`RuntimeError：模型构建函数没有返回一个有效的 Keras 模型实例`在调整堆叠回归器的混合器时

如何解决`RuntimeError：模型构建函数没有返回一个有效的 Keras 模型实例`在调整堆叠回归器的混合器时

我正在尝试使用 keras_tuner 来调整堆叠回归器的混合器（XGBRegressor）的超参数。已经找到了它的低级回归量的超参数，所以我只对搅拌机的 max_depth 和 learning_rate 的最佳值感兴趣。（有一些类似的问题，比如 this one，据我所知，没有一个可以解决这个问题。）这是我这样做的程序：

import tensorflow as tf
from sklearn.datasets import fetch_california_housing
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import StackingRegressor
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeRegressor
import xgboost
import keras_tuner as kt


housing = fetch_california_housing()

X_train_full,X_test,y_train_full,y_test = train_test_split(housing.data,housing.target,train_size=0.8,test_size=0.2)
X_train,X_valid,y_train,y_valid = train_test_split(X_train_full,test_size=0.2)

scaler = StandardScaler()

X_train = scaler.fit_transform(X_train)
X_test = scaler.fit_transform(X_test)

def build_dnn_reg_opt():
    model = tf.keras.Sequential()
    model.add(tf.keras.layers.InputLayer(input_shape=X_train.shape[1:]))
    model.add(tf.keras.layers.Batchnormalization(momentum=0.999))
    model.add(tf.keras.layers.Dense(42,tf.keras.activations.selu,kernel_initializer="lecun_normal"))
    model.add(tf.keras.layers.Batchnormalization(momentum=0.999))
    model.add(tf.keras.layers.Dense(42,kernel_initializer="lecun_normal"))
    model.add(tf.keras.layers.Batchnormalization(momentum=0.999))
    model.add(tf.keras.layers.Dense(1,kernel_initializer="lecun_normal"))
    optimizer = tf.keras.optimizers.Adam(learning_rate=0.05)
    model.compile(loss="mae",optimizer=optimizer,metrics=["mse"])
    return model

dnn_reg_opt = build_dnn_reg_opt()

rnd_reg_opt = DecisionTreeRegressor(max_depth=8,min_samples_leaf=32,max_leaf_nodes=10)

rf_reg_opt = RandomForestRegressor(n_estimators=76,max_leaf_nodes=20)

def build_model_stack(hp):
    max_depth = hp.Int("max_depth",min_value=1,max_value=10,step=1)
    learning_rate = hp.Choice("learning_rate",values=[0.01,0.02,0.03,0.04,0.05,0.06,0.07])
    model = StackingRegressor(estimators=[("rnd_reg_opt",rnd_reg_opt),("rf_reg_opt",rf_reg_opt),("dnn_reg_opt",dnn_reg_opt)],final_estimator=xgboost.XGBRegressor(max_depth=max_depth,learning_rate=learning_rate))
    return model

rnd_reg_opt.fit(X_train,y_train)


def exponential_decay(lr0,s):
    def exponential_decay_fn(epoch):
        return lr0 * 0.1 ** (epoch / s)
    return exponential_decay_fn

exponential_decay_fn = exponential_decay(lr0=0.01,s=20)

lr_scheduler_cb = tf.keras.callbacks.LearningRateScheduler(exponential_decay_fn)
early_stop_cb = tf.keras.callbacks.EarlyStopping(monitor="val_loss",patience=5)

dnn_reg_opt.fit(X_train,validation_data = (X_valid,y_valid),epochs=50,callbacks =[early_stop_cb,lr_scheduler_cb])


rf_reg_opt.fit(X_train,y_train)

tuner_BO = kt.Bayesianoptimization(build_model_stack,objective=kt.Objective("val_loss",direction="min"),max_trials=10,seed=seed_value)

tuner_BO.search(X_train,lr_scheduler_cb])

best_hps_BO = tuner_BO.get_best_hyperparameters(num_trials=1)[0]

print("BO results:")
print("max_depth: {0}".format(best_hps_BO.get("max_depth")))
print("learning_rate: {0}".format(best_hps_BO.get("learning_rate")))

但是，抛出以下错误：

RuntimeError: Model-building function did not return a valid Keras Model instance,found StackingRegressor(estimators=[('rnd_reg_opt',DecisionTreeRegressor(max_depth=8,max_leaf_nodes=10,min_samples_leaf=32)),('rf_reg_opt',RandomForestRegressor(max_leaf_nodes=20,n_estimators=76)),('dnn_reg_opt',<tensorflow.python.keras.engine.sequential.Sequential object at 0x0000012D308E0D30>)],final_estimator=XGBRegressor(base_score=None,booster=None,col...
                                               importance_type='gain',interaction_constraints=None,learning_rate=0.01,max_delta_step=None,max_depth=1,min_child_weight=None,missing=nan,monotone_constraints=None,n_estimators=100,n_jobs=None,num_parallel_tree=None,random_state=None,reg_alpha=None,reg_lambda=None,scale_pos_weight=None,subsample=None,tree_method=None,validate_parameters=None,verbosity=None))

解决此问题的方法是什么？