更新 function.py

function.py

@@ -37,13 +37,12 @@ def compute_gradient(X_batch, y_batch, weights, lambda_reg):
     z = np.dot(X_batch, weights)
     h = sigmoid(z)
     gradient = np.dot(X_batch.T, (h - y_batch)) / len(y_batch)
-    # 添加L2正则化
+    gradient += (lambda_reg / len(y_batch)) * weights  # L2正则化
-    gradient += (lambda_reg / len(y_batch)) * weights
     return gradient
 
-def train_logreg(X, y, learning_rate=0.01, iterations=5000, batch_size=64, lambda_reg=0.01):
+def train_logreg(X, y, learning_rate=0.001, iterations=10000, batch_size=64, lambda_reg=0.1):
     m, n = X.shape
-    weights = np.random.randn(n) * 0.01
+    weights = np.random.randn(n) * np.sqrt(2. / n)  # He初始化
     best_weights = weights.copy()
     best_accuracy = 0
 
@@ -109,7 +108,7 @@ if __name__ == "__main__":
     X_val = scaler.transform(X_val)
 
     # 训练模型
-    final_weights = train_logreg(X_train, y_train, learning_rate=0.01, iterations=5000, batch_size=64, lambda_reg=0.1)
+    final_weights = train_logreg(X_train, y_train, learning_rate=0.001, iterations=10000, batch_size=64, lambda_reg=0.1)
 
     # 验证模型
     val_accuracy = accuracy(X_val, y_val, final_weights)