def run_exp_lib(dataset_feat_net_triples,
get_model=get_model_with_default_configs):
results = []
exp_nums = len(dataset_feat_net_triples)
print("-----\nTotal %d experiments in this run:" % exp_nums)
for exp_id, (dataset_name, feat_str,
net) in enumerate(dataset_feat_net_triples):
print('{}/{} - {} - {} - {}'.format(exp_id + 1, exp_nums, dataset_name,
feat_str, net))
print("Here we go..")
sys.stdout.flush()
for exp_id, (dataset_name, feat_str,
net) in enumerate(dataset_feat_net_triples):
print('-----\n{}/{} - {} - {} - {}'.format(exp_id + 1, exp_nums,
dataset_name, feat_str,
net))
sys.stdout.flush()
dataset = get_dataset(dataset_name,
sparse=True,
feat_str=feat_str,
root=args.data_root)
model_func = get_model(net)
if 'MNIST' in dataset_name or 'CIFAR' in dataset_name:
train_dataset, test_dataset = dataset
train_acc, acc, duration = single_train_test(
train_dataset,
test_dataset,
model_func,
epochs=args.epochs,
batch_size=args.batch_size,
lr=args.lr,
lr_decay_factor=args.lr_decay_factor,
lr_decay_step_size=args.lr_decay_step_size,
weight_decay=0,
epoch_select=args.epoch_select,
with_eval_mode=args.with_eval_mode)
std = 0
else:
cross_validation_with_val_set(
dataset,
model_func,
folds=10,
epochs=args.epochs,
batch_size=args.batch_size,
lr=args.lr,
lr_decay_factor=args.lr_decay_factor,
lr_decay_step_size=args.lr_decay_step_size,
weight_decay=0,
epoch_select=args.epoch_select,
with_eval_mode=args.with_eval_mode,
logger=logger,
dataset_name=args.dataset,
aug1=args.aug1,
aug_ratio1=args.aug_ratio1,
aug2=args.aug2,
aug_ratio2=args.aug_ratio2,
suffix=args.suffix)
"""
def run_exp_lib(dataset_feat_net_triples,
get_model=get_model_with_default_configs):
results = []
exp_nums = len(dataset_feat_net_triples)
print("-----\nTotal %d experiments in this run:" % exp_nums)
for exp_id, (dataset_name, feat_str,
net) in enumerate(dataset_feat_net_triples):
print('{}/{} - {} - {} - {}'.format(exp_id + 1, exp_nums, dataset_name,
feat_str, net))
print("Here we go..")
sys.stdout.flush()
for exp_id, (dataset_name, feat_str,
net) in enumerate(dataset_feat_net_triples):
print('-----\n{}/{} - {} - {} - {}'.format(exp_id + 1, exp_nums,
dataset_name, feat_str,
net))
sys.stdout.flush()
dataset = get_dataset(dataset_name,
sparse=True,
feat_str=feat_str,
root=args.data_root)
model_func = get_model(net)
if 'MNIST' in dataset_name or 'CIFAR' in dataset_name:
train_dataset, test_dataset = dataset
train_acc, acc, duration = single_train_test(
train_dataset,
test_dataset,
model_func,
epochs=args.epochs,
batch_size=args.batch_size,
lr=args.lr,
lr_decay_factor=args.lr_decay_factor,
lr_decay_step_size=args.lr_decay_step_size,
weight_decay=0,
epoch_select=args.epoch_select,
with_eval_mode=args.with_eval_mode)
std = 0
else:
train_acc, acc, std, duration = cross_validation_with_val_set(
dataset,
model_func,
folds=10,
epochs=args.epochs,
batch_size=args.batch_size,
lr=args.lr,
lr_decay_factor=args.lr_decay_factor,
lr_decay_step_size=args.lr_decay_step_size,
weight_decay=0,
epoch_select=args.epoch_select,
with_eval_mode=args.with_eval_mode,
logger=logger)
summary1 = 'data={}, model={}, feat={}, eval={}'.format(
dataset_name, net, feat_str, args.epoch_select)
summary2 = 'train_acc={:.2f}, test_acc={:.2f} ± {:.2f}, sec={}'.format(
train_acc * 100, acc * 100, std * 100, round(duration, 2))
results += ['{}: {}, {}'.format('fin-result', summary1, summary2)]
print('{}: {}, {}'.format('mid-result', summary1, summary2))
sys.stdout.flush()
print('-----\n{}'.format('\n'.join(results)))
sys.stdout.flush()