BP神经网络实现
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互联网集市收集整理的这篇技术教程文章主要介绍了BP神经网络实现，小编现在分享给大家，供广大互联网技能从业者学习和参考。文章包含4944字，纯文字阅读大概需要8分钟。
内容图文
#
-*- coding: utf-8 -*-
#
---------------------------------------------------------------------------
#
BP-ANN.py
#
Created on: 2014-06-12 09:49:56.00000
#
Description:
#
---------------------------------------------------------------------------
import
os
import
math
import
time
import
datetime
#
输入层n个神经元
n = 4
#隐含层p个神经元
p = 6
#输出层q个神经元
q = 1
#输入向量
trainsample = 60
x = [[0 for i in range(0, n)] for j in range(trainsample)]
x = [[0.27500,0.60000,0.14085,0.07143],[0.22500,0.43333,0.14085,0.07143],[0.17500,0.50000,0.12676,0.07143],[0.15000,0.46667,0.15493,0.07143],[0.25000,0.63333,0.14085,0.07143],[0.35000,0.73333,0.18310,0.14286],[0.15000,0.56667,0.14085,0.10714],[0.25000,0.56667,0.15493,0.07143],[0.10000,0.40000,0.14085,0.07143],[0.22500,0.46667,0.15493,0.03571],[0.35000,0.66667,0.15493,0.07143],[0.20000,0.56667,0.16901,0.07143],[0.20000,0.43333,0.14085,0.03571],[0.07500,0.43333,0.09859,0.03571],[0.45000,0.76667,0.11268,0.07143],[0.42500,0.90000,0.15493,0.14286],[0.35000,0.73333,0.12676,0.14286],[0.27500,0.60000,0.14085,0.10714],[0.42500,0.70000,0.18310,0.10714],[0.27500,0.70000,0.15493,0.10714],[0.75000,0.50000,0.60563,0.50000],[0.60000,0.50000,0.57746,0.53571],[0.72500,0.46667,0.63380,0.53571],[0.37500,0.20000,0.50704,0.46429],[0.62500,0.36667,0.59155,0.53571],[0.42500,0.36667,0.57746,0.46429],[0.57500,0.53333,0.60563,0.57143],[0.22500,0.23333,0.40845,0.35714],[0.65000,0.40000,0.59155,0.46429],[0.30000,0.33333,0.49296,0.50000],[0.25000,0.10000,0.43662,0.35714],[0.47500,0.43333,0.53521,0.53571],[0.50000,0.16667,0.50704,0.35714],[0.52500,0.40000,0.60563,0.50000],[0.40000,0.40000,0.45070,0.46429],[0.67500,0.46667,0.56338,0.50000],[0.40000,0.43333,0.57746,0.53571],[0.45000,0.33333,0.52113,0.35714],[0.55000,0.16667,0.57746,0.53571],[0.40000,0.26667,0.49296,0.39286],[0.57500,0.53333,0.78873,0.89286],[0.45000,0.33333,0.66197,0.67857],[0.77500,0.43333,0.77465,0.75000],[0.57500,0.40000,0.73239,0.64286],[0.62500,0.43333,0.76056,0.78571],[0.90000,0.43333,0.87324,0.75000],[0.22500,0.26667,0.57746,0.60714],[0.82500,0.40000,0.83099,0.64286],[0.67500,0.26667,0.76056,0.64286],[0.80000,0.63333,0.80282,0.89286],[0.62500,0.50000,0.66197,0.71429],[0.60000,0.33333,0.69014,0.67857],[0.70000,0.43333,0.71831,0.75000],[0.42500,0.26667,0.64789,0.71429],[0.45000,0.36667,0.66197,0.85714],[0.60000,0.50000,0.69014,0.82143],[0.62500,0.43333,0.71831,0.64286],[0.92500,0.70000,0.88732,0.78571],[0.92500,0.30000,0.91549,0.82143],[0.50000,0.16667,0.64789,0.53571]]
d = [0 for i in range(0, trainsample)]
d = [0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.7,0.7,0.7,0.7,0.7,0.7,0.7,0.7,0.7,0.7,0.7,0.7,0.7,0.7,0.7,0.7,0.7,0.7,0.7,0.7]
#隐含层(p个隐含层神经元, 每个隐含层神经元有对应输入层神经元有一个系数)
hi = [0 for i in range(0, p)]
ho = [0 for i in range(0, p)]
b1 = [0.3 for i in range(0, p)]
Whi = [[0.5 for i in range(0, n)] for j in range(p)]
pp = [0 for i in range(0, p)]
rateWhi = 0.05
#输出层(q个输出层神经元, 每个输出层神经元有对应隐含层神经元有一个系数)
yi = [0 for i in range(0, q)]
yo = [1 for i in range(0, q)]
b2 = [0.3 for i in range(0, q)]
Woh = [[0.5 for i in range(0, p)] for j in range(q)]
qq = [0 for i in range(0, q)]
rateWoh = 0.05
#误差函数
e=1.0
def f1(x):
y = 1.0/(1.0+math.exp(-1.0*x))
return y
def f2(x):
y = f1(x)
y = y * (1.0 - y)
return y
def train():
e = 0.0
for trainIndex in range(0, trainsample): # trainsample#print(x[trainIndex])#隐含层 输入 输出for i in range(0, p):
hi[i] = 0
for j in range(0, n):
hi[i] = hi[i] + Whi[i][j] * x[trainIndex][j]
ho[i] = f1(hi[i]+b1[i])
#输出层 输入 输出for i in range(0, q):
yi[i] = 0
for j in range(0, p):
yi[i] = yi[i] + Woh[i][j] * ho[j]
yo[i] = f1(yi[i]+b2[i])
#print(hi)#误差函数对输出层的各神经元的偏导数for i in range(0, q):
qq[i] = 0
qq[i] = (d[trainIndex] - yo[i]) * yo[i] * (1 - yo[i])# * ho[i]for k in range(0, p):
Woh[i][k] = Woh[i][k] + rateWoh * qq[i] * ho[k]
for i in range(0, p):
pp[i] = 0
for j in range(0, q):
pp[i] = pp[i] + qq[j] * Woh[j][i]
pp[i] = pp[i] * ho[i] * (1 - ho[i])
for k in range(0, n):
Whi[i][k] = Whi[i][k] + rateWhi * pp[i] * x[trainIndex][k]
for i in range(0, q):
e = e + 0.5 * math.pow((d[trainIndex] - yo[i]), 2)
#
for i in range(0, q):
b2[i] = b2[i] + rateWoh * qq[i]
for i in range(0, p):
b1[i] = b1[i] + rateWhi * pp[i]
return e
def recognize(vv):
#隐含层 输入 输出for i in range(0, p):
hi[i] = 0
for j in range(0, n):
hi[i] = hi[i] + Whi[i][j] * vv[j]
ho[i] = f1(hi[i]+b1[i])
#输出层 输入 输出for i in range(0, q):
yi[i] = 0
for j in range(0, p):
yi[i] = yi[i] + Woh[i][j] * ho[j]
yo[i] = f1(yi[i]+b2[i])
print(‘-------------------------------------------------------‘)
print(yo)
times = 0
while e > 0.001:
#计算各层输入和输出
e = train()
times = times + 1
print(str(times)+"" + str(e))
if times > 10000:
break
recognize([0.7, 0.5, 0.774647887, 0.821428571])
recognize([0.4,0.433333333,0.521126761,0.464285714])
recognize([0.25,0.5,0.112676056,0.071428571])
原文：http://www.cnblogs.com/gispathfinder/p/5783403.html
内容总结
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