## python实现简单神经网络算法

| |
[ 所属分类 开发（python） | 发布者 店小二03 | 时间 | 作者 红领巾 ] 0人收藏点击收藏

python实现简单神经网络算法，供大家参考，具体内容如下

python实现二层神经网络

import numpy as np

#sigmoid function
def nonlin(x, deriv = False):
if(deriv == True):
return x*(1-x)
return 1/(1+np.exp(-x))

#input dataset
x = np.array([[0,0,1],
[0,1,1],
[1,0,1],
[1,1,1]])

#output dataset
y = np.array([[0,0,1,1]]).T

np.random.seed(1)

#init weight value
syn0 = 2*np.random.random((3,1))-1

for iter in xrange(100000):
l0 = x #the first layer,and the input layer
l1 = nonlin(np.dot(l0,syn0)) #the second layer,and the output layer

l1_error = y-l1

l1_delta = l1_error*nonlin(l1,True)

syn0 += np.dot(l0.T, l1_delta)
print "outout after Training:"
print l1

import numpy as np

#sigmoid function
def nonlin(x, deriv = False):
if(deriv == True):
return x*(1-x)
return 1/(1+np.exp(-x))

#input dataset
x = np.array([[0,0,1],
[0,1,1],
[1,0,1],
[1,1,1]])

#output dataset
y = np.array([[0,0,1,1]]).T

np.random.seed(1)

#init weight value
syn0 = 2*np.random.random((3,1))-1

for iter in xrange(100000):
l0 = x #the first layer,and the input layer
l1 = nonlin(np.dot(l0,syn0)) #the second layer,and the output layer

l1_error = y-l1

l1_delta = l1_error*nonlin(l1,True)

syn0 += np.dot(l0.T, l1_delta)
print "outout after Training:"
print l1

l0:输入层

l1:输出层

syn0:初始权值

l1_error:误差

l1_delta:误差校正系数

func nonlin:sigmoid函数

python实现三层神经网络

import numpy as np

def nonlin(x, deriv = False):
if(deriv == True):
return x*(1-x)
else:
return 1/(1+np.exp(-x))

#input dataset
X = np.array([[0,0,1],
[0,1,1],
[1,0,1],
[1,1,1]])

#output dataset
y = np.array([[0,1,1,0]]).T

syn0 = 2*np.random.random((3,4)) - 1 #the first-hidden layer weight value
syn1 = 2*np.random.random((4,1)) - 1 #the hidden-output layer weight value

for j in range(60000):
l0 = X #the first layer,and the input layer
l1 = nonlin(np.dot(l0,syn0)) #the second layer,and the hidden layer
l2 = nonlin(np.dot(l1,syn1)) #the third layer,and the output layer

l2_error = y-l2 #the hidden-output layer error

if(j%10000) == 0:
print "Error:"+str(np.mean(l2_error))

l2_delta = l2_error*nonlin(l2,deriv = True)

l1_error = l2_delta.dot(syn1.T) #the first-hidden layer error

l1_delta = l1_error*nonlin(l1,deriv = True)

syn1 += l1.T.dot(l2_delta)
syn0 += l0.T.dot(l1_delta)
print "outout after Training:"
print l2
import numpy as np

def nonlin(x, deriv = False):
if(deriv == True):
return x*(1-x)
else:
return 1/(1+np.exp(-x))

#input dataset
X = np.array([[0,0,1],
[0,1,1],
[1,0,1],
[1,1,1]])

#output dataset
y = np.array([[0,1,1,0]]).T

syn0 = 2*np.random.random((3,4)) - 1 #the first-hidden layer weight value
syn1 = 2*np.random.random((4,1)) - 1 #the hidden-output layer weight value

for j in range(60000):
l0 = X #the first layer,and the input layer
l1 = nonlin(np.dot(l0,syn0)) #the second layer,and the hidden layer
l2 = nonlin(np.dot(l1,syn1)) #the third layer,and the output layer

l2_error = y-l2 #the hidden-output layer error

if(j%10000) == 0:
print "Error:"+str(np.mean(l2_error))

l2_delta = l2_error*nonlin(l2,deriv = True)

l1_error = l2_delta.dot(syn1.T) #the first-hidden layer error

l1_delta = l1_error*nonlin(l1,deriv = True)

syn1 += l1.T.dot(l2_delta)
syn0 += l0.T.dot(l1_delta)
print "outout after Training:"
print l2

tags: l1,np,layer,l2,error,#the,nonlin,delta,dot,random,syn0,l0,deriv,True

1.凡CodeSecTeam转载的文章,均出自其它媒体或其他官网介绍,目的在于传递更多的信息,并不代表本站赞同其观点和其真实性负责；
2.转载的文章仅代表原创作者观点,与本站无关。其原创性以及文中陈述文字和内容未经本站证实,本站对该文以及其中全部或者部分内容、文字的真实性、完整性、及时性，不作出任何保证或承若；
3.如本站转载稿涉及版权等问题,请作者及时联系本站,我们会及时处理。