also, don't allow bias to be wider type

src/utils.jl

@@ -514,14 +514,14 @@ to the constructor's keyword `bias=bias`.
 * `bias == true` creates a trainable array of the given size, of the same type as `weights`, initialised to zero.
 * `bias == false` returns `false`, which is understood by AD to be non-differentiable.
 * `bias::AbstractArray` uses the array provided, provided it has the correct size.
-  It does not at present correct the `eltype` to match that of `weights`.
+  It will also correct the `eltype` to match that of `weights`.
 """
 function create_bias(weights::AbstractArray, bias::Bool, dims::Integer...)
   bias ? fill!(similar(weights, dims...), 0) : false
 end
 function create_bias(weights::AbstractArray, bias::AbstractArray, dims::Integer...)
   size(bias) == dims || throw(DimensionMismatch("expected bias of size $(dims), got size $(size(bias))"))
-  bias
+  convert(AbstractArray{eltype(weights)}, bias)
 end
 
 

test/layers/basic.jl

@@ -58,10 +58,10 @@ import Flux: activations
       @test Dense(rand(100,10), false, tanh).σ == tanh
       @test Dense(rand(100,10), rand(100)).σ == identity
       @test Dense(rand(Float16, 100,10), true).bias isa Vector{Float16}  # creates matching type
-      @test_skip Dense(rand(Float16, 100,10), rand(100)).bias isa Vector{Float16}  # converts to match
+      @test Dense(rand(Float16, 100,10), rand(100)).bias isa Vector{Float16}  # converts to match
 
       @test Dense(3,4; init=Base.randn, bias=true).bias isa Vector{Float64}
-      @test_skip Dense(3,4; init=Base.randn, bias=[1,2,3,4]).bias isa Vector{Float64}
+      @test Dense(3,4; init=Base.randn, bias=[1,2,3,4]).bias isa Vector{Float64}
 
       @test_throws MethodError Dense(10, 10.5)
       @test_throws MethodError Dense(10, 10.5, tanh)