在张量流中实现余弦相似度
在张量流中实现余弦相似度
提问于 2019-05-09 03:27:29
我的问题是关于下面的方程式
上面的单向量方程。但是如果我有一批向量，比如我的X和Y的维数是(None，32)，那么就会有一些问题。
还要记住，在编码环境中，批处理中的一个示例已经处于转置状态。我的问题是，当我们需要对None，32进行转置时，代码将不会接受和转置为None dimenation.So，我用以下方式解决了它：
def Cosine_similarity(X, Y, feature_dim):
L = tf.compat.v1.initializers.glorot_normal()(shape=[feature_dim, feature_dim])
out1 = tf.matmul(X, L)
out2 = tf.matmul(Y, L)
out_numerator = tf.reduce_sum(tf.multiply(out1, out2), axis = 1)
out3 = tf.reduce_sum(tf.multiply(out1, out1), axis = 1)
out3 = tf.sqrt(out3)
out4 = tf.reduce_sum(tf.multiply(out2, out2), axis = 1)
out4 = tf.sqrt(out4)
out_denominator = tf.multiply(out3, out4)
final_out = tf.divide(out_numerator, out_denominator)
return final_out这来自于以下内容：
<XA.YA> = (XA)^T (YA)
= tf.reduce_sum(tf.multiply((X A) , (Y A)), axis = 1)所以我只想知道这个实现是否正确？或者，如果我遗漏了什么，你可以纠正我
回答 1
Stack Overflow用户
回答已采纳
发布于 2019-05-09 05:54:19
我不确定我是否理解您对(无)维度的担忧。
如果我理解正确的话，两个形状相同的矩阵X和Y ([batch, target_dim])之间的余弦相似性只是X * Y^T与一些L2归一化的矩阵乘法。注意：X将是您的out1，Y将是您的out2。
def Cosine_similarity(x, y, A):
"""Pair-wise Cosine similarity.
First `x` and `y` are transformed by A.
`X = xA^T` with shape [batch, target_dim],
`Y = yA^T` with shape [batch, target_dim].
Args:
x: shaped [batch, feature_dim].
y: shaped [batch, feature_dim].
A: shaped [targte_dim, feature_dim]. Transformation matrix to project
from `feature_dim` to `target_dim`.
Returns:
A cosine similarity matrix shaped [batch, batch]. The entry
at (i, j) is the cosine similarity value between vector `X[i, :]` and
`Y[j, :]` where `X`, `Y` are the transformed `x` and y` by `A`
respectively. In the other word, entry at (i, j) is the pair-wise
cosine similarity value between the i-th example of `x` and the j-th
example of `y`.
"""
x = tf.matmul(x, A, transpose_b=True)
y = tf.matmul(y, A, transpose_b=True)
x_norm = tf.nn.l2_normalize(x, axis=-1)
y_norm = tf.nn.l2_normalize(y, axis=-1)
y_norm_trans = tf.transpose(y_norm, [1, 0])
sim = tf.matmul(x_norm, y_norm_trans)
return sim
import numpy as np
feature_dim = 8
target_dim = 4
batch_size = 2
x = tf.placeholder(tf.float32, shape=(None, dim))
y = tf.placeholder(tf.float32, shape=(None, dim))
A = tf.placeholder(tf.float32, shape=(target_dim, feature_dim))
sim = Cosine_similarity(x, y, A)
with tf.Session() as sess:
x, y, sim = sess.run([x, y, sim], feed_dict={
x: np.ones((batch_size, feature_dim)),
y: np.random.rand(batch_size, feature_dim),
A: np.random.rand(target_dim, feature_dim)})
print 'x=\n', x
print 'y=\n', y
print 'sim=\n', sim结果：
x=
[[ 1. 1. 1. 1. 1. 1. 1. 1.]
[ 1. 1. 1. 1. 1. 1. 1. 1.]]
y=
[[ 0.01471654 0.76577073 0.97747731 0.06429122 0.91344446 0.47987637
0.09899797 0.773938 ]
[ 0.8555786 0.43403915 0.92445409 0.03393625 0.30154493 0.60895061
0.1233703 0.58597666]]
sim=
[[ 0.95917791 0.98181278]
[ 0.95917791 0.98181278]]页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/56047777
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