Features and Story graph || input to keras policy
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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
python-3.x keras rasa-nlu rasa-core
asked Nov 10 at 21:03
SUBHOJEET
72
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up vote
0
down vote
favorite
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
python-3.x keras rasa-nlu rasa-core
asked Nov 10 at 21:03
SUBHOJEET
72
add a comment |
up vote
0
down vote
favorite
up vote
0
down vote
favorite
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
python-3.x keras rasa-nlu rasa-core
asked Nov 10 at 21:03
SUBHOJEET
72
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
python-3.x keras rasa-nlu rasa-core
python-3.x keras rasa-nlu rasa-core
asked Nov 10 at 21:03
SUBHOJEET
72
asked Nov 10 at 21:03
SUBHOJEET
72
asked Nov 10 at 21:03
SUBHOJEET
72
asked Nov 10 at 21:03
SUBHOJEET
72
asked Nov 10 at 21:03
SUBHOJEET
72
72
add a comment |
add a comment |
1 Answer
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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.
answered Nov 14 at 9:12
Tobias
24118
add a comment |
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
0
down vote
accepted
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.
answered Nov 14 at 9:12
Tobias
24118
add a comment |
up vote
0
down vote
accepted
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.
answered Nov 14 at 9:12
Tobias
24118
add a comment |
up vote
0
down vote
accepted
up vote
0
down vote
accepted
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.
answered Nov 14 at 9:12
Tobias
24118
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.
answered Nov 14 at 9:12
Tobias
24118
answered Nov 14 at 9:12
Tobias
24118
answered Nov 14 at 9:12
Tobias
24118
answered Nov 14 at 9:12
Tobias
24118
24118
add a comment |
add a comment |
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