text stringlengths 1 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()
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.