Jönsson, DanielEilertsen, GabrielShi, HeziZheng, JianminYnnerman, AndersUnger, JonasArchambault, Daniel and Nabney, Ian and Peltonen, Jaakko2020-05-242020-05-242020978-3-03868-113-7https://doi.org/10.2312/mlvis.20201101https://diglib.eg.org:443/handle/10.2312/mlvis20201101We present an analysis of the impact of hyper-parameters for an ensemble of neural networks using tailored visualization techniques to understand the complicated relationship between hyper-parameters and model performance. The high-dimensional error surface spanned by the wide range of hyper-parameters used to specify and optimize neural networks is difficult to characterize - it is non-convex and discontinuous, and there could be complex local dependencies between hyper-parameters. To explore these dependencies, we make use of a large number of sampled relations between hyper-parameters and end performance, retrieved from thousands of individually trained convolutional neural network classifiers. We use a structured selection of visualization techniques to analyze the impact of different combinations of hyper-parameters. The results reveal how complicated dependencies between hyper-parameters influence the end performance, demonstrating how the complete picture painted by considering a large number of trainings simultaneously can aid in understanding the impact of hyper-parameter combinations.Computing methodologiesNeural networksHumancentered computingVisual analytics10.2312/mlvis.2020110113-17