diff --git a/src/Flux.jl b/src/Flux.jl
index 5f906a0528..a38115f1ef 100644
--- a/src/Flux.jl
+++ b/src/Flux.jl
@@ -14,7 +14,7 @@ using Zygote: Params, @adjoint, gradient, pullback, @nograd
 export gradient
 using ChainRulesCore
 
-export Chain, Dense, Maxout, SkipConnection, Parallel,
+export Chain, Dense, Maxout, SkipConnection, Parallel, Embedding,
        RNN, LSTM, GRU, GRUv3,
        SamePad, Conv, CrossCor, ConvTranspose, DepthwiseConv,
        AdaptiveMaxPool, AdaptiveMeanPool, GlobalMaxPool, GlobalMeanPool, MaxPool, MeanPool,
diff --git a/src/layers/basic.jl b/src/layers/basic.jl
index 952ff7d444..9f47cba309 100644
--- a/src/layers/basic.jl
+++ b/src/layers/basic.jl
@@ -483,7 +483,8 @@ function Base.show(io::IO, m::Parallel)
 end
 
 """
-    Embedding(in => out; init=randn)
+    Embedding(in => out; init=randn32)
+    Embedding(weight::AbstractMatrix)
 
 A lookup table that stores embeddings of dimension `out` 
 for a vocabulary of size `in`.
@@ -493,41 +494,39 @@ The input to the layer can be either a vector of indexes
 or the corresponding [onehot encoding](@ref Flux.OneHotArray). 
 
 # Examples
-```jldoctest
-julia> vocab_size, embed_size = 1000, 4;
-
-julia> model = Flux.Embedding(vocab_size => embed_size)
-Embedding(1000 => 4)  # 4_000 parameters
 
-julia> vocab_idxs = [1, 722, 53, 220, 3];
+```jldoctest
+julia> m = Embedding(reshape(-6:45, 2, 26) .+ 0.01f0)
+Embedding(26 => 2)
 
-julia> x = Flux.OneHotMatrix(vocab_idxs, vocab_size); summary(x)
-"1000×5 OneHotMatrix(::Vector{Int64}) with eltype Bool"
+julia> m(5)  # embedding vector for 5th element
+2-element Vector{Float32}:
+ 2.01
+ 3.01
 
-julia> model(x) |> summary
-"4×5 Matrix{Float32}"
+julia> m([6, 15, 15])  # applied to a batch
+2×3 Matrix{Float32}:
+ 4.01  22.01  22.01
+ 5.01  23.01  23.01
 
