python打卡day45

知识点回顾：

1. tensorboard的发展历史和原理
2. tensorboard的常见操作
3. tensorboard在cifar上的实战：MLP和CNN模型

   作业：对resnet18在cifar10上采用微调策略下，用tensorboard监控训练过程。

   import torch
   import torch.nn as nn
   import torch.optim as optim
   from torchvision import datasets, transforms, models
   from torch.utils.data import DataLoader
   from torch.utils.tensorboard import SummaryWriter
   import os# 设置随机种子保证可重复性
   torch.manual_seed(42)# 定义数据预处理
   transform = {    'train': transforms.Compose([transforms.RandomCrop(32, padding=4),transforms.RandomHorizontalFlip(),transforms.ToTensor(),transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]),'test': transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
   }# 加载 CIFAR-10 数据集
   train_dataset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform['train'])
   test_dataset = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform['test'])train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
   test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)# 初始化 TensorBoard 的 SummaryWriter
   log_dir = 'runs/resnet18_cifar10_finetune'
   if os.path.exists(log_dir):i = 1while os.path.exists(f"{log_dir}_{i}"):i += 1log_dir = f"{log_dir}_{i}"
   writer = SummaryWriter(log_dir)# 加载预训练的 ResNet18 模型
   model = models.resnet18(pretrained=True)# 修改最后一层全连接层以适应 CIFAR-10 的 10 个类别
   num_ftrs = model.fc.in_features
   model.fc = nn.Linear(num_ftrs, 10)# 定义损失函数和优化器
   criterion = nn.CrossEntropyLoss()
   optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)# 设置训练设备
   device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
   model.to(device)# 训练和验证函数
   num_epochs = 10
   for epoch in range(num_epochs):# 训练阶段model.train()running_loss = 0.0running_corrects = 0for inputs, labels in train_loader:inputs = inputs.to(device)labels = labels.to(device)optimizer.zero_grad()outputs = model(inputs)_, preds = torch.max(outputs, 1)loss = criterion(outputs, labels)loss.backward()optimizer.step()running_loss += loss.item() * inputs.size(0)running_corrects += torch.sum(preds == labels.data)epoch_loss = running_loss / len(train_dataset)epoch_acc = running_corrects.double() / len(train_dataset)# 使用 TensorBoard 记录训练损失和准确率writer.add_scalar('Train/Loss', epoch_loss, epoch)writer.add_scalar('Train/Accuracy', epoch_acc, epoch)# 验证阶段model.eval()val_running_loss = 0.0val_running_corrects = 0with torch.no_grad():for inputs, labels in test_loader:inputs = inputs.to(device)labels = labels.to(device)outputs = model(inputs)_, preds = torch.max(outputs, 1)loss = criterion(outputs, labels)val_running_loss += loss.item() * inputs.size(0)val_running_corrects += torch.sum(preds == labels.data)val_epoch_loss = val_running_loss / len(test_dataset)val_epoch_acc = val_running_corrects.double() / len(test_dataset)# 使用 TensorBoard 记录验证损失和准确率writer.add_scalar('Validation/Loss', val_epoch_loss, epoch)writer.add_scalar('Validation/Accuracy', val_epoch_acc, epoch)print(f'Epoch {epoch+1}/{num_epochs}, Train Loss: {epoch_loss:.4f}, Train Acc: {epoch_acc:.4f}, Val Loss: {val_epoch_loss:.4f}, Val Acc: {val_epoch_acc:.4f}')# 可视化模型结构
   dataiter = iter(train_loader)
   images, labels = next(dataiter)
   images = images.to(device)
   writer.add_graph(model, images)
   writer.close()