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.
 
-    m = Chain(x -> x^2, x -> x+1)
-    m(5) == 26
+```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))
+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.
@@ -45,13 +47,15 @@ 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> d = Dense(5, 2)
-    Dense(5, 2)
+```julia
+julia> d = Dense(5, 2)
+Dense(5, 2)
 
-    julia> d(rand(5))
-    Tracked 2-element Array{Float64,1}:
-      0.00257447
-      -0.00449443
+julia> d(rand(5))
+Tracked 2-element Array{Float64,1}:
+  0.00257447
+  -0.00449443
+```
 """
 struct Dense{F,S,T}
   σ::F

src/layers/recurrent.jl

@@ -13,13 +13,15 @@ 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.
 
-    accum(h, x) = (h+x, x)
-    rnn = Flux.Recur(accum, 0)
-    rnn(2) # 2
-    rnn(3) # 3
-    rnn.state # 5
-    rnn.(1:10) # apply to a sequence
-    rnn.state # 60
+```julia
+accum(h, x) = (h+x, x)
+rnn = Flux.Recur(accum, 0)
+rnn(2) # 2
+rnn(3) # 3
+rnn.state # 5
+rnn.(1:10) # apply to a sequence
+rnn.state # 60
+```
 """
 mutable struct Recur{T}
   cell::T