import tensorflow as tf
weights = tf.constant([[1.0, -2.0],[-3.0 , 4.0]])

>>> sess.run(tf.contrib.layers.l1_regularizer(0.5)(weights))
5.0
>>> sess.run(tf.keras.regularizers.l1(0.5)(weights))
5.0
>>> sess.run(tf.keras.regularizers.l1()(weights))
0.099999994
>>> sess.run(tf.keras.regularizers.l1(1)(weights))
10.0
>>> sess.run(tf.nn.l2_loss(weights))
15.0
>>> sess.run(tf.keras.regularizers.l2(1)(weights))
30.0
>>> sess.run(tf.keras.regularizers.l2(0.5)(weights))
15.0
>>> sess.run(tf.contrib.layers.l1_regularizer(0.5)(weights))
5.0
>>> sess.run(tf.contrib.layers.l2_regularizer(0.5)(weights))
7.5
>>> sess.run(tf.contrib.layers.l2_regularizer(1.0)(weights))
15.0

在tensorflow中，tf.nn中只有tf.nn.l2_loss，却没有l1_loss，于是自己网上查阅资料，了解到tf.contrib.layers中有tf.contrib.layers.l1_regularizer(),但是tf.contrib目前新版本已经被弃用了，后来发现tf.keras.regularizers下面有l1和l2正则化器，但是该正则化器的l2有点不一样，从上面的结果可以看出，scale都为1时，它要多一倍。可以查看源代码，tf.nn.l2_loss和 tf.contrib.layers.l2_regularizer 中都统一除以了2.所以值要少一半。

>>> sess.run(tf.nn.l2_loss(weights))
15.0
>>> sess.run(tf.keras.regularizers.l2(1)(weights))
30.0
>>> sess.run(tf.contrib.layers.l2_regularizer(1.0)(weights))
15.0
将scale设为0.5后，可以得到一样的值，因此，以后在损失函数中可以使用这样的方式来求l2损失和l1损失。
>>> sess.run(tf.keras.regularizers.l2(0.5)(weights))
15.0

参考了  day-17 L1和L2正则化的tensorflow示例 - 派森蛙 - 博客园

https://www.cnblogs.com/python-frog/p/9416970.html

'''
输入：
x = [[1.0,2.0]]
w = [[1.0,2.0],[3,0,4.0]]

输出：
y = x*w = [[7.0,10.0]]
l1 = (1.0+2.0+3.0+4.0)*0.5 = 5.0
l2 = (1.0**2 + 2.0**2 + 3.0**2 + 4.0**2) / 2)*0.5 = 7.5
'''

import tensorflow as tf
from tensorflow.contrib.layers import *

w = tf.constant([[1.0,2.0],[3.0,4.0]])
x = tf.placeholder(dtype=tf.float32,shape=[None,2])
y = tf.matmul(x,w)

with tf.Session()  as sess:
    init = tf.global_variables_initializer()
    sess.run(init)
    print(sess.run(y,feed_dict={x:[[1.0,2.0]]}))
    print("=========================")
    print(sess.run(l1_regularizer(scale=0.5)(w)))
    #(1.0+2.0+3.0+4.0)*0.5 = 5.0
    print("=========================")
    print(sess.run(l2_regularizer(scale=0.5)(w)))
    #(1.0**2 + 2.0**2 + 3.0**2 + 4.0**2) / 2)*0.5 = 7.5