TextCNN.py 文件源码

def forward(self, x):
        x = self.embeding(x)

        x = x.unsqueeze(1)
        x= [F.relu(conv(x).squeeze(3)) for conv in self.convs]
        x = [F.max_pool1d(i, i.size(2)).squeeze(2) for i in x]
        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)
        out = self.fc(x)
        return out