I’ve been working through a PyTorch tutorial here: https://jovian.ml/aakashns/fdaae0bf32cf4917a931ac415a5c31b0 1 and have been trying to implement the neural network from the first chapter of http://neuralnetworksanddeeplearning.com/. One of the changes to make to the tutorial’s neural network is changing the loss function from cross entropy to MSE. After initialising the network, the tutorial runs the following
for images, labels in train_loader:
    outputs = model(images)

    print(outputs.shape,labels.shape)
    loss = F.cross_entropy(outputs, labels)
    
    print('Loss:', loss.item())
    break

print('outputs.shape : ', outputs.shape)
print('Sample outputs :\n', outputs[:2].data)

which prints
torch.Size([128, 10]) torch.Size([128])
Loss: 2.3188605308532715
outputs.shape :  torch.Size([128, 10])
Sample outputs: ...

However when running essentially the same code after changing the loss function to MSELoss:
for images, labels in train_loader:
    outputs = model(images)

    print(outputs.shape, labels.shape)

    loss_fn = nn.MSELoss()
    loss = loss_fn(outputs, labels) # Calculate loss
    
    print('Loss:', loss.item())
    break

print('outputs.shape : ', outputs.shape)
print('Sample outputs :\n', outputs[:2].data)

I get the following output (note a different batch size is used) and then warning
torch.Size([10, 10]) torch.Size([10])
Loss: 42.0984992980957
...
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/loss.py:432: UserWarning: Using a target size (torch.Size([10])) that is different to the input size (torch.Size([10, 10])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.

Why is this warning appearing for one loss function and not the other?
Exra: in addition, why doesn’t writing loss = nn.MSELoss(outputs, labels) rather than the loss_fn workaround I’ve been using work?