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To Raise or Not To Raise: The Autonomous Learning Rate Question
Published 16 Jun 2021 in cs.LG | (2106.08767v3)
Abstract: There is a parameter ubiquitous throughout the deep learning world: learning rate. There is likewise a ubiquitous question: what should that learning rate be? The true answer to this question is often tedious and time consuming to obtain, and a great deal of arcane knowledge has accumulated in recent years over how to pick and modify learning rates to achieve optimal training performance. Moreover, the long hours spent carefully crafting the perfect learning rate can come to nothing the moment your network architecture, optimizer, dataset, or initial conditions change ever so slightly. But it need not be this way. We propose a new answer to the great learning rate question: the Autonomous Learning Rate Controller. Find it at https://github.com/fastestimator/ARC/tree/v2.0
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Science 350(6266), 1332–1338 (2015) https://science.sciencemag.org/content/350/6266/1332.full.pdf. https://doi.org/10.1126/science.aab3050 (38) Koch, Gregory, Zemel, R., Salakhutdinov, R.: Siamese neural networks for one-shot image recognition. ICML Deep Learning Workshop (2015) (39) Karpathy, A.: The Unreasonable Effectiveness of Recurrent Neural Networks. http://karpathy.github.io/2015/05/21/rnn-effectiveness/. Accessed: 2020-11-04 (40) Cho, K., van Merrienboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: Encoder-decoder approaches. In: Wu, D., Carpuat, M., Carreras, X., Vecchi, E.M. (eds.) Proceedings of SSST@EMNLP 2014, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation, Doha, Qatar, 25 October 2014, pp. 103–111. 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Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada, pp. 3787–3798 (2019). https://proceedings.neurips.cc/paper/2019/hash/f8c0c968632845cd133308b1a494967f-Abstract.html (44) Chicco, D., Jurman, G.: The advantages of the matthews correlation coefficient (mcc) over f1 score and accuracy in binary classification evaluation. BMC genomics 21(1), 1–13 (2020) (45) Zagoruyko, S., Komodakis, N.: Wide residual networks. In: Wilson, R.C., Hancock, E.R., Smith, W.A.P. (eds.) Proceedings of the British Machine Vision Conference 2016, BMVC 2016, York, UK, September 19-22, 2016. BMVA Press, York, UK (2016). http://www.bmva.org/bmvc/2016/papers/paper087/index.html (46) Page, D.C.: Cifar10-Fast Dawn Benchmark Implementation. https://github.com/davidcpage/cifar10-fast. 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Science 350(6266), 1332–1338 (2015) https://science.sciencemag.org/content/350/6266/1332.full.pdf. https://doi.org/10.1126/science.aab3050 (38) Koch, Gregory, Zemel, R., Salakhutdinov, R.: Siamese neural networks for one-shot image recognition. ICML Deep Learning Workshop (2015) (39) Karpathy, A.: The Unreasonable Effectiveness of Recurrent Neural Networks. http://karpathy.github.io/2015/05/21/rnn-effectiveness/. Accessed: 2020-11-04 (40) Cho, K., van Merrienboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: Encoder-decoder approaches. In: Wu, D., Carpuat, M., Carreras, X., Vecchi, E.M. (eds.) Proceedings of SSST@EMNLP 2014, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation, Doha, Qatar, 25 October 2014, pp. 103–111. 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Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada, pp. 3787–3798 (2019). https://proceedings.neurips.cc/paper/2019/hash/f8c0c968632845cd133308b1a494967f-Abstract.html (44) Chicco, D., Jurman, G.: The advantages of the matthews correlation coefficient (mcc) over f1 score and accuracy in binary classification evaluation. BMC genomics 21(1), 1–13 (2020) (45) Zagoruyko, S., Komodakis, N.: Wide residual networks. In: Wilson, R.C., Hancock, E.R., Smith, W.A.P. (eds.) Proceedings of the British Machine Vision Conference 2016, BMVC 2016, York, UK, September 19-22, 2016. BMVA Press, York, UK (2016). http://www.bmva.org/bmvc/2016/papers/paper087/index.html (46) Page, D.C.: Cifar10-Fast Dawn Benchmark Implementation. https://github.com/davidcpage/cifar10-fast. 