torch.mean()和mean(dim=None, keepdim=False)的使用举例怎么分析

torch.mean()和mean(dim=None, keepdim=False)的使用举例怎么分析，针对这个问题，这篇文章详细介绍了相对应的分析和解答，希望可以帮助更多想解决这个问题的小伙伴找到更简单易行的方法。

代码实验展示:

Microsoft Windows [版本 10.0.18363.1256](c) 2019 Microsoft Corporation。保留所有权利。C:\Users\chenxuqi>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)>>> a = torch.randn(4, 3)>>> atensor([[ 0.2824, -0.3715,  0.9088],[-1.7601, -0.1806,  2.0937],[ 1.0406, -1.7651,  1.1216],[ 0.8440,  0.1783,  0.6859]])>>> torch.mean(a)tensor(0.2565)>>>>>> atensor([[ 0.2824, -0.3715,  0.9088],[-1.7601, -0.1806,  2.0937],[ 1.0406, -1.7651,  1.1216],[ 0.8440,  0.1783,  0.6859]])>>> torch.mean(a, 1)tensor([0.2732, 0.0510, 0.1324, 0.5694])>>> torch.mean(a, 0)tensor([ 0.1017, -0.5347,  1.2025])>>>>>> torch.mean(input=a, dim=0, keepdim=False)tensor([ 0.1017, -0.5347,  1.2025])>>>>>> torch.mean(input=a, dim=1, keepdim=False)tensor([0.2732, 0.0510, 0.1324, 0.5694])>>>>>> torch.mean(input=a, dim=0, keepdim=True)tensor([[ 0.1017, -0.5347,  1.2025]])>>> torch.mean(input=a, dim=1, keepdim=True)tensor([[0.2732],[0.0510],[0.1324],[0.5694]])>>>>>>>>>

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