https://github.com/darsnack/flux.jl
Relax! Flux is the ML library that doesn't make you tensor
Science Score: 41.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
○codemeta.json file
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○.zenodo.json file
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✓DOI references
Found 3 DOI reference(s) in README -
✓Academic publication links
Links to: joss.theoj.org -
○Academic email domains
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (12.3%) to scientific vocabulary
Repository
Relax! Flux is the ML library that doesn't make you tensor
Basic Info
- Host: GitHub
- Owner: darsnack
- License: other
- Language: Julia
- Default Branch: master
- Homepage: https://fluxml.ai/
- Size: 9.82 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 4
- Releases: 0
Metadata Files
README.md
[![][action-img]][action-url] [![][codecov-img]][codecov-url] [](https://github.com/SciML/ColPrac)
Flux is an elegant approach to machine learning. It's a 100% pure-Julia stack, and provides lightweight abstractions on top of Julia's native GPU and AD support. Flux makes the easy things easy while remaining fully hackable.
Works best with Julia 1.9 or later. Here's a very short example to try it out: ```julia using Flux, Plots data = [([x], 2x-x^3) for x in -2:0.1f0:2]
model = Chain(Dense(1 => 23, tanh), Dense(23 => 1, bias=false), only)
optim = Flux.setup(Adam(), model) for epoch in 1:1000 Flux.train!((m,x,y) -> (m(x) - y)^2, model, data, optim) end
plot(x -> 2x-x^3, -2, 2, legend=false) scatter!(x -> model([x]), -2:0.1f0:2) ```
The quickstart page has a longer example. See the documentation for details, or the model zoo for examples. Ask questions on the Julia discourse or slack.
If you use Flux in your research, please cite our work.
Owner
- Name: Kyle Daruwalla
- Login: darsnack
- Kind: user
- Location: Cold Spring Harbor Lab, NY
- Website: darsnack.github.io
- Repositories: 67
- Profile: https://github.com/darsnack
NeuroAI scholar at CSHL
Citation (CITATION.bib)
@article{Flux.jl-2018,
author = {Michael Innes and
Elliot Saba and
Keno Fischer and
Dhairya Gandhi and
Marco Concetto Rudilosso and
Neethu Mariya Joy and
Tejan Karmali and
Avik Pal and
Viral Shah},
title = {Fashionable Modelling with Flux},
journal = {CoRR},
volume = {abs/1811.01457},
year = {2018},
url = {https://arxiv.org/abs/1811.01457},
archivePrefix = {arXiv},
eprint = {1811.01457},
timestamp = {Thu, 22 Nov 2018 17:58:30 +0100},
biburl = {https://dblp.org/rec/bib/journals/corr/abs-1811-01457},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{innes:2018,
author = {Mike Innes},
title = {Flux: Elegant Machine Learning with Julia},
journal = {Journal of Open Source Software},
year = {2018},
doi = {10.21105/joss.00602},
}