Azure ML Hyperdrive

Question

When running the hyperdrive step, I would like to get the hyper parameters that were selected for the best model and export them to use in a subsequent model. How would I got about doing that? I saw a method get_hyperparameters but from what I can tell that just gets all child runs. I am essentially wanting to use the same model but change alpha levels.

Answer 1

Hello.

I think below is what you are looking for. Could you please take a look?

Once all of the hyperparameter tuning runs have completed, identify the best performing configuration and hyperparameter values:

best_run = hyperdrive_run.get_best_run_by_primary_metric()
best_run_metrics = best_run.get_metrics()
parameter_values = best_run.get_details()['runDefinition']['Arguments']

print('Best Run Id: ', best_run.id)
print('\n Accuracy:', best_run_metrics['accuracy'])
print('\n learning rate:',parameter_values[3])
print('\n keep probability:',parameter_values[5])
print('\n batch size:',parameter_values[7])

Regards,
Yutong