function [net,epochs,rms] = trainnet(in,targets,S1,validation)% WBL 22 August 2002 Use P450 training data to train Matlab MLP Nural Network% $Revision: 1.5 $  $Date: 2002/10/31 11:22:31 $%WBL 13 Sep 2002 as trainnet - but pass net via arguments, rather than saving to file%WBL  9 Sep 2002 implement early stopping%WBL  9 Sep 2002 Based on read.m %    DEFINING THE NETWORK%    ====================% S1 = number of nodes in hidden layer% S2 = number of output nodes (ie 2)S2 =  2;net = newff(minmax(in),[S1 S2],{'logsig' 'logsig'},'traingdx');%net.LW{2,1} = net.LW{2,1}*0.01;%net.b{2} = net.b{2}*0.01;%    TRAINING THE NETWORK WITHOUT NOISE%    ==================================net.performFcn = 'sse';        % Sum-Squared Error performance function%net.trainParam.goal = 0.1;     % Sum-squared error goal.net.trainParam.show = inf;      % Frequency of progress displays (in epochs).net.trainParam.epochs = 5000;  % Maximum number of epochs to train.%net.trainParam.epochs = 5;  % Maximum number of epochs to train.%net.trainParam.mc = 0.95;      % Momentum constant.%    Training begins...please wait...%[net,tr] = train(net,in,targets);[net,tr] = train(net,in,targets,[],[],validation);%trepochs = tr.epoch(size(tr.epoch,2));rms = sqrt(tr.perf(size(tr.perf,2))/size(in,2));%filename = sprintf('13-sep-2002_%dx%d', S1, ntests);%save (filename, 'net');