model_DeepCNN.py 文件源码

def forward(self, x):
        one_layer = self.embed(x)  # (N,W,D) #  torch.Size([64, 43, 300])
        # one_layer = self.dropout(one_layer)
        one_layer = one_layer.unsqueeze(1)  # (N,Ci,W,D)  #  torch.Size([64, 1, 43, 300])
        # one layer
        one_layer = [torch.transpose(F.relu(conv(one_layer)).squeeze(3), 1, 2) for conv in self.convs1] # torch.Size([64, 100, 36])
        # two layer
        two_layer = [F.relu(conv(one_layer.unsqueeze(1))).squeeze(3) for (conv, one_layer) in zip(self.convs2, one_layer)]
        print("two_layer {}".format(two_layer[0].size()))
        # pooling
        output = [F.max_pool1d(i, i.size(2)).squeeze(2) for i in two_layer]   #  torch.Size([64, 100]) torch.Size([64, 100])
        output = torch.cat(output, 1)  # torch.Size([64, 300])
        # dropout
        output = self.dropout(output)
        # linear
        output = self.fc1(F.relu(output))
        logit = self.fc2(F.relu(output))
        return logit