向量化与for循环耗时对比
Nani_xiao 人气:0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
以上为个人经验,希望能给大家一个参考,也希望大家多多支持。
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