# -*- 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 "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

# -*- 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 "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