mnist_hogwild.py 文件源码

def train_epoch(epoch, args, model, data_loader, optimizer):
    model.train()
    pid = os.getpid()
    for batch_idx, (data, target) in enumerate(data_loader):
        data, target = Variable(data), Variable(target)
        optimizer.zero_grad()
        output = model(data)
        loss = F.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % args.log_interval == 0:
            print('{}\tTrain Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                pid, epoch, batch_idx * len(data), len(data_loader.dataset),
                            100. * batch_idx / len(data_loader), loss.data[0]))