How to prevent Inf or NaN at low floating number precision


I am programming at Float32 to improve the speed. In my calculations, however, Inf or NaNs occur occasionally due to the use of exp.(x). For example x=90.30891f0; exp(x) will yield Inf32, even if x is fine.

I was wondering if there is a way to prevent this behaviour, without increase the floating number precision? In general, I have no idea where these values might occur.

Thank you very much!

I mean…

julia> prevfloat(typemax(Float32))

julia> exp(90.30891)

x is fine but exp(x) is simply outside of Float32's range, nothing Julia can do here sorry


Thank you. Maybe I’ll go back to Float64…
Just a bit curious why Pytorch use Float32 as default, since using exp at Float32 can easily cause an overflow.

:man_shrugging: speed

because ML typically don’t need Float64 as their “weights” and their data are “normalized”

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If you need exp(x) as an intermediate value but you’re going to mix it with another calculation, then there are ways to do the entire calculation in a more stable way.

An example exp(x)/exp(y) = exp(x-y) and similar things. This is pretty standard numerical methods stuff. If on the other hand you just need to output the value of exp(x) for a large x, then you’ll have to switch to a higher precision. For example exp(convert(Float64,x))

You can work with Float32 but convert it before applying exp or other functions that grow large.