Deep Dive into Deep Neural Networks with Flows

Abstract

Deep neural networks are becoming omnipresent in reason of their growing popularity in media and their daily use. However, their global complexity makes them hard to understand which emphasizes their black-box aspect and the lack of confidence given by their potential users. The use of tailored visual and interactive representations is one way to improve their explainability and trustworthy. Inspired by parallel coordinates and Sankey diagrams, this paper proposes a novel visual representation allowing tracing the progressive classification of a trained classification neural network by examining how each evaluation data is being processed by each network's layer. It is thus possible to observe which data classes are quickly recognized, unstable, or lately recognized. Such information provides insights to the user about the model architecture's pertinence and can guide on its improvement. The method has been validated on two classification neural networks inspired from the literature (LeNet5 and VGG16) using two public databases (MNIST and FashionMNIST).