deep_gru_lstm_sample_word.py

def sample_word(vocabulary: list, prob: np.ndarray, threshold: float) -> str:
    # sample a word from the vocabulary according to 'prob'
    # probability distribution (the softmax output of our model)
    
    prob = prob.tolist()
    vocab_prob = [[vocabulary[i], prob[i]] for i in range(len(prob))]
    vocab_prob.sort(reverse=True, key=lambda e: e[1])
    
    s = 0
    for i in range(len(vocab_prob)):
        if s > threshold:
            vocab_prob[i][1] = 0
        s += vocab_prob[i][1]
        
    vocab = [w for w, p in vocab_prob]
    prob = np.array([p/s for w, p in vocab_prob])
    
    return np.random.choice(vocab, p=prob)