给定数据利用神经网络算法模型进行计算

给定数据利用神经网络算法模型进行计算，利用fp、bp算法，求得模型最优值。

神经网络初步学习使用。

 1 import numpy as np
 2 import matplotlib.pylab as plt
 3 from numpy import *
 4 from pylab import *
 5 
 6 from sklearn.neural_network import mlpclassifier
 7 
 8 # 中文 负号
 9 mpl.rcparams['font.sans-serif']=['simhei']
10 matplotlib.rcparams['axes.unicode_minus']=false
11 
12 x1 = [0.697,0.774,0.634,0.608,0.556,0.403,0.481,0.437,0.666,0.243,0.245,0.343,0.639,0.657,0.360,0.593,0.719]
13 x2 = [0.460,0.376,0.264,0.318,0.215,0.237,0.149,0.211,0.091,0.267,0.057,0.099,0.161,0.198,0.370,0.042,0.103]
14 y = [1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0]
15 
16 # 数据初始化
17 m = len(x1)
18 x = np.c_[np.ones(m),x1,x2]
19 y = np.c_[y]
20 
21 # 数据重新洗牌/打乱
22 np.random.seed(3)
23 order=np.random.permutation(m)
24 x=x[order]
25 y=y[order]
26 
27 # 将数据切割 分为训练集和测试集
28 d = int(m*0.75)
29 train_x,test_x=np.split(x,[d,])
30 train_y,test_y=np.split(y,[d,])
31 
32 # 定义sigmoid函数g(z)函数
33 def g(z,deriv=false):
34     if deriv == true:
35         return z*(1-z)
36     return 1.0/(1.0+np.exp(-z))
37 
38 # 初始化theta值 定义神经网络网格结构  l1=3,l2=17,l3=1
39 np.random.seed(3)
40 theta1=2*np.random.random((3,17))-1
41 theta2=2*np.random.random((17,1))-1
42 
43 # 初始化代价值
44 j_hietory = np.zeros(15000)
45 
46 # 开始神经网络迭代
47 for i in range(15000):
48     # 前项传播算法
49     a1 = train_x
50     z2 = a1.dot(theta1)
51     a2 = g(z2)
52     z3 = a2.dot(theta2)
53     a3 = g(z3)
54     h = a3
55 
56 
57     # 记录每次代价
58     j_hietory[i] = -1.0/m*(np.dot(train_y.t,np.log(h))+np.dot((1-train_y).t,np.log((1-h))))
59 
60     # 反向传播算法
61 
62     # 每层的delta
63     delta_l3 = a3-train_y
64     delta_l2 = delta_l3.dot(theta2.t)*g(a2,true)
65     # 每层的deltatheta
66     deltatheta2 = 1.0/m*np.dot(a2.t,delta_l3)
67     deltatheta1 = 1.0/m*np.dot(a1.t,delta_l2)
68     # 每层的theta更新
69     theta2 -= 0.8*deltatheta2
70     theta1 -= 0.8*deltatheta1
71 
72 plt.plot(j_hietory)
73 plt.show()
74 
75 
76 # 定义准确率函数
77 def testaccuracy(x,y):
78     m = x.shape[0]
79     count = 0
80 
81     for i in range(m):
82         a1 = x[i]
83         z2 = a1.dot(theta1)
84         a2 = g(z2)
85         z3 = a2.dot(theta2)
86         a3 = g(z3)
87         h = a3
88 
89         if bool(np.where(h>=0.5,1,0)) == bool(y[i]):
90             count += 1
91     return count/m
92 
93 # 分别计算训练集和测试集的准确率
94 print('训练集的准确率：',testaccuracy(train_x,train_y)*100,'%')
95 print('训练集的准确率：',testaccuracy(test_x,test_y)*100,'%')