I am working on a video animation project using PyTorch. My dataset contains 3904x60 mfcc audio features(input) and corresponding 3904x3 video features(output). The goal is to train a neural network model such that given an unknown audio feature, the model maps it into its corresponding video feature. In other words, the neural network performs a 60 to 3 feature mapping. I have already built the neural network following this tutorial 21 and my CNN architecture looks like:
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

and my training code looks like:
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))

But as I ran the code I received an error:
—> 13         loss = criterion(outputs, v)
RuntimeError: multi-target not supported at /Users/soumith/miniconda2/conda-bld/pytorch_1532623076075/work/aten/src/THNN/generic/ClassNLLCriterion.c:21
I defined the batch size to be 4 so each a and v in the iteration should be a 4 by 60 tensor and a 4 by 3 tensor, respectively. One solution 47 suggests that I should reshape the tensor into a categorical vector. However, this solution doesn’t actually apply to this case since I want the 3 output feature integrated as a whole instead of treating each feature as an independent label. How do I solve this problem?