Sources

[1]	Input pipeline structure. Accessed: 2019-01-10. URL: https://www.tensorflow.org/guide/performance/datasets.

[2]	Sacred read the docs. Accessed: 2019-01-10. URL: https://sacred.readthedocs.io/en/latest/.

[3]	Tensorflow data import. Accessed: 2019-01-10. URL: https://www.tensorflow.org/guide/datasets.

[4]	Tensorflow losses. Accessed: 2019-01-10. URL: https://www.tensorflow.org/api_docs/python/tf/losses.

[5]	Tensorflow train optimizer. Accessed: 2019-01-10. URL: https://www.tensorflow.org/api_docs/python/tf/train.

[6]

Mart’ın Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek Gordon Murray, Benoit Steiner, Paul A. Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zhang. Tensorflow: A system for large-scale machine learning. CoRR, 2016. URL: http://arxiv.org/abs/1605.08695, arXiv:1605.08695.

[7]	Martin Arjovsky, Soumith Chintala, and Léon Bottou. Wasserstein gan. –, 2017. URL: https://arxiv.org/abs/1701.07875.

[8]

Heng-Tze Cheng, Zakaria Haque, Lichan Hong, Mustafa Ispir, Clemens Mewald, Illia Polosukhin, Georgios Roumpos, D. Sculley, Jamie Smith, David Soergel, Yuan Tang, Philipp Tucker, Martin Wicke, Cassandra Xia, and Jianwei Xie. Tensorflow estimators: managing simplicity vs. flexibility in high-level machine learning frameworks. CoRR, 2017. URL: http://arxiv.org/abs/1708.02637, arXiv:1708.02637.

[9]	Yann N. Dauphin, Harm de Vries, Junyoung Chung, and Yoshua Bengio. Rmsprop and equilibrated adaptive learning rates for non-convex optimization. CoRR, 2015. URL: http://arxiv.org/abs/1502.04390, arXiv:1502.04390.

[10]

Jeffrey Dean, Greg S. Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Quoc V. Le, Mark Z. Mao, Marc’Aurelio Ranzato, Andrew Senior, Paul Tucker, Ke Yang, and Andrew Y. Ng. Large scale distributed deep networks. In Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1, NIPS‘12, 1223–1231. USA, 2012. Curran Associates Inc. URL: http://dl.acm.org/citation.cfm?id=2999134.2999271.

[11]	Li Deng. The mnist database of handwritten digit images for machine learning research. IEEE Signal Processing Magazine, November 2012. URL: https://www.microsoft.com/en-us/research/publication/the-mnist-database-of-handwritten-digit-images-for-machine-learning-research/.

[12]	Pedro Domingos. A few useful things to know about machine learning. Commun. ACM, 55(10):78–87, October 2012. URL: http://doi.acm.org/10.1145/2347736.2347755, doi:10.1145/2347736.2347755.

[13]	Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial networks. –, 2014. URL: https://arxiv.org/abs/1406.2661.

[14]	Klaus Greff, Aaron Klein, Martin Chovanec, Frank Hutter, and Jürgen Schmidhuber. The sacred infrastructure for computational research. -, 2017. URL: http://conference.scipy.org/proceedings/scipy2017/pdfs/klaus_greff.pdf.