def test_convert_optimizer_trial_to_hps(self):
hps = hp_module.HyperParameters()
hps.Choice('learning_rate', [1e-4, 1e-3, 1e-2])
optimizer_trial = {
'name':
'trial_name',
'state':
'ACTIVE',
'parameters': [{
'parameter': 'learning_rate',
'floatValue': 0.0001
}, {
'parameter': 'num_layers',
'intValue': '2'
}, {
'parameter': 'units_0',
'floatValue': 96
}, {
'parameter': 'units_1',
'floatValue': 352
}]
}
trial_hps = cloud_tuner_utils.convert_optimizer_trial_to_hps(
hps, optimizer_trial)
self.assertEqual(trial_hps.values, EXPECTE_TRIAL_HPS)
def get_best_trials(self, num_trials = 1):
"""Returns the trials with the best objective values found so far.
Arguments:
num_trials: positive int, number of trials to return.
Returns:
List of KerasTuner Trials.
"""
if len(self.objective) > 1:
raise ValueError(
'Getting the best trials for multi-objective optimization '
'is not supported. ')
maximizing = cloud_tuner_utils.format_goal(
self.objective[0].direction) == 'MAXIMIZE'
# List all trials associated with the same study
trial_list = self.service.list_trials()
optimizer_trials = [t for t in trial_list if t['state'] == 'COMPLETED']
if not optimizer_trials:
return []
sorted_trials = sorted(
optimizer_trials,
key=lambda t: t['finalMeasurement']['metrics'][0]['value'],
reverse=maximizing)
best_optimizer_trials = sorted_trials[:num_trials]
best_trials = []
# Convert Optimizer trials to KerasTuner Trial instance
for optimizer_trial in best_optimizer_trials:
final_measurement = optimizer_trial['finalMeasurement']
kerastuner_trial = trial_module.Trial(
hyperparameters=cloud_tuner_utils.convert_optimizer_trial_to_hps(
self.hyperparameters.copy(), optimizer_trial),
trial_id=cloud_tuner_utils.get_trial_id(optimizer_trial),
status=trial_module.TrialStatus.COMPLETED)
# If trial had ended before having intermediate metric reporting, set
# epoch = 1.
kerastuner_trial.best_step = final_measurement.get('stepCount', 1)
kerastuner_trial.score = final_measurement['metrics'][0]['value']
best_trials.append(kerastuner_trial)
return best_trials
def create_trial(self, tuner_id):
"""Create a new `Trial` to be run by the `Tuner`.
Arguments:
tuner_id: An ID that identifies the `Tuner` requesting a `Trial`. `Tuners`
that should run the same trial (for instance, when running a
multi-worker model) should have the same ID. If multiple
suggestTrialsRequests have the same tuner_id, the service will return
the identical suggested trial if the trial is PENDING, and provide a new
trial if the last suggest trial was completed.
Returns:
A `Trial` object containing a set of hyperparameter values to run
in a `Tuner`.
Raises:
SuggestionInactiveError: Indicates that a suggestion was requested from an
inactive study.
"""
# List all trials from the same study and see if any trial.status=STOPPED or
# if number of trials >= max_limit.
trial_list = self.service.list_trials()
# Note that KerasTunerTrialStatus - 'STOPPED' is equivalent to
# OptimizerTrialState - 'STOPPING'.
stopping_trials = [t for t in trial_list if t['state'] == 'STOPPING']
if (self.max_trials
and len(trial_list) >= self.max_trials) or stopping_trials:
trial_id = 'n'
hyperparameters = self.hyperparameters.copy()
hyperparameters.values = None
# This will break the search loop later.
return trial_module.Trial(hyperparameters=hyperparameters,
trial_id=trial_id,
status=trial_module.TrialStatus.STOPPED)
# Get suggestions
suggestions = self.service.get_suggestions(tuner_id)
if 'trials' not in suggestions:
return trial_module.Trial(hyperparameters={},
status=trial_module.TrialStatus.STOPPED)
# Fetches the suggested trial.
# Optimizer Trial instance
optimizer_trial = suggestions['trials'][0]
trial_id = cloud_tuner_utils.get_trial_id(optimizer_trial)
# KerasTuner Trial instance
kerastuner_trial = trial_module.Trial(
hyperparameters=cloud_tuner_utils.convert_optimizer_trial_to_hps(
self.hyperparameters.copy(), optimizer_trial),
trial_id=trial_id,
status=trial_module.TrialStatus.RUNNING)
tf.get_logger().info(
'Hyperparameters requested by tuner ({}): {} '.format(
tuner_id, kerastuner_trial.hyperparameters.values))
self._start_time = time.time()
self.trials[trial_id] = kerastuner_trial
self.ongoing_trials[tuner_id] = kerastuner_trial
self._save_trial(kerastuner_trial)
self.save()
return kerastuner_trial