The short answer is that train(only if all weights are zero) or configure( anytime) assign weights depending on the state of the RNG.
If you are training in a loop over random initial weights. The best statistically unbiased choice for the best net is determined by the minimum mse of the validation set.
rng(4151941) % Initialize RNG with famous birthday
for i = 1:Ntrials
s{i} = rng; %Save the ith state of the rng (may not need a cell)
net = configure(net,x,t);
[ net tr ] = train(net,x,t);
mseval(i) = tr.best_vperf; % Best mseval over all epochs of ith run
end
[ minmseval ibest ] = min(mseval);
rng = s{ibest}; % For repeating the best design
bestnet = configure(net,x,t);
bestIW0 = bestnet.IW
bestb0 = bestnet.b
bestLW0 = bestnet.LW
[ bestnet tr ] = train(bestnet,x,t) % NO SEMICOLON TO REVEAL ALL DETAILS!!!
Hope this helps.
Thank you for formally accepting my answer
Greg
