import tensorflow as tf
from data_loader.data_generator import DataGenerator
from models.example_model import ExampleModel
from trainers.example_trainer import ExampleTrainer
from utils.config import process_config
from utils.dirs import create_dirs
from utils.logger import Logger
from utils.utils import get_args
def main():
# capture the config path from the run arguments
# then process the json configuration file
try:
args = get_args()
config = process_config(args.config)
except:
print("missing or invalid arguments")
exit(0)
# create the experiments dirs
create_dirs([config.summary_dir, config.checkpoint_dir])
# create tensorflow session
sess = tf.Session()
# create your data generator
data = DataGenerator(config)
# create an instance of the model you want
model = ExampleModel(config)
# create tensorboard logger
logger = Logger(sess, config)
# create trainer and pass all the previous components to it
trainer = ExampleTrainer(sess, model, data, config, logger)
#load model if exists
model.load(sess)
# here you train your model
trainer.train()
if __name__ == '__main__':
main()
