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")