contrib.keras.datasets.reuters.load_data

tf.contrib.keras.datasets.reuters.load_data

tf.contrib.keras.datasets.reuters.load_data

load_data(
    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
)

Defined in tensorflow/contrib/keras/python/keras/datasets/reuters.py.

Loads the Reuters newswire classification 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.
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.

Returns:

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.