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autodocs 2017-05-03 19:17:54 +00:00
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latest/apis/backends.html
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</em>
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<p>
Flux is a machine learning library, 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.
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.
</p>
<p>
This gives you great flexibility. 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&#39;s utilities (like optimisers) with a completely different backend (like
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&#39;s utilities (like optimisers) with a completely different backend (like
<a href="https://github.com/denizyuret/Knet.jl">
Knet
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<p>
We noted above that a model is a function with trainable parameters. Normal functions like
<code>exp</code>
are actually models too, that happen to have 0 parameters. Flux doesn&#39;t care, and anywhere that you use one, you can use the other. For example,
are actually models too – they just happen to have 0 parameters. Flux doesn&#39;t care, and anywhere that you use one, you can use the other. For example,
<code>Chain</code>
will happily work with regular functions:
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@ -13,7 +13,7 @@ var documenterSearchIndex = {"docs": [
"page": "Home",
"title": "Flux",
"category": "section",
"text": "... Initialising Photon Beams ...Flux is a machine learning library, 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.This gives you great flexibility. 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 Knet) or mix and match approaches.Note that Flux is in alpha. Many things work but the API is still in a state of... well, it might change.Note: If you're using Julia v0.5 please see this version of the docs instead."
"text": "... Initialising Photon Beams ...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.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 Knet) or mix and match approaches.Note that Flux is in alpha. Many things work but the API is still in a state of... well, it might change.Note: If you're using Julia v0.5 please see this version of the docs instead."
},
{
@ -93,7 +93,7 @@ var documenterSearchIndex = {"docs": [
"page": "Model Building Basics",
"title": "Dressed like a model",
"category": "section",
"text": "We noted above that a model is a function with trainable parameters. Normal functions like exp are actually models too, that happen to have 0 parameters. Flux doesn't care, and anywhere that you use one, you can use the other. For example, Chain will happily work with regular functions:foo = Chain(exp, sum, log)\nfoo([1,2,3]) == 3.408 == log(sum(exp([1,2,3])))"
"text": "We noted above that a model is a function with trainable parameters. Normal functions like exp are actually models too – they just happen to have 0 parameters. Flux doesn't care, and anywhere that you use one, you can use the other. For example, Chain will happily work with regular functions:foo = Chain(exp, sum, log)\nfoo([1,2,3]) == 3.408 == log(sum(exp([1,2,3])))"
},
{