fix tests

NEWS.md

@@ -2,7 +2,7 @@
 
 ## v0.13.7
 * Added [`@autosize` macro](https://github.com/FluxML/Flux.jl/pull/2078)
-* New method of `train!` using Zygote's "explicit" mode, allows changing AD back-end.
+* New method of `train!` using Zygote's "explicit" mode. Part of a move away from "implicit" `Params`.
 
 ## v0.13.4
 * Added [`PairwiseFusion` layer](https://github.com/FluxML/Flux.jl/pull/1983)

docs/make.jl

@@ -1,10 +1,10 @@
-using Documenter, Flux, NNlib, Functors, MLUtils, BSON, Optimisers, OneHotArrays, Zygote, ChainRulesCore
+using Documenter, Flux, NNlib, Functors, MLUtils, BSON, Optimisers, OneHotArrays, Zygote, ChainRulesCore, Statistics
 
 
 DocMeta.setdocmeta!(Flux, :DocTestSetup, :(using Flux); recursive = true)
 
 makedocs(
-    modules = [Flux, NNlib, Functors, MLUtils, BSON, Optimisers, OneHotArrays, Zygote, ChainRulesCore, Base],
+    modules = [Flux, NNlib, Functors, MLUtils, BSON, Optimisers, OneHotArrays, Zygote, ChainRulesCore, Base, Statistics],
     doctest = false,
     sitename = "Flux",
     # strict = [:cross_references,],

docs/src/models/overview.md

@@ -17,7 +17,7 @@ This example will predict the output of the function `4x + 2`. Making such predi
 
 First, import `Flux` and define the function we want to simulate:
 
-```jldoctest overview
+```jldoctest overview setup = :(using Statistics)
 julia> using Flux
 
 julia> actual(x) = 4x + 2
@@ -77,13 +77,13 @@ julia> predict(x_train)
 In order to make better predictions, you'll need to provide a *loss function* to tell Flux how to objectively *evaluate* the quality of a prediction. Loss functions compute the cumulative distance between actual values and predictions. 
 
 ```jldoctest overview; filter = r"[+-]?([0-9]*[.])?[0-9]+(f[+-]*[0-9])?"
-julia> loss(model, x, y) = mean(abs2.(model(x) .- y));
+julia> loss(model, x, y) = Statistics.mean(abs2.(model(x) .- y));
 
 julia> loss(predict, x_train, y_train)
 122.64734f0
 ```
 
-More accurate predictions will yield a lower loss. You can write your own loss functions or rely on those already provided by Flux. This loss function is called [mean squared error](https://www.statisticshowto.com/probability-and-statistics/statistics-definitions/mean-squared-error/). Flux works by iteratively reducing the loss through *training*.
+More accurate predictions will yield a lower loss. You can write your own loss functions or rely on those already provided by Flux. This loss function is called [mean squared error](https://www.statisticshowto.com/probability-and-statistics/statistics-definitions/mean-squared-error/) (and built-in as [`mse`](@ref Flux.Losses.mse)). Flux works by iteratively reducing the loss through *training*.
 
 ## 3. Improve the Prediction