Selected article for: "initial learning rate and learning rate"

Author: Shuo Jin; Bo Wang; Haibo Xu; Chuan Luo; Lai Wei; Wei Zhao; Xuexue Hou; Wenshuo Ma; Zhengqing Xu; Zhuozhao Zheng; Wenbo Sun; Lan Lan; Wei Zhang; Xiangdong Mu; Chenxi Shi; Zhongxiao Wang; Jihae Lee; Zijian Jin; Minggui Lin; Hongbo Jin; Liang Zhang; Jun Guo; Benqi Zhao; Zhizhong Ren; Shuhao Wang; Zheng You; Jiahong Dong; Xinghuan Wang; Jianming Wang; Wei Xu
Title: AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system in four weeks
  • Document date: 2020_3_23
  • ID: e6q92shw_57
    Snippet: Model training. For neural network training, we trained all models from scratch with random initial parameters. Table S4 described the training and test data distribution of both segmentation and classification tasks. We trained the models on a server with eight Nvidia TITAN RTX GPUs using the PyTorch 22 framework. We used Adam optimizer with an initial learning rate of 1e 4.....
    Document: Model training. For neural network training, we trained all models from scratch with random initial parameters. Table S4 described the training and test data distribution of both segmentation and classification tasks. We trained the models on a server with eight Nvidia TITAN RTX GPUs using the PyTorch 22 framework. We used Adam optimizer with an initial learning rate of 1e 4

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