import torch import torch.nn as nn import torch.optim as optim from torchvision import datasets, transforms from torch.utils.data import DataLoader import matplotlib.pyplot as plt import numpy as np # 设置中文字体支持 plt.rcParams["font.family"] = ["SimHei"] plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题 # 检查GPU是否可用 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(f"使用设备: {device}") # 1. 数据预处理 # 训练集：使用多种数据增强方法提高模型泛化能力 train_transform = transforms.Compose([ # 随机裁剪图像，从原图中随机截取32x32大小的区域 transforms.RandomCrop(32, padding=4), # 随机水平翻转图像（概率0.5） transforms.RandomHorizontalFlip(), # 随机颜色抖动：亮度、对比度、饱和度和色调随机变化 transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1), # 随机旋转图像（最大角度15度） transforms.RandomRotation(15), # 将PIL图像或numpy数组转换为张量 transforms.ToTensor(), # 标准化处理：每个通道的均值和标准差，使数据分布更合理 transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)) ]) # 测试集：仅进行必要的标准化，保持数据原始特性，标准化不损失数据信息，可还原 test_transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)) ]) # 2. 加载CIFAR-10数据集 train_dataset = datasets.CIFAR10( root='./data', train=True, download=True, transform=train_transform # 使用增强后的预处理 ) test_dataset = datasets.CIFAR10( root='./data', train=False, transform=test_transform # 测试集不使用增强 ) # 3. 创建数据加载器 batch_size = 64 train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True) test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False) import torch import torch.nn as nn import torch.optim as optim import torch.nn.functional as F # 设置设备 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(f"使用设备: {device}") # 1. 定义CNN模型 class CNN(nn.Module): def __init__(self): super(CNN, self).__init__() # ---------------------- 卷积特征提取部分 ---------------------- # 第一个卷积块 self.conv_block1 = nn.Sequential( nn.Conv2d(3, 32, kernel_size=3, padding=1), # [batch, 3, 32, 32] -> [batch, 32, 32, 32] nn.BatchNorm2d(32), nn.ReLU(inplace=True), nn.MaxPool2d(2) # [batch, 32, 32, 32] -> [batch, 32, 16, 16] ) # 第二个卷积块 self.conv_block2 = nn.Sequential( nn.Conv2d(32, 64, kernel_size=3, padding=1), # [batch, 32, 16, 16] -> [batch, 64, 16, 16] nn.BatchNorm2d(64), nn.ReLU(inplace=True), nn.MaxPool2d(2) # [batch, 64, 16, 16] -> [batch, 64, 8, 8] ) # 第三个卷积块 self.conv_block3 = nn.Sequential( nn.Conv2d(64, 128, kernel_size=3, padding=1), # [batch, 64, 8, 8] -> [batch, 128, 8, 8] nn.BatchNorm2d(128), nn.ReLU(inplace=True), nn.MaxPool2d(2) # [batch, 128, 8, 8] -> [batch, 128, 4, 4] ) # ---------------------- 全连接分类部分 ---------------------- self.classifier = nn.Sequential( nn.Linear(128 * 4 * 4, 512), nn.ReLU(inplace=True), nn.Dropout(0.5), nn.Linear(512, 256), nn.ReLU(inplace=True), nn.Dropout(0.3), nn.Linear(256, 10) ) def forward(self, x): # 卷积特征提取 x = self.conv_block1(x) x = self.conv_block2(x) x = self.conv_block3(x) # 展平 x = x.view(x.size(0), -1) # [batch, 128, 4, 4] -> [batch, 2048] # 分类 x = self.classifier(x) return x # 2. 初始化模型 model = CNN().to(device) print(f"模型参数量: {sum(p.numel() for p in model.parameters()):,}") print(f"可训练参数量: {sum(p.numel() for p in model.parameters() if p.requires_grad):,}") # 3. 定义损失函数和优化器 criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=0.001, weight_decay=1e-4) # 添加L2正则化 scheduler = optim.lr_scheduler.ReduceLROnPlateau( optimizer, # 第一个参数是optimizer，不要用关键字参数 mode='min', factor=0.5, patience=5, threshold=0.01, min_lr=1e-5 )
