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https://github.com/chris-santiago/autoencoders
Experimenting with various autoencoder architectures.
https://github.com/chris-santiago/autoencoders
autoencoder denoising-autoencoders hydra pytorch pytorch-lightning self-supervised-learning siamese-network simsiam-pytorch taskfile
Last synced: 23 days ago
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Experimenting with various autoencoder architectures.
- Host: GitHub
- URL: https://github.com/chris-santiago/autoencoders
- Owner: chris-santiago
- License: mit
- Created: 2023-08-26T05:50:14.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2023-11-03T16:08:17.000Z (about 1 year ago)
- Last Synced: 2024-12-07T19:36:09.158Z (about 1 month ago)
- Topics: autoencoder, denoising-autoencoders, hydra, pytorch, pytorch-lightning, self-supervised-learning, siamese-network, simsiam-pytorch, taskfile
- Language: Jupyter Notebook
- Homepage:
- Size: 3.49 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE.txt
Awesome Lists containing this project
README
# 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:
```bash
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](https://hydra.cc/docs/intro/) for managing configuration CLI arguments. See `autoencoders/conf` for full configuration details.
#### Task
This project uses [Task](https://taskfile.dev/) as a task runner. Though the underlying Python
commands can be executed without it, we recommend [installing Task](https://taskfile.dev/installation/)
for ease of use. Details located in `Taskfile.yml`.
#### Current commands
```bash
> 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.*
```bash
task train -- model=sidae2 data=simsiam callbacks=siam
```
#### Weights and Biases
This project is set up to log experiment results with [Weights and Biases](https://wandb.ai/). It
expects an API key within a `.env` file in the root directory:
```toml
WANDB_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/](https://chris-santiago.github.io/autoencoders/)~~