我正在使用PyTorch进行一个视频动画项目。我的数据集包含3904x60 mfcc音频特征（输入）和相应的3904x3视频特征（输出）。目标是训练一个神经网络模型，使得给定一个未知的音频特征，模型将其映射到其相应的视频特征中。换句话说，神经网络执行60到3的特征映射。我已经按照本教程构建了神经网络：

class ConvNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.layer1 = nn.Sequential(
            nn.Conv1d(1, 32, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool1d(kernel_size=2, stride=2))
        self.layer2 = nn.Sequential(
            nn.Conv1d(32, 64, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool1d(kernel_size=2, stride=2))
        self.drop_out = nn.Dropout()
        self.fc1 = nn.Linear(15 * 64, 1000)
        self.fc2 = nn.Linear(1000, 3)

    def forward(self, x):
        out = self.layer1(x)
        out = self.layer2(out)
        out = out.reshape(out.size(0), -1)
        out = self.drop_out(out)
        out = self.fc1(out)
        out = self.fc2(out)
        return out

我的训练代码如下所示：

model = ConvNet()

criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)


for epoch in range(num_epochs):
    for i, (a, v) in enumerate(train_loader):
        # Run the forward pass
        a = a.float()
        v = v.long()
        outputs = model(a.view(a.size(0),1,a.size(1)))
        loss = criterion(outputs, v)
        loss_list.append(loss.item())

        # Backprop and perform Adam optimisation
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        # Track the accuracy
        total = labels.size(0)
        _, predicted = torch.max(outputs.data, 1)
        correct = (predicted == labels).sum().item()
        acc_list.append(correct / total)

        if (i + 1) % 100 == 0:
            print('Epoch[{}/{}],Step[{}/{}],Loss{:.4f},Accuracy{:.2f}%'
              .format(epoch + 1, num_epochs, i + 1, total_step, loss.item(),
                      (correct / total) * 100))

但在训练中收到错误：

---

/Users/soumith/miniconda2/conda-bld/pytorch_1532623076075/work/aten/src/THNN/generic/ClassNLLCriterion.c： 21不支持多目标

我将批量大小定义为4，因此迭代中的每个a和v应该分别是4 x 60张量和4 x 3张量。我如何解决这个问题？