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Year 2020, , 169 - 182, 30.12.2020

Abdullah Talha Kabakuş

https://doi.org/10.35377/saucis.03.03.776573
Cited By: 4

Abstract

References

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  • N. Brancati, G. De Pietro, M. Frucci, and D. Riccio, “A Deep Learning Approach for Breast Invasive Ductal Carcinoma Detection and Lymphoma Multi-Classification in Histological Images,” IEEE Access, vol. 7, pp. 44709–44720, 2019, doi: 10.1109/ACCESS.2019.2908724.
  • Y. Weng, F. Bell, H. Zheng, and G. Tur, “OCC: A Smart Reply System for Efficient In-App Communications,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD ’19), 2019, pp. 1–8, doi: 10.1145/3292500.3330694.
  • W. G. Hatcher and W. Yu, “A Survey of Deep Learning: Platforms, Applications and Emerging Research Trends,” IEEE Access, vol. 6, pp. 24411–24432, 2018, doi: 10.1109/ACCESS.2018.2830661.
  • X. W. Chen and X. Lin, “Big Data Deep Learning: Challenges and Perspectives,” IEEE Access, vol. 2, pp. 514–525, 2014, doi: 10.1109/ACCESS.2014.2325029.
  • N. D. Nguyen, T. Nguyen, and S. Nahavandi, “System Design Perspective for Human-Level Agents Using Deep Reinforcement Learning: A Survey,” IEEE Access, vol. 5, pp. 27091–27102, 2017, doi: 10.1109/ACCESS.2017.2777827.
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  • V. Nair and G. E. Hinton, “Rectified Linear Units Improve Restricted Boltzmann Machines,” in Proceedings of the 27th International Conference on Machine Learning (ICML 2010), 2010, pp. 807–814.
  • Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nat. Methods, vol. 521, pp. 436–444, 2015, doi: 10.1038/nmeth.3707.
  • P. Goldsborough, “A Tour of TensorFlow,” arXiv Prepr., vol. 1610.01178, pp. 1–16, 2016.
  • L. Rampasek and A. Goldenberg, “TensorFlow: Biology’s Gateway to Deep Learning?,” Cell Syst., vol. 2, no. 1, pp. 12–14, 2016, doi: 10.1016/j.cels.2016.01.009.
  • S. Ioffe and C. Szegedy, “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,” in Proceedings of the 32nd International Conference on Machine Learning (ICML 2015), 2015, pp. 448–456.
  • F. Chollet, “Keras: the Python deep learning API,” 2015. https://keras.io (accessed Sep. 03, 2020).
  • A. Paszke et al., “PyTorch: An Imperative Style, High-Performance Deep Learning Library,” in Proceedings of the Thirty-third Conference on Neural Information Processing Systems (NIPS 2019), 2019, pp. 8026–8037.
  • Y. Jia et al., “Caffe: Convolutional Architecture for Fast Feature Embedding,” in Proceedings of the 22nd ACM International Conference on Multimedia (MM 2014), 2014, pp. 675–678, doi: 10.1145/2647868.2654889.
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  • M. Abadi et al., “TensorFlow: A System for Large-Scale Machine Learning,” in Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 2016), 2016, pp. 265–283.
  • R. Collobert, K. Kavukcuoglu, and C. Farabet, “Torch7: A Matlab-like Environment for Machine Learning,” in Proceedings of the Twenty-fifth Conference on Neural Information Processing Systems (NIPS 2011), 2011, pp. 1–6.
  • S. Bahrampour, N. Ramakrishnan, L. Schott, and M. Shah, “Comparative Study of Caffe, Neon, Theano, and Torch for Deep Learning,” in Proceedings of the 4th International Conference on Learning Representations (ICLR 2016), 2016, pp. 1–11, doi: 10.1227/01.NEU.0000297044.82035.57.
  • “NervanaSystems/neon: Intel® NervanaTM reference deep learning framework committed to best performance on all hardware,” Intel, 2015. https://github.com/NervanaSystems/neon (accessed Aug. 02, 2020).
  • S. Shi, Q. Wang, P. Xu, and X. Chu, “Benchmarking State-of-the-Art Deep Learning Software Tools,” in Proceedings of the 2016 7th International Conference on Cloud Computing and Big Data (CCBD 2016), 2016, pp. 99–104, doi: 10.1109/CCBD.2016.029.
  • S. Chintala, “Easy benchmarking of all publicly accessible implementations of convnets,” 2017. https://github.com/soumith/convnet-benchmarks (accessed Aug. 02, 2020).
