I’m trying to calculate MSELoss when mask is used. Suppose that I have tensor with batch_size of 2:
[2, 33, 1] as my target, and another input target with the same shape. Since sequence length might differ for each example, I have also a binary mask indicating the existence of each element in the input sequence. So here is what I’m doing:
mse_loss = nn.MSELoss(reduction='none') loss = mse_loss(input, target) loss = (loss * mask.float()).sum() # gives \sigma_euclidean over unmasked elements mse_loss_val = loss / loss.numel() # now doing backpropagation mse_loss_val.backward()
loss / loss.numel() a good practice? I’m skeptical, as I have to use
reduction='none' and when calculating final loss value, I think I should calculate the loss only considering those loss elements that are nonzero (i.e., unmasked), however, I’m taking the average over all tensor elements with
torch.numel(). I’m actually trying to take
1/n factor of MSELoss into account. Any thoughts?