How to test a neural network which is trained for multiple input patterns (Own code and not nntool)?

Hi

I have developed and trained a neural network (3 layers: 1 input, 1 hidden and 1 output) for following situation

(The code was written step by step, as i do not want to jump directly to nntool without understanding the computations)

Data set (40 input patterns):

Input: 40 samples 5 elements

Output: 40 samples 1 element

number of neurons (Input = 5; hidden = 5; output = 1)

Using the delta rule with backpropagation algorithm, i was able to achieve error = 9.39E-06 for 1000 iterations

My final "input to hidden layer" weight matrix size is 200 x 5 (as i have 40 samples x 5 input neurons and 5 hidden neurons)

"hidden to output layer" weight matrix size is 200 x 1 (as i have 40 samples x 5 hidden neurons and 1 output neuron)

Now my question is for a given test sample having 5 elements (input is 1 sample 5 elements), i need to run feed-forward computation to get a single element output.

For running this which weights i need to select in "input to hidden layer" and "hidden to output layer" from the trained set??

I have 200 x 5 and 200 x 1 weight matrices; but i require only 5 x 5 and 5 x 1 weight matrices for testing.

Kindly let me know if i am missing something here?

Thanks in advance

Ravi

Answer 1

[Ravi mutturi]

There is fundamental mistake in my problem formulation. I am supposed to take same weight matrix for all patterns rather than changing for each pattern. I could fix my code. Thanks for reading the question.