| 109 | |
| 110 | class bTextCNN(nn.Module): |
| 111 | def __init__(self, fea_dim, vocab_size): |
| 112 | super(bTextCNN, self).__init__() |
| 113 | self.vocab_size = vocab_size |
| 114 | self.fea_dim=fea_dim |
| 115 | |
| 116 | self.channel_in = 1 |
| 117 | self.filter_num = 14 |
| 118 | self.window_size = [3,4,5] |
| 119 | |
| 120 | self.textcnn =nn.ModuleList([nn.Conv2d(self.channel_in, self.filter_num, (K,self.vocab_size)) for K in self.window_size]) |
| 121 | self.linear = nn.Sequential(torch.nn.Linear(len(self.window_size) * self.filter_num, self.fea_dim),torch.nn.ReLU()) |
| 122 | self.classifier = nn.Linear(self.fea_dim,2) |
| 123 | |
| 124 | def forward(self, **kwargs): |
| 125 | title_w2v = kwargs['title_w2v'] |