Multiagent Reinforcement Learning for Hyperparameter Optimization of Convolutional Neural Networks
Nowadays, deep convolutional neural networks (DCNNs) play a significant role in many application domains, such as computer vision, medical imaging, and image processing. Nonetheless, designing a DCNN, able to defeat the state of the art, is a manual, challenging, and time-consuming task, due to the...
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| Vydáno v: | IEEE transactions on computer-aided design of integrated circuits and systems Ročník 41; číslo 4; s. 1034 - 1047 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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IEEE
01.04.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0278-0070, 1937-4151 |
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| Abstract | Nowadays, deep convolutional neural networks (DCNNs) play a significant role in many application domains, such as computer vision, medical imaging, and image processing. Nonetheless, designing a DCNN, able to defeat the state of the art, is a manual, challenging, and time-consuming task, due to the extremely large design space, as a consequence of a large number of layers and their corresponding hyperparameters. In this work, we address the challenge of performing hyperparameter optimization of DCNNs through a novel multiagent reinforcement learning (MARL)-based approach, eliminating the human effort. In particular, we adapt <inline-formula> <tex-math notation="LaTeX">Q </tex-math></inline-formula>-learning and define learning agents per layer to split the design space into independent smaller design subspaces such that each agent fine tunes the hyperparameters of the assigned layer concerning a global reward. Moreover, we provide a novel formation of <inline-formula> <tex-math notation="LaTeX">Q </tex-math></inline-formula>-tables along with a new update rule that facilitates agents' communication. Our MARL-based approach is data driven and able to consider an arbitrary set of design objectives and constraints. We apply our MARL-based solution to different well-known DCNNs, including GoogLeNet, VGG, and U-Net, and various datasets for image classification and semantic segmentation. Our results have shown that compared to the original CNNs, the MARL-based approach can reduce the model size, training time, and inference time by up to, respectively, <inline-formula> <tex-math notation="LaTeX">83\times </tex-math></inline-formula>, 52%, and 54% without any degradation in accuracy. Moreover, our approach is very competitive to state-of-the-art neural architecture search methods in terms of the designed CNN accuracy and its number of parameters while significantly reducing the optimization cost. |
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| AbstractList | Nowadays, deep convolutional neural networks (DCNNs) play a significant role in many application domains, such as computer vision, medical imaging, and image processing. Nonetheless, designing a DCNN, able to defeat the state of the art, is a manual, challenging, and time-consuming task, due to the extremely large design space, as a consequence of a large number of layers and their corresponding hyperparameters. In this work, we address the challenge of performing hyperparameter optimization of DCNNs through a novel multiagent reinforcement learning (MARL)-based approach, eliminating the human effort. In particular, we adapt [Formula Omitted]-learning and define learning agents per layer to split the design space into independent smaller design subspaces such that each agent fine tunes the hyperparameters of the assigned layer concerning a global reward. Moreover, we provide a novel formation of [Formula Omitted]-tables along with a new update rule that facilitates agents’ communication. Our MARL-based approach is data driven and able to consider an arbitrary set of design objectives and constraints. We apply our MARL-based solution to different well-known DCNNs, including GoogLeNet, VGG, and U-Net, and various datasets for image classification and semantic segmentation. Our results have shown that compared to the original CNNs, the MARL-based approach can reduce the model size, training time, and inference time by up to, respectively, [Formula Omitted], 52%, and 54% without any degradation in accuracy. Moreover, our approach is very competitive to state-of-the-art neural architecture search methods in terms of the designed CNN accuracy and its number of parameters while significantly reducing the optimization cost. Nowadays, deep convolutional neural networks (DCNNs) play a significant role in many application domains, such as computer vision, medical imaging, and image processing. Nonetheless, designing a DCNN, able to defeat the state of the art, is a manual, challenging, and time-consuming task, due to the extremely large design space, as a consequence of a large number of layers and their corresponding hyperparameters. In this work, we address the challenge of performing hyperparameter optimization of DCNNs through a novel multiagent reinforcement learning (MARL)-based approach, eliminating the human effort. In particular, we adapt <inline-formula> <tex-math notation="LaTeX">Q </tex-math></inline-formula>-learning and define learning agents per layer to split the design space into independent smaller design subspaces such that each agent fine tunes the hyperparameters of the assigned layer concerning a global reward. Moreover, we provide a novel