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2026.1.17总结

news 2026/5/11 19:51:59

2: 计算机视觉基础

以下是代码的完整执行流程：
导入库 → 导入PyTorch及相关工具库
定义CNN模型 → 构建两层卷积+全连接的神经网络结构
数据准备 → 下载MNIST数据集并进行标准化预处理
初始化 → 创建模型、损失函数和优化器
训练循环（10个epoch）：
训练阶段：前向传播 → 计算损失 → 反向传播 → 参数更新
测试阶段：评估模型准确率
记录结果 → 保存每个epoch的训练损失和测试准确率
可视化 → 绘制损失和准确率变化曲线
结束 → 显示训练完成的模型性能图表
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt

2.1 CNN基础架构

class CNN(nn.Module):
def init(self):
super(CNN, self).init()
# 卷积层
self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1)
self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)

    # 池化层self.pool = nn.MaxPool2d(2, 2)# 全连接层self.fc1 = nn.Linear(64 * 7 * 7, 128)self.fc2 = nn.Linear(128, 10)# Dropoutself.dropout = nn.Dropout(0.5)def forward(self, x):# 卷积层1 + 激活函数 + 池化x = self.pool(F.relu(self.conv1(x)))# 卷积层2 + 激活函数 + 池化x = self.pool(F.relu(self.conv2(x)))# 展平x = x.view(-1, 64 * 7 * 7)# 全连接层x = F.relu(self.fc1(x))x = self.dropout(x)x = self.fc2(x)return x

2.2 数据加载和预处理

def load_mnist():
"""加载MNIST数据集"""
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
])

# 训练集
trainset = torchvision.datasets.MNIST(root='./data', train=True,download=True, transform=transform
)# 测试集
testset = torchvision.datasets.MNIST(root='./data',train=False,download=True,transform=transform
)trainloader = DataLoader(trainset, batch_size=64, shuffle=True)
testloader = DataLoader(testset, batch_size=64, shuffle=False)return trainloader, testloader

2.3 完整训练流程

def train_cnn():
# 加载数据
trainloader, testloader = load_mnist()

# 初始化模型
model = CNN()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)# 训练循环
num_epochs = 10
train_losses = []
test_accuracies = []for epoch in range(num_epochs):model.train()running_loss = 0.0# 训练阶段for images, labels in trainloader:# 前向传播outputs = model(images)loss = criterion(outputs, labels)# 反向传播optimizer.zero_grad()loss.backward()optimizer.step()running_loss += loss.item()avg_loss = running_loss / len(trainloader)train_losses.append(avg_loss)# 测试阶段model.eval()correct = 0total = 0with torch.no_grad():for images, labels in testloader:outputs = model(images)_, predicted = torch.max(outputs.data, 1)total += labels.size(0)correct += (predicted == labels).sum().item()accuracy = 100 * correct / totaltest_accuracies.append(accuracy)print(f'Epoch [{epoch+1}/{num_epochs}], 'f'Loss: {avg_loss:.4f}, 'f'Accuracy: {accuracy:.2f}%')return model, train_losses, test_accuracies

2.4 可视化训练结果

def plot_training_results(train_losses, test_accuracies):
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))

# 损失曲线
ax1.plot(train_losses, label='Training Loss')
ax1.set_xlabel('Epoch')
ax1.set_ylabel('Loss')
ax1.set_title('Training Loss over Epochs')
ax1.legend()
ax1.grid(True)# 准确率曲线
ax2.plot(test_accuracies, label='Test Accuracy', color='orange')
ax2.set_xlabel('Epoch')
ax2.set_ylabel('Accuracy (%)')
ax2.set_title('Test Accuracy over Epochs')
ax2.legend()
ax2.grid(True)plt.tight_layout()
plt.show()