Merge pull request #381 from piever/pv/docs

fix julia 1 changes in tutorial

docs/src/models/basics.md

@@ -172,7 +172,7 @@ using Flux
 
 layers = [Dense(10, 5, σ), Dense(5, 2), softmax]
 
-model(x) = foldl((x, m) -> m(x), x, layers)
+model(x) = foldl((x, m) -> m(x), layers, init = x)
 
 model(rand(10)) # => 2-element vector
 ```

docs/src/models/regularisation.md

@@ -1,7 +1,7 @@
 # Regularisation
 
 Applying regularisation to model parameters is straightforward. We just need to
-apply an appropriate regulariser, such as `vecnorm`, to each model parameter and
+apply an appropriate regulariser, such as `norm`, to each model parameter and
 add the result to the overall loss.
 
 For example, say we have a simple regression.
@@ -15,12 +15,12 @@ loss(x, y) = crossentropy(softmax(m(x)), y)
 We can regularise this by taking the (L2) norm of the parameters, `m.W` and `m.b`.
 
 ```julia
-penalty() = vecnorm(m.W) + vecnorm(m.b)
+penalty() = norm(m.W) + norm(m.b)
 loss(x, y) = crossentropy(softmax(m(x)), y) + penalty()
 ```
 
 When working with layers, Flux provides the `params` function to grab all
-parameters at once. We can easily penalise everything with `sum(vecnorm, params)`.
+parameters at once. We can easily penalise everything with `sum(norm, params)`.
 
 ```julia
 julia> params(m)
@@ -28,7 +28,7 @@ julia> params(m)
  param([0.355408 0.533092; … 0.430459 0.171498])
  param([0.0, 0.0, 0.0, 0.0, 0.0])
 
-julia> sum(vecnorm, params(m))
+julia> sum(norm, params(m))
 26.01749952921026 (tracked)
 ```
 
@@ -40,7 +40,7 @@ m = Chain(
   Dense(128, 32, relu),
   Dense(32, 10), softmax)
 
-loss(x, y) = crossentropy(m(x), y) + sum(vecnorm, params(m))
+loss(x, y) = crossentropy(m(x), y) + sum(norm, params(m))
 
 loss(rand(28^2), rand(10))
 ```
@@ -57,6 +57,6 @@ julia> activations(c, rand(10))
  param([0.0330606, -0.456104])
  param([0.61991, 0.38009])
 
-julia> sum(vecnorm, ans)
+julia> sum(norm, ans)
 2.639678767773633 (tracked)
 ```