-
Notifications
You must be signed in to change notification settings - Fork 0
/
Multi_NetWork.py
68 lines (52 loc) · 1.61 KB
/
Multi_NetWork.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
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 3, 3)
self.pool1 = nn.MaxPool2d(2)
self.conv2 = nn.Conv2d(3, 6, 3)
self.pool2 = nn.MaxPool2d(2)
self.fc1 = nn.Linear(6 * 123 * 123, 150)
self.drop = nn.Dropout(0.5)
self.fc21 = nn.Linear(150, 2)
self.fc22 = nn.Linear(150, 3)
self.soft1 = nn.Softmax(dim=1)
def forward(self, x):
x = self.pool1(self.conv1(x))
x = F.relu(x)
x = self.pool2(self.conv2(x))
# x = self.drop(x)
x = F.relu(x)
x = x.view(-1, 6 * 123 * 123)
x = self.fc1(x)
x = self.drop(x)
x = F.relu(x)
x1 = self.fc21(x)
x2 = self.fc22(x)
x1 = self.soft1(x1)
x2 = self.soft1(x2)
return x1, x2
class Net_BCE(nn.Module):
def __init__(self):
super(Net_BCE, self).__init__()
self.conv1 = nn.Conv2d(3, 3, 3)
self.pool1 = nn.MaxPool2d(2)
self.conv2 = nn.Conv2d(3, 6, 3)
self.pool2 = nn.MaxPool2d(2)
self.fc1 = nn.Linear(6 * 123 * 123, 150)
self.drop = nn.Dropout(0.5)
self.fc2 = nn.Linear(150, 5)
self.sig=nn.Sigmoid()
def forward(self, x):
x = self.pool1(self.conv1(x))
x = F.relu(x)
x = self.pool2(self.conv2(x))
x = F.relu(x)
x = x.view(-1, 6 * 123 * 123)
x = self.fc1(x)
x = self.drop(x)
x = F.relu(x)
x = self.fc2(x)
x=self.sig(x)
return x