[Pytorch] F.softmax() method description

1、Function syntax format and role：
F.sofrmax(x,dim)作用：
根据不同的dimrules to do the normalization operation.
xrefers to the input tensor,dimRefers to the way of normalization.
2、F.softmax()Example under 2D tensors：
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()Example under 3D tensors：
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,The two can be compared for learning.链接如下：
【Pytorch】torch.argmax()用法