keras-team/keras · error · ValueError

Unknown vectorization mode:

Error message

Unknown vectorization mode:

What it means

sequences_to_matrix accepts only the modes 'binary', 'count', 'tfidf', and 'freq'; anything else falls through to raise ValueError('Unknown vectorization mode:', mode). Note the Keras bug of passing mode as a second positional argument to ValueError, so it shows as a tuple in the traceback rather than in the message text.

Source

Thrown at keras/src/legacy/preprocessing/text.py:281

                counts[j] += 1
            for j, c in list(counts.items()):
                if mode == "count":
                    x[i][j] = c
                elif mode == "freq":
                    x[i][j] = c / len(seq)
                elif mode == "binary":
                    x[i][j] = 1
                elif mode == "tfidf":
                    # Use weighting scheme 2 in
                    # https://en.wikipedia.org/wiki/Tf%E2%80%93idf
                    tf = 1 + np.log(c)
                    idf = np.log(
                        1
                        + self.document_count / (1 + self.index_docs.get(j, 0))
                    )
                    x[i][j] = tf * idf
                else:
                    raise ValueError("Unknown vectorization mode:", mode)
        return x

    def get_config(self):
        json_word_counts = json.dumps(self.word_counts)
        json_word_docs = json.dumps(self.word_docs)
        json_index_docs = json.dumps(self.index_docs)
        json_word_index = json.dumps(self.word_index)
        json_index_word = json.dumps(self.index_word)

        return {
            "num_words": self.num_words,
            "filters": self.filters,
            "lower": self.lower,
            "split": self.split,
            "char_level": self.char_level,
            "oov_token": self.oov_token,
            "document_count": self.document_count,
            "word_counts": json_word_counts,

View on GitHub (pinned to 7a34a03db6)

Solutions

1. Use one of the exact lowercase strings: 'binary', 'count', 'tfidf', 'freq'
2. Check for hyphens and casing like 'tf-idf' or 'Tfidf'
3. For one-hot behavior use to_categorical on sequences instead

Example fix

# before
tok.texts_to_matrix(texts, mode='tf-idf')
# after
tok.texts_to_matrix(texts, mode='tfidf')

MODES = {'binary', 'count', 'tfidf', 'freq'}
if mode not in MODES:
    raise ValueError(f'mode must be one of {MODES}')

def valid_mode(m):
    return m in {'binary', 'count', 'tfidf', 'freq'}

When it happens

Trigger: Calling texts_to_matrix(texts, mode='tf-idf') (hyphen), mode='TFIDF' (uppercase), or a nonexistent mode like 'onehot'.

Common situations: Typos in the mode string; copying 'tf-idf' from other libraries' docs; case-sensitivity surprises.

Understand the failure class

Background: "Must be a positive integer", "Invalid value", "Unsupported": the invalid-argument-value error family, when a library rejects the value you pass — this error's family across 35 libraries.

Related errors

• Unrecognized keyword arguments: {str(kwargs)}
• Specify a dimension (`num_words` argument), or fit on some t
• Fit the Tokenizer on some data before using tfidf mode.
• Unknown activation function '{activation}' cannot be seriali
• Could not interpret activation function identifier: {identif

AI-assisted analysis of keras-team/keras@7a34a03db6 (2026-08-25). Data as JSON: /api/errors/e574d09a0cb21032. Report an issue: GitHub.