convoluted connor @notwa@witches.town

heh, taking out LayerNorms from my network results in accuracies of 0.20 ± 0.08.
and just realized that saying 20% ± 8% seems kinda misleading… like it's not 8% of 20%, it's percent as a unit! do i write 20±8% then? augh

try not to turn into a vampire werewolf skeleton this impending super duper moon

didn't burn it this time but still kinda underwhelming, although this stuff is a little old at this point

wish I still had the drive to make stupid things for dumb fun as a kid. totally pointless webpages and aimless game maps/mods, incomprehensible video/audio shitposts... eventually you make something pretty neat by fluke, you know?

gonna make some gween tea tonight

i should probably change the default matplotlib font

DET. i want to make this prettier sometime but it's not a priority. https://witches.town/media/ZMDw73xiH_u8sIjQ-CQ

current mood: ba!
https://www.youtube.com/watch?v=L0DAQoqVzAw

all my good posts are just random excerpts of random chats i have

read-write-execute: the life of an author

implementing it was kinda fun. MNIST comes in some arbitrary order, so i first arg-sorted samples by class, then interleaved them (0 to 9 repeating) by swapping axes 0 and 1. after that i can flatten, roll, and split the samples into training and validation sets for each fold.

however this only works when all the classes have an equal number of samples. also, my batch training function shuffles by default, so i omitted it from the procedure.

another one off my list of stuff i should've implemented ages ago: stratified k-folding. i think? i'll double-check my code tomorrow.

valid loss: 0.50343 ± 0.02206
valid accuracy: 89.794% ± 0.631%

sometimes i just like looking at plots

zoomed in. https://witches.town/media/WvShtebTD-yhUMCHCns

whee. https://witches.town/media/w_EsNJrMn6rxYu2jiiA