{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/cyclical-learning-rates-for-training-neural","title":"Cyclical Learning Rates for Training Neural Networks","arxiv_id":"1506.01186","date":"2015-06-03","proceeding":null,"authors":["Leslie N. Smith"],"abstract":"It is known that the learning rate is the most important hyper-parameter to\ntune for training deep neural networks. This paper describes a new method for\nsetting the learning rate, named cyclical learning rates, which practically\neliminates the need to experimentally find the best values and schedule for the\nglobal learning rates. Instead of monotonically decreasing the learning rate,\nthis method lets the learning rate cyclically vary between reasonable boundary\nvalues. Training with cyclical learning rates instead of fixed values achieves\nimproved classification accuracy without a need to tune and often in fewer\niterations. This paper also describes a simple way to estimate \"reasonable\nbounds\" -- linearly increasing the learning rate of the network for a few\nepochs. In addition, cyclical learning rates are demonstrated on the CIFAR-10\nand CIFAR-100 datasets with ResNets, Stochastic Depth networks, and DenseNets,\nand the ImageNet dataset with the AlexNet and GoogLeNet architectures. These\nare practical tools for everyone who trains neural networks.","url_abs":"http://arxiv.org/abs/1506.01186v6","url_pdf":"http://arxiv.org/pdf/1506.01186v6.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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