Python实现感知器模型、两层神经网络

import numpy as np#sigmoid functiondef nonlin(x, deriv = False):  if(deriv==True):    return x*(1-x)  return 1/(1+np.exp(-x))# input datasetX=np.array([[0,0,1],      [0,1,1],      [1,0,1],      [1,1,1]])# output datasety=np.array([[0,1,0,1]]).T#seed( ) 用于指定随机数生成时所用算法开始的整数值，#如果使用相同的seed( )值，则每次生成的随即数都相同，#如果不设置这个值，则系统根据时间来自己选择这个值，#此时每次生成的随机数因时间差异而不同。np.random.seed(1)  # init weight value with mean 0syn0 = 2*np.random.random((3,1))-1   for iter in range(1000):  # forward propagation  L0=X  L1=nonlin(np.dot(L0,syn0))  # error  L1_error=y-L1  L1_delta = L1_error*nonlin(L1,True)  # updata weight  syn0+=np.dot(L0.T,L1_delta)print("Output After Training:")print(L1)

import numpy as npdef nonlin(x, deriv = False):  if(deriv == True):    return x*(1-x)  else:    return 1/(1+np.exp(-x))#input datasetX = np.array([[0,0,1],       [0,1,1],       [1,0,1],       [1,1,1]])#output datasety = np.array([[0,1,1,0]]).T#the first-hidden layer weight valuesyn0 = 2*np.random.random((3,4)) - 1 #the hidden-output layer weight valuesyn1 = 2*np.random.random((4,1)) - 1 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