不断优化
Example:
for input, target in dataset:
optimizer.zero_grad()
output = model(input)
loss = loss_fn(output, target)
loss.backward()
optimizer.step()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()
optim = torch.optim.SGD(xzy.parameters(),lr=0.01)
for epoch in range(20):
running_loss = 0.0
for data in dataloader:
imgs,targets = data
outputs = xzy(imgs)
result_loss = loss(outputs,targets)
optim.zero_grad()
result_loss.backward()
optim.step()
running_loss = result_loss + result_loss
print(running_loss)
