Tensorflow: Getting LSTM to 100% in converting date time formats
I think, that the issues lies in the LSTM I use, even though its bidirectional, but since the day is the first element that is predicted, the model does not have alot of information for predicting the correct day, since at timestep 0 the model hasnt seen any date values yet.

Try generate 27.12.1992 - Wednesday instead to see if that does anything.
Honestly i don't think any model can predict correctly with that data
I created a bidirectional seq2seq endcoder-decoder network, which aims at formating different datetypes to a german datetime format as: day_written_out, day.month.year
So as an example I have the string 12-27-1992 and I expect the model to predict Sunday, 27.12.1992.
After training, the model reaches a valid_accuracy of 98%. Further investigation showed, that the model nearly always predicted the date itself correct, but the model is not able to extract the correct day. So instead of predicting Sunday, 27.12.1992 it predicts Wednesday, 27.12.1992.
Only 5% of the days are predicted correctly.
I think, that the issues lies in the LSTM I use, even though its bidirectional, but since the day is the first element that is predicted, the model does not have alot of information for predicting the correct day, since at timestep 0 the model hasnt seen any date values yet. Is this correct?
So my question is, why is this model not capable of predicting the correct day? And would a tranformer architecture, which is capable of reading the whole sequence at a time, be able to solve this issue?
Thank you