From d13e52abdf57b69ef7b713d3348d72aa181bc13c Mon Sep 17 00:00:00 2001
From: Michael Abbott <32575566+mcabbott@users.noreply.github.com>
Date: Wed, 12 Oct 2022 18:24:06 -0400
Subject: [PATCH] fix doctests

---
 src/Optimisers.jl | 10 +++++-----
 src/adjust.jl     |  6 +++---
 2 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/src/Optimisers.jl b/src/Optimisers.jl
index 73c56305..8e8cb19f 100644
--- a/src/Optimisers.jl
+++ b/src/Optimisers.jl
@@ -77,7 +77,7 @@ or [`update!`](@ref).
 julia> m = (x = rand(3), y = (true, false), z = tanh);
 
 julia> Optimisers.setup(Momentum(), m)  # same field names as m
-(x = Leaf(Momentum{Float32}(0.01, 0.9), [0.0, 0.0, 0.0]), y = (nothing, nothing), z = nothing)
+(x = Leaf(Momentum{Float32}(0.01, 0.9), [0.0, 0.0, 0.0]), y = ((), ()), z = ())
 ```
 
 The recursion into structures uses Functors.jl, and any new `struct`s containing parameters
@@ -90,7 +90,7 @@ julia> struct Layer; mat; fun; end
 julia> model = (lay = Layer([1 2; 3 4f0], sin), vec = [5, 6f0]);
 
 julia> Optimisers.setup(Momentum(), model)  # new struct is by default ignored
-(lay = nothing, vec = Leaf(Momentum{Float32}(0.01, 0.9), Float32[0.0, 0.0]))
+(lay = (), vec = Leaf(Momentum{Float32}(0.01, 0.9), Float32[0.0, 0.0]))
 
 julia> destructure(model)
 (Float32[5.0, 6.0], Restructure(NamedTuple, ..., 2))
@@ -98,7 +98,7 @@ julia> destructure(model)
 julia> using Functors; @functor Layer  # annotate this type as containing parameters
 
 julia> Optimisers.setup(Momentum(), model)
-(lay = (mat = Leaf(Momentum{Float32}(0.01, 0.9), Float32[0.0 0.0; 0.0 0.0]), fun = nothing), vec = Leaf(Momentum{Float32}(0.01, 0.9), Float32[0.0, 0.0]))
+(lay = (mat = Leaf(Momentum{Float32}(0.01, 0.9), Float32[0.0 0.0; 0.0 0.0]), fun = ()), vec = Leaf(Momentum{Float32}(0.01, 0.9), Float32[0.0, 0.0]))
 
 julia> destructure(model)
 (Float32[1.0, 3.0, 2.0, 4.0, 5.0, 6.0], Restructure(NamedTuple, ..., 6))
@@ -120,12 +120,12 @@ See also [`update!`](@ref), which will be faster for models of ordinary `Array`s
 julia> m = (x = Float32[1,2,3], y = tanh);
 
 julia> t = Optimisers.setup(Descent(0.1f0), m)
-(x = Leaf(Descent{Float32}(0.1), nothing), y = nothing)
+(x = Leaf(Descent{Float32}(0.1), nothing), y = ())
 
 julia> g = (x = [1,1,1], y = nothing);  # fake gradient
 
 julia> Optimisers.update(t, m, g)
-((x = Leaf(Descent{Float32}(0.1), nothing), y = nothing), (x = Float32[0.9, 1.9, 2.9], y = tanh))
+((x = Leaf(Descent{Float32}(0.1), nothing), y = ()), (x = Float32[0.9, 1.9, 2.9], y = tanh))
 ```
 """
 update
diff --git a/src/adjust.jl b/src/adjust.jl
index d6f3647d..78b3d452 100644
--- a/src/adjust.jl
+++ b/src/adjust.jl
@@ -13,15 +13,15 @@ To change just the learning rate, provide a number `η::Real`.
 julia> m = (vec = rand(Float32, 2), fun = sin);
 
 julia> st = Optimisers.setup(Nesterov(), m)  # stored momentum is initialised to zero
-(vec = Leaf(Nesterov{Float32}(0.001, 0.9), Float32[0.0, 0.0]), fun = nothing)
+(vec = Leaf(Nesterov{Float32}(0.001, 0.9), Float32[0.0, 0.0]), fun = ())
 
 julia> st, m = Optimisers.update(st, m, (vec = [16, 88], fun = nothing));  # with fake gradient
 
 julia> st
-(vec = Leaf(Nesterov{Float32}(0.001, 0.9), Float32[-0.016, -0.088]), fun = nothing)
+(vec = Leaf(Nesterov{Float32}(0.001, 0.9), Float32[-0.016, -0.088]), fun = ())
 
 julia> st = Optimisers.adjust(st, 0.123)  # change learning rate, stored momentum untouched
-(vec = Leaf(Nesterov{Float32}(0.123, 0.9), Float32[-0.016, -0.088]), fun = nothing)
+(vec = Leaf(Nesterov{Float32}(0.123, 0.9), Float32[-0.016, -0.088]), fun = ())
 ```
 
 To change other parameters, `adjust` also accepts keyword arguments matching the field