# Флукс

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Flux is a library for machine learning, implemented in Julia.

At the core of it, Flux simply lets you run your normal Julia code on a dataflow backend like TensorFlow.

```julia
@net f(x) = x .* x
f([1,2,3]) == [1,4,9]
f_tensorflow = tf(f)
f_tensorflow([1,2,3]) == [1.0, 4.0, 9.0]
```

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.

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
Chain(
  Input(784),
  Affine(128), relu,
  Affine( 64), relu,
  Affine( 10), softmax)
```

Define a custom recurrent layer:

```julia
@net type Recurrent
  Wxy; Wyy; by
  y
  function (x)
    y = tanh( x * Wxy .+ y{-1} * Wyy .+ by )
  end
end
```