官方文档中[1]，如下解释：
numpy.expand_dims(a, axis)
Expand the shape of an array.
Insert a new axis that will appear at the axis position in the expanded array shape.
Note
Previous to NumPy 1.13.0, neither axis < -a.ndim - 1 nor axis > a.ndim raised errors or put the new axis where documented. Those axis values are now deprecated and will raise an AxisError in the future.
Parameters:a : array_like
Input array.
axis : int
Position in the expanded axes where the new axis is placed.
Returns:res : ndarray
Output array. The number of dimensions is one greater than that of the input array.
See also
squeeze
The inverse operation, removing singleton dimensions
reshape
Insert, remove, and combine dimensions, and resize existing ones
doc.indexing, atleast_1d, atleast_2d, atleast_3d
test codes:<自行脑补>
import numpyas np
x= np.array([[1,2,3],[4,5,6]])
print(x)
print(x.shape)
print("-----------------------------")
y= np.expand_dims(x, axis=0)
print(y)
print("y.shape: ", y.shape)
print("-----------------------------")
y= np.expand_dims(x, axis=1)
print(y)
print("y.shape: ", y.shape)
print("-----------------------------")
y= np.expand_dims(x, axis=2)
print(y)
print("y.shape: ", y.shape)
结果：
[[1 2 3]
[4 5 6]]
(2, 3)
-----------------------------
[[[1 2 3]
[4 5 6]]]
y.shape: (1, 2, 3)
-----------------------------
[[[1 2 3]]
[[4 5 6]]]
y.shape: (2, 1, 3)
-----------------------------
[[[1]
[2]
[3]]
[[4]
[5]
[6]]]
y.shape: (2, 3, 1)
顺带着：<张量拼接>
y1= np.array([ [[1,0],[1,0]] , [[2,2],[3,3]] ])
print(y1.shape)
y2= np.array([ [[4,4],[5,5]] , [[0,1],[0,1]] ])
print(y2.shape)
y3= np.concatenate((y1,y2),axis=1)
print(y3.shape)
print(y3)
输出结果：
(2, 2, 2)
(2, 2, 2)
(2, 4, 2)
[[[1 0]
[1 0]
[4 4]
[5 5]]
[[2 2]
[3 3]
[0 1]
[0 1]]]
此处为按1轴的拼接：首先我们找到y1和y2的0轴，在对应0轴内每个元素按照1轴进行拼接。y1的0轴第一个元素[[1,0],[1,0]]与y2的0轴第一个元素[[0,0],[0,0]]按照2轴拼接成：
[[1, 0],
[1, 0],
[4, 4],
[5,5]]
[1] https://docs.scipy.org/doc/numpy/reference/generated/numpy.expand_dims.html
[2] https://blog.csdn.net/qq_16234613/article/details/79380957