def _parameters_sanity_check(self):
""" Parameteres sanity check. """
if self.discriminator:
assert self.lr_patch_size * self.scale == self.discriminator.patch_size
self.adam_optimizer
if self.feature_extractor:
assert self.lr_patch_size * self.scale == self.feature_extractor.patch_size
check_parameter_keys(
self.learning_rate,
needed_keys=['initial_value'],
optional_keys=['decay_factor', 'decay_frequency'],
default_value=None,
)
check_parameter_keys(
self.flatness,
needed_keys=[],
optional_keys=['min', 'increase_frequency', 'increase', 'max'],
default_value=0.0,
)
check_parameter_keys(
self.adam_optimizer,
needed_keys=['beta1', 'beta2'],
optional_keys=['epsilon'],
default_value=None,
)
check_parameter_keys(self.log_dirs, needed_keys=['logs', 'weights'])
def test_check_parameter_keys(self):
par = {'a': 0}
utils.check_parameter_keys(parameter=par, needed_keys=['a'])
utils.check_parameter_keys(parameter=par,
needed_keys=None,
optional_keys=['b'],
default_value=-1)
self.assertTrue(par['b'] == -1)
try:
utils.check_parameter_keys(parameter=par, needed_keys=['c'])
except:
self.assertTrue(True)
else:
self.assertTrue(False)
def check_parameter_keys(parameter,
needed_keys,
optional_keys=None,
default_value=None):
if needed_keys:
for key in needed_keys:
if key not in parameter:
logger.error('{p} is missing key {k}'.format(
p=parameter, k=key))
raise
if optional_keys:
for key in optional_keys:
if key not in parameter:
logger.info('Setting {k} in {p} to {d}'.format(
k=key, p=parameter, d=default_value))
parameter[key] = default_value