make Zeros a dimensionlesss number

src/utils.jl

@@ -139,30 +139,40 @@ function throttle(f, timeout; leading=true, trailing=false)
   end
 end
 
-import Base: +, reshape, size
+import Base: +, -, reshape, size
+import Base.Broadcast: broadcasted
 
 """
     Zeros()
-    Zeros(T, a::Union{Colon, Int}...)
 
-Acts as a stand-in for an array of zeros that can be used during training which is
-ignored by the optimisers.
+Acts as a stand-in for an array of zeros that can be
+used during training which is ignored by the optimisers.
+
+Used to turn bias off for a forward pass of a layer.
+
+## Examples
+
+```julia
+julia> rand(3,3) .+ Flux.Zeros()
+3×3 Array{Float64,2}:
+ 0.198739  0.490459  0.785386
+ 0.779074  0.39986   0.66383
+ 0.854981  0.447292  0.314497
+
+julia> bias = Conv((2,2), 1=>3, bias = Flux.Zeros())
+Conv((2, 2), 1=>3)
+```
 """
-struct Zeros{T} <: Number
-  size::Tuple
+struct Zeros <: Number end
+for f in (:+, :-)
+ @eval $f(a::Union{Number, Zeros}, b::Zeros) = a
 end
+Base.:*(a::Union{Number, Zeros}, b::Zeros) = zero(a)
 
-Zeros(::Type{T}, sz...) where T = Zeros{T}(sz)
-Zeros(sz::Union{Integer, Colon}...) = Zeros(Bool, sz...)
-
-+(a::Number, ::Zeros) = a
-+(::Zeros, a::Number) = a
-
-size(xs::Zeros) = xs.size
-reshape(z::Zeros{T}, args...) where T = Zeros(T, args...)
-
-@adjoint reshape(xs::Zeros{T}, dims...) where T =
-  Zeros(T, dims...), Δ -> (Zeros(T, size(xs)...), map(_ -> nothing, dims)...)
+broadcasted(::typeof(+), arr::AbstractArray, ::Zeros) = arr
+broadcasted(::typeof(*), arr::AbstractArray, ::Zeros) = zero(arr)
+Base.reshape(xs::Zeros, args...) = xs
+@adjoint reshape(xs::Zeros, dims...) = reshape(xs, dims...), _ -> nothing
 
 """
     @jit ...