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IEEE Computer Society, Las Vegas, NV, USA (2016). https://doi.org/10.1109/CVPR.2016.308. https://doi.org/10.1109/CVPR.2016.308 (30) Kendall, A., Gal, Y., Cipolla, R.: Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18-22, 2018, pp. 7482–7491. IEEE Computer Society, Salt Lake City, UT, USA (2018). https://doi.org/10.1109/CVPR.2018.00781. http://openaccess.thecvf.com/content_cvpr_2018/html/Kendall_Multi-Task_Learning_Using_CVPR_2018_paper.html (31) Welinder, P., Branson, S., Mita, T., Wah, C., Schroff, F., Belongie, S., Perona, P.: Caltech-UCSD Birds 200. Technical Report CNS-TR-2010-001, California Institute of Technology (2010) (32) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. 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Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada, pp. 3787–3798 (2019). https://proceedings.neurips.cc/paper/2019/hash/f8c0c968632845cd133308b1a494967f-Abstract.html (44) Chicco, D., Jurman, G.: The advantages of the matthews correlation coefficient (mcc) over f1 score and accuracy in binary classification evaluation. BMC genomics 21(1), 1–13 (2020) (45) Zagoruyko, S., Komodakis, N.: Wide residual networks. In: Wilson, R.C., Hancock, E.R., Smith, W.A.P. (eds.) Proceedings of the British Machine Vision Conference 2016, BMVC 2016, York, UK, September 19-22, 2016. BMVA Press, York, UK (2016). http://www.bmva.org/bmvc/2016/papers/paper087/index.html (46) Page, D.C.: Cifar10-Fast Dawn Benchmark Implementation. https://github.com/davidcpage/cifar10-fast. 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IEEE Computer Society, Las Vegas, NV, USA (2016). https://doi.org/10.1109/CVPR.2016.308. https://doi.org/10.1109/CVPR.2016.308 (30) Kendall, A., Gal, Y., Cipolla, R.: Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18-22, 2018, pp. 7482–7491. IEEE Computer Society, Salt Lake City, UT, USA (2018). https://doi.org/10.1109/CVPR.2018.00781. http://openaccess.thecvf.com/content_cvpr_2018/html/Kendall_Multi-Task_Learning_Using_CVPR_2018_paper.html (31) Welinder, P., Branson, S., Mita, T., Wah, C., Schroff, F., Belongie, S., Perona, P.: Caltech-UCSD Birds 200. Technical Report CNS-TR-2010-001, California Institute of Technology (2010) (32) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. 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Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada, pp. 3787–3798 (2019). https://proceedings.neurips.cc/paper/2019/hash/f8c0c968632845cd133308b1a494967f-Abstract.html (44) Chicco, D., Jurman, G.: The advantages of the matthews correlation coefficient (mcc) over f1 score and accuracy in binary classification evaluation. BMC genomics 21(1), 1–13 (2020) (45) Zagoruyko, S., Komodakis, N.: Wide residual networks. In: Wilson, R.C., Hancock, E.R., Smith, W.A.P. (eds.) Proceedings of the British Machine Vision Conference 2016, BMVC 2016, York, UK, September 19-22, 2016. BMVA Press, York, UK (2016). http://www.bmva.org/bmvc/2016/papers/paper087/index.html (46) Page, D.C.: Cifar10-Fast Dawn Benchmark Implementation. https://github.com/davidcpage/cifar10-fast. 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Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada, pp. 3787–3798 (2019). https://proceedings.neurips.cc/paper/2019/hash/f8c0c968632845cd133308b1a494967f-Abstract.html (44) Chicco, D., Jurman, G.: The advantages of the matthews correlation coefficient (mcc) over f1 score and accuracy in binary classification evaluation. BMC genomics 21(1), 1–13 (2020) (45) Zagoruyko, S., Komodakis, N.: Wide residual networks. In: Wilson, R.C., Hancock, E.R., Smith, W.A.P. (eds.) Proceedings of the British Machine Vision Conference 2016, BMVC 2016, York, UK, September 19-22, 2016. BMVA Press, York, UK (2016). http://www.bmva.org/bmvc/2016/papers/paper087/index.html (46) Page, D.C.: Cifar10-Fast Dawn Benchmark Implementation. https://github.com/davidcpage/cifar10-fast. 