问如何在keras神经网络管道中添加一个sklearn目标变压器(用于输出变量)？

EN

import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasRegressor
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import MinMaxScaler
from sklearn.datasets import load_boston
from sklearn.compose import TransformedTargetRegressor
from sklearn.model_selection import train_test_split

#load data
X, y = load_boston(return_X_y=True)

#define simple neural network
def simple_nn():
    model = Sequential()
    model.add(Dense(13, input_dim=13, activation='relu'))
    model.add(Dense(1))
    model.compile(loss='mean_squared_error', optimizer = 'adam')
    return model

#create pipeline for input variables (X) preprocessing
estimators = []
estimators.append(('standardize', StandardScaler()))
estimators.append(('mlp', KerasRegressor(build_fn=simple_nn, epochs=100, batch_size=5, verbose=True)))
pipeline = Pipeline(estimators)

#Section in question
transformer = MinMaxScaler()
model = TransformedTargetRegressor(regressor=pipeline,
                                   transformer=transformer)
results = cross_val_score(model, X, y, cv=KFold(n_splits=5))