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. 2018 Nov 8;8:16520. doi: 10.1038/s41598-018-34525-1

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© The Author(s) 2018

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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Best predictions of CDI NN. (A) Histogram of the error computed for the test samples. (B) Close-up view of the predictions with the lowest χ² error. (C) Input diffraction patterns, (D) predicted intensities computed by taking an FT of the complex object obtained from the predicted shape and phase. (E,F) Corresponding actual shape and predicted shape. (G,H) Corresponding actual phase and predicted phase. CDI NN fares well when faced with large objects with weak phase. Intensity units are dimensionless while phase is in radians.