_logger = log.get_logger()
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="""Use the test dataset to check for the total accuracy of
the highest iteration's checkpoint model.""")
parser.add_argument(
"--test-dir",
default="./test",
type=str,
help="Directory containing CSV files used in testing.")
parser.add_argument(
"--checkpoint-dir",
default="./checkpoints",
type=str,
help="Location to restore checkpoint files.")
parser.add_argument("-v", "--verbosity", action="count", default=0)
args = parser.parse_args()
log.set_verbosity(args.verbosity)
test_features, test_species = network.read_data_set(
"{test_dir}/*.csv".format(test_dir=args.test_dir))
network.test(
test_features,
test_species,
args.checkpoint_dir)
import argparse
from iris import network
from iris import log
_logger = log.get_logger()
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="""Use the test dataset to check for the total accuracy of
the highest iteration's checkpoint model.""")
parser.add_argument("--test-dir",
default="./test",
type=str,
help="Directory containing CSV files used in testing.")
parser.add_argument("--checkpoint-dir",
default="./checkpoints",
type=str,
help="Location to restore checkpoint files.")
parser.add_argument("-v", "--verbosity", action="count", default=0)
args = parser.parse_args()
log.set_verbosity(args.verbosity)
test_features, test_species = network.read_data_set(
"{test_dir}/*.csv".format(test_dir=args.test_dir))
network.test(test_features, test_species, args.checkpoint_dir)
"--checkpoint-dir",
default="./checkpoints",
type=str,
help="Location to save checkpoint files.")
parser.add_argument(
"--checkpoint-save-every",
type=int,
help="Save a checkpoint every X iterations.")
parser.add_argument(
"--train-iterations",
default=20000,
type=int,
help="Number of train iterations to run.")
parser.add_argument("-v", "--verbosity", action="count", default=0)
args = parser.parse_args()
log.set_verbosity(args.verbosity)
_logger.info(
"Reading CSV files from %s.", args.train_dir)
train_features, train_species = network.read_data_set(
"{train_dir}/*.csv".format(train_dir=args.train_dir))
network.train(
train_features,
train_species,
args.checkpoint_dir,
args.train_iterations,
args.checkpoint_save_every)
