-
Notifications
You must be signed in to change notification settings - Fork 47
/
model_SentCNN.py
58 lines (44 loc) · 1.64 KB
/
model_SentCNN.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
import torch
import torch.nn as nn
import torch.nn.functional as F
class CNN_Text(nn.Module):
def __init__(self, args):
super(CNN_Text,self).__init__()
self.args = args
V = args.embed_num
D = args.embed_dim
C = args.class_num
Ci = 1
Co = args.kernel_num
Ks = args.kernel_sizes
self.embed = nn.Embedding(V, D)
#self.convs1 = [nn.Conv2d(Ci, Co, (K, D)) for K in Ks]
self.convs1 = nn.ModuleList([nn.Conv2d(Ci, Co, (K, D)) for K in Ks])
'''
self.conv13 = nn.Conv2d(Ci, Co, (3, D))
self.conv14 = nn.Conv2d(Ci, Co, (4, D))
self.conv15 = nn.Conv2d(Ci, Co, (5, D))
'''
self.dropout = nn.Dropout(args.dropout)
self.fc1 = nn.Linear(len(Ks)*Co, C)
def conv_and_pool(self, x, conv):
x = F.relu(conv(x)).squeeze(3) #(N,Co,W)
x = F.max_pool1d(x, x.size(2)).squeeze(2)
return x
def forward(self, x):
x = self.embed(x) # (N,W,D)
if self.args.static:
x = Variable(x)
x = x.unsqueeze(1) # (N,Ci,W,D)
x = [F.relu(conv(x)).squeeze(3) for conv in self.convs1] #[(N,Co,W), ...]*len(Ks)
x = [F.max_pool1d(i, i.size(2)).squeeze(2) for i in x] #[(N,Co), ...]*len(Ks)
x = torch.cat(x, 1)
'''
x1 = self.conv_and_pool(x,self.conv13) #(N,Co)
x2 = self.conv_and_pool(x,self.conv14) #(N,Co)
x3 = self.conv_and_pool(x,self.conv15) #(N,Co)
x = torch.cat((x1, x2, x3), 1) # (N,len(Ks)*Co)
'''
x = self.dropout(x) # (N,len(Ks)*Co)
logit = self.fc1(x) # (N,C)
return logit