TF-池化函数 tf.nn.max_pool 的介绍

转载自此大神 http://blog.csdn.net/mao_xiao_feng/article/details/53453926

 

max pooling是CNN当中的最大值池化操作，其实用法和卷积很类似

有些地方可以从卷积去参考【TensorFlow】tf.nn.conv2d是怎样实现卷积的？ 

 

tf.nn.max_pool(value, ksize, strides, padding, name=None)

参数是四个，和卷积很类似：

 

第一个参数value：需要池化的输入，一般池化层接在卷积层后面，所以输入通常是feature map，依然是[batch, height, width, channels]这样的shape

第二个参数ksize：池化窗口的大小，取一个四维向量，一般是[1, height, width, 1]，因为我们不想在batch和channels上做池化，所以这两个维度设为了1

第三个参数strides：和卷积类似，窗口在每一个维度上滑动的步长，一般也是[1, stride,stride, 1]

第四个参数padding：和卷积类似，可以取'VALID' 或者'SAME'

返回一个Tensor，类型不变，shape仍然是[batch, height, width, channels]这种形式

示例源码：

假设有这样一张图，双通道

第一个通道：

第二个通道：

用程序去做最大值池化：

import tensorflow as tf  
  
a=tf.constant([  
        [[1.0,2.0,3.0,4.0],  
        [5.0,6.0,7.0,8.0],  
        [8.0,7.0,6.0,5.0],  
        [4.0,3.0,2.0,1.0]],  
        [[4.0,3.0,2.0,1.0],  
         [8.0,7.0,6.0,5.0],  
         [1.0,2.0,3.0,4.0],  
         [5.0,6.0,7.0,8.0]]  
    ])  
  
a=tf.reshape(a,[1,4,4,2])  
  
pooling=tf.nn.max_pool(a,[1,2,2,1],[1,1,1,1],padding='VALID')  
with tf.Session() as sess:  
    print("image:")  
    image=sess.run(a)  
    print (image)  
    print("reslut:")  
    result=sess.run(pooling)  
    print (result)  

这里步长为1，窗口大小2×2，输出结果：

image:  
[[[[ 1.  2.]  
   [ 3.  4.]  
   [ 5.  6.]  
   [ 7.  8.]]  
  
  [[ 8.  7.]  
   [ 6.  5.]  
   [ 4.  3.]  
   [ 2.  1.]]  
  
  [[ 4.  3.]  
   [ 2.  1.]  
   [ 8.  7.]  
   [ 6.  5.]]  
  
  [[ 1.  2.]  
   [ 3.  4.]  
   [ 5.  6.]  
   [ 7.  8.]]]]  
reslut:  
[[[[ 8.  7.]  
   [ 6.  6.]  
   [ 7.  8.]]  
  
  [[ 8.  7.]  
   [ 8.  7.]  
   [ 8.  7.]]  
  
  [[ 4.  4.]  
   [ 8.  7.]  
   [ 8.  8.]]]]  

池化后的图就是：

证明了程序的结果是正确的。

我们还可以改变步长

pooling=tf.nn.max_pool(a,[1,2,2,1],[1,2,2,1],padding='VALID')  

最后的result就变成：

reslut:  
[[[[ 8.  7.]  
   [ 7.  8.]]  
  
  [[ 4.  4.]  
   [ 8.  8.]]]] 

 

 

 

下面是我自己写的测试代码和测试结果：

import tensorflow as tf

# def max_pool(value, ksize, strides, padding, data_format="NHWC", name=None)
#8x8的4维全1矩阵
value = tf.ones([1, 8, 8, 1], dtype=tf.float32)

oplist = []

ksize   = [1, 2, 2, 1]
strides = [1, 1, 1, 1]
reth = tf.nn.max_pool(value, ksize, strides, padding='VALID')
oplist.append([reth, 'case 1'])


ksize   = [1, 4, 4, 1]
strides = [1, 1, 1, 1]
reth = tf.nn.max_pool(value, ksize, strides, padding='VALID')
oplist.append([reth, 'case 2'])

ksize   = [1, 6, 6, 1]
strides = [1, 1, 1, 1]
reth = tf.nn.max_pool(value, ksize, strides, padding='VALID')
oplist.append([reth, 'case 3'])

