diff --git a/docs/src/models/layers.md b/docs/src/models/layers.md
index c2056bb4..4bbb2ba0 100644
--- a/docs/src/models/layers.md
+++ b/docs/src/models/layers.md
@@ -6,6 +6,8 @@ These core layers form the foundation of almost all neural networks.
 Chain
 Dense
 Conv
+MaxPool
+MeanPool
 ```
 
 ## Recurrent Layers
diff --git a/src/Flux.jl b/src/Flux.jl
index 614eeaf7..8c959fec 100644
--- a/src/Flux.jl
+++ b/src/Flux.jl
@@ -5,7 +5,7 @@ module Flux
 using MacroTools, Juno, Requires, Reexport, Statistics, Random
 using MacroTools: @forward
 
-export Chain, Dense, RNN, LSTM, GRU, Conv,
+export Chain, Dense, RNN, LSTM, GRU, Conv, MaxPool, MeanPool,
        Dropout, LayerNorm, BatchNorm,
        params, mapleaves, cpu, gpu
 
diff --git a/src/layers/conv.jl b/src/layers/conv.jl
index 78509c84..dbf8ccf9 100644
--- a/src/layers/conv.jl
+++ b/src/layers/conv.jl
@@ -1,6 +1,6 @@
 using NNlib: conv
 
-@generated sub2(::Type{Val{N}}) where N = :(Val($(N-2)))
+@generated sub2(::Val{N}) where N = :(Val($(N-2)))
 
 expand(N, i::Tuple) = i
 expand(N, i::Integer) = ntuple(_ -> i, N)
@@ -28,7 +28,7 @@ end
 
 Conv(w::AbstractArray{T,N}, b::AbstractVector{T}, σ = identity;
      stride = 1, pad = 0, dilation = 1) where {T,N} =
-  Conv(σ, w, b, expand.(sub2(Val{N}), (stride, pad, dilation))...)
+  Conv(σ, w, b, expand.(sub2(Val(N)), (stride, pad, dilation))...)
 
 Conv(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity; init = initn,
      stride = 1, pad = 0, dilation = 1) where N =
@@ -50,3 +50,48 @@ function Base.show(io::IO, l::Conv)
   l.σ == identity || print(io, ", ", l.σ)
   print(io, ")")
 end
+
+
+"""
+    MaxPool(k)
+
+Max pooling layer. `k` stands for the size of the window for each dimension of the input.
+
+Takes the keyword arguments `pad` and `stride`.
+"""
+struct MaxPool{N}
+    k::NTuple{N,Int}
+    pad::NTuple{N,Int}
+    stride::NTuple{N,Int}
+end
+
+MaxPool(k::NTuple{N,Integer}; pad = 0, stride = k) where N =
+  MaxPool(k, expand(Val(N), pad), expand(Val(N), stride))
+
+(m::MaxPool)(x) = maxpool(x, m.k; pad = m.pad, stride = m.stride)
+
+function Base.show(io::IO, m::MaxPool)
+  print(io, "MaxPool(", m.k, ", pad = ", m.pad, ", stride = ", m.stride, ")")
+end
+
+"""
+    MeanPool(k)
+
+Mean pooling layer. `k` stands for the size of the window for each dimension of the input.
+
+Takes the keyword arguments `pad` and `stride`.
+"""
+struct MeanPool{N}
+    k::NTuple{N,Int}
+    pad::NTuple{N,Int}
+    stride::NTuple{N,Int}
+end
+
+MeanPool(k::NTuple{N,Integer}; pad = 0, stride = k) where N =
+  MeanPool(k, expand(Val(N), pad), expand(Val(N), stride))
+
+(m::MeanPool)(x) = meanpool(x, m.k; pad = m.pad, stride = m.stride)
+
+function Base.show(io::IO, m::MeanPool)
+  print(io, "MeanPool(", m.k, ", pad = ", m.pad, ", stride = ", m.stride, ")")
+end
diff --git a/test/layers/conv.jl b/test/layers/conv.jl
new file mode 100644
index 00000000..5928bd75
--- /dev/null
+++ b/test/layers/conv.jl
@@ -0,0 +1,23 @@
+using Flux, Test
+using Flux: maxpool, meanpool
+
+@testset "Pooling" begin
+  x = randn(10, 10, 3, 2)
+  mp = MaxPool((2, 2))
+  @test mp(x) == maxpool(x, (2,2))
+  mp = MeanPool((2, 2))
+  @test mp(x) == meanpool(x, (2,2))
+end
+
+@testset "CNN" begin
+  r = zeros(28, 28, 1, 5)
+  m = Chain(
+    Conv((2, 2), 1=>16, relu),
+    MaxPool((2,2)),
+    Conv((2, 2), 16=>8, relu),
+    MaxPool((2,2)),
+    x -> reshape(x, :, size(x, 4)),
+    Dense(288, 10), softmax)
+
+  @test size(m(r)) == (10, 5)
+end
diff --git a/test/runtests.jl b/test/runtests.jl
index fd48e547..70d929bf 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -28,6 +28,7 @@ include("onehot.jl")
 include("tracker.jl")
 include("layers/normalisation.jl")
 include("layers/stateless.jl")
+include("layers/conv.jl")
 include("optimise.jl")
 include("data.jl")