def eval_child_model(self,
model,
data_loader,
mode,
only_noise_class=False):
"""Evaluate the child model.
Args:
model: image model that will be evaluated.
data_loader: dataset object to extract eval data from.
mode: will the model be evalled on train, val or test.
only_noise_class: If True, evaluate the model only on examples from the
noised class.
Returns:
Accuracy of the model on the specified dataset.
"""
tf.logging.info('Evaluating child model in mode %s', mode)
while True:
try:
with self._new_session(model):
accuracy, logit_norm_val, hidden_norm_val, cost = helper_utils.eval_child_model(
self.session, model, data_loader, mode,
only_noise_class)
tf.logging.info(
'Eval child model accuracy: {}'.format(accuracy))
# If epoch trained without raising the below errors, break
# from loop.
break
except (tf.errors.AbortedError, tf.errors.UnavailableError) as e:
tf.logging.info('Retryable error caught: %s. Retrying.', e)
return accuracy, logit_norm_val, hidden_norm_val, cost
def eval_child_model(self, model, data_loader, mode):
"""Evaluate the child model.
Args:
model: image model that will be evaluated.
data_loader: dataset object to extract eval data from.
mode: will the model be evalled on train, val or test.
Returns:
Accuracy of the model on the specified dataset.
"""
tf.logging.info('Evaluating child model in mode %s', mode)
while True:
try:
with self._new_session(model):
accuracy = helper_utils.eval_child_model(
self.session,
model,
data_loader,
mode)
tf.logging.info('Eval child model accuracy: {}'.format(accuracy))
# If epoch trained without raising the below errors, break
# from loop.
break
except (tf.errors.AbortedError, tf.errors.UnavailableError) as e:
tf.logging.info('Retryable error caught: %s. Retrying.', e)
return accuracy