numpy 中的Axis（轴）含义 np.newaxis numpy.expand_dims

理解numpy中的轴：

:表示当前维的所有索引值都取
import numpy as np
t = np.array(
  [
      [
          [
           [1,2,3],  
           [4,5,6]
          ],  
          [
           [7,8,9],  
           [10,11,12]
          ],  
          [
           [13,14,15],  
           [16,17,18]
          ]
      ],  
      [
          [
           [19,20,21],  
           [22,23,24]
          ],  
          [
           [25,26,27],  
           [28,29,30]
          ],  
          [
           [31,32,33],
           [34,35,36]
          ]
      ]
  ])

print(t[0,:,:,:])
[[[ 1 2 3]
 [ 4 5 6]]

[[ 7 8 9]
 [10 11 12]]

[[13 14 15]
 [16 17 18]]]

print(t[:,0,:,:])
[[[ 1 2 3]
 [ 4 5 6]]

[[19 20 21]
 [22 23 24]]]

print(t[:,:,0,:])
[[[ 1 2 3]
 [ 7 8 9]
 [13 14 15]]

[[19 20 21]
 [25 26 27]
 [31 32 33]]]

print(t[:,:,:,0])
[[[ 1 4]
 [ 7 10]
 [13 16]]

[[19 22]
 [25 28]
 [31 34]]]

np.newaxis：
np.newaxis相当于新插入一个轴

a=np.array([1,2,3,4,5])
b=a[np.newaxis,:]
print a.shape,b.shape
print a
print b

输出结果：
(5,) (1, 5)
[1 2 3 4 5]
[[1 2 3 4 5]]

a=np.array([1,2,3,4,5])
b=a[:,np.newaxis]
print a.shape,b.shape
print a
print b

输出结果
(5,) (5, 1)
[1 2 3 4 5]
[[1]
 [2]
 [3]
 [4]
 [5]]

numpy.expand_dims
numpy.expand_dims同样是用于扩充数组维度

>>> x = np.array([1,2])
>>> x.shape
(2,)

>>> y = np.expand_dims(x, axis=0)  #等价于 x[np.newaxis,:]或x[np.newaxis]
>>> y
array([[1, 2]])
>>> y.shape
(1, 2)   #看np.newaxis位置（在:之前）可知插入在2之前

>>> y = np.expand_dims(x, axis=1)  #等价于x[:,newaxis]
>>> y
array([[1],
 [2]])
>>> y.shape
(2, 1)  #看np.newaxis位置（在:之后）可知插入在2之后

>>> np.newaxis is None
True

二维情况：

x = np.array([[1,2,3],[4,5,6]])
print x
print x.shape

[[1 2 3]
 [4 5 6]]
(2, 3)

y = np.expand_dims(x,axis=0)
print y
print "y.shape: ",y.shape
print "y[0][1]: ",y[0][1]

[[[1 2 3]
  [4 5 6]]]
y.shape:  (1, 2, 3)
y[0][1]:  [4 5 6]

y = np.expand_dims(x,axis=1)
print y
print "y.shape: ",y.shape
print "y[1][0]: ",y[1][0]

[[[1 2 3]]

 [[4 5 6]]]
y.shape:  (2, 1, 3)
y[1][0]:  [4 5 6]

y = np.expand_dims(x,axis=3)
print y
print "y.shape: ",y.shape


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

[[4]
 [5]
 [6]]]
y.shape:  (2, 3, 1)