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在工作用了tensorboard来可视化模型训练过程后，发现还挺香的。另外pytorch也正式支持tensorboard了，这里记录一下。

前置条件

安装tensorboard：

pip install tensorboard

实现步骤

1. 指定tensorboard输出日志：writer = SummaryWriter(log_dir=LOG_DIR)
2. 将模型和数据集添加到writer中：writer.add_graph(model, images.to(device))
3. 记录过程数据指标：writer.add_scalar('Test Loss', avg_loss, epoch)
4. 当模型开始训练后，启动tensorboard：tensorboard --logdir=runs。打开链接就能看到模型过程指标了：http://localhost:6006/

代码示例

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from torch.utils.tensorboard import SummaryWriter
from datetime import datetime# 1. 设置参数
BATCH_SIZE = 64
EPOCHS = 100
LEARNING_RATE = 0.001
NUM_CLASSES = 10
LOG_DIR = "runs/fashion_mnist_experiment_" + datetime.now().strftime("%Y%m%d_%H%M%S")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")# 2. 准备数据集
transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.5,), (0.5,))
])train_set = torchvision.datasets.FashionMNIST(root='./data',train=True,download=True,transform=transform
)test_set = torchvision.datasets.FashionMNIST(root='./data',train=False,download=True,transform=transform
)train_loader = torch.utils.data.DataLoader(train_set,batch_size=BATCH_SIZE,shuffle=True
)test_loader = torch.utils.data.DataLoader(test_set,batch_size=BATCH_SIZE,shuffle=False
)# 3. 定义模型
class FashionMNISTModel(nn.Module):def __init__(self, num_classes):super().__init__()self.features = nn.Sequential(nn.Conv2d(1, 32, kernel_size=3, padding=1),nn.ReLU(),nn.MaxPool2d(2),nn.Conv2d(32, 64, kernel_size=3, padding=1),nn.ReLU(),nn.MaxPool2d(2))self.classifier = nn.Sequential(nn.Flatten(),nn.Linear(64 * 7 * 7, 128),nn.ReLU(),nn.Dropout(0.5),nn.Linear(128, num_classes))def forward(self, x):x = self.features(x)x = self.classifier(x)return xmodel = FashionMNISTModel(NUM_CLASSES).to(device)# 4. 初始化TensorBoard Writer
writer = SummaryWriter(log_dir=LOG_DIR)# 5. 添加模型结构和数据集到TensorBoard
images, _ = next(iter(train_loader))
# note: 模型和数据集要么都在cpu，要么都在gpu；不然报错
writer.add_graph(model, images.to(device))# 6. 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE)# 7. 训练循环
def train():model.train()# 用累加loss，不然单个batch loss下降不明显running_loss = 0.0for batch_idx, (images, labels) in enumerate(train_loader):images = images.to(device)labels = labels.to(device)optimizer.zero_grad()outputs = model(images)loss = criterion(outputs, labels)loss.backward()optimizer.step()running_loss += loss.item()# 每100个batch记录一次if batch_idx % 100 == 0:writer.add_scalar('Training Loss',loss.item(),epoch * len(train_loader) + batch_idx)running_loss = 0# 8. 测试函数
def test():model.eval()test_loss = 0.0correct = 0total = 0with torch.no_grad():for images, labels in test_loader:images = images.to(device)labels = labels.to(device)outputs = model(images)loss = criterion(outputs, labels)test_loss += loss.item()_, predicted = torch.max(outputs.data, 1)total += labels.size(0)correct += (predicted == labels).sum().item()accuracy = 100 * correct / totalavg_loss = test_loss / len(test_loader)# 记录测试结果writer.add_scalar('Test Loss', avg_loss, epoch)writer.add_scalar('Test Accuracy', accuracy, epoch)print(f"Epoch [{epoch + 1}/{EPOCHS}], "f"Test Loss: {avg_loss:.4f}, "f"Test Accuracy: {accuracy:.2f}%")# 9. 主训练循环
for epoch in range(EPOCHS):train()test()# 10. 关闭Writer
writer.close()print("训练完成！")