machine-learning - `THIndexTensor_(size)(target, 0) == batch_size' 失败。在 d :\projects\pytorch\torch\lib\thnn\generic/ClassNLLCriterion. c:54

Question

我正在尝试在狗品种数据集上训练我的神经网络。前馈后，在损失计算期间它会抛出此错误:

RuntimeError: Assertion `THIndexTensor_(size)(target, 0) == batch_size' failed.  at d:\projects\pytorch\torch\lib\thnn\generic/ClassNLLCriterion.c:54 

代码:

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


for epoch in range(10):  # loop over the dataset multiple times
        running_loss = 0.0
        print(len(trainloader))
        for i, data in enumerate(trainloader, 0):
            # get the inputs
            inputs, labels  = data

            # wrap them in Variable
            inputs, labels = Variable(inputs).float(), Variable(labels).float().type(torch.LongTensor)


            # zero the parameter gradients
            optimizer.zero_grad()

            # forward + backward + optimize
            outputs = net(inputs)

            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()
            # print statistics
            running_loss += loss.data[0]
            if i % 2000 == 1999:    # print every 2000 mini-batches
                print('[%d, %5d] loss: %.3f' %
                      (epoch + 1, i + 1, running_loss / 2000))
                running_loss = 0.0

print('Finished Training')

此行生成错误:

loss = criterion(outputs, labels)

什么问题？

Answer 1

我认为问题是您缺少张量标签上的批处理维度。该错误表明第 0 维的大小不等于批量大小。

尝试更改此设置:

loss = criterion(outputs, labels.unsqueeze(0))

请注意，outputs 张量应比对应于每个标签分数的 labels 张量多一维，并且 labels 应只需包含正确标签的索引。