add randn

src/backend/tensorflow/graph.jl

@@ -38,6 +38,7 @@ graph(::typeof(Flux.cast), args...) = TensorFlow.cast(args...)
 graph(::typeof(solve), A, b) = TensorFlow.matrix_solve(A, b)
 graph(::typeof(triangular_solve), A, b) = TensorFlow.matrix_triangular_solve(A, b; lower=false)
 graph(::typeof(randu), x) = Ops.random_uniform(convert(Tensor{Int32},x);dtype=Float32)
+graph(::typeof(randn), x) = TensorFlow.random_normal(convert(Tensor{Int32},x);dtype=Float32)
 
 for op in (*, .*, .+, .^, log, exp, ceil, floor, sqrt, abs, cos,
            sin, tan, atan, asin, acos, tanh, lgamma, erf, erfc, real, imag, conj,

src/ops.jl

@@ -1,6 +1,6 @@
-export reshape, tile, fill, cast, solve, triangular_solve, randu
+export reshape, tile, fill, cast, solve, triangular_solve, randu, randn
 
-import Base: reshape, fill
+import Base: reshape, fill, randn
 
 reshape(x::AbstractArray, dims::AbstractArray) = reshape(x,tuple(dims...))
 tile(x::AbstractArray, mult::AbstractArray) = repeat(x,outer=tuple(mult...))
@@ -9,3 +9,4 @@ cast{T}(x::AbstractArray, ::Type{T}) = convert(Array{T},x)
 solve(A::AbstractArray, b::AbstractArray) = A\b
 triangular_solve(A::AbstractArray, b::AbstractArray) = A\b
 randu(x::AbstractArray) = rand(tuple(x...))
+randn(x::AbstractArray) = randn(tuple(x...))

test/backend/tensorflow.jl

@@ -59,6 +59,7 @@ end
   _,A,_ = lu(A)
   @test tf(@net (x,y) -> triangular_solve(x,y))(A,b) ≈ A\b
   @test size(tf(@net x -> randu(x))([2,3])) == (2,3)
+  @test size(tf(@net x -> randn(x))([2,3])) == (2,3)
 end
 
 end