• Kajika@lemmy.mlOP
    0·
    9 months ago

    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.

      • Kajika@lemmy.mlOP
        0·
        9 months ago

        That 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 while MyStruct input {}; will (that was the fix). Long story.

        • TheFadingOne@feddit.de
          0·
          9 months ago

          If you use the GNU libc the feenableexcept function, which you can use to enable certain floating point exceptions, could be useful to catch unexpected/unwanted NaNs