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JMLR.org, Lille, France (2015). http://proceedings.mlr.press/v37/ioffe15.html (25) Verma, G., Swami, A.: Error correcting output codes improve probability estimation and adversarial robustness of deep neural networks. In: Wallach, H.M., Larochelle, H., Beygelzimer, A., d’Alché-Buc, F., Fox, E.B., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, 8-14 December 2019, Vancouver, BC, Canada, pp. 8643–8653 (2019). https://papers.nips.cc/paper/2019/hash/cd61a580392a70389e27b0bc2b439f49-Abstract.html (26) Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. In: Bengio, Y., LeCun, Y. 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(eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015). http://arxiv.org/abs/1409.1556 (24) Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: Bach, F.R., Blei, D.M. (eds.) Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 6-11 July 2015. JMLR Workshop and Conference Proceedings, vol. 37, pp. 448–456. JMLR.org, Lille, France (2015). http://proceedings.mlr.press/v37/ioffe15.html (25) Verma, G., Swami, A.: Error correcting output codes improve probability estimation and adversarial robustness of deep neural networks. In: Wallach, H.M., Larochelle, H., Beygelzimer, A., d’Alché-Buc, F., Fox, E.B., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, 8-14 December 2019, Vancouver, BC, Canada, pp. 8643–8653 (2019). https://papers.nips.cc/paper/2019/hash/cd61a580392a70389e27b0bc2b439f49-Abstract.html (26) Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015). http://arxiv.org/abs/1412.6572 (27) Bossard, L., Guillaumin, M., Van Gool, L.: Food-101 – mining discriminative components with random forests. In: European Conference on Computer Vision (2014) (28) Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017, pp. 2261–2269. 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IEEE Computer Society, Salt Lake City, UT, USA (2018). https://doi.org/10.1109/CVPR.2018.00781. http://openaccess.thecvf.com/content_cvpr_2018/html/Kendall_Multi-Task_Learning_Using_CVPR_2018_paper.html (31) Welinder, P., Branson, S., Mita, T., Wah, C., Schroff, F., Belongie, S., Perona, P.: Caltech-UCSD Birds 200. Technical Report CNS-TR-2010-001, California Institute of Technology (2010) (32) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016, pp. 770–778. IEEE Computer Society, Las Vegas, NV, USA (2016). https://doi.org/10.1109/CVPR.2016.90. https://doi.org/10.1109/CVPR.2016.90 (33) Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., III, W.M.W., Frangi, A.F. (eds.) 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Science 350(6266), 1332–1338 (2015) https://science.sciencemag.org/content/350/6266/1332.full.pdf. https://doi.org/10.1126/science.aab3050 (38) Koch, Gregory, Zemel, R., Salakhutdinov, R.: Siamese neural networks for one-shot image recognition. ICML Deep Learning Workshop (2015) (39) Karpathy, A.: The Unreasonable Effectiveness of Recurrent Neural Networks. http://karpathy.github.io/2015/05/21/rnn-effectiveness/. Accessed: 2020-11-04 (40) Cho, K., van Merrienboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: Encoder-decoder approaches. In: Wu, D., Carpuat, M., Carreras, X., Vecchi, E.M. (eds.) Proceedings of SSST@EMNLP 2014, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation, Doha, Qatar, 25 October 2014, pp. 103–111. 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Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada, pp. 3787–3798 (2019). https://proceedings.neurips.cc/paper/2019/hash/f8c0c968632845cd133308b1a494967f-Abstract.html (44) Chicco, D., Jurman, G.: The advantages of the matthews correlation coefficient (mcc) over f1 score and accuracy in binary classification evaluation. BMC genomics 21(1), 1–13 (2020) (45) Zagoruyko, S., Komodakis, N.: Wide residual networks. In: Wilson, R.C., Hancock, E.R., Smith, W.A.P. (eds.) Proceedings of the British Machine Vision Conference 2016, BMVC 2016, York, UK, September 19-22, 2016. BMVA Press, York, UK (2016). http://www.bmva.org/bmvc/2016/papers/paper087/index.html (46) Page, D.C.: Cifar10-Fast Dawn Benchmark Implementation. https://github.com/davidcpage/cifar10-fast. 