PyTorch的class torch.nn.Sigmoid怎么使用

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代码实验展示:

C:\Users\chenxuqi>conda activate ssd4pytorch2_2_0(ssd4pytorch2_2_0) C:\Users\chenxuqi>pythonPython 3.7.7 (default, May  6 2020, 11:45:54) [MSC v.1916 64 bit (AMD64)] :: Anaconda, Inc. on win32Type "help", "copyright", "credits" or "license" for more information.>>> import torch>>> torch.manual_seed(seed=20200910)>>>>>> data = torch.randn(3,5)>>> datatensor([[ 0.2824, -0.3715,  0.9088, -1.7601, -0.1806],[ 2.0937,  1.0406, -1.7651,  1.1216,  0.8440],[ 0.1783,  0.6859, -1.5942, -0.2006, -0.4050]])>>> data[0,1] = 0.0>>> datatensor([[ 0.2824,  0.0000,  0.9088, -1.7601, -0.1806],[ 2.0937,  1.0406, -1.7651,  1.1216,  0.8440],[ 0.1783,  0.6859, -1.5942, -0.2006, -0.4050]])>>>>>> torch.nn.functional.sigmoid(data)D:\Anaconda3\envs\ssd4pytorch2_2_0\lib\site-packages\torch\nn\functional.py:1350: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.  warnings.warn("nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.")tensor([[0.5701, 0.5000, 0.7127, 0.1468, 0.4550],[0.8903, 0.7390, 0.1461, 0.7543, 0.6993],[0.5445, 0.6650, 0.1688, 0.4500, 0.4001]])>>>>>>>>> model = torch.nn.Sigmoid()>>> model(data)tensor([[0.5701, 0.5000, 0.7127, 0.1468, 0.4550],[0.8903, 0.7390, 0.1461, 0.7543, 0.6993],[0.5445, 0.6650, 0.1688, 0.4500, 0.4001]])>>>>>>

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