dlfeval returns incorrect gradient

Hi Everyone,

I am using the Deep Learning toolbox, dlfeval and dlgradient, to evaluate a function's derivatives for an optimization problem. Although the code runs, the gradient it is calculating is incorrect. Are there resouces I can explore to find out what is going wrong with the code, or any sense of what might be causing issues int he calculations? Thank you!

I am including the portion of my code in question below, but can provide more if it helps find a solution. All code until the "CODE ABOVE HAS BEEN VERIFIED" line has already been confirmed to be functional in another code.

x = dlarray(x);

Unrecognized function or variable 'x'.

[c,dc,vms] = dlfeval(@stress_objVal_objGrad,x,penal,nelx,nely,KE,H,Hs,beta,E0,Emin,...
    F,freedofs,fixeddofs,nu);

function  [c,dc,vms] = stress_objVal_objGrad(x,penal,nelx,nely,KE,H,Hs,beta,E0,Emin,...
    F,freedofs,fixeddofs,nu)

%% FILTER
x = x(:);
xTilde = (H*x)./Hs;
xPhys = 1-exp(-beta*xTilde)+xTilde*exp(-beta);
xPhys = reshape(xPhys,[nely,nelx]);

%% FEA
[U] = FE(nelx,nely,xPhys,penal,KE,E0,Emin,F,freedofs,fixeddofs);
%% CODE ABOVE HAS BEEN VERIFIED

nele = nelx*nely;
nodenrs = reshape(1:(1+nelx)*(1+nely),1+nely,1+nelx);
edofVec = reshape(2*nodenrs(1:end-1,1:end-1)+1,nelx*nely,1);
edofMat = repmat(edofVec,1,8)+repmat([0 1 2*nely+[2 3 0 1] -2 -1],nelx*nely,1);

%Strain-Displacement matrix for 4-node element
B = [-1/2 0 1/2 0 1/2 0 -1/2 0
0 -1/2 0 -1/2 0 1/2 0 1/2
-1/2 -1/2 -1/2 1/2 1/2 1/2 1/2 -1/2];
% UE = reshape(sum(U(edofMat),2),nely,nelx);

%Constitutive matrix for plane stress 
DE = 1/(1-nu^2)*[ 1 nu 0
nu 1 0
0 0 (1-nu)/2];

%% CODE OF CONCERN BEGINS HERE:
q = 1;
xPhys = xPhys(:);
E = Emin+xPhys.^q*(E0-Emin);

% Stress Calculation
s = (U(edofMat)*(DE*B)').*repmat(E,1,3);
vms = sqrt(sum(s.^2,2)-s(:,1).*s(:,2)+2.*s(:,3).^2);
c=max(vms);

%% CALCULATE GRADIENT
 dc = -1*dlgradient(c,x);
 dc = reshape(dc,[nely,nelx]);
 vms = reshape(vms,[nely,nelx]);
end

[Torsten]

We must have executable code that reproduces the computation for what you think gives wrong results.

The code from above does not help in this respect.

[Jackson Jewett]

Thank you for noting this. The code has many files, I have created a github repository here:

https://github.com/jljtt/Top88_AD