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视频地址损失函数与反向传播_哔哩哔哩_bilibili

Loss两个作用

1,计算实际输出和目标之间的差距

2. 为我们更新输出提供一定的依据（反向传播)

import torch
from torch import nn
from torch.nn import L1Lossinputs = 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 = L1Loss(reduction='sum')
result = loss(inputs, targets)loss_mse = nn.MSELoss()
result_mse = loss_mse(inputs, targets)print(result)
print(result_mse)x = torch.tensor([0.1, 0.2, 0.3])
y = torch.tensor([1])
x = torch.reshape(x, (1, 3))
loss_cross = nn.CrossEntropyLoss()
result_cross = loss_cross(x, y)
print (result_cross)

上方代码举例了几种loss函数的使用

下方代码是loss函数在模型中如何使用

import torchvision
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear, Sequential
from torch.utils.data import DataLoaderdataset = torchvision.datasets.CIFAR10("CIFAR10", train=False, transform=torchvision.transforms.ToTensor(),download=True)dataloader = DataLoader(dataset, batch_size=1)class Tudui(nn.Module):def __init__(self):super(Tudui, self).__init__()self.model1 = Sequential(Conv2d(3, 32, 5, padding=2),MaxPool2d(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)result_loss.backward()