如何在pytorch中运行一批？

Question

我是AI和python的新手，我尝试只运行一个批处理以达到过度拟合的目的。我发现了以下代码： iter(train_loader).next()

但我不确定在我的代码中在哪里实现它。即使我这样做了，我如何在每次迭代后检查以确保我在训练同一批人

train_loader = torch.utils.data.DataLoader(
    dataset_train,
    batch_size=48,
    shuffle=True,
    num_workers=2
)

net = nn.Sequential(
    nn.Flatten(),
    nn.Linear(128*128*3,10)
)


nepochs = 3
statsrec = np.zeros((3,nepochs))

loss_fn = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.001)


for epoch in range(nepochs):  # loop over the dataset multiple times

    running_loss = 0.0
    n = 0
    for i, data in enumerate(train_loader, 0):
        inputs, labels = data
        
         # Zero the parameter gradients
        optimizer.zero_grad() 

        # Forward, backward, and update parameters
        outputs = net(inputs)
        loss = loss_fn(outputs, labels)
        loss.backward()
        optimizer.step()
    
        # accumulate loss
        running_loss += loss.item()
        n += 1
    
    ltrn = running_loss/n
    ltst, atst = stats(train_loader, net)
    statsrec[:,epoch] = (ltrn, ltst, atst)
    print(f"epoch: {epoch} training loss: {ltrn: .3f}  test loss: {ltst: .3f} test accuracy: {atst: .1%}")

请给我一个提示

Answer 1

如果您希望在单个批次上进行培训，则删除数据加载器上的循环：

for i, data in enumerate(train_loader, 0):
    inputs, labels = data

只需在遍历历代之前获取train_loader迭代器的第一个元素：

inputs, labels = next(itr(train_loader))
for epoch in range(nepochs):
    optimizer.zero_grad() 
    outputs = net(inputs)
    loss = loss_fn(outputs, labels)
    loss.backward()
    optimizer.step()
    # ...