如何使用GridSearchCV在keras模型的超参数优化中使用简单的验证集？

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

def create_model(optimizer='adam', activation='relu'):
    model = Sequential()
    # 添加模型层
    model.add(...)
    # 编译模型
    model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy'])
    return model

model = KerasClassifier(build_fn=create_model, verbose=0)

param_grid = {
    'optimizer': ['adam', 'sgd'],
    'activation': ['relu', 'sigmoid']
}

grid = GridSearchCV(estimator=model, param_grid=param_grid, cv=3)

grid_result = grid.fit(X_train, y_train, validation_data=(X_val, y_val))

print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))