add randu

src/backend/tensorflow/graph.jl

@@ -37,6 +37,7 @@ graph(::typeof(fill), x, dims) = Ops.fill(convert(Tensor{Int32}, dims), Tensor(x
 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)
 
 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

@@ -1,4 +1,4 @@
-export reshape, tile, fill, cast, solve, triangular_solve
+export reshape, tile, fill, cast, solve, triangular_solve, randu
 
 import Base: reshape, fill
 
@@ -8,3 +8,4 @@ fill{T}(x::T, dims::AbstractArray) = fill(x,tuple(dims...))
 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...))

test/backend/tensorflow.jl

@@ -58,6 +58,7 @@ end
   @test tf(@net (x,y) -> solve(x,y))(A,b) ≈ A\b
   _,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)
 end
 
 end