I have a pattern data with 12 categories. And I want to separate these data into two categories. So Can anyone tell me is it possible to do with a single layer neural network with 12 input values with the bias term? And also I implemented it with matlab but i'm having some doubt what should be the best initial weight values(range) and possible learning rate? can you please guide me on these cases.
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Anushka Ekanayake
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Is a single layer enough?
Whether a single hidden layer suffices to correctly label your input data depends on the complexity of your data. You should empirically try different topologies (combinations of layers and number of neurons) until you discover a setting that works for you.
What are the best weight ranges?
The recommended weight ranges depends on the activation function you intend to use. For the sigmoid function, the range is a small interval centered around 0, eg: [-0.1, 0.1]
What is the ideal learning rate?
The learning rate often set to a small value such as 0.03, but if the data is easily learned by your network you can often increase the rate drastically eg: 0.3. Check out this discussion on how learning rates affect the learning process: https://stackoverflow.com/a/11415434/1149632
A side note
You should search the Web for a few pointers and tips, and rather post more to the point questions on StackOverflow.
Check this out:
