I wrote a code for classification, using a” patternnet “neural network to classify a dataset which is 2D two spiral dataset, all my data were 40 in two classes each class population was 20, I manually separated it in two parts, part one training and validation and part two testing, so 32 out of 40 is associated in training and validation phase and 8 for testing, the separation process is randomly, I give data of part one to the net 90% for training, 10% for validation.
net = patternnet(70);
net.divideParam.trainRatio=0.9
net.divideParam.valRatio=0.1
net.divideParam.testRatio=0
then I use the trained net for testing the data of part two, my problem is no matter how many neurons I use, the classification result is perfect , over fitting doesn’t happen , even if I use 70 neurons , how such thing is possible?
I should work with neural networks which are capable of over fitting easily, I use small size data sets which are distributed in a complex pattern, like spirals , or banana-shaped data sets , I would like to have them in higher dimension space but unfortunately I couldn’t generate them in higher spaces .
