diff --git a/function.py b/function.py
index e4a3502..be01a25 100644
--- a/function.py
+++ b/function.py
@@ -1,12 +1,18 @@
 import os
 import csv
 import numpy as np
-import tensorflow as tf
+import torch
+import torch.nn as nn
+import torch.optim as optim
 from sklearn.model_selection import train_test_split
 from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, roc_auc_score
 from sklearn.preprocessing import StandardScaler
 from imblearn.over_sampling import SMOTE
 
+# 姓名和学号
+ id = 202210423118 
+ name = 刘国庆
+
 # 读取 CSV 数据的函数
 def load_data(file_path):
     if not os.path.exists(file_path):
@@ -34,6 +40,15 @@ def evaluate_model(y_true, y_pred, y_prob=None):
         auc = roc_auc_score(y_true, y_prob)
         print(f"AUC Score: {auc:.4f}")
 
+# 定义模型类
+class LogisticRegressionModel(nn.Module):
+    def __init__(self, input_size):
+        super(LogisticRegressionModel, self).__init__()
+        self.fc = nn.Linear(input_size, 1)
+        
+    def forward(self, x):
+        return torch.sigmoid(self.fc(x))
+
 # 主函数
 def main():
     # 加载数据
@@ -52,24 +67,40 @@ def main():
     X_train_res = scaler.fit_transform(X_train_res)
     X_test = scaler.transform(X_test)
 
-    # 创建 Keras 模型
-    model = tf.keras.Sequential([
-        tf.keras.layers.InputLayer(input_shape=(X_train_res.shape[1],)),
-        tf.keras.layers.Dense(1, activation='sigmoid')  # 逻辑回归输出
-    ])
+    # 转换为 PyTorch 张量
+    X_train_res = torch.tensor(X_train_res, dtype=torch.float32)
+    y_train_res = torch.tensor(y_train_res, dtype=torch.float32).view(-1, 1)
+    X_test = torch.tensor(X_test, dtype=torch.float32)
+    y_test = torch.tensor(y_test, dtype=torch.float32).view(-1, 1)
 
-    # 编译模型
-    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
+    # 创建模型
+    model = LogisticRegressionModel(input_size=X_train_res.shape[1])
+
+    # 定义损失函数和优化器
+    criterion = nn.BCELoss()
+    optimizer = optim.Adam(model.parameters(), lr=0.001)
 
     # 训练模型
-    model.fit(X_train_res, y_train_res, epochs=100, batch_size=32, verbose=1)
+    model.train()
+    for epoch in range(100):
+        optimizer.zero_grad()  # 清空梯度
+        outputs = model(X_train_res)  # 前向传播
+        loss = criterion(outputs, y_train_res)  # 计算损失
+        loss.backward()  # 反向传播
+        optimizer.step()  # 更新权重
+        
+        if (epoch + 1) % 10 == 0:
+            print(f'Epoch [{epoch + 1}/100], Loss: {loss.item():.4f}')
 
     # 评估模型
-    y_pred_prob = model.predict(X_test).flatten()  # 获取预测概率
-    y_pred = (y_pred_prob > 0.5).astype(int)
+    model.eval()
+    with torch.no_grad():
+        y_pred_prob = model(X_test).numpy().flatten()  # 获取预测概率
+        y_pred = (y_pred_prob > 0.5).astype(int)
 
     # 评估模型
-    evaluate_model(y_test, y_pred, y_pred_prob)
+    evaluate_model(y_test.numpy(), y_pred, y_pred_prob)
 
 if __name__ == "__main__":
     main()
+