In neural net function, how can I see normalized input data?

 % Automatic Normalization
[x,t] = simplefit_dataset;
net = newfit(x,t,10);
net = setwb(net,0.5*(ones(10+1))); 
net.divideFcn = 'divideind';   
net.divideParam.trainInd = 1:60; 
net.divideParam.valInd   = 61:94; 
net.divideParam.testInd  = 94; 
[net, tr] = train(net,x,t);
y = net(4)
 % Manual Normalization
[x,t] = simplefit_dataset;
net = newfit(x,t,10);
net.inputs{1}.processFcns={};
net.outputs{2}.processFcns={};
[x,ps] = mapminmax(x);
[t,pt] = mapminmax(t);
net = setwb(net,0.5*(ones(10+1)));
net.divideFcn = 'divideind';   
net.divideParam.trainInd = 1:60; 
net.divideParam.valInd   = 61:94;
net.divideParam.testInd  = 94; 
[net, tr] = train(net,x,t);
y = net(mapminmax('apply',4,ps));
y = mapminmax('reverse',y,pt)
The first code uses the normalization 
function of the neural network, the second 
code turns off the normalization function 
of the neural network, and the mapminmax 
function is entered manually.

I think the result of this code should be the same. However, the result y was different. Could you tell me why?

Greg Heath 2017-7-31

在 MATLAB Online 中打开

Do not just look at just one output point when you have 94. The best measure I can think of is the normalized mean square difference

NMSD = mse(y2-y1)/var(t,1)

However, use the default 0.7/0.15/0.15 data division ratio and compute the differences for the trn, val and tst subsets.

Hope this helps.

Thank you for formally accepting my answer

Greg

Greg Heath 2017-7-31

Thank you for this post. I really learned from it. As I result, see

http://www.mathworks.com/matlabcentral/... newsreader/view_thread/349217#955612

Thanks again,

Greg

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