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损失函数和反向传播

news 2025/9/15 2:37:12

1. 损失函数的基础

import torch
from torch.nn import L1Loss
from torch import nninputs = torch.tensor([1, 2, 3], dtype=torch.float32)
targets = torch.tensor([1, 2, 5], dtype=torch.float32)inputs = torch.reshape(inputs, (1, 1, 1, 3))
targets = torch.reshape(targets, (1, 1, 1, 3))loss_l1 = L1Loss(reduction='sum') #默认为mean
result_l1  = loss_l1(inputs,targets)loos_mse = nn.MSELoss()
result_mes = loos_mse(inputs, targets)print(result_l1, result_mes)x = torch.tensor([0.1,0.2,0.3])
y = torch.tensor([1])
x = torch.reshape(x,(1,3)) #(N,C)
loss_cross = nn.CrossEntropyLoss()  #注意输入输出的维度 多看官网
result_cross = loss_cross(x,y)
print(result_cross)

2. 损失函数的运用

import torchvision
from torch import nn
from torch.nn import Sequential, Conv2d, MaxPool2d, Flatten, Linear
from torch.utils.data import DataLoaderdataset = torchvision.datasets.CIFAR10('./dataset',train=False, transform=torchvision.transforms.ToTensor())
dataloader = DataLoader(dataset, batch_size=1)class Tudui(nn.Module):def __init__(self):super().__init__()self.model1 = Sequential(Conv2d(3, 32, 5, padding=2),MaxPool2d(kernel_size=2),Conv2d(32, 32, 5, padding=2),MaxPool2d(2),Conv2d(32, 64, 5, padding=2),MaxPool2d(2),Flatten(),Linear(1024, 64),Linear(64, 10))def forward(self, x):x = self.model1(x)return xloss = nn.CrossEntropyLoss()tudui = Tudui()for data in dataloader:imgs, targets = dataoutputs = tudui(imgs)result_loss = loss(outputs, targets)print(result_loss)result_loss.backward() #梯度print('ok')

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