根据question的答案,也根据numpy的答案,与a.dot(b)相比,二维数组的矩阵乘法最好通过a b或numpy.matmul(a,b)完成.

If both a and b are 2-D arrays, it is matrix multiplication, but using

matmul or a @ b is preferred.

我做了以下基准测试,发现相反的结果.

问题：我的基准测试有问题吗？如果不是,当Numpy比a @ b或numpy.matmul(a,b)快时,为什么Numpy不推荐a.dot(b)？

基准使用python 3.5 numpy 1.15.0.

$pip3 list | grep numpy

numpy 1.15.0

$python3 --version

Python 3.5.2

基准代码：

import timeit

setup = '''

import numpy as np

a = np.arange(16).reshape(4,4)

b = np.arange(16).reshape(4,4)

'''

test = '''

for i in range(1000):

a @ b

'''

test1 = '''

for i in range(1000):

np.matmul(a,b)

'''

test2 = '''

for i in range(1000):

a.dot(b)

'''

print( timeit.timeit(test, setup, number=100) )

print( timeit.timeit(test1, setup, number=100) )

print( timeit.timeit(test2, setup, number=100) )

结果：

test : 0.11132473500038031

test1 : 0.10812476599676302

test2 : 0.06115105600474635

附加结果：

>>> a = np.arange(16).reshape(4,4)

>>> b = np.arange(16).reshape(4,4)

>>> a@b

array([[ 56, 62, 68, 74],

[152, 174, 196, 218],

[248, 286, 324, 362],

[344, 398, 452, 506]])

>>> np.matmul(a,b)

array([[ 56, 62, 68, 74],

[152, 174, 196, 218],

[248, 286, 324, 362],

[344, 398, 452, 506]])

>>> a.dot(b)

array([[ 56, 62, 68, 74],

[152, 174, 196, 218],

[248, 286, 324, 362],

[344, 398, 452, 506]])