45 lines
1.8 KiB
Markdown
45 lines
1.8 KiB
Markdown
# Флукс
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[](https://travis-ci.org/FluxML/Flux.jl) [](https://fluxml.github.io/Flux.jl/stable/) [](https://gitter.im/FluxML/Lobby) [Slack](https://discourse.julialang.org/t/announcing-a-julia-slack/4866)
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Flux is a library for machine learning, implemented in Julia.
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At the core of it, Flux simply lets you run your normal Julia code on a dataflow backend like TensorFlow.
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```julia
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@net f(x) = x .* x
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f([1,2,3]) == [1,4,9]
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f_tensorflow = tf(f)
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f_tensorflow([1,2,3]) == [1.0, 4.0, 9.0]
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```
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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.
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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.
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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.
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## Brief Examples
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Simple multi-layer-perceptron for MNIST, using the high-level API:
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```julia
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Chain(
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Input(784),
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Affine(128), relu,
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Affine( 64), relu,
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Affine( 10), softmax)
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```
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Define a custom recurrent layer:
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```julia
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@net type Recurrent
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Wxy; Wyy; by
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y
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function (x)
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y = tanh( x * Wxy .+ y{-1} * Wyy .+ by )
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end
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end
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```
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