def _predict_input_fn():
feature_map = parsing_ops.parse_example(
input_lib.limit_epochs(serialized_examples, num_epochs=1),
feature_spec)
_, features = graph_io.queue_parsed_features(feature_map)
features.pop('y')
return features, None
def _predict_input_fn():
feature_map = parsing_ops.parse_example(
input_lib.limit_epochs(serialized_examples, num_epochs=1),
feature_spec)
_, features = graph_io.queue_parsed_features(feature_map)
features.pop('y')
return features, None
def test_queue_parsed_features_single_tensor(self):
with ops.Graph().as_default() as g, self.session(graph=g) as session:
features = {"test": constant_op.constant([1, 2, 3])}
_, queued_features = graph_io.queue_parsed_features(features)
coord = coordinator.Coordinator()
threads = queue_runner_impl.start_queue_runners(session, coord=coord)
out_features = session.run(queued_features["test"])
self.assertAllEqual([1, 2, 3], out_features)
coord.request_stop()
coord.join(threads)
def test_queue_parsed_features_single_tensor(self):
with ops.Graph().as_default() as g, self.test_session(graph=g) as session:
features = {"test": constant_op.constant([1, 2, 3])}
_, queued_features = graph_io.queue_parsed_features(features)
coord = coordinator.Coordinator()
threads = queue_runner_impl.start_queue_runners(session, coord=coord)
out_features = session.run(queued_features["test"])
self.assertAllEqual([1, 2, 3], out_features)
coord.request_stop()
coord.join(threads)
def _train_input_fn():
feature_map = parsing_ops.parse_example(serialized_examples,
feature_spec)
_, features = graph_io.queue_parsed_features(feature_map)
labels = features.pop('y')
return features, labels
def _train_input_fn():
feature_map = parsing_ops.parse_example(
serialized_examples, feature_spec)
_, features = graph_io.queue_parsed_features(feature_map)
labels = features.pop('y')
return features, labels
