def main():
args = parser.parse_args()
print('Options:')
for (key, value) in iteritems(vars(args)):
print("{:12}: {}".format(key, value))
assert os.path.exists(args.xp_dir)
# default value for basefile: string basis for all exported file names
if args.out_name:
base_file = "{}/{}".format(args.xp_dir, args.out_name)
else:
base_file = "{}/{}_{}_{}".format(args.xp_dir, args.dataset,
args.solver, args.loss)
# if pickle file already there, consider run already done
if (os.path.exists("{}_weights.p".format(base_file))
and os.path.exists("{}_results.p".format(base_file))):
sys.exit()
# computation device
if 'gpu' in args.device:
try: # Theano-1.0.2
theano.gpuarray.use(args.device)
except: # Theano-0.8.2
theano.sandbox.cuda.use(args.device)
np.random.seed(args.seed)
# set save_at to n_epochs if not provided
save_at = args.n_epochs if not args.save_at else args.save_at
log_file = "{}/log_{}.txt".format(args.xp_dir, args.dataset)
save_to = "{}_weights.p".format(base_file)
weights = "{}/{}_weights.p".format(args.xp_dir, args.in_name) \
if args.in_name else None
# update config data
Cfg.C.set_value(args.C)
Cfg.batch_size = args.batch_size
Cfg.compile_lwsvm = False
Cfg.learning_rate.set_value(args.lr)
Cfg.softmax_loss = (args.loss == 'ce')
# train
nnet = NeuralNet(dataset=args.dataset, use_weights=weights)
nnet.train(solver=args.solver,
n_epochs=args.n_epochs,
save_at=save_at,
save_to=save_to)
# log
nnet.log.save_to_file("{}_results.p".format(base_file))
nnet.dump_weights("{}_final_weights.p".format(base_file))
logged = open(log_file, "a")
logged.write("{}\t{}\t{}: OK\n".format(args.dataset, args.solver,
args.loss))
logged.close()
def main():
args = parser.parse_args()
print('Options:')
for (key, value) in iteritems(vars(args)):
print("{:12}: {}".format(key, value))
assert os.path.exists(args.xp_dir)
Cfg.C.set_value(args.C)
Cfg.D.set_value(args.D)
Cfg.batch_size = args.batch_size
Cfg.compile_lwsvm = True
Cfg.softmax_loss = False
# default value for basefile: string basis for all exported file names
if args.out_name:
base_file = "{}/{}".format(args.xp_dir, args.out_name)
else:
base_file = "{}/{}_lwsvm".format(args.xp_dir, args.in_name)
# if pickle file already there, consider run already done
if (os.path.exists("{}_final_weights.p".format(base_file))
and os.path.exists("{}_results.p".format(base_file))):
sys.exit()
# computation device
if 'gpu' in args.device:
try: # Theano-1.0.2
theano.gpuarray.use(args.device)
except: # Theano-0.8.2
theano.sandbox.cuda.use(args.device)
np.random.seed(args.seed)
log_file = "{}/log_{}.txt".format(args.xp_dir, args.dataset)
if args.dataset != 'imagenet':
weights = "{}/{}_weights.p".format(args.xp_dir, args.in_name)
else:
weights = "{}/vgg16.pkl".format(args.xp_dir)
nnet = NeuralNet(dataset=args.dataset, use_weights=weights)
nnet.train(solver="svm")
# log
nnet.log.save_to_file("{}_results.p".format(base_file))
nnet.dump_weights("{}_final_weights.p".format(base_file))
logged = open(log_file, "a")
logged.write("{}\t{}\tlwsvm: OK\n".format(args.dataset, args.in_name))
logged.close()
