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I have a completely beginner question about the setup of artificial neural networks. Basically it boils down to:

  • How do I put in data and get results? For example, classification: let say I have 10 input features, all numerical, and 5 output classes.
  • How do I set up my input layer and my output layer? Would it be good to have 10 input nodes, one for each feature and feed each node a single numerical value during feed forward evaluation?
  • How about the output layer? Should I set up 1 node, 5 nodes?
  • What is the classification rule for neural networks?
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  • $\begingroup$ I think you will have more luck with this question on the CrossValidated StackExchange, the statistics SE site. There are already quite a few NN questions, and a neural-net tag. $\endgroup$ – Michael Grant Apr 21 '13 at 1:32
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From the top of my mind, or the rest of knowlegd about back-propagation neural networks, I would start with two layers with 10 perceptron in the first and 5 in the last using a the sigmoid logistic activation function.

$$ 1/(1+exp(-\beta x)) $$

Entering dimension 10, output dimension 5.

Better take a look at Haykin, S. (1999). Neural Networks: A comprehensive foundation (2nd ed). Prentice Hall International.

Chapter 4 Multlayer Perceptrons. If back-propagation NN most commom feed forward NN.

Also if you did not take a look, check this

If you give more details about your problem we might be able to give a better asnwer.

What you mean by classification rule?

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