SavedAdd your journal

Journals / Sakarya University Journal of Computer and Information Sciences / 2020 / Cilt: 3 Sayı: 3

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

Abdullah Talha KABAKUŞ
Pages
169–182
Publication Date
2020-12-30
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

This experimental study should help deep learning engineers and researchers to choose the most suitable platform for the implementations of their deep neural networks.

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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.
📊
Key Findings & Data: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.
💡
Conclusion & Impact:This experimental study should help deep learning engineers and researchers to choose the most suitable platform for the implementations of their deep neural networks.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:deep learning · deep neural networks · feedforward neural networks

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.

🔖

Cite This Article

KABAKUŞ, A. T. (2020). A Comparison of the State-of-the-Art Deep Learning Platforms: An Experimental Study. Sakarya University Journal of Computer and Information Sciences, Cilt: 3 Sayı: 3, 169–182. https://ajindex.com/articles/946668

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (3)

  1. O. Russakovsky et al., “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis., vol. 115, pp. 211–252, 2015, doi: 10.1007/s11263-015-0816-y. 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. 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).
  2. 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.
  3. 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