python實現(xiàn)簡單神經(jīng)網(wǎng)絡(luò)算法

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 

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