# Python implements simple neural network algorithms and python neural network algorithms

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Python implements simple neural network algorithms and python neural network algorithms

Python implements simple neural network algorithms for your reference. The specific content is as follows:

Python implements L2 Neural Networks

Including the input layer and output layer

`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 `

Here,
L0: input layer

L1: output layer

Syn0: initial weight

L1_error: Error

L1_delta: Error Correction Coefficient

Func nonlin: sigmoid Function

It can be seen that the more iterations, the closer the prediction result to the ideal value, the longer the time consumption.

Python implements a layer-3 neural network

Including the input layer, hidden layer, and output layer

`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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