給定數據利用神經網路演算法模型進行計算

給定數據利用神經網路演算法模型進行計算，利用FP、BP演算法，求得模型最優值。

神經網路初步學習使用。

 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 負號
 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 # 數據初始化
17 m = len(X1)
18 X = np.c_[np.ones(m),X1,X2]
19 y = np.c_[Y]
20 
21 # 數據重新洗牌/打亂
22 np.random.seed(3)
23 order=np.random.permutation(m)
24 X=X[order]
25 y=y[order]
26 
27 # 將數據切割 分為訓練集和測試集
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 # 定義Sigmoid函數g(z)函數
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 定義神經網路網格結構  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 # 初始化代價值
44 J_hietory = np.zeros(15000)
45 
46 # 開始神經網路迭代
47 for i in range(15000):
48     # 前項傳播演算法
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     # 記錄每次代價
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     # 反向傳播演算法
61 
62     # 每層的delta
63     delta_L3 = a3-train_y
64     delta_L2 = delta_L3.dot(theta2.T)*g(a2,True)
65     # 每層的deltatheta
66     deltatheta2 = 1.0/m*np.dot(a2.T,delta_L3)
67     deltatheta1 = 1.0/m*np.dot(a1.T,delta_L2)
68     # 每層的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 # 定義準確率函數
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 # 分別計算訓練集和測試集的準確率
94 print('訓練集的準確率：',testAccuracy(train_X,train_y)*100,'%')
95 print('訓練集的準確率：',testAccuracy(test_X,test_y)*100,'%')