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Flux.jl/v0.1.0/apis/backends.html

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2017-03-01 12:37:00 +00:00
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Backends · Flux
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Backends
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Basic Usage
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<pre><code class="language-julia">model = Chain(Affine(10, 20), σ, Affine(20, 15), softmax)
xs = rand(10)</code></pre>
<p>
Currently, Flux&#39;s pure-Julia backend has no optimisations. This means that calling
</p>
<pre><code class="language-julia">model(rand(10)) #&gt; [0.0650, 0.0655, ...]</code></pre>
<p>
directly won&#39;t have great performance. In order to run a computationally intensive training process, we rely on a backend like MXNet or TensorFlow.
</p>
<p>
This is easy to do. Just call either
<code>mxnet</code>
or
<code>tf</code>
on a model to convert it to a model of that kind:
</p>
<pre><code class="language-julia">mxmodel = mxnet(model, (10, 1))
mxmodel(xs) #&gt; [0.0650, 0.0655, ...]
# or
tfmodel = tf(model)
tfmodel(xs) #&gt; [0.0650, 0.0655, ...]</code></pre>
<p>
These new models look and feel exactly like every other model in Flux, including returning the same result when you call them, and can be trained as usual using
<code>Flux.train!()</code>
. The difference is that the computation is being carried out by a backend, which will usually give a large speedup.
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Native Integration
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<p>
Flux aims to provide high-level APIs that work well across backends, but in some cases you may want to take advantage of features specific to a given backend. In these cases it&#39;s easy to &quot;drop down&quot; and use the backend&#39;s API directly, where appropriate. For example:
</p>
<pre><code class="language-julia">using MXNet
Flux.loadmx()
mxmodel = mx.FeedForward(model)</code></pre>
<p>
This returns a standard
<code>mx.FeedForward</code>
instance, just like you might have created using MXNet&#39;s usual API. You can then use this with MXNet&#39;s data provider implementation, custom optimisers, or distributed training processes.
</p>
<p>
Same goes for TensorFlow, where it&#39;s easy to create a
<code>Tensor</code>
object:
</p>
<pre><code class="language-julia">using TensorFlow
Flux.loadtf()
x = placeholder(Float32)
y = Tensor(model, x)</code></pre>
<p>
This makes makes it easy to take advantage of Flux&#39;s model description and debugging tools while also getting the benefit of the work put into these backends. You can check out how this looks with the integration examples
<a href="https://github.com/MikeInnes/Flux.jl/tree/master/examples">
here
</a>
.
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