In my Neural network implemented usign backpropagation in Matlab, I train the network for a regression problem with 98000 data points, I calculate the training accuracy for each epoch and then save weights and biases in a .mat file for testing.
Since my training data has 98000 points, therefore there are 98000 possile weight matrices in the .mat file.
Network Architecture: I-H1-O´= 5-8-1
I have the following questions:
- How to select the weight at which I should start the testing?
- Since during testing, there are no updates on weight, does it mean that I use only 1 weight and bias matrix for testing?
- Should I be selecting the weight and bias corresponding to minimum error in training set? If not, what else shoud be the criteria for selection?
Any leads would be appreciated.
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