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Using the ```keras_tuner` API, implement hyper tuning so that our model can be tuned and optimized for our data.
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Although Optuna is used for hyperparameter tuning in the project, the tuning results may vary across different environments. How can the reproducibility of hyperparameter tuning be ensured?
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Hyperparameter tuning requires that tunable parameters be identified for each step and assigned functions that generate suitable grid ranges. The current implementation provides `hom_degree()` and `ma…
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Hello Bayesian Wizards,
I recently worked on something for which the client was not sure about the adstock decay rates.
They said it should be somewhere between 30 to 50% for each of the 5 channels.…
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**Is your feature request related to a problem? Please describe.**
Recently I saw a review of this library where the user suggested that we should include hyperparameter tuning. I do realize it will …
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### Search before asking
- [X] I have searched the HUB [issues](https://github.com/ultralytics/hub/issues) and [discussions](https://github.com/ultralytics/hub/discussions) and found no similar quest…
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Hi! Thank you for publishing your code.
Your released code has a section dedicated to configuration files corresponding to different tracker modules https://github.com/PinataFarms/FEARTracker/tree/…
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### Search before asking
- [X] I have searched the YOLOv8 [issues](https://github.com/ultralytics/ultralytics/issues) and found no similar bug report.
### YOLOv8 Component
Hyperparameter Tuning
#…
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### What happened + What you expected to happen
I am running the notebook in Kaggle.
!pip install -U "ray[tune]"
```python
from ray import train, tune
def objective(config):
project = …
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I'm doing some hyperparameter tuning and it really seems to prefer migrating the hall of fame, rather than between populations.
It could be because the hyperparameter tuning was only for 2-minute runs…