import itertools
from awave.utils.scheduling import run_serial
DIR_FILE = os.path.dirname(
os.path.realpath(__file__)) # directory of the config file
if __name__ == '__main__':
params_to_vary = {
'seed': [1],
'wave': ['db5'],
'J': [4],
'mode': ['zero'],
'init_factor': [1],
'noise_factor': [0],
'const_factor': [0],
'batch_size': [100],
'lr': [0.001],
'num_epochs': [50],
'attr_methods': ['Saliency'],
'lamL1wave': [0.005, 0.01, 0.02],
'lamL1attr': np.round([0] + list(np.geomspace(0.001, 0.05, 10)), 5),
'dirname': ['db5_saliency_warmstart_seed=1_new'],
'warm_start': [True]
}
ks = sorted(params_to_vary.keys())
vals = [params_to_vary[k] for k in ks]
param_combinations = list(itertools.product(*vals)) # list of tuples
# iterate
run_serial(ks, param_combinations, path=opj(DIR_FILE, "ex_cosmology.py"))
opj = os.path.join
import itertools
from awave.utils.scheduling import run_serial
DIR_FILE = os.path.dirname(
os.path.realpath(__file__)) # directory of the config file
if __name__ == '__main__':
params_to_vary = {
'seed': [1],
'wave': ['db5'],
'J': [4],
'init_factor': [1],
'noise_factor': [0.3],
'const_factor': [0],
'batch_size': [100],
'lr': [0.001],
'num_epochs': [50],
'attr_methods': ['Saliency'],
'lamL1wave': np.round(list(np.geomspace(0.00001, 0.0001, 5)), 5),
'lamL1attr': np.round([0] + list(np.geomspace(0.00001, 50, 20)), 5),
'dirname': ['db5_saliency_warmstart_seed=1'],
'warm_start': [True]
}
ks = sorted(params_to_vary.keys())
vals = [params_to_vary[k] for k in ks]
param_combinations = list(itertools.product(*vals)) # list of tuples
# iterate
run_serial(ks, param_combinations, path=opj(DIR_FILE, "ex_simulation.py"))
DIR_FILE = os.path.dirname(
os.path.realpath(__file__)) # directory of the config file
if __name__ == '__main__':
params_to_vary = {
'seed': [1],
'wave': ['db3'],
'J': [3],
'mode': ['periodization'],
'init_factor': [1],
'noise_factor': [0],
'const_factor': [0],
'batch_size': [100],
'lr': [0.001],
'num_epochs': [50],
'attr_methods': ['Saliency'],
'lamL1wave': [0.05],
'lamL1attr': np.round([0] + list(np.geomspace(0.01, 5, 20)), 5),
'target': [-1],
'model': ['cnn'],
'dirname': ['db3_saliency_warmstart_mode=per_cnn_seed=1'],
'warm_start': [True]
}
ks = sorted(params_to_vary.keys())
vals = [params_to_vary[k] for k in ks]
param_combinations = list(itertools.product(*vals)) # list of tuples
# iterate
run_serial(ks, param_combinations, path=opj(DIR_FILE, "ex_mnist.py"))