Keras Tokenizer num_words doesn't seem to work

问题:

>>> 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?

回答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.

回答2:

Just a add on Marcin's answer ("it will keep the counter of all words - even when it's obvious that it will not use it later.").

The reason it keeps counter on all words is that you can call fit_on_texts multiple times. Each time it will update the internal counters, and when transformations are called, it will use the top words based on the updated counters.

Hope it helps.

回答3:

Limiting num_words to a small number (eg, 3) has no effect on fit_on_texts outputs such as word_index, word_counts, word_docs. It does have effect on texts_to_matrix. The resulting matrix will have num_words (3) columns.

>>> 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)
>>> print(t.word_index)
{'world': 1, 'this': 2, 'is': 3, 'hello': 4, 'so': 5, 'fantastic': 6, 'there': 7, 'no': 8, 'other': 9, 'like': 10, 'one': 11}

>>> t.texts_to_matrix(l, mode='count')
array([[0., 1., 1.],       
       [0., 1., 1.]])