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. 2020 Jan 21;10:834. doi: 10.1038/s41598-020-57866-2

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© The Author(s) 2020

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Box and whisker plots showing the distribution of the classification error against the structural hyperparameters (i.e. VC-dimension, Depth, Number of Convolutional Layers, Number of Fully Connected Layers and Number of Max-Pooling Layers) for subsampled CIFAR-10, MNIST and the Mitosis dataset (columns). Notice the difference in the trends followed for each dataset, indicating that the effect of the structural hyperparameters on the performance of each structure, for each dataset is unique. Also note the difference in the values of the structural attributes at which the overall lowest validation classification error is achieved, indicating a unique optimal structure for each dataset.