import torch import torch.nn as nn import torch.optim as optim import torchvision import torchvision.transforms as transforms import matplotlib.pyplot as plt import numpy as npprint("林丽坤参与了 CIFAR - 10 图像分类实验")transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ])trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform) trainloader = torch.utils.data.DataLoader(trainset, batch_size=4, shuffle=True, num_workers=2)testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform) testloader = torch.utils.data.DataLoader(testset, batch_size=4, shuffle=False, num_workers=2)classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')class Net(nn.Module):def __init__(self):super(Net, self).__init__()self.conv1 = nn.Conv2d(3, 6, 5)self.pool = nn.MaxPool2d(2, 2)self.conv2 = nn.Conv2d(6, 16, 5)self.fc1 = nn.Linear(16 * 5 * 5, 120)self.fc2 = nn.Linear(120, 84)self.fc3 = nn.Linear(84, 10)def forward(self, x):x = self.pool(torch.relu(self.conv1(x)))x = self.pool(torch.relu(self.conv2(x)))x = x.view(-1, 16 * 5 * 5)x = torch.relu(self.fc1(x))x = torch.relu(self.fc2(x))x = self.fc3(x)return x net = Net()criterion = nn.CrossEntropyLoss() optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)for epoch in range(2): running_loss = 0.0for i, data in enumerate(trainloader, 0):inputs, labels = dataoptimizer.zero_grad()outputs = net(inputs)loss = criterion(outputs, labels)loss.backward()optimizer.step()running_loss += loss.item()if i % 2000 == 1999: print(f'Epoch: {epoch + 1}, Batch: {i + 1}, Loss: {running_loss / 2000}')running_loss = 0.0 print("林丽坤，训练完成")correct = 0 total = 0 with torch.no_grad():for data in testloader:images, labels = dataoutputs = net(images)_, predicted = torch.max(outputs.data, 1)total += labels.size(0)correct += (predicted == labels).sum().item() print(f'林丽坤，准确率 of the network on the 10000 test images: {100 * correct / total}%')