  • M. Marcus, B. Santorini, and M. Marcinkiewicz, “Building a Large Annotated Corpus of English: The Penn Treebank,” Comput. Linguist., vol. 19, no. 2, pp. 313–330, 1993.
  • A. Shatnawi, G. Al-Bdour, R. Al-Qurran, and M. Al-Ayyoub, “A Comparative Study of Open Source Deep Learning Frameworks,” in Proceedings of the 2018 9th International Conference on Information and Communication Systems (ICICS 2018), 2018, pp. 72–77, doi: 10.1109/IACS.2018.8355444.
  • Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE, vol. 86, no. 11, pp. 2278–2324, 1998, doi: 10.1109/5.726791.
  • A. Krizhevsky, “Learning Multiple Layers of Features from Tiny Images,” 2009. doi: 10.1.1.222.9220.
  • V. Kovalev, A. Kalinovsky, and S. Kovalev, “Deep Learning with Theano, Torch, Caffe, TensorFlow, and Deeplearning4J: Which One Is the Best in Speed and Accuracy?,” in Proceedings of the 13th International Conference on Pattern Recognition and Information Processing (PRIP 2016), 2016, pp. 99–103.
  • “Deeplearning4j: Deep Learning for Java,” Konduit, 2020. https://deeplearning4j.org (accessed Aug. 02, 2020).
  • “NVIDIA cuDNN,” NVIDIA, 2020. https://developer.nvidia.com/cudnn (accessed Aug. 02, 2020).
  • A. Vedaldi and K. Lenc, “MatConvNet: Convolutional Neural Networks for MATLAB,” in Proceedings of the 23rd ACM International Conference on Multimedia (MM’15), 2015, pp. 689–692.
  • F. Chollet, Deep Learning with Python. Manning Publications, 2017.
  • N. Ketkar, Deep Learning with Python. Springer, 2017.
  • S. Chintala, “Roadmap for torch and pytorch,” 2017. https://discuss.pytorch.org/t/roadmap-for-torch-and-pytorch/38/2 (accessed Aug. 02, 2020).
  • B. Hayes, “Programming Languages Most Used and Recommended by Data Scientists,” Business Over Broadway, 2019. https://businessoverbroadway.com/2019/01/13/programming-languages-most-used-and-recommended-by-data-scientists/ (accessed Aug. 02, 2020).
  • “Caffe2 and PyTorch join forces to create a Research + Production platform PyTorch 1.0,” 2018. https://caffe2.ai/blog/2018/05/02/Caffe2_PyTorch_1_0.html (accessed Aug. 02, 2020).
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  • D. Yu et al., “An Introduction to Computational Networks and the Computational Network Toolkit,” 2015. [Online]. Available: https://www.microsoft.com/en-us/research/wp-content/uploads/2014/08/CNTKBook-20160217.pdf.
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  • “Colaboratory,” Google, 2020. https://colab.research.google.com (accessed Sep. 03, 2020).
  • O. Y. Al-Jarrah, P. D. Yoo, S. Muhaidat, G. K. Karagiannidis, and K. Taha, “Efficient Machine Learning for Big Data: A Review,” Big Data Res., vol. 2, no. 3, pp. 87–93, 2015, doi: 10.1016/j.bdr.2015.04.001.
  • T. Condie, P. Mineiro, N. Polyzotis, and M. Weimer, “Machine learning on Big Data,” in Proceedings of the 2013 IEEE 29th International Conference on Data Engineering (ICDE 2013), 2013, pp. 1242–1244.
  • D. C. Cireşan, U. Meier, L. M. Gambardella, and J. Schmidhuber, “Deep, Big, Simple Neural Nets for Handwritten Digit Recognition,” Neural Comput., vol. 22, no. 12, pp. 3207–3220, 2010, doi: 10.1162/NECO_a_00052.
  • D. P. Kingma and J. L. Ba, “Adam: A Method for Stochastic Optimization,” in Proceeding of the 3rd International Conference on Learning Representations (ICLR 2015), 2015, pp. 1–15.
  • H. Robbins and S. Monro, “A Stochastic Approximation Method,” Ann. Math. Stat., vol. 22, no. 3, pp. 400–407, 1951, doi: 10.1214/aoms/1177729586.
  • K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv Prepr., pp. 1–14, 2014, [Online]. Available: http://arxiv.org/abs/1409.1556.
  • H. Wang, Y. Zhang, and X. Yu, “An Overview of Image Caption Generation Methods,” Comput. Intell. Neurosci., vol. 2020, pp. 1–13, 2020, doi: 10.1155/2020/3062706.
  • A. L. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng, and C. Potts, “Learning Word Vectors for Sentiment Analysis,” 2011.

