(Tensorflow之十八）tf.nn.l2_normalize的使用

tf.nn.l2_normalize(x, dim, epsilon=1e-12, name=None)
上式：
x为输入的向量；
dim为l2范化的维数，dim取值为0或0或1；
epsilon的范化的最小值边界；

按例计算

例1：

import tensorflow as tf
input_data = tf.constant([[1.0,2,3],[4.0,5,6],[7.0,8,9]])

output = tf.nn.l2_normalize(input_data, dim = 0)
with tf.Session() as sess:
print sess.run(input_data)
print sess.run(output)

结果：

[[1. 2. 3.]
[4. 5. 6.]
[7. 8. 9.]]
[[0.12309149 0.20739034 0.26726127]
[0.49236596 0.51847583 0.53452253]
[0.86164045 0.82956135 0.80178374]]

计算方法：
dim = 0, 为按列进行l2范化
norm(1)=12+42+72−−−−−−−−−−√=66−−√
norm(2)=22+52+82−−−−−−−−−−√=93−−√
norm(3)=32+62+92−−−−−−−−−−√=126−−−√

[[1./norm(1), 2./norm(2) , 3./norm(3) ]
[4./norm(1) , 5./norm(2) , 6./norm(3) ]    =
[7./norm(1) , 8./norm(2) , 9./norm(3) ]]
[[0.12309149 0.20739034 0.26726127]
[0.49236596 0.51847583 0.53452253]
[0.86164045 0.82956135 0.80178374]]

按行计算

例2：

import tensorflow as tf
input_data = tf.constant([[1.0,2,3],[4.0,5,6],[7.0,8,9]])

output = tf.nn.l2_normalize(input_data, dim = 1)
with tf.Session() as sess:
print sess.run(input_data)
print sess.run(output)

结果：

[[1. 2. 3.]
[4. 5. 6.]
[7. 8. 9.]]
[[0.26726124 0.5345225 0.8017837 ]
[0.45584232 0.5698029 0.6837635 ]
[0.5025707 0.5743665 0.64616233]]

计算方法：
dim = 0, 为按行进行l2范化
norm(1)=12+22+32−−−−−−−−−−√=14−−√
norm(2)=22+52+82−−−−−−−−−−√=77−−√
norm(3)=32+62+92−−−−−−−−−−√=194−−−√

[[1./norm(1), 2./norm(1) , 3./norm(1) ]
[4./norm(2) , 5./norm(2) , 6./norm(2) ]    =
[7./norm(3) , 8..norm(3) , 9./norm(3) ]]
[[0.12309149 0.20739034 0.26726127]
[0.49236596 0.51847583 0.53452253]
[0.86164045 0.82956135 0.80178374]]