Qnet 2000 |
The input layer of a neural network has the sole purpose of distributing input data values to the first hidden layer. The number of nodes in the input layer will be equal to the number of input data values in the model. For example, assume a lender wishes to create a neural network that will accept or reject automobile loan applications. Inputs could include things such as the loan applicant’s age, marital status, the number of dependents, education status, total family income, the total monthly debt payments (house, cars, credit cards, etc.) and the monthly payment required for the new loan. If this is the extent of input information for the model, then this network would be designed with 7 input nodes. The output of this model is simply whether the person is qualified or not (1=yes, 0=no). Therefore, the output layer would consist of one node. Each case of 7 inputs and 1 output is referred to as one training pattern. If there is previous loan information for 5000 people, the model could be trained using 5000 patterns.
The number of nodes in the input and output layers are set in the network design dialog.