MATLAB: How to get validation test and training errors of a neural network

Deep Learning Toolboxneural networkneural networks

I have created and trained a neural network using the following code .I want to know how to get the training testing and validation errors/mis-classifications the way we get using the matlab GUI.
trainFcn = 'trainscg';  % Scaled conjugate gradient backpropagation.
   % Create a Pattern Recognition Network
   hiddenLayerSize = 25;
   net = patternnet(hiddenLayerSize);
% Setup Division of Data for Training, Validation, Testing
net.divideParam.trainRatio = trainper/100;
net.divideParam.valRatio = valper/100;
net.divideParam.testRatio = testper/100; 
% Train the Network
[net,tr] = train(net,x,t);

Best Answer

BOTH documentation commands
 help patternnet
and
 doc patternnet
have the following sample code for CLASSIFICATION & PATTERN-RECOGNITION:
    [x,t] = iris_dataset;
     net  = patternnet(10);
     net  = train(net,x,t);
     view(net)
     y = net(x);
     perf = perform(net,t,y);
     classes = vec2ind(y);
However, the following are missing
 1. Dimensions of x and t 
 2. Plots of x, t, and t vs x
 3. Minimum possible number of hidden nodes
 4. Initial state of the RNG (Needed for duplication)
 5. Training record, tr
 6. Plots of e = y-t vs x
 7. Misclassified cases
 8. trn/val/tst Error rates
For details see my NEWSGROUP posts
 SIZES OF MATLAB CLASSIFICATION EXAMPLE DATA SETS
http://www.mathworks.com/matlabcentral/newsreader/...
       view_thread/339984
 and
 BEYOND THE HELP/DOC DOCUMENTATION : PATTERNNET for 
 NN Classification and PatternRecognition
 http://www.mathworks.com/matlabcentral/newsreader/...
     view_thread/344832
Hope this helps.
Thank you for formally accepting my answer
Greg
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