The test dataset (-ti, -to) is used as a stopping criterion. The learning does not actually stop but learning algorithm checks after every successful training epoch if the error of the test set is smaller than before during the training and saves the network weights. After the maximum number of the training epochs have been reached, these weights are saved to the namespace.
Example (ex5.6): Train a three-layered (input + hidden + output layer) network and save the network output.
NDA> load sin.dat NDA> select sinx -f sin.x NDA> select siny -f sin.y NDA> bp -di sinx -do siny -net 3 1 10 1 -types s s s -em 2000 -nout wei -ef virhe -bs 0.01 NDA> fbp -di sinx -do out -win wei NDA> select output -f sin.x out.0 NDA> save output