tf.keras.datasets.imdb.load_data

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.

path where to cache the data (relative to ~/.keras/dataset).
num_words integer or None. Words are ranked by how often they occur (in the training set) and only the num_words most frequent words are kept. Any less frequent word will appear as oov_char value in the sequence data. If None, all words are kept. Defaults to None.
skip_top skip the top N most frequently occurring words (which may not be informative). These words will appear as oov_char value in the dataset. When 0, no words are skipped. Defaults to 0.
maxlen int or None. Maximum sequence length. Any longer sequence will be truncated. None, means no truncation. Defaults to None.
seed int. Seed for reproducible data shuffling.
start_char int. The start of a sequence will be marked with this character. 0 is usually the padding character. Defaults to 1.
oov_char int. The out-of-vocabulary character. Words that were cut out because of the num_words or skip_top limits will be replaced with this character.
index_from int. Index actual words with this index and higher.

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).