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https://github.com/alecokas/flux-vae
Implementation of a Convolutional Variational Autoencoder in Flux.jl
https://github.com/alecokas/flux-vae
blog cvae fashion-mnist flux julia julialang machine-learning vae variational-autoencoder
Last synced: 2 months ago
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Implementation of a Convolutional Variational Autoencoder in Flux.jl
- Host: GitHub
- URL: https://github.com/alecokas/flux-vae
- Owner: alecokas
- License: mit
- Created: 2020-06-26T21:04:10.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2020-07-29T17:33:12.000Z (over 4 years ago)
- Last Synced: 2023-03-05T15:05:06.440Z (almost 2 years ago)
- Topics: blog, cvae, fashion-mnist, flux, julia, julialang, machine-learning, vae, variational-autoencoder
- Language: Julia
- Homepage: http://alecokas.github.io/julia/flux/vae/2020/07/22/convolutional-vae-in-flux.html
- Size: 85.9 KB
- Stars: 8
- Watchers: 1
- Forks: 5
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# flux-vae
This repository accompanies my blog post ["Convolutional VAE in Flux"](http://alecokas.github.io/julia/flux/vae/2020/07/22/convolutional-vae-in-flux.html) where we take a look at variational autoencoders and do a walk-through demo of a [Flux](https://fluxml.ai/) implementation of a convolutional VAE using the FashionMNIST benchmark dataset.
## Training
You can train the model directly from these scripts by running the following in your terminal:
```
julia --project=vaeenv conv-vae/main.jl
```
You should see a progress tracker and the training loss displayed in your terminal.
## Visualisation
Similarly, you can visualise reconstructed images from the test set by running:
```
julia --project=vaeenv conv-vae/visualise.jl
```
Some original test set samples (on the left) with the coresponding reconstructions (on the right):
## Dependencies
This code has been tested using Julia version 1.4.1. The package environment status is as follows:
```
[fbb218c0] BSON v0.2.6
[336ed68f] CSV v0.7.1
[3895d2a7] CUDAapi v4.0.0
[35d6a980] ColorSchemes v3.9.0
[3a865a2d] CuArrays v2.2.0
[a93c6f00] DataFrames v0.21.4
[31c24e10] Distributions v0.23.2
[ced4e74d] DistributionsAD v0.5.2
[587475ba] Flux v0.10.4
[6a3955dd] ImageFiltering v0.6.13
[82e4d734] ImageIO v0.2.0
[916415d5] Images v0.22.2
[c8e1da08] IterTools v1.3.0
[eb30cadb] MLDatasets v0.5.2
[442fdcdd] Measures v0.3.1
[a3a9e032] NIfTI v0.4.1
[d96e819e] Parameters v0.12.1
[91a5bcdd] Plots v1.3.5
[92933f4c] ProgressMeter v1.3.1
[e88e6eb3] Zygote v0.4.20
[9a3f8284] Random
[10745b16] Statistics
```
You can also have a look in the `vaeenv` directory where I have commited my `Project.toml` and `Manifest.toml` files for you to inspect the dependences.
To cite this work, please site the linked blog post:
@article{kastanos20fluxvae,
title = "Convolutional VAE in Flux",
author = "Alexandros Kastanos",
journal = "alecokas.github.io",
year = "2020",
url = "http://alecokas.github.io/julia/flux/vae/2020/07/22/convolutional-vae-in-flux.html"
}