如何使用keras序贯模型使用gridsearchCV调整l2正则化器

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

def create_model(l2_regularizer=0.01):
    model = Sequential()
    model.add(Dense(64, input_dim=100, activation='relu', kernel_regularizer=regularizers.l2(l2_regularizer)))
    model.add(Dense(1, activation='sigmoid'))
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model

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

param_grid = {'l2_regularizer': [0.01, 0.1, 1]}

grid = GridSearchCV(estimator=model, param_grid=param_grid, cv=3)
grid_result = grid.fit(X, y)

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