def main(_):
GPU_ID = FLAGS.gpu
os.environ[
"CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 on stackoverflow
os.environ["CUDA_VISIBLE_DEVICES"] = str(GPU_ID)
RUN = FLAGS.run
EXP_DIR = FLAGS.exp_dir
SEED = int(FLAGS.seed)
ARCH = FLAGS.arch
model = Model(arch=ARCH)
train_ops = TrainOps(model, EXP_DIR, RUN, ARCH)
train_ops.load_exp_config()
if FLAGS.mode == 'train':
print 'Training'
train_ops.train(seed=SEED)
if FLAGS.mode == 'test':
print 'Testing'
train_ops.test()
def main(_):
#npr.seed(int(FLAGS.seed))
GPU_ID = FLAGS.gpu
os.environ[
"CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 on stackoverflow
os.environ["CUDA_VISIBLE_DEVICES"] = str(GPU_ID)
EXP_DIR = FLAGS.exp_dir
model = Model()
tr_ops = TrainOps(model, EXP_DIR)
if 'train' in FLAGS.mode:
npr.seed(int(FLAGS.seed))
if FLAGS.mode == 'train_ERM':
print 'Training model with standard ERM'
tr_ops.load_exp_config()
tr_ops.train()
if FLAGS.mode == 'train_RDA':
print 'Training model with RDA'
tr_ops.load_exp_config()
tr_ops.train(random_transf=True)
if FLAGS.mode == 'train_RSDA':
print 'Training model with RSDA'
tr_ops.load_exp_config()
tr_ops.train_search(search_algorithm='random_search')
if FLAGS.mode == 'train_ESDA':
print 'Training model with ESDA'
tr_ops.load_exp_config()
tr_ops.train_search(search_algorithm='evolution_search')
elif FLAGS.mode == 'test_all':
print 'Testing all'
tr_ops.load_exp_config()
tr_ops.test_all()
elif FLAGS.mode == 'test_RS':
print 'Random search'
tr_ops.load_exp_config()
tr_ops.test_random_search(run=str(FLAGS.run),
seed=int(FLAGS.seed),
no_iters=int(FLAGS.search_no_iters),
string_length=int(
FLAGS.transf_string_length))
elif FLAGS.mode == 'test_ES':
print 'Evolution search'
tr_ops.load_exp_config()
tr_ops.test_evolution_search(run=str(FLAGS.run),
seed=int(FLAGS.seed),
no_iters=int(FLAGS.search_no_iters),
string_length=int(
FLAGS.transf_string_length),
pop_size=int(FLAGS.pop_size),
mutation_rate=float(FLAGS.mutation_rate))