tensorflow中的tf.tile

转自：FesianXu

tf.tile()进行张量扩展

tf.tile()应用于需要张量扩展的场景，具体说来就是：
如果现有一个形状如[width, height]的张量，需要得到一个基于原张量的，形状如[batch_size,width,height]的张量，其中每一个batch的内容都和原张量一模一样。tf.tile使用方法如：

tile(
    input,
    multiples,
    name=None
)
    
    

     
     
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其中输出将会重复input输入multiples次。例子如：

import tensorflow as tf

raw = tf.Variable(tf.random_normal(shape=(1, 3, 2)))
multi = tf.tile(raw, multiples=[2, 1, 1])

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    print(raw.eval())
    print('-----------------------------')
    print(sess.run(multi))

    
    

     
     
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输出如：

[[[-0.50027871 -0.48475555]
  [-0.52617502 -0.2396145 ]
  [ 1.74173343 -0.20627949]]]
\-----------------------------
[[[-0.50027871 -0.48475555]
  [-0.52617502 -0.2396145 ]
  [ 1.74173343 -0.20627949]]

 [[-0.50027871 -0.48475555]
  [-0.52617502 -0.2396145 ]
  [ 1.74173343 -0.20627949]]]
    
    

     
     
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可见，multi重复了raw的0 axes两次，1和2 axes不变。

还有几个例子：

import tensorflow as tf
temp = tf.tile([1,2,3],[2])
temp2 = tf.tile([[1,2],[3,4],[5,6]],[2,3])
with tf.Session() as sess:
    print(sess.run(temp))
    print(sess.run(temp2))
     
     

      
      
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6
     
     

[1 2 3 1 2 3]

[[1 2 1 2 1 2]
[3 4 3 4 3 4]
[5 6 5 6 5 6]
[1 2 1 2 1 2]
[3 4 3 4 3 4]
[5 6 5 6 5 6]]

import tensorflow as tf
temp = tf.tile([[1,2,3],[1,2,3]],[1,1])
temp2 = tf.tile([[1,2,3],[1,2,3]],[2,1])
temp3 = tf.tile([[1,2,3],[1,2,3]],[2,2])
with tf.Session() as sess:
    print(sess.run(temp))
    print(sess.run(temp2))
    print(sess.run(temp3))
     
     

      
      
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[[1 2 3]
[1 2 3]]

[[1 2 3]
[1 2 3]
[1 2 3]
[1 2 3]]

[[1 2 3 1 2 3]
[1 2 3 1 2 3]
[1 2 3 1 2 3]
[1 2 3 1 2 3]]

tf.tile()进行张量扩展

tf.tile()应用于需要张量扩展的场景，具体说来就是：
如果现有一个形状如[width, height]的张量，需要得到一个基于原张量的，形状如[batch_size,width,height]的张量，其中每一个batch的内容都和原张量一模一样。tf.tile使用方法如：

tile(
    input,
    multiples,
    name=None
)
  
  

   
   
1
   
   
2
   
   
3
   
   
4
   
   
5
  
  

其中输出将会重复input输入multiples次。例子如：

import tensorflow as tf

raw = tf.Variable(tf.random_normal(shape=(1, 3, 2)))
multi = tf.tile(raw, multiples=[2, 1, 1])

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    print(raw.eval())
    print('-----------------------------')
    print(sess.run(multi))

  
  

   
   
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11
  
  

输出如：

[[[-0.50027871 -0.48475555]
  [-0.52617502 -0.2396145 ]
  [ 1.74173343 -0.20627949]]]
\-----------------------------
[[[-0.50027871 -0.48475555]
  [-0.52617502 -0.2396145 ]
  [ 1.74173343 -0.20627949]]

 [[-0.50027871 -0.48475555]
  [-0.52617502 -0.2396145 ]
  [ 1.74173343 -0.20627949]]]
  
  

   
   
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8
   
   
9
   
   
10
   
   
11
  
  

可见，multi重复了raw的0 axes两次，1和2 axes不变。

还有几个例子：

import tensorflow as tf
temp = tf.tile([1,2,3],[2])
temp2 = tf.tile([[1,2],[3,4],[5,6]],[2,3])
with tf.Session() as sess:
    print(sess.run(temp))
    print(sess.run(temp2))
   
   

    
    
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3
    
    
4
    
    
5
    
    
6
   
   

[1 2 3 1 2 3]

[[1 2 1 2 1 2]
[3 4 3 4 3 4]
[5 6 5 6 5 6]
[1 2 1 2 1 2]
[3 4 3 4 3 4]
[5 6 5 6 5 6]]

import tensorflow as tf
temp = tf.tile([[1,2,3],[1,2,3]],[1,1])
temp2 = tf.tile([[1,2,3],[1,2,3]],[2,1])
temp3 = tf.tile([[1,2,3],[1,2,3]],[2,2])
with tf.Session() as sess:
    print(sess.run(temp))
    print(sess.run(temp2))
    print(sess.run(temp3))
   
   

    
    
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8
   
   

[[1 2 3]
[1 2 3]]

[[1 2 3]
[1 2 3]
[1 2 3]
[1 2 3]]

[[1 2 3 1 2 3]
[1 2 3 1 2 3]
[1 2 3 1 2 3]
[1 2 3 1 2 3]]

import tensorflow as tf
temp = tf.tile([1,2,3],[2])
temp2 = tf.tile([[1,2],[3,4],[5,6]],[2,3])
with tf.Session() as sess:
    print(sess.run(temp))
    print(sess.run(temp2))
  
  

   
   
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6
  
  

[1 2 3 1 2 3]

[[1 2 1 2 1 2]
[3 4 3 4 3 4]
[5 6 5 6 5 6]
[1 2 1 2 1 2]
[3 4 3 4 3 4]
[5 6 5 6 5 6]]

import tensorflow as tf
temp = tf.tile([[1,2,3],[1,2,3]],[1,1])
temp2 = tf.tile([[1,2,3],[1,2,3]],[2,1])
temp3 = tf.tile([[1,2,3],[1,2,3]],[2,2])
with tf.Session() as sess:
    print(sess.run(temp))
    print(sess.run(temp2))
    print(sess.run(temp3))
  
  

   
   
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[[1 2 3]
[1 2 3]]

[[1 2 3]
[1 2 3]
[1 2 3]
[1 2 3]]

[[1 2 3 1 2 3]
[1 2 3 1 2 3]
[1 2 3 1 2 3]
[1 2 3 1 2 3]]