-
Notifications
You must be signed in to change notification settings - Fork 0
/
nn_arch.py
75 lines (64 loc) · 2.63 KB
/
nn_arch.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
import torch
import torch.nn as nn
seq_len = 30
class Dnn(nn.Module):
def __init__(self, embed_mat, class_num):
super(Dnn, self).__init__()
vocab_num, embed_len = embed_mat.size()
self.embed = nn.Embedding(vocab_num, embed_len, _weight=embed_mat)
self.la1 = nn.Sequential(nn.Linear(embed_len, 200),
nn.ReLU())
self.la2 = nn.Sequential(nn.Linear(200, 200),
nn.ReLU())
self.dl = nn.Sequential(nn.Dropout(0.2),
nn.Linear(200, class_num))
def forward(self, x):
x = self.embed(x)
x = torch.mean(x, dim=1)
x = self.la1(x)
x = self.la2(x)
return self.dl(x)
class Cnn(nn.Module):
def __init__(self, embed_mat, class_num):
super(Cnn, self).__init__()
vocab_num, embed_len = embed_mat.size()
self.embed = nn.Embedding(vocab_num, embed_len, _weight=embed_mat)
self.cap1 = nn.Sequential(nn.Conv1d(embed_len, 64, kernel_size=1, padding=0),
nn.ReLU(),
nn.MaxPool1d(seq_len))
self.cap2 = nn.Sequential(nn.Conv1d(embed_len, 64, kernel_size=2, padding=1),
nn.ReLU(),
nn.MaxPool1d(seq_len + 1))
self.cap3 = nn.Sequential(nn.Conv1d(embed_len, 64, kernel_size=3, padding=1),
nn.ReLU(),
nn.MaxPool1d(seq_len))
self.la = nn.Sequential(nn.Linear(192, 200),
nn.ReLU())
self.dl = nn.Sequential(nn.Dropout(0.2),
nn.Linear(200, class_num))
def forward(self, x):
x = self.embed(x)
x = x.permute(0, 2, 1)
x1 = self.cap1(x)
x2 = self.cap2(x)
x3 = self.cap3(x)
x = torch.cat((x1, x2, x3), dim=1)
x = x.view(x.size(0), -1)
x = self.la(x)
return self.dl(x)
class Rnn(nn.Module):
def __init__(self, embed_mat, class_num):
super(Rnn, self).__init__()
vocab_num, embed_len = embed_mat.size()
self.embed = nn.Embedding(vocab_num, embed_len, _weight=embed_mat)
self.ra = nn.LSTM(embed_len, 200, batch_first=True, bidirectional=True)
self.mp = nn.MaxPool1d(seq_len)
self.dl = nn.Sequential(nn.Dropout(0.2),
nn.Linear(400, class_num))
def forward(self, x):
x = self.embed(x)
h, hc_n = self.ra(x)
h = h.permute(0, 2, 1)
x = self.mp(h)
x = x.view(x.size(0), -1)
return self.dl(x)