add cast

src/backend/tensorflow/graph.jl

@@ -34,6 +34,7 @@ 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))
+graph(::typeof(Flux.cast), args...) = TensorFlow.cast(args...)
 
 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

@@ -1,7 +1,8 @@
-export reshape, tile, fill
+export reshape, tile, fill, cast
 
 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...))
+cast{T}(x::AbstractArray, ::Type{T}) = convert(Array{T},x)

test/backend/tensorflow.jl

@@ -52,6 +52,7 @@ end
   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))
+  @test typeof(tf(@net x -> Flux.cast(x,Int32))(A)) == Array{Int32,3}
 end
 
 end