import torch.optim as optim
from .bgd_optimizer import BGD
def bgd(model, **kwargs):
logger = kwargs.get("logger", None)
assert(logger is not None)
bgd_params = {
"mean_eta": kwargs.get("mean_eta", 1),
"std_init": kwargs.get("std_init", 0.02),
"mc_iters": kwargs.get("mc_iters", 10)
}
logger.info("BGD params: " + str(bgd_params))
all_params = [{'params': params} for l, (name, params) in enumerate(model.named_parameters())]
return BGD(all_params, **bgd_params)
def sgd(model, **kwargs):
logger = kwargs.get("logger", None)
assert(logger is not None)
sgd_params = {
"momentum": kwargs.get("momentum", 0.9),
"lr": kwargs.get("lr", 0.1),
"weight_decay": kwargs.get("weight_decay", 5e-4)
}
logger.info("SGD params: " + str(sgd_params))
all_params = [{'params': params, 'name': name, 'initial_lr': kwargs.get("lr", 0.1)} for l, (name, params) in enumerate(model.named_parameters())]
return optim.SGD(all_params, **sgd_params)
def adam(model, **kwargs):
logger = kwargs.get("logger", None)
assert(logger is not None)
adam_params = {
"eps": kwargs.get("eps", 1e-08),
"lr": kwargs.get("lr", 0.001),
"betas": kwargs.get("betas", (0.9, 0.999)),
"weight_decay": kwargs.get("weight_decay", 0)
}
logger.info("ADAM params: " + str(adam_params))
all_params = [{'params': params, 'name': name, 'initial_lr': kwargs.get("lr", 0.001)} for l, (name, params) in enumerate(model.named_parameters())]
return optim.Adam(all_params, **adam_params)
def adagrad(model, **kwargs):
logger = kwargs.get("logger", None)
assert(logger is not None)
adam_params = {
"lr": kwargs.get("lr", 0.01),
"weight_decay": kwargs.get("weight_decay", 0)
}
logger.info("Adagrad params: " + str(adam_params))
all_params = [{'params': params, 'name': name, 'initial_lr': kwargs.get("lr", 0.01)} for l, (name, params) in enumerate(model.named_parameters())]
return optim.Adagrad(all_params, **adam_params)
