对numpy中轴与维度的理解

[[ 1., 0., 0.],
 [ 0., 1., 2.]]

 X = np.reshape(np.arange(24), (2, 3, 4))
  # 也即 2 行 3 列的 4 个平面（plane）
 X
array([[[ 0, 1, 2, 3],
    [ 4, 5, 6, 7],
    [ 8, 9, 10, 11]],
    [[12, 13, 14, 15],
    [16, 17, 18, 19],
    [20, 21, 22, 23]]])

 X[:, :, 0]
array([[ 0, 4, 8],
    [12, 16, 20]])
 X[:, :, 1]
array([[ 1, 5, 9],
    [13, 17, 21]])
 X[:, :, 2]
array([[ 2, 6, 10],
    [14, 18, 22]])
 X[:, :, 3]
array([[ 3, 7, 11],
    [15, 19, 23]])

#!/usr/bin/env python 
# coding=utf-8 
import numpy as np 
 
a=np.array([1, 2, 3, 4, 5, 6, 7, 8]) 
print a 
d=a.reshape((2,4)) 
print d 

#!/usr/bin/env python 
# coding=utf-8 
import numpy as np 
 
a=np.array([1, 2, 3, 4, 5, 6, 7, 8]) 
print a 
f=a.reshape((2, 2, 2)) 
print f 

#!/usr/bin/env python 
# coding=utf-8 
import numpy as np 
 
a=np.array([1, 2, 3, 4, 5, 6, 7, 8]) 
print a 
print a.dtype 
e=a.reshape((2,2)) 
print e 

#!/usr/bin/env python 
# coding=utf-8 
import numpy as np 
 
a=np.array([1, 2, 3, 4, 5, 6, 7, 8]) 
print a 
e=a.reshape((2, 4)) 
print e 
a[1]=100 
print a 
print e 

a=np.arange(0, 60, 10)
>a
array([0,10,20,30,40,50])
>a.reshape(-1,1)
array([[0],
[10],
[20],
[30],
[40],
[50]])

> a = np.array([[1,2,3], [4,5,6]])
> np.reshape(a, (3,-1)) # the unspecified value is inferred to be 2
array([[1, 2],
    [3, 4],
    [5, 6]])

# 下面是两张2*3大小的照片(不知道有几张照片用-1代替)，如何把所有二维照片给摊平成一维
> image = np.array([[[1,2,3], [4,5,6]], [[1,1,1], [1,1,1]]])
> image.shape
(2, 2, 3)
> image.reshape((-1, 6))
array([[1, 2, 3, 4, 5, 6],
    [1, 1, 1, 1, 1, 1]])