def get_optimizer(self):
if self.optimizer_name == 'Adam-Exp':
opt = torch.optim.Adam(self.parameters(), lr=self.lr)
sch = torch.optim.lr_scheduler.StepLR(opt, step_size=2, gamma=0.9)
return opt, sch
if self.optimizer_name == 'Adam-Tri':
opt = torch.optim.Adam(self.parameters(), lr=self.lr)
sch = CyclicLR(opt,
base_lr=0.00005,
max_lr=0.0002,
step_size=200,
mode='triangular')
return opt, sch
if self.optimizer_name == 'SGD':
opt = torch.optim.SGD(self.parameters(), lr=self.lr)
sch = CyclicLR(opt,
base_lr=1e-4,
max_lr=5e-4,
step_size=20,
mode='triangular')
return opt, sch
else:
raise NotImplementedError
def get_optimizer(self):
if self.optimizer_name == 'Adam':
opt = torch.optim.Adam(self.parameters(),
lr=self.lr)
sch = CyclicLR(opt,
base_lr=1e-4,
max_lr=5e-4,
step_size=200,
mode='triangular'
)
return opt, sch
if self.optimizer_name == 'SGD':
opt = torch.optim.SGD(self.parameters(),
lr=self.lr)
sch = CyclicLR(opt,
base_lr=1e-4,
max_lr=5e-4,
step_size=20,
mode='triangular'
)
return opt, sch
else:
raise NotImplementedError
