|TensorFlow 2.0 version||View source on GitHub|
Loads the Reuters newswire classification dataset.
tf.keras.datasets.reuters.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, **kwargs )
path: where to cache the data (relative to
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
skip_toplimit 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.