本文實例講述了Python編程實現(xiàn)的簡單神經(jīng)網(wǎng)絡(luò)算法。分享給大家供大家參考,具體如下:
python實現(xiàn)二層神經(jīng)網(wǎng)絡(luò)
包括輸入層和輸出層
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# -*- 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 "服務(wù)器之家測試結(jié)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 |
這里,
l0:輸入層
l1:輸出層
syn0:初始權(quán)值
l1_error:誤差
l1_delta:誤差校正系數(shù)
func nonlin:sigmoid函數(shù)
這里迭代次數(shù)為100時,預(yù)測結(jié)果為
迭代次數(shù)為1000時,預(yù)測結(jié)果為:
迭代次數(shù)為10000,預(yù)測結(jié)果為:
迭代次數(shù)為100000,預(yù)測結(jié)果為:
可見迭代次數(shù)越多,預(yù)測結(jié)果越接近理想值,當(dāng)時耗時也越長。
python實現(xiàn)三層神經(jīng)網(wǎng)絡(luò)
包括輸入層、隱含層和輸出層
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# -*- 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 "服務(wù)器之家測試結(jié)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 |
運行結(jié)果:
希望本文所述對大家Python程序設(shè)計有所幫助。
原文鏈接:http://blog.csdn.net/xiao_lxl/article/details/51721312