How to get validation test and training errors of a neural network?
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);
John Williams answered .
2025-11-20
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