Understanding neural networks through deep visualization

Recent years have produced great advances in training large, deep neural networks (DNNs), in-cluding notable successes in training convolu-tional neural networks (convnets) to recognize natural images. However, our understanding of how these models work, especially what compu-tations they perform at intermediate layers, has lagged behind. Progress in the field will be further accelerated by the development of bet-ter tools for visualizing and interpreting neural nets. We introduce two such tools here. The first is a tool that visualizes the activations pro-duced on each layer of a trained convnet as it processes an image or video (e.g. a live web-cam stream). We have found that looking at live activations that change in response to user input helps build valuable intuitions about how con-vnets work. The second tool enables visualizing features at each layer of a DNN via regularized optimization in image space. Because previous versions of this idea produced less recognizable images, here we introduce several new regular-ization methods that combine to produce qualita-tively clearer, more interpretable visualizations. Both tools are open source and work on a pre-trained convnet with minimal setup