-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathtest_treeconvfc.py
More file actions
159 lines (138 loc) · 6.47 KB
/
Copy pathtest_treeconvfc.py
File metadata and controls
159 lines (138 loc) · 6.47 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
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision
from torchvision import datasets, transforms
from torch.autograd import Variable
import numpy as np
import scipy.io
import os
import sys
import pickle
from sklearn.metrics import roc_auc_score
import time
from lib.Tox21_Data import Dataset, read
from lib.utils import readData, TextDataset
from lib.treeConvNetFC import TreeConvNetFC
from lib.maskedDAEwithFC import MaskedDenoisingAutoencoderFC
# Training settings
parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
parser.add_argument('--batch-size', type=int, default=256, metavar='N',
help='input batch size for training (default: 64)')
parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
help='input batch size for testing (default: 1000)')
parser.add_argument('--epochs', type=int, default=10, metavar='N',
help='number of epochs to train (default: 10)')
parser.add_argument('--dataset', type=str, default="agnews",
help='dataset')
parser.add_argument('--target', type=int, default=0, metavar='N',
help='target task 0-11')
parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
help='learning rate (default: 0.01)')
parser.add_argument('--name', type=str, default="tree-mnist",
help='name for this run')
args = parser.parse_args()
use_cuda = torch.cuda.is_available()
if args.dataset=="agnews":
label_name = ['World', 'Sports', 'Business', 'Sci/Tech']
training_num, valid_num, test_num, vocab_size = 110000, 10000, 7600, 10000
training_file = 'dataset/agnews_training_110K_10K-TFIDF-words.txt'
valid_file = 'dataset/agnews_valid_10K_10K-TFIDF-words.txt'
test_file = 'dataset/agnews_test_7600_10K-TFIDF-words.txt'
# 10000 -> 1000 -> 500 -> 250
# should produce 91.91%
# kernel_stride = [(11, 10), (5, 2), (5, 2)]
# fcwidths = [100, 50, 25]
kernel_stride = [(6, 5), (5, 4), (5, 2)]
fcwidths = [100, 50, 25]
elif args.dataset=="dbpedia":
label_name = ['Company','EducationalInstitution','Artist','Athlete','OfficeHolder','MeanOfTransportation','Building','NaturalPlace','Village','Animal','Plant','Album','Film','WrittenWork']
training_num, valid_num, test_num, vocab_size = 549990, 10010, 70000, 10000
training_file = 'dataset/dbpedia_training_549990_10K-frequent-words.txt'
valid_file = 'dataset/dbpedia_valid_10010_10K-frequent-words.txt'
test_file = 'dataset/dbpedia_test_70K_10K-frequent-words.txt'
# 10000 -> 1000 -> 500 -> 250
# kernel_stride = [(9, 10), (5, 2), (5, 2)]
# fcwidths = [200, 100, 50]
# kernel_stride = [(10, 15), (5, 1), (5, 2)]
# fcwidths = [100, 100, 50]
kernel_stride = [(6, 5), (5, 4), (5, 2)]
fcwidths = [100, 50, 25]
elif args.dataset=="sogounews":
label_name = ['sports','finance','entertainment','automobile','technology']
training_num, valid_num, test_num, vocab_size = 440000, 10000, 60000, 10000
training_file = 'dataset/sogounews_training_440K_10K-frequent-words.txt'
valid_file = 'dataset/sogounews_valid_10K_10K-frequent-words.txt'
test_file = 'dataset/sogounews_test_60K_10K-frequent-words.txt'
# 10000 -> 1000 -> 500 -> 250
# kernel_stride = [(9, 10), (5, 2), (5, 2)]
# fcwidths = [200, 100, 50]
kernel_stride = [(6, 5), (5, 4), (5, 2)]
fcwidths = [100, 50, 25]
elif args.dataset=="yelp":
label_name = ['1','2','3','4','5']
training_num, valid_num, test_num, vocab_size = 640000, 10000, 50000, 10000
training_file = 'dataset/yelpfull_training_640K_10K-frequent-words.txt'
valid_file = 'dataset/yelpfull_valid_10K_10K-frequent-words.txt'
test_file = 'dataset/yelpfull_test_50K_10K-frequent-words.txt'
# 10000 -> 1000 -> 500 -> 250
# kernel_stride = [(9, 10), (5, 2), (5, 2)]
# fcwidths = [200, 100, 50]
kernel_stride = [(6, 5), (5, 4), (5, 2)]
fcwidths = [100, 50, 25]
elif args.dataset=="yahoo":
label_name = ["Society & Culture","Science & Mathematics","Health","Education & Reference","Computers & Internet","Sports","Business & Finance","Entertainment & Music","Family & Relationships","Politics & Government"]
training_num, valid_num, test_num, vocab_size = 1390000, 10000, 60000, 10000
training_file = 'dataset/yahoo_training_1.39M_10K-frequent-words.txt'
valid_file = 'dataset/yahoo_valid_10K_10K-frequent-words.txt'
test_file = 'dataset/yahoo_test_60K_10K-frequent-words.txt'
# 10000 -> 1000 -> 500 -> 250
kernel_stride = [(9, 10), (5, 2), (5, 2)]
fcwidths = [200, 100, 50]
# 10000 -> 2000 -> 500 -> 250
# kernel_stride = [(6, 5), (5, 4), (5, 2)]
# fcwidths = [200, 100, 50]
# 10000 -> 500 -> 500 -> 250
# kernel_stride = [(10, 15), (5, 1), (5, 2)]
# fcwidths = [100, 100, 50]
# 10000 -> 1000 -> 500 -> 500
# kernel_stride = [(9, 10), (5, 2), (5, 1)]
# fcwidths = [100, 100, 100]
# 10000 -> 1000 -> 500 -> 500
# kernel_stride = [(10, 15), (5, 1), (5, 1)]
# fcwidths = [500, 500, 300]
print("Running Dataset %s with [lr=%f, epochs=%d, batch_size=%d]"
% (args.dataset, args.lr, args.epochs, args.batch_size))
print("kernel_stride: ", kernel_stride)
print("fcwidths: ", fcwidths)
start_time = time.time()
num_classes = len(label_name)
randgen = np.random.RandomState(13)
trainset = TextDataset(training_file, training_num, vocab_size, randgen)
validset = TextDataset(valid_file, valid_num, vocab_size)
testset = TextDataset(test_file, test_num, vocab_size)
end_time = time.time()
print("reading data cost: ", end_time - start_time)
input_dim = trainset.shape[1]
net = TreeConvNetFC(args.name)
start_time = time.time()
net.learn_structure(trainset, validset, num_classes, kernel_stride, fcwidths, corrupt=0.5,
lr=1e-3, batch_size=args.batch_size, epochs=10)
# net.net = torch.load("checkpoint/ckpt-agnews-treeconvfc-5-structure.t7")
end_time = time.time()
print("learning structure cost: ", end_time - start_time)
# net.net = torch.load('./checkpoint/ckpt-'+args.name+'-structure.t7')
# use skeleton only, initialize weight randomly
# for l in net.net:
# if isinstance(l, MaskedDenoisingAutoencoder):
# l.reset_parameters()
# if isinstance(l, nn.Dropout):
# l.p = 0.5
start_time = time.time()
net.fit(trainset, validset, testset, batch_size=args.batch_size,
lr=args.lr, epochs=args.epochs)
end_time = time.time()
print("finetuning cost: ", end_time - start_time)
print("Done.")