numpy 实现求范数，softmax等函数

版权声明：本文为博主CSDN Rosefun96原创文章。 https://blog.csdn.net/rosefun96/article/details/88763569

1 实践

# -*- coding: utf-8 -*-
"""
Created on Sat Mar 23 11:17:12 2019

@author: win10
"""

import numpy as np 

def softmax(x):
    totalSum = np.sum(exp(x), axis = 0)
    return exp(x)/totalSum
#求范数
def norm(x):
    return np.sqrt(np.sum(np.square(x), axis = -1,keepdims =True))

#squash压缩函数
def squash(x):
    s_squared_norm = np.sum(np.square(x), -1, keepdims = True) 
    scale = np.sqrt(s_squared_norm)/(0.5 + s_squared_norm)
    return scale*x

a = 2*np.random.random((10,5))-1
c = a*norm(a)
c = softmax(c)