Testing a Backpropagation Neural Network

I have developed a backpropagation Neural Network in Matlab. The script for training the Network works fine and predicted output follows the expected output.

The weights, biases, network architecture,inputs, targets from training network are stored in the .mat file. This .mat file is then loaded in the testing script and only the forward propagation part is implemented in the testing script to get the expected output.

However, the output from testing is not anywhere close to the expected output even though the training accuracy is quite high (more than 95% in each run).

Is it because the network is not being trained enough or Is it some problem with the code that needs debugging? Can someone tell the reason for it and any possible solutions to the problem.

Attached are the files used for training and testing.

Any leads would be appreciated.

Answer 1

[Greg Heath]

You are probably

OVERTRAINING AN OVERFIT NET

OVERFITTING: Using more unknown hidden nodes than number of training equations

OVERTRAINING:Training longer than is practical for a good solution.

I have written several zillion posts in both the NEWSGROUP and ANSWERS

Hope this helps

Thank you for formally accepting my answer

Greg

[Zeeshan Ahmad Khan]

I have reduced the number of hidden nodes and have calculated the number of training equations and weights accroding to the following equation:

For a double hidden layer network,

Ntrneq = Ntrn*O

Nw = (I+1)*H1 + (H1+1)*H2+(H2+1)*O

However, regarding Overtraining, can you provide a lead on how to get rid of Overtraining.

I have already implemened L2 regularization in the script.

Any leads would be appreciated.

Answer 2

[Miss Jenny]

Can someone tell me how to test the networks, i have training the dataset by using the pattern recognition apps in the toolbox. But i don't know how to proceed with testing the networks.