formation of <inline-formula> <tex-math notation="LaTeX">Q </tex-math></inline-formula>-tables along with a new update rule that facilitates agents' communication. Our MARL-based approach is data driven and able to consider an arbitrary set of design objectives and constraints. We apply our MARL-based solution to different well-known DCNNs, including GoogLeNet, VGG, and U-Net, and various datasets for image classification and semantic segmentation. Our results have shown that compared to the original CNNs, the MARL-based approach can reduce the model size, training time, and inference time by up to, respectively, <inline-formula> <tex-math notation="LaTeX">83\times </tex-math></inline-formula>, 52%, and 54% without any degradation in accuracy. Moreover, our approach is very competitive to state-of-the-art neural architecture search methods in terms of the designed CNN accuracy and its number of parameters while significantly reducing the optimization cost. |
| Author | Zapater, Marina Iranfar, Arman Atienza, David |
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| References | ref13 ref34 Bakas (ref20) 2018 ref37 ref14 ref31 Miller (ref24); 89 ref30 ref10 ref2 Hsu (ref33) 2018 Zoph (ref9) 2016 Kingma (ref15) 2014 ref17 ref39 Baker (ref12) 2016 ref16 ref38 ref19 ref18 Codella (ref22) 2019 Carlucci (ref35) 2019 Krizhevsky (ref45) 2009 Simonyan (ref4) 2014 Feurer (ref11) 2018 ref26 ref25 Pham (ref32) 2018 ref42 ref41 Alom (ref1) 2018 Liu (ref29) 2018 ref44 ref21 ref43 Bergstra (ref6) 2012; 13 ref28 ref27 Xie (ref36) 2018 ref7 Lanctot (ref40) 2019 ref3 ref5 Kandasamy (ref8) 2018 Elsken (ref23) 2018 |
| References_xml | – volume-title: Adam: A method for stochastic optimization year: 2014 ident: ref15 – ident: ref16 doi: 10.1007/978-3-7908-2604-3_16 – volume-title: Very deep convolutional networks for large-scale image recognition year: 2014 ident: ref4 – ident: ref19 doi: 10.1038/sdata.2017.117 – ident: ref44 doi: 10.1109/CVPR.2009.5206848 – volume-title: Neural architecture search with reinforcement learning year: 2016 ident: ref9 – ident: ref34 doi: 10.1088/1757-899x/750/1/012223 – volume-title: Designing neural network architectures using reinforcement learning year: 2016 ident: ref12 – volume-title: Neural architecture search: A survey year: 2018 ident: ref23 – ident: ref37 doi: 10.1109/TNN.1998.712192 – ident: ref43 doi: 10.1007/978-3-319-46723-8_48 – ident: ref3 doi: 10.1109/5.726791 – ident: ref39 doi: 10.1017/CBO9780511811654 – volume-title: MONAS: Multi-objective neural architecture search using reinforcement learning year: 2018 ident: ref33 – ident: ref5 doi: 10.1109/CVPR.2016.90 – year: 2018 ident: ref1 article-title: The history began from AlexNet: A comprehensive survey on deep learning approaches – ident: ref21 doi: 10.1038/sdata.2018.161 – ident: ref13 doi: 10.1007/978-3-642-14435-67 – volume-title: Practical transfer learning for Bayesian optimization year: 2018 ident: ref11 – ident: ref17 doi: 10.1007/978-3-319-24574-4_28 – ident: ref7 doi: 10.1145/3292500.3330648 – ident: ref14 doi: 10.5555/2999134.2999257 – ident: ref28 doi: 10.1609/aaai.v32i1.11709 – ident: ref26 doi: 10.1007/978-3-030-05318-5_8 – start-page: 2016 volume-title: Advances in Neural Information Processing Systems year: 2018 ident: ref8 article-title: Neural architecture search with Bayesian optimisation and optimal transport – ident: ref18 doi: 10.1109/TMI.2014.2377694 – ident: ref31 doi: 10.1007/978-3-030-01246-5_2 – volume-title: MANAS: multi-agent neural architecture search year: 2019 ident: ref35 – volume: 89 start-page: 379 volume-title: Proc. 3rd Int. Conf. Genet. Algorithms (ICGA) ident: ref24 article-title: Designing neural networks using genetic algorithms – volume-title: Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (ISIC) year: 2019 ident: ref22 – ident: ref27 doi: 10.1109/WACV.2018.00083 – ident: ref10 doi: 10.1609/aaai.v33i01.33014780 – ident: ref42 doi: 10.4324/9781410605337-29 – ident: ref25 doi: 10.1145/3071178.3071229 – volume: 13 start-page: 281 issue: 10 year: 2012 ident: ref6 article-title: Random search for hyper-parameter optimization publication-title: J. Mach. Learn. Res. – ident: ref30 doi: 10.1109/CVPR.2018.00257 – ident: ref38 doi: 10.1007/978-3-030-60990-0_12 – volume-title: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge year: 2018 ident: ref20 – volume-title: Efficient neural architecture search via parameter sharing year: 2018 ident: ref32 – year: 2009 ident: ref45 article-title: Learning multiple layers of features from tiny images – ident: ref41 doi: 10.1007/978-3-319-28929-8 – ident: ref2 doi: 10.1162/neco.1989.1.4.541 – volume-title: DARTS: Differentiable architecture search year: 2018 ident: ref29 – volume-title: SNAS: Stochastic neural architecture search year: 2018 ident: ref36 – volume-title: OpenSpiel: A framework for reinforcement learning in games year: 2019 ident: ref40 |
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| SubjectTerms | Accuracy Artificial neural networks Computer architecture Computer vision Convolution Convolutional neural network (CNN) hyperparameter optimization Image classification Image processing Image segmentation Kernel Learning Medical imaging Multiagent systems neural architecture search (NAS) Neural networks Optimization Reinforcement learning reinforcement Learning (RL) Search problems Subspaces Training |
| Title | Multiagent Reinforcement Learning for Hyperparameter Optimization of Convolutional Neural Networks |
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