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)