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For example, if I test dropout and l2_regularization through hyperas optimization, is there a way to return the set of data which relates the loss metrics of each NN configuration? Or would I have to hardcode that utility myself, likely using pandas?
The text was updated successfully, but these errors were encountered:
Everything returned in the function you want to minimise should be accessible from the Trials object. Of course, you can always have your function output to disk or a database or w/e you like.
For example, if I test dropout and l2_regularization through hyperas optimization, is there a way to return the set of data which relates the loss metrics of each NN configuration? Or would I have to hardcode that utility myself, likely using pandas?
The text was updated successfully, but these errors were encountered: