『TensorFlow』正則化添加方法整理

1、基礎正則化函數

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

返回一個用來執行L1正則化的函數,函數的簽名是func(weights). 
參數:

• scale: 正則項的係數.
• scope: 可選的scope name

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

先看看tf.contrib.layers.l2_regularizer(weight_decay)都執行了什麼：

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) 
"""
#**上面代碼的等價代碼
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)))

咱們很容易能夠模擬出tf.contrib.layers.l2_regularizer都作了什麼，不過會讓代碼變醜。

如下比較完整實現L2 正則化。

import tensorflow as tf
sess=tf.Session()
weight_decay=0.1                                                #(1)定義weight_decay
l2_reg=tf.contrib.layers.l2_regularizer(weight_decay)           #(2)定義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)建立variable，l2_regularizer複製給regularizer參數。
                                                                #目測REXXX_LOSSES集合
#regularizer定義會將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)則REGULARIAZTION_LOSSES集合會包含全部被weight_decay後的參數和，將其相加
l2_loss=tf.add_n(reg_set)
print("loss=%s" %(sess.run(l2_loss)))
"""
此處輸出0.7,即:
   weight_decay*sigmal(w*2)/2=0.1*(0*0+1*1+2*2+3*3)/2=0.7
其實代碼本身寫也很方便，用API看着比較正規。
在網絡模型中，直接將l2_loss加入loss就行了。(loss變大，執行train天然會decay)
"""

2、添加正則化方法

a、原始辦法

正則化經常使用到集合，下面是最原始的添加正則辦法（直接在變量聲明後將之添加進'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)

先看參數

• regularizer:就是咱們上一步建立的正則化方法
• weights_list: 想要執行正則化方法的參數列表,若是爲None的話,就取GraphKeys.WEIGHTS中的weights.

函數返回一個標量Tensor,同時,這個標量Tensor也會保存到GraphKeys.REGULARIZATION_LOSSES中.這個Tensor保存了計算正則項損失的方法.

tensorflow中的Tensor是保存了計算這個值的路徑(方法),當咱們run的時候,tensorflow後端就經過路徑計算出Tensor對應的值

如今,咱們只需將這個正則項損失加到咱們的損失函數上就能夠了.

若是是本身手動定義weight的話,須要手動將weight保存到GraphKeys.WEIGHTS中,可是若是使用layer的話,就不用這麼麻煩了,別人已經幫你考慮好了.(最好本身驗證一下tf.GraphKeys.WEIGHTS中是否包含了全部的weights,防止被坑)

c、使用slim

使用slim會簡單不少：

 with slim.arg_scope([slim.conv2d, slim.fully_connected],
                            activation_fn=tf.nn.relu,
                            weights_regularizer=slim.l2_regularizer(weight_decay)）：
    pass

此時添加集合爲tf.GraphKeys.REGULARIZATION_LOSSES。