Please use 'ReverseDiff.value' about reversediff.jl HOT 7 CLOSED

import numpy as np

from julia.api import Julia
jl = Julia(compiled_modules=False)
from julia import Main

a = np.random.rand(408);
b = 8
c = 34
d = 41
l = np.random.rand(41,34)
m = np.random.rand(41,34)
p=1
q=1
r = np.random.rand(41,34)
s = np.random.rand(34)
t = np.random.rand(41,8)
u = np.random.rand(41,34)
v = np.random.rand(34)
w = 1
;

Main.a = a
Main.b = b
Main.c = c
Main.d = d
Main.l = l
Main.m = m
Main.p = p
Main.q = q
Main.r = r
Main.s = s
Main.t = t
Main.u = u 
Main.v = v
Main.w = w

%%time

jl.eval('include("Dummy Julia v2.jl")')

%%time

jl.eval('calc_sse(a)')

%%time

jl.eval('optimization(a)')

import Pkg
Pkg.add("Nonconvex")

using AbstractDifferentiation, ReverseDiff
backend = AbstractDifferentiation.ReverseDiffBackend()

using Nonconvex
Nonconvex.@load NLopt

function calc_sse(a)
    
    f = a[1:c]
    g= a[c+1:2*c]
    h= a[2*c+1:3*c]
    k= a[3*c+1:4*c]
    
    n = rand(b,c) 
 
    o = zeros((d, c))
    
    mf = 1.0./(1.0 .- (r * f))
    
    fi = ((t.*mf)*n).*u
    
    qt = g
    
    for e in 1:d
        
        for j in 1:c
            
            if l[e,j] != 0.0
                
                o[e,j] =  qt[j]+(1-n[j])*(fi[e,j]+v[j])
                
                qt[j] = o[e,j]
            end
            
        end
        
    end
                    
    diff = sum((o .- l).^2, dims=1) ./transpose(s)
    
    sse = sum(diff)/c
                    
    return sse
    
end

function optimization(a)
    model = Model(calc_sse)
    addvar!(model, zeros(408), zeros(408).+1000, init=a)
    alg = NLoptAlg(Symbol(:LD_MMA))
    options = NLoptOptions(ftol_rel = 1.0)
    ad_model = abstractdiffy(model,backend)
    result = optimize(ad_model, alg, a, options = options)
    return result.minimum, result.minimizer, result
    
end

JuliaError: Exception 'ArgumentError: Converting an instance of ReverseDiff.TrackedReal{Float64, Float64, Nothing} to Float64 is not defined. Please use `ReverseDiff.value` instead.' occurred while calling julia code:
optimization(a)