Use Classification Neural Network Model for another Dataset

Dear All; I have built an AI model to classify the data using a dataset. Then i try to test this model to classify an external data set but it does not work properly because the code is not properly made , the code is below , could you help me in that :

clc; clear; close all; %Read The data [x1,TXT,RAW]=xlsread('ALL2.xlsx','lnRe'); [t1,TXT2,RAW2]=xlsread('ALL2.xlsx','OUT2');

 x=x1'; 
t=t1';
% Build the model
net= patternnet ([100]);
% net.divideParam.trainRatio = 70/100;
% net.divideParam.valRatio   = 15/100;
% net.divideParam.testRatio  = 15/100;
%  view(net)
net=init(net);
[net,tr] = train(net,x,t);

% Test the Network [x2,TXT3,RAW3]=xlsread('expsettest.xlsx','Ln(Re)'); [t2,TXT4,RAW4]=xlsread('expsettest.xlsx','out-test'); xt=x2'; tt=t2'; outputs = net( xt); errors = gsubtract(tt,outputs); performance = perform(net,tt,outputs)

figure, plotconfusion(tt,outputs)

Answer 1

[Greg Heath]

100 hidden nodes appears to be a ridiculous number.

 Why don't you start by just using all defaults.

 help patternnet
 doc patternnet

 Then Search NEWSGROUP and ANSWERS using

 greg patternnet

Hope this helps.

Thank you for formally accepting my answer

Greg

[mustafa alnasser]

Dear Greg

Thank you for your help

Let me explain for you my issue:

My problem is in the second part of the code, which is test part . in the confusion matrix , i got unreasonable results in terms of data points for each group , it gave more data that it should be for each group , i feel that i am not programing the test code in right way

Best Regards

[Greg Heath]

Please explain your variables and their dimensions