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Flux.jl/v0.2.1/models/templates.html

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build based on 7a85eff
2017-05-04 16:25:27 +00:00
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Model Templates · Flux
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Model Templates
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Models in templates
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<h1>
<a class="nav-anchor" id="Model-Templates-1" href="#Model-Templates-1">
Model Templates
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<p>
We mentioned that we could factor out the repetition of defining affine layers with something like:
</p>
<pre><code class="language-julia">function create_affine(in, out)
W = param(randn(out,in))
b = param(randn(out))
@net x -&gt; W * x + b
end</code></pre>
<p>
<code>@net type</code>
syntax provides a shortcut for this:
</p>
<pre><code class="language-julia">@net type MyAffine
W
b
x -&gt; x * W + b
end
# Convenience constructor
MyAffine(in::Integer, out::Integer) =
MyAffine(randn(out, in), randn(out))
model = Chain(MyAffine(5, 5), MyAffine(5, 5))
model(x1) # [-1.54458,0.492025,0.88687,1.93834,-4.70062]</code></pre>
<p>
This is almost exactly how
<code>Affine</code>
is defined in Flux itself. Using
<code>@net type</code>
gives us some extra conveniences:
</p>
<ul>
<li>
<p>
It creates default constructor
<code>MyAffine(::AbstractArray, ::AbstractArray)</code>
which initialises
<code>param</code>
s for us;
</p>
</li>
<li>
<p>
It subtypes
<code>Flux.Model</code>
to explicitly mark this as a model;
</p>
</li>
<li>
<p>
We can easily define custom constructors or instantiate
<code>Affine</code>
with arbitrary weights of our choosing;
</p>
</li>
<li>
<p>
We can dispatch on the
<code>Affine</code>
type, for example to override how it gets converted to MXNet, or to hook into shape inference.
</p>
</li>
</ul>
<h2>
<a class="nav-anchor" id="Models-in-templates-1" href="#Models-in-templates-1">
Models in templates
</a>
</h2>
<p>
<code>@net</code>
models can contain sub-models as well as just array parameters:
</p>
<pre><code class="language-julia">@net type TLP
first
second
function (x)
l1 = σ(first(x))
l2 = softmax(second(l1))
end
end</code></pre>
<p>
Clearly, the
<code>first</code>
and
<code>second</code>
parameters are not arrays here, but should be models themselves, and produce a result when called with an input array
<code>x</code>
. The
<code>Affine</code>
layer fits the bill, so we can instantiate
<code>TLP</code>
with two of them:
</p>
<pre><code class="language-julia">model = TLP(Affine(10, 20),
Affine(20, 15))
x1 = rand(20)
model(x1) # [0.057852,0.0409741,0.0609625,0.0575354 ...</code></pre>
<p>
You may recognise this as being equivalent to
</p>
<pre><code class="language-julia">Chain(
Affine(10, 20), σ
Affine(20, 15), softmax)</code></pre>
<h2>
<a class="nav-anchor" id="Supported-syntax-1" href="#Supported-syntax-1">
Supported syntax
</a>
</h2>
<p>
The syntax used to define a forward pass like
<code>x -&gt; x*W + b</code>
behaves exactly like Julia code for the most part. However, it&#39;s important to remember that it&#39;s defining a dataflow graph, not a general Julia expression. In practice this means that anything side-effectful, or things like control flow and
<code>println</code>
s, won&#39;t work as expected. In future we&#39;ll continue to expand support for Julia syntax and features.
</p>
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