# 5. 训练模型（记录每个 iteration 的损失） def train(model, train_loader, test_loader, criterion, optimizer, scheduler, device, epochs): model.train() # 设置为训练模式 # 记录每个 iteration 的损失 all_iter_losses = [] # 存储所有 batch 的损失 iter_indices = [] # 存储 iteration 序号 # 记录每个 epoch 的准确率和损失 train_acc_history = [] test_acc_history = [] train_loss_history = [] test_loss_history = [] for epoch in range(epochs): running_loss = 0.0 correct = 0 total = 0 for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device), target.to(device) # 移至GPU optimizer.zero_grad() # 梯度清零 output = model(data) # 前向传播 loss = criterion(output, target) # 计算损失 loss.backward() # 反向传播 optimizer.step() # 更新参数 # 记录当前 iteration 的损失 iter_loss = loss.item() all_iter_losses.append(iter_loss) iter_indices.append(epoch * len(train_loader) + batch_idx + 1) # 统计准确率和损失 running_loss += iter_loss _, predicted = output.max(1) total += target.size(0) correct += predicted.eq(target).sum().item() # 每100个批次打印一次训练信息 if (batch_idx + 1) % 100 == 0: print(f'Epoch: {epoch+1}/{epochs} | Batch: {batch_idx+1}/{len(train_loader)} ' f'| 单Batch损失: {iter_loss:.4f} | 累计平均损失: {running_loss/(batch_idx+1):.4f}') # 计算当前epoch的平均训练损失和准确率 epoch_train_loss = running_loss / len(train_loader) epoch_train_acc = 100. * correct / total train_acc_history.append(epoch_train_acc) train_loss_history.append(epoch_train_loss) # 测试阶段 model.eval() # 设置为评估模式 test_loss = 0 correct_test = 0 total_test = 0 with torch.no_grad(): for data, target in test_loader: data, target = data.to(device), target.to(device) output = model(data) test_loss += criterion(output, target).item() _, predicted = output.max(1) total_test += target.size(0) correct_test += predicted.eq(target).sum().item() epoch_test_loss = test_loss / len(test_loader) epoch_test_acc = 100. * correct_test / total_test test_acc_history.append(epoch_test_acc) test_loss_history.append(epoch_test_loss) # 更新学习率调度器 scheduler.step(epoch_test_loss) print(f'Epoch {epoch+1}/{epochs} 完成 | 训练准确率: {epoch_train_acc:.2f}% | 测试准确率: {epoch_test_acc:.2f}%') # 绘制所有 iteration 的损失曲线 plot_iter_losses(all_iter_losses, iter_indices) # 绘制每个 epoch 的准确率和损失曲线 plot_epoch_metrics(train_acc_history, test_acc_history, train_loss_history, test_loss_history) return epoch_test_acc # 返回最终测试准确率 # 6. 绘制每个 iteration 的损失曲线 def plot_iter_losses(losses, indices): plt.figure(figsize=(10, 4)) plt.plot(indices, losses, 'b-', alpha=0.7, label='Iteration Loss') plt.xlabel('Iteration（Batch序号）') plt.ylabel('损失值') plt.title('每个 Iteration 的训练损失') plt.legend() plt.grid(True) plt.tight_layout() plt.show() # 7. 绘制每个 epoch 的准确率和损失曲线 def plot_epoch_metrics(train_acc, test_acc, train_loss, test_loss): epochs = range(1, len(train_acc) + 1) plt.figure(figsize=(12, 4)) # 绘制准确率曲线 plt.subplot(1, 2, 1) plt.plot(epochs, train_acc, 'b-', label='训练准确率') plt.plot(epochs, test_acc, 'r-', label='测试准确率') plt.xlabel('Epoch') plt.ylabel('准确率 (%)') plt.title('训练和测试准确率') plt.legend() plt.grid(True) # 绘制损失曲线 plt.subplot(1, 2, 2) plt.plot(epochs, train_loss, 'b-', label='训练损失') plt.plot(epochs, test_loss, 'r-', label='测试损失') plt.xlabel('Epoch') plt.ylabel('损失值') plt.title('训练和测试损失') plt.legend() plt.grid(True) plt.tight_layout() plt.show() # 8. 执行训练和测试 epochs = 20 # 增加训练轮次以获得更好效果 print("开始使用CNN训练模型...") final_accuracy = train(model, train_loader, test_loader, criterion, optimizer, scheduler, device, epochs) print(f"训练完成！最终测试准确率: {final_accuracy:.2f}%") # # 保存模型 # torch.save(model.state_dict(), 'cifar10_cnn_model.pth') # print("模型已保存为: cifar10_cnn_model.pth")
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