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Neural architecture search via standard machine learning methodologies

  • Received: 24 March 2021 Revised: 19 October 2021 Accepted: 13 January 2022 Published: 11 February 2022
  • In the context of deep learning, the more expensive computational phase is the full training of the learning methodology. Indeed, its effectiveness depends on the choice of proper values for the so-called hyperparameters, namely the parameters that are not trained during the learning process, and such a selection typically requires an extensive numerical investigation with the execution of a significant number of experimental trials. The aim of the paper is to investigate how to choose the hyperparameters related to both the architecture of a Convolutional Neural Network (CNN), such as the number of filters and the kernel size at each convolutional layer, and the optimisation algorithm employed to train the CNN itself, such as the steplength, the mini-batch size and the potential adoption of variance reduction techniques. The main contribution of the paper consists in introducing an automatic Machine Learning technique to set these hyperparameters in such a way that a measure of the CNN performance can be optimised. In particular, given a set of values for the hyperparameters, we propose a low-cost strategy to predict the performance of the corresponding CNN, based on its behavior after only few steps of the training process. To achieve this goal, we generate a dataset whose input samples are provided by a limited number of hyperparameter configurations together with the corresponding CNN measures of performance obtained with only few steps of the CNN training process, while the label of each input sample is the performance corresponding to a complete training of the CNN. Such dataset is used as training set for a Support Vector Machines for Regression and/or Random Forest techniques to predict the performance of the considered learning methodology, given its performance at the initial iterations of its learning process. Furthermore, by a probabilistic exploration of the hyperparameter space, we are able to find, at a quite low cost, the setting of a CNN hyperparameters which provides the optimal performance. The results of an extensive numerical experimentation, carried out on CNNs, together with the use of our performance predictor with NAS-Bench-101, highlight how the proposed methodology for the hyperparameter setting appears very promising.

    Citation: Giorgia Franchini, Valeria Ruggiero, Federica Porta, Luca Zanni. Neural architecture search via standard machine learning methodologies[J]. Mathematics in Engineering, 2023, 5(1): 1-21. doi: 10.3934/mine.2023012

    Related Papers:

  • In the context of deep learning, the more expensive computational phase is the full training of the learning methodology. Indeed, its effectiveness depends on the choice of proper values for the so-called hyperparameters, namely the parameters that are not trained during the learning process, and such a selection typically requires an extensive numerical investigation with the execution of a significant number of experimental trials. The aim of the paper is to investigate how to choose the hyperparameters related to both the architecture of a Convolutional Neural Network (CNN), such as the number of filters and the kernel size at each convolutional layer, and the optimisation algorithm employed to train the CNN itself, such as the steplength, the mini-batch size and the potential adoption of variance reduction techniques. The main contribution of the paper consists in introducing an automatic Machine Learning technique to set these hyperparameters in such a way that a measure of the CNN performance can be optimised. In particular, given a set of values for the hyperparameters, we propose a low-cost strategy to predict the performance of the corresponding CNN, based on its behavior after only few steps of the training process. To achieve this goal, we generate a dataset whose input samples are provided by a limited number of hyperparameter configurations together with the corresponding CNN measures of performance obtained with only few steps of the CNN training process, while the label of each input sample is the performance corresponding to a complete training of the CNN. Such dataset is used as training set for a Support Vector Machines for Regression and/or Random Forest techniques to predict the performance of the considered learning methodology, given its performance at the initial iterations of its learning process. Furthermore, by a probabilistic exploration of the hyperparameter space, we are able to find, at a quite low cost, the setting of a CNN hyperparameters which provides the optimal performance. The results of an extensive numerical experimentation, carried out on CNNs, together with the use of our performance predictor with NAS-Bench-101, highlight how the proposed methodology for the hyperparameter setting appears very promising.



