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How can i evaluate my network performance as i have trained my model?

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I have trained my model with 100% accuracy,but i want to evaluate my trained work from test data set or unseen data.what should i add in my code for testing purpose? i-e to test validation data and test data
p = u; %inputs
t = f; %targets
[pn,ps] = mapminmax(p);
[tn,ts] = mapminmax(t);
%net = newff(p,t,10,10{},'trainlm');
%net = init(net);
% net.IW{1,1}=wts0;
% net.b{1}=bias0; =2;
net.trainParam.epochs =5000;
net.trainParam.goal =1e-7;;;
[net,tr] = train(net,pn,tn);
ANN = sim(net,pn);
output1= mapminmax('reverse',ANN,ts);

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Walter Roberson
Walter Roberson il 18 Feb 2017
  7 Commenti
Walter Roberson
Walter Roberson il 18 Feb 2017
The code for that example does not create a network named "net". Are you trying to apply that to deepnet just before
% Train the deep network on the wine data.
Machine Learning Enthusiast
Modificato: Machine Learning Enthusiast il 20 Feb 2017
yes....i am not sure about the syntax "deepnet" or only "net".i want to divide my data into train,validation and test as given below and at the end i am trying to get confusion matrix with validation and test set as shown in attached figure.
% Setup Division of Data for Training, Validation, Testing
deepnet.divideParam.trainRatio = 80/100;
deepnet.divideParam.valRatio = 10/100;
deepnet.divideParam.testRatio = 10/100;

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