A Comparison of the State-of-the-Art Deep Learning Platforms: An Experimental Study

Year 2020, , 169 - 182, 30.12.2020

Abdullah Talha Kabakuş

https://doi.org/10.35377/saucis.03.03.776573
Cited By: 4

Abstract

Deep learning, a subfield of machine learning, has proved its efficacy on a wide range of applications including but not limited to computer vision, text analysis and natural language processing, algorithm enhancement, computational biology, physical sciences, and medical diagnostics by producing results superior to the state-of-the-art approaches. When it comes to the implementation of deep neural networks, there exist various state-of-the-art platforms. Starting from this point of view, a qualitative and quantitative comparison of the state-of-the-art deep learning platforms is proposed in this study in order to shed light on which platform should be utilized for the implementations of deep neural networks. Two state-of-the-art deep learning platforms, namely, (i) Keras, and (ii) PyTorch were included in the comparison within this study. The deep learning platforms were quantitatively examined through the models based on three most popular deep neural networks, namely, (i) Feedforward Neural Network (FNN), (ii) Convolutional Neural Network (CNN), and (iii) Recurrent Neural Network (RNN). The models were evaluated on three evaluation metrics, namely, (i) training time, (ii) testing time, and (iii) prediction accuracy. According to the experimental results, while Keras provided the best performance for both FNNs and CNNs, PyTorch provided the best performance for RNNs expect for one evaluation metric, which was the testing time. This experimental study should help deep learning engineers and researchers to choose the most suitable platform for the implementations of their deep neural networks.

Keywords

deep learning deep neural networks feedforward neural networks convolutional neural networks recurrent neural networks

