How To Use My NeuRal Network Model?

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Hi,
I am new in NN and like test some strategies with that. I am using one C++ Modelling Code for NN that design NN.
this code makes for me weights and other important parameters of NN.
my problem is how use this info for predict other my data and test how my model will work?
For example I have 2000 data.
3 Inputs and 1 output
Using 1 hidden layer.
model makes 6 weights for me.
I want know How I can use this data for test my NN?
Regards,

Posted 25-Jul-11 3:22am

rezafx

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DaanSieben · Answer 1 · 2011-07-25T03:31:00

Solution 1

Hi,

There isn't a way to determine when your Neural Network is 100% done "learning" the given equation. After feeding it all input you have, you can randomly input some data, see if the given result is what you expect. If repeated muliple times you can determine a "correctness" of the Neural Network, for example 9 out of 10.

-- Daan

Posted 25-Jul-11 3:31am

DaanSieben

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rezafx 25-Jul-11 9:38am

Hi,
Thanks.
I mean how to add my new data and check output for that?
Regards,

DaanSieben 25-Jul-11 9:46am

Hi,

That depends on your code... Do you have a sample?

-- Daan

rezafx 25-Jul-11 10:41am

Hi,
Yes, I am using this code:
http://takinginitiative.net/2008/04/23/basic-neural-network-tutorial-c-implementation-and-source-code/

Regards,

Guyverthree · Answer 2 · 2011-07-25T04:31:00

There are many ways to test that a Neural Network is working correctly.

The most common I have come across are:

You could have a learning set and then assess it to be finished learning when the sum square of the errors drops below a certain threshold.
The problem with this approach is over training can become an issue. I.E. The Neural Net learns only the patterns you present and not that general problem solution.

You can also have a set of validation data which is not in the learning set but is used to test that the neural net has learnt the general problem solution and so it's only the error on this set which is compared to the threshold. This can lead to long training times as some Nets will not learn the validation set no matter how well trained if the validation set is unusual.

You could also set an arbitrary limit on the number of epochs, that you will learn for. This is the worst method because you cannot guarantee that it has learnt anything at all in the time limit.