def main():
args = parser.parse_args()
config = vars(args)
config['gf_dim'] = 64
config['df_dim'] = 64
config['test_is_adaptive_eps'] = True
pp.pprint(config)
if not os.path.exists(args.log_dir):
os.makedirs(args.log_dir)
if not os.path.exists(args.sample_dir):
os.makedirs(args.sample_dir)
decoder = get_decoder(args.decoder, config)
encoder = get_encoder(args.encoder, config)
iaf_layers = [
get_iaf_layer(args.encoder, config, 'iaf_layer_%d' % i) for i in range(config['iaf_nlayers'])
]
if args.is_train:
x_train = inputs.get_inputs('train', config)
x_val = inputs.get_inputs('val', config)
train(encoder, decoder, iaf_layers, x_train, x_val, config)
else:
x_test = inputs.get_inputs('test', config)
test(encoder, decoder, iaf_layers, x_test, config)
def main():
args = parser.parse_args()
config = vars(args)
config['gf_dim'] = 64
config['df_dim'] = 64
config['test_is_adaptive_eps'] = True
pp.pprint(config)
if not os.path.exists(args.log_dir):
os.makedirs(args.log_dir)
if not os.path.exists(args.sample_dir):
os.makedirs(args.sample_dir)
decoder = get_decoder(args.decoder, config)
encoder = get_encoder(args.encoder, config)
adversary = get_adversary(args.adversary, config)
if args.is_train:
x_train = inputs.get_inputs('train', config)
x_val = inputs.get_inputs('val', config)
train(encoder, decoder, adversary, x_train, x_val, config)
else:
x_test = inputs.get_inputs('test', config)
test(encoder, decoder, adversary, x_test, config)