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
)


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.
• **kwargs: Used for backwards compatibility.

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.