details of input & output of LSTM

[machanic]

I repeat my understanding about your codes, can you tell me is it exactly what you mean?
In this example , I omit the procedure of random sample, just look it.
suppose we have sequence of a,b,c,d,e,f,g.
the LSTM's 1st time input : a,b,c and output: o_a, o_b, o_c. you need to concatenate (o_a, o_b, o_c) to calculate loss, all ground truth of (label_a, label_b, label_c) will be used in calculate loss.
the LSTM's 2nd time input: b,c,d and output: o_b,o_c, o_d you need to concatenate (o_b, o_c, o_d) to calculate loss, all ground truth of (label_b, label_c, label_d) will be used in calculate loss.

so my question is:

1. is this example I showed exactly what you do in your experiment?
2. In this example, In order to train the whole dataset, so overlap sequence will repeatly input to LSTM in order to calcuate all position of sequence.
   3..Because there exists a lot of 0-label (image frame with no action on its face) frame, Do you train every sample in train set OR just pick that frame which have AU label != 0. input to LSTM?