Hello Harvey,
I don’t use the topic functions at the same time. When the model is a regression, there is one neuron in the output layer and the loss function is nn.MSE or nn.L1 (MAE) or the custom AdjMSE.
When the model classifies, there are as many neurons in the output layer as there are classes, and the loss function is nn.CrossEntropy…
The function is generic and allows both uses (regression and classification), hence the confusion.
My issue is when I do a regression, I don’t get the same backward propagation if I use nn.MSELoss or if I use the custom AdjMSE while there are assumed to be perfectly identical…