学习pytorch14 损失函数与反向传播-编程知识

学习pytorch14 损失函数与反向传播

神经网络-损失函数与反向传播

官网
损失函数
- L1Loss MAE 平均
- MSELoss 平方差
- CROSSENTROPYLOSS 交叉熵损失
- - 注意
  - code
反向传播
- 在debug中的显示
- - code

B站小土堆pytorch视频学习

官网

https://pytorch.org/docs/stable/nn.html#loss-functions
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损失函数

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L1Loss MAE 平均

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import torchinput = torch.tensor([1, 2, 3], dtype=float)
# target = torch.tensor([1, 2, 5], dtype=float)
target = torch.tensor([[[[1, 2, 5]]]], dtype=float) # shape [1, 1, 1, 3]
input = torch.reshape(input, (1,1,1,3))
# target = torch.reshape(target, (1,1,1,3))
print(input.shape)
print(target.shape)loss1 = torch.nn.L1Loss()
loss2 = torch.nn.L1Loss(reduction="sum")
result1 = loss1(input, target)
print(result1) # tensor(0.6667, dtype=torch.float64)
result2 = loss2(input, target)
print(result2) # tensor(2., dtype=torch.float64)

MSELoss 平方差

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import torchinput = torch.tensor([1, 2, 3], dtype=float)
# target = torch.tensor([1, 2, 5], dtype=float)
target = torch.tensor([[[[1, 2, 5]]]], dtype=float) # shape [1, 1, 1, 3]
input = torch.reshape(input, (1,1,1,3))
# target = torch.reshape(target, (1,1,1,3))
print(input.shape)
print(target.shape)loss_mse = torch.nn.MSELoss(reduction='mean')
result_mse = loss_mse(input, target)
print(result_mse) # tensor(1.3333, dtype=torch.float64)
loss_mse2 = torch.nn.MSELoss(reduction='sum')
result_mse2 = loss_mse2(input, target)
print(result_mse2)   # tensor(4., dtype=torch.float64)

CROSSENTROPYLOSS 交叉熵损失

https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss
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在神经网络中，默认log是以e为底的，所以也可以写成ln

注意

根据需求选择对应的loss函数
注意loss函数的输入输出shape

code

import torch
import torchvision
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear, Sequential
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWritertest_set = torchvision.datasets.CIFAR10("./dataset", train=False, transform=torchvision.transforms.ToTensor(),download=True)dataloader = DataLoader(test_set, batch_size=1)class MySeq(nn.Module):def __init__(self):super(MySeq, self).__init__()self.model1 = Sequential(Conv2d(3, 32, kernel_size=5, stride=1, padding=2),MaxPool2d(2),Conv2d(32, 32, kernel_size=5, stride=1, padding=2),MaxPool2d(2),Conv2d(32, 64, kernel_size=5, stride=1, padding=2),MaxPool2d(2),Flatten(),Linear(1024, 64),Linear(64, 10))def forward(self, x):x = self.model1(x)return xloss = nn.CrossEntropyLoss()
myseq = MySeq()
print(myseq)
for data in dataloader:imgs, targets = dataprint(imgs.shape)output = myseq(imgs)result = loss(output, targets)print(result)

反向传播

在debug中的显示

显示在网络结构中，每一层的保护属性中，都有weight属性，梯度属性在weitht属性里面
先找模型结构在找每一层在找weight权重，梯度在weight权重里面

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code

核心代码：result_loss.backward() # 要在最后获取 backward函数要挂在通过loss函数计算后的结果上。

# 模型定义、数据加载 同上个代码
for data in dataloader:imgs, targets = dataprint(imgs.shape)output = myseq(imgs)result_loss= loss(output, targets)result_loss.backward()  # 要在最后获取print(result_loss)print(result_loss.grad)