如何在Numpy中实现ReLU功能 [英] How to implement the ReLU function in Numpy

问题描述

我想做一个简单的神经网络，我想使用ReLU函数.有人可以告诉我如何使用numpy实现该功能的线索. 感谢您的宝贵时间！

I want to make a simple neural network and I wish to use the ReLU function. Can someone give me a clue of how can I implement the function using numpy. Thanks for your time!

推荐答案

有两种方法.

>>> x = np.random.random((3, 2)) - 0.5
>>> x
array([[-0.00590765,  0.18932873],
       [-0.32396051,  0.25586596],
       [ 0.22358098,  0.02217555]])
>>> np.maximum(x, 0)
array([[ 0.        ,  0.18932873],
       [ 0.        ,  0.25586596],
       [ 0.22358098,  0.02217555]])
>>> x * (x > 0)
array([[-0.        ,  0.18932873],
       [-0.        ,  0.25586596],
       [ 0.22358098,  0.02217555]])
>>> (abs(x) + x) / 2
array([[ 0.        ,  0.18932873],
       [ 0.        ,  0.25586596],
       [ 0.22358098,  0.02217555]])

如果使用以下代码计时结果:

If timing the results with the following code:

import numpy as np

x = np.random.random((5000, 5000)) - 0.5
print("max method:")
%timeit -n10 np.maximum(x, 0)

print("multiplication method:")
%timeit -n10 x * (x > 0)

print("abs method:")
%timeit -n10 (abs(x) + x) / 2

我们得到:

max method:
10 loops, best of 3: 239 ms per loop
multiplication method:
10 loops, best of 3: 145 ms per loop
abs method:
10 loops, best of 3: 288 ms per loop

所以乘法似乎是最快的.

So the multiplication seems to be the fastest.

这篇关于如何在Numpy中实现ReLU功能的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！