return 2**-6
elif 2000 <= x < 4000:
return 2**-6
elif 4000 <= x < 6000:
return 2**-8
elif 6000 <= x < 8000:
return 2**-8
elif 8000 <= x:
return 2**-11
update_step_size_method = 'schedule'
else:
raise NotImplementedError
# Set up the data, parameters, model, optimizer, and results objects
data = opt_structures.Data(train_loader, valid_loader, test_loader)
params = opt_structures.Params(num_classes=num_classes,
ckn=True,
train_w_layers=None,
lambda_filters=0,
normalize=True,
w_last_init=None,
update_step_size_method=update_step_size_method,
step_size_init=args.step_size,
step_size_schedule=step_size_schedule,
tau=args.hessian_reg,
num_iters=args.num_iters,
save_path=save_file + '_params.pickle',
eval_test_every=args.eval_test_every,
save_every=500)
model = opt_structures.Model(model, save_path=save_file + '_model.pickle')
params = parse_config.load_config('../../cfg/ckn-gm.cfg')
if args.num_filters > 0:
nlayers = len(params['num_filters'])
params['num_filters'] = [args.num_filters] * nlayers
params['patch_sigma'] = [bw] * nlayers
layers = parse_config.create_layers(params)
model = net.CKN(layers).to(defaults.device)
model.init(train_loader)
print('Done with initialization')
# Set up the data, parameters, model, results, and optimizer objects
if args.num_labeled > 0:
data = opt_structures.Data(train_labeled_loader,
train_unlabeled_loader,
valid_loader,
train_valid_loader,
test_loader,
deepcluster_loader=train_loader)
else:
data = opt_structures.Data(None,
train_unlabeled_loader,
None,
None,
test_loader,
deepcluster_loader=train_loader)
params = opt_structures.Params(
nclasses=nclasses,
min_frac_points_class=min_frac_points_class,
max_frac_points_class=max_frac_points_class,
ckn=ckn,
project=True,
