python实现简单神经网络算法

Python  /  管理员 发布于 7年前   232

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

包括输入层和输出层

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 

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

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

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 

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持。