import tensorflow as tf
import numpy as np
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Embedding, LSTM, SimpleRNN, Dense

maxFeatures = 100
inputTrain = np.random.randint(low = 1, high = 99, size=(1000,100))
yTrain = np.concatenate((np.ones((500,1)), np.zeros((500,1))))
for i in range(0,2):
  model = Sequential()
  model.add(Embedding(maxFeatures, 32))
  if i == 0:
    model.add(LSTM((32)))
  else:
    model.add(SimpleRNN(32, return_sequences=True))
    model.add(SimpleRNN(32, return_sequences=True))
  model.add(Dense(1, activation='sigmoid'))

  model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])

  history = model.fit(inputTrain, yTrain, epochs=10, batch_size=128, validation_split=.2)