Hi I am trying to understand bidirectional RNN.
> class RNN(nn.Module):
>
>
> def __init__(self,n_vocab,n_embed,hidden_size,output_size):
>
> super().__init__()
>
> self.hidden_size = hidden_size
>
> self.embedding = nn.Embedding(n_vocab+1,n_embed) ## n_vocab is unique words in dictionary ## n_embed is hyperparameter
> self.rnn = nn.RNN(n_embed, hidden_size, num_layers = 1, batch_first = True,bidirectional = True) #
>
> self.fc = nn.Linear(hidden_size,output_size)
>
> def forward(self,x):>
>
> x = x # input batch_size * seq_length
>
> batch_size = x.size(0)
>
> #print('Batch Size is',batch_size)
>
> x = self.embedding(x) # batch-size x seq_length x embedding_dimension
>
> x,hidden =self.rnn(x) #batch-size x seq_length x hidden_size
>
>
>
> return x,hidden
I am returning both hidden state and output while going through tutorials some says that I need to concatenate hidden state (torch.cat((hidden[-2,:,:], hidden[-1,:,:]), dim = 1)) and in some tutorials take output state (x[:,-1,:]) but both of results come difference.
What is the correct way of doing Bidirectional RNN.