torch.nn.functional中softmax的作用及其参数说明

我就是我 2022-01-09 09:59 400阅读 0赞

参考：https://pytorch-cn.readthedocs.io/zh/latest/package\_references/functional/\#\_1

class torch.nn.Softmax(input, dim)

或：

torch.nn.functional.softmax(input, dim)

对n维输入张量运用Softmax函数，将张量的每个元素缩放到（0,1）区间且和为1。Softmax函数定义如下：

![1446032-20190409103827224-1240655551.png][]

参数：

dim:指明维度，dim=0表示按列计算；dim=1表示按行计算。默认dim的方法已经弃用了，最好声明dim，否则会警告：

UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.

shape：

*  输入：(N, L)
 *  输出：(N, L)

返回结果是一个与输入维度dim相同的张量，每个元素的取值范围在（0,1）区间。

例子：

import torch
    
    from torch import nn
    from torch import autograd
    
    m = nn.Softmax()
    input = autograd.Variable(torch.randn(2, 3))
    print(input)
    print(m(input))

(deeplearning) userdeMBP:pytorch user$ python test.py 
    tensor([[ 0.2854,  0.1708,  0.4308],
            [-0.1983,  2.0705,  0.1549]])
    test.py:9: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
      print(m(input))
    tensor([[0.3281, 0.2926, 0.3794],
            [0.0827, 0.7996, 0.1177]])

可见默认按行计算，即dim=1

更明显的例子：

import torch
    
    import torch.nn.functional as F
    
    x= torch.Tensor( [ [1,2,3,4],[1,2,3,4],[1,2,3,4]])
    
    y1= F.softmax(x, dim = 0) #对每一列进行softmax
    print(y1)
    
    y2 = F.softmax(x,dim =1) #对每一行进行softmax
    print(y2)
    
    x1 = torch.Tensor([1,2,3,4])
    print(x1)
    
    y3 = F.softmax(x1,dim=0) #一维时使用dim=0，使用dim=1报错
    print(y3)

(deeplearning) userdeMBP:pytorch user$ python test.py 
    tensor([[0.3333, 0.3333, 0.3333, 0.3333],
            [0.3333, 0.3333, 0.3333, 0.3333],
            [0.3333, 0.3333, 0.3333, 0.3333]])
    tensor([[0.0321, 0.0871, 0.2369, 0.6439],
            [0.0321, 0.0871, 0.2369, 0.6439],
            [0.0321, 0.0871, 0.2369, 0.6439]])
    tensor([1., 2., 3., 4.])
    tensor([0.0321, 0.0871, 0.2369, 0.6439])

因为列的值相同，所以按列计算时每一个所占的比重都是0.3333；行都是\[1,2,3,4\]，所以按行计算，比重结果都为\[0.0321, 0.0871, 0.2369, 0.6439\]

一维使用dim=1报错：

RuntimeError: Dimension out of range (expected to be in range of [-1, 0], but got 1)

转载于:https://www.cnblogs.com/wanghui-garcia/p/10675588.html

[1446032-20190409103827224-1240655551.png]: /images/20220109/3841d0c515804afaa6e1b0e68482a2ff.png