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 hope it was garlic NaN at least.
I guess you can always just add an
assert not data.isna().any()in strategic locations
this is just like in regular math too. not being a number is just so fun that nobody wants to go back to being a number once they get a taste of it
Thanks. This is great
Fucking over-dramatic divisions by 0, sigh.
Also applies to nulls in SQL queries.
It's not fun tracing where nulls are coming from when dealing with a 1500 line data warehouse pipeline query that aggregates 20 different tables.
"Bounds checking, mobof--ker! Do you speak it?"
NaN is such a fun floating point virus. Some really wonky gameplay after we hit NaN in a few spots.
Nanananana! Batman!
As I was coding in C++ my own Engine with OpenGL. I forgot something to do. Maybe forgot to assign a pointer or forgot to pass a variable. At the end I had copied a NaN value to a vertieces of my Model as the Model should be a wrapper for Data I wanted to read and visualize.
Printing the entire Model into the terminal confused me why everything is NaN suddenly when it started nicely.
Consider IEEE754 arithmetic as monadic, simple!
This gave me some real Agent Smith vibes
