-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathmodel.py
198 lines (176 loc) · 6.04 KB
/
model.py
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
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt
import random as rd
import pickle
from time import time
from nntli import *
from utils import *
torch.set_default_dtype(torch.float64)
def nntli_train(train_data, train_label, val_data, val_label, dataname):
nsample = train_data.shape[0]
dim = train_data.shape[1]
length = train_data.shape[-1]
val_nsample = val_data.shape[0]
# initialization
STE= STEstimator.apply
clip = Clip.apply
f_num = 8
f_conj = 1
t1 = np.zeros((f_num,1))
t1 = torch.tensor(t1, requires_grad=True)
t2 = np.ones((f_num,1))*(length-1)
t2 = torch.tensor(t2, requires_grad=True)
Wc = torch.ones((f_conj,f_num), requires_grad=True)
Wd = torch.ones(f_conj, requires_grad=False)
a = np.array([[1,0],[-1,0],[0,1],[0,-1],[1,0],[-1,0],[0,1],[0,-1]]).reshape(f_num,1,dim)
a = torch.tensor(a, dtype=torch.float64, requires_grad=False)
b = torch.rand((f_num,1), requires_grad=True)
at = torch.tensor(1, requires_grad=False)
W = RMinTimeWeight(at,t1,t2)
W1 = torch.tensor(range(length), requires_grad=False)
Formula = []
Spatial = []
tl1 = 0
tl2 = length-1
j = 0
beta = 1
am = 0
scale = 2.5
# scale = 1
fn = int(f_num/2)
for i in range(fn):
Formula.append(Eventually(a[j],b[j],tl1,tl2))
Formula[j].init_sparsemax(beta,am,scale,2)
Spatial.append('F')
j += 1
for i in range(fn):
Formula.append(Always(a[j],b[j],tl1,tl2))
Formula[j].init_sparsemax(beta,am,scale,2)
Spatial.append('G')
j += 1
beta = 1
am = 0
scale = 1.1
# scale = 1
conjunc = Conjunction()
conjunc.init_sparsemax(beta,am,scale,1)
beta = 1
am = 0
scale = 1
disjunc = Disjunction()
disjunc.init_sparsemax(beta,am,scale,1)
optimizer1 = torch.optim.Adam([Wc], lr=0.1)
optimizer2 = torch.optim.Adam([b], lr=0.1)
optimizer3 = torch.optim.Adam([t1,t2], lr=0.1)
n_iters = 10000
batch_size = 10
delta = 1
acc = 0
acc_best = 0
log = []
W1s = W.get_weight(W1)
Wcs = STE(Wc)
Wds = STE(Wd)
x = val_data
y = val_label
r1o = torch.empty((val_nsample,f_num,1))
for k, formula in enumerate(Formula):
xo1 = formula.robustness_trace(x,W1s[k,:],val_nsample,need_trace=False)
r1o[:,k,:] = xo1[:,0]
r2i = torch.squeeze(r1o)
r2o = torch.empty((val_nsample,f_conj))
for k in range(f_conj):
xo2 = conjunc.forward(r2i,Wcs[k,:])
r2o[:,k] = xo2[:,0]
R = disjunc.forward(r2o,Wds)
Rl = clip(R)
acc = sum(val_label==Rl[:,0])/(val_nsample)
print('before training, accuracy = {acc}'.format(acc = acc))
training_time = 0
for epoch in range(1,n_iters):
start = time()
rand_num = rd.sample(range(0,nsample),batch_size)
x = train_data[rand_num,:,:]
y = train_label[rand_num]
Wt = W.get_weight(W1)
W1s = Wt
W1s = STE(Wt)
r1o = torch.empty((batch_size,f_num,1))
for k, formula in enumerate(Formula):
if sum(W1s[k,:])==0:
with torch.no_grad():
Wc[:,k] = 0.4
xo1 = formula.robustness_trace(x,W1s[k,:],batch_size,need_trace=False)
r1o[:,k,:] = xo1[:,0]
Wcs = STE(Wc)
r2i = torch.squeeze(r1o)
r2o = torch.empty((batch_size,f_conj))
for k in range(f_conj):
if sum(Wcs[k,:])==0:
Wd[k] = 0
else:
Wd[k] = 1
xo2 = conjunc.forward(r2i,Wcs[k,:])
r2o[:,k] = xo2[:,0]
Wd = torch.sum(Wcs,1)
Wds = clip(Wd)
R = disjunc.forward(r2o,Wds)
Rl = clip(R)
Rl = Rl[:,0]
l = torch.sum(torch.exp(-delta * y * Rl)) #+ 0.1*torch.sum(Wcs)#- 0.001*torch.sum(t2-t1) #+ 0.1*torch.sum(Wcs)
log.append(l.detach().numpy())
l.backward()
optimizer1.step()
optimizer2.step()
optimizer3.step()
optimizer1.zero_grad()
optimizer2.zero_grad()
optimizer3.zero_grad()
with torch.no_grad():
Wc[Wc<=0] = 0
Wc[Wc>=1] = 1
with torch.no_grad():
t1[t1<0] = 0
t2[t2<0] = 0
t1[t1>tl2] = tl2
t2[t2>tl2] = tl2
for k, t in enumerate(t1):
if t>t2[k]:
t1[k] = t2[k]-1
end = time()
training_time += end - start
if epoch % 100 ==0:
x = val_data
y = val_label
r1o = torch.empty((val_nsample,f_num,1))
for k, formula in enumerate(Formula):
xo1 = formula.robustness_trace(x,W1s[k,:],val_nsample,need_trace=False)
r1o[:,k,:] = xo1[:,0]
r2i = torch.squeeze(r1o)
r2o = torch.empty((val_nsample,f_conj))
for k in range(f_conj):
xo2 = conjunc.forward(r2i,Wcs[k,:])
r2o[:,k] = xo2[:,0]
R = disjunc.forward(r2o,Wds)
Rl = clip(R)
acc_val = sum(val_label==Rl[:,0])/(val_nsample)
acc_stl = STL_accuracy(x,y,Formula,Spatial,W1s,Wcs,Wds,clip)
acc = acc_stl
acc = acc_val
print('epoch_num = {epoch}, loss = {l}, accuracy_val = {acc_val}, accuracy_stl = {acc_stl}'.format(epoch=epoch,l=l, acc_val=acc_val, acc_stl=acc_stl))
if acc>acc_best:
best_training_time = training_time
if acc>=acc_best:
acc_best = acc
Wcss, Wdss = extract_formula(train_data,train_label,Formula,conjunc,disjunc,clip,W1s,Wcs,Wds)
print_formula(Formula, Spatial, W1s, Wcss, Wdss)
f = open('W_best_'+dataname+'.pkl', 'wb')
pickle.dump([W1s, Wcss, Wdss, a, b, t1, t2, Spatial], f)
f.close()
f = open('network_best_'+dataname+'.pkl', 'wb')
pickle.dump([Formula, conjunc, disjunc, clip], f)
f.close()
print(acc_best)
return acc_best, best_training_time