python - 将输入字符串张量转换为 python 字符串

Question

我有以下两本词典:

word2int = {}
int2word = {}

for i,word in enumerate(words):
    word2int[word] = i
    int2word[i] = word

def euclidean_dist(vec1, vec2):
    return np.sqrt(np.sum((vec1-vec2)**2))

def find_closest(word_index, vectors):
    min_dist = 10000 # to act like positive infinity
    min_index = -1
    query_vector = vectors[word_index]
    for index, vector in enumerate(vectors):
        if euclidean_dist(vector, query_vector) < min_dist and not np.array_equal(vector, query_vector):
            min_dist = euclidean_dist(vector, query_vector)
            min_index = index
    return min_index

我有一个输入字符串张量X。我想在 word2int 中使用 X 索引，如下所示:

X = tf.placeholder(tf.string)
find_closest_word = tf.convert_to_tensor(int2word[find_closest(word2int[X], vectors)], dtype=tf.string)

问题:

如何将字符串张量 X 转换为 python 字符串，以便将其用作 word2int 中的索引？

Answer 1

How can I convert a string tensor X to a python string so that it can be used as index in word2int?

除非调用sess.run(string_tensor)，否则无法获取字符串值。

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值得一提的是，有一种更简洁的方法可以将 id 转换为单词，反之亦然，使用 index_table_from_file 。 Here这是一个如何使用它的示例。