predicted.eq(targets_b.data).cpu().sum().float())

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        progress_bar(batch_idx, len(trainloader),
                     'Loss: %.3f | Reg: %.5f | Acc: %.3f%% (%d/%d)'
                     % (train_loss/(batch_idx+1), reg_loss/(batch_idx+1),
                        100.*correct/total, correct, total))
    return (train_loss/batch_idx, reg_loss/batch_idx, 100.*correct/total)