CODEWITHSUNDEEP
machine-learningartificial-intelligenceneural-networkencog
Nate Cook3
Nate Cook3

Reputation: 596

Encog Neural Network - How to actually run testing data

I've been able to train the network, and gotten it trained down to the minimal error I want...

I don't actually see anywhere, even when I looked through the guide book, how to test the trained network on new data... I split part of my training data apart so that I can test the network's results on untrained data since I'm using it for classification. Here is the code I've got, not sure what to do with the MLData output. For classification, I just want to take the output neuron with the highest value... aka, most likely to be the correct classification node.

    MLDataSet testingSet = new BasicMLDataSet(testingTraining, testingIdeal);
    System.out.println("Test Results:");
    for(MLDataPair pair: testingSet ) {
        final MLData output = network.compute(pair.getInput());
        //what do I do with this output?
    }

(My testing data is obviously tagged with the correct classifications...)

Upvotes: 0

Views: 610

Answers (1)

nitbix
nitbix

Reputation: 153

Well it depends on what problem you have at hand, but the idea is that your output should be as close as possible to the test dataset output, so I suggest comparing that. For example, if this is a classification task, your output will be iterable and you should be able to work out what the selected output class is and compare it to the target. You can the work out a misclassification rate, or any other measure of accuracy (precision, recall, F1-score..). So something like:

int bad = 0;
for(MLDataPair pair: testingSet)
{
    MLData output = network.compute(pair.getInput());
    if(outputClass(output) != outputClass(pair.getIdeal()))
        bad++;
}
double misclassificationRate = bad / testingSet.size()

You would have to write outputClass appropriately so that it returns the classification output, of course. For regression you can do something similar, but instead of the mapping you would be looking at some distance measure between the two outputs to work out your error.

Upvotes: 2

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