add fill

src/backend/tensorflow/graph.jl

@@ -33,6 +33,7 @@ graph(::typeof(size), x) = TensorFlow.size(x)
 graph(::typeof(chol), args...) = TensorFlow.transpose(TensorFlow.cholesky(args...))
 graph(::typeof(reshape), x, dims) = TensorFlow.reshape(x,convert(Tensor{Int32},dims))
 graph(::typeof(Flux.tile), args...) = TensorFlow.tile(args...)
+graph(::typeof(fill), x, dims) = Ops.fill(convert(Tensor{Int32}, dims), Tensor(x))
 
 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 +1,7 @@
-export reshape, tile
+export reshape, tile, fill
 
-import Base: reshape
+import Base: reshape, fill
 
 reshape(x::AbstractArray, dims::AbstractArray) = reshape(x,tuple(dims...))
 tile(x::AbstractArray, mult::AbstractArray) = repeat(x,outer=tuple(mult...))
+fill{T}(x::T, dims::AbstractArray) = fill(x,tuple(dims...))

test/backend/tensorflow.jl

@@ -51,6 +51,7 @@ end
   @test transpose(tf(@net (x,y) -> reshape(x,y))(transpose(A),[2,9])) ≈ reshape(A,(9,2)) # Note: TF is row major and julia is not
   A = randn(Float32,(4,3,1))
   @test tf(@net (x,y) -> Flux.tile(x,y))(A,[1,1,3]) ≈ repeat(A,outer=(1,1,3))
+  @test tf(@net (x,y) -> fill(x,y))(3.2,[3,2]) ≈ convert(Array{Float32},3.2*ones(3,2))
 end
 
 end