...符号用来省略：
代码如下
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print(a, a.shape)
b = a[..., 1]
c = a[1, ...]
print("切片b:\n", b, b.shape)
print("切片c:\n", c, c.shape)
输出：
D:\Anaconda3\envs\tensorflow_gpu\python.exe E:/programSpace/PythonPrograme/test/test.py
[[1 2 3]
[4 5 6]
[7 8 9]] (3, 3)
切片b:
[2 5 8] (3,)
切片c:
[4 5 6] (3,)
可见，选取矩阵中的一行或者一列数据切片出来都是一维数组
对多行或者多列进行切片操作
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print(a, a.shape)
b = a[..., 1:3]
c = a[1:3, ...]
print("切片b:\n", b, b.shape)
print("切片c:\n", c, c.shape)
输出结果：
D:\Anaconda3\envs\tensorflow_gpu\python.exe E:/programSpace/PythonPrograme/test/test.py
[[1 2 3]
[4 5 6]
[7 8 9]] (3, 3)
切片b:
[[2 3]
[5 6]
[8 9]] (3, 2)
切片c:
[[4 5 6]
[7 8 9]] (2, 3)
可见对二维数组进行多行或者多列切片会保留原来的排列
对于三维数组（张量）进行切片操作
a = np.array([[[1, 2, 3], [4, 5, 6], [7, 8, 9]], [[11, 12, 13], [14, 15, 16], [17, 18, 19]]])
print(a, a.shape)
b = a[..., 1]
c = a[1, ...]
print("切片b\n", b, b.shape)
print("切片c\n", c, c.shape)
输出结果:
D:\Anaconda3\envs\tensorflow_gpu\python.exe E:/programSpace/PythonPrograme/test/test.py
[[[ 1 2 3]
[ 4 5 6]
[ 7 8 9]]
[[11 12 13]
[14 15 16]
[17 18 19]]] (2, 3, 3)
切片b
[[ 2 5 8]
[12 15 18]] (2, 3)
切片c
[[11 12 13]
[14 15 16]
[17 18 19]] (3, 3)
可见经过切片操作,三维张量变成了二维张量
更改b的切片操作
a = np.array([[[1, 2, 3], [4, 5, 6], [7, 8, 9]], [[11, 12, 13], [14, 15, 16], [17, 18, 19]]])
print(a, a.shape)
b = a[..., 1:2]
print("切片b\n", b, b.shape)
结果:
D:\Anaconda3\envs\tensorflow_gpu\python.exe E:/programSpace/PythonPrograme/test/test.py
[[[ 1 2 3]
[ 4 5 6]
[ 7 8 9]]
[[11 12 13]
[14 15 16]
[17 18 19]]] (2, 3, 3)
切片b
[[[ 2]
[ 5]
[ 8]]
[[12]
[15]
[18]]] (2, 3, 1)
区别使用'b = a[..., 1:2]和b = a[..., 1]'可以达到不变或者降维的效果
