Keras KerasClassifier gridsearch TypeError:无法pickle _thread.lock对象

from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import GridSearchCV

# 定义Keras模型
def create_model():
    # 构建和编译模型
    model = ...
    return model

# 创建KerasClassifier对象
model = KerasClassifier(build_fn=create_model)

# 定义参数网格
param_grid = {
    'batch_size': [16, 32, 64],
    'epochs': [10, 20, 30]
}

# 使用GridSearchCV进行参数搜索
grid = GridSearchCV(estimator=model, param_grid=param_grid)
grid_result = grid.fit(X, y)

from keras.models import Sequential
from keras.layers import Dense
from sklearn.model_selection import ParameterGrid

# 定义Keras模型
def create_model():
    # 构建和编译模型
    model = ...
    return model

# 定义参数网格
param_grid = {
    'batch_size': [16, 32, 64],
    'epochs': [10, 20, 30]
}

best_score = None
best_params = None

# 遍历参数网格
for params in ParameterGrid(param_grid):
    # 创建模型
    model = create_model()
    model.set_params(**params)
    
    # 训练模型
    model.fit(X, y, ...)
    
    # 评估性能
    score = model.score(X_test, y_test)
    
    # 更新最佳配置
    if best_score is None or score > best_score:
        best_score = score
        best_params = params

print("Best score:", best_score)
print("Best params:", best_params)