diff --git a/src/layers/normalise.jl b/src/layers/normalise.jl
index 7562e84f..054ca08b 100644
--- a/src/layers/normalise.jl
+++ b/src/layers/normalise.jl
@@ -203,9 +203,9 @@ InstanceNorm(chs::Integer, λ = identity;
   InstanceNorm(λ, param(initβ(chs)), param(initγ(chs)),
             zeros(chs), ones(chs), ϵ, momentum, true)
 
-function (IN::InstanceNorm)(x)
-  size(x, ndims(x)-1) == length(IN.β) ||
-    error("InstanceNorm expected $(length(IN.β)) channels, got $(size(x, ndims(x)-1))")
+function (in::InstanceNorm)(x)
+  size(x, ndims(x)-1) == length(in.β) ||
+    error("InstanceNorm expected $(length(in.β)) channels, got $(size(x, ndims(x)-1))")
   ndims(x) > 2 ||
     error("InstanceNorm requires at least 3 dimensions. With 2 dimensions an array of zeros would be returned")
   # these are repeated later on depending on the batch size
@@ -216,39 +216,39 @@ function (IN::InstanceNorm)(x)
   affine_shape[end-1] = c
   affine_shape[end] = bs
   m = prod(size(x)[1:end-2])
-  γ, β = expand_inst(IN.γ, affine_shape), expand_inst(IN.β, affine_shape)
+  γ, β = expand_inst(in.γ, affine_shape), expand_inst(in.β, affine_shape)
 
-  if !IN.active
-    μ = expand_inst(IN.μ, affine_shape)
-    σ² = expand_inst(IN.σ², affine_shape)
-    ϵ = IN.ϵ
+  if !in.active
+    μ = expand_inst(in.μ, affine_shape)
+    σ² = expand_inst(in.σ², affine_shape)
+    ϵ = in.ϵ
   else
     T = eltype(x)
 
-    ϵ = data(convert(T, IN.ϵ))
+    ϵ = data(convert(T, in.ϵ))
     axes = 1:dims-2 # axes to reduce along (all but channels and batch size axes)
     μ = mean(x, dims = axes)
     σ² = mean((x .- μ) .^ 2, dims = axes)
 
     # update moving mean/std
-    mtm = data(convert(T, IN.momentum))
-    IN.μ = dropdims(mean(repeat((1 - mtm) .* IN.μ, outer=[1, bs]) .+ mtm .* reshape(data(μ), (c, bs)), dims = 2), dims=2)
-    IN.σ² = dropdims(mean((repeat((1 - mtm) .* IN.σ², outer=[1, bs]) .+ (mtm * m / (m - 1)) .* reshape(data(σ²), (c, bs))), dims = 2), dims=2)
+    mtm = data(convert(T, in.momentum))
+    in.μ = dropdims(mean(repeat((1 - mtm) .* in.μ, outer=[1, bs]) .+ mtm .* reshape(data(μ), (c, bs)), dims = 2), dims=2)
+    in.σ² = dropdims(mean((repeat((1 - mtm) .* in.σ², outer=[1, bs]) .+ (mtm * m / (m - 1)) .* reshape(data(σ²), (c, bs))), dims = 2), dims=2)
   end
 
-  let λ = IN.λ
+  let λ = in.λ
     x̂ = (x .- μ) ./ sqrt.(σ² .+ ϵ)
     λ.(γ .* x̂ .+ β)
   end
 end
 
-children(IN::InstanceNorm) =
-  (IN.λ, IN.β, IN.γ, IN.μ, IN.σ², IN.ϵ, IN.momentum, IN.active)
+children(in::InstanceNorm) =
+  (in.λ, in.β, in.γ, in.μ, in.σ², in.ϵ, in.momentum, in.active)
 
-mapchildren(f, IN::InstanceNorm) =  # e.g. mapchildren(cu, IN)
-  InstanceNorm(IN.λ, f(IN.β), f(IN.γ), f(IN.μ), f(IN.σ²), IN.ϵ, IN.momentum, IN.active)
+mapchildren(f, in::InstanceNorm) =  # e.g. mapchildren(cu, in)
+  InstanceNorm(in.λ, f(in.β), f(in.γ), f(in.μ), f(in.σ²), in.ϵ, in.momentum, in.active)
 
-_testmode!(IN::InstanceNorm, test) = (IN.active = !test)
+_testmode!(in::InstanceNorm, test) = (in.active = !test)
 
 function Base.show(io::IO, l::InstanceNorm)
   print(io, "InstanceNorm($(join(size(l.β), ", "))")