|TensorFlow 2.0 version||View source on GitHub|
Loads the IMDB dataset.
tf.keras.datasets.imdb.load_data( path='imdb.npz', num_words=None, skip_top=0, maxlen=None, 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: sequences longer than this will be filtered out.
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).
ValueError: in case
maxlenis so low that no input sequence could be kept.
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.