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(Tensorflow之十八)tf.nn.l2_normalize的使用

发布时间:

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]]



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