float32 param initialisers

src/layers/basic.jl

@@ -114,3 +114,11 @@ end
 function Base.show(io::IO, l::Diagonal)
   print(io, "Diagonal(", length(l.α), ")")
 end
+
+# Try to avoid hitting generic matmul in some simple cases
+# Base's matmul is so slow that it's worth the extra conversion to hit BLAS
+(a::Dense{<:Any,W})(x::AbstractArray{T}) where {T <: Union{Float32,Float64}, W <: AbstractArray{T}} =
+  invoke(a, Tuple{AbstractArray}, x)
+
+(a::Dense{<:Any,W})(x::AbstractArray{<:Real}) where {T <: Union{Float32,Float64}, W <: AbstractArray{T}} =
+  a(T.(x))

src/utils.jl

@@ -1,6 +1,12 @@
 # Arrays
-glorot_uniform(dims...) = (rand(dims...) .- 0.5) .* sqrt(24.0/(sum(dims)))
-glorot_normal(dims...) = randn(dims...) .* sqrt(2.0/sum(dims))
+glorot_uniform(dims...) = (rand(Float32, dims...) .- 0.5f0) .* sqrt(24.0f0/sum(dims))
+glorot_normal(dims...) = randn(Float32, dims...) .* sqrt(2.0f0/sum(dims))
+
+ones(T::Type, dims...) = Base.ones(T, dims...)
+zeros(T::Type, dims...) = Base.zeros(T, dims...)
+
+ones(dims...) = Base.ones(Float32, dims...)
+zeros(dims...) = Base.zeros(Float32, dims...)
 
 unsqueeze(xs, dim) = reshape(xs, (size(xs)[1:dim-1]..., 1, size(xs)[dim:end]...))
 

test/layers/conv.jl

@@ -2,7 +2,7 @@ using Flux, Test
 using Flux: maxpool, meanpool
 
 @testset "Pooling" begin
-  x = randn(10, 10, 3, 2)
+  x = randn(Float32, 10, 10, 3, 2)
   mp = MaxPool((2, 2))
   @test mp(x) == maxpool(x, (2,2))
   mp = MeanPool((2, 2))
@@ -10,7 +10,7 @@ using Flux: maxpool, meanpool
 end
 
 @testset "CNN" begin
-  r = zeros(28, 28, 1, 5)
+  r = zeros(Float32, 28, 28, 1, 5)
   m = Chain(
     Conv((2, 2), 1=>16, relu),
     MaxPool((2,2)),