损失函数和反向传播

损失函数Loss

1.概念理解

2.官方文档
使用不难，要明白loss是如何计算的需要一定数学功底

（1）L1loss

X:1,2,3
Y:1,2,5
L1loss = (0+0+2) / 3 = 0.6
MSE = (0+0+2^2) / 3 = 1.333

import torchfrom torch.nn import L1Lossinputs = torch.tensor([1, 2, 3], dtype=torch.float32)targets = torch.tensor([1, 2, 5], dtype=torch.float32)print(inputs.shape)inputs = torch.reshape(inputs, (1, 1, 1, 3))targets = torch.reshape(targets, (1, 1, 1, 3))print(inputs.shape)loss = L1Loss(reduction='mean')result = loss(inputs, targets)print(result)

结果：
（2）MSELOSS

loss_mse = nn.MSELoss()result2 = loss_mse(inputs,targets)print(result2)

结果：
（3）CROSSENTROPYLOSS

x = torch.tensor([0.1, 0.2, 0.3])y = torch.tensor([1])x = torch.reshape(x, (1, 3))loss_cross = nn.CrossEntropyLoss()result3 = loss_cross(x,y)print(result3)

结果：

与卷积网络结合

import torchvisionfrom torch import nnfrom torch.nn import Sequential, Conv2d, MaxPool2d, Flatten, Linearfrom torch.utils.data import DataLoaderdataset = torchvision.datasets.CIFAR10("data", train=False, download=True,    transform=torchvision.transforms.ToTensor())dataloader = DataLoader(dataset, batch_size=1)class Peipei(nn.Module):    def __init__(self) -> None: super(Peipei, self).__init__() self.model1 = Sequential(     Conv2d(3, 32, 5, padding=2, stride=1),     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 xpeipei = Peipei()loss = nn.CrossEntropyLoss()for data in dataloader:    imgs, targets = data    outputs = peipei(imgs)    result_loss = loss(outputs,targets)    print(result_loss)    # 反向传播，计算每个节点的梯度/参数，以便于后续选择合适的优化器    result_loss.backward()

结果：