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
)
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.