diff --git a/src/utils.jl b/src/utils.jl
index d3d01a11..b2fe76bf 100644
--- a/src/utils.jl
+++ b/src/utils.jl
@@ -1,12 +1,14 @@
 # Arrays
-nfan(n_in, n_out) = n_in, n_out #fan-in, fan-out
+nfan() = 1, 1 #fan_in, fan_out
+nfan(n) = 1, n #A vector is treated as a n×1 matrix
+nfan(n_out, n_in) = n_in, n_out #In case of Dense kernels: arranged as matrices
 nfan(dims...) = prod(dims[1:end-2]) .* (dims[end-1], dims[end]) #In case of convolution kernels
 
 glorot_uniform(dims...) = (rand(Float32, dims...) .- 0.5f0) .* sqrt(24.0f0 / sum(nfan(dims...)))
 glorot_normal(dims...) = randn(Float32, dims...) .* sqrt(2.0f0 / sum(nfan(dims...)))
 
-he_uniform(dims...) = (rand(Float32, dims...) .- 0.5f0) .* sqrt(24.0f0 / first(nfan(dims...)))
-he_normal(dims...) = randn(Float32, dims...) .* sqrt(2.0f0 / first(nfan(dims...)))
+he_uniform(dims...) = (rand(Float32, dims...) .- 0.5f0) .* sqrt(24.0f0 / last(nfan(dims...)))
+he_normal(dims...) = randn(Float32, dims...) .* sqrt(2.0f0 / last(nfan(dims...)))
 
 ones(T::Type, dims...) = Base.ones(T, dims...)
 zeros(T::Type, dims...) = Base.zeros(T, dims...)
diff --git a/test/utils.jl b/test/utils.jl
index 99492d4e..22b8f26a 100644
--- a/test/utils.jl
+++ b/test/utils.jl
@@ -58,7 +58,9 @@ end
   Random.seed!(0)
 
   @testset "Fan in/out" begin
-    @test nfan(100, 200) == (100, 200) #For Dense layer
+    @test nfan() == (1, 1) #For a constant
+    @test nfan(100) == (1, 100) #For vector
+    @test nfan(100, 200) == (200, 100) #For Dense layer
     @test nfan(2, 30, 40) == (2 * 30, 2 * 40) #For 1D Conv layer
     @test nfan(2, 3, 40, 50) == (2 * 3 * 40, 2 * 3 * 50) #For 2D Conv layer
     @test nfan(2, 3, 4, 50, 60) == (2 * 3 * 4 * 50, 2 * 3 * 4 * 60) #For 3D Conv layer
@@ -67,7 +69,7 @@ end
   @testset "glorot" begin
     # glorot_uniform and glorot_normal should both yield a kernel with
     # variance ≈ 2/(fan_in + fan_out)
-    for dims ∈ [(100, 100), (100, 400), (2, 3, 32, 64), (2, 3, 4, 32, 64)]
+    for dims ∈ [(1000,), (100, 100), (100, 400), (2, 3, 32, 64), (2, 3, 4, 32, 64)]
       for init ∈ [glorot_uniform, glorot_normal]
         v = init(dims...)
         fan_in, fan_out = nfan(dims...)
@@ -79,11 +81,11 @@ end
 
   @testset "he" begin
     # he_uniform and he_normal should both yield a kernel with variance ≈ 2/fan_in
-    for dims ∈ [(100, 100), (100, 400), (2, 3, 32, 64), (2, 3, 4, 32, 64)]
+    for dims ∈ [(1000,), (100, 100), (100, 400), (2, 3, 32, 64), (2, 3, 4, 32, 64)]
       for init ∈ [he_uniform, he_normal]
         v = init(dims...)
         fan_in, fan_out = nfan(dims...)
-        σ2 = 2 / fan_in
+        σ2 = 2 / fan_out
         @test 0.9σ2 < var(v) < 1.1σ2
       end
     end