fix doctest

FluxML · Dec 13, 2024 · 89fc89f · 89fc89f
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diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
@@ -75,4 +75,4 @@ jobs:
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
           DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }}
-          DATADEPS_ALWAYS_ACCEPT: true
+
diff --git a/docs/make.jl b/docs/make.jl
@@ -2,6 +2,7 @@ using Documenter, Flux, NNlib, Functors, MLUtils, BSON, Optimisers,
       OneHotArrays, Zygote, ChainRulesCore, Plots, MLDatasets, Statistics, 
       DataFrames, JLD2, MLDataDevices
 
+ENV["DATADEPS_ALWAYS_ACCEPT"] = true
 
 DocMeta.setdocmeta!(Flux, :DocTestSetup, :(using Flux); recursive = true)
 

diff --git a/docs/src/guide/models/basics.md b/docs/src/guide/models/basics.md
@@ -185,7 +185,7 @@ These matching nested structures are at the core of how Flux works.
 <h3><img src="../../../assets/zygote-crop.png" width="40px"/>&nbsp;<a href="https://github.com/FluxML/Zygote.jl">Zygote.jl</a></h3>
 ```
 
-Flux's [`gradient`](@ref) function by default calls a companion packages called [Zygote](https://github.com/FluxML/Zygote.jl).
+Flux's [`gradient`](@ref Flux.gradient) function by default calls a companion packages called [Zygote](https://github.com/FluxML/Zygote.jl).
 Zygote performs source-to-source automatic differentiation, meaning that `gradient(f, x)`
 hooks into Julia's compiler to find out what operations `f` contains, and transforms this
 to produce code for computing `∂f/∂x`.
@@ -372,7 +372,7 @@ How does this `model3` differ from the `model1` we had before?
   Its contents is stored in a tuple, thus `model3.layers[1].weight` is an array.
 * Flux's layer [`Dense`](@ref Flux.Dense) has only minor differences from our `struct Layer`:
   - Like `struct Poly3{T}` above, it has type parameters for its fields -- the compiler does not know exactly what type `layer3s.W` will be, which costs speed.
-  - Its initialisation uses not `randn` (normal distribution) but [`glorot_uniform`](@ref) by default.
+  - Its initialisation uses not `randn` (normal distribution) but [`glorot_uniform`](@ref  Flux.glorot_uniform) by default.
   - It reshapes some inputs (to allow several batch dimensions), and produces more friendly errors on wrong-size input.
   - And it has some performance tricks: making sure element types match, and re-using some memory.
 * The function [`σ`](@ref NNlib.sigmoid) is calculated in a slightly better way,

diff --git a/docs/src/reference/models/layers.md b/docs/src/reference/models/layers.md
@@ -104,6 +104,7 @@ PairwiseFusion
 Much like the core layers above, but can be used to process sequence data (as well as other kinds of structured data).
 
 ```@docs
+Recurrence
 RNNCell
 RNN
 LSTMCell

diff --git a/src/layers/recurrent.jl b/src/layers/recurrent.jl
@@ -12,7 +12,7 @@ function scan(cell, x, state)
 end
 
 """
-    Recurrent(cell)
+    Recurrence(cell)
 
 Create a recurrent layer that processes entire sequences out
 of a recurrent `cell`, such as an [`RNNCell`](@ref), [`LSTMCell`](@ref), or [`GRUCell`](@ref),
@@ -52,7 +52,7 @@ stack(out, dims = 2)
 # Examples
 
 ```jldoctest
-julia> rnn = Recurrent(RNNCell(2 => 3))
+julia> rnn = Recurrence(RNNCell(2 => 3))
 
 julia> x = rand(Float32, 2, 3, 4); # in x len x batch_size