Keras IMDB - words from indexes

keras_imdb_words_from_indexes.py

from __future__ import print_function

from keras.datasets imporrt imdb

(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=20000)
x_train = sequence.pad_sequences(x_train, maxlen=80)

word_index = keras.datasets.imdb.get_word_index()
# or items() in Python 3
inverted_word_index = {index:word for (word, index) in word_index.iteritems()}
print([inverted_word_index[i] for i in x_train[0]])

# [u'for',
#  u'anyone',
#  u'of',
#  u'and',
#  u'br',
#  u"show's",
#  u'to',
#  u'whether',
#  u'from',
#  u'than',
#  u'out',
#  u'themselves',
#  u'history',
#  u'he',
#  u'name',
#  u'half',
#  u'some',
#  u'br',
#  u'of',
#  u"'n",
#  u'odd',
#  u'was',
#  u'two',
#  u'most',
#  u'of',
#  u'mean',
#  u'for',
#  u'1',
#  u'any',
#  u'an',
#  u'boat',
#  u'she',
#  u'he',
#  u'should',
#  u'is',
#  u'thought',
#  u'frog',
#  u'but',
#  u'of',
#  u'script',
#  u'you',
#  u'not',
#  u'while',
#  u'history',
#  u'he',
#  u'heart',
#  u'to',
#  u'real',
#  u'at',
#  u'barrel',
#  u'but',
#  u'when',
#  u'from',
#  u'one',
#  u'bit',
#  u'then',
#  u'have',
#  u'two',
#  u'of',
#  u'script',
#  u'their',
#  u'with',
#  u'her',
#  u'nobody',
#  u'most',
#  u'that',
#  u'with',
#  u"wasn't",
#  u'to',
#  u'with',
#  u'armed',
#  u'acting',
#  u'watch',
#  u'an',
#  u'for',
#  u'with',
#  u'heartfelt',
#  u'film',
#  u'want',
#  u'an']