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Phase diagram of early training dynamics in deep neural networks: effect of the learning rate, depth, and width

23 Feb 2023NeurIPS 2023 11arXiv:2302.12250archive 2025-07-28

We systematically analyze optimization dynamics in deep neural networks (DNNs) trained with stochastic gradient descent (SGD) and study the effect of learning rate η, depth d, and width w of the neural network. By analyzing the maximum eigenvalue λᴴₜ of the Hessian of the loss, which is a measure of sharpness of the loss landscape, we find that the dynamics can show four distinct regimes: (i) an early time transient regime, (ii) an intermediate saturation regime, (iii) a progressive sharpening regime, and (iv) a late time ``edge of stability" regime. The early and intermediate regimes (i) and (ii) exhibit a rich phase diagram depending on η≡c / λ₀ᴴ, d, and w. We identify several critical values of c, which separate qualitatively distinct phenomena in the early time dynamics of training loss and sharpness. Notably, we discover the opening up of a ``sharpness reduction" phase, where sharpness decreases at early times, as d and 1/w are increased.

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