集成keras LSTM和scikit boost

Question

我能以一种“包装器”的方式将LSTM层与AdaBoost或Random Forest（scikit）以一种接近于此的方式进行集成吗

import numpy as np
import pandas as pd
import xgboost as xgb
from keras.models import Sequential
from keras.layers.core import Dense, Activation, Dropout
from keras.layers.advanced_activations import PReLU
from keras.models import Sequential
from keras.utils import np_utils
import tensorflow as tf
from sklearn.preprocessing import StandardScaler
from keras.layers import Bidirectional

class LSTM_block(keras.layers.Layer):

  def init__(self):
    super(LSTMBlock, self).__init__()
    self.lstm1 = tf.keras.Bidirectional(LSTM(50, activation='relu'), input_shape=(1, 1))
    self.lstm2 = tf.keras.Dense(10)
    self.lstm3 = compile(optimizer='adam', loss='mse')
  def call(self, inputs):
    x = self.lstm2(inputs)
    return self.lstm2(x)

 Ada_estimator = KerasRegressor(build_fn= simple_model, epochs=100, 
 batch_size=10, verbose=0)
 lstm = LSTM_Block()
 y = lstm(tf.ones(shape=(3, 64)))  # The 
 boosted_ann = AdaBoostRegressor(base_estimator= ada_estimator)
 linear_4 = boosted_ann
 boosted_ann.fit(X_train, y_train)# scale  data 
 boosted_ann.predict(rescaledX_Test)

谢谢你的帮助