Affine
is defined in Flux itself! There's no difference between "library-level" and "user-level" models, so making your code reusable doesn't involve a lot of extra complexity. Moreover, much more complex models than
Affine
- are equally simple to define, and equally close to the mathematical notation; read on to find out how.
+ are equally simple to define.
+
+
+@net
+ models can contain sub-models as well as just array parameters:
+
@net type TLP
+ first
+ second
+ function (x)
+ l1 = σ(first(x))
+ l2 = softmax(second(l1))
+ end
+end+Just as above, this is roughly equivalent to writing: +
+type TLP
+ first
+ second
+end
+
+function (self::TLP)(x)
+ l1 = σ(self.first)
+ l2 = softmax(self.second(l1))
+end
+Clearly, the
+first
+ and
+second
+ parameters are not arrays here, but should be models themselves, and produce a result when called with an input array
+x
+. The
+Affine
+ layer fits the bill so we can instantiate
+TLP
+ with two of them:
+
model = TLP(Affine(10, 20),
+ Affine(20, 15))
+x1 = rand(20)
+model(x1) # [0.057852,0.0409741,0.0609625,0.0575354 ...+You may recognise this as being equivalent to +
+Chain(
+ Affine(10, 20), σ
+ Affine(20, 15)), softmax
+given that it's just a sequence of calls. For simple networks
+Chain
+ is completely fine, although the
+@net
+ version is more powerful as we can (for example) reuse the output
+l1
+ more than once.