如何从GridSearchCV结果中提取最佳ANN的权重？

from sklearn.model_selection import GridSearchCV
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier

# 创建一个函数来构建ANN模型
def create_model(optimizer='adam'):
    model = Sequential()
    model.add(Dense(12, input_dim=8, activation='relu'))
    model.add(Dense(1, activation='sigmoid'))
    model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])
    return model

# 创建KerasClassifier包装器
model = KerasClassifier(build_fn=create_model, verbose=0)

# 定义参数网格
param_grid = {'optimizer': ['adam', 'sgd'], 'batch_size': [10, 20], 'epochs': [10, 20]}

# 创建GridSearchCV对象
grid = GridSearchCV(estimator=model, param_grid=param_grid, cv=3)
grid_result = grid.fit(X_train, y_train)

# 输出最佳参数和得分
print(f"Best: {grid_result.best_score_} using {grid_result.best_params_}")

# 获取最佳模型
best_model = grid_result.best_estimator_.model

# 提取并打印最佳模型的权重
weights = best_model.get_weights()
for layer in weights:
    print(layer)