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shape函数是Numpy中的函数，它的功能是读取矩阵的长度，比如shape[0]就是读取矩阵第一维度的长度。
直接用.shape可以快速读取矩阵的形状，使用shape[0]读取矩阵第一维度的长度。
.shape的使用方法
>>> import numpy as np >>> x=np.array([[1,2,3],[4,5,6]]) >>> print(x.shape) (2,
3)
shape[0]的使用方法
>>> import numpy as np >>> x=np.array([[1,2,3],[4,5,6]]) >>> print(x.shape[0])
2
其实，我们可以发现:
>>> print(len(x)) 2
shape[0]读取矩阵第一维度的长度，即数组的行数。
shape[1]的使用方法
>>> print(x.shape[1]) 3
是我们的数组的列数。
有时我们会遇到一种新的表示方法:shape[-1]
首先需要知道，对于二维张量，shape[0]代表行数，shape[1]代表列数，同理三维张量还有shape[2]
对于图像来说：
image.shape[0]——图片高
image.shape[1]——图片长
image.shape[2]——图片通道数
而对于矩阵来说：
shape[0]：表示矩阵的行数
shape[1]：表示矩阵的列数
一般来说，-1代表最后一个，所以shape[-1]代表最后一个维度，如在二维张量里，shape[-1]表示列数，注意，即使是一维行向量，shape[-1]表示行向量的元素总数，换言之也是列数：
我们还是举上面的例子:
>>> print(x.shape[-1]) 3
就是求得的列数。
