chris-santiago/autoencoders

Experimenting with various autoencoder architectures.

Experimenting with AutoEncoders

This repo (roughly) experiments with various autoencoder architectures to tease out their ability to learn meaningful representations of data in an unsupervised setting.

• Each model is trained (unsupervised) on the 60k MNIST training set.
• Models are evaluated via transfer learning on the 10k MNIST test set.
• Each model's respective encodings of varying subsets of the 10k MNIST test set are input to train a linear classifier:
  • Each linear classifier is trained on increasing split sizes ([10, 100, 1000, 2000, 4000, 8000]) and evaluated on remaining subset of the 10K MNIST test set.
  • See autoencoders/compare.py for details.
• Classifier performance is measured by mulit-class accuracy and (ROC)AUC.

Notes

• Most models follow the architectures from their respective papers (see autoencoders/models/).
• Hyperparameter optimization is not performed on the encoder models nor the linear classifiers.
• Model specification/configuration located in the outputs/ directory.

Results

Install

Create a virtual environment with Python >= 3.10 and install from git:

pip install git+https://github.com/chris-santiago/autoencoders.git
conda env create -f environment.yml
cd autoencoders
pip install -e .

Use

Prerequisites

Hydra

This project uses Hydra for managing configuration CLI arguments. See autoencoders/conf for full configuration details.

Task

This project uses Task as a task runner. Though the underlying Python commands can be executed without it, we recommend installing Task for ease of use. Details located in Taskfile.yml.

Current commands

> task -l
task: Available tasks for this project:
* check-config:          Check Hydra configuration
* compare:               Compare encoders using linear baselines
* eval-downstream:       Evaluate encoders using linear baselines
* plot-downstream:       Plot encoder performance on downstream tasks
* train:                 Train a model
* wandb:                 Login to Weights & Biases

Example: Train a SiDAE model

The -- forwards CLI arguments to Hydra.

task train -- model=sidae2 data=simsiam callbacks=siam

Weights and Biases

This project is set up to log experiment results with Weights and Biases. It expects an API key within a .env file in the root directory:

WANDB_KEY=<my-super-secret-key>

Users can configure different logger(s) within the conf/trainer/default.yaml file.

Documentation

Documentation hosted on Github Pages: https://chris-santiago.github.io/autoencoders/