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
)
This is a dataset of 25,000 movies reviews from IMDB, labeled by sentiment (positive/negative). Reviews have been preprocessed, and each review is encoded as a list of word indexes (integers). For convenience, words are indexed by overall frequency in the dataset, so that for instance the integer "3" encodes the 3rd most frequent word in the data. This allows for quick filtering operations such as: "only consider the top 10,000 most common words, but eliminate the top 20 most common words".
As a convention, "0" does not stand for a specific word, but instead is used to encode the pad token.
Returns | |
---|---|
Tuple of Numpy arrays: (x_train, y_train), (x_test, y_test) .
|
x_train, x_test: lists of sequences, which are lists of indexes
(integers). If the num_words argument was specific, the maximum
possible index value is num_words - 1
. If the maxlen
argument was
specified, the largest possible sequence length is maxlen
.
y_train, y_test: lists of integer labels (1 or 0).
Raises | |
---|---|
ValueError
|
in case maxlen is 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.