How to provide training data to the neural network?

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Vineet
Vineet 2013-4-23
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 个评论
Greg Heath
Greg Heath 2013-4-23
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.

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Greg Heath
Greg Heath 2013-4-23
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 !

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