How to provide training data to the neural network?

1 view (last 30 days)
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.
  1 Comment
Greg Heath
Greg Heath on 23 Apr 2013
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.

Sign in to comment.

Accepted Answer

Greg Heath
Greg Heath on 23 Apr 2013
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 !

More Answers (0)

Categories

Find more on Sequence and Numeric Feature Data Workflows in Help Center and File Exchange

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!