# FAQ and additional resources¶

## Extending Selene¶

The main modules that users may want to extend are

• selene_sdk.samplers.OnlineSampler

• selene_sdk.samplers.file_samplers.FileSampler

• selene_sdk.sequences.Sequence

• selene_sdk.targets.Target

Please refer to the documentation for these classes. If you are encounter a bug or have a feature request, please post to our Github issues. E-mail kchen@flatironinstitute.org if you are interested in being a contributor to Selene.

Join our Google group if you have questions about the package, case studies, or model development.

## Exporting a Selene-trained model to Kipoi¶

We have provided an example of how to prepare a model for upload to Kipoi’s model zoo using a model trained during case study 2. You can use this example as a starting point for preparing your own model for Kipoi. We have provided a script that can help to automate parts of the process.

We are also working on an export function that will be built into Selene and accessible through the CLI.

## Hyperparameter optimization¶

Hyperparameter optimization is the process of finding the set of hyperparameters that yields an optimal model against a predefined score (e.g. minimizing a loss function). Hyperparameters are the variables that govern the training process (i.e. these parameters are constant during training, compared to model parameters which are optimized/”tuned” by the training process itself). Hyperparameter tuning works by running multiple trials of a single training run with different values for your chosen hyperparameters, set within some specified limit. Some examples of hyperparameters:

• learning rate

• number of hidden units

• convolutional kernel size

You can select hyperparameters yourself (manually) or automatically. For automatic hyperparameter optimization, you can look into grid search or random search.

Some resources that may be useful:

To use hyperparameter optimization on models being developed with Selene, you could implement a method that runs Selene (via a command-line call) with a set of hyperparameters and then monitors the validation performance based on the output to selene_sdk.train_model.validation.txt.