损失函数与反向传播

计算l1loss mseloss

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 = 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 DataLoader

dataset = 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 x

loss = nn.CrossEntropyLoss()
xzy = XuZhenyu()
for data in dataloader:
    imgs,targets = data
    outputs = 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 DataLoader

dataset = 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 x

loss = nn.CrossEntropyLoss()
xzy = XuZhenyu()
for data in dataloader:
    imgs,targets = data
    outputs = xzy(imgs)
    result_loss = loss(outputs,targets)
    #print(result_loss)
    result_loss.backward()
    print("ok")