"Syed Amin" wrote in message <email@example.com>... > "Greg Heath" <firstname.lastname@example.org> wrote in message <email@example.com>... > > ... > > > function mse_calc = mse_test(x, net, inputs, targets) > > > % 'x' contains the weights and biases vector > > > % in row vector form as passed to it by the > > > % genetic algorithm. This must be transposed > > > % when being set as the weights and biases > > > % vector for the network. > > > % To set the weights and biases vector to the > > > % one given as input > > > net = setwb(net, x); > > > % To evaluate the ouputs based on the given > > > % weights and biases vector > > > y = net(inputs); > > > % Calculating the mean squared error > > > mse_calc = sum((y-targets).^2)/length(y); > > > > No. This is only valid for vectors. For matrices > > there are many equivalent forms > > > > Neq = prod(size(targets)) > > e = targets - y; > > > > MSE = sum(sum(e.^2))/Neq > > MSE = sumsqr(e)/Neq > > MSE = sse(e)/Neq > > MSE = mse(e) > > > > There are several ways to mitigate overfitting, > > > > 1. Reduce H so that Neq >>Nw. An adequate value of r = Neq/Nw depends on the data. > > 2. Adjust MSE above by replacing Neq with Ndof = Neq - Nw, the number of degrees of freedom for estimation. > > 3. Use a holdout (from training) validation subset to stop the minimization of MSEtrn when MSEval is a minimum. > > 4. Use regularization with the modified MSE > > > > MSEreg = MSE + alpha*mse(x) > > > > I don't remember how alpha is determined. Check the source codes of msereg and > > trainbr; e.g., > > > > help msereg > > doc msereg > > type msereg > > > > Hope this helps. > > > > Greg > > > > > > > > > > Thanks Greg, > It worked quite well .Now that I have optimized the weights could you tell me how to use these optimized weights to train neural network or patternnet >
I don't understand. Optimizing the weights IS the training. All that is left is to put them in the net using setwb or net.IW, net.LW and net.b.