diff --git a/src/Flux.jl b/src/Flux.jl
index 9969b32346..6a987fdfde 100644
--- a/src/Flux.jl
+++ b/src/Flux.jl
@@ -11,7 +11,7 @@ export gradient
 
 export Chain, Dense, Maxout, RNN, LSTM, GRU, Conv, CrossCor, ConvTranspose, MaxPool, MeanPool,
        DepthwiseConv, Dropout, AlphaDropout, LayerNorm, BatchNorm, InstanceNorm, GroupNorm,
-       SkipConnection, params, fmap, cpu, gpu, f32, f64
+       WeightNorm, WeightNormParam, SkipConnection, params, fmap, cpu, gpu, f32, f64
 
 include("optimise/Optimise.jl")
 using .Optimise
diff --git a/src/layers/normalise.jl b/src/layers/normalise.jl
index b421d3e7f0..ba03ac2250 100644
--- a/src/layers/normalise.jl
+++ b/src/layers/normalise.jl
@@ -365,3 +365,91 @@ function Base.show(io::IO, l::GroupNorm)
   (l.λ == identity) || print(io, ", λ = $(l.λ)")
   print(io, ")")
 end
+
+
+"""
+Weight Normalization.
+This layer reparametrizes weights (w) of a layer with its decomposition into magnitude (g) and direction (v).
+WeightNorm has been implemented solely for `Dense` layers in Flux.
+
+	WeightNorm(layer, weight, dim)
+
+``layer`` is the layer being normalized.
+
+``weight`` are the parameters to be normalized.
+
+``dim`` are the dimension of normalization.
+Often, its the dimension encoding the output channels.
+
+Example:
+```
+d = Dense(10, 9, tanh);
+wndA = WeightNorm(d, :W, 2); #The param d.W is now normalized in the second dimension, i.e normalization per output channel
+wndB = WeightNorm(d, :W, [1:2]); #Now we normalize all directions together, keeping a single magnitude
+```
+
+Link : https://arxiv.org/pdf/1602.07868.pdf
+"""
+
+struct WeightNormParam{T,N,I}
+  g::AbstractArray{T,N}
+  v::AbstractArray{T,N}
+  dim::I
+end
+
+Base.size(w::WeightNormParam, i...) = size(w.v, i...)
+Base.size(w::WeightNormParam) = size(w.v)
+Base.iterate(w::WeightNormParam, i...) = iterate(w.g .* w.v ./ WN_mag(w.v, w.dim), i...)
+Base.getindex(w::WeightNormParam, i...) = getindex(w.g .* w.v ./ WN_mag(w.v, w.dim), i...)
+Base.ndims(w::WeightNormParam) = ndims(w.v)
+Base.length(w::WeightNormParam) = length(w.v)
+
+@functor WeightNormParam
+
+WN_mag(p, dim, eps) = sqrt.(sum(abs2.(p), dims = dim)) .+ eps
+WN_mag(p, dim) = WN_mag(p, dim, eps(eltype(p)))
+WN_dir(p, mag) = p ./ mag
+
+import Base.*, Base./, Base.+, Base.-
+for f in (:+, :-, :*, :/)
+  @eval ($f)(z::AbstractArray, w::WeightNormParam) = ($f)(z, w.g .* w.v ./ WN_mag(w.v, w.dim))
+  @eval ($f)(w::WeightNormParam, z::AbstractArray) = ($f)(w.g .* w.v ./ WN_mag(w.v, w.dim), z)
+end
+
+struct WeightNorm{L}
+  layer::L
+  eps::Number
+  weight::Vector
+  dim::Vector
+end
+
+@functor WeightNorm
+
+function Base.show(io::IO, wn::WeightNorm)
+  print(io, "WeightNorm(", wn.layer, ", ", wn.weight, ", ", wn.dim, ")")
+end
+
+function WeightNorm(layer, weight::Vector, dim::Vector)
+  if !isa(layer, Dense)
+    error("WeightNorm is defined only for Dense layers!")
+  end
+  #Expose layer fields and constructor
+  func, re = Flux.functor(layer)
+  #Get the fields
+  par = [getfield(layer, fn) for fn in keys(func)]
+  w = map(weight) do W
+      getfield(layer, W)
+  end
+  g = map((W, D) -> WN_mag(W, D), w, dim)
+  v = map((W, G) -> WN_dir(W, G), w, g)
+  par[indexin(weight,collect(keys(func)))] = WeightNormParam.(g, v, dim)
+  return WeightNorm(re(par), eps(Float32), weight, dim)
+end
+
+WeightNorm(layer, weight::Symbol, dim::Vector) = WeightNorm(layer, [weight], dim)
+WeightNorm(layer, weight::Symbol, dim::Integer) = WeightNorm(layer, [weight], [dim])
+WeightNorm(layer, weight::Vector, dim::Integer) = WeightNorm(layer, weight, [dim for _ in axes(weight,1)])
+
+function (wn::WeightNorm)(x)
+  wn.layer(x)
+end
\ No newline at end of file
diff --git a/test/layers/normalisation.jl b/test/layers/normalisation.jl
index 4399a25608..fa060a04fc 100644
--- a/test/layers/normalisation.jl
+++ b/test/layers/normalisation.jl
@@ -191,6 +191,35 @@ end
 
 end
 
+@testset "WeightNorm" begin
+  let fake_data = randn(Float32, 10,3)
+    d = Dense(10, 9, tanh)
+    gs = gradient(() -> sum(abs2, d(fake_data)), params(d))
+    W = d.W
+    for WN_dim in [[1], 1, [2], 2, [1:2]]
+      wnd = WeightNorm(d, :W, WN_dim)
+      gswn = gradient(() -> sum(abs2, wnd(fake_data)), params(wnd))
+      g = wnd.layer.W.g
+      v = wnd.layer.W.v
+      
+      ΔW = gs[W]
+      Δg = gswn[g]
+      Δv = gswn[v]
+      @test wnd(fake_data) ≈ d(fake_data)
+      if isa(WN_dim, Int)
+        normv = sum(abs2, v, dims = WN_dim)
+        @test sum(ΔW .* v ./ normv, dims = WN_dim) ≈ Δg
+      else
+        normv = sum(abs2, v, dims = WN_dim[1])
+        @test sum(ΔW .* v ./ normv, dims = WN_dim[1]) ≈ Δg
+      end
+      @test g ./ normv .* ΔW - g .* Δg .* v ./ (normv.^2) ≈ Δv
+      @test size(Δv) == size(ΔW)
+      @test isa(wnd.layer.W, Flux.WeightNormParam)
+    end
+  end
+end
+
 if VERSION >= v"1.1"
 @testset "GroupNorm" begin
   # begin tests