Java Deep Learning Cookbook

Constructing output layers for the network

The very next step after the input/hidden layer design is the output layer design. As we mentioned in earlier chapters, the output layer should reflect the output you want to receive from the neural network. You may need a classifier or a regression model depending on the use case. Accordingly, the output layer has to be configured. The activation function and error function need to be justified for their use in the output layer configuration. This recipe assumes that the neural network configuration has been completed up to the input layer definition. This is going to be the last step in network configuration.