def instantiate(self, model: Model) -> torch.optim.Adadelta:
return torch.optim.Adadelta(filter(lambda p: p.requires_grad,
model.parameters()),
lr=self.lr,
rho=self.rho,
eps=self.eps,
weight_decay=self.weight_decay)
def instantiate(self, model: Model) -> torch.optim.Adam:
if self.layer_groups:
parameters = mu.to_parameter_groups(model.get_layer_groups())
if isinstance(self.lr, collections.