https://github.com/pseudomanifold/bestiary-autoencoders
A Bestiary of Autoencoders
https://github.com/pseudomanifold/bestiary-autoencoders
autoencoder pytorch-lightning variational-autoencoder
Last synced: 5 months ago
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A Bestiary of Autoencoders
- Host: GitHub
- URL: https://github.com/pseudomanifold/bestiary-autoencoders
- Owner: Pseudomanifold
- Created: 2021-03-04T09:37:25.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2021-03-09T11:09:42.000Z (over 4 years ago)
- Last Synced: 2024-12-26T11:09:19.421Z (10 months ago)
- Topics: autoencoder, pytorch-lightning, variational-autoencoder
- Language: Python
- Homepage:
- Size: 11.7 KB
- Stars: 3
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# A Bestiary of Autoencoders
This repository illustrates some simple autoencoder architectures. You
might want to [check out the slides](https://bastian.rieck.me/talks/A_Bestiary_of_Autoencoders.pdf).
To run the example, follow these steps:
1. Install [poetry](https://python-poetry.org), a framework for
simplifying the management of virtual environments.
2. Issue `poetry install` to set up and install all dependencies.
3. To run the model with default parameters, issue `poetry run python -m bestiary.mnist`.
This should result in the following output:
```
| Name | Type | Params
-----------------------------------
0 | encoder | Linear | 1.6 K
1 | decoder | Linear | 2.4 K
-----------------------------------
3.9 K Trainable params
0 Non-trainable params
3.9 K Total params
0.016 Total estimated model params size (MB)
Epoch 1: 62%|█████████████████████████████████████████████████████████████████▊ | 534/860 [00:06<00:04, 80.91it/s, loss=0.0557, v_num=127]
```
Afterwards, see the folder `output` for some latent space reconstructions. To see available options, run `poetry run python -m bestiary.mnist --help`.
The current options of the code are as follows:
```
usage: mnist.py [-h] [-m {linear,vae}] [--max-epochs MAX_EPOCHS]
optional arguments:
-h, --help show this help message and exit
-m {linear,vae}, --model {linear,vae}
Model to use for training
--max-epochs MAX_EPOCHS
Maximum number of epochs to train
```
If you are interested in autoencoders, check out these awesome other
projects:
- [latent-lego](https://github.com/quadbiolab/latent-lego): a Python library for building autoencoders for single-cell genomics, developed by the Quantitative Developmental Biology Lab of D-BSSE.
- [topological-autoencoders](https://github.com/BorgwardtLab/topological-autoencoders): a new type of architecture for preserving topological properties in your data, developed by the Machine Learning and Computational Bioloy Lab of D-BSSE.