I tested a pattern recognition neural network (using resilient backpropagation) with 2 hidden layers [5 4] on the iris dataset, and the mean-squared error of the network on the training data appears to be worse than test or validation. I found the same result with the Levenberg-Marquardt training algorithm. Does anyone have any ideas what would cause this?
edit: this does not happen every time that I run the code, but out of 100 trials it happened more often than not that the test, validation, or train (or two of the three) was separated from the rest.
load('iris_dataset')%use default parameters
net = patternnet([5 4], 'trainrp');net.trainParam.showWindow=0;[net, tr] = train(net, irisInputs, irisTargets);plotperform(tr);
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