def get_problem(dataset, K, p, lmbd, rho, batch_size, save_dir):
# Setup the training constant and a test set
if dataset == 'artificial':
from Lcod.simple_problem_generator import SimpleProblemGenerator
from Lcod.simple_problem_generator import create_dictionary
D = create_dictionary(K, p, seed=290890)
pb = SimpleProblemGenerator(D, lmbd, rho=rho, batch_size=batch_size,
corr=corr, seed=422742)
elif dataset == 'mnist':
from Lcod.mnist_problem_generator import MnistProblemGenerator
from Lcod.mnist_problem_generator import create_dictionary_dl
D = create_dictionary_dl(lmbd, K, N=10000, dir_mnist=save_dir)
pb = MnistProblemGenerator(D, lmbd, batch_size=batch_size,
dir_mnist=save_dir, seed=42242)
elif dataset == 'images':
from Lcod.image_problem_generator import ImageProblemGenerator
from Lcod.image_problem_generator import create_dictionary_haar
p = int(np.sqrt(p))
D = create_dictionary_haar(p, wavelet='haar')
pb = ImageProblemGenerator(D, lmbd, batch_size=batch_size,
seed=1234)
else:
raise NameError("dataset {} not reconized by the script"
"".format(dataset))
return pb, D
def get_problem(config):
# retrieve the parameter of the problem
dataset = config['data']
batch_size, lmbd = config['batch_size'], config['lmbd']
seed = config.get('seed')
# Setup the training constant and a test set
if dataset == 'artificial':
from Lcod.simple_problem_generator import SimpleProblemGenerator
from Lcod.simple_problem_generator import create_dictionary
# retrieve specific parameters for the problem
K, p, rho = config['K'], config['p'], config['rho']
seed_D, corr = config.get('seed_D'), config.get('corr', 0)
D = create_dictionary(K, p, seed=seed_D)
pb = SimpleProblemGenerator(D,
lmbd,
rho=rho,
batch_size=batch_size,
corr=corr,
seed=seed)
elif dataset == 'adverse':
from Lcod.simple_problem_generator import SimpleProblemGenerator
from data_handlers.dictionaries import create_adversarial_dictionary
# retrieve specific parameters for the problem
K, p, rho = config['K'], config['p'], config['rho']
seed_D, corr = config.get('seed_D'), config.get('corr', 0)
D = create_adversarial_dictionary(K, p, seed=seed_D)
pb = SimpleProblemGenerator(D,
lmbd,
rho=rho,
batch_size=batch_size,
corr=corr,
seed=seed)
elif dataset == 'mnist':
from Lcod.mnist_problem_generator import MnistProblemGenerator
from Lcod.mnist_problem_generator import create_dictionary_dl
K, save_dir = config['K'], config['save_dir']
D = create_dictionary_dl(lmbd, K, N=10000, dir_mnist=save_dir)
pb = MnistProblemGenerator(D,
lmbd,
batch_size=batch_size,
dir_mnist=save_dir,
seed=seed)
elif dataset == 'images':
from Lcod.image_problem_generator import ImageProblemGenerator
from Lcod.image_problem_generator import create_dictionary_haar
p = config['p']
D = create_dictionary_haar(p)
pb = ImageProblemGenerator(D, lmbd, batch_size=batch_size, seed=seed)
else:
raise NameError("dataset {} not reconized by the script"
"".format(dataset))
return pb, D
def get_problem(config):
# retrieve the parameter of the problem
dataset = config['data']
batch_size, lmbd = config['batch_size'], config['lmbd']
seed = config.get('seed')
# Setup the training constant and a test set
if dataset == 'artificial':
from Lcod.simple_problem_generator import SimpleProblemGenerator
from Lcod.simple_problem_generator import create_dictionary
# retrieve specific parameters for the problem
K, p, rho = config['K'], config['p'], config['rho']
seed_D, corr = config.get('seed_D'), config.get('corr', 0)
D = create_dictionary(K, p, seed=seed_D)
pb = SimpleProblemGenerator(D, lmbd, rho=rho, batch_size=batch_size,
corr=corr, seed=seed)
elif dataset == 'adverse':
from Lcod.simple_problem_generator import SimpleProblemGenerator
from data_handlers.dictionaries import create_adversarial_dictionary
# retrieve specific parameters for the problem
K, p, rho = config['K'], config['p'], config['rho']
seed_D, corr = config.get('seed_D'), config.get('corr', 0)
D = create_adversarial_dictionary(K, p, seed=seed_D)
pb = SimpleProblemGenerator(D, lmbd, rho=rho, batch_size=batch_size,
corr=corr, seed=seed)
elif dataset == 'mnist':
from Lcod.mnist_problem_generator import MnistProblemGenerator
from Lcod.mnist_problem_generator import create_dictionary_dl
K, save_dir = config['K'], config['save_dir']
D = create_dictionary_dl(lmbd, K, N=10000, dir_mnist=save_dir)
pb = MnistProblemGenerator(D, lmbd, batch_size=batch_size,
dir_mnist=save_dir, seed=seed)
elif dataset == 'images':
from Lcod.image_problem_generator import ImageProblemGenerator
from Lcod.image_problem_generator import create_dictionary_haar
p = config['p']
D = create_dictionary_haar(p)
pb = ImageProblemGenerator(D, lmbd, batch_size=batch_size,
seed=seed)
else:
raise NameError("dataset {} not reconized by the script"
"".format(dataset))
return pb, D
save_dir = _assert_exist('save_exp', NAME_EXP)
_assert_exist(save_dir, 'ckpt')
save_curve = os.path.join(save_dir, "curve_cost.npy")
# Setup the training constant and a test set
if dataset == 'artificial':
from Lcod.simple_problem_generator import SimpleProblemGenerator
from Lcod.simple_problem_generator import create_dictionary
p = 64 # Dimension of the data
D = create_dictionary(K, p, seed=290890)
pb = SimpleProblemGenerator(D, lmbd, rho=rho, batch_size=batch_size,
corr=corr, seed=422742)
elif dataset == 'mnist':
from Lcod.mnist_problem_generator import MnistProblemGenerator
from Lcod.mnist_problem_generator import create_dictionary_dl
D = create_dictionary_dl(lmbd, K, N=10000, dir_mnist=save_dir)
pb = MnistProblemGenerator(D, lmbd, batch_size=batch_size,
dir_mnist=save_dir, seed=42242)
elif dataset == 'images':
from Lcod.image_problem_generator import ImageProblemGenerator
from Lcod.image_problem_generator import create_dictionary_haar
p = 8
reg_scale = 1e-4
D = create_dictionary_haar(p)
pb = ImageProblemGenerator(D, lmbd, batch_size=batch_size,
data_dir='data/VOC', seed=1234)
elif dataset == 'cifar':
from Lcod.cifar_generator import CifarProblemGenerator
from Lcod.cifar_generator import create_dictionary_dl
p = 8
reg_scale = 1e-4