I am trying to understand how the dialogue management actually works at Rasa core. I am well aware of the high-level architecture but interested to understand how
LSTM is used. So I am looking into the keras_policy.py file
I am a bit confused about some of the logic in
model_architecture method. I see you have
if-else based on the length of the output shape
len(output_shape). Although it is well commented that the target label
y can be either
(num examples, num features) or
(num examples, max_dialogue_len, num features), I am not sure why.
Can anyone please explain?