Need help from experts here @souvikg10 @Juste
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?