Question

Julia的ForwardDiff可以处理闭包吗？如果不是，那么它就不是很有用了，但是如果是，那么我在下面哪里出错了？

using ForwardDiff
function make_add(x)
   foo = y::Vector -> y+x
   return foo
end

zulu = make_add(17)

g = x-> ForwardDiff.gradient(zulu, x)

g([1, 2, 3])

MethodError: no method matching extract_gradient! 
  (::Type{ForwardDiff.Tag{##1#2{Int64},Int64}},

 `::Array{Array{ForwardDiff.Dual{ForwardDiff.Tag{##1#2{Int64},Int64},Int64,3},1},1}, ::Array{ForwardDiff.Dual{ForwardDiff.Tag{##1#2{Int64},Int64},Int64,3},1})
Closest candidates are:
  extract_gradient!(::Type{T}, ::AbstractArray, ::ForwardDiff.Dual) where T at /home/jrun/.julia/v0.6/ForwardDiff/src/gradient.jl:76
  extract_gradient!(::Type{T}, ::AbstractArray, ::Real) where T at /home/jrun/.julia/v0.6/ForwardDiff/src/gradient.jl:75
  extract_gradient!(::Type{T}, ::DiffResults.DiffResult, ::ForwardDiff.Dual) where T at /home/jrun/.julia/v0.6/ForwardDiff/src/gradient.jl:70
  ...

Stacktrace:
 [1] gradient(::Function, ::Array{Int64,1}, ::ForwardDiff.GradientConfig{ForwardDiff.Tag{##1#2{Int64},Int64},Int64,3,Array{ForwardDiff.Dual{ForwardDiff.Tag{##1#2{Int64},Int64},Int64,3},1}}, ::Val{true}) at /home/jrun/.julia/v0.6/ForwardDiff/src/gradient.jl:17
 [2] gradient(::Function, ::Array{Int64,1}, ::ForwardDiff.GradientConfig{ForwardDiff.Tag{##1#2{Int64},Int64},Int64,3,Array{ForwardDiff.Dual{ForwardDiff.Tag{##1#2{Int64},Int64},Int64,3},1}}) at /home/jrun/.julia/v0.6/ForwardDiff/src/gradient.jl:15
 [3] (::##3#4)(::Array{Int64,1}) at ./In[8]:1`

编辑实际上，这与闭包无关。简单地：

h = x-> ForwardDiff.gradient(x-> x+17.0, x)

炸弹完全一样

Answer 1

gradient是为数组定义的。在标量上使用derivative。

Answer 2

ForwardDiff.gadient的文档中指出：

此方法假定isa(f(x), Real)。

问题是您的函数返回的是向量而不是标量，因此您需要使用jacobian（接受数组作为返回值）：

julia> function make_add(x)
          foo = y::Vector -> y .+ x
             return foo
             end
make_add (generic function with 1 method)

julia> zulu = make_add(17)
#27 (generic function with 1 method)

julia> g = x-> ForwardDiff.jacobian(zulu, x)
#29 (generic function with 1 method)

julia> g([1, 2, 3])
3×3 Array{Int64,2}:
 1  0  0
 0  1  0
 0  0  1

还请注意，我在+之前添加了一个点（因此其读为y .+ x），因为在当前版本的Julia 1.0中，不允许在不广播的情况下向向量添加标量。 / p>

朱莉娅（Julia）的自动区分功能-是否完全无效？

2 个答案: