Keras Tokenizer num_words doesn't seem to work

Question

>>> t = Tokenizer(num_words=3) >>> l = ["Hello, World! This is so&#$ fantastic!", "There is no other world like this one"] >>> t.fit_on_texts(l) >>> t.word_index {'fantastic': 6, 'like': 10, 'no': 8, 'this': 2, 'is': 3, 'there': 7, 'one': 11, 'other': 9, 'so': 5, 'world': 1, 'hello': 4} 

I'd have expected t.word_index to have just the top 3 words. What am I doing wrong?

Answer 1

There is nothing wrong in what you are doing. word_index is computed the same way no matter how many most frequent words you will use later (as you may see here). So when you will call any transformative method - Tokenizer will use only three most common words and at the same time, it will keep the counter of all words - even when it's obvious that it will not use it later.