References

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  • Y. Weng, F. Bell, H. Zheng, and G. Tur, “OCC: A Smart Reply System for Efficient In-App Communications,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD ’19), 2019, pp. 1–8, doi: 10.1145/3292500.3330694.
  • W. G. Hatcher and W. Yu, “A Survey of Deep Learning: Platforms, Applications and Emerging Research Trends,” IEEE Access, vol. 6, pp. 24411–24432, 2018, doi: 10.1109/ACCESS.2018.2830661.
  • X. W. Chen and X. Lin, “Big Data Deep Learning: Challenges and Perspectives,” IEEE Access, vol. 2, pp. 514–525, 2014, doi: 10.1109/ACCESS.2014.2325029.
  • N. D. Nguyen, T. Nguyen, and S. Nahavandi, “System Design Perspective for Human-Level Agents Using Deep Reinforcement Learning: A Survey,” IEEE Access, vol. 5, pp. 27091–27102, 2017, doi: 10.1109/ACCESS.2017.2777827.
  • A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1 (NIPS’12), 2012, pp. 1097–1105.
  • M. Nielsen, “Neural Networks and Deep Learning,” 2019. http://neuralnetworksanddeeplearning.com (accessed Sep. 03, 2020).
  • N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res., vol. 15, no. 1, pp. 1929–1958, 2014.
  • V. Nair and G. E. Hinton, “Rectified Linear Units Improve Restricted Boltzmann Machines,” in Proceedings of the 27th International Conference on Machine Learning (ICML 2010), 2010, pp. 807–814.
  • Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nat. Methods, vol. 521, pp. 436–444, 2015, doi: 10.1038/nmeth.3707.
  • P. Goldsborough, “A Tour of TensorFlow,” arXiv Prepr., vol. 1610.01178, pp. 1–16, 2016.
  • L. Rampasek and A. Goldenberg, “TensorFlow: Biology’s Gateway to Deep Learning?,” Cell Syst., vol. 2, no. 1, pp. 12–14, 2016, doi: 10.1016/j.cels.2016.01.009.
  • S. Ioffe and C. Szegedy, “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,” in Proceedings of the 32nd International Conference on Machine Learning (ICML 2015), 2015, pp. 448–456.
  • F. Chollet, “Keras: the Python deep learning API,” 2015. https://keras.io (accessed Sep. 03, 2020).
  • A. Paszke et al., “PyTorch: An Imperative Style, High-Performance Deep Learning Library,” in Proceedings of the Thirty-third Conference on Neural Information Processing Systems (NIPS 2019), 2019, pp. 8026–8037.
  • Y. Jia et al., “Caffe: Convolutional Architecture for Fast Feature Embedding,” in Proceedings of the 22nd ACM International Conference on Multimedia (MM 2014), 2014, pp. 675–678, doi: 10.1145/2647868.2654889.
  • R. Al-Rfou, “Theano: A Python framework for fast computation of mathematical expressions,” arXiv Prepr., vol. 1605.02688, pp. 1–19, 2016.
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  • L. Liu, Y. Wu, W. Wei, W. Cao, S. Sahin, and Q. Zhang, “Benchmarking Deep Learning Frameworks: Design Considerations, Metrics and Beyond,” in Proceedings of the 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS 2018), 2018, pp. 1258–1269, doi: 10.1109/ICDCS.2018.00125.
  • M. Abadi et al., “TensorFlow: A System for Large-Scale Machine Learning,” in Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 2016), 2016, pp. 265–283.
  • R. Collobert, K. Kavukcuoglu, and C. Farabet, “Torch7: A Matlab-like Environment for Machine Learning,” in Proceedings of the Twenty-fifth Conference on Neural Information Processing Systems (NIPS 2011), 2011, pp. 1–6.
  • S. Bahrampour, N. Ramakrishnan, L. Schott, and M. Shah, “Comparative Study of Caffe, Neon, Theano, and Torch for Deep Learning,” in Proceedings of the 4th International Conference on Learning Representations (ICLR 2016), 2016, pp. 1–11, doi: 10.1227/01.NEU.0000297044.82035.57.
  • “NervanaSystems/neon: Intel® NervanaTM reference deep learning framework committed to best performance on all hardware,” Intel, 2015. https://github.com/NervanaSystems/neon (accessed Aug. 02, 2020).
  • S. Shi, Q. Wang, P. Xu, and X. Chu, “Benchmarking State-of-the-Art Deep Learning Software Tools,” in Proceedings of the 2016 7th International Conference on Cloud Computing and Big Data (CCBD 2016), 2016, pp. 99–104, doi: 10.1109/CCBD.2016.029.
  • S. Chintala, “Easy benchmarking of all publicly accessible implementations of convnets,” 2017. https://github.com/soumith/convnet-benchmarks (accessed Aug. 02, 2020).
  • M. Marcus, B. Santorini, and M. Marcinkiewicz, “Building a Large Annotated Corpus of English: The Penn Treebank,” Comput. Linguist., vol. 19, no. 2, pp. 313–330, 1993.
