【Pytorch】F.softmax()方法说明

1、函数语法格式和作用：
F.sofrmax(x,dim)作用：
根据不同的dim规则来做归一化操作。
x指的是输入的张量，dim指的是归一化的方式。
2、F.softmax()在二维张量下的例子：
2.1、举例代码：

import torch
import torch.nn.functional as F

input = torch.randn(3, 4)
print("input=",input)
b = F.softmax(input, dim=0)  # 按列SoftMax,列和为1（即0维度进行归一化）
print("b=",b)

c = F.softmax(input, dim=1)  # 按行SoftMax,行和为1（即1维度进行归一化）
print("c=",c)

2.2运行结果：

input= tensor([[-0.4918,  2.5391, -0.3338, -0.4989],
        [-0.2537,  0.1675,  1.1313,  0.0916],
        [ 0.9846, -1.4170, -0.7165,  1.8283]])
b= tensor([[0.1505, 0.8989, 0.1664, 0.0766],
        [0.1909, 0.0839, 0.7201, 0.1383],
        [0.6586, 0.0172, 0.1135, 0.7851]])
c= tensor([[0.0419, 0.8675, 0.0490, 0.0416],
        [0.1261, 0.1921, 0.5037, 0.1781],
        [0.2779, 0.0252, 0.0507, 0.6462]])

3、F.softmax()在三维张量下的例子：
3.1、举例代码：

import torch
import torch.nn.functional as F
a = torch.rand(3,4,5)
print("aa=",aa)
bb = F.softmax(aa,dim=0)  # 维度为0进行归一化
print("bb=",bb)
cc = F.softmax(aa,dim=1)  # 维度为1进行归一化
print("cc=",cc)
dd = F.softmax(aa,dim=2)  # 维度为2进行归一化
print("dd=",dd)

3.2、运行结果：

aa= tensor([[[0.0532, 0.9631, 0.9244, 0.9132, 0.7016],
         [0.5757, 0.2128, 0.6454, 0.6925, 0.1175],
         [0.0750, 0.5791, 0.6225, 0.7012, 0.5312],
         [0.9914, 0.1633, 0.7572, 0.9257, 0.3213]],

        [[0.6944, 0.5708, 0.5255, 0.3559, 0.6915],
         [0.7808, 0.3902, 0.6919, 0.7571, 0.5835],
         [0.0716, 0.9227, 0.8213, 0.3502, 0.7966],
         [0.9457, 0.4547, 0.4147, 0.8405, 0.3674]],

        [[0.9406, 0.8854, 0.6632, 0.5422, 0.1366],
         [0.1791, 0.1090, 0.2523, 0.5594, 0.8374],
         [0.7514, 0.2770, 0.8544, 0.5708, 0.2875],
         [0.8299, 0.9569, 0.1342, 0.2009, 0.3595]]])
bb= tensor([[[0.1877, 0.3845, 0.4096, 0.4419, 0.3909],
         [0.3448, 0.3231, 0.3673, 0.3399, 0.2152],
         [0.2523, 0.3175, 0.2873, 0.3873, 0.3239],
         [0.3563, 0.2198, 0.4452, 0.4162, 0.3240]],

        [[0.3564, 0.2597, 0.2749, 0.2531, 0.3870],
         [0.4233, 0.3858, 0.3848, 0.3626, 0.3429],
         [0.2515, 0.4477, 0.3505, 0.2727, 0.4223],
         [0.3404, 0.2942, 0.3161, 0.3822, 0.3393]],

        [[0.4559, 0.3558, 0.3155, 0.3049, 0.2222],
         [0.2319, 0.2912, 0.2479, 0.2975, 0.4420],
         [0.4962, 0.2347, 0.3623, 0.3400, 0.2538],
         [0.3032, 0.4860, 0.2388, 0.2016, 0.3366]]])
cc= tensor([[[0.1596, 0.3842, 0.2992, 0.2760, 0.3242],
         [0.2692, 0.1814, 0.2264, 0.2213, 0.1808],
         [0.1632, 0.2617, 0.2212, 0.2233, 0.2734],
         [0.4080, 0.1727, 0.2531, 0.2794, 0.2216]],

        [[0.2556, 0.2411, 0.2262, 0.1956, 0.2680],
         [0.2787, 0.2013, 0.2672, 0.2922, 0.2405],
         [0.1371, 0.3429, 0.3041, 0.1945, 0.2977],
         [0.3286, 0.2147, 0.2025, 0.3176, 0.1938]],

        [[0.3135, 0.3248, 0.2888, 0.2661, 0.1842],
         [0.1464, 0.1494, 0.1915, 0.2708, 0.3713],
         [0.2595, 0.1768, 0.3496, 0.2739, 0.2142],
         [0.2807, 0.3489, 0.1701, 0.1892, 0.2302]]])
dd= tensor([[[0.0985, 0.2447, 0.2355, 0.2328, 0.1884],
         [0.2210, 0.1538, 0.2370, 0.2484, 0.1398],
         [0.1277, 0.2114, 0.2207, 0.2388, 0.2015],
         [0.2720, 0.1188, 0.2152, 0.2547, 0.1392]],

        [[0.2253, 0.1991, 0.1903, 0.1606, 0.2247],
         [0.2278, 0.1542, 0.2085, 0.2225, 0.1870],
         [0.1131, 0.2648, 0.2393, 0.1494, 0.2334],
         [0.2732, 0.1672, 0.1606, 0.2459, 0.1532]],

        [[0.2616, 0.2475, 0.1982, 0.1756, 0.1171],
         [0.1562, 0.1456, 0.1680, 0.2285, 0.3017],
         [0.2384, 0.1484, 0.2643, 0.1990, 0.1499],
         [0.2637, 0.2994, 0.1315, 0.1406, 0.1647]]])

结语：在弄清楚F.softmax()后，在Pytorch中还有一个torch.max（）方法，可以将两个进行对比学习。链接如下：
【Pytorch】torch.argmax()用法