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[![Build Status](https://travis-ci.org/FluxML/Flux.jl.svg?branch=master)](https://travis-ci.org/FluxML/Flux.jl) [![](https://img.shields.io/badge/docs-stable-blue.svg)](https://fluxml.github.io/Flux.jl/stable/) [![Join the chat at https://gitter.im/FluxML](https://badges.gitter.im/FluxML/Lobby.svg)](https://gitter.im/FluxML/Lobby) [Slack](https://discourse.julialang.org/t/announcing-a-julia-slack/4866) [![Build Status](https://travis-ci.org/FluxML/Flux.jl.svg?branch=master)](https://travis-ci.org/FluxML/Flux.jl) [![](https://img.shields.io/badge/docs-stable-blue.svg)](https://fluxml.github.io/Flux.jl/stable/) [![Join the chat at https://gitter.im/FluxML](https://badges.gitter.im/FluxML/Lobby.svg)](https://gitter.im/FluxML/Lobby) [Slack](https://discourse.julialang.org/t/announcing-a-julia-slack/4866)
Flux is a library for machine learning, implemented in Julia. Flux is a library for machine learning, implemented in Julia. Flux is high-level yet extremely lightweight, providing only simple abstractions on top of Julia's native GPU support and automatic differentiation.
At the core of it, Flux simply lets you run your normal Julia code on a dataflow backend like TensorFlow. Define a simple model using any Julia code:
```julia ```julia
@net f(x) = x .* x using Flux.Tracker
f([1,2,3]) == [1,4,9] x, y = rand(10), rand(5) # Dummy input / output
f_tensorflow = tf(f) # `track` defines parameters that we can train
f_tensorflow([1,2,3]) == [1.0, 4.0, 9.0] W, b = track(randn(5,10)), track(randn(5))
# Transform `x` and calculate the mean squared error
loss = Flux.mse(W*x .+ b, y)
# Calculate and store gradients of `track`ed parameters
back!(loss)
Tracker.grad(W) # Get the gradient of `W` wrt the loss
``` ```
After adding the `@net` annotation we can take advantage of various optimisations, parallelism, and access to GPUs that TensorFlow provides. Unlike a TensorFlow graph, `f` continues to behave like Julia code; you still get good stack traces, can step through in the debugger, etc. Define a larger model using high-level abstractions:
On top of this foundation we build a set of flexible machine learning abstractions and utilities that interoperate well with other approaches like [Knet](https://github.com/denizyuret/Knet.jl). This gives you great flexibility; you can go high level or stay mathematical, write custom GPU kernels, build your own abstractions, and mix and match approaches.
Check out the [docs](https://fluxml.github.io/Flux.jl/stable/) to get started. Flux is in alpha so **please open issues liberally**; we would love to help you get started.
## Brief Examples
Simple multi-layer-perceptron for MNIST, using the high-level API:
```julia ```julia
Chain( using Flux
Input(784),
Affine(128), relu, m = Chain(
Affine( 64), relu, Dense(10, 32, relu),
Affine( 10), softmax) Dense(32, 10), softmax)
m(rand(10))
``` ```
Define a custom recurrent layer: Mix and match the two:
```julia ```julia
@net type Recurrent using Flux.Tracker
Wxy; Wyy; by x, y = rand(10), rand(5)
y d = Dense(10, 5)
function (x) loss = Flux.mse(d(x), y)
y = tanh( x * Wxy .+ y{-1} * Wyy .+ by )
end
end
``` ```
See the [documentation](http://fluxml.github.io/Flux.jl/stable/) or the [model zoo](https://github.com/FluxML/model-zoo/) for more examples.