← back to paste›Edit / fork

Untitled paste

#5exuaaxywUpublic / publicnew version

anonymous created 2 days ago Expires in 5 days 4.5 KB syntax: text

Your changes create a new paste linked to this one — the original is untouched.

new version Your changes create a new paste linked to this one — the original is untouched.

Visibility Public feed Access public

Change note (optional)

This paste will be listed on the public feed. Change Visibility if you only want people with the link to see it.

Cancel

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.optim as optim
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

# ========== 全局配置 中文+随机种子 ==========
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
torch.manual_seed(42)
np.random.seed(42)

# ========== 1.读取并预处理数据 ==========
df = pd.read_csv("pima-diabetes.csv")
X = df.drop("Outcome", axis=1).values
y = df["Outcome"].values.reshape(-1, 1)

# 划分训练集、测试集，分层抽样保证正负样本比例
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42, stratify=y
)
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# 转为torch张量
X_train = torch.tensor(X_train, dtype=torch.float32)
y_train = torch.tensor(y_train, dtype=torch.float32)
X_test = torch.tensor(X_test, dtype=torch.float32)
y_test = torch.tensor(y_test, dtype=torch.float32)

# ========== 2.定义网络 ==========
class DiabetesNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(8, 64),
            nn.ReLU(),
            nn.Linear(64, 32),
            nn.ReLU(),
            nn.Linear(32, 1),
            nn.Sigmoid()
        )
    def forward(self, x):
        return self.net(x)

loss_fn = nn.BCELoss()
lr = 0.001
max_epoch = 150

# ===================== 第一部分：无早停，完整训练（观察过拟合） =====================
print("===== 开始无早停训练 =====")
model1 = DiabetesNet()
opt1 = optim.Adam(model1.parameters(), lr=lr)
train_loss_no_early = []
test_loss_no_early = []

for epoch in range(max_epoch):
    model1.train()
    opt1.zero_grad()
    pred_train = model1(X_train)
    tr_loss = loss_fn(pred_train, y_train)
    tr_loss.backward()
    opt1.step()

model1.eval()
    with torch.no_grad():
        pred_test = model1(X_test)
        te_loss = loss_fn(pred_test, y_test)

train_loss_no_early.append(tr_loss.item())
    test_loss_no_early.append(te_loss.item())
    if (epoch+1) % 20 == 0:
        print(f"Epoch {epoch+1:3d} | Train:{tr_loss:.4f} | Test:{te_loss:.4f}")

# 绘图：无早停loss曲线
plt.figure(figsize=(8, 5))
plt.plot(train_loss_no_early, label="Train Loss")
plt.plot(test_loss_no_early, label="Test Loss")
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.title("Training and Testing Loss Curves（无早停，出现过拟合）")
plt.legend()
plt.grid(alpha=0.3)
plt.tight_layout()
plt.savefig("loss_no_earlystop.jpg", dpi=300)
plt.show()

# ===================== 第二部分：带早停法训练 =====================
print("\n===== 开始带早停训练 =====")
model2 = DiabetesNet()
opt2 = optim.Adam(model2.parameters(), lr=lr)
train_loss_early = []
test_loss_early = []
patience = 15          # 连续15轮测试loss不下降就停止
best_test_loss = float("inf")
wait = 0
stop_epoch = max_epoch # 记录停止轮次

for epoch in range(max_epoch):
    model2.train()
    opt2.zero_grad()
    pred_train = model2(X_train)
    tr_loss = loss_fn(pred_train, y_train)
    tr_loss.backward()
    opt2.step()

model2.eval()
    with torch.no_grad():
        pred_test = model2(X_test)
        te_loss = loss_fn(pred_test, y_test)

train_loss_early.append(tr_loss.item())
    test_loss_early.append(te_loss.item())

# 早停判断逻辑
    if te_loss < best_test_loss:
        best_test_loss = te_loss
        wait = 0
    else:
        wait += 1
        if wait >= patience:
            stop_epoch = epoch + 1
            print(f"✅ 触发早停，Epoch={stop_epoch}，最佳测试损失：{best_test_loss:.4f}")
            break

if (epoch+1) % 20 == 0:
        print(f"Epoch {epoch+1:3d} | Train:{tr_loss:.4f} | Test:{te_loss:.4f}")

# 绘图：带早停loss曲线，标记停止点
plt.figure(figsize=(8, 5))
plt.plot(train_loss_early, label="Train Loss")
plt.plot(test_loss_early, label="Test Loss")
plt.axvline(x=stop_epoch-1, color="red", linestyle="--", label=f"早停点 Epoch={stop_epoch}")
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.title("Training and Testing Loss Curves（加入早停法 Early Stopping）")
plt.legend()
plt.grid(alpha=0.3)
plt.tight_layout()
plt.savefig("loss_with_earlystop.jpg", dpi=300)
plt.show()

print("\n✅ 全部训练完成，两张图片已保存：")
print("loss_no_earlystop.jpg    无早停（过拟合对照）")
print("loss_with_earlystop.jpg  带早停法")