{"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/deep-learning-using-rectified-linear-units","title":"Deep Learning using Rectified Linear Units (ReLU)","arxiv_id":"1803.08375","date":"2018-03-22","proceeding":null,"authors":["Abien Fred Agarap"],"abstract":"We introduce the use of rectified linear units (ReLU) as the classification\nfunction in a deep neural network (DNN). Conventionally, ReLU is used as an\nactivation function in DNNs, with Softmax function as their classification\nfunction. However, there have been several studies on using a classification\nfunction other than Softmax, and this study is an addition to those. We\naccomplish this by taking the activation of the penultimate layer $h_{n - 1}$\nin a neural network, then multiply it by weight parameters $\\theta$ to get the\nraw scores $o_{i}$. Afterwards, we threshold the raw scores $o_{i}$ by $0$,\ni.e. $f(o) = \\max(0, o_{i})$, where $f(o)$ is the ReLU function. We provide\nclass predictions $\\hat{y}$ through argmax function, i.e. argmax $f(x)$.","url_abs":"http://arxiv.org/abs/1803.08375v2","url_pdf":"http://arxiv.org/pdf/1803.08375v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deep-learning-using-rectified-linear-units","repo_url":"https://github.com/AFAgarap/relu-classifier","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1803.08375","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.08375"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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