pytorch中交叉熵损失(nn.CrossEntropyLoss())的计算过程详解

脚本专栏 2025/1/6 佚名

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公式

首先需要了解CrossEntropyLoss的计算过程，交叉熵的函数是这样的：

其中，其中yi表示真实的分类结果。这里只给出公式，关于CrossEntropyLoss的其他详细细节请参照其他博文。

测试代码（一维）

import torch
import torch.nn as nn
import math

criterion = nn.CrossEntropyLoss()
output = torch.randn(1, 5, requires_grad=True)
label = torch.empty(1, dtype=torch.long).random_(5)
loss = criterion(output, label)

print("网络输出为5类:")
print(output)
print("要计算label的类别:")
print(label)
print("计算loss的结果:")
print(loss)

first = 0
for i in range(1):
  first = -output[i][label[i]]
second = 0
for i in range(1):
  for j in range(5):
    second += math.exp(output[i][j])
res = 0
res = (first + math.log(second))
print("自己的计算结果：")
print(res)

测试代码（多维）

import torch
import torch.nn as nn
import math
criterion = nn.CrossEntropyLoss()
output = torch.randn(3, 5, requires_grad=True)
label = torch.empty(3, dtype=torch.long).random_(5)
loss = criterion(output, label)

print("网络输出为3个5类:")
print(output)
print("要计算loss的类别:")
print(label)
print("计算loss的结果:")
print(loss)

first = [0, 0, 0]
for i in range(3):
  first[i] = -output[i][label[i]]
second = [0, 0, 0]
for i in range(3):
  for j in range(5):
    second[i] += math.exp(output[i][j])
res = 0
for i in range(3):
  res += (first[i] + math.log(second[i]))
print("自己的计算结果：")
print(res/3)