Question

我有两个归一化张量，我需要计算这些张量之间的余弦相似度。我如何使用TensorFlow进行操作？

cosine(normalize_a,normalize_b)

    a = tf.placeholder(tf.float32, shape=[None], name="input_placeholder_a")
    b = tf.placeholder(tf.float32, shape=[None], name="input_placeholder_b")
    normalize_a = tf.nn.l2_normalize(a,0)        
    normalize_b = tf.nn.l2_normalize(b,0)

Answer 1

时代变了。使用最新的TF API，可以通过调用tf.losses.cosine_distance来计算。

示例：

import tensorflow as tf
import numpy as np


x = tf.constant(np.random.uniform(-1, 1, 10)) 
y = tf.constant(np.random.uniform(-1, 1, 10))
s = tf.losses.cosine_distance(tf.nn.l2_normalize(x, 0), tf.nn.l2_normalize(y, 0), dim=0)
print(tf.Session().run(s))

当然，1 - s是余弦相似度！

Answer 2

这将完成这项工作：

ssl on;

这会打印a = tf.placeholder(tf.float32, shape=[None], name="input_placeholder_a") b = tf.placeholder(tf.float32, shape=[None], name="input_placeholder_b") normalize_a = tf.nn.l2_normalize(a,0) normalize_b = tf.nn.l2_normalize(b,0) cos_similarity=tf.reduce_sum(tf.multiply(normalize_a,normalize_b)) sess=tf.Session() cos_sim=sess.run(cos_similarity,feed_dict={a:[1,2,3],b:[2,4,6]})

Answer 3

您可以像这样标准化矢量或矩阵：

[batch_size*hidden_num]
states_norm=tf.nn.l2_normalize(states,dim=1)
[batch_size * embedding_dims]
embedding_norm=tf.nn.l2_normalize(embedding,dim=1)
#assert hidden_num == embbeding_dims
after mat [batch_size*embedding]
user_app_scores = tf.matmul(states_norm,embedding_norm,transpose_b=True)

如何计算两个张量之间的余弦相似度？

3 个答案: