BP神經網路

2022-10-09 01:48:09 字數 2703 閱讀 5351

import math


import numpy as np


import pandas as pd


from pandas import dataframe


y =[0.14 ,0.64 ,0.28 ,0.33 ,0.12 ,0.03 ,0.02 ,0.11 ,0.08 ]


x1 =[0.29 ,0.50 ,0.00 ,0.21 ,0.10 ,0.06 ,0.13 ,0.24 ,0.28 ]


x2 =[0.23 ,0.62 ,0.53 ,0.53 ,0.33 ,0.15 ,0.03 ,0.23 ,0.03 ]


theata = [-1,-1,-1,-1,-1,-1,-1,-1,-1]


x = np.array([x1,x2,theata])


w_mid = dataframe(0.5,index=['input1','input2','theata'],columns=['mid1','mid2','mid3','mid4'])


w_out = dataframe(0.5,index=['input1','input2','input3','input4','theata'],columns=['a'])


def sigmoid(x): #對映函式


return 1/(1+math.exp(-x))


#訓練神經元


def train(w_out, w_mid,data,real):


#中間層神經元輸入和輸出層神經元輸入


net_in = dataframe(data,index=['input1','input2','theata'],columns=['a'])


out_in = dataframe(0,index=['input1','input2','input3','input4','theata'],columns=['a'])


out_in.loc['theata'] = -1


#中間層和輸出層神經元權值


w_mid_delta = dataframe(0,index=['input1','input2','theata'],columns=['mid1','mid2','mid3','mid4'])


w_out_delta = dataframe(0,index=['input1','input2','input3','input4','theata'],columns=['a'])


#中間層的輸出


for i in range(0,4):


out_in.iloc[i] = sigmoid(sum(w_mid.iloc[:,i]*net_in.iloc[:,0]))


#輸出層的輸出/網路輸出


res = sigmoid(sum(out_in.iloc[:,0]*w_out.iloc[:,0]))


#誤差error = abs(res-real)


#輸出層權值變化量


#yita =學習率


yita =0.8


w_out_delta.iloc[:,0] = yita*res*(1-res)*(real-res)*out_in.iloc[:,0]


w_out_delta.iloc[4,0] = -(yita*res*(1-res)*(real-res))


w_out = w_out + w_out_delta #輸出層權值更新


#中間層權值變化量


for i in range(0,4):


w_mid_delta.iloc[:,i] = yita*out_in.iloc[i,0]*(1-out_in.iloc[i,0])*w_out.iloc[i,0]*res*(1-res)*(real-res)*net_in.iloc[:,0]


w_mid_delta.iloc[2,i] = -(yita*out_in.iloc[i,0]*(1-out_in.iloc[i,0])*w_out.iloc[i,0]*res*(1-res)*(real-res))


w_mid = w_mid + w_mid_delta #中間層權值更新


return w_out,w_mid,res,error


def reault(data,w_out, w_mid):


net_in = dataframe(data,index=['input1','input2','theata'],columns=['a'])


out_in = dataframe(0,index=['input1','input2','input3','input4','theata'],columns=['a'])


out_in.loc['theata'] = -1


#中間層的輸出


for i in range(0,4):


out_in.iloc[i] = sigmoid(sum(w_mid.iloc[:,i]*net_in.iloc[:,0]))


#輸出層的輸出/網路輸出


res = sigmoid(sum(out_in.iloc[:,0]*w_out.iloc[:,0]))


return res


for i in range(0,9):


w_out,w_mid,res,error = train(w_out,w_mid,x[0:,i],y[i])


res1 = reault([0.38 ,0.49,-1 ], w_out, w_mid)


res2 = reault([0.29 ,0.47 ,-3], w_out, w_mid)


print(res1,res2)

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