def main():
# open session
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
# declare instance for GAN
classes = ['angry']
gan = WGAN(classes=classes,
sess=sess,
epoch=EPOCH,
batch_size=BATCH_SIZE,
z_dim=Z_DIM,
dataset_name=DATASET,
augmentation=False,
aug_ratio=12,
checkpoint_dir=CHECKPOINT_DIR,
result_dir=RESULT_DIR,
log_dir=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(EPOCH - 1)
print(" [*] Testing finished!")
def main(_):
check_dir()
print_config()
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.5)
run_option = tf.ConfigProto(gpu_options=gpu_options)
with tf.Session(config=run_option) as sess:
wgan = WGAN(config=FLAGS, sess=sess)
wgan.build_model()
if FLAGS.is_training:
wgan.train_model()
if FLAGS.is_testing:
wgan.test_model()
def main():
with tf.Session() as sess:
gan = WGAN(sess,
epoch=10000,
batch_size=16,
dataset_name='fire2.tfrecords',
checkpoint_dir='checkpoint',
result_dir='results',
log_dir='logs')
# build graph
gan.build_model()
show_all_variables()
gan.train()
print(" [*] Training finished!")
gan.visualize_results(20 - 1)
print(" [*] Testing finished!")
import argparse
desc = "dimension"
parser = argparse.ArgumentParser(description=desc)
parser.add_argument('--dim', type=int, help='input dimension')
args = parser.parse_args()
dim = args.dim
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
gan = WGAN(sess,
epoch=20,
batch_size=128,
z_dim=dim,
dataset_name='fashion-mnist',
checkpoint_dir='checkpoints',
result_dir='results',
log_dir='logs')
if gan is None:
raise Exception("[!] There is no option for " + args.gan_type)
# build graph
gan.build_model()
# show network architecture
show_all_variables()
# launch the graph in a session
gan.train()
print(" [*] Training finished!")