Author: Xueyan Mei; Hao-Chih Lee; Kaiyue Diao; Mingqian Huang; Bin Lin; Chenyu Liu; Zongyu Xie; Yixuan Ma; Philip M. Robson; Michael Chung; Adam Bernheim; Venkatesh Mani; Claudia Calcagno; Kunwei Li; Shaolin Li; Hong Shan; Jian Lv; Tongtong Zhao; Junli Xia; Qihua Long; Sharon Steinberger; Adam Jacobi; Timothy Deyer; Marta Luksza; Fang Liu; Brent P. Little; Zahi A. Fayad; Yang Yang
Title: Artificial intelligence for rapid identification of the coronavirus disease 2019 (COVID-19) Document date: 2020_4_17
ID: 79tozwzq_63
Snippet: We used binary cross entropy as the objective function. Adam optimizer 29 with a learning rate 0.001 was used to train the neural network. The learning rate was decreased by a factor of 0.95 each epoch. We applied random rotation, grid distortion, and cutout 30 to images for data augmentation. 20% of training samples were held out as the tuning set to monitor the progress of the training process. The training process was iterated for 40 epochs wi.....
Document: We used binary cross entropy as the objective function. Adam optimizer 29 with a learning rate 0.001 was used to train the neural network. The learning rate was decreased by a factor of 0.95 each epoch. We applied random rotation, grid distortion, and cutout 30 to images for data augmentation. 20% of training samples were held out as the tuning set to monitor the progress of the training process. The training process was iterated for 40 epochs with a batch size of 16 samples.
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