config = ConfigProto()
config.gpu_options.allow_growth = True
session = InteractiveSession(config=config)
arg_parser = argparse.ArgumentParser(
description='Run model training for each tagged label')
arg_parser.add_argument('--config_path',
metavar='config_path',
default='./al_3_train.cfg',
type=str,
help='Path to the training config file',
required=False)
args = arg_parser.parse_args()
config = configparser.ConfigParser()
config.read(args.config_path)
MAX_TAGS = int(config.get('training', 'MAX_TAGS'))
BATCH_SIZE = int(config.get('training', 'BATCH_SIZE'))
BUFFER_SIZE = int(config.get('training', 'BUFFER_SIZE'))
INPUT_FILE = config.get('data', 'INPUT_FILE')
OUTPUT_PATH = config.get('data', 'OUTPUT_PATH')
EARLY_STOPPING_ROUNDS = int(config.get('training', 'EARLY_STOPPING_ROUNDS'))
EMBED_TRAINABLE = bool(config.get('training', 'EMBED_TRAINABLE') == 'True')
RANDOM_EMBED = bool(config.get('training', 'RANDOM_EMBED') == 'True')
DROPOUT_LEVEL = float(config.get('training', 'DROPOUT_LEVEL'))
LEARNING_RATE = float(config.get('training', 'LEARNING_RATE'))
N_LSTM_UNITS = int(config.get('training', 'N_LSTM_UNITS'))
N_FC_NEURONS = int(config.get('training', 'N_FC_NEURONS'))
Path(OUTPUT_PATH).mkdir(parents=True, exist_ok=True)
from pathlib import Path
import logging
import configparser
import ui_utils
import nlp_tools
# allow gpu memory growth
from tensorflow.compat.v1 import ConfigProto
from tensorflow.compat.v1 import InteractiveSession
config = ConfigProto()
config.gpu_options.allow_growth = True
session = InteractiveSession(config=config)
config = configparser.ConfigParser()
config.read('./config_core_train.cfg')
OUTPUT_DIR = config.get('data', 'OUTPUT_DIR')
FILE_NOTE = config.get('data', 'FILE_NOTE')
APPLY_FILE = config.get('applying', 'APPLY_FILE')
CLF_THRESHOLD = float(config.get('applying', 'CLF_THRESHOLD'))
APPLY_BATCH_SIZE = int(config.get('applying', 'APPLY_BATCH_SIZE'))
OUTPUT_PATH = f"{OUTPUT_DIR}{FILE_NOTE}/"
SCORED_PATH = f"{OUTPUT_PATH}scored/"
Path(SCORED_PATH).mkdir(parents=True, exist_ok=True)
# initialize logger
root_logger = logging.getLogger()
formatter = logging.Formatter('%(asctime)s: %(levelname)s:: %(message)s')
