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import torch
import torchvision
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear, Sequential
from torch.utils.data import DataLoaderdataset = torchvision.datasets.CIFAR10("CIFAR10", train=False, transform=torchvision.transforms.ToTensor(),download=True)dataloader = DataLoader(dataset, batch_size=1)class Tudui(nn.Module):def __init__(self):super(Tudui, self).__init__()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()
tudui = Tudui()
optim = torch.optim.SGD(tudui.parameters(), lr=0.01)
for epoch in range(20):running_loss = 0.0for data in dataloader:imgs, targets = dataoutputs = tudui(imgs)result_loss = loss(outputs, targets)optim.zero_grad()result_loss.backward()optim.step()running_loss = running_loss + result_loss.item()print(running_loss)