272 lines
7.4 KiB
HTML
272 lines
7.4 KiB
HTML
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Home · Flux
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<h1>
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Flux
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Where do I start?
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Installation
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Building Models
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In Action
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Simple MNIST
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Char RNN
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Contributing & Help
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Internals
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<a class="edit-page" href="https://github.com/MikeInnes/Flux.jl/tree/7a85eff370b7c68d587b49699fa3f71e44993397/docs/src/index.md">
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Edit on GitHub
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<h1>
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<a class="nav-anchor" id="Flux-1" href="#Flux-1">
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Flux
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</a>
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</h1>
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<p>
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<em>
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... Initialising Photon Beams ...
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</em>
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</p>
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<p>
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Flux is a library for machine learning, implemented in Julia. In a nutshell, it simply lets you run normal Julia code on a backend like TensorFlow. It also provides many conveniences for doing deep learning.
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</p>
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<p>
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Flux is very flexible. You can use a convenient Keras-like API if you want something simple, but you can also drop down to straight mathematics, or build your own abstractions. You can even use Flux's utilities (like optimisers) with a completely different backend (like
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<a href="https://github.com/denizyuret/Knet.jl">
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Knet
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</a>
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) or mix and match approaches.
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</p>
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<p>
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Note that Flux is in alpha. Many things work but the API is still in a state of... well, it might change.
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</p>
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<p>
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<strong>
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Note:
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</strong>
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If you're using Julia v0.5 please see
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<a href="http://mikeinnes.github.io/Flux.jl/v0.1.1/">
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this version
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</a>
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of the docs instead.
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</p>
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<h2>
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<a class="nav-anchor" id="Where-do-I-start?-1" href="#Where-do-I-start?-1">
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Where do I start?
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</a>
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</h2>
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<p>
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<em>
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... Charging Ion Capacitors ...
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</em>
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</p>
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<p>
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The
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<a href="examples/logreg.html">
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examples
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</a>
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give a feel for high-level usage.
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</p>
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<p>
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If you want to know why Flux is unique, or just don't want to see
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<em>
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those digits
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</em>
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again, check out the
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<a href="models/basics.html">
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model building guide
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</a>
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instead.
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</p>
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<p>
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Flux is meant to be played with. These docs have lots of code snippets; try them out in
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<a href="http://junolab.org">
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Juno
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</a>
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!
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</p>
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<h2>
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<a class="nav-anchor" id="Installation-1" href="#Installation-1">
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Installation
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</a>
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</h2>
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<p>
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<em>
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... Inflating Graviton Zeppelins ...
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</em>
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</p>
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<pre><code class="language-julia">Pkg.update()
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Pkg.add("Flux.jl")</code></pre>
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<p>
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You'll also need a backend to run real training, if you don't have one already. Choose from
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<a href="https://github.com/dmlc/MXNet.jl">
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MXNet
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</a>
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or
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<a href="https://github.com/malmaud/TensorFlow.jl">
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TensorFlow
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</a>
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(MXNet is the recommended option if you're not sure):
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</p>
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<pre><code class="language-julia">Pkg.add("MXNet") # or "TensorFlow"
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Pkg.test("Flux") # Make sure everything installed properly</code></pre>
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<p>
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<strong>
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Note:
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</strong>
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TensorFlow integration may not work properly on Julia v0.6 yet.
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</p>
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Model Building Basics
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