Pytorch中Softmax和LogSoftmax的使用详解

一、函数解释

1.Softmax函数常用的用法是指定参数dim就可以：

（1）dim=0：对每一列的所有元素进行softmax运算，并使得每一列所有元素和为1。

（2）dim=1：对每一行的所有元素进行softmax运算，并使得每一行所有元素和为1。

class Softmax(Module):
   r"""Applies the Softmax function to an n-dimensional input Tensor
   rescaling them so that the elements of the n-dimensional output Tensor
   lie in the range [0,1] and sum to 1.
   Softmax is defined as:
   .. math::
       \text{Softmax}(x_{i}) = \frac{\exp(x_i)}{\sum_j \exp(x_j)}
   Shape:
       - Input: :math:`(*)` where `*` means, any number of additional
         dimensions
       - Output: :math:`(*)`, same shape as the input
   Returns:
       a Tensor of the same dimension and shape as the input with
       values in the range [0, 1]
   Arguments:
       dim (int): A dimension along which Softmax will be computed (so every slice
           along dim will sum to 1).
   .. note::
       This module doesn't work directly with NLLLoss,
       which expects the Log to be computed between the Softmax and itself.
       Use `LogSoftmax` instead (it's faster and has better numerical properties).
   Examples::
       >>> m = nn.Softmax(dim=1)
       >>> input = torch.randn(2, 3)
       >>> output = m(input)
   """
   __constants__ = ['dim']

def __init__(self, dim=None):
       super(Softmax, self).__init__()
       self.dim = dim

def __setstate__(self, state):
       self.__dict__.update(state)
       if not hasattr(self, 'dim'):
           self.dim = None

def forward(self, input):
       return F.softmax(input, self.dim, _stacklevel=5)

def extra_repr(self):
       return 'dim={dim}'.format(dim=self.dim)

2.LogSoftmax其实就是对softmax的结果进行log，即Log(Softmax(x))

class LogSoftmax(Module):
   r"""Applies the :math:`\log(\text{Softmax}(x))` function to an n-dimensional
   input Tensor. The LogSoftmax formulation can be simplified as:
   .. math::
       \text{LogSoftmax}(x_{i}) = \log\left(\frac{\exp(x_i) }{ \sum_j \exp(x_j)} \right)
   Shape:
       - Input: :math:`(*)` where `*` means, any number of additional
         dimensions
       - Output: :math:`(*)`, same shape as the input
   Arguments:
       dim (int): A dimension along which LogSoftmax will be computed.
   Returns:
       a Tensor of the same dimension and shape as the input with
       values in the range [-inf, 0)
   Examples::
       >>> m = nn.LogSoftmax()
       >>> input = torch.randn(2, 3)
       >>> output = m(input)
   """
   __constants__ = ['dim']

def __init__(self, dim=None):
       super(LogSoftmax, self).__init__()
       self.dim = dim

def __setstate__(self, state):
       self.__dict__.update(state)
       if not hasattr(self, 'dim'):
           self.dim = None

def forward(self, input):
       return F.log_softmax(input, self.dim, _stacklevel=5)

二、代码示例

输入代码

import torch
import torch.nn as nn
import numpy as np

batch_size = 4
class_num = 6
inputs = torch.randn(batch_size, class_num)
for i in range(batch_size):
   for j in range(class_num):
       inputs[i][j] = (i + 1) * (j + 1)

print("inputs:", inputs)

得到大小batch_size为4，类别数为6的向量（可以理解为经过最后一层得到）

tensor([[ 1.,  2.,  3.,  4.,  5.,  6.],
           [ 2.,  4.,  6.,  8., 10., 12.],
           [ 3.,  6.,  9., 12., 15., 18.],
           [ 4.,  8., 12., 16., 20., 24.]])

