Advertisement

Stochastic Optimization of Contextual Neural Networks with RMSprop

  • Maciej HukEmail author
Conference paper
  • 283 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12034)

Abstract

The paper presents modified version of Generalized Error Backpropagation algorithm (GBP) merged with RMSprop optimizer. This solution is compared with analogous method based on Stochastic Gradient Descent. Both algorithms are used to train MLP and CxNN neural networks solving selected benchmark and real–life classification problems. Results indicate that usage of GBP-RMSprop can be beneficial in terms of increasing classification accuracy as well as decreasing activity of neurons’ connections and length of training. This suggests that RMSprop can effectively solve optimization problems of variable dimensionality. In the effect, merging GBP with RMSprop as well as with other optimizers such as Adam and AdaGrad can lead to construction of better algorithms for training of contextual neural networks.

Keywords

Classification Self-consistency Aggregation functions 

References

  1. 1.
    Suleymanova, I., et al.: A deep convolutional neural network approach for astrocyte detection. Sci. Rep. 8(12878), 1–7 (2018)Google Scholar
  2. 2.
    Chen, S., Zhang, S., Shang, J., Chen, B., Zheng, N.: Brain-inspired cognitive model with attention for self-driving cars. IEEE Trans. Cogn. Dev. Syst. 11(1), 13–25 (2019)CrossRefGoogle Scholar
  3. 3.
    Zhang, S., Zheng, W.X.: Recursive adaptive sparse exponential functional link neural network for nonlinear AEC in impulsive noise environment. IEEE Trans. Neural Netw. Learn. Syst. 29(9), 4314–4323 (2018)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Guest, D., Cranmer, K., Whiteson, D.: Deep learning and its application to LHC physics. Annu. Rev. Nucl. Part. Sci. 68, 1–22 (2018)CrossRefGoogle Scholar
  5. 5.
    Bao, W.N., Yue, J.H., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS ONE 12(7), 1–24 (2017)CrossRefGoogle Scholar
  6. 6.
    Tsai, Y.-C., et al.: FineNet: a joint convolutional and recurrent neural network model to forecast and recommend anomalous financial items. In: Proceedings of the 13th ACM Conference on Recommender Systems RecSys 2019, pp. 536–537. ACM, New York (2019)Google Scholar
  7. 7.
    Gao, D., Li, X., Dong, Y., Peers, P., Xu, K., Tong, X.: Deep inverse rendering for high-resolution SVBRDF estimation from an arbitrary number of images. ACM Trans. Graphics (SIGGRAPH) 38(4), 1–15 (2019). Article no. 134CrossRefGoogle Scholar
  8. 8.
    Liu, L., et al.: Automatic skin binding for production characters with deep graph networks. ACM Trans. Graphics (SIGGRAPH) 38(4), 1–12 (2019). Article no. 114Google Scholar
  9. 9.
    Gong, K., et al.: Iterative PET image reconstruction using convolutional neural network representation. IEEE Trans. Med. Imaging 38(3), 675–685 (2019)CrossRefGoogle Scholar
  10. 10.
    Athiwaratkun, B., Stokes, J.W.: Malware classification with LSTM and GRU language models and a character-level CNN. In: Proceedings of the 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), USA, pp. 2482–2486. IEEE (2017)Google Scholar
  11. 11.
    Huang, X., Tan, H., Lin, G., Tian, Y.: A LSTM-based bidirectional translation model for optimizing rare words and terminologies. In: 2018 IEEE International Conference on Artificial Intelligence and Big Data (ICAIBD), China, pp. 5077–5086. IEEE (2018)Google Scholar
  12. 12.
    Dozono, H., Niina, G., Araki, S.: Convolutional self organizing map. In: 2016 IEEE International Conference on Computational Science and Computational Intelligence (CSCI), pp. 767–771. IEEE (2016)Google Scholar
  13. 13.
    Higgins, I., et al.: beta-VAE: learning basic visual concepts with a constrained variational framework. In: International Conference on Learning Represent, ICLR 2017, vol 2, no. 5, pp. 1–22 (2017)Google Scholar
  14. 14.
    Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of GANs for improved quality, stability, and variation. In: International Conference on Learning Representations, ICLR 2018, pp. 1–26 (2018)Google Scholar
  15. 15.
