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 )
Loads the IMDB dataset.
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.
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.
oov_charvalue in the dataset. Defaults to 0, so no words are skipped.
skip_toplimits will be replaced with this character.
(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).
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.
Retrieves a dict mapping words to their index in the IMDB dataset.
The word index dictionary. Keys are word strings, values are their index.
# Use the default parameters to keras.datasets.imdb.load_data start_char = 1 oov_char = 2 index_from = 3 # Retrieve the training sequences. (x_train, _), _ = keras.datasets.imdb.load_data( start_char=start_char, oov_char=oov_char, index_from=index_from ) # Retrieve the word index file mapping words to indices word_index = keras.datasets.imdb.get_word_index() # Reverse the word index to obtain a dict mapping indices to words # And add `index_from` to indices to sync with `x_train` inverted_word_index = dict( (i + index_from, word) for (word, i) in word_index.items() ) # Update `inverted_word_index` to include `start_char` and `oov_char` inverted_word_index[start_char] = "[START]" inverted_word_index[oov_char] = "[OOV]" # Decode the first sequence in the dataset decoded_sequence = " ".join(inverted_word_index[i] for i in x_train)