I. Introduction
The world of deep learning is constantly seeing new results as researchers find new ways to configure networks or find new data sets to train neural networks. One such new advancement in deep learning is the introduction of the capsule network presented in [1, 2]. The capsule network is a novel new idea for a neural network wherein neurons are replaced by capsules. One of the primary differences between a neuron and a capsule is that the input and output to a capsule is a vector, whereas a neuron has a scalar input and output. Capsule networks are also better at identifying objects that are rotated differently in images by keeping track of pose information when learning on a training set [1]. Finally, capsule networks can train with less data than similar convolutional neural networks.