diff --git a/src/cuda/cudnn.jl b/src/cuda/cudnn.jl
index 9948ef37..dd1775ad 100644
--- a/src/cuda/cudnn.jl
+++ b/src/cuda/cudnn.jl
@@ -27,15 +27,16 @@ const BATCHNORM_SPATIAL = 1
 const BATCHNORM_ACTIVATION = 0
 const BATCHNORM_MIN_EPS = 1e-5
 
-@inline _wsize(y) = ((1 for _=1:ndims(y)-2)..., size(y)[end-1], 1)
-@inline _reddims(y) = ((i for i=1:ndims(y)-2)..., ndims(y))
+@inline _wsize(y) = (map(_ -> 1, size(y)[1:end-2])..., size(y)[end-1], 1)
 
-mutable struct bncache
+@inline _reddims(y) = (collect(1:ndims(y)-2)..., ndims(y))
+
+mutable struct BNCache
   mean
   ivar
 end
 
-bncache() = bncache(nothing, nothing)
+BNCache() = BNCache(nothing, nothing)
 
 # CuDNN supports only 4D and 5D Tensors for BatchNorm Operations
 # so use the native julia code when doing batchnorm on a 2D Array
@@ -56,7 +57,7 @@ function cudnnBNForward!(y::CuArray{T}, g::CuArray{T}, b::CuArray{T}, x::CuArray
                         alpha = T(1), beta = T(0),
                         eps = T(1e-5), training = true) where T<:Union{Float32, Float64}
   dims = _wsize(x)
-  if(eps < BATCHNORM_MIN_EPS)
+  if eps < BATCHNORM_MIN_EPS
     warn("eps ",eps," is too small for CuDNN so eps has been assigned the value ", BATCHNORM_MIN_EPS)
     eps = BATCHNORM_MIN_EPS
   end
@@ -64,11 +65,11 @@ function cudnnBNForward!(y::CuArray{T}, g::CuArray{T}, b::CuArray{T}, x::CuArray
   yd = TensorDesc(y)
   gd = TensorDesc(T, (1,1,length(g),1))
 
-  if(training)
+  if training
 
-    if(cache !== nothing)
-      mean = cu(zeros(T, dims...))
-      ivar = cu(ones(T, dims...))
+    if cache !== nothing
+      mean = zeros(CuArray{T}, dims...)
+      ivar = ones(CuArray{T}, dims...)
     else
       mean = C_NULL
       ivar = C_NULL
@@ -90,7 +91,7 @@ function cudnnBNForward!(y::CuArray{T}, g::CuArray{T}, b::CuArray{T}, x::CuArray
                   momentum, running_mean, running_var,
                   eps, mean, ivar)
 
-    if(cache !== nothing)
+    if cache !== nothing
       cache.mean = mean
       cache.ivar = ivar
     end
@@ -131,7 +132,7 @@ function cudnnBNBackward!(dg::CuArray{T}, g::CuArray{T}, db::CuArray{T},
                           momentum; cache = nothing, eps = T(1e-5),
                           alpha = T(1), beta = T(0),
                           dalpha = T(1), dbeta = T(0), training = true) where T<:Union{Float32, Float64}
-  if(training)
+  if training
     xd = TensorDesc(x)
     dyd = TensorDesc(dy)
     dxd = TensorDesc(dx)
@@ -143,7 +144,7 @@ function cudnnBNBackward!(dg::CuArray{T}, g::CuArray{T}, db::CuArray{T},
       mean, ivar = C_NULL, C_NULL
     end
 
-    if(eps < BATCHNORM_MIN_EPS)
+    if eps < BATCHNORM_MIN_EPS
       warn("eps ",eps," is too small for CuDNN so eps has been assigned the value ", BATCHNORM_MIN_EPS)
       eps = BATCHNORM_MIN_EPS
     end
@@ -175,7 +176,8 @@ end
 
 # Flux Interface
 
-import Flux.Tracker: track, back, @back, istracked
+import ..Flux: Flux
+import ..Tracker: track, back, @back, istracked, TrackedArray
 
 _batchnorm(g, b, x, running_mean, running_var, momentum,
            cache, alpha, beta, eps, training) =
@@ -195,7 +197,7 @@ batchnorm(g::TrackedArray, b::TrackedArray, x::CuArray{T}, running_mean::CuArray
 function back(::typeof(_batchnorm), Δ, g, b, x, running_mean, running_var, momentum, cache, alpha, beta, eps, training)
   deriv_tup = ∇batchnorm(data(g), data(b), data(x), Δ, running_mean, running_var, momentum,
                          cache = cache, alpha = alpha, beta = beta, eps = eps, training = training)
-  istracked(x) && @back(x, deriv_tup[1])
+  @back(x, deriv_tup[1])
   @back(b, deriv_tup[2])
   @back(g, deriv_tup[3])
 end