fixup

Author: mcabbott

docs/make.jl

@@ -1,13 +1,13 @@
-using Documenter, Flux, NNlib, Functors, MLUtils, BSON, Optimisers, OneHotArrays
+using Documenter, Flux, NNlib, Functors, MLUtils, BSON, Optimisers, OneHotArrays, Zygote, ChainRulesCore
 
 
 DocMeta.setdocmeta!(Flux, :DocTestSetup, :(using Flux); recursive = true)
 
 makedocs(
-    modules = [Flux, NNlib, Functors, MLUtils, BSON, Optimisers, OneHotArrays],
+    modules = [Flux, NNlib, Functors, MLUtils, BSON, Optimisers, OneHotArrays, Zygote, ChainRulesCore, Base],
     doctest = false,
     sitename = "Flux",
-    strict = [:cross_references,],
+    # strict = [:cross_references,],
     pages = [
         "Home" => "index.md",
         "Building Models" => [

docs/src/data/onehot.md

@@ -51,6 +51,8 @@ julia> onecold(ans, [:a, :b, :c])
 
 Note that these operations returned `OneHotVector` and `OneHotMatrix` rather than `Array`s. `OneHotVector`s behave like normal vectors but avoid any unnecessary cost compared to using an integer index directly. For example, multiplying a matrix with a one-hot vector simply slices out the relevant row of the matrix under the hood.
 
+### Function listing
+
 ```@docs
 OneHotArrays.onehot
 OneHotArrays.onecold

docs/src/models/losses.md

@@ -3,7 +3,7 @@
 Flux provides a large number of common loss functions used for training machine learning models.
 They are grouped together in the `Flux.Losses` module.
 
-Loss functions for supervised learning typically expect as inputs a target `y`, and a prediction `ŷ`.
+Loss functions for supervised learning typically expect as inputs a target `y`, and a prediction `ŷ` from your model.
 In Flux's convention, the order of the arguments is the following
 
 ```julia
@@ -21,7 +21,7 @@ loss(ŷ, y, agg=x->mean(w .* x))    # weighted mean
 loss(ŷ, y, agg=identity)           # no aggregation.
 ```
 
-## Losses Reference
+### Function listing
 
 ```@docs
 Flux.Losses.mae

docs/src/outputsize.md

@@ -1,4 +1,4 @@
-## Model Building
+# Size Propagation
 
 Flux provides some utility functions to help you generate models in an automated fashion.
 

docs/src/utilities.md

@@ -27,7 +27,7 @@ julia> Dense(4 => 5, tanh; init=Flux.randn32(MersenneTwister(1)))
 Dense(4 => 5, tanh)  # 25 parameters
 ```
 
-## Initialisation Functions
+## Initialisation functions
 
 ```@docs
 Flux.glorot_uniform
@@ -52,7 +52,7 @@ Flux.default_rng_value
 Flux.nfan
 ```
 
-## Changing the type of model parameters
+## Changing the type of all parameters
 
 The default `eltype` for models is `Float32` since models are often trained/run on GPUs.
 The `eltype` of model `m` can be changed to `Float64` by `f64(m)`: