{"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/searching-for-activation-functions","title":"Searching for Activation Functions","arxiv_id":"1710.05941","date":"2017-10-16","proceeding":"ICLR 2018 1","authors":["Prajit Ramachandran","Barret Zoph","Quoc V. Le"],"abstract":"The choice of activation functions in deep networks has a significant effect\non the training dynamics and task performance. Currently, the most successful\nand widely-used activation function is the Rectified Linear Unit (ReLU).\nAlthough various hand-designed alternatives to ReLU have been proposed, none\nhave managed to replace it due to inconsistent gains. In this work, we propose\nto leverage automatic search techniques to discover new activation functions.\nUsing a combination of exhaustive and reinforcement learning-based search, we\ndiscover multiple novel activation functions. We verify the effectiveness of\nthe searches by conducting an empirical evaluation with the best discovered\nactivation function. Our experiments show that the best discovered activation\nfunction, $f(x) = x \\cdot \\text{sigmoid}(\\beta x)$, which we name Swish, tends\nto work better than ReLU on deeper models across a number of challenging\ndatasets. For example, simply replacing ReLUs with Swish units improves top-1\nclassification accuracy on ImageNet by 0.9\\% for Mobile NASNet-A and 0.6\\% for\nInception-ResNet-v2. The simplicity of Swish and its similarity to ReLU make it\neasy for practitioners to replace ReLUs with Swish units in any neural network.","url_abs":"http://arxiv.org/abs/1710.05941v2","url_pdf":"http://arxiv.org/pdf/1710.05941v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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