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ForwardDiff.gradient(x->log(1/max(1f-20,x[1])), zeros(Float32,1))) returns [NaN32] #287
Description
Hi and thanks for ForwardDiff!
I've ran into what seems like a bug:
import ForwardDiff
println("This should be 0: ",
ForwardDiff.gradient(x->log(1/max(1f-10,x[1])), zeros(Float32,1)))
println("This should be 0 too: ",
ForwardDiff.gradient(x->log(1/max(1f-20,x[1])), zeros(Float32,1)))
output is:
This should be 0: Float32[-0.0]
This should be 0 too: Float32[NaN32]
I guess this looks like some kind of overflow problem, but the number isn't that big, so I wouldn't expect overflow here.
ForwardDiff version is 0.7
julia version is 0.6.2 (2017-12-13 18:08 UTC) x86_64-linux-gnu.
HTH.
Activity
fredrikekre commentedon Jan 5, 2018
You can set
ForwardDiff.jl/src/prelude.jl
Line 1 in 49292ee
true, see #179 and linked issuesmicklat commentedon Jan 5, 2018
Thanks, that worked around it! I wonder how nans got into this in the first place.
micklat commentedon Jan 5, 2018
Oh I see, the drivative of 1/x overflows when x=1f-20.
jrevels commentedon Jan 18, 2018
Closing since this seems resolved, but feel to reopen if there's still an issue here!