text_to_word_sequence

keras.preprocessing.text.text_to_word_sequence(text, 
    filters=base_filter(), lower=True, split=" ")

Split a sentence into a list of words.

• Return: List of words (str).

• Arguments:

  • text: str.
  • filters: list (or concatenation) of characters to filter out, such as punctuation. Default: base_filter(), includes basic punctuation, tabs, and newlines.
  • lower: boolean. Whether to set the text to lowercase.
  • split: str. Separator for word splitting.

one_hot

keras.preprocessing.text.one_hot(text, n,
    filters=base_filter(), lower=True, split=" ")

One-hot encode a text into a list of word indexes in a vocabulary of size n.

• Return: List of integers in [1, n]. Each integer encodes a word (unicity non-guaranteed).

• Arguments: Same as text_to_word_sequence above.

  • n: int. Size of vocabulary.

Tokenizer

keras.preprocessing.text.Tokenizer(num_words=None, filters=base_filter(), 
    lower=True, split=" ")

Class for vectorizing texts, or/and turning texts into sequences (=list of word indexes, where the word of rank i in the dataset (starting at 1) has index i).

• Arguments: Same as text_to_word_sequence above.

  • num_words: None or int. Maximum number of words to work with (if set, tokenization will be restricted to the top num_words most common words in the dataset).

• Methods:

  • fit_on_texts(texts):

    • Arguments:
      • texts: list of texts to train on.

  • texts_to_sequences(texts)

    • Arguments:
      • texts: list of texts to turn to sequences.
    • Return: list of sequences (one per text input).

  • texts_to_sequences_generator(texts): generator version of the above.

    • Return: yield one sequence per input text.

  • texts_to_matrix(texts):

    • Return: numpy array of shape (len(texts), num_words).
    • Arguments:
      • texts: list of texts to vectorize.
      • mode: one of "binary", "count", "tfidf", "freq" (default: "binary").

  • fit_on_sequences(sequences):

    • Arguments:
      • sequences: list of sequences to train on.

  • sequences_to_matrix(sequences):

    • Return: numpy array of shape (len(sequences), num_words).
    • Arguments:
      • sequences: list of sequences to vectorize.
      • mode: one of "binary", "count", "tfidf", "freq" (default: "binary").

• Attributes:

  • word_counts: dictionary mapping words (str) to the number of times they appeared on during fit. Only set after fit_on_texts was called.
  • word_docs: dictionary mapping words (str) to the number of documents/texts they appeared on during fit. Only set after fit_on_texts was called.
  • word_index: dictionary mapping words (str) to their rank/index (int). Only set after fit_on_texts was called.
  • document_count: int. Number of documents (texts/sequences) the tokenizer was trained on. Only set after fit_on_texts or fit_on_sequences was called.