trw.hparams.params_optimizer_random_search

Module Contents

Classes

HyperParametersOptimizerRandomSearchLocal

Random hyper parameter run on a single machine

Functions

log_random(string)	Log string to console
store_loss_params(output_location, loss, infos, hyper_parameters)	Export the loss as self contained .pkl file to be analyzed later
load_loss_params(output_location)	reload the loss as self contained .pkl file to be analyzed later

Attributes

logger

trw.hparams.params_optimizer_random_search.logger

trw.hparams.params_optimizer_random_search.log_random(string)

Log string to console

trw.hparams.params_optimizer_random_search.store_loss_params(output_location, loss, infos, hyper_parameters)

Export the loss as self contained .pkl file to be analyzed later

trw.hparams.params_optimizer_random_search.load_loss_params(output_location)

reload the loss as self contained .pkl file to be analyzed later

class trw.hparams.params_optimizer_random_search.HyperParametersOptimizerRandomSearchLocal(evaluate_hparams_fn, repeat, log_string=log_random, result_prefix='hparams-random')

Random hyper parameter run on a single machine

We need to define the hyper parameter evaluation function:

def evaluate_hparams(hparams):
    # evaluate an hyper-parameter configuration and return a loss value and some additional information
    # e.g., result report
    return 0.0, {}

optimize(self, result_path)

Optimize the hyper-parameter search using random search

Parameters

result_path – where to save the information of each run. Can be None, in this case nothing is exported.

Returns

the results of all the runs