python实现简单神经网络算法

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 

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