add triangular_solve

src/backend/tensorflow/graph.jl

@@ -36,6 +36,7 @@ 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)
+graph(::typeof(triangular_solve), A, b) = TensorFlow.matrix_triangular_solve(A, b; lower=false)
 
 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
+export reshape, tile, fill, cast, solve, triangular_solve
 
 import Base: reshape, fill
 
@@ -7,3 +7,4 @@ 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
+triangular_solve(A::AbstractArray, b::AbstractArray) = A\b

test/backend/tensorflow.jl

@@ -56,6 +56,8 @@ end
   A = randn(Float32,(5,5))
   b = randn(Float32,(5,1))
   @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
 end
 
 end