Day37

知识点回顾：

1. 过拟合的判断：测试集和训练集同步打印指标
2. 模型的保存和加载
   1. 仅保存权重
   2. 保存权重和模型
   3. 保存全部信息checkpoint，还包含训练状态
3. 早停策略

对信贷数据集训练后保存权重，加载权重后继续训练50轮，并采取早停策略

import torch
import torch.nn as nn
import torch.optim as optim
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
import time
import matplotlib.pyplot as plt
from tqdm import tqdm  # 导入tqdm库用于进度条显示
import warnings
warnings.filterwarnings("ignore")  # 忽略警告信息
 
# 设置GPU设备
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(f"使用设备: {device}")
 
# 加载鸢尾花数据集
iris = load_iris()
X = iris.data  # 特征数据
y = iris.target  # 标签数据
 
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
 
# 归一化数据
scaler = MinMaxScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
 
# 将数据转换为PyTorch张量并移至GPU
X_train = torch.FloatTensor(X_train).to(device)
y_train = torch.LongTensor(y_train).to(device)
X_test = torch.FloatTensor(X_test).to(device)
y_test = torch.LongTensor(y_test).to(device)
 
class MLP(nn.Module):
    def __init__(self):
        super(MLP, self).__init__()
        self.fc1 = nn.Linear(4, 10)  # 输入层到隐藏层
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(10, 3)  # 隐藏层到输出层
 
    def forward(self, x):
        out = self.fc1(x)
        out = self.relu(out)
        out = self.fc2(out)
        return out
 
# 实例化模型并移至GPU
model = MLP().to(device)
 
# 分类问题使用交叉熵损失函数
criterion = nn.CrossEntropyLoss()
 
# 使用随机梯度下降优化器
optimizer = optim.SGD(model.parameters(), lr=0.01)
 
# 训练模型
num_epochs = 20000  # 训练的轮数
 
# 用于存储每200个epoch的损失值和对应的epoch数
train_losses = []  # 存储训练集损失
test_losses = []   # 存储测试集损失
epochs = []
 
# ===== 新增早停相关参数 =====
best_test_loss = float('inf')  # 记录最佳测试集损失
best_epoch = 0                 # 记录最佳epoch
patience = 50                # 早停耐心值（连续多少轮测试集损失未改善时停止训练）
counter = 0                    # 早停计数器
early_stopped = False          # 是否早停标志
# ==========================
 
start_time = time.time()  # 记录开始时间
 
# 创建tqdm进度条
with tqdm(total=num_epochs, desc="训练进度", unit="epoch") as pbar:
    # 训练模型
    for epoch in range(num_epochs):
        # 前向传播
        outputs = model(X_train)  # 隐式调用forward函数
        train_loss = criterion(outputs, y_train)
 
        # 反向传播和优化
        optimizer.zero_grad()
        train_loss.backward()
        optimizer.step()
 
        # 记录损失值并更新进度条
        if (epoch + 1) % 200 == 0:
            # 计算测试集损失
            model.eval()
            with torch.no_grad():
                test_outputs = model(X_test)
                test_loss = criterion(test_outputs, y_test)
            model.train()
            
            train_losses.append(train_loss.item())
            test_losses.append(test_loss.item())
            epochs.append(epoch + 1)
            
            # 更新进度条的描述信息
            pbar.set_postfix({'Train Loss': f'{train_loss.item():.4f}', 'Test Loss': f'{test_loss.item():.4f}'})
            
            # ===== 新增早停逻辑 =====
            if test_loss.item() < best_test_loss: # 如果当前测试集损失小于最佳损失
                best_test_loss = test_loss.item() # 更新最佳损失
                best_epoch = epoch + 1 # 更新最佳epoch
                counter = 0 # 重置计数器
                # 保存最佳模型
                torch.save(model.state_dict(), 'best_model.pth')
            else:
                counter += 1
                if counter >= patience:
                    print(f"早停触发！在第{epoch+1}轮，测试集损失已有{patience}轮未改善。")
                    print(f"最佳测试集损失出现在第{best_epoch}轮，损失值为{best_test_loss:.4f}")
                    early_stopped = True
                    break  # 终止训练循环
            # ======================
 
        # 每1000个epoch更新一次进度条
        if (epoch + 1) % 1000 == 0:
            pbar.update(1000)  # 更新进度条
 
    # 确保进度条达到100%
    if pbar.n < num_epochs:
        pbar.update(num_epochs - pbar.n)  # 计算剩余的进度并更新
 
time_all = time.time() - start_time  # 计算训练时间
print(f'Training time: {time_all:.2f} seconds')
 
# ===== 新增：加载最佳模型用于最终评估 =====
if early_stopped:
    print(f"加载第{best_epoch}轮的最佳模型进行最终评估...")
    model.load_state_dict(torch.load('best_model.pth'))
# ================================
 
# 可视化损失曲线
plt.figure(figsize=(10, 6))
plt.plot(epochs, train_losses, label='Train Loss')
plt.plot(epochs, test_losses, label='Test Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.title('Training and Test Loss over Epochs')
plt.legend()
plt.grid(True)
plt.show()
 
# 在测试集上评估模型
model.eval()
with torch.no_grad():
    outputs = model(X_test)
    _, predicted = torch.max(outputs, 1)
    correct = (predicted == y_test).sum().item()
    accuracy = correct / y_test.size(0)
    print(f'测试集准确率: {accuracy * 100:.2f}%')  

@浙大疏锦行