ksize   = [1, 2, 2, 1]
strides = [1, 2, 2, 1]
reth = tf.nn.max_pool(value, ksize, strides, padding='VALID')
oplist.append([reth, 'case 4'])

ksize   = [1, 2, 2, 1]
strides = [1, 2, 2, 1]
reth = tf.nn.max_pool(value, ksize, strides, padding='SAME')
oplist.append([reth, 'case 5'])

with tf.Session() as a_sess:
    a_sess.run(tf.global_variables_initializer())
    for aop in oplist:
        print("----------{}---------".format(aop[1]))
        print("shape  =",aop[0].shape)
        print("content=",a_sess.run(aop[0]))
        print('---------------------\n\n')

结果为

C:\Users\Administrator\Anaconda3\python.exe C:/Users/Administrator/PycharmProjects/p3test/tf_maxpool.py
2017-05-10 16:43:25.690336: W c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE instructions, but these are available on your machine and could speed up CPU computations.
2017-05-10 16:43:25.691336: W c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE2 instructions, but these are available on your machine and could speed up CPU computations.
2017-05-10 16:43:25.691336: W c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.
2017-05-10 16:43:25.692336: W c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-05-10 16:43:25.692336: W c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-05-10 16:43:25.692336: W c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
----------case 1---------
shape  = (1, 7, 7, 1)
content= [[[[ 1.]
   [ 1.]
   [ 1.]
   [ 1.]
   [ 1.]
   [ 1.]
   [ 1.]]

  [[ 1.]
   [ 1.]
   [ 1.]
   [ 1.]
   [ 1.]
   [ 1.]
   [ 1.]]

  [[ 1.]
   [ 1.]
   [ 1.]
   [ 1.]
   [ 1.]
   [ 1.]
   [ 1.]]

  [[ 1.]
   [ 1.]
   [ 1.]
   [ 1.]
   [ 1.]
   [ 1.]
   [ 1.]]

  [[ 1.]
   [ 1.]
   [ 1.]
   [ 1.]
   [ 1.]
   [ 1.]
   [ 1.]]

  [[ 1.]
   [ 1.]
   [ 1.]
   [ 1.]
   [ 1.]
   [ 1.]
   [ 1.]]

  [[ 1.]
   [ 1.]
   [ 1.]
   [ 1.]
   [ 1.]
   [ 1.]
   [ 1.]]]]
---------------------


----------case 2---------
shape  = (1, 5, 5, 1)
content= [[[[ 1.]
   [ 1.]
   [ 1.]
   [ 1.]
   [ 1.]]

  [[ 1.]
   [ 1.]
   [ 1.]
   [ 1.]
   [ 1.]]

  [[ 1.]
   [ 1.]
   [ 1.]
   [ 1.]
   [ 1.]]

  [[ 1.]
   [ 1.]
   [ 1.]
   [ 1.]
   [ 1.]]

  [[ 1.]
   [ 1.]
   [ 1.]
   [ 1.]
   [ 1.]]]]
---------------------


----------case 3---------
shape  = (1, 3, 3, 1)
content= [[[[ 1.]
   [ 1.]
   [ 1.]]

  [[ 1.]
   [ 1.]
   [ 1.]]

  [[ 1.]
   [ 1.]
   [ 1.]]]]
---------------------


----------case 4---------
shape  = (1, 4, 4, 1)
content= [[[[ 1.]
   [ 1.]
   [ 1.]
   [ 1.]]

  [[ 1.]
   [ 1.]
   [ 1.]
   [ 1.]]

  [[ 1.]
   [ 1.]
   [ 1.]
   [ 1.]]

  [[ 1.]
   [ 1.]
   [ 1.]
   [ 1.]]]]
---------------------


----------case 5---------
shape  = (1, 4, 4, 1)
content= [[[[ 1.]
   [ 1.]
   [ 1.]
   [ 1.]]

  [[ 1.]
   [ 1.]
   [ 1.]
   [ 1.]]

  [[ 1.]
   [ 1.]
   [ 1.]
   [ 1.]]

  [[ 1.]
   [ 1.]
   [ 1.]
   [ 1.]]]]
---------------------



Process finished with exit code 0