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[Discussion][D] Gradient norm tracking

Are there any best practices on how one should track gradient norms during training? Surprisingly, I haven’t been able to find much reliable information on it, except the classical Glorot’s paper.

My current approach is to track 2-norm of weights raw gradients. However, I don’t have any practical intuition on which values should make me worried. Tracking the actual weight updates (e.g adjusted by Adam) makes make much more sense, but I haven’t seen anyone doing so.

A few words why am I concerned: I’m working on some exotic NN architecture for 3D, where different architecture choices implicate gradient behavior drastically, up to blow up.

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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.