# How can I derive lambda and alpha in SELU activation?

I came across SELU, and I was wondering how to derive $$alpha$$ and $$lambda$$ in the function, because I wasn't able to understand the math in the reseach article. I did, however, find this website that drew the following values from the paper: $$alpha = 1.673263...$$ $$lambda = 1.050701...$$ But, now I'm slightly confused because I thought self-normalizing meant the function would adjust its $$alpha$$ and $$lambda$$ values over time with the influx of training data. Or have I misread something? If I could provide a link to a video or a website or if I could manage an explanation of the functions which define the alpha and lambda values I'd really appreciate it.

I don't claim to understand the full proof provided in the paper. However, I do know that $$\alpha$$ and $$\lambda$$ are constants, not learned.