diff --git a/function.py b/function.py
index bf62866..007ba98 100644
--- a/function.py
+++ b/function.py
@@ -21,7 +21,9 @@ def train_logreg(X, y, learning_rate=0.001, iterations=10000, batch_size=64, lam
     """对率回归训练函数。"""
     m, n = X.shape
     weights = np.random.randn(n) * np.sqrt(2. / n)  # He初始化
-    for _ in range(iterations):
+    print("开始训练对率回归模型")
+    
+    for i in range(iterations):
         indices = np.random.permutation(m)
         X_shuffled = X[indices]
         y_shuffled = y[indices]
@@ -32,6 +34,9 @@ def train_logreg(X, y, learning_rate=0.001, iterations=10000, batch_size=64, lam
             gradient = compute_gradient(X_batch, y_batch, weights, lambda_reg)
             weights -= learning_rate * gradient
             
+        if i % 1000 == 0:  # 每1000次迭代打印一次信息
+            print(f"迭代 {i}: 当前权重形状: {weights.shape}")
+
     return weights
 
 def predict_logreg(inX, weights):
@@ -46,6 +51,7 @@ def load_data(filepath):
         raise FileNotFoundError(f"文件 {filepath} 不存在！")
     
     data, labels = [], []
+    print(f"加载数据中: {filepath}")
     with open(filepath, 'r') as f:
         for line in f:
             line = line.strip().split()
@@ -66,6 +72,7 @@ def load_data(filepath):
     data = np.delete(data, indices_to_remove, axis=1)
     target_variable = np.where((labels == 2) | (labels == 3), 0, 1)
 
+    print(f"数据加载成功，数据形状: {data.shape}, 标签形状: {target_variable.shape}")
     return data, target_variable
 
 def k_fold_cross_validation(X, y, k=5, learning_rate=0.001, iterations=10000, batch_size=64, lambda_reg=0.1):