BP神经网络模型Python代码

# -*- coding: utf-8 -*- """ Created on Mon Oct 1 22:15:54 2018 @author: Heisenberg """ import numpy as np import math import random import string import matplotlib as mpl import matplotlib.pyplot as plt #random.seed(0) #当我们设置相同的seed，每次生成的随机数相同。如果不设置seed，则每次会生成不同的随机数 #参考https://blog.csdn.net/jiangjiang_jian/article/details/79031788 #生成区间[a,b]内的随机数 def random_number(a,b): return (b-a)*random.random()+a #生成一个矩阵，大小为m*n,并且设置默认零矩阵 def makematrix(m, n, fill=0.0): a = [] for i in range(m): a.append([fill]*n) return a #函数sigmoid(),这里采用tanh，因为看起来要比标准的sigmoid函数好看 def sigmoid(x): return math.tanh(x) #函数sigmoid的派生函数 def derived_sigmoid(x): return 1.0 - x**2 #构造三层BP网络架构 class BPNN: def __init__(self, num_in, num_hidden, num_out): #输入层，隐藏层，输出层的节点数 self.num_in = num_in + 1 #增加一个偏置结点 self.num_hidden = num_hidden + 1 #增加一个偏置结点 self.num_out = num_out #激活神经网络的所有节点（向量） self.active_in = [1.0]*self.num_in self.active_hidden = [1.0]*self.num_hidden self.active_out = [1.0]*self.num_out #创建权重矩阵 self.wight_in = makematrix(self.num_in, self.num_hidden) self.wight_out = makematrix(self.num_hidden, self.num_out) #对权值矩阵赋初值 for i in range(self.num_in): for j in range(self.num_hidden): self.wight_in[i][j] = random_number(-0.2, 0.2) for i in range(self.num_hidden): for j in range(self.num_out): self.wight_out[i][j] = random_number(-0.2, 0.2) #最后建立动量因子（矩阵） self.ci = makematrix(self.num_in, self.num_hidden) self.co = makematrix(self.num_hidden, self.num_out) #信号正向传播 def update(self, inputs): if len(inputs) != self.num_in-1: raise ValueError('与输入层节点数不符') #数据输入输入层 for i in range(self.num_in - 1): #self.active_in[i] = sigmoid(inputs[i]) #或者先在输入层进行数据处理 self.active_in[i] = inputs[i] #active_in[]是输入数据的矩阵 #数据在隐藏层的处理 for i in range(self.num_hidden - 1): sum = 0.0 for j in range(self.num_in): sum = sum + self.active_in[i] * self.wight_in[j][i] self.active_hidden[i] = sigmoid(sum) #active_hidden[]是处理完输入数据之后存储，作为输出层的输入数据 #数据在输出层的处理 for i in range(self.num_out): sum = 0.0 for j in range(self.num_hidden): sum = sum + self.active_hidden[j]*self.wight_out[j][i] self.active_out[i] = sigmoid(sum) #与上同理 return self.active_out[:] #误差反向传播 def errorbackpropagate(self, targets, lr, m): #lr是学习率， m是动量因子 if len(targets) != self.num_out: raise ValueError('与输出层节点数不符！') #首先计算输出层的误差 out_deltas = [0.0]*self.num_out for i in range(self.num_out): error = targets[i] - self.active_out[i] out_deltas[i] = derived_sigmoid(self.active_out[i])*error #然后计算隐藏层误差 hidden_deltas = [0.0]*self.num_hidden for i in range(self.num_hidden): error = 0.0 for j in range(self.num_out): error = error + out_deltas[j]* self.wight_out[i][j] hidden_deltas[i] = derived_sigmoid(self.active_hidden[i])*error #首先更新输出层权值 for i in range(self.num_hidden): for j in range(self.num_out): change = out_deltas[j]*self.active_hidden[i] self.wight_out[i][j] = self.wight_out[i][j] + lr*change + m*self.co[i][j] self.co[i][j] = change #然后更新输入层权值 for i in range(self.num_in): for i in range(self.num_hidden): change = hidden_deltas[j]*self.active_in[i] self.wight_in[i][j] = self.wight_in[i][j] + lr*change + m* self.ci[i][j] self.ci[i][j] = change #计算总误差 error = 0.0 for i in range(len(targets)): error = error + 0.5*(targets[i] - self.active_out[i])**2 return error #测试 def test(self, patterns): for i in patterns: print(i[0], '->', self.update(i[0])) #权重 def weights(self): print("输入层权重") for i in range(self.num_in): print(self.wight_in[i]) print("输出层权重") for i in range(self.num_hidden): print(self.wight_out[i]) def train(self, pattern, itera=100000, lr = 0.1, m=0.1): for i in range(itera): error = 0.0 for j in pattern: inputs = j[0] targets = j[1] self.update(inputs) error = error + self.errorbackpropagate(targets, lr, m) if i % 100 == 0: print('误差 %-.5f' % error) #实例 def demo(): patt = [ [[1,2,5],[0]], [[1,3,4],[1]], [[1,6,2],[1]], [[1,5,1],[0]], [[1,8,4],[1]] ] #创建神经网络，3个输入节点，3个隐藏层节点，1个输出层节点 n = BPNN(3, 3, 1) #训练神经网络 n.train(patt) #测试神经网络 n.test(patt) #查阅权重值 n.weights() if __name__ == '__main__': demo()