我試圖確定一個(gè)句子和其他句子之間的語(yǔ)義相似性，如下所示：import tensorflow as tfimport tensorflow_hub as hubimport numpy as npimport os, sysfrom sklearn.metrics.pairwise import cosine_similarity# get cosine similairty matrixdef cos_sim(input_vectors):    similarity = cosine_similarity(input_vectors)    return similarity# get topN similar sentencesdef get_top_similar(sentence, sentence_list, similarity_matrix, topN):    # find the index of sentence in list    index = sentence_list.index(sentence)    # get the corresponding row in similarity matrix    similarity_row = np.array(similarity_matrix[index, :])    # get the indices of top similar    indices = similarity_row.argsort()[-topN:][::-1]    return [sentence_list[i] for i in indices]module_url = "https://tfhub.dev/google/universal-sentence-encoder/2" #@param ["https://tfhub.dev/google/universal-sentence-encoder/2", "https://tfhub.dev/google/universal-sentence-encoder-large/3"]# Import the Universal Sentence Encoder's TF Hub moduleembed = hub.Module(module_url)# Reduce logging output.tf.logging.set_verbosity(tf.logging.ERROR)sentences_list = [    # phone related    'My phone is slow',    'My phone is not good',    'I need to change my phone. It does not work well',    'How is your phone?',    # age related    'What is your age?',    'How old are you?',    'I am 10 years old',    # weather related    'It is raining today',    'Would it be sunny tomorrow?',    'The summers are here.']with tf.Session() as session:  session.run([tf.global_variables_initializer(), tf.tables_initializer()])  sentences_embeddings = session.run(embed(sentences_list))similarity_matrix = cos_sim(np.array(sentences_embeddings))sentence = "It is raining today"top_similar = get_top_similar(sentence, sentences_list, similarity_matrix, 3)# printing the list using loop for x in range(len(top_similar)):     print(top_similar[x])#view raw