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filename = 'model.h1.24_jan_19' 

# set checkpoint
checkpoint = ModelCheckpoint(filename, monitor='val_loss',  
                             verbose=1, save_best_only=True, 
                             mode='min') 


# train model 
history = model.fit(trainX, trainY.reshape(trainY.shape[0], trainY.shape[1], 1), 

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model = load_model('model.h1.24_jan_19') 

preds = model.predict_classes(testX.reshape((testX.shape[0], testX.shape[1])))

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def get_word(n, tokenizer):  
      for word, index in tokenizer.word_index.items():                       
          if index == n: 
              return word 
      return None

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preds_text = [] 
for i in preds:        
       temp = []        
       for j in range(len(i)):             
            t = get_word(i[j], eng_tokenizer)             
            if j > 0:                 
                if (t==get_word(i[j-1],eng_tokenizer))or(t== None):                       
                     temp.append('')                 
                else:                      
                     temp.append(t)             

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import pandas as pd
import numpy as np
import spacy
from tqdm import tqdm
import re
import time
import pickle
pd.set_option('display.max_colwidth', 200)

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# remove URL's from train and test
train['clean_tweet'] = train['tweet'].apply(lambda x: re.sub(r'http\S+', '', x))

test['clean_tweet'] = test['tweet'].apply(lambda x: re.sub(r'http\S+', '', x))

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# just a random sentence
x = ["Roasted ants are a popular snack in Columbia"]

# Extract ELMo features 
embeddings = elmo(x, signature="default", as_dict=True)["elmo"]

embeddings.shape

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def elmo_vectors(x):
  embeddings = elmo(x.tolist(), signature="default", as_dict=True)["elmo"]

  with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    sess.run(tf.tables_initializer())
    # return average of ELMo features
    return sess.run(tf.reduce_mean(embeddings,1))

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# save elmo_train_new
pickle_out = open("elmo_train_03032019.pickle","wb")
pickle.dump(elmo_train_new, pickle_out)
pickle_out.close()

# save elmo_test_new
pickle_out = open("elmo_test_03032019.pickle","wb")
pickle.dump(elmo_test_new, pickle_out)
pickle_out.close()

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# load elmo_train_new
pickle_in = open("elmo_train_03032019.pickle", "rb")
elmo_train_new = pickle.load(pickle_in)

# load elmo_train_new
pickle_in = open("elmo_test_03032019.pickle", "rb")
elmo_test_new = pickle.load(pickle_in)