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 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()
optim = torch.optim.SGD(xzy.parameters(),lr=0.01)
for epoch in range(20):running_loss = 0.0for data in dataloader:imgs,targets = dataoutputs = xzy(imgs)result_loss = loss(outputs,targets)optim.zero_grad()result_loss.backward()optim.step()running_loss = result_loss + result_lossprint(running_loss)
