Broadcasing即扩展，推广的意思，是为了使不同shape的array相互能够进行运算。 例子如下：
a = np.array([1.0, 2.0, 3.0]) b = np.array([2.0, 2.0, 2.0]) a * b array([ 2., 4., 6.])
>>> a = np.array([1.0, 2.0, 3.0]) >>> b = 2.0 >>> a * b array([ 2., 4., 6.])其中扩展规则如下，从后往前依次对shape中的各个值进行比较，兼容的情况有如下三种：
二者的数值一样其中有一个数值为1shape长度不一样时，扩展时长度不够的那一个自动进行长度扩展，扩展值为1.下面是详细的例子：
Image (3d array): 256 x 256 x 3 Scale (1d array): 3 Result (3d array): 256 x 256 x 3 A (4d array): 8 x 1 x 6 x 1 B (3d array): 7 x 1 x 5 Result (4d array): 8 x 7 x 6 x 5 A (2d array): 5 x 4 B (1d array): 1 Result (2d array): 5 x 4 A (2d array): 5 x 4 B (1d array): 4 Result (2d array): 5 x 4 A (3d array): 15 x 3 x 5 B (3d array): 15 x 1 x 5 Result (3d array): 15 x 3 x 5 A (3d array): 15 x 3 x 5 B (2d array): 3 x 5 Result (3d array): 15 x 3 x 5 A (3d array): 15 x 3 x 5 B (2d array): 3 x 1 Result (3d array): 15 x 3 x 5