publication . Preprint . 2016

Should I use TensorFlow

Schrimpf, Martin;
Open Access English
  • Published: 27 Nov 2016
Abstract
Google's Machine Learning framework TensorFlow was open-sourced in November 2015 [1] and has since built a growing community around it. TensorFlow is supposed to be flexible for research purposes while also allowing its models to be deployed productively. This work is aimed towards people with experience in Machine Learning considering whether they should use TensorFlow in their environment. Several aspects of the framework important for such a decision are examined, such as the heterogenity, extensibility and its computation graph. A pure Python implementation of linear classification is compared with an implementation utilizing TensorFlow. I also contrast Tens...
Subjects
free text keywords: Computer Science - Learning, Statistics - Machine Learning
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23 references, page 1 of 2

[1] Mart n Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Je rey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geo rey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mane, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viegas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. TensorFlow: Large-scale machine learning on heterogeneous systems, 2015. Software available from tensor ow.org.

[2] Mart n Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Je rey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geo rey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mane, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viegas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. Tensor ow repository. https://github:com/tensor ow/tensor ow, 2016.

[3] James Bergstra, Olivier Breuleux, Frederic Bastien, Pascal Lamblin, Razvan Pascanu, Guillaume Desjardins, Joseph Turian, David Warde-Farley, and Yoshua Bengio. Theano: a CPU and GPU math expression compiler. In Proceedings of the Python for Scienti c Computing Conference (SciPy), June 2010. Oral Presentation. [OpenAIRE]

[4] Jack Clark. Google turning its lucrative web search over to ai machines. Bloomberg, oct 2015.

[5] Christer Clerwall. Enter the robot journalist: Users' perceptions of automated content. Journalism Practice, 8(5):519{531, 2014.

[6] Ronan Collobert, Samy Bengio, and Johnny Mariethoz. Torch: a modular machine learning software library. Technical report, IDIAP, 2002.

[7] Je Dean. Large-scale deep learning for intelligent computer systems. https: //www:youtube:com/watch?v=QSaZGT4-6EY, March 2016. Tech Talk.

[8] Je rey Dean, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Mark Mao, Andrew Senior, Paul Tucker, Ke Yang, Quoc V Le, et al. Large scale distributed deep networks. In Advances in Neural Information Processing Systems, pages 1223{1231, 2012.

[9] See Bruce Fecheyr-Lippens, Bill Schaninger, and Karen Tanner. Power to the new people analytics. McKinsey Quarterly, March 2015.

[10] Otavio Good. How google translate squeezes deep learning onto a phone, jul 2015.

[11] Ian J. Goodfellow, Yaroslav Bulatov, Julian Ibarz, Sacha Arnoud, and Vinay D. Shet. Multi-digit number recognition from street view imagery using deep convolutional neural networks. CoRR, abs/1312.6082, 2013. [OpenAIRE]

[12] Peter Harrington. Machine learning in action. Manning, 2012.

[13] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385, 2015.

[14] Stacey Higginbotham. How facebook is teaching computers to see. Fortune, June 2015.

[15] Xuedong Huang. Microsoft computational network toolkit o ers most efcient distributed deep learning computational performance. https : / / blogs:technet:microsoft:com / inside microsoft research / 2015 / 12 / 07 / microsoft - computational- network- toolkit- o ers- most- e cient- distributed- deep- learningcomputational-performance, December 2015.

23 references, page 1 of 2
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