python实现多层感知器MLP（基于双月数据集）

Python  /  管理员 发布于 7年前   233

本文实例为大家分享了python实现多层感知器MLP的具体代码，供大家参考，具体内容如下

1、加载必要的库，生成数据集

import mathimport randomimport matplotlib.pyplot as pltimport numpy as npclass moon_data_class(object):  def __init__(self,N,d,r,w):    self.N=N    self.w=w       self.d=d    self.r=r      def sgn(self,x):    if(x>0):      return 1;    else:      return -1;      def sig(self,x):    return 1.0/(1+np.exp(x))        def dbmoon(self):    N1 = 10*self.N    N = self.N    r = self.r    w2 = self.w/2    d = self.d    done = True    data = np.empty(0)    while done:      #generate Rectangular data      tmp_x = 2*(r+w2)*(np.random.random([N1, 1])-0.5)      tmp_y = (r+w2)*np.random.random([N1, 1])      tmp = np.concatenate((tmp_x, tmp_y), axis=1)      tmp_ds = np.sqrt(tmp_x*tmp_x + tmp_y*tmp_y)      #generate double moon data ---upper      idx = np.logical_and(tmp_ds > (r-w2), tmp_ds < (r+w2))      idx = (idx.nonzero())[0]         if data.shape[0] == 0:        data = tmp.take(idx, axis=0)      else:        data = np.concatenate((data, tmp.take(idx, axis=0)), axis=0)      if data.shape[0] >= N:        done = False    #print (data)    db_moon = data[0:N, :]    #print (db_moon)    #generate double moon data ----down    data_t = np.empty([N, 2])    data_t[:, 0] = data[0:N, 0] + r    data_t[:, 1] = -data[0:N, 1] - d    db_moon = np.concatenate((db_moon, data_t), axis=0)    return db_moon

2、定义激活函数

def rand(a,b):  return (b-a)* random.random()+adef sigmoid(x):  #return np.tanh(-2.0*x)  return 1.0/(1.0+math.exp(-x))def sigmoid_derivate(x):  #return -2.0*(1.0-np.tanh(-2.0*x)*np.tanh(-2.0*x))  return x*(1-x) #sigmoid函数的导数

3、定义神经网络

class BP_NET(object):  def __init__(self):    self.input_n = 0    self.hidden_n = 0    self.output_n = 0    self.input_cells = []    self.bias_input_n = []    self.bias_output = []    self.hidden_cells = []    self.output_cells = []    self.input_weights = []    self.output_weights = []        self.input_correction = []    self.output_correction = []    def setup(self, ni,nh,no):    self.input_n = ni+1#输入层+偏置项    self.hidden_n = nh    self.output_n = no    self.input_cells = [1.0]*self.input_n    self.hidden_cells = [1.0]*self.hidden_n    self.output_cells = [1.0]*self.output_n        self.input_weights = make_matrix(self.input_n,self.hidden_n)    self.output_weights = make_matrix(self.hidden_n,self.output_n)        for i in range(self.input_n):      for h in range(self.hidden_n):        self.input_weights[i][h] = rand(-0.2,0.2)        for h in range(self.hidden_n):      for o in range(self.output_n):        self.output_weights[h][o] = rand(-2.0,2.0)        self.input_correction = make_matrix(self.input_n , self.hidden_n)    self.output_correction = make_matrix(self.hidden_n,self.output_n)          def predict(self,inputs):    for i in range(self.input_n-1):      self.input_cells[i] = inputs[i]        for j in range(self.hidden_n):      total = 0.0      for i in range(self.input_n):        total += self.input_cells[i] * self.input_weights[i][j]      self.hidden_cells[j] = sigmoid(total)          for k in range(self.output_n):      total = 0.0      for j in range(self.hidden_n):        total+= self.hidden_cells[j]*self.output_weights[j][k]# + self.bias_output[k]  self.output_cells[k] = sigmoid(total)    return self.output_cells[:]    def back_propagate(self, case,label,learn,correct):    #计算得到输出output_cells    self.predict(case)    output_deltas = [0.0]*self.output_n    error = 0.0    #计算误差 = 期望输出-实际输出    for o in range(self.output_n):      error = label[o] - self.output_cells[o] #正确结果和预测结果的误差：0,1，-1      output_deltas[o]= sigmoid_derivate(self.output_cells[o])*error#误差稳定在0~1内     hidden_deltas = [0.0] * self.hidden_n    for j in range(self.hidden_n):      error = 0.0      for k in range(self.output_n):        error+= output_deltas[k]*self.output_weights[j][k]      hidden_deltas[j] = sigmoid_derivate(self.hidden_cells[j])*error     for h in range(self.hidden_n):      for o in range(self.output_n):        change = output_deltas[o]*self.hidden_cells[h]        #调整权重：上一层每个节点的权重学习*变化+矫正率        self.output_weights[h][o] += learn*change     #更新输入->隐藏层的权重    for i in range(self.input_n):      for h in range(self.hidden_n):        change = hidden_deltas[h]*self.input_cells[i]        self.input_weights[i][h] += learn*change     error = 0    for o in range(len(label)):      for k in range(self.output_n):        error+= 0.5*(label[o] - self.output_cells[k])**2          return error      def train(self,cases,labels, limit, learn,correct=0.1):    for i in range(limit):  error = 0.0      # learn = le.arn_speed_start /float(i+1)          for j in range(len(cases)):        case = cases[j]        label = labels[j]          error+= self.back_propagate(case, label, learn,correct)      if((i+1)%500==0):        print("error:",error)          def test(self): #学习异或        N = 200    d = -4    r = 10    width = 6        data_source = moon_data_class(N, d, r, width)    data = data_source.dbmoon()# x0 = [1 for x in range(1,401)]    input_cells = np.array([np.reshape(data[0:2*N, 0], len(data)), np.reshape(data[0:2*N, 1], len(data))]).transpose()        labels_pre = [[1.0] for y in range(1, 201)]    labels_pos = [[0.0] for y in range(1, 201)]    labels=labels_pre+labels_pos        self.setup(2,5,1) #初始化神经网络：输入层，隐藏层，输出层元素个数    self.train(input_cells,labels,2000,0.05,0.1) #可以更改        test_x = []    test_y = []    test_p = []        y_p_old = 0      for x in np.arange(-15.,25.,0.1):      for y in np.arange(-10.,10.,0.1):        y_p =self.predict(np.array([x, y]))        if(y_p_old <0.5 and y_p[0] > 0.5):          test_x.append(x)          test_y.append(y)          test_p.append([y_p_old,y_p[0]])        y_p_old = y_p[0]    #画决策边界    plt.plot( test_x, test_y, 'g--')      plt.plot(data[0:N, 0], data[0:N, 1], 'r*', data[N:2*N, 0], data[N:2*N, 1], 'b*')    plt.show()if __name__ == '__main__':  nn = BP_NET()  nn.test()

4、运行结果

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持。