一、基础正则化函数

tf.contrib.layers.l1_regularizer(scale, scope=None)

返回一个用来执行L1正则化的函数,函数的签名是func(weights).

参数:

scale: 正则项的系数.

scope: 可选的scope name

tf.contrib.layers.l2_regularizer(scale, scope=None)

先看看tf.contrib.layers.l2_regularizer(weight_decay)都执行了什么：

import tensorflow as tf

sess=tf.Session()

weight_decay=0.1

tmp=tf.constant([0,1,2,3],dtype=tf.float32)

"""

l2_reg=tf.contrib.layers.l2_regularizer(weight_decay)

a=tf.get_variable("I_am_a",regularizer=l2_reg,initializer=tmp)

"""

#**上面代码的等价代码

a=tf.get_variable("I_am_a",initializer=tmp)

a2=tf.reduce_sum(a*a)*weight_decay/2;

a3=tf.get_variable(a.name.split(":")[0]+"/Regularizer/l2_regularizer",initializer=a2)

tf.add_to_collection(tf.GraphKeys.REGULARIZATION_LOSSES,a2)

#**

sess.run(tf.global_variables_initializer())

keys = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)

for key in keys:

print("%s : %s" %(key.name,sess.run(key)))

我们很容易可以模拟出tf.contrib.layers.l2_regularizer都做了什么，不过会让代码变丑。

以下比较完整实现L2 正则化。

import tensorflow as tf

sess=tf.Session()

weight_decay=0.1 #(1)定义weight_decay

l2_reg=tf.contrib.layers.l2_regularizer(weight_decay) #(2)定义l2_regularizer()

tmp=tf.constant([0,1,2,3],dtype=tf.float32)

a=tf.get_variable("I_am_a",regularizer=l2_reg,initializer=tmp) #(3)创建variable，l2_regularizer复制给regularizer参数。

#目测REXXX_LOSSES集合

#regularizer定义会将a加入REGULARIZATION_LOSSES集合

print("Global Set:")

keys = tf.get_collection("variables")

for key in keys:

print(key.name)

print("Regular Set:")

keys = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)

for key in keys:

print(key.name)

print("--------------------")

sess.run(tf.global_variables_initializer())

print(sess.run(a))

reg_set=tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES) #(4)则REGULARIAZTION_LOSSES集合会包含所有被weight_decay后的参数和，将其相加

l2_loss=tf.add_n(reg_set)

print("loss=%s" %(sess.run(l2_loss)))

"""

此处输出0.7,即:

weight_decay*sigmal(w*2)/2=0.1*(0*0+1*1+2*2+3*3)/2=0.7

其实代码自己写也很方便，用API看着比较正规。

在网络模型中，直接将l2_loss加入loss就好了。(loss变大，执行train自然会decay)

"""

二、添加正则化方法

a、原始办法

正则化常用到集合，下面是最原始的添加正则办法(直接在变量声明后将之添加进'losses'集合或tf.GraphKeys.LOESSES也行)：

import tensorflow as tf

import numpy as np

def get_weights(shape, lambd):

var = tf.Variable(tf.random_normal(shape), dtype=tf.float32)

tf.add_to_collection('losses', tf.contrib.layers.l2_regularizer(lambd)(var))

return var

x = tf.placeholder(tf.float32, shape=(None, 2))

y_ = tf.placeholder(tf.float32, shape=(None, 1))

batch_size = 8

layer_dimension = [2, 10, 10, 10, 1]

n_layers = len(layer_dimension)

cur_lay = x

in_dimension = layer_dimension[0]

for i in range(1, n_layers):

out_dimension = layer_dimension[i]

weights = get_weights([in_dimension, out_dimension], 0.001)

bias = tf.Variable(tf.constant(0.1, shape=[out_dimension]))

cur_lay = tf.nn.relu(tf.matmul(cur_lay, weights)+bias)

in_dimension = layer_dimension[i]

mess_loss = tf.reduce_mean(tf.square(y_-cur_lay))

tf.add_to_collection('losses', mess_loss)

loss = tf.add_n(tf.get_collection('losses'))

b、tf.contrib.layers.apply_regularization(regularizer, weights_list=None)

先看参数

regularizer:就是我们上一步创建的正则化方法

weights_list: 想要执行正则化方法的参数列表,如果为None的话,就取GraphKeys.WEIGHTS中的weights.

函数返回一个标量Tensor,同时,这个标量Tensor也会保存到GraphKeys.REGULARIZATION_LOSSES中.这个Tensor保存了计算正则项损失的方法.

tensorflow中的Tensor是保存了计算这个值的路径(方法),当我们run的时候,tensorflow后端就通过路径计算出Tensor对应的值

现在,我们只需将这个正则项损失加到我们的损失函数上就可以了.

如果是自己手动定义weight的话,需要手动将weight保存到GraphKeys.WEIGHTS中,但是如果使用layer的话,就不用这么麻烦了,别人已经帮你考虑好了.(最好自己验证一下tf.GraphKeys.WEIGHTS中是否包含了所有的weights,防止被坑)

c、使用slim

使用slim会简单很多：

with slim.arg_scope([slim.conv2d, slim.fully_connected],

activation_fn=tf.nn.relu,

weights_regularizer=slim.l2_regularizer(weight_decay))：

pass

此时添加集合为tf.GraphKeys.REGULARIZATION_LOSSES。