|TensorFlow 1 version||View source on GitHub|
Loads the IMDB dataset.
Compat aliases for migration
See Migration guide for more details.
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 any unknown word.
path: where to cache the data (relative to
num_words: integer or None. Words are ranked by how often they occur (in the training set) and only the
num_wordsmost frequent words are kept. Any less frequent word will appear as
oov_charvalue in the sequence data. If None, all words are kept. Defaults to None, so all words are kept.
skip_top: skip the top N most frequently occurring words (which may not be informative). These words will appear as
oov_charvalue in the dataset. Defaults to 0, so no words are skipped.
maxlen: int or None. Maximum sequence length. Any longer sequence will be truncated. Defaults to None, which means no truncation.
seed: int. Seed for reproducible data shuffling.
start_char: int. The start of a sequence will be marked with this character. Defaults to 1 because 0 is usually the padding character.
oov_char: int. The out-of-vocabulary character. Words that were cut out because of the
skip_toplimits will be replaced with this character.
index_from: int. 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).
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
y_train, y_test: lists of integer labels (1 or 0).
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.