text: from __future__ import absolute_import
  from ..utils.data_utils import get_file
  from ..preprocessing.sequence import _remove_long_seq
  from six.moves import zip
  import numpy as np
  import json
  import warnings
  
  
  def load_data(path='imdb.npz', num_words=None, skip_top=0,
                maxlen=None, seed=113,
                start_char=1, oov_char=2, index_from=3, **kwargs):
      """Loads the IMDB dataset.
  
      # Arguments
          path: where to cache the data (relative to `~/.keras/dataset`).
          num_words: max number of words to include. Words are ranked
              by how often they occur (in the training set) and only
              the most frequent words are kept
          skip_top: skip the top N most frequently occurring words
              (which may not be informative).
          maxlen: truncate sequences after this length.
          seed: random seed for sample shuffling.
          start_char: The start of a sequence will be marked with this character.
              Set to 1 because 0 is usually the padding character.
          oov_char: words that were cut out because of the `num_words`
              or `skip_top` limit will be replaced with this character.
          index_from: index actual words with this index and higher.
  
      # Returns
          Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
  
      # Raises
          ValueError: in case `maxlen` is so low
              that no input sequence could be kept.
  
      Note that the 'out of vocabulary' character is only used for
      words that were present in the training set but are not included
      because they're not making the `num_words` cut here.
      Words that were not seen in the training set but are in the test set
      have simply been skipped.
      """
      # Legacy support
      if 'nb_words' in kwargs:
          warnings.warn('The `nb_words` argument in `load_data` '
                        'has been renamed `num_words`.')
          num_words = kwargs.pop('nb_words')
      if kwargs:
          raise TypeError('Unrecognized keyword arguments: ' + str(kwargs))
  
      path = get_file(path, origin='https://s3.amazonaws.com/text-datasets/imdb.npz')
      with np.load(path) as f:
          x_train, labels_train = f['x_train'], f['y_train']
          x_test, labels_test = f['x_test'], f['y_test']
  
      np.random.seed(seed)
      np.random.shuffle(x_train)
      np.random.seed(seed)
      np.random.shuffle(labels_train)
  
      np.random.seed(seed * 2)
      np.random.shuffle(x_test)
      np.random.seed(seed * 2)
      np.random.shuffle(labels_test)
  
      xs = np.concatenate([x_train, x_test])
      labels = np.concatenate([labels_train, labels_test])
  
      if start_char is not None:
          xs = [[start_char] + [w + index_from for w in x] for x in xs]
      elif index_from:
          xs = [[w + index_from for w in x] for x in xs]
  
      if maxlen:
          xs, labels = _remove_long_seq(maxlen, xs, labels)
          if not xs:
              raise ValueError('After filtering for sequences shorter than maxlen=' +
                               str(maxlen) + ', no sequence was kept. '
                               'Increase maxlen.')
      if not num_words:
          num_words = max([max(x) for x in xs])
  
      # by convention, use 2 as OOV word
      # reserve 'index_from' (=3 by default) characters:
      # 0 (padding), 1 (start), 2 (OOV)
      if oov_char is not None:
          xs = [[w if (skip_top <= w < num_words) else oov_char for w in x] for x in xs]
      else:
          xs = [[w for w in x if (skip_top <= w < num_words)] for x in xs]
  
      idx = len(x_train)
      x_train, y_train = np.array(xs[:idx]), np.array(labels[:idx])
      x_test, y_test = np.array(xs[idx:]), np.array(labels[idx:])
  
      return (x_train, y_train), (x_test, y_test)
  
  
  def get_word_index(path='imdb_word_index.json'):
      """Retrieves the dictionary mapping word indices back to words.
  
      # Arguments
          path: where to cache the data (relative to `~/.keras/dataset`).
  
      # Returns
          The word index dictionary.
      """
      path = get_file(path,
                      origin='https://s3.amazonaws.com/text-datasets/imdb_word_index.json')
      f = open(path)
      data = json.load(f)
      f.close()
      return data
  