为什么 numpy 比 python 慢？如何让代码执行得更好

我将我的神经网络从纯 python 重新编写为 numpy，但现在它的运行速度甚至更慢。所以我尝试了这两个功能：

def d():
    a = [1,2,3,4,5]
    b = [10,20,30,40,50]
    c = [i*j for i,j in zip(a,b)]
    return c

def e():
    a = np.array([1,2,3,4,5])
    b = np.array([10,20,30,40,50])
    c = a*b
    return c

时间 d = 1.77135205057

时间 e = 17.2464673758

Numpy 慢 10 倍。为什么会这样以及如何正确使用 numpy？

我认为差异是因为您正在构建列表和数组e而你只是在构建列表d。考虑：

import numpy as np

def d():
    a = [1,2,3,4,5]
    b = [10,20,30,40,50]
    c = [i*j for i,j in zip(a,b)]
    return c

def e():
    a = np.array([1,2,3,4,5])
    b = np.array([10,20,30,40,50])
    c = a*b
    return c

#Warning:  Functions with mutable default arguments are below.
# This code is only for testing and would be bad practice in production!
def f(a=[1,2,3,4,5],b=[10,20,30,40,50]):
    c = [i*j for i,j in zip(a,b)]
    return c

def g(a=np.array([1,2,3,4,5]),b=np.array([10,20,30,40,50])):
    c = a*b
    return c


import timeit
print timeit.timeit('d()','from __main__ import d')
print timeit.timeit('e()','from __main__ import e')
print timeit.timeit('f()','from __main__ import f')
print timeit.timeit('g()','from __main__ import g')

这里的功能f and g避免每次都重新创建列表/数组，我们会得到非常相似的性能：

1.53083586693
15.8963699341
1.33564996719
1.69556999207

请注意 list-comp +zip仍然获胜。然而，如果我们让数组足够大，numpy 就会轻松获胜：

t1 = [1,2,3,4,5] * 100
t2 = [10,20,30,40,50] * 100
t3 = np.array(t1)
t4 = np.array(t2)
print timeit.timeit('f(t1,t2)','from __main__ import f,t1,t2',number=10000)
print timeit.timeit('g(t3,t4)','from __main__ import g,t3,t4',number=10000)

我的结果是：

0.602419137955
0.0263929367065