MultiLayerNetwork'MultiLayerNetwork' consists of a single input layer and a single output layer with a stack of layers in between them.

This page describes how to build more complicated networks, using DL4J's Computation Graph functionality. New Features!!! as of 7.3 Computation Graph will support a parameterless LossLayer and vertices for performing triplet loss. As a general rule, when building networks with a single input layer, a single output layer, and an input- a- b- c- output type connection structure: MultiLayerNetwork is usually the preferred network. However, everything that MultiLayerNetwork can do, ComputationGraph can do as well - though the configuration may be a little more complicated. The basic idea is that in the ComputationGraph, the core building block is the GraphVertex, instead of layers.

For example, suppose we want to use a RNN to predict the weather, one hour in advance (based on the weather at say the previous 100 hours as input). If we were to use the output method, at each hour we would need to feed in the full 100 hours of data to predict the weather for hour 101. Then to predict the weather for hour 102, we would need to feed in the full 100 (or 101) hours of data; and so on for hours 103 . Alternatively, we could use the rnnTimeStep method.