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Jamming in neural networks

Code used in https://arxiv.org/abs/1809.09349

Depends on https://github.com/mariogeiger/hessian

Fully-connected network trained with a quadratic hinge loss

The main file is train.py

Usage example:

python train.py --log_dir R40d40h5L --dataset random --dim 40 --width 40 --depth 5 --p 24000 --optimizer adam --init orth --n_steps_max 1M --learning_rate 0.1 --max_learning_rate 0.1

It will train a neural network of 5 hidden layers of 40 units on a random dataset in dimension 40. It will create a directory called R40d40h5L and save the logs and and many measures of the run.

The script has many parameters, see here.

Here is an example of code that load the results of a run

from functions import *  # functions.py contains useful functions

log_dir = "R40d40h5L"
runs = list(load_dir2(log_dir))
print("{} runs in this directory".format(len(runs))  # 1 if you ran the command above

run = runs[0]  # pick the first one
print("{depth} layers of {width} units trained on {p} points".format(**run['desc']))

dynamics = run['dynamics']  # list containing many measures during the training
print("The finall loss is {}".format(dynamics[-1]['train'][1]))

model_init, model_last, trainset, testset = load_run(runs[0])

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