https://github.com/martinuzzifrancesco/luxrecurrentlayers.jl
Novel recurrent layers for Lux.jl
https://github.com/martinuzzifrancesco/luxrecurrentlayers.jl
Last synced: 7 months ago
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Novel recurrent layers for Lux.jl
- Host: GitHub
- URL: https://github.com/martinuzzifrancesco/luxrecurrentlayers.jl
- Owner: MartinuzziFrancesco
- License: mit
- Created: 2025-02-18T12:40:09.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-02-18T12:53:03.000Z (8 months ago)
- Last Synced: 2025-02-18T13:37:28.797Z (8 months ago)
- Language: Julia
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
| **Documentation** | **Build Status** | **Julia** | **Testing** |
|:-----------------:|:----------------:|:---------:|:-----------:|
| [![docsstbl][docs-stbl]][docsstbl-url] [![docsdev][docs-dev]][docsdev-url] | [![CI][ci-img]][ci-url] | [![Julia][julia-img]][julia-url] [![Code Style: Blue][style-img]][style-url] | [![Aqua QA][aqua-img]][aqua-url] [![JET][jet-img]][jet-url] [![codecov][cc-img]][cc-url] |
[docs-stbl]: https://img.shields.io/badge/docs-stable-blue.svg
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[style-img]: https://img.shields.io/static/v1?label=code%20style&message=SciML&color=9558b2&labelColor=389826
[style-url]: https://github.com/SciML/SciMLStyle
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[jet-img]: https://img.shields.io/badge/%E2%9C%88%EF%B8%8F%20tested%20with%20-%20JET.jl%20-%20red
[jet-url]: https://github.com/aviatesk/JET.jl
LuxRecurrentLayers.jl
> [!WARNING]
> Currently heavy work in progress, please check out
> [RecurrentLayers.jl](https://github.com/MartinuzziFrancesco/RecurrentLayers.jl)
> for a more polished recurrent layers library.
LuxRecurrentLayers.jl extends [Lux.jl](https://github.com/LuxDL/Lux.jl)
recurrent layers offering by providing implementations of additional
recurrent layers not available in base deep learning libraries.
## Quick Example
```julia
using Lux, LuxRecurrentLayers, Random
# Seeding
rng = Random.default_rng()
Random.seed!(rng, 0)
# Define the recurrent model (a cell in this case)
rnn = AntisymmetricRNNCell(3=>5)
# Get parameters and states
ps, st = Lux.setup(rng, rnn)
# Random input
inp = rand(Float32, 3)
# Forward pass with random input
output, st = Lux.apply(rnn, inp, ps, st)
```
## See also
[RecurrentLayers.jl](https://github.com/MartinuzziFrancesco/RecurrentLayers.jl):
Equivalent library, providing recurrent layers for Flux.jl.
[ReservoirComputing.jl](https://github.com/SciML/ReservoirComputing.jl):
Reservoir computing utilities for scientific machine learning.
Essentially gradient free trained neural networks.