本文实例讲述了Python编程实现的简单神经网络算法。分享给大家供大家参考，具体如下：

python实现二层神经网络

包括输入层和输出层

# -*- coding:utf-8 -*-

#! python2

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

print "脚本之家测试结果："

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

这里，

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

这里迭代次数为100时，预测结果为

迭代次数为1000时，预测结果为：

迭代次数为10000，预测结果为：

迭代次数为100000，预测结果为：

可见迭代次数越多，预测结果越接近理想值，当时耗时也越长。

python实现三层神经网络

包括输入层、隐含层和输出层

# -*- coding:utf-8 -*-

#! python2

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

print "脚本之家测试结果："

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

运行结果：

希望本文所述对大家Python程序设计有所帮助。