-
Notifications
You must be signed in to change notification settings - Fork 0
/
bash_gaussian_covariance.sh
90 lines (77 loc) · 1.56 KB
/
bash_gaussian_covariance.sh
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
#!/bin/bash
# configuration for estimation the covariance of a gaussian distribution
epoch=2000
dataset=covariance
train_size=10000
batch_size=10000
cg_maxiter=16
save_iter=1
print_iter=1
# 2000 epoch 215s
# # 2ts-gda
# d_optim=gd
# d_step_size=0.2
# d_num_step=1
# g_optim=gd
# g_step_size=0.02
# simultaneous=0
# 1462 epoch 215s
# gda-20
d_optim=gd
d_step_size=0.02
d_num_step=20
g_optim=gd
g_step_size=0.02
simultaneous=0
# # sd
# d_optim=gd
# d_step_size=0.2
# d_num_step=1
# g_optim=sd
# g_step_size=0.02
# simultaneous=1
# # fr
# d_optim=fr
# d_step_size=0.2
# d_num_step=1
# g_optim=gd
# g_step_size=0.02
# simultaneous=1
# # gd-newton
# d_optim=newton
# d_step_size=1.
# d_num_step=1
# g_optim=gd
# g_step_size=0.02
# simultaneous=0
# # complete newton
# d_optim=newton
# d_step_size=1.
# d_num_step=1
# g_optim=newton
# g_step_size=1.
# simultaneous=0
# # EG
d_optim=eg
d_step_size=0.02
d_num_step=1
g_optim=eg
g_step_size=0.02
simultaneous=1
python run.py --epoch $epoch \
--dataset $dataset \
--train_size $train_size \
--batch_size $batch_size \
--pretrain 0 \
--d_optim $d_optim \
--d_step_size $d_step_size \
--d_num_step $d_num_step \
--g_optim $g_optim \
--g_step_size $g_step_size \
--cg_maxiter $cg_maxiter \
--cg_tol 1e-30 \
--cg_lam 0. \
--cg_lam_cn 0. \
--simultaneous $simultaneous \
--save_iter $save_iter \
--print_iter $print_iter