syntax highlighting

docs/src/models/layers.md

@@ -14,7 +14,7 @@ Much like the core layers above, but can be used to process sequence data (as we
 ```@docs
 RNN
 LSTM
-Recur
+Flux.Recur
 ```
 
 ## Activation Functions

src/layers/basic.jl

@@ -4,12 +4,14 @@
 Chain multiple layers / functions together, so that they are called in sequence
 on a given input.
 
+```julia
 m = Chain(x -> x^2, x -> x+1)
 m(5) == 26
 
 m = Chain(Dense(10, 5), Dense(5, 2))
 x = rand(10)
 m(x) == m[2](m[1](x))
+```
 
 `Chain` also supports indexing and slicing, e.g. `m[2]` or `m[1:end-1]`.
 `m[1:3](x)` will calculate the output of the first three layers.
@@ -45,6 +47,7 @@ Creates a traditional `Dense` layer with parameters `W` and `b`.
 The input `x` must be a vector of length `in`, or a batch of vectors represented
 as an `in × N` matrix. The out `y` will be a vector or batch of length `out`.
 
+```julia
 julia> d = Dense(5, 2)
 Dense(5, 2)
 
@@ -52,6 +55,7 @@ as an `in × N` matrix. The out `y` will be a vector or batch of length `out`.
 Tracked 2-element Array{Float64,1}:
   0.00257447
   -0.00449443
+```
 """
 struct Dense{F,S,T}
   σ::F

src/layers/recurrent.jl

@@ -13,6 +13,7 @@ in the background. `cell` should be a model of the form:
 
 For example, here's a recurrent network that keeps a running total of its inputs.
 
+```julia
 accum(h, x) = (h+x, x)
 rnn = Flux.Recur(accum, 0)
 rnn(2) # 2
@@ -20,6 +21,7 @@ For example, here's a recurrent network that keeps a running total of its inputs
 rnn.state # 5
 rnn.(1:10) # apply to a sequence
 rnn.state # 60
+```
 """
 mutable struct Recur{T}
   cell::T