hello
I’m doing regression and my network output is a 14 values vector
by using MSE loss function the training converge but I’d like to use different criteria for the different values
as a starter, I tried something like this
lass MyLoss(nn.Module):
    def __init__(self):
        super().__init__()
        self.mse = nn.MSELoss()

    def forward(self, yhat, y):
        loss = 2*self.mse(yhat[:2], y[:2]) + 5*self.mse(yhat[2:5], y[2:5]) + self.mse(yhat[5:], y[5:])
        return loss

however this results in (I set batch size to 2 for testing purposes)
UserWarning: Using a target size (torch.Size([0, 14])) that is different to the input size (torch.Size([2, 14])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
I reckon I’m not slicing the tensor correctly, or is this completely the wrong way to go about this?
The alternative I have in mind is to have multiple output layers with a single loss function each; is that a better idea?