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Consider installing `keras-tuner` so make it easier to perform hyper-parameter optimization for `tf.keras` models.
https://github.com/keras-team/keras-tuner
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Freeing users from the responsibility of manual hyper-parameter optimization, allows the user to focus on the domain problem being solved and it will reduce the cycle time needed to develop performant…
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rockt updated
9 years ago
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Main goal would be to define the `Objective` analogous to kopt' `CompileFN` but now using the gin-config. Arguments of Objective would be same as arguments to `gin_train` but where the gin-config file…
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Dear all,
I am trying to use exact GPs and the hyperparameter optimization keeps producing errors, such as `AssertionError: isfinite(phi_c) && isfinite(dphi_c)`.
Consider this minimal example:
`N…
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This issue is opened for journaling the hyper parameter optimization process:
- The metrics are being monitored via WANDB dashboard located [here](https://app.wandb.ai/hakanonal/geodashml).
- There …
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**Scenario**
Let's see how far our model can get us without using GPUs
**Implementation**
Optimize a selection of the network hyper-parameters using an optimization method that is smarter than g…
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Current options for hyper-parameter optimization (grid search, random search) construct a task graph to search many options, submit that graph, and then wait for the result. However, we might instead…
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Currently there is some Hyper parameter optimization using optuna. I believe we can increase the currency of this search using Ray on Spark
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Right now, integration between dask and various single node machine learning libraries are implemented as standalone dask extensions like dask-ml and dask-optuna. These can be used with xgboost when …