接着我们对该向量每一行进行Softmax

Softmax = nn.Softmax(dim=1)
probs = Softmax(inputs)
print("probs:\n", probs)

得到

tensor([[4.2698e-03, 1.1606e-02, 3.1550e-02, 8.5761e-02, 2.3312e-01, 6.3369e-01],
           [3.9256e-05, 2.9006e-04, 2.1433e-03, 1.5837e-02, 1.1702e-01, 8.6467e-01],
           [2.9067e-07, 5.8383e-06, 1.1727e-04, 2.3553e-03, 4.7308e-02, 9.5021e-01],
           [2.0234e-09, 1.1047e-07, 6.0317e-06, 3.2932e-04, 1.7980e-02, 9.8168e-01]])

此外，我们对该向量每一行进行LogSoftmax

LogSoftmax = nn.LogSoftmax(dim=1)
log_probs = LogSoftmax(inputs)
print("log_probs:\n", log_probs)

得到

tensor([[-5.4562e+00, -4.4562e+00, -3.4562e+00, -2.4562e+00, -1.4562e+00, -4.5619e-01],
           [-1.0145e+01, -8.1454e+00, -6.1454e+00, -4.1454e+00, -2.1454e+00, -1.4541e-01],
           [-1.5051e+01, -1.2051e+01, -9.0511e+00, -6.0511e+00,  -3.0511e+00, -5.1069e-02],
           [-2.0018e+01, -1.6018e+01, -1.2018e+01, -8.0185e+00, -4.0185e+00, -1.8485e-02]])

验证每一行元素和是否为1

# probs_sum in dim=1
probs_sum = [0 for i in range(batch_size)]

for i in range(batch_size):
   for j in range(class_num):
       probs_sum[i] += probs[i][j]
   print(i, "row probs sum:", probs_sum[i])

得到每一行的和，看到确实为1

0 row probs sum: tensor(1.)
1 row probs sum: tensor(1.0000)
2 row probs sum: tensor(1.)
3 row probs sum: tensor(1.)

验证LogSoftmax是对Softmax的结果进行Log

# to numpy
np_probs = probs.data.numpy()
print("numpy probs:\n", np_probs)

# np.log()
log_np_probs = np.log(np_probs)
print("log numpy probs:\n", log_np_probs)

得到

numpy probs:
[[4.26977826e-03 1.16064614e-02 3.15496325e-02 8.57607946e-02 2.33122006e-01 6.33691311e-01]
 [3.92559559e-05 2.90064461e-04 2.14330270e-03 1.58369839e-02 1.17020354e-01 8.64669979e-01]
 [2.90672347e-07 5.83831024e-06 1.17265590e-04 2.35534250e-03 4.73083146e-02 9.50212955e-01]
 [2.02340233e-09 1.10474026e-07 6.03167746e-06 3.29318427e-04 1.79801770e-02 9.81684387e-01]]
log numpy probs:
[[-5.4561934e+00 -4.4561934e+00 -3.4561934e+00 -2.4561932e+00 -1.4561933e+00 -4.5619333e-01]
 [-1.0145408e+01 -8.1454077e+00 -6.1454072e+00 -4.1454072e+00 -2.1454074e+00 -1.4540738e-01]
 [-1.5051069e+01 -1.2051069e+01 -9.0510693e+00 -6.0510693e+00 -3.0510693e+00 -5.1069155e-02]
 [-2.0018486e+01 -1.6018486e+01 -1.2018485e+01 -8.0184851e+00 -4.0184855e+00 -1.8485421e-02]]

验证完毕

三、整体代码

import torch
import torch.nn as nn
import numpy as np

batch_size = 4
class_num = 6
inputs = torch.randn(batch_size, class_num)
for i in range(batch_size):
   for j in range(class_num):
       inputs[i][j] = (i + 1) * (j + 1)

print("inputs:", inputs)
Softmax = nn.Softmax(dim=1)
probs = Softmax(inputs)
print("probs:\n", probs)

LogSoftmax = nn.LogSoftmax(dim=1)
log_probs = LogSoftmax(inputs)
print("log_probs:\n", log_probs)

# probs_sum in dim=1
probs_sum = [0 for i in range(batch_size)]

for i in range(batch_size):
   for j in range(class_num):
       probs_sum[i] += probs[i][j]
   print(i, "row probs sum:", probs_sum[i])

# to numpy
np_probs = probs.data.numpy()
print("numpy probs:\n", np_probs)

# np.log()
log_np_probs = np.log(np_probs)
print("log numpy probs:\n", log_np_probs)

基于pytorch softmax,logsoftmax 表达

import torch
import numpy as np
input = torch.autograd.Variable(torch.rand(1, 3))

print(input)
print('softmax={}'.format(torch.nn.functional.softmax(input, dim=1)))
print('logsoftmax={}'.format(np.log(torch.nn.functional.softmax(input, dim=1))))

来源：https://blog.csdn.net/qq_36556893/article/details/105889978