实验题

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
import matplotlib.pyplot as plt

1. 数据加载与预处理

transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

加载训练集和测试集

train_set = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
test_set = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)

train_loader = torch.utils.data.DataLoader(train_set, batch_size=64, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_set, batch_size=64, shuffle=False)

类别标签

classes = ('飞机', '汽车', '鸟', '猫', '鹿', '狗', '青蛙', '马', '船', '卡车')

2. 构建卷积神经网络

class Net(nn.Module):
def init(self):
super(Net, self).init()
self.conv1 = nn.Conv2d(3, 16, 3, padding=1)
self.relu = nn.ReLU()
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(16, 32, 3, padding=1)
self.fc1 = nn.Linear(32 * 8 * 8, 128)
self.fc2 = nn.Linear(128, 10)

def forward(self, x):
x = self.pool(self.relu(self.conv1(x)))
x = self.pool(self.relu(self.conv2(x)))
x = x.view(-1, 32 * 8 * 8)
x = self.relu(self.fc1(x))
x = self.fc2(x)
return x

net = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.001)

3. 训练网络

epochs = 10
train_losses = []
train_accs = []
test_accs = []

for epoch in range(epochs):
running_loss = 0.0
correct = 0
total = 0
for inputs, labels in train_loader:
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()

running_loss += loss.item()
_, predicted = outputs.max(1)
total += labels.size(0)
correct += predicted.eq(labels).sum().item()

train_loss = running_loss / len(train_loader)
train_acc = 100. * correct / total
train_losses.append(train_loss)
train_accs.append(train_acc)

测试集评估

net.eval()
correct = 0
total = 0
with torch.no_grad():
for inputs, labels in test_loader:
outputs = net(inputs)
_, predicted = outputs.max(1)
total += labels.size(0)
correct += predicted.eq(labels).sum().item()
test_acc = 100. * correct / total
test_accs.append(test_acc)
net.train()

print(f'Epoch {epoch+1}/{epochs}, Loss: {train_loss:.3f}, Train Acc: {train_acc:.2f}%, Test Acc: {test_acc:.2f}%')

print('训练完成')

4. 可视化训练过程

plt.figure(figsize=(12, 4))
plt.subplot(1, 2, 1)
plt.plot(train_losses)
plt.title('训练损失')
plt.xlabel('Epoch')
plt.ylabel('Loss')

plt.subplot(1, 2, 2)
plt.plot(train_accs, label='训练准确率')
plt.plot(test_accs, label='测试准确率')
plt.title('准确率变化')
plt.xlabel('Epoch')
plt.ylabel('Accuracy (%)')
plt.legend()
plt.show()

5. 查看各类别准确率

class_correct = list(0. for i in range(10))
class_total = list(0. for i in range(10))
with torch.no_grad():
for inputs, labels in test_loader:
outputs = net(inputs)
_, predicted = torch.max(outputs, 1)
c = (predicted == labels).squeeze()
for i in range(len(labels)):
label = labels[i]
class_correct[label] += c[i].item()
class_total[label] += 1

for i in range(10):
print(f'类别 {classes[i]} 的准确率: {100 * class_correct[i] / class_total[i]:.2f}%')