def predict(self, features):
self.assert_is_loaded()
# Forcing both session and graph to be defaults here to force a graph
# context if this ever gets used during Eager execution. Makes testing
# easier too.
with self._session.as_default(), self._graph.as_default():
return self._session.run(
self._predictions,
tensorspec_utils.map_feed_dict(self._features, features))
def predict(self, features):
"""Predicts based on feature input using the loaded model.
Args:
features: A dict containing the features used for predictions.
Returns:
The result of the queried model predictions.
"""
self.assert_is_loaded()
return self._sess.run(
self._predictions,
tensorspec_utils.map_feed_dict(self._features, features))
def preprocess(preprocessor, feature_spec, label_spec, flatten=False):
with tf.Session() as sess:
feature_placeholders = tensorspec_utils.make_placeholders(
feature_spec, batch_size=1)
label_placeholders = None
if label_spec is not None:
label_placeholders = tensorspec_utils.make_placeholders(
label_spec, batch_size=1)
# Normally we want our features and labels to be flattened.
# However we support not flattened hierarchies as well.
if flatten:
feature_placeholders = tensorspec_utils.flatten_spec_structure(
feature_placeholders)
if label_spec is not None:
label_placeholders = tensorspec_utils.flatten_spec_structure(
label_placeholders)
(features_preprocessed, labels_preprocessed) = preprocessor.preprocess(
features=feature_placeholders,
labels=label_placeholders,
mode=tf.estimator.ModeKeys.TRAIN)
# We create a mapping of {key: np.array} or a namedtuple spec structure.
np_feature_spec = tensorspec_utils.make_random_numpy(
feature_spec, batch_size=1)
if label_placeholders is not None:
np_label_spec = tensorspec_utils.make_random_numpy(
label_spec, batch_size=1)
# We create our feed dict which basically consists of
# {placeholders: np.array}.
feed_dict = tensorspec_utils.map_feed_dict(feature_placeholders,
np_feature_spec,
ignore_batch=True)
if label_placeholders is not None:
feed_dict = tensorspec_utils.map_feed_dict(label_placeholders,
np_label_spec,
feed_dict,
ignore_batch=True)
fetch_results = [features_preprocessed]
if label_placeholders is not None:
fetch_results.append(labels_preprocessed)
np_preprocessed = sess.run(
fetch_results, feed_dict=feed_dict)
np_features_preprocessed = np_preprocessed[0]
if label_placeholders is not None:
np_labels_preprocessed = np_preprocessed[1]
np_feature_spec = tensorspec_utils.flatten_spec_structure(
np_feature_spec)
if label_placeholders is not None:
np_label_spec = tensorspec_utils.flatten_spec_structure(np_label_spec)
for key, value in np_feature_spec.items():
np.testing.assert_allclose(value, np_features_preprocessed[key])
if label_placeholders is not None:
for key, value in np_label_spec.items():
np.testing.assert_allclose(value, np_labels_preprocessed[key])
