def test_extended_summary_working(kwargs, capsys):
state = TunerState('test', None, **kwargs)
state.summary()
summary_out = capsys.readouterr()
state.summary(extended=True)
extended_summary_out = capsys.readouterr()
assert summary_out.out.count(":") < extended_summary_out.out.count(":")
def __init__(self, model_fn, objective, name, distributions, **kwargs):
""" Tuner abstract class
Args:
model_fn (function): Function that return a Keras model
name (str): name of the tuner
objective (str): Which objective the tuner optimize for
distributions (Distributions): distributions object
Notes:
All meta data and varialbles are stored into self.state
defined in ../states/tunerstate.py
"""
# hypertuner state init
self.state = TunerState(name, objective, **kwargs)
self.stats = self.state.stats # shorthand access
self.cloudservice = CloudService()
# check model function
if not model_fn:
fatal("Model function can't be empty")
try:
mdl = model_fn()
except:
traceback.print_exc()
fatal("Invalid model function")
if not isinstance(mdl, Model):
t = "tensorflow.keras.models.Model"
fatal("Invalid model function: Doesn't return a %s object" % t)
# function is valid - recording it
self.model_fn = model_fn
# Initializing distributions
hparams = config._DISTRIBUTIONS.get_hyperparameters_config()
if len(hparams) == 0:
warning("No hyperparameters used in model function. Are you sure?")
# set global distribution object to the one requested by tuner
# !MUST be after _eval_model_fn()
config._DISTRIBUTIONS = distributions(hparams)
# instances management
self.max_fail_streak = 5 # how many failure before giving up
self.instance_states = InstanceStatesCollection()
# previous models
print("Loading from %s" % self.state.host.results_dir)
count = self.instance_states.load_from_dir(self.state.host.results_dir,
self.state.project,
self.state.architecture)
self.stats.instance_states_previously_trained = count
info("Tuner initialized")
def test_summary(kwargs, capsys):
state = TunerState('test', None, **kwargs)
state.summary()
captured = capsys.readouterr()
to_test = [
'results: %s' % kwargs.get('results_dir'),
'tmp: %s' % kwargs.get('tmp_dir'),
'export: %s' % kwargs.get('export_dir'),
]
for s in to_test:
assert s in captured.out
def test_invalid_max_epochs(kwargs):
with pytest.raises(ValueError):
TunerState('test', None, max_epochs=[], **kwargs)
def test_invalid_epoch_budget(kwargs):
with pytest.raises(ValueError):
TunerState('test', None, epoch_budget=[], **kwargs)
def test_invalid_user_info(kwargs):
with pytest.raises(ValueError):
TunerState('test', None, user_info=[], **kwargs)
with pytest.raises(ValueError):
TunerState('test', None, user_info='bad', **kwargs)
def test_is_serializable(kwargs):
st = TunerState('test', None, **kwargs)
is_serializable(st)