title: Integrate keras LSTM and scikit boost
body: <p>Can I integrate LSTM layers in a &quot;wrapper&quot; way with AdaBoost or Random Forest (scikit) in a way close to this?</p>
  <pre><code>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)
  </code></pre>
  <p>Thanx for any help!</p>