def _create_test_input_config(self): """Generate test input_config_pb2.InputConfig proto.""" feature_group_1 = self._create_test_feature_group( encoder_type=input_config_pb2.EncoderType.BOW) feature_context_movie_genre = input_config_pb2.Feature( feature_name='context_movie_genre', feature_type=input_config_pb2.FeatureType.STRING, vocab_name='movie_genre_vocab.txt', vocab_size=19, embedding_dim=3) feature_group_2 = input_config_pb2.FeatureGroup( features=[feature_context_movie_genre], encoder_type=input_config_pb2.EncoderType.BOW) feature_label = input_config_pb2.Feature( feature_name='label_movie_id', feature_type=input_config_pb2.FeatureType.INT, vocab_size=3952, embedding_dim=4) input_config = input_config_pb2.InputConfig( activity_feature_groups=[feature_group_1, feature_group_2], label_feature=feature_label) return input_config
def _create_test_input_config(self, encoder_type: input_config_pb2.EncoderType): """Generate test input_config_pb2.InputConfig proto.""" feature_context_movie_id = input_config_pb2.Feature( feature_name='context_movie_id', feature_type=input_config_pb2.FeatureType.INT, vocab_size=20, embedding_dim=4) feature_context_movie_rating = input_config_pb2.Feature( feature_name='context_movie_rating', feature_type=input_config_pb2.FeatureType.FLOAT) feature_group_1 = input_config_pb2.FeatureGroup( features=[feature_context_movie_id, feature_context_movie_rating], encoder_type=encoder_type) feature_label = input_config_pb2.Feature( feature_name='label_movie_id', feature_type=input_config_pb2.FeatureType.INT, vocab_size=20, embedding_dim=4) input_config = input_config_pb2.InputConfig( activity_feature_groups=[feature_group_1], label_feature=feature_label) return input_config
def load_input_config(): """Load input config.""" assert FLAGS.input_config_file, 'input_config_file cannot be empty.' with tf.io.gfile.GFile(FLAGS.input_config_file, 'rb') as reader: return text_format.Parse(reader.read(), input_config_pb2.InputConfig())
feature_type: STRING vocab_name: "movie_genre_vocab.txt" vocab_size: 19 embedding_dim: 8 feature_length: 8 } encoder_type: BOW } label_feature { feature_name: "label_movie_id" feature_type: INT vocab_size: 3952 embedding_dim: 8 feature_length: 1 } """, input_config_pb2.InputConfig()) EXAMPLE1 = text_format.Parse( """ features { feature { key: "context_movie_id" value { int64_list { value: [1, 2, 0, 0, 0] } } } feature { key: "context_movie_rating" value {