add solve

src/backend/tensorflow/graph.jl

@@ -35,6 +35,7 @@ graph(::typeof(reshape), x, dims) = TensorFlow.reshape(x,convert(Tensor{Int32},d
 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...)
+graph(::typeof(solve), A, b) = TensorFlow.matrix_solve(A, b)
 
 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
+export reshape, tile, fill, cast, solve
 
 import Base: reshape, fill
 
@@ -6,3 +6,4 @@ 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)
+solve(A::AbstractArray, b::AbstractArray) = A\b

test/backend/tensorflow.jl

@@ -53,6 +53,9 @@ end
   @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}
+  A = randn(Float32,(5,5))
+  b = randn(Float32,(5,1))
+  @test tf(@net (x,y) -> solve(x,y))(A,b) ≈ A\b
 end
 
 end