更新 function.py

function.py

@@ -10,7 +10,7 @@ def sigmoid(z):
     """Sigmoid 激活函数。"""
     return 1 / (1 + np.exp(-z))
 
-def train_logreg(X, y, learning_rate=0.01, iterations=1000, lambda_reg=0.01, batch_size=32):
+def train_logreg(X, y, learning_rate=0.1, iterations=2000, lambda_reg=0.01, batch_size=32):
     """逻辑回归训练函数，随机初始化权重，并在每次迭代前打乱数据。"""
     m, n = X.shape
     weights = np.random.randn(n) * 0.01  # 随机初始化权重，取较小值
@@ -36,7 +36,8 @@ def train_logreg(X, y, learning_rate=0.01, iterations=1000, lambda_reg=0.01, bat
             weights -= learning_rate * gradient
 
         # 动态调整学习率，逐渐减小
-        learning_rate *= 0.99
+        if i % 100 == 0:  # 每100次迭代输出一次学习率
+            print(f"Iteration {i}, Learning Rate: {learning_rate}")
 
     return weights
 
@@ -102,7 +103,7 @@ if __name__ == "__main__":
 
         # 训练逻辑回归模型，设置 batch size
         batch_size = 32
-        weights = train_logreg(X_train_kf, y_train_kf, learning_rate=0.1, iterations=1000, lambda_reg=0.01, batch_size=batch_size)
+        weights = train_logreg(X_train_kf, y_train_kf, learning_rate=0.1, iterations=2000, lambda_reg=0.01, batch_size=batch_size)
 
         # 验证模型
         val_predictions = [predict_logreg(X_val_kf[i], weights) for i in range(len(X_val_kf))]