torch.nn.BCELoss用法

1. 定义
　　数学公式为 Loss = -w * [p * log(q) + (1-p) * log(1-q)] ，其中p、q分别为理论标签、实际预测值，w为权重。这里的log对应数学上的ln。

　　PyTorch对应函数为：
　　　　torch.nn.BCELoss(weight=None, size_average=None, reduce=None, reduction=‘mean’)
　　计算目标值和预测值之间的二进制交叉熵损失函数。

　　有四个可选参数：weight、size_average、reduce、reduction

1. weight必须和target的shape一致，默认为none。定义BCELoss的时候指定即可。
2. 默认情况下 nn.BCELoss()，reduce = True，size_average = True。
3. 如果reduce为False，size_average不起作用，返回向量形式的loss。
4. 如果reduce为True，size_average为True，返回loss的均值，即loss.mean()。
5. 如果reduce为True，size_average为False，返回loss的和，即loss.sum()。
6. 如果reduction = ‘none’，直接返回向量形式的 loss。
7. 如果reduction = ‘sum’，返回loss之和。
8. 如果reduction = ”elementwise_mean，返回loss的平均值。
9. 如果reduction = ”mean，返回loss的平均值

2. 验证代码

1>

import torch import torch.nn as nn m = nn.Sigmoid() loss = nn.BCELoss(size_average=False, reduce=False) input = torch.randn(3, requires_grad=True) target = torch.empty(3).random_(2) lossinput = m(input) output = loss(lossinput, target) print("输入值:") print(lossinput) print("输出的目标值:") print(target) print("计算loss的结果:") print(output)

 2>

import torch import torch.nn as nn m = nn.Sigmoid() loss = nn.BCELoss(size_average=True, reduce=False) input = torch.randn(3, requires_grad=True) target = torch.empty(3).random_(2) lossinput = m(input) output = loss(lossinput, target) print("输入值:") print(lossinput) print("输出的目标值:") print(target) print("计算loss的结果:") print(output)

 3>

import torch import torch.nn as nn m = nn.Sigmoid() loss = nn.BCELoss(size_average=True, reduce=True) input = torch.randn(3, requires_grad=True) target = torch.empty(3).random_(2) lossinput = m(input) output = loss(lossinput, target) print("输入值:") print(lossinput) print("输出的目标值:") print(target) print("计算loss的结果:") print(output)

 4>

import torch import torch.nn as nn m = nn.Sigmoid() loss = nn.BCELoss(size_average=False, reduce=True) input = torch.randn(3, requires_grad=True) target = torch.empty(3).random_(2) lossinput = m(input) output = loss(lossinput, target) print("输入值:") print(lossinput) print("输出的目标值:") print(target) print("计算loss的结果:") print(output)

 5>

import torch import torch.nn as nn m = nn.Sigmoid() loss = nn.BCELoss(reduction = 'none') input = torch.randn(3, requires_grad=True) target = torch.empty(3).random_(2) lossinput = m(input) output = loss(lossinput, target) print("输入值:") print(lossinput) print("输出的目标值:") print(target) print("计算loss的结果:") print(output)

 6>

import torch import torch.nn as nn m = nn.Sigmoid() weights=torch.randn(3) loss = nn.BCELoss(weight=weights,size_average=False, reduce=False) input = torch.randn(3, requires_grad=True) target = torch.empty(3).random_(2) lossinput = m(input) output = loss(lossinput, target) print("输入值:") print(lossinput) print("输出的目标值:") print(target) print("权重值") print(weights) print("计算loss的结果:") print(output)

2. 验证代码
1>
import torchimport torch.nn as nn
m = nn.Sigmoid()
loss = nn.BCELoss(size_average=False, reduce=False)input = torch.randn(3, requires_grad=True)target = torch.empty(3).random_(2)lossinput = m(input)output = loss(lossinput, target)
print(“输入值:”)print(lossinput)print(“输出的目标值:”)print(target)print(“计算loss的结果:”)print(output)1234567891011121314151617
2>
import torchimport torch.nn as nn
m = nn.Sigmoid()
loss = nn.BCELoss(size_average=True, reduce=False)input = torch.randn(3, requires_grad=True)target = torch.empty(3).random_(2)lossinput = m(input)output = loss(lossinput, target)
print(“输入值:”)print(lossinput)print(“输出的目标值:”)print(target)print(“计算loss的结果:”)print(output)1234567891011121314151617
3>
import torchimport torch.nn as nn
m = nn.Sigmoid()
loss = nn.BCELoss(size_average=True, reduce=True)input = torch.randn(3, requires_grad=True)target = torch.empty(3).random_(2)lossinput = m(input)output = loss(lossinput, target)
print(“输入值:”)print(lossinput)print(“输出的目标值:”)print(target)print(“计算loss的结果:”)print(output)1234567891011121314151617
4>
import torchimport torch.nn as nn
m = nn.Sigmoid()
loss = nn.BCELoss(size_average=False, reduce=True)input = torch.randn(3, requires_grad=True)target = torch.empty(3).random_(2)lossinput = m(input)output = loss(lossinput, target)
print(“输入值:”)print(lossinput)print(“输出的目标值:”)print(target)print(“计算loss的结果:”)print(output)1234567891011121314151617
5>
import torchimport torch.nn as nn
m = nn.Sigmoid()
loss = nn.BCELoss(reduction = ‘none’)input = torch.randn(3, requires_grad=True)target = torch.empty(3).random_(2)lossinput = m(input)output = loss(lossinput, target)
print(“输入值:”)print(lossinput)print(“输出的目标值:”)print(target)print(“计算loss的结果:”)print(output)1234567891011121314151617
6>
import torchimport torch.nn as nn
m = nn.Sigmoid()weights=torch.randn(3)
loss = nn.BCELoss(weight=weights,size_average=False, reduce=False)input = torch.randn(3, requires_grad=True)target = torch.empty(3).random_(2)lossinput = m(input)output = loss(lossinput, target)
print(“输入值:”)print(lossinput)print(“输出的目标值:”)print(target)print(“权重值”)print(weights)print(“计算loss的结果:”)print(output)1234567891011121314151617181920
————————————————版权声明：本文为CSDN博主「qq_29631521」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。原文链接：https://blog.csdn.net/qq_29631521/article/details/104907401

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