The dynamics of representation learning in shallow, non-linear autoencoders

Last update: Jun 8, 2022

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The dynamics of representation learning in shallow, non-linear autoencoders

The package is written in python and uses the pytorch implementation to ML. Repository src contains the source files.

train_autoencoder.py
Trains (online and off-line) a shallow autoencoder both on a synthetic dataset and on benchmark datasets. Includes an implementation of the analytical equations allowing to track the dynamics of online training at all times. Included benchmark datasets : Cifar 10 (in gray scale) and FashionMNIST

Example of command to train online, integrating the analytical equations, on a synthetic dataset:
python3 train_autoencoder.py --D 500 --K 2 --dataset sinusoidal --analytical_updates 1

Example of command to train on finite dataset:
python3 train_autoencoder.py --dataset fmnist

truncated_vanilla_SGD.py
Trains an AE using different learning rules for reconstruction:

sanger's rule
vanilla SGD
truncated version of SGD introduced in the article Includes the implementation of the analytical equations tracking the dynamics of learning.

Example of command to train online, integrating the analytical equations, on a synthetic dataset:
python3 truncated_vanilla_SGD.py --K 5 --low_rank 5 --D 500 --analytical 1

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The dynamics of representation learning in shallow, non-linear autoencoders

Related tags

Overview

The dynamics of representation learning in shallow, non-linear autoencoders

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Owner

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