python如何实现多层感知器MLP-创新互联
这篇文章主要介绍了python如何实现多层感知器MLP,具有一定借鉴价值,感兴趣的朋友可以参考下,希望大家阅读完这篇文章之后大有收获,下面让小编带着大家一起了解一下。
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1、加载必要的库,生成数据集
import math import random import matplotlib.pyplot as plt import numpy as np class 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()+a def 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
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