# How to setup a Neural Network?

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?
• 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. – Michael Grant Apr 21 '13 at 1:32

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.

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?