M.sess.run(tf.global_variables_initializer()) saver = tf.train.Saver() if args.dirt > 0: run = args.run if args.run < 999 else 0 setup = [ ('model={:s}', 'dirtt'), ('src={:s}', args.src), ('trg={:s}', args.trg), ('nn={:s}', args.nn), ('trim={:d}', args.trim), ('dw={:.0e}', args.dw), ('bw={:.0e}', 0), ('sw={:.0e}', args.sw), ('tw={:.0e}', args.tw), ('dirt={:05d}', 0), ('run={:04d}', run) ] vada_name = '_'.join([t.format(v) for (t, v) in setup]) path = tf.train.latest_checkpoint(os.path.join('checkpoints', vada_name)) saver.restore(M.sess, path) print("Restored from {}".format(path)) src = get_data(args.src) trg = get_data(args.trg) train(M, src, trg, saver=saver, has_disc=args.dirt == 0, model_name=model_name)
parser = argparse.ArgumentParser( formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('--trg', type=str, default='svhn', help="Trg data") parser.add_argument('--nn', type=str, default='v1', help="Architecture") parser.add_argument('--Z', type=int, default=10, help="Z dimensionality") parser.add_argument('--lr', type=float, default=1e-3, help="Learning rate") parser.add_argument('--run', type=int, default=999, help="Run index") parser.add_argument('--datadir', type=str, default=DATA, help="Data directory") parser.add_argument('--logdir', type=str, default='log', help="Log directory") codebase_args.args = args = parser.parse_args() pprint(vars(args)) from codebase.datasets import get_data from codebase.models.vae import vae from codebase.train import train # Make model name setup = [('model={:s}', 'vae'), ('trg={:s}', args.trg), ('nn={:s}', args.nn), ('Z={:d}', args.Z), ('lr={:.0e}', args.lr), ('run={:04d}', args.run)] model_name = '_'.join([t.format(v) for (t, v) in setup]) print "Model name:", model_name M = vae() M.sess.run(tf.global_variables_initializer()) src = None trg = get_data(args.trg) saver = None # tf.train.Saver() train(M, src, trg, saver=saver, model_name=model_name)