How to change Neural Network performance from MSE to MAE?

Is it possible to run a neural network that uses MAE instead of MSE?

I am trying to create a neural network that describes the relationship between numerous factors and the binary outcome. This is the code that I have written so far.

clear all; format compact
load ShotData;
num_reps=20; C=[2 4 6]; n=length(A);
I = A(1:n,C)'; T = B(1:n)';
tic; for k=1:num_reps
net = feedforwardnet(10,'trainscg'); net = train(net,I,T); a = net(I);
e = T - a; perf(1:num_reps) = mae(e)';
end
perf_final=mean(perf); z=toc;

I previously analyzed the data using logistic regression and then computed MAE. In order to be about to compare methods I need help to create a neural network that uses MAE.

Answer 1

[Greg Heath]

 clear all; 
format compact
load ShotData; % Contains A and B. 
%What are size(A) and size(B)?
[rA cA]  = size(A)
[rB cB ] = size(B)
num_reps = 20; 
C = [2 4 6]; 
n = length(A); % Improper syntax if A is 2-dimensional;
I = A(1:n,C)'; 
T = B(1:n)';

 net               = feedforwardnet(10,'trainscg'); 
net.performFcn    =  'mae';
tic;
for k = 1:num_reps
    net = configure(net,I,T); % Initialize weights
    [ net tr a e ]    = train(net,I,T); 
   %a = net(I); e    = T - a; % Unnecessary see above
   %perf(1:num_reps) = mae(e)'; % Improper syntax 
   perf(k,1)         = mae(e);
end

 perf_final = mean(perf); 

 z = toc;

% 1. You didn't take into account that some of the designs may have not converged to a low % minimum because of poor initial weights. Discard useless designs before taking mean. Can use % min, median, mean, std, and max to discard outliers.

% 2. You did not take into account the default trn/val/tst division. The corresponding indices % are contained in tr. Look at the results of the command

 tr = tr

Thank you for formally accepting my answer

Greg