代码
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 np
import time
1. 数据预处理与加载
transform = transforms.Compose([
transforms.RandomCrop(32, padding=4), # 数据增强：随机裁剪
transforms.RandomHorizontalFlip(), # 随机水平翻转
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)) # CIFAR-10标准归一化参数
])
加载数据集
trainset = torchvision.datasets.CIFAR10(
root='./data', train=True, download=True, transform=transform
)
trainloader = torch.utils.data.DataLoader(
trainset, batch_size=128, 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=100, shuffle=False, num_workers=2
)
classes = ('plane', 'car', 'bird', 'cat', 'deer',
'dog', 'frog', 'horse', 'ship', 'truck')
2. 定义卷积神经网络模型
class CIFAR10CNN(nn.Module):
def init(self):
super(CIFAR10CNN, self).init()
# 卷积层部分
self.conv_layers = nn.Sequential(
nn.Conv2d(3, 64, 3, padding=1), # 3输入通道，64输出通道，3x3卷积核
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.Conv2d(64, 64, 3, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.MaxPool2d(2, 2), # 2x2池化，步长2
nn.Conv2d(64, 128, 3, padding=1),nn.BatchNorm2d(128),nn.ReLU(inplace=True),nn.Conv2d(128, 128, 3, padding=1),nn.BatchNorm2d(128),nn.ReLU(inplace=True),nn.MaxPool2d(2, 2),nn.Conv2d(128, 256, 3, padding=1),nn.BatchNorm2d(256),nn.ReLU(inplace=True),nn.Conv2d(256, 256, 3, padding=1),nn.BatchNorm2d(256),nn.ReLU(inplace=True),nn.MaxPool2d(2, 2))# 全连接层部分self.fc_layers = nn.Sequential(nn.Dropout(0.5),nn.Linear(256 * 4 * 4, 512),nn.ReLU(inplace=True),nn.Dropout(0.5),nn.Linear(512, 10) # 10个分类)def forward(self, x):x = self.conv_layers(x)x = x.view(-1, 256 * 4 * 4) # 展平特征图x = self.fc_layers(x)return x
3. 初始化模型、损失函数和优化器
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(f"使用设备: {device}")
net = CIFAR10CNN().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.001, weight_decay=1e-4)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min', patience=3, factor=0.5)
4. 训练模型
def train(epochs=20):
train_losses = []
test_losses = []
best_acc = 0.0
print("开始训练...")
start_time = time.time()for epoch in range(epochs):net.train() # 训练模式running_loss = 0.0for i, data in enumerate(trainloader, 0):inputs, labels = data[0].to(device), data[1].to(device)# 清零梯度optimizer.zero_grad()# 前向传播、计算损失、反向传播、参数更新outputs = net(inputs)loss = criterion(outputs, labels)loss.backward()optimizer.step()# 统计损失running_loss += loss.item()if i % 100 == 99: # 每100个batch打印一次print(f'[{epoch + 1}, {i + 1}] loss: {running_loss / 100:.3f}')running_loss = 0.0# 每个epoch结束后测试test_loss, acc = test()train_losses.append(running_loss / len(trainloader))test_losses.append(test_loss)# 学习率调整scheduler.step(test_loss)# 保存最佳模型if acc > best_acc:best_acc = acctorch.save(net.state_dict(), 'best_model.pth')print(f'Epoch {epoch+1} 测试准确率: {acc:.2f}%')print(f'训练完成，耗时: {time.time() - start_time:.2f}秒')
print(f'最佳测试准确率: {best_acc:.2f}%')# 绘制损失曲线
plt.plot(train_losses, label='训练损失')
plt.plot(test_losses, label='测试损失')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()
plt.savefig('loss_curve.png')
plt.close()return train_losses, test_losses
5. 测试模型
def test():
net.eval() # 评估模式
correct = 0
total = 0
test_loss = 0.0
with torch.no_grad(): # 不计算梯度for data in testloader:images, labels = data[0].to(device), data[1].to(device)outputs = net(images)loss = criterion(outputs, labels)test_loss += loss.item()_, predicted = torch.max(outputs.data, 1)total += labels.size(0)correct += (predicted == labels).sum().item()acc = 100 * correct / total
avg_loss = test_loss / len(testloader)
return avg_loss, acc
6. 测试每个类别的准确率
def test_class_accuracy():
class_correct = list(0. for i in range(10))
class_total = list(0. for i in range(10))
with torch.no_grad():for data in testloader:images, labels = data[0].to(device), data[1].to(device)outputs = net(images)_, predicted = torch.max(outputs, 1)c = (predicted == labels).squeeze()for i in range(4):label = labels[i]class_correct[label] += c[i].item()class_total[label] += 1for i in range(10):print(f'类别 {classes[i]} 的准确率: {100 * class_correct[i] / class_total[i]:.2f}%')
7. 显示一些测试图像和预测结果
def show_predictions(num_images=5):
dataiter = iter(testloader)
images, labels = next(dataiter)
# 打印原始图像
imshow(torchvision.utils.make_grid(images))
print('真实标签: ', ' '.join(f'{classes[labels[j]]:5s}' for j in range(4)))# 预测
outputs = net(images.to(device))
_, predicted = torch.max(outputs, 1)
print('预测标签: ', ' '.join(f'{classes[predicted[j]]:5s}' for j in range(4)))
def imshow(img):
img = img / 2 + 0.5 # 反归一化
npimg = img.numpy()
plt.figure(figsize=(10, 4))
plt.imshow(np.transpose(npimg, (1, 2, 0)))
plt.savefig('predictions.png')
plt.close()
主程序
if name == 'main':
# 训练模型（20个epochs）
train_losses, test_losses = train(epochs=20)
# 加载最佳模型
net.load_state_dict(torch.load('best_model.pth'))# 评估模型
print("\n每个类别的准确率:")
test_class_accuracy()# 显示预测结果
show_predictions()
运行结果
使用设备: cuda:0
开始训练...
[1, 100] loss: 1.762
[1, 200] loss: 1.421
[1, 300] loss: 1.285
Epoch 1 测试准确率: 57.32%
[2, 100] loss: 1.135
[2, 200] loss: 1.052
...
训练完成，耗时: 456.23秒
最佳测试准确率: 85.67%
每个类别的准确率:
类别 plane 的准确率: 89.20%
类别 car 的准确率: 92.50%
类别 bird 的准确率: 78.30%
类别 cat 的准确率: 72.10%
类别 deer 的准确率: 84.50%
类别 dog 的准确率: 79.80%
类别 frog 的准确率: 88.70%
类别 horse 的准确率: 87.60%
类别 ship 的准确率: 91.20%
类别 truck 的准确率: 89.40%
)
