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 做到这一点？
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)
最佳答案
这将完成工作:
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]})
这会打印 0.99999988
关于python - 如何计算两个张量之间的余弦相似度？，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/43357732/