Merge pull request #50 from alha02/add-more-tf-ops

Add more tf ops

src/Flux.jl

@@ -23,7 +23,7 @@ include("core.jl")
 import .FluxCore: back!, update!, graph
 
 include("utils.jl")
-
+include("ops.jl")
 include("params.jl")
 
 include("compiler/code.jl")

src/backend/tensorflow/graph.jl

@@ -31,6 +31,15 @@ graph(::typeof(svd), x) = svd(x)
 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...)
+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)
+graph(::typeof(randu), x) = Ops.random_uniform(convert(Tensor{Int32},x);dtype=Float32)
+graph(::typeof(randn), x) = TensorFlow.random_normal(convert(Tensor{Int32},x);dtype=Float32)
+graph(::typeof(Flux.expand_dims), x, dim) = TensorFlow.expand_dims(x,convert(Tensor{Int32},dim))
 
 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

@@ -0,0 +1,18 @@
+export reshape, tile, fill, cast, solve, triangular_solve, randu, randn,
+       expand_dims
+
+import Base: reshape, fill, randn
+
+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
+triangular_solve(A::AbstractArray, b::AbstractArray) = A\b
+randu(x::AbstractArray) = rand(tuple(x...))
+randn(x::AbstractArray) = randn(tuple(x...))
+
+function expand_dims(x,dim)
+    s = [size(x)...]
+    reshape(x,tuple(vcat(s[1:dim-1],1,s[dim:end])...))
+end

test/backend/tensorflow.jl

@@ -47,6 +47,24 @@ end
   A = randn(6,5)
   A = A'*A
   @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))
+  @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
+  _,A,_ = lu(A)
+  @test tf(@net (x,y) -> triangular_solve(x,y))(A,b) ≈ A\b
+  @test size(tf(@net x -> randu(x))([2,3])) == (2,3)
+  @test size(tf(@net x -> randn(x))([2,3])) == (2,3)
+  m = tf(@net (x,y) -> Flux.expand_dims(x,y))
+  A = randn(Float32,(3,2))
+  @test m(A,1) ≈ Flux.expand_dims(A,1)
+  @test m(A,2) ≈ Flux.expand_dims(A,2)
+  @test m(A,3) ≈ Flux.expand_dims(A,3)
 end
 
 end