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    [1] T. Akiba, S. Sano, T. Yanase, T. Ohta, M. Koyama, Optuna: A next-generation hyperparameter optimization framework, In: Proceedings of the 25rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2019, 2623–2631. http://dx.doi.org/10.1145/3292500.3330701
    [2] B. Baker, O. Gupta, R. Raskar, N. Naik, Accelerating neural architecture search using performance prediction, 2017, arXiv: 1705.10823.
    [3] B. Baker, O. Gupta, N. Naik, R. Raskar, Designing neural network architectures using reinforcement learning, 2017, arXiv: 1611.02167.
    [4] J. F. Barrett, N. Keat, Artifacts in CT: recognition and avoidance, RadioGraphics, 24 (2004), 1679–1691. http://dx.doi.org/10.1148/rg.246045065 doi: 10.1148/rg.246045065
    [5] J. Bergstra, R. Bardenet, Y. Bengio, B. Kégl, Algorithms for hyper-parameter optimization, In: Advances in Neural Information Processing Systems, 2011, 2546–2554.
    [6] J. Bergstra, Y. Bengio, Random search for hyper-parameter optimization, J. Mach. Learn. Res., 13 (2012), 281–305.
    [7] L. Bottou, F. E. Curtis, J. Nocedal, Optimization methods for large-scale machine learning, SIAM Rev., 60 (2018), 223–311. http://dx.doi.org/10.1137/16M1080173 doi: 10.1137/16M1080173
    [8] L. Breiman, Random forests, Machine Learning, 45 (2001), 5–32. http://dx.doi.org/10.1023/A:1010933404324 doi: 10.1023/A:1010933404324
    [9] C. J. C. Burges, A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery, 2 (1998), 121–167. http://dx.doi.org/10.1023/A:1009715923555 doi: 10.1023/A:1009715923555
    [10] H. Cai, T. Chen, W. Zhang, Y. Yu, J. Wang, Efficient architecture search by network transformation, 2017, arXiv/1707.04873.
    [11] T. Domhan, J. T. Springenberg, F. Hutter, Speeding Up Automatic Hyperparameter Optimization of Deep Neural Networks by Extrapolation of Learning Curves, In: IJCAI International Joint Conference on Artificial Intelligence, 2015, 3460–3468.
    [12] T. Elsken, J. H. Metzen, F. Hutter, Neural architecture search: a survey, J. Mach. Learn. Res., 20 (2019), 1997–2017.
    [13] T. Elsken, J.-H. Metzen, F. Hutter, Simple and efficient architecture search for convolutional neural networks, 2017, arXiv: 1711.04528.
    [14] G. Franchini, M. Galinier, M. Verucchi, Mise en abyme with artificial intelligence: how to predict the accuracy of NN, applied to hyper-parameter tuning, In: INNSBDDL 2019: Recent advances in big data and deep learning, Cham: Springer, 2020,286–295. http://dx.doi.org/10.1007/978-3-030-16841-4_30
    [15] D. E. Goldberg, Genetic algorithms in search, optimization, and machine learning, Addison Wesley Publishing Co. Inc., 1989.
    [16] T. Hospedales, A. Antoniou, P. Micaelli, A. Storkey, Meta-learning in neural networks: a survey, IEEE Transactions on Pattern Analysis and Machine Intelligence, in press. http://dx.doi.org/10.1109/TPAMI.2021.3079209
    [17] F. Hutter, L. Kotthoff, J. Vanschoren, Automatic machine learning: methods, systems, challenges, Cham: Springer, 2019. http://dx.doi.org/10.1007/978-3-030-05318-5
    [18] F. Hutter, H. Hoos, K. Leyton-Brown, Sequential model-based optimization for general algorithm configuration, In: LION 2011: Learning and Intelligent Optimization, Berlin, Heidelberg: Springer, 2011,507–523. http://dx.doi.org/10.1007/978-3-642-25566-3_40
    [19] D. P. Kingma, J. Ba, Adam: a method for stochastic optimization, 2017, arXiv: 1412.6980.
    [20] N. Loizou, P. Richtarik, Momentum and stochastic momentum for stochastic gradient, Newton, proximal point and subspace descent methods, Comput. Optim. Appl., 77 (2020), 653–710. http://dx.doi.org/10.1007/s10589-020-00220-z doi: 10.1007/s10589-020-00220-z
    [21] J. Mockus, V. Tiesis, A. Zilinskas, The application of Bayesian methods for seeking the extremum, In: Towards global optimisation, North-Holand, 2012,117–129.
    [22] B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, N. De Freitas, Taking the human out of the loop: A review of bayesian optimization, Proc. IEEE, 104 (2016), 148–175. http://dx.doi.org/10.1109/JPROC.2015.2494218 doi: 10.1109/JPROC.2015.2494218
    [23] S. Thrun, L. Pratt, Learning to learn: introduction and overview, In: Learning to learn, Boston, MA: Springer, 1998, 3–17. http://dx.doi.org/10.1007/978-1-4615-5529-2_1
    [24] C. Ying, A. Klein, E. Real, E. Christiansen, K. Murphy, F. Hutter, NAS-Bench-101: Towards reproducible neural architecture search, In: Proceedings of the 36–th International Conference on Machine Learning, 2019, 7105–7114.
    [25] Z. Zhong, J. Yan, W. Wei, J. Shao, C.-L. Liu, Practical block-wise neural network architecture generation, In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, 2423–2432. http://dx.doi.org/10.1109/CVPR.2018.00257
    [26] B. Zoph, Q. V. Le, Neural architecture search with reinforcemente learning, 2017, arXiv: 1611.01578.
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