Solved by John_Smith in post #2 
              
              
                When you run a rnn net over a sequence of data, the output has two tensors: (output, h_n). While h_n is the hidden state from the last timestamp, the tensor output actually has the hidden states for every timestamps in the sequence. 
rnn_net = nn.RNN(4, 3)
X = torch.rand((6,5,4))
out, hidden = rnn_n…