Abstract
We describe and implement a visualization tool for a self-splitting neural network (SSNN). The SSNN is a modular neural network that partitions the input domain during training through the identification of solved chunks and a divide-and-conquer strategy. The visualization tool shows a 2D projection of the input domain as partitioning proceeds, highlighting the boundaries of trained regions. Greyscale can be used to contrast the ranges of outputs so that generalization can be visually assessed. The tool is useful for illustrating how the SSNN works and for comparing different learning and splitting strategies.