计算l1loss mseloss

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 = L1Loss(reduction='sum')
result = loss(inputs,targets)loss_mse = nn.MSELoss()
result_mse = loss_mse(inputs,targets)print(result)
print(result_mse)

交叉熵·

 

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)

import torch
import torchvision.datasets
from torch import nn
from torch.nn import Sequential,Conv2d,MaxPool2d,Flatten,Linear
from torch.utils.data import DataLoaderdataset = torchvision.datasets.CIFAR10("../data",train=False,transform=torchvision.transforms.ToTensor(),download=True)
dataloader = DataLoader(dataset,batch_size=1)
class XuZhenyu(nn.Module):def __init__(self, *args, **kwargs) -> None:super().__init__(*args, **kwargs)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()
xzy = XuZhenyu()
for data in dataloader:imgs,targets = dataoutputs = xzy(imgs)result_loss = loss(outputs,targets)print(result_loss)

反向传播grad对参数优化，梯度下降，对参数更新，达到降阶。

import torch
import torchvision.datasets
from torch import nn
from torch.nn import Sequential,Conv2d,MaxPool2d,Flatten,Linear
from torch.utils.data import DataLoaderdataset = torchvision.datasets.CIFAR10("../data",train=False,transform=torchvision.transforms.ToTensor(),download=True)
dataloader = DataLoader(dataset,batch_size=1)
class XuZhenyu(nn.Module):def __init__(self, *args, **kwargs) -> None:super().__init__(*args, **kwargs)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()
xzy = XuZhenyu()
for data in dataloader:imgs,targets = dataoutputs = xzy(imgs)result_loss = loss(outputs,targets)#print(result_loss)result_loss.backward()print("ok")