Datasets:
jblitzar
/
github-python

Dataset card Data Studio Files
xet
 Community
text
stringlengths
0
93.6k
if __name__ == '__main__':
# url = 'http://zcsy.sxu.edu.cn/'
# project_name = 'tmp'
target_txt = sys.argv[1]
project_name = sys.argv[2] if sys.argv[2] else 'tmp'
main(target_txt, project_name)
# <FILESEP>
from TripleGAN import TripleGAN
from utils import show_all_variables
from utils import check_folder
import tensorflow as tf
import argparse
"""parsing and configuration"""
def parse_args():
desc = "Tensorflow implementation of TripleGAN"
parser = argparse.ArgumentParser(description=desc)
parser.add_argument('--n', type=int, default=4000, help='The number of dataset')
parser.add_argument('--dataset', type=str, default='cifar10', choices=['mnist', 'fashion-mnist', 'celebA', 'cifar10'],
help='The name of dataset')
# In now, only cifar 10...
parser.add_argument('--epoch', type=int, default=1000, help='The number of epochs to run')
parser.add_argument('--batch_size', type=int, default=20, help='The size of batch')
parser.add_argument('--unlabel_batch_size', type=int, default=250, help='The size of unlabel batch')
parser.add_argument('--z_dim', type=int, default=100, help='Dimension of noise vector')
parser.add_argument('--gan_lr', type=float, default=2e-4, help='learning rate of GAN')
parser.add_argument('--cla_lr', type=float, default=2e-3, help='learning rate of Classify')
parser.add_argument('--checkpoint_dir', type=str, default='checkpoint',
help='Directory name to save the checkpoints')
parser.add_argument('--result_dir', type=str, default='results',
help='Directory name to save the generated images')
parser.add_argument('--log_dir', type=str, default='logs',
help='Directory name to save training logs')
return check_args(parser.parse_args())
"""checking arguments"""
def check_args(args):
# --checkpoint_dir
check_folder(args.checkpoint_dir)
# --result_dir
check_folder(args.result_dir)
# --result_dir
check_folder(args.log_dir)
# --epoch
try:
assert args.epoch >= 1
except:
print('number of epochs must be larger than or equal to one')
# --batch_size
try:
assert args.batch_size >= 1
except:
print('batch size must be larger than or equal to one')
# --z_dim
try:
assert args.z_dim >= 1
except:
print('dimension of noise vector must be larger than or equal to one')
return args
"""main"""
def main():
# parse arguments
args = parse_args()
if args is None:
exit()
# open session
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
gan = TripleGAN(sess, epoch=args.epoch, batch_size=args.batch_size, unlabel_batch_size=args.unlabel_batch_size,
z_dim=args.z_dim, dataset_name=args.dataset, n=args.n, gan_lr = args.gan_lr, cla_lr = args.cla_lr,
checkpoint_dir=args.checkpoint_dir, result_dir=args.result_dir, log_dir=args.log_dir)
# build graph
gan.build_model()
# show network architecture
show_all_variables()
# launch the graph in a session
gan.train()
print(" [*] Training finished!")
# visualize learned generator
gan.visualize_results(args.epoch-1)
print(" [*] Testing finished!")
if __name__ == '__main__':
main()