-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathRNN.py
More file actions
200 lines (140 loc) · 3.15 KB
/
RNN.py
File metadata and controls
200 lines (140 loc) · 3.15 KB
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
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
import numpy as np
import random
import csv
np.random.seed(0)
## Initializing Weight
wx1 = random.random()
wx2 = random.random()
wh1 = random.random()
wh2 = random.random()
wb1 = random.random()
wb2 = random.random()
alpha = 0.1
def sigmoid(x):
return 1/(1+np.exp(-x))
def sigmoidDerivative(x):
return sigmoid(x)*(1-sigmoid(x))
def feedforward(x):
x1,x2 = x[0],x[1]
x10 = x1
x20 = x2
h10 = 0
h20 = 0
x11 = 0
x21 = 0
h11 = sigmoid(x10*wx1+h20*wh2+wb1)
h21 = sigmoid(x20*wx2+h10*wh1+wb2)
x12 = 0
x22 = 0
h12 = sigmoid(x11*wx1+h21*wh2+wb1)
h22 = sigmoid(x21*wx2+h11*wh1+wb2)
t1 = sigmoid(x12*wx1+h22*wh2+wb1)
t2 = sigmoid(x22*wx2+h12*wh1+wb2)
return (t1,t2)
def backpropagate(x,y):
global wx1
global wx2
global wb1
global wb2
global wh1
global wh2
y1,y2 = y[0],y[1]
x1,x2 = x[0],x[1]
x10 = x1
x20 = x2
h10 = 0
h20 = 0
x11 = 0
x21 = 0
h11 = sigmoid(x10*wx1+h20*wh2+wb1)
h21 = sigmoid(x20*wx2+h10*wh1+wb2)
x12 = 0
x22 = 0
h12 = sigmoid(x11*wx1+h21*wh2+wb1)
h22 = sigmoid(x21*wx2+h11*wh1+wb2)
t1 = sigmoid(x12*wx1+h22*wh2+wb1)
t2 = sigmoid(x22*wx2+h12*wh1+wb2)
dwx11 = h11*(1-h11)*x10
dwx12 = h22*(1-h22)*(wh1*dwx11)
dwx13 = t1*(1-t1)*(x12+wh2*dwx12)
dwx1 = (t1-y1)*dwx13
dwx21 = h21*(1-h21)*x20
dwx22 = h12*(1-h12)*(wh2*dwx21)
dwx23 = t2*(1-t2)*(x22+wh1*dwx22)
dwx2 = (t2-y2)*dwx23
dwh11 = h22*(1-h22)*h11
dwh12 = t1*(1-t1)*wh2*dwh11
dwh1 = (t1-y1)*dwh12
dwh21 = h12*(1-h12)*h21
dwh22 = t2*(1-t2)*wh1*dwh21
dwh2 = (t2-y2)*dwh22
dwb11 = h11*(1-h11)
dwb12 = h22*(1-h22)*(wh1*dwb11)
dwb13 = t1*(1-t1)*(1+dwb12)
dwb1 = (t1-y1)*dwb13
dwb21 = h21*(1-h21)
dwb22 = h12*(1-h12)*(wh2*dwb21)
dwb23 = t2*(1-t2)*(1+dwb22)
dwb2 = (t2-y2)*dwb23
wx1 -= alpha*dwx1
wx2 -= alpha*dwx2
wb1 -= alpha*dwb1
wb2 -= alpha*dwb2
wh1 -= alpha*dwh1
wh2 -= alpha*dwh2
## Get data
X = []
Y = []
with open('q3data.csv','rb') as csvfile:
spr = csv.reader(csvfile, delimiter=' ', quotechar='|')
for row in spr:
r = map(float,row[0].split(','))
X.append((r[0],r[1]))
Y.append((r[2],r[3]))
acc = 0
for x,y in zip(X,Y):
t = feedforward(x)
acc += 0.5*(t[0]-y[0])*(t[0]-y[0])
acc += 0.5*(t[1]-y[1])*(t[1]-y[1])
accp = acc*100.00/(2*len(X))
print accp
X = []
Y = []
with open('foo.csv','rb') as csvfile:
spr = csv.reader(csvfile, delimiter=' ', quotechar='|')
for row in spr:
r = map(float,row[0].split(','))
X.append((r[0],r[1]))
Y.append((r[2],r[3]))
acc = 0
for x,y in zip(X,Y):
t = feedforward(x)
acc += 0.5*(t[0]-y[0])*(t[0]-y[0])
acc += 0.5*(t[1]-y[1])*(t[1]-y[1])
accp = acc*100.00/(2*len(X))
print accp
for _ in range(1000):
for x,y in zip(X,Y):
backpropagate(x,y)
acc = 0
for x,y in zip(X,Y):
t = feedforward(x)
acc += 0.5*(t[0]-y[0])*(t[0]-y[0])
acc += 0.5*(t[1]-y[1])*(t[1]-y[1])
accp = acc*100.00/(2*len(X))
print accp
X = []
Y = []
with open('q3data.csv','rb') as csvfile:
spr = csv.reader(csvfile, delimiter=' ', quotechar='|')
for row in spr:
r = map(float,row[0].split(','))
X.append((r[0],r[1]))
Y.append((r[2],r[3]))
acc = 0
for x,y in zip(X,Y):
t = feedforward(x)
acc += 0.5*(t[0]-y[0])*(t[0]-y[0])
acc += 0.5*(t[1]-y[1])*(t[1]-y[1])
accp = acc*100.00/(2*len(X))
print accp