Hey guys
I defined a GRU layer like this with n_layers = 2
self.gru = nn.GRU(embed_dim, hidden_dim, n_layers)
 self.fc = nn.Linear(hidden_dim, output_dim)
in the forward function, what should I give to the fc layer?
output, hidden = self.gru(x)
fc_input = output[-1]
or
fc_input  = torch.cat((hidden[-1, :, :],  hidden[-2, :, :]), dim = 1)
return torch.sigmoid(self.fc(fc_input  ))
What about having a bidirectional model? what would be the fc_input in that case?
Thank you.