    Alcin, M., Koyuncu, I., Tuna, M., Varan, M., Pehlivan, I.: A novel high speed artificial neural network–based chaotic true random number generator on field programmable gate array. Int. J. Circuit Theory Appl. 47(3), 365–378 (2019)CrossRefGoogle Scholar
  16. 16.
    Huk, M.: Backpropagation generalized delta rule for the selective attention Sigma-if artificial neural network. Int. J. Appl. Math. Comput. Sci. 22, 449–459 (2012)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Huk, M.: Notes on the generalized backpropagation algorithm for contextual neural networks with conditional aggregation functions. J. Intell. Fuzzy Syst. 32, 1365–1376 (2017)CrossRefGoogle Scholar
  18. 18.
    Huk, M.: Training contextual neural networks with rectifier activation functions: role and adoption of sorting methods. J. Intell. Fuzzy Syst. 38, 1–10 (2019)Google Scholar
  19. 19.
    Huk, M.: Learning distributed selective attention strategies with the Sigma-if neural network. In: Akbar, M., Hussain, D. (eds.) Advances in Computer Science and IT, pp. 209–232. InTech, Vukovar (2009)Google Scholar
  20. 20.
    Szczepanik, M., Jóźwiak, I.: Data management for fingerprint recognition algorithm based on characteristic points’ groups. In: Pechenizkiy, M., Wojciechowski, M. (eds.) New Trends in Databases and Information Systems. Advances in Intelligent Systems and Computing, vol. 185, pp. 425–432. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-32518-2_40CrossRefGoogle Scholar
  21. 21.
    Janusz, B.J., Wołk, K.: Implementing contextual neural networks in distributed machine learning framework. In: Nguyen, N.T., Hoang, D.H., Hong, T.-P., Pham, H., Trawiński, B. (eds.) ACIIDS 2018. LNCS (LNAI), vol. 10752, pp. 212–223. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-75420-8_20CrossRefGoogle Scholar
  22. 22.
    Wołk, K., Burnell, E.: Implementation and analysis of contextual neural networks in H2O framework. In: Nguyen, N.T., Gaol, F.L., Hong, T.-P., Trawiński, B. (eds.) ACIIDS 2019. LNCS (LNAI), vol. 11432, pp. 429–440. Springer, Cham (2019).  https://doi.org/10.1007/978-3-030-14802-7_37CrossRefGoogle Scholar
  23. 23.
    Ruder, S.: An overview of gradient descent optimization algorithms, pp. 1–14. eprint arXiv:1609.04747v2 (2017)
  24. 24.
    Armstrong, S.A.: MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia. Nat. Genet. 30, 41–47 (2002)CrossRefGoogle Scholar
  25. 25.
    Golub, T.R., et al.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999)CrossRefGoogle Scholar
  26. 26.
    Khan, J., et al.: Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat. Med. 7(6), 673–679 (2001)CrossRefGoogle Scholar
  27. 27.
    UCI Machine Learning Repository. http://archive.ics.uci.edu/ml
  28. 28.
    Huk, M.: Non-uniform initialization of inputs groupings in contextual neural networks. In: Nguyen, N.T., Gaol, F.L., Hong, T.-P., Trawiński, B. (eds.) ACIIDS 2019. LNCS (LNAI), vol. 11432, pp. 420–428. Springer, Cham (2019).  https://doi.org/10.1007/978-3-030-14802-7_36CrossRefGoogle Scholar
  29. 29.
    Dauphin Y., Pascanu, R., Gulcehre, C., Cho, K., Ganguli, S., Bengio, Y.: Identifying and attacking the saddle point problem in high dimensional non-convex optimization, pp. 1–14. eprint arXiv:1406.2572 (2014)
  30. 30.
    Darken, C., Chang, J., Moody, J.: Learning rate schedules for faster stochastic gradient search. In: Proceedings of the 1992 IEEE Workshop on Neural Networks for Signal Processing II, September, pp. 1–11 (1992)Google Scholar
  31. 31.
    Bouckaert, R.R., Frank, E.: Evaluating the replicability of significance tests for comparing learning algorithms. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 3–12. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-24775-3_3CrossRefGoogle Scholar
  32. 32.
    Matsumoto, M., Nishimura, T.: Mersenne twister: a 623-dimensionally equidistributed uniform pseudorandom number generator. ACM Trans. Model. Comput. Simul. 8(3), 3–30 (1998)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of Computer Science and ManagementWroclaw University of Science and TechnologyWroclawPoland

Personalised recommendations