new_contents: import os
  from six.moves import cPickle as pickle
  from six.moves import xrange
  import numpy as np
  import keras
  from keras.preprocessing import sequence
  from keras.utils import np_utils
  import random, sys
  
  maxlend = 25
  maxlenh = 25
  maxlen = maxlend + maxlenh
  rnn_size = 512 
  rnn_layers = 3  
  batch_norm = False
  
  #Number of nodes from the top LSTM layer used for activation 
  activation_rnn_size = 40 if maxlend else 0
  
  #Training parameters
  seed = 42
  p_W, p_U, p_dense, p_emb, weight_decay = 0, 0, 0, 0, 0
  optimizer = 'adam'
  batch_size = 64
  
  nb_train_samples = 30000
  nb_val_samples = 3000
  
  
  ############################## Read Word embedding ####################################
  
  with open('data/vocabulary-embedding.pkl', 'rb') as fp:
  	embedding, idx2word, word2idx, glove_idx2idx = pickle.load(fp)
  vocab_size, embedding_size = embedding.shape
  
  nb_unknown_words = 10
  
  for i in range(nb_unknown_words):
  	idx2word[vocab_size - 1 - i] = '<%d>'%i
  
  for i in range(vocab_size-nb_unknown_words, len(idx2word)):
      idx2word[i] = idx2word[i] + 
  
  empty = 0
  eos = 1
  idx2word[empty] = '_'
  idx2word[eos] = '~'
subject: read vocabulary-embedding