def create_observation_dict(suggestion):
start = time.time()
accuracy = calculate_objective(
suggestion.assignments,
data,
with_architecture=with_architecture,
)
end = time.time()
failed = True
values = None
duration = start - end
if accuracy > 75 and duration > -250:
values = [
{
'name': 'accuracy',
'value': accuracy
},
{
'name': 'negative_train_time',
'value': duration
},
]
failed = False
return {
'suggestion': suggestion.id,
'values': values,
'failed': failed,
}
def create_observation_dict(suggestion):
start = time.time()
accuracy = calculate_objective(
suggestion.assignments,
data,
with_architecture=with_architecture,
)
end = time.time()
return {
'suggestion': suggestion.id,
'values': [
{'name': 'accuracy', 'value': accuracy},
{'name': 'negative_train_time', 'value': start - end},
],
}
if with_architecture:
exp_name = 'GPU-powered Sentiment Analysis (SGD + Architecture)'
param_filepath = 'cnn_text/long_hyperparams.json'
else:
exp_name = 'GPU-powered Sentiment Analysis (SGD Only)'
param_filepath = 'cnn_text/hyperparams.json'
with open(param_filepath) as f:
hyperparams = f.read()
hyperparams = json.loads(hyperparams)
experiment = conn.experiments().create(name=exp_name,
project='sigopt-examples',
parameters=hyperparams,
observation_budget=40 *
len(hyperparams))
print("Created experiment: https://sigopt.com/experiment/" + experiment.id)
else:
experiment = conn.experiments(experiment_id).fetch()
# Optimization Loop
data = get_data()
for _ in range(experiment.observation_budget):
suggestion = conn.experiments(experiment.id).suggestions().create()
value = calculate_objective(suggestion.assignments,
data,
with_architecture=with_architecture)
observation = conn.experiments(experiment.id).observations().create(
value=value, suggestion=suggestion.id)