fix bug

src/layers/basic.jl

@@ -120,12 +120,13 @@ end
 
 @functor Dense
 
-function (a::Dense)(x::Union{AbstractVector, AbstractMatrix})
+function (a::Dense)(x::AbstractVecOrMat)
   W, b, σ = a.W, a.b, a.σ
   return σ.(W*x .+ b)
 end
 
-(a::Dense)(x::AbstractArray) = reshape(a(mat(x)), :, size(x)[2:end]...)
+(a::Dense)(x::AbstractArray) = 
+  reshape(a(reshape(x, size(x,1), :)), :, size(x)[2:end]...)
 
 function Base.show(io::IO, l::Dense)
   print(io, "Dense(", size(l.W, 2), ", ", size(l.W, 1))
@@ -418,7 +419,7 @@ end
 
 (m::Embedding)(x::Union{OneHotVector, OneHotMatrix}) = m.weight * x # equivalent to m.weight[:,onecold(x)]
 (m::Embedding)(x::Union{Int,AbstractVector}) = m.weight[:, x]
-(m::Embedding)(x::AbstractArray) = reshape(m(mat(x)), :, size(x)[2:end]...)
+(m::Embedding)(x::AbstractArray) = reshape(m(vec(x)), :, size(x)...)
 
 function Base.show(io::IO, m::Embedding)
   print(io, "Embedding($(size(m.weight, 2)), $(size(m.weight, 1)))")

src/layers/stateless.jl

@@ -4,7 +4,7 @@
 Reshape arbitrarly-shaped input into a matrix-shaped output,
 preserving the size of the last dimension.
 
-See also [`unsqueeze`](@ref) and [`mat`](@ref).
+See also [`unsqueeze`](@ref).
 
 # Examples
 ```jldoctest
@@ -26,18 +26,6 @@ function flatten(x::AbstractArray)
   return reshape(x, :, size(x)[end])
 end
 
-"""
-    mat(x::AbstractArray)
-
-Reshape arbitrarly-shaped input into a matrix-shaped output,
-preserving the size of the first dimension.
-
-See also [`flatten`](@ref) and [`unsqueeze`](@ref).
-"""
-function mat(x::AbstractArray)
-  return reshape(x, size(x,1), :)
-end
-
 """
     normalise(x; dims=ndims(x), ϵ=1e-5)
 

test/cuda/layers.jl

@@ -87,9 +87,6 @@ pixelshuffle = [PixelShuffle]
 gpu_gradtest("PixelShuffle 2d", pixelshuffle, rand(Float32, 3, 4, 18, 3), 3)
 gpu_gradtest("PixelShuffle 1d", pixelshuffle, rand(Float32, 3, 18, 3), 3)
 
-embedding = [Embedding]
-gpu_gradtest("Embedding", embedding, rand(1:10, 3), 10, 4)
-
 @testset "function layers" begin
   x = rand(Float32, 3,3)
   gpu_autodiff_test(x -> sum(Flux.normalise(x; dims=1)), x)

test/layers/basic.jl

@@ -163,13 +163,17 @@ import Flux: activations
     y = m(x)
     @test y isa Matrix{Float32}
     @test y ≈ m.weight[:,x]
-
     x2 = OneHotMatrix(x, vocab_size)
     y2 = m(x2)
     @test y2 isa Matrix{Float32}
     @test y2 ≈ y
     @test_throws DimensionMismatch m(OneHotMatrix(x, 1000))
 
+    x = rand(1:vocab_size, 3, 4)
+    y = m(x)
+    @test y isa Array{Float32, 3}
+    @test size(y) ==  (embed_size, 3, 4)
+
     @test m(2) ≈ m.weight[:,2]
     @test m(OneHotVector(3, vocab_size)) ≈ m.weight[:,3]
     @test_throws DimensionMismatch m(OneHotVector(3, 1000))