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)