获得 LSTM 的 top 3 预测，而不是仅获得 top

Question

I have a LSTM model trained on text content. And now I want to use that model to generate some sentences. But instead of always picking the best option, i want it to select from for example the top 3, so that it can produce different sentences with the same input, because now I get the same answer for almost every input. How do i modify this code so that is possible, I know I need to remove the np.argmax but i don't know how to the return the index of the top 3 highest values.

Current code:

def prediction(seed_text, next_words): 
  for _ in range(next_words):
    token_list = tokenizer.texts_to_sequences([seed_text])[0]
    token_list = pad_sequences([token_list], maxlen=max_seq_length-1, padding='pre')
    predicted = np.argmax(model.predict(token_list, verbose=0), axis=-1)
    ouput_word = ""
    for word, index in tokenizer.word_index.items():
      if index == predicted:
        output_word = word
        break
  
    seed_text += ' '+output_word
  return seed_text

Answer 1

np.argsort will give you the indices of the items in an array in the order that sorts them small to large: https://numpy.org/doc/stable/reference/generated/numpy.argsort.html

Here's an example using argsort . Note that the one with the lowest prediction (index 2, "c" with the predicted value of 0.05) is left out of what is printed.

import numpy as np

word_index = {'a': 0, 'b': 1, 'c': 2, 'd': 3}

predictions = np.array([0.1, 0.7, 0.05, 0.15])

# add negative to sort large to small; slice to select just up to 3rd index
top_3 = np.argsort(-predictions)[:3]

for word, index in word_index.items():
    if index in top_3:
        print(word)
#> a
#> b
#> d