add all-keyword constructors

src/interface.jl

@@ -241,7 +241,8 @@ like this:
 struct Rule
   eta::Float64
   beta::Tuple{Float64, Float64}
-  Rule(eta = 0.1, beta = (0.7, 0.8)) = eta < 0 ? error() : new(eta, beta)
+  Rule(eta, beta = (0.7, 0.8)) = eta < 0 ? error() : new(eta, beta)
+  Rule(; eta = 0.1, beta = (0.7, 0.8)) = Rule(eta, beta)
 end
 ```
 Any field called `eta` is assumed to be a learning rate, and cannot be negative.
@@ -259,12 +260,17 @@ macro def(expr)
     lines[i] = :($name::$typeof($val))
   end
   rule = Meta.isexpr(expr.args[2], :<:) ? expr.args[2].args[1] : expr.args[2]
+  params = [Expr(:kw, nv...) for nv in zip(names,vals)]
   check = :eta in names ? :(eta < 0 && throw(DomainError(eta, "the learning rate cannot be negative"))) : nothing
-  inner = :(function $rule($([Expr(:kw, nv...) for nv in zip(names,vals)]...))
+  # Positional-argument method, has defaults for all but the first arg:
+  inner = :(function $rule($(names[1]), $(params[2:end]...))
     $check
     new($(names...))
   end)
-  push!(lines, inner)
+  # Keyword-argument method. (Made an inner constructor only to allow
+  # resulting structs to be @doc... cannot if macro returns a block.)
+  kwmethod = :($rule(; $(params...)) = $rule($(names...)))
+  push!(lines, inner, kwmethod)
   esc(expr)
 end
 

src/rules.jl

@@ -19,7 +19,7 @@ For each parameter `p` and its gradient `dp`, this runs `p -= η*dp`.
 struct Descent{T} <: AbstractRule
   eta::T
 end
-Descent() = Descent(1f-1)
+Descent(; eta = 1f-1) = Descent(eta)
 
 init(o::Descent, x::AbstractArray) = nothing
 
@@ -115,10 +115,11 @@ struct RMSProp <: AbstractRule
   centred::Bool
 end
 
-function RMSProp(η = 0.001, ρ = 0.9, ϵ = 1e-8; centred::Bool = false, centered::Bool = false)
+function RMSProp(η, ρ = 0.9, ϵ = 1e-8; centred::Bool = false, centered::Bool = false)
   η < 0 && throw(DomainError(η, "the learning rate cannot be negative"))
   RMSProp(η, ρ, ϵ, centred | centered)
 end
+RMSProp(; eta = 0.001, rho = 0.9, epsilon = 1e-8, kw...) = RMSProp(eta, rho, epsilon; kw...)
 
 init(o::RMSProp, x::AbstractArray) = (zero(x), o.centred ? zero(x) : false)
 
@@ -501,9 +502,12 @@ weight decay regularization.
 - Machine epsilon (`ϵ`): Constant to prevent division by zero
                          (no need to change default)
 """
-AdamW(η = 0.001, β = (0.9, 0.999), γ = 0, ϵ = 1e-8) =
+AdamW(η, β = (0.9, 0.999), γ = 0, ϵ = 1e-8) =
   OptimiserChain(Adam(η, β, ϵ), WeightDecay(γ))
 
+AdamW(; eta = 0.001, beta = (0.9, 0.999), lambda = 0, epsilon = 1e-8) =
+  OptimiserChain(Adam(eta, beta, epsilon), WeightDecay(lambda))
+
 """
     AdaBelief(η = 0.001, β = (0.9, 0.999), ϵ = 1e-16)