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
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
|
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
|
1
2
3
4
5
6
7
8
9
|
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
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
|
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服务器之家。
原文链接：https://blog.csdn.net/moge19/article/details/83004811