python向量化与for循环耗时对比分析

向量化与for循环耗时对比

深度学习中，可采用向量化替代for循环，优化耗时问题

对比例程如下，参考Andrew NG的课程笔记

import time
import numpy as np
a = np.random.rand(1000000)
b = np.random.rand(1000000)

tic = time.time()
c = np.dot(a,b)
toc = time.time()
print(c)
print("Vectorized version: " , str(1000*(toc-tic)) + "ms")

c = 0
tic1 = time.time()
for i in range(1000000):
   c += a[i]*b[i]
toc1 = time.time()
print(c)
print("For loop version: " , str(1000*(toc1-tic1)) + "ms")

处理百万数据，耗时相差400多倍。

效果图：

向量化数据的相比于for循环的优势

例子

import numpy as np
import time
a = np.random.rand(1000000)
b = np.random.rand(1000000)
tic = time.time()

c = np.dot(a,b)
toc = time.time()
print©
print(“vectorized version:” + str((toc-tic))+“s”)

c1 = 0
tic = time.time()
for i in range(1000000):
c1 += a[i]*b[i]
toc = time.time()
print(c1)
print(“Nonvectorized version:” + str(toc-tic)+“s”)

结果

250487.97870397285
vectorized version:0.002000093460083008s
250487.9787039739
Nonvectorized version:0.957054615020752s

可以看出向量化后执行时间比使用for循环快478倍

来源：https://blog.csdn.net/xiao_lxl/article/details/78134537