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Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks

Description

Repository for the paper Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks. To appear in NeurIPS 2022.

Prerequisites

Structure

In this repository we provide the code and some guided examples to help the reader to reproduce the figures. The repository is structured as follows.

File Description
/sim sim.py is the simulation class, which imports cython code from simcy.pyx. setup.py is an auxiliar buinding file for cython
/ode ode.py is the ODE solver class, which imports cython code from odecy.pyx. setup.py is an auxiliar buinding file for cython

The notebooks are self-explanatory.

Building cython code

Both /sim and /ode use cython code. To build, run python setup.py build_ext --inplace on the respective folder. Then simply start a python session and do whether from sim import sim or from ode import ode and use the imported function as described in the how_to.ipynb notebooks.

Reference

  • Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks; R. Veiga, L. Stephan, B. Loureiro, F. Krzakala, L. Zdeborová; arXiv:2202.00293 [stat.ML]