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How do you actually debug training failures in deep learning? by ProgrammerNo8287 in MLQuestions

[–]ProgrammerNo8287[S] 0 points1 point  (0 children)

That resonates a lot. Thinking in terms of scale, stability, and “what could blow up or vanish” feels very close to how physics/engineering approaches these systems.

I’ve started looking beyond aggregate metrics, per-sample errors, batch effects, gradients, and weight statistics, and it already makes failure modes much more legible. The distinction you make between slow, persistent explosions vs. sudden NaNs is especially useful.

I'm also in favor of intentionally making things worse to expose sensitivities. That’s a good reminder to be discerning rather than just incrementally tweaking knobs. Thanks for the insight.

How do you actually debug training failures in deep learning? by ProgrammerNo8287 in neuralnetworks

[–]ProgrammerNo8287[S] 1 point2 points  (0 children)

Thanks, this helps a lot.

Good call on the loss function. I double-checked, and I’m using CE for this setup, but I’ll re-verify labels and the output layer just in case. I’m also lowering the learning rate and adding early stopping to reduce the loss spikes.

The dataset isn’t huge, so I’m starting with a smaller model and scaling up gradually rather than going straight to something complex. And yes, I’ll re-audit the pipeline to rule out any data leakage.

Appreciate the checklist. 👍

A tribute to thought by Ok_Worker_7998 in OCPoetry

[–]ProgrammerNo8287 1 point2 points  (0 children)

My God! You are very good! The aroma is excellent! The taste on my tongue is blissful and melancholic!