1. torch.Tensor 生成一个 tensor 类型。

>>> import torch
>>> x=torch.Tensor([[1,2,3],[4,5,6]])
>>> x
tensor([[1., 2., 3.],
        [4., 5., 6.]])

参考: https://www.jianshu.com/p/314b6cfce1c3 https://pytorch-cn.readthedocs.io/zh/latest/package_references/Tensor/ https://blog.csdn.net/weixin_42018112/article/details/91383574

2. torch.nn.functional.softmax(input, dim=None, _stacklevel=3, dtype=None) 用于对设置的维度上的数进行收缩，让他的和为 1.

>>> import torch
>>> from torch import nn as nn
>>> input = torch.randn(2, 3, 4)
>>> input
tensor([[[-0.1335,  0.1574, -0.4618, -0.1629],
         [-1.1302, -0.2782,  0.2689,  1.4722],
         [ 1.8547,  3.0593,  1.7146, -0.4395]],

        [[-0.0102,  1.4679,  0.0138,  0.5245],
         [ 2.2345,  2.0089,  2.0074,  0.4197],
         [-1.4187, -0.0887,  0.9257,  0.2516]]])
>>> torch.nn.functional.softmax(input)
tensor([[[0.4692, 0.2124, 0.3833, 0.3346],
         [0.0334, 0.0922, 0.1495, 0.7413],
         [0.9635, 0.9588, 0.6876, 0.3338]],

        [[0.5308, 0.7876, 0.6167, 0.6654],
         [0.9666, 0.9078, 0.8505, 0.2587],
         [0.0365, 0.0412, 0.3124, 0.6662]]])
>>>
>>> torch.nn.functional.softmax(input, dim=0)
tensor([[[0.4692, 0.2124, 0.3833, 0.3346],
         [0.0334, 0.0922, 0.1495, 0.7413],
         [0.9635, 0.9588, 0.6876, 0.3338]],

        [[0.5308, 0.7876, 0.6167, 0.6654],
         [0.9666, 0.9078, 0.8505, 0.2587],
         [0.0365, 0.0412, 0.3124, 0.6662]]])
>>> torch.nn.functional.softmax(input, dim=1)
tensor([[[0.1153, 0.0504, 0.0841, 0.1452],
         [0.0426, 0.0326, 0.1746, 0.7447],
         [0.8421, 0.9171, 0.7413, 0.1101]],

        [[0.0936, 0.3415, 0.0923, 0.3757],
         [0.8835, 0.5866, 0.6779, 0.3383],
         [0.0229, 0.0720, 0.2298, 0.2860]]])
>>> torch.nn.functional.softmax(input, dim=2)
tensor([[[0.2482, 0.3320, 0.1788, 0.2410],
         [0.0479, 0.1122, 0.1939, 0.6460],
         [0.1885, 0.6287, 0.1638, 0.0190]],

        [[0.1232, 0.5403, 0.1262, 0.2103],
         [0.3626, 0.2894, 0.2889, 0.0591],
         [0.0487, 0.1843, 0.5081, 0.2589]]])

默认是对于第一维进行伸缩。 dim = 0, 0.4692 + 0.5308 = 1 dim = 1, 0.1153 + 0.0426 + 0.8421 = 1 dim = 2, 0.2482 + 0.3320 + 0.1788 + 0.2410 = 1

参考: https://blog.csdn.net/m0_46653437/article/details/111610571 https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.softmax https://blog.csdn.net/chengyq116/article/details/106893773

3. torch.clamp(input, min, max, *, out=None) → Tensor 用于限制数值的上限和下限，把所有数据限制在设定好的上下限之间。

>>> a = torch.randn(4)
>>> a
tensor([-1.7120,  0.1734, -0.0478, -0.0922])
>>> torch.clamp(a, min=-0.5, max=0.5)
tensor([-0.5000,  0.1734, -0.0478, -0.0922])

参考: https://pytorch.org/docs/stable/generated/torch.clamp.html

参考数目：

https://pytorch.apachecn.org/docs/1.0/tensors.html