I have implemented a Bidirectional LSTM which predicts a certain profile by using a windowing input in tensorflow. Conceptually it makes sense to me during the training phase.
Once the model is trained I use it to predict future and evaluate against a test set. However, I am unsure what happens to the backward LSTM cells that were originally trained with information passed from future to past. Does the network first do a forward pass in time and then a backward pass to give an output? Could someone please elaborate how does the network work in testing phase.
Network architecture
Thanks in advance.
I have tried to find some resources online but couldn't find a convincing answer