gensim_word2vec.py 文件源码

def create_embedding_matrix(model):
    # convert the wv word vectors into a numpy matrix that is suitable for insertion
    # into our TensorFlow and Keras models
    embedding_matrix = np.zeros((len(model.wv.vocab), vector_dim))
    for i in range(len(model.wv.vocab)):
        embedding_vector = model.wv[model.wv.index2word[i]]
        if embedding_vector is not None:
            embedding_matrix[i] = embedding_vector
    return embedding_matrix