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- Chicco, D., Jurman, G.: The advantages of the matthews correlation coefficient (mcc) over f1 score and accuracy in binary classification evaluation. BMC genomics 21(1), 1–13 (2020) (45) Zagoruyko, S., Komodakis, N.: Wide residual networks. In: Wilson, R.C., Hancock, E.R., Smith, W.A.P. (eds.) Proceedings of the British Machine Vision Conference 2016, BMVC 2016, York, UK, September 19-22, 2016. BMVA Press, York, UK (2016). http://www.bmva.org/bmvc/2016/papers/paper087/index.html (46) Page, D.C.: Cifar10-Fast Dawn Benchmark Implementation. https://github.com/davidcpage/cifar10-fast. Accessed: 2020-11-04 (47) Lin, T., Goyal, P., Girshick, R.B., He, K., Dollár, P.: Focal loss for dense object detection. In: IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22-29, 2017, pp. 2999–3007. IEEE Computer Society, Venice, Italy (2017). https://doi.org/10.1109/ICCV.2017.324. https://doi.org/10.1109/ICCV.2017.324 (48) Marcus, M.P.: Treebank-3 LDC99T42. Web Download. https://catalog.ldc.upenn.edu/LDC99T42. Accessed: 2020-11-04 Zagoruyko, S., Komodakis, N.: Wide residual networks. In: Wilson, R.C., Hancock, E.R., Smith, W.A.P. (eds.) Proceedings of the British Machine Vision Conference 2016, BMVC 2016, York, UK, September 19-22, 2016. BMVA Press, York, UK (2016). http://www.bmva.org/bmvc/2016/papers/paper087/index.html (46) Page, D.C.: Cifar10-Fast Dawn Benchmark Implementation. https://github.com/davidcpage/cifar10-fast. Accessed: 2020-11-04 (47) Lin, T., Goyal, P., Girshick, R.B., He, K., Dollár, P.: Focal loss for dense object detection. In: IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22-29, 2017, pp. 2999–3007. IEEE Computer Society, Venice, Italy (2017). https://doi.org/10.1109/ICCV.2017.324. https://doi.org/10.1109/ICCV.2017.324 (48) Marcus, M.P.: Treebank-3 LDC99T42. Web Download. https://catalog.ldc.upenn.edu/LDC99T42. Accessed: 2020-11-04 Page, D.C.: Cifar10-Fast Dawn Benchmark Implementation. https://github.com/davidcpage/cifar10-fast. Accessed: 2020-11-04 (47) Lin, T., Goyal, P., Girshick, R.B., He, K., Dollár, P.: Focal loss for dense object detection. In: IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22-29, 2017, pp. 2999–3007. IEEE Computer Society, Venice, Italy (2017). https://doi.org/10.1109/ICCV.2017.324. https://doi.org/10.1109/ICCV.2017.324 (48) Marcus, M.P.: Treebank-3 LDC99T42. Web Download. https://catalog.ldc.upenn.edu/LDC99T42. Accessed: 2020-11-04 Lin, T., Goyal, P., Girshick, R.B., He, K., Dollár, P.: Focal loss for dense object detection. In: IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22-29, 2017, pp. 2999–3007. IEEE Computer Society, Venice, Italy (2017). https://doi.org/10.1109/ICCV.2017.324. https://doi.org/10.1109/ICCV.2017.324 (48) Marcus, M.P.: Treebank-3 LDC99T42. Web Download. https://catalog.ldc.upenn.edu/LDC99T42. Accessed: 2020-11-04 Marcus, M.P.: Treebank-3 LDC99T42. Web Download. https://catalog.ldc.upenn.edu/LDC99T42. Accessed: 2020-11-04
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- Page, D.C.: Cifar10-Fast Dawn Benchmark Implementation. https://github.com/davidcpage/cifar10-fast. Accessed: 2020-11-04 (47) Lin, T., Goyal, P., Girshick, R.B., He, K., Dollár, P.: Focal loss for dense object detection. In: IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22-29, 2017, pp. 2999–3007. IEEE Computer Society, Venice, Italy (2017). https://doi.org/10.1109/ICCV.2017.324. https://doi.org/10.1109/ICCV.2017.324 (48) Marcus, M.P.: Treebank-3 LDC99T42. Web Download. https://catalog.ldc.upenn.edu/LDC99T42. Accessed: 2020-11-04 Lin, T., Goyal, P., Girshick, R.B., He, K., Dollár, P.: Focal loss for dense object detection. In: IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22-29, 2017, pp. 2999–3007. IEEE Computer Society, Venice, Italy (2017). https://doi.org/10.1109/ICCV.2017.324. https://doi.org/10.1109/ICCV.2017.324 (48) Marcus, M.P.: Treebank-3 LDC99T42. Web Download. https://catalog.ldc.upenn.edu/LDC99T42. Accessed: 2020-11-04 Marcus, M.P.: Treebank-3 LDC99T42. Web Download. https://catalog.ldc.upenn.edu/LDC99T42. Accessed: 2020-11-04
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- Marcus, M.P.: Treebank-3 LDC99T42. Web Download. https://catalog.ldc.upenn.edu/LDC99T42. Accessed: 2020-11-04
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