this post was submitted on 08 Feb 2024
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Took me 2 hours to find out why the final output of a neural network was a bunch of NaN. This is always very annoying but I can't really complain, it make sense. Just sucks.
I guess you can always just add an
assert not data.isna().any()in strategic locationsThat could be a nice way. Sadly it was in a C++ code base (using tensorflow). Therefore no such nice things (would be slow too). I skill-issued myself thinking a struct would be 0 -initialized but
MyStruct input;would not whileMyStruct input {};will (that was the fix). Long story.If you use the GNU libc the
feenableexceptfunction, which you can use to enable certain floating point exceptions, could be useful to catch unexpected/unwanted NaNs