How to provide training data to the neural network?

I created a feed forward neural network using the newff function.

The code is below:

    net=newff(P,T, [5 5], {'tansig', 'purelin'},'trainlm', 'learngdm'); 

    net.trainParam.show = 10; %showing results after every 10 iterations
    net.trainParam.lr = 0.01; %learning rate
    net.trainParam.epochs = 50; %no. of iterations
    net.trainParam.goal = 0.0001; % percentage error goal

    net1 = train(net, P, T);%training the network

I need to know as to how do I provide the testing data after the training is done.

[Greg Heath]

If you can, change the title to use "additional testing" instead of "training". The adjective "additional" recognizes that the default data division already creates a nondesign testing subset.

Answer 1

[Greg Heath]

Always start with default values. If they don't work, change one at a time.

You have two hidden layers. One is sufficient.

 close all, clear all, clc

 [ p, t ] = simplefit_dataset;
 whos
 % Name  Size   Bytes  Class 
 %  p    1x94    752   double               
 %  t    1x94    752   double

 [ I N ] = size( p)      % [ 1 94 ]
 [ O N ] = size( t)      % [ 1 94 ]
 Ntst = round(0.15*N)    % 14 default data division
 Nval = Ntst             % 14 
 Ntrn = N-2*Ntst         % 66

 Ntrneq = Ntrn*O         % 66 No. of training equations

% Nw = (I+1)*H+(H+1)*O % No. of unknown weights

% Ntrneq > Nw when H < = Hub

 Hub = -1 + ceil( ( Ntrneq-O)/(I+O+1)  )  % 21
 H   = 10                 % Choose default value
 Nw  = O + (I+O+1)*H      % 31 unknown weights

% Initialize RNG so default random data division and random initial weights can be duplicated

 rng(0)
 net = newff( p, t, H );
 view(net)
 [ net tr y0 e0 ] = train( net, p, t );
 y = net(p);        % same as y0
 e = t-y;           % same as e0
 MSE = mse(e)       %  4.988e-8
 tr = tr            % training details

% New data

 ynew = net(pnew);

Hope this helps.

Thank you for formally accepting my answer

Greg

P.S. See tr for the separate training, validation and test results !