CODEWITHSUNDEEP
python-3.xkerasrasa-nlurasa-core
SUBHOJEET
SUBHOJEET

Reputation: 408

Features and Story graph || input to keras policy

Could you please let me know how are you identifying the features you're passing to keras policy. I could see story graphs are being created during agent.load_data.

Could you please share live example so that I can tune the parameters and hyperparameters to get the best out of keras lstm model.

Rasa Core version: 0.11.12

Python version: 3.5

Operating system (windows, osx, ...):windows 10

Upvotes: 0

Views: 187

Answers (1)

Tobias
Tobias

Reputation: 1910

The selected feature depends on the used policy and its configuration. You can specify your configuration in a policy configuration file. If you use the "embedding policy", you can also define the layers etc. of the used LSTM in this configuration file.

The features are identified from

  • intents so far
  • last actions
  • current slot values / entities values

Have a look at the documentation on featurization for more details since this highly depends on the used policy configuration (you can select different featurizers).

With rasa_core version 0.12 you can only compare the accuracy of different policies with the command python -m rasa_core.train compare. This is probably helpful if you want to finetune that.

Upvotes: 2

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