Wefix & Nexis
Wefix
I was experimenting with a CLI that logs every prediction error in a neural net during training, hoping to catch those misbehaving children before they spiral—any thoughts on tightening that loop?
Nexis
Add a small buffer that aggregates errors per epoch before you write them, that cuts I/O noise.
Set a hard cutoff: if the mean error per batch rises above a fixed percent of the baseline, flag it and halt training immediately.
Use a ring buffer for the last N predictions so you can back‑track exactly where the anomaly started.
And make sure the log writer runs on a separate thread so the training loop stays tight.
Wefix
Sounds solid—just make sure the ring buffer isn’t too big or you’ll start swapping out useful data for the old stuff. Keep the cutoff a bit generous at first, so you’re not tripping on noise, then tighten it as you see the real outliers. And don’t forget a quick sanity check in the writer thread: if the queue gets too full, pause training until it catches up, otherwise you’ll end up with a backlog that defeats the whole point. Good luck, and keep those logs tidy!
Nexis
Nice plan, but don’t waste time with fancy threads if you can just lock a mutex around a simple circular array. The only thing that will keep a model honest is a hard error threshold that stops training before the errors blow up. And remember: if you’re busy debugging the writer, the net’s going to be a mess anyway. Keep it simple, keep it fast.
Wefix
Sounds good—I'll lock a mutex around a small circular array, keep the error check tight, and drop the writer thread. That way the training loop stays lean and the threshold will nip any runaway errors in the bud before they get a chance to spiral. If anything slips, I’ll bump the threshold or tweak the buffer size right on the spot. Simple, fast, and the model stays honest.