-
Notifications
You must be signed in to change notification settings - Fork 6
/
Copy pathLOO_experiments_mnist_clustering.py
142 lines (133 loc) · 5.29 KB
/
LOO_experiments_mnist_clustering.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
"""
This performs clustering and then samples from those clusters in a stratified
manner
"""
import augmentations
import dataset_loaders
import experiments_util
from experiments import run_test_clustered
import tensorflow as tf
from keras import backend as K
def main():
rounds = 5
n_aug_sample_points = [1, 10, 50, 100, 250, 500, 750, 1000]
n_train = 1000
n_jobs = 1
cv = 1
use_GPU = True
batch_size = 512
CNN_extractor_max_iter = 40
use_loss = False
n_clusters_arr = [1, 10, 50, 100, 250, 500, 750, 1000]
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
K.set_session(sess)
# Can use multiple valus of C for cross-validation
logistic_reg__Cs = [[10]]
classes_datasets = [
((3, 8), dataset_loaders.Dataset.MNIST),
]
selected_augmentations = [
(augmentations.Image_Transformation.translate, {"mag_aug": 2}),
(augmentations.Image_Transformation.rotate, {"mag_aug": 30,
"n_rotations": 15}),
(augmentations.Image_Transformation.crop,
{"mag_augs": [1, 2, 3, 4, 5, 6]}),
]
experiment_configs = [
("baseline", False, False),
("random_proportional", False, False),
# ("random_proportional", False, True),
# ("random_proportional", True, False),
# ("random_proportional", True, True),
# ("random_inverse_proportional", False, False),
# ("random_inverse_proportional", True, False),
# ("random_softmax_proportional", False, False),
# ("random_softmax_proportional", False, True),
# ("random_softmax_proportional", True, False),
# ("random_softmax_proportional", True, True),
# ("random_inverse_softmax_proportional", False, False),
# ("random_inverse_softmax_proportional", True, False),
# ("deterministic_proportional", False, False),
# ("deterministic_proportional", False, True),
# ("deterministic_proportional", True, False),
# ("deterministic_proportional", True, True),
# ("deterministic_inverse_proportional", False, False),
# ("deterministic_inverse_proportional", True, False),
]
for n_clusters in n_clusters_arr:
for logistic_reg__C in logistic_reg__Cs:
for classes, dataset in classes_datasets:
for aug_transformation, aug_kw_args in selected_augmentations:
dataset_class_str = experiments_util.classes_to_class_str(
classes
)
print("Class types: {}".format(dataset_class_str))
reg_str = "-".join(list(map(str, logistic_reg__C)))
results_filename = "aug_results_{}_{}_{}_{}_{}{}".format(
dataset.name,
dataset_class_str,
aug_transformation.name,
reg_str,
n_clusters,
"_loss" if use_loss else "",
)
run_test_clustered(
classes,
rounds,
n_aug_sample_points,
n_train,
n_jobs,
cv,
use_GPU,
batch_size,
dataset,
aug_transformation,
aug_kw_args,
logistic_reg__C,
CNN_extractor_max_iter,
use_loss,
experiment_configs,
results_filename,
n_clusters,
)
use_loss = True
for n_clusters in n_clusters_arr:
for logistic_reg__C in logistic_reg__Cs:
for classes, dataset in classes_datasets:
for aug_transformation, aug_kw_args in selected_augmentations:
dataset_class_str = experiments_util.classes_to_class_str(
classes
)
print("Class types: {}".format(dataset_class_str))
reg_str = "-".join(list(map(str, logistic_reg__C)))
results_filename = "aug_results_{}_{}_{}_{}_{}{}".format(
dataset.name,
dataset_class_str,
aug_transformation.name,
reg_str,
n_clusters,
"_loss" if use_loss else "",
)
run_test_clustered(
classes,
rounds,
n_aug_sample_points,
n_train,
n_jobs,
cv,
use_GPU,
batch_size,
dataset,
aug_transformation,
aug_kw_args,
logistic_reg__C,
CNN_extractor_max_iter,
use_loss,
experiment_configs,
results_filename,
n_clusters,
)
if __name__ == "__main__":
main()