损失函数和反向传播

1. 损失函数的基础

import torch
from torch.nn import L1Loss
from torch import nn
 
inputs = 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 DataLoader
 
dataset = 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 x
 
loss = nn.CrossEntropyLoss()
 
tudui = Tudui()
 
for data in dataloader:
    imgs, targets = data
    outputs = tudui(imgs)
    result_loss = loss(outputs, targets)
    print(result_loss)
 
    result_loss.backward() #梯度
    print('ok')