https://github.com/FluxML/Flux.jl

Relax! Flux is the ML library that doesn't make you tensor
https://github.com/FluxML/Flux.jl

data-science deep-learning flux machine-learning neural-networks the-human-brain

Last synced: 12 days ago
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Relax! Flux is the ML library that doesn't make you tensor

• Host: GitHub
• URL: https://github.com/FluxML/Flux.jl
• Owner: FluxML
• License: other
• Created: 2016-04-01T21:11:05.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2024-06-07T11:51:59.000Z (5 months ago)
• Last Synced: 2024-06-11T19:42:30.111Z (5 months ago)
• Topics: data-science, deep-learning, flux, machine-learning, neural-networks, the-human-brain
• Language: Julia
• Homepage: https://fluxml.ai/
• Size: 10.6 MB
• Stars: 4,410
• Watchers: 93
• Forks: 596
• Open Issues: 300
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • Funding: .github/FUNDING.yml
  • License: LICENSE.md
  • Citation: CITATION.bib

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README

        


    

    





[![](https://img.shields.io/badge/Documentation-stable-blue.svg)](https://fluxml.github.io/Flux.jl/stable/) [![DOI](https://joss.theoj.org/papers/10.21105/joss.00602/status.svg)](https://doi.org/10.21105/joss.00602) [![Flux Downloads](https://img.shields.io/badge/dynamic/json?url=http%3A%2F%2Fjuliapkgstats.com%2Fapi%2Fv1%2Fmonthly_downloads%2FFlux&query=total_requests&suffix=%2Fmonth&label=Downloads)](http://juliapkgstats.com/pkg/Flux)




[![][action-img]][action-url] [![][codecov-img]][codecov-url] [![ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor's%20Guide-blueviolet)](https://github.com/SciML/ColPrac)



[action-img]: https://github.com/FluxML/Flux.jl/workflows/CI/badge.svg

[action-url]: https://github.com/FluxML/Flux.jl/actions

[codecov-img]: https://codecov.io/gh/FluxML/Flux.jl/branch/master/graph/badge.svg

[codecov-url]: https://codecov.io/gh/FluxML/Flux.jl

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](https://julialang.org/downloads/) 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](https://fluxml.ai/Flux.jl/stable/guide/models/quickstart/) has a longer example. See the [documentation](https://fluxml.github.io/Flux.jl/) for details, or the [model zoo](https://github.com/FluxML/model-zoo/) for examples. Ask questions on the [Julia discourse](https://discourse.julialang.org/) or [slack](https://discourse.julialang.org/t/announcing-a-julia-slack/4866).

If you use Flux in your research, please [cite](CITATION.bib) our work.