如何使用joblib或pickle导出从KerasClassifier和Gridsearchcv创建的模型？

from sklearn.externals import joblib
import pickle

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

# 定义Keras模型
def create_model():
    model = Sequential()
    model.add(Dense(units=16, activation='relu', input_dim=8))
    model.add(Dense(units=8, activation='relu'))
    model.add(Dense(units=1, activation='sigmoid'))
    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
    return model

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

# 定义参数网格
param_grid = {'batch_size': [10, 20, 40],
              'epochs': [10, 50, 100]}

# 使用GridSearchCV进行参数调优
grid = GridSearchCV(estimator=model, param_grid=param_grid, cv=3)
grid_result = grid.fit(X, y)

# 使用joblib导出模型
joblib.dump(grid_result.best_estimator_, 'model.joblib')

# 使用pickle导出模型
with open('model.pkl', 'wb') as file:
    pickle.dump(grid_result.best_estimator_, file)