Building Complex Network Architectures with Computation Graph - Deeplearning4j: Open-source, Distributed Deep Learning for the JVM

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.