TensorFlow 1 version View source on GitHub

Loads the Reuters newswire classification dataset.

    path='reuters.npz', num_words=None, skip_top=0, maxlen=None, test_split=0.2,
    seed=113, start_char=1, oov_char=2, index_from=3, **kwargs


  • 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.
  • test_split: Fraction of the dataset to be used as test data.
  • 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.
  • **kwargs: Used for backwards compatibility.


Tuple of Numpy arrays: (x_train, y_train), (x_test, y_test).

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.