I have an auto-encoder trained with BCEWithLogitsLoss(). Say the output dimension is 10, and I feed a data batch of size 20 into the network during testing. Say, y_hat is my output vector (i.e., reconstruction of the input of dimension 10), and say y is my actual input data:
criterion = nn.BCEWithLogitsLoss()
e = criterion(y_hat, data)
Unfortunately, e has is just a scalar! What I need is individual error vectors. In particular, I want e to be NOT aggregated, and have the dimensionality of (batchsize, data_dimension)= (20,10). Then I can average my ‘e’ across the second dimension and end up with an average error vector of size 20!
This way for 20 input data, I will have 20 individual error values and I can compute my performance (e.g., AUC). Any ideas how I can do this?
Thanks a lot
If it was just BCE() error function, I could have just written the math and computed it manually, but BCEWithLogitsLoss() is tricky to code from scratch (I think!)