BP人工神经网络

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):  # 映射函数
    return 1 / (1 + math.exp(-x))


# 训练神经元
def train(W_out, W_mid, data, real):
    # 中间层神经元输入和输出层神经元输入
    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

    # 中间层和输出层神经元权值
    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'])

    # 中间层的输出
    for i in range(0, 4):
        Out_in.iloc[i] = sigmoid(sum(W_mid.iloc[:, i] * Net_in.iloc[:, 0]))
    # 输出层的输出/网络输出
    res = sigmoid(sum(Out_in.iloc[:, 0] * W_out.iloc[:, 0]))
    # 误差
    error = abs(res - real)

    # 输出层权值变化量
    # yita =学习率
    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  # 输出层权值更新

    # 中间层权值变化量
    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  # 中间层权值更新
    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

    # 中间层的输出
    for i in range(0, 4):
        Out_in.iloc[i] = sigmoid(sum(W_mid.iloc[:, i] * Net_in.iloc[:, 0]))
    # 输出层的输出/网络输出
    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)

标签：res,mid,人工神经网络,BP,theata,iloc,Out,out
来源： https://www.cnblogs.com/chendongcheng/p/16027896.html