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| import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.utils.tensorboard import SummaryWriter
# 定义 TensorBoard 的写入器
writer = SummaryWriter("./log")
# 定义超参数
BATCH_SIZE = 512
EPOCHS = 20
LEARNING_RATE = 1e-3
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# 数据加载和预处理，在 data 目录下载 MNIST 数据集
train_loader = torch.utils.data.DataLoader(
datasets.MNIST(
"data",
train=True,
download=True,
transform=transforms.Compose(
[
transforms.RandomRotation(10), # 随机旋转，提高模型的泛化能力
transforms.RandomAffine(
0, shear=10, scale=(0.8, 1.2)
), # 仿射变换，提高模型的泛化能力
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,)),
]
),
),
batch_size=BATCH_SIZE,
shuffle=True,
)
test_loader = torch.utils.data.DataLoader(
datasets.MNIST(
"data",
train=False,
transform=transforms.Compose(
[transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
),
),
batch_size=BATCH_SIZE,
shuffle=False,
)
# 定义模型的结构，这里是一个简单的卷积神经网络
class ConvNet(nn.Module):
def __init__(self):
super(ConvNet, self).__init__()
self.conv1 = nn.Conv2d(1, 32, 3)
self.conv2 = nn.Conv2d(32, 64, 3)
self.dropout1 = nn.Dropout(0.25)
self.fc1 = nn.Linear(64 * 5 * 5, 128)
self.dropout2 = nn.Dropout(0.5)
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = F.relu(self.conv1(x))
x = F.max_pool2d(x, 2)
x = F.relu(self.conv2(x))
x = F.max_pool2d(x, 2)
x = self.dropout1(x)
x = x.view(-1, 64 * 5 * 5)
x = F.relu(self.fc1(x))
x = self.dropout2(x)
x = self.fc2(x)
return F.log_softmax(x, dim=1)
# 初始化模型和优化器
model = ConvNet().to(DEVICE)
optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE)
scheduler = torch.optim.lr_scheduler.StepLR(
optimizer, step_size=5, gamma=0.5
) # 学习率衰减
# 添加模型结构到 TensorBoard
example_input = torch.randn(1, 1, 28, 28).to(DEVICE) # 创建一个示例输入
writer.add_graph(model, example_input) # 添加模型图
# 训练函数
def train(model, device, train_loader, optimizer, epoch):
model.train()
running_loss = 0.0 # 用于记录训练过程中累计的损失
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data)
loss = F.nll_loss(output, target)
loss.backward()
optimizer.step()
running_loss += loss.item()
if (batch_idx + 1) % 30 == 0:
print(
f"Train Epoch: {epoch} [{batch_idx * len(data)}/{len(train_loader.dataset)} "
f"({100. * batch_idx / len(train_loader):.0f}%)]\tLoss: {loss.item():.6f}"
)
# 将训练的平均损失写入 TensorBoard
writer.add_scalar("Loss/train", running_loss / len(train_loader), epoch)
# 测试函数
def test(model, device, test_loader, epoch):
model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
data, target = data.to(device), target.to(device)
output = model(data)
test_loss += F.nll_loss(output, target, reduction="sum").item()
pred = output.argmax(dim=1, keepdim=True)
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
accuracy = 100.0 * correct / len(test_loader.dataset)
print(
f"\nTest set: Average loss: {test_loss:.4f}, Accuracy: {correct}/{len(test_loader.dataset)} ({accuracy:.2f}%)\n"
)
# 将测试的损失和准确率写入 TensorBoard
writer.add_scalar("Loss/test", test_loss, epoch)
writer.add_scalar("Accuracy/test", accuracy, epoch)
# 训练和测试循环
for epoch in range(1, EPOCHS + 1):
train(model, DEVICE, train_loader, optimizer, epoch)
test(model, DEVICE, test_loader, epoch)
scheduler.step() # 更新学习率
# 保存模型
torch.save(model.state_dict(), "mnist_cnn.pth")
print("Model saved to mnist_cnn.pth")
# 关闭 TensorBoard 写入器
writer.close()
|