  • A. Shatnawi, G. Al-Bdour, R. Al-Qurran, and M. Al-Ayyoub, “A Comparative Study of Open Source Deep Learning Frameworks,” in Proceedings of the 2018 9th International Conference on Information and Communication Systems (ICICS 2018), 2018, pp. 72–77, doi: 10.1109/IACS.2018.8355444.
  • Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE, vol. 86, no. 11, pp. 2278–2324, 1998, doi: 10.1109/5.726791.
  • A. Krizhevsky, “Learning Multiple Layers of Features from Tiny Images,” 2009. doi: 10.1.1.222.9220.
  • V. Kovalev, A. Kalinovsky, and S. Kovalev, “Deep Learning with Theano, Torch, Caffe, TensorFlow, and Deeplearning4J: Which One Is the Best in Speed and Accuracy?,” in Proceedings of the 13th International Conference on Pattern Recognition and Information Processing (PRIP 2016), 2016, pp. 99–103.
  • “Deeplearning4j: Deep Learning for Java,” Konduit, 2020. https://deeplearning4j.org (accessed Aug. 02, 2020).
  • “NVIDIA cuDNN,” NVIDIA, 2020. https://developer.nvidia.com/cudnn (accessed Aug. 02, 2020).
  • A. Vedaldi and K. Lenc, “MatConvNet: Convolutional Neural Networks for MATLAB,” in Proceedings of the 23rd ACM International Conference on Multimedia (MM’15), 2015, pp. 689–692.
  • F. Chollet, Deep Learning with Python. Manning Publications, 2017.
  • N. Ketkar, Deep Learning with Python. Springer, 2017.
  • S. Chintala, “Roadmap for torch and pytorch,” 2017. https://discuss.pytorch.org/t/roadmap-for-torch-and-pytorch/38/2 (accessed Aug. 02, 2020).
  • B. Hayes, “Programming Languages Most Used and Recommended by Data Scientists,” Business Over Broadway, 2019. https://businessoverbroadway.com/2019/01/13/programming-languages-most-used-and-recommended-by-data-scientists/ (accessed Aug. 02, 2020).
  • “Caffe2 and PyTorch join forces to create a Research + Production platform PyTorch 1.0,” 2018. https://caffe2.ai/blog/2018/05/02/Caffe2_PyTorch_1_0.html (accessed Aug. 02, 2020).
  • T. E. Oliphant, A Guide to NumPy. Trelgol Publishing, 2006.
  • Y. Bengio, “MILA and the future of Theano,” 2017. https://groups.google.com/forum/#!msg/theano-users/7Poq8BZutbY/rNCIfvAEAwAJ (accessed Aug. 02, 2020).
  • D. Yu et al., “An Introduction to Computational Networks and the Computational Network Toolkit,” 2015. [Online]. Available: https://www.microsoft.com/en-us/research/wp-content/uploads/2014/08/CNTKBook-20160217.pdf.
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  • “Google Trends,” Google, 2020. https://trends.google.com/trends (accessed Aug. 02, 2020).
  • “Colaboratory,” Google, 2020. https://colab.research.google.com (accessed Sep. 03, 2020).
  • O. Y. Al-Jarrah, P. D. Yoo, S. Muhaidat, G. K. Karagiannidis, and K. Taha, “Efficient Machine Learning for Big Data: A Review,” Big Data Res., vol. 2, no. 3, pp. 87–93, 2015, doi: 10.1016/j.bdr.2015.04.001.
  • T. Condie, P. Mineiro, N. Polyzotis, and M. Weimer, “Machine learning on Big Data,” in Proceedings of the 2013 IEEE 29th International Conference on Data Engineering (ICDE 2013), 2013, pp. 1242–1244.
  • D. C. Cireşan, U. Meier, L. M. Gambardella, and J. Schmidhuber, “Deep, Big, Simple Neural Nets for Handwritten Digit Recognition,” Neural Comput., vol. 22, no. 12, pp. 3207–3220, 2010, doi: 10.1162/NECO_a_00052.
  • D. P. Kingma and J. L. Ba, “Adam: A Method for Stochastic Optimization,” in Proceeding of the 3rd International Conference on Learning Representations (ICLR 2015), 2015, pp. 1–15.
  • H. Robbins and S. Monro, “A Stochastic Approximation Method,” Ann. Math. Stat., vol. 22, no. 3, pp. 400–407, 1951, doi: 10.1214/aoms/1177729586.
  • K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv Prepr., pp. 1–14, 2014, [Online]. Available: http://arxiv.org/abs/1409.1556.
  • H. Wang, Y. Zhang, and X. Yu, “An Overview of Image Caption Generation Methods,” Comput. Intell. Neurosci., vol. 2020, pp. 1–13, 2020, doi: 10.1155/2020/3062706.
  • A. L. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng, and C. Potts, “Learning Word Vectors for Sentiment Analysis,” 2011.
There are 53 citations in total.

Details

Primary Language English
Subjects Artificial Intelligence
Journal Section Articles
Authors

Abdullah Talha Kabakuş 0000-0003-2181-4292

Publication Date December 30, 2020
Submission Date August 3, 2020
Acceptance Date September 18, 2020
Published in Issue Year 2020

Cite

IEEE A. T. Kabakuş, “A Comparison of the State-of-the-Art Deep Learning Platforms: An Experimental Study”, SAUCIS, vol. 3, no. 3, pp. 169–182, 2020, doi: 10.35377/saucis.03.03.776573.

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