Hamid, SagadDerstroff, AdrianKlemm, SörenNgo, Quynh QuangJiang, XiaoyiLinsen, LarsArchambault, Daniel and Nabney, Ian and Peltonen, Jaakko2019-06-022019-06-022019978-3-03868-089-5https://doi.org/10.2312/mlvis.20191160https://diglib.eg.org:443/handle/10.2312/mlvis20191160A good deep neural network design allows for efficient training and high accuracy. The training step requires a suitable choice of several hyper-parameters. Limited knowledge exists on how the hyper-parameters impact the training process, what is the interplay of multiple hyper-parameters, and what is the interrelation of hyper-parameters and network topology. In this paper, we present a structured analysis towards these goals by investigating an ensemble of training runs.We propose a visual ensemble analysis based on hyper-parameter space visualizations, performance visualizations, and visualizing correlations of topological structures. As a proof of concept, we apply our approach to deep convolutional neural networks.10.2312/mlvis.2019116019-23