-julia> model(vocab_idxs) == model(x)
+julia> ans == m(Flux.onehotbatch("foo", 'a':'z'))
 true
 ```
 """
-struct Embedding{W}
+struct Embedding{W <: AbstractMatrix}
   weight::W
 end
 
 @functor Embedding
 
-Embedding((in, out)::Pair{<:Integer, <:Integer}; init = randn32) = Embedding(init(out, in))
+Embedding(dims::Pair{<:Integer, <:Integer}; init = randn32) = Embedding(init(last(dims), first(dims)))
   
 (m::Embedding)(x::Integer) = m.weight[:, x]
 (m::Embedding)(x::AbstractVector) = NNlib.gather(m.weight, x)
 (m::Embedding)(x::AbstractArray) = reshape(m(vec(x)), :, size(x)...)
-
-function (m::Embedding)(x::Union{OneHotVector{T,L}, OneHotMatrix{T,L}}) where {T,L}
-  size(m.weight, 2) == L || throw(DimensionMismatch("Matrix column must correspond with OneHot size: $(size(m.weight, 2)) != $L"))
-  return m(onecold(x))
-end
+(m::Embedding)(x::AbstractArray{Bool}) = reshape(m(reshape(x, size(x, 1), :)), :, size(x)[2:end]...)
+(m::Embedding)(x::AbstractVecOrMat{Bool}) = m.weight * x  # handles OneHotLikeVector, OneHotLikeMatrix
  
 function Base.show(io::IO, m::Embedding)
-  print(io, "Embedding(", size(m.weight, 2), " => ", size(m.weight, 1), ")")
+  print(io, "Embedding($(size(m.weight, 2)) => $(size(m.weight, 1)))")
 end
diff --git a/src/onehot.jl b/src/onehot.jl
index 86afd513dc..814645c6d2 100644
--- a/src/onehot.jl
+++ b/src/onehot.jl
@@ -33,6 +33,9 @@ const OneHotLike{T, L, N, var"N+1", I} =
   Union{OneHotArray{T, L, N, var"N+1", I},
         Base.ReshapedArray{Bool, var"N+1", <:OneHotArray{T, L, <:Any, <:Any, I}}}
 
+const OneHotLikeVector{T, L} = OneHotLike{T, L, 0, 1, T}
+const OneHotLikeMatrix{T, L, I} = OneHotLike{T, L, 1, 2, I}  
+
 _isonehot(x::OneHotArray) = true
 _isonehot(x::Base.ReshapedArray{<:Any, <:Any, <:OneHotArray{<:Any, L}}) where L = (size(x, 1) == L)
 
diff --git a/src/outputsize.jl b/src/outputsize.jl
index 1caea9e16b..02c92c7174 100644
--- a/src/outputsize.jl
+++ b/src/outputsize.jl
@@ -168,3 +168,7 @@ for (fn, Dims) in ((:conv, DenseConvDims), (:depthwiseconv, DepthwiseConvDims))
     end
   end
 end
+
+function NNlib.gather(src::AbstractArray{Tsrc, Nsrc}, idx::AbstractArray{<:Nil}) where {Tsrc, Nsrc}
+  fill(nil, (size(src)[1:Nsrc-1]..., size(idx)...))
+end
\ No newline at end of file
diff --git a/test/cuda/layers.jl b/test/cuda/layers.jl
index 396e6c0ab5..3c28c6a268 100644
--- a/test/cuda/layers.jl
+++ b/test/cuda/layers.jl
@@ -127,13 +127,13 @@ gpu_gradtest("PixelShuffle 2d", pixelshuffle, rand(Float32, 3, 4, 18, 3), 3)
 gpu_gradtest("PixelShuffle 1d", pixelshuffle, rand(Float32, 3, 18, 3), 3)
 
 embedding = [Flux.Embedding]
-gpu_gradtest("Embedding", embedding, [1,3,5], 5, 2)
-gpu_gradtest("Embedding repeated indices", embedding, [1,3,5,3], 5, 2)
-gpu_gradtest("Embedding integer index", embedding, 1, 5, 2)
-gpu_gradtest("Embedding 2d index", embedding, [1 2; 3 4], 5, 2)
-gpu_gradtest("Embedding OneHotVec index", embedding, OneHotVector(1, 5), 5, 2)
-gpu_gradtest("Embedding OneHotMatrix index", embedding,  OneHotMatrix([1,2,3], 5), 5, 2)
-gpu_gradtest("Embedding OneHotMatrix repeated indices", embedding, OneHotMatrix([1,2,2], 5), 5, 2)
+gpu_gradtest("Embedding", embedding, [1,3,5], 5 => 2)
+gpu_gradtest("Embedding repeated indices", embedding, rand(1:10, 10^3), 10 => 2)
+gpu_gradtest("Embedding integer index", embedding, 1, 5 => 2)
+gpu_gradtest("Embedding 2d index", embedding, [1 2; 3 4], 5 => 2)
+gpu_gradtest("Embedding OneHotVec index", embedding, OneHotVector(1, 5), 5 => 2)
+gpu_gradtest("Embedding OneHotMatrix index", embedding,  OneHotMatrix([1,2,3], 5), 5 => 2)
+gpu_gradtest("Embedding OneHotMatrix repeated indices", embedding, OneHotMatrix(rand(1:10, 10^3), 10), 10 => 2)
 
 @testset "function layers" begin
   x = rand(Float32, 3,3)
diff --git a/test/layers/basic.jl b/test/layers/basic.jl
index ca8e15a643..8ad58b26f1 100644
--- a/test/layers/basic.jl
+++ b/test/layers/basic.jl
@@ -276,7 +276,7 @@ import Flux: activations
 
   @testset "Embedding" begin
     vocab_size, embed_size = 10, 4
-    m = Flux.Embedding(vocab_size, embed_size)
+    m = Flux.Embedding(vocab_size => embed_size)
     @test size(m.weight) == (embed_size, vocab_size)
 
     x = rand(1:vocab_size, 3)
@@ -284,9 +284,9 @@ import Flux: activations
     @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 m(x2) isa Matrix{Float32}
+    @test m(x2) ≈ y 
+    @test m(collect(x2)) ≈ y
     @test_throws DimensionMismatch m(OneHotMatrix(x, 1000))
 
     x = rand(1:vocab_size, 3, 4)
@@ -297,6 +297,9 @@ import Flux: activations
     @test m(2) ≈ m.weight[:,2]
     @test m(OneHotVector(3, vocab_size)) ≈ m.weight[:,3]
     @test_throws DimensionMismatch m(OneHotVector(3, 1000))
+
+    x = onehotbatch(rand(1:vocab_size, 4, 3, 4, 5), 1:vocab_size)
+    @test m(x) ≈ m(onecold(x))
   end
 end
 
diff --git a/test/outputsize.jl b/test/outputsize.jl
index 2c90811dcb..d1ae94223a 100644
--- a/test/outputsize.jl
+++ b/test/outputsize.jl
@@ -155,3 +155,10 @@ end
   @test outputsize(m, (32, 32, 16, 16)) == (32, 32, 16, 16)
   @test outputsize(m, (32, 32, 16); padbatch=true) == (32, 32, 16, 1)
 end
+
+@testset "embedding" begin
+  m = Embedding(3=>5)
+  @test outputsize(m, (2,)) == (5, 2)
+  @test outputsize(m, (2, 3)) == (5, 2, 3)
+  @test outputsize(m, (2, 3, 4)) == (5, 2, 3, 4)
+end