作业：对信贷数据集进行训练后保持权重，后继续训练50次，采取早停策略
python
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
import warnings
warnings.filterwarnings("ignore")
# 检查GPU是否可用，优先使用GPU
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(f"使用设备: {device}")
# 若有多个GPU，可指定具体GPU，例如cuda:1
# 验证GPU是否真的在使用（可选）
if torch.cuda.is_available():
print(f"GPU名称: {torch.cuda.get_device_name(0)}")
torch.cuda.empty_cache() # 清空GPU缓存
# 加载信贷数据集
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)
# 转换为PyTorch张量并强制移至指定设备（GPU/CPU）
X_train = torch.FloatTensor(X_train).to(device, non_blocking=True)
y_train = torch.LongTensor(y_train).to(device, non_blocking=True)
X_test = torch.FloatTensor(X_test).to(device, non_blocking=True)
y_test = torch.LongTensor(y_test).to(device, non_blocking=True)
class MLP(nn.Module):
def __init__(self):
super(MLP, self).__init__()
self.fc1 = nn.Linear(4, 10) # 输入层（信贷数据集需修改输入维度）
self.relu = nn.ReLU()
self.fc2 = nn.Linear(10, 3) # 输出层（信贷数据集需修改输出维度）
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)
criterion = nn.CrossEntropyLoss() # 分类损失函数
optimizer = optim.SGD(model.parameters(), lr=0.01) # 优化器
# 首次训练参数
first_train_epochs = 20000
train_losses = [] # 首次训练损失
test_losses = []
epochs = []
# 早停参数（首次训练和继续训练共用相同策略）
best_test_loss = float('inf')
best_epoch = 0
patience = 50
counter = 0
early_stopped = False
print("\n===== 开始首次训练 =====")
start_time = time.time()
with tqdm(total=first_train_epochs, desc="首次训练进度", unit="epoch") as pbar:
for epoch in range(first_train_epochs):
model.train()
# 前向传播
outputs = model(X_train)
train_loss = criterion(outputs, y_train)
# 反向传播和优化
optimizer.zero_grad()
train_loss.backward()
optimizer.step()
# 每200轮记录损失并检查早停
if (epoch + 1) % 200 == 0:
model.eval()
with torch.no_grad():
test_outputs = model(X_test)
test_loss = criterion(test_outputs, y_test)
train_losses.append(train_loss.item())
test_losses.append(test_loss.item())
epochs.append(epoch + 1)
# 更新进度条
pbar.set_postfix({'Train Loss': f'{train_loss.item():.4f}', 'Test Loss': f'{test_loss.item():.4f}'})
# 早停逻辑
if test_loss.item() < best_test_loss:
best_test_loss = test_loss.item()
best_epoch = epoch + 1
counter = 0
# 保存最佳模型
torch.save(model.state_dict(), 'best_model.pth')
else:
counter += 1
if counter >= patience:
print(f"\n首次训练早停触发！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
# 更新进度条
if (epoch + 1) % 1000 == 0:
pbar.update(1000)
# 补全进度条
if pbar.n < first_train_epochs:
pbar.update(first_train_epochs - pbar.n)
# 保存首次训练结束后的模型权重（核心修改点1）
torch.save({
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'epoch': epoch + 1,
'best_loss': best_test_loss
}, 'trained_model.pth')
print(f"\n首次训练完成，权重已保存至 trained_model.pth")
print(f"首次训练总耗时: {time.time() - start_time:.2f} 秒")
print("\n===== 加载权重并开始继续训练 =====")
# 加载保存的权重（核心修改点2）
checkpoint = torch.load('trained_model.pth', map_location=device)
model.load_state_dict(checkpoint['model_state_dict'])
print(f"加载了首次训练至第{checkpoint['epoch']}轮的权重，最佳损失: {checkpoint['best_loss']:.4f}")
# 重新初始化优化器（核心修改点3：继续训练必须重置优化器）
optimizer = optim.SGD(model.parameters(), lr=0.01)
# 若需要延续优化器状态，可取消下面注释（视场景选择）
# optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
# 继续训练的参数
continue_train_epochs = 50 # 目标继续训练50轮
continue_train_losses = [] # 继续训练损失
continue_test_losses = []
continue_epochs = []
# 重置早停参数（针对继续训练）
continue_best_loss = checkpoint['best_loss']
continue_counter = 0
continue_early_stop = False
start_continue_time = time.time()
with tqdm(total=continue_train_epochs, desc="继续训练进度", unit="epoch") as pbar:
for epoch in range(continue_train_epochs):
model.train()
# 前向传播
outputs = model(X_train)
train_loss = criterion(outputs, y_train)
# 反向传播和优化
optimizer.zero_grad()
train_loss.backward()
optimizer.step()
# 每1轮就检查损失和早停（继续训练轮数少，无需间隔）
model.eval()
with torch.no_grad():
test_outputs = model(X_test)
test_loss = criterion(test_outputs, y_test)
continue_train_losses.append(train_loss.item())
continue_test_losses.append(test_loss.item())
continue_epochs.append(epoch + 1)
# 更新进度条
pbar.set_postfix({'Train Loss': f'{train_loss.item():.4f}', 'Test Loss': f'{test_loss.item():.4f}'})
pbar.update(1)
# 继续训练的早停逻辑
if test_loss.item() < continue_best_loss:
continue_best_loss = test_loss.item()
continue_counter = 0
# 保存继续训练后的最佳模型
torch.save(model.state_dict(), 'continue_best_model.pth')
else:
continue_counter += 1
if continue_counter >= patience:
print(f"\n继续训练早停触发！在第{epoch+1}轮，测试集损失已有{patience}轮未改善。")
print(f"继续训练最佳损失: {continue_best_loss:.4f}")
continue_early_stop = True
break
print(f"继续训练完成，总耗时: {time.time() - start_continue_time:.2f} 秒")
print(f"继续训练实际轮数: {len(continue_epochs)} 轮（早停触发则少于50轮）")
print("\n===== 最终模型评估 =====")
model.load_state_dict(torch.load('continue_best_model.pth', map_location=device))
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'测试集最终准确率: {accuracy * 100:.2f}%')
# ====================== 8. 可视化 ======================
plt.figure(figsize=(12, 6))
# 绘制首次训练损失
plt.subplot(1, 2, 1)
plt.plot(epochs, train_losses, label='Train Loss')
plt.plot(epochs, test_losses, label='Test Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.title('首次训练损失曲线')
plt.legend()
plt.grid(True)
# 绘制继续训练损失
plt.subplot(1, 2, 2)
plt.plot(continue_epochs, continue_train_losses, label='Train Loss')
plt.plot(continue_epochs, continue_test_losses, label='Test Loss')
plt.xlabel('Continue Epoch')
plt.ylabel('Loss')
plt.title('继续训练50轮损失曲线')
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()