Python实现单隐层神经网络

import randomimport math #---神经网络Model---class Ann:    #构造函数 初始化模型参数    def __init__(self, i_num, h_num, o_num):        #可调参数        self.learn_rate = 0.1    #学习率        self.num_long = 2        #输出结果位数        self.random_long = 10    #随机种子位数         #输入参数        self.input_num = i_num   #输入层 数量        self.hidden_num = h_num  #隐层 数量        self.output_num = o_num  #输出层 数量         #模型参数        self.input = []          #输入层        self.hidden = []         #隐层        self.output = []         #输出层        self.error = []          #误差        self.expectation = []    #期望        self.weight_ih = self.__ini_weight(self.input_num, self.hidden_num)   #输入层->隐层 连接权        self.weight_ho = self.__ini_weight(self.hidden_num, self.output_num)  #隐层->输出层 连接权        self.threshold_h = self.__ini_threshold(self.hidden_num)              #隐层 阈值        self.threshold_o = self.__ini_threshold(self.output_num)              #输出层 阈值      #初始连接权生成器    def __ini_weight(self, x, y):        result = []        long = math.pow(10, self.random_long)        for i in range(0, x, 1):            res = []            for j in range(0, y, 1):                num = round(random.randint(-1*long,long)/long, self.random_long)                res.insert(j, num)            result.insert(i, res)        return result     #初始阈值生成器    def __ini_threshold(self, n):        result = []        long = pow(10, self.random_long)        for i in range(0, n, 1):            num = round(random.randint(-1*long,long)/long, self.random_long)            result.insert(i, num)        return result     #激励函数 sigma    def excitation(self, value):        sigma = 1/(1+(math.exp(-1*value)))        return sigma     #输入数据    def input_param(self, data, expectation = []):        self.input = []        for value in data:            self.input.append(value)        if(expectation):            self.expectation = []            for value in expectation:                self.expectation.append(value)     #隐层计算    def count_hidden(self):        self.hidden = []        for h in range(0, self.hidden_num, 1):            Hval = 0            for i in range(len(self.input)):                Hval += self.input[i] * self.weight_ih[i][h]            Hval = self.excitation(Hval+self.threshold_h[h])            self.hidden.insert(h, Hval)     #输出层计算    def count_output(self):        self.output = []        for o in range(0, self.output_num, 1):            Oval = 0            for h in range(len(self.hidden)):                Oval += self.hidden[h] * self.weight_ho[h][o]            Oval += self.threshold_o[o]            Oval = round(Oval, self.num_long)            self.output.insert(o, Oval)     #误差计算    def count_error(self):        self.error = []        for key in range(len(self.output)):            self.error.insert(key, self.expectation[key] - self.output[key])     #连接权反馈训练 输入层->隐层    def train_weight_ih(self):        for i in range(len(self.weight_ih)):            for h in range(len(self.weight_ih[i])):                tmp = 0                for o in range(0, self.output_num, 1):                    tmp += self.weight_ho[h][o] * self.error[o]                self.weight_ih[i][h] = self.weight_ih[i][h] + self.learn_rate * self.hidden[h] * (1 - self.hidden[h]) * self.input[i] * tmp      #连接权反馈训练 隐层->输出层    def train_weight_ho(self):        for h in range(len(self.weight_ho)):            for o in range(len(self.weight_ho[h])):                self.weight_ho[h][o] = self.weight_ho[h][o] + self.learn_rate * self.hidden[h] * self.error[o]     #阈值反馈训练 隐层    def train_threshold_h(self):        for h in range(len(self.threshold_h)):            tmp = 0            for o in range(0, self.output_num, 1):                tmp += self.weight_ho[h][o] * self.error[o]            self.threshold_h[h] = self.threshold_h[h] + self.learn_rate * self.hidden[h] * (1 - self.hidden[h]) * tmp      #阈值反馈训练 输出层    def train_threshold_o(self):        for o in range(len(self.threshold_o)):            self.threshold_o[o] = self.threshold_o[o] + self.error[o]     #反馈训练    def train(self):        self.train_weight_ih()        self.train_weight_ho()        self.train_threshold_h()        self.train_threshold_o()     #归一化函数    def normal_num(self, max, min, data):        data = (data - min)/(max - min)        return data  #---业务部分(示例)--- #要训练的规则，输入两个值，如果两值相等返回[1,0]，反之返回[0,1]def testFunc(val):    if(val[0] == val[1]):        return [1,0]    else:        return [0,1] #构造神经网络模型ann = Ann(2,3,2) #生成训练数据，随机生成5000组[0,1][1,0][1,1][0,0]随机数组data = []for i in range(0, 10000, 1):    x = random.randint(0,1)    y = random.randint(0,1)    data.append([x,y]) #取得训练数据中的最大值和最小值for i in range(len(data)):    for j in range(len(data[i])):        if(i == 0 and j == 0):            max = min = data[i][j]        elif(data[i][j] > max):            max = data[i][j]        elif(data[i][j] < min):            min = data[i][j] #训练数据归一化dataNormal = []for i in range(len(data)):    dataNormal.insert(i, [])    for j in range(len(data[i])):        dataNormal[i].append(ann.normal_num(max, min, data[i][j])) #计算训练数据期望值，并进行反馈训练for i in range(len(data)):    #计算期望值    exp = testFunc(data[i])    #输入训练数据与期望    ann.input_param(dataNormal[i], exp)    #计算隐层    ann.count_hidden()    #计算输出层    ann.count_output()    #计算误差    ann.count_error()    #反馈训练    ann.train() #生成测试数据，随机生成20组testdata = []for i in range(0, 20, 1):    x = random.randint(0,1)    y = random.randint(0,1)    testdata.append([x,y]) #进行测试，同时输出神经网络预测值与实际期望值for i in range(len(testdata)):    exp = testFunc(testdata[i])    ann.input_param(testdata[i])    ann.count_hidden()    ann.count_output()    print("Ann:")    print(ann.output)    print("Exp:")    print(exp)    print("\r")