How can I create a many to many RNN with fix number of unrolling steps in decoder?

[leolcling (Leo)]

I suppose I should define a RNNCell (or LSTMCell/GRUCell) in my decoder and use a for loop in the forward method in decoder. Something like this:

Different Between LSTM and LSTMCell Function

LSTMCell is more flexible and you need less code with LSTM . So with LSTMCell, def forward(self, x): h = self.get_hidden() for input in x: h = self.rnn(input, h) # self.rnn = self.LSTMCell(input_size, hidden_size) while with LSTM it is def forward(self, x): h_0 = self.get_hidden() output, h = self.rnn(x, h_0) # self.rnn = self.LSTM(input_size, hidden_size) output is the blue rectangles in your fig.

But when I look at the tutorial of seq-seq translation. I got confused because the tutorial use nn.GRU as oppose to GRUCell, and a for loop was used in the training loop… Is is related to attention?

If I want to implement a many-to-many RNN with attention and control the output to be 5 steps, should I use RNN or RNNCell? Thanks.