2022 bp神经网络

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
def sigmoid(x):
return 1/(1+np.exp(-x))
data_tr = pd.read_csv('D:\\智能\\test1.txt')
data_te = pd.read_csv('D:\\智能\\test2.txt')
n = len(data_tr)
yita = 0.
out_in = np.array([0.0, 0, 0, 0, -1])
w_mid = np.zeros([3,4])
w_out = np.zeros([5])
delta_w_out = np.zeros([5])
delta_w_mid = np.zeros([3,4])
Err = []
for j in range(800):
error = []
for it in range(n):
net_in = np.array([data_tr.iloc[it, 0], data_tr.iloc[it, 1], -1])
real = data_tr.iloc[it, 2]
for i in range(4):
out_in[i] = sigmoid(sum(net_in * w_mid[:, i]))
res = sigmoid(sum(out_in * w_out))
error.append(abs(real-res)

print('第',it, '个样本的模型输出：', res, 'real:', real)

delta_w_out = yita*res*(1-res)*(real-res)*out_in

delta_w_out[4] = -yita*res*(1-res)*(real-res)
w_out = w_out + delta_w_out
for i in range(4):
delta_w_mid[:, i] = yita*out_in[i]*(1-out_in[i])*w_out[i]*res*(1-res)*(real-res)*net_in
delta_w_mid[2, i] = -yita*out_in[i]*(1-out_in[i])*w_out[i]*res*(1-res)*(real-res)
w_mid = w_mid + delta_w_mid
Err.append(np.mean(error))
print(w_mid,w_out)
plt.plot(Err)
plt.show()
plt.close()
for it in range(len(data_te)):
net_in = np.array([data_te.iloc[it, 0], data_te.iloc[it, 1], -1])
for i in range(4):
out_in[i] = sigmoid(sum(net_in * w_mid[:, i]))
res = sigmoid(sum(out_in * w_out))
print('第',it+1,'个测试值：',res)