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