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Latest commit

* use NNlib.dropout, deprecate Flux.dropout

* improve Dropout's docstring

* make Dropout(0) === identity, cannot mutate

* NNlibCUDA = 0.2.5

* NNlibCUDA = 0.2.6

* simplify default_rng etc

* Revert "simplify default_rng etc"

This reverts commit 0e396a6.

* un-revert the removal of the active=true method

* avoid a branch

* Update src/layers/normalise.jl

Co-authored-by: Carlo Lucibello <>

* Apply suggestions from code review

Co-authored-by: Kyle Daruwalla <>


Co-authored-by: Carlo Lucibello <>
Co-authored-by: Kyle Daruwalla <>

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DOI Flux Downloads
ColPrac: Contributor's Guide on Collaborative Practices for Community Packages

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 1.8 or later. Here's a very short example to try it out:

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)

plot(x -> 2x-x^3, -2, 2, legend=false)
scatter!(-2:0.1:2, [model([x]) for x in -2:0.1:2])

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.