Hi all,

Suppose I have a multi-class text classification problem and I want to use LSTMs. I found several codes to implement this but I have been confused since there are different implementation about how to use the output of the LSTMs some of them are like the below

```
def forward(self, x):
h_embedding = self.embedding(x)
h_embedding = torch.squeeze(torch.unsqueeze(h_embedding, 0))
h_lstm, _ = self.lstm(h_embedding)
avg_pool = torch.mean(h_lstm, 1)
max_pool, _ = torch.max(h_lstm, 1)
conc = torch.cat(( avg_pool, max_pool), 1)
conc = self.relu(self.linear(conc))
conc = self.dropout(conc)
out = self.out(conc)
return out
```

but I cannot understand what they did.

and others like the below:

```
def forward(self, x):
h_embedding = self.embedding(x)
h_lstm, _ = self.lstm(h_embedding)
max_pool, _ = torch.max(h_lstm, 1)
linear = self.relu(self.linear(max_pool))
out = self.out(linear)
return out
```

Why the use max_pool?

What’s one correct architecture (for multi-class problem) since I want as output the number of class one sentence belong? which is the right Loss function? any help

Regards,