add tile

src/backend/tensorflow/graph.jl

@@ -32,6 +32,7 @@ graph(::typeof(size), x, dim) = TensorFlow.size(x,convert(Tensor{Int32}, dim))
 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...)
 
 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

@@ -1,5 +1,6 @@
-export reshape
+export reshape, tile
 
 import Base: reshape
 
 reshape(x::AbstractArray, dims::AbstractArray) = reshape(x,tuple(dims...))
+tile(x::AbstractArray, mult::AbstractArray) = repeat(x,outer=tuple(mult...))

test/backend/tensorflow.jl

@@ -49,6 +49,8 @@ end
   @test tf(@net x -> chol(x))(A) ≈ chol(A)
   A = randn(Float32,(6,3))
   @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))
 end
 
 end