Selected article for: "adam learning and loss function"
Author: Chuansheng Zheng; Xianbo Deng; Qing Fu; Qiang Zhou; Jiapei Feng; Hui Ma; Wenyu Liu; Xinggang Wang
Title: Deep Learning-based Detection for COVID-19 from Chest CT using Weak Label
  • Document date: 2020_3_17
    • ID: ll4rxd9p_19
    Snippet: Training and Testing Procedures The DeCoVNet software was developed based on the PyTorch framework [21] . Our proposed DeCoVNet was trained in an end-to-end manner, which meant that the CT volumes were provided as input and only the final output was supervised without any manual intervention. The network was trained for 100 epochs using Adam optimizer [22] with a constant learning rate of 1e-5. Because the length of CT volume of each patient was .....
    Document: Training and Testing Procedures The DeCoVNet software was developed based on the PyTorch framework [21] . Our proposed DeCoVNet was trained in an end-to-end manner, which meant that the CT volumes were provided as input and only the final output was supervised without any manual intervention. The network was trained for 100 epochs using Adam optimizer [22] with a constant learning rate of 1e-5. Because the length of CT volume of each patient was not fixed, the batch size was set to 1. The binary cross-entropy loss function was used to calculate the loss between predictions and ground-truth labels.

    Search related documents:
    Co phrase search for related documents
    • Adam optimizer and binary cross entropy loss function: 1, 2, 3
    • Adam optimizer and cross entropy loss function: 1, 2, 3, 4, 5
    • Adam optimizer and learning rate: 1, 2, 3, 4, 5, 6, 7, 8, 9
    • Adam optimizer and loss function: 1, 2, 3, 4, 5, 6, 7, 8, 9
    • Adam optimizer epochs and batch size: 1, 2
    • Adam optimizer epochs and cross entropy loss function: 1, 2
    • Adam optimizer epochs and learning rate: 1
    • Adam optimizer epochs and loss function: 1, 2, 3
    • batch size and cross entropy loss function: 1, 2, 3, 4, 5
    • batch size and learning rate: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13
    • batch size and loss function: 1, 2, 3, 4, 5
    • binary cross entropy loss function and cross entropy loss function: 1, 2, 3, 4, 5, 6, 7
    • binary cross entropy loss function and loss function: 1, 2, 3, 4, 5, 6, 7
    • constant learning rate and learning rate: 1, 2, 3
    • cross entropy loss function and learning rate: 1, 2
    • CT volume and ground truth: 1, 2, 3, 4
    • end end and final output: 1
    • end end and ground truth: 1, 2, 3, 4, 5, 6
    • end end and input provide: 1