def sng_builder(category):
if cfg.SNG.NAME == 'MDENAS':
return CategoricalMDENAS(category, cfg.SNG.THETA_LR)
elif cfg.SNG.NAME == 'DDPNAS':
return CategoricalDDPNAS(category, cfg.SNG.PRUNING_STEP)
elif cfg.SNG.NAME == 'SNG':
return SNG(category)
elif cfg.SNG.NAME == 'ASNG':
return ASNG(category)
elif cfg.SNG.NAME == 'dynamic_SNG':
return Dynamic_SNG(category,
step=cfg.SNG.PRUNING_STEP,
pruning=cfg.SNG.PRUNING)
elif cfg.SNG.NAME == 'dynamic_ASNG':
return Dynamic_ASNG(category,
step=cfg.SNG.PRUNING_STEP,
pruning=cfg.SNG.PRUNING)
elif cfg.SNG.NAME == 'MIGO':
return MIGO(categories=category,
step=cfg.SNG.PRUNING_STEP,
pruning=cfg.SNG.PRUNING,
sample_with_prob=cfg.SNG.PROB_SAMPLING,
utility_function=cfg.SNG.UTILITY,
utility_function_hyper=cfg.SNG.UTILITY_FACTOR,
momentum=cfg.SNG.MOMENTUM,
gamma=cfg.SNG.GAMMA)
else:
raise NotImplementedError
def get_optimizer(name,
category,
step=4,
gamma=0.9,
sample_with_prob=True,
utility_function='log',
utility_function_hyper=0.4):
if name == 'DDPNAS':
return CategoricalDDPNAS(category, 3)
elif name == 'MDENAS':
return CategoricalMDENAS(category, 0.01)
elif name == 'SNG':
return SNG(categories=category)
elif name == 'ASNG':
return ASNG(categories=category)
elif name == 'dynamic_ASNG':
return Dynamic_ASNG(categories=category,
step=step,
pruning=True,
sample_with_prob=sample_with_prob)
elif name == 'dynamic_SNG':
return Dynamic_SNG(categories=category,
step=step,
pruning=True,
sample_with_prob=sample_with_prob)
elif name == 'MIGO':
return MIGO(categories=category,
step=step,
lam=6,
pruning=True,
sample_with_prob=sample_with_prob,
utility_function='log',
utility_function_hyper=utility_function_hyper,
momentum=True,
gamma=gamma,
dynamic_sampling=False)
elif name == 'GridSearch':
return GridSearch(category)
else:
raise NotImplementedError