As this approach is relatively new, there has not been much work on analyzing the properties and behavior of these models. For instance: What are the properties of sentences on which this approach performs better? How does the choice of source/target vocabulary affect the performance? In which cases does the neural machine translation fail?
raw data : for each subjects(S1,S2 …) , each action(walking, waiting, smoking …), each sub sequence(1/2): $(n) \times 99$ (np.ndarray, float32)
From data_utils.load_data() used by translate.read_all_data()
train data: the composed dictionary ((suject_id, action, subaction_id, ‘even’) as key) of raw data (just even rows), with one hot encoding columns for action type, if action is specified (normal case), just append an all 1 column to rawdata. Size of each dictionary value: $(n/2) \times (99 + actions;count)$
complete data: all data joint together, from different subjects, actions, sub sequences: $(n) \times 99$
From translate.read_all_data() used by translate.train()
train set : normalized train data, throw out data with $std < 1e-4$ (accroding to complete data). Size of each dictionary value: $(n/2) \times ((99-used;dimension;count) + actions;count)$
Human Dimension
After the analyzztion of the complete data, human dimension has been fixed to $54$.
From Seq2SeqModel.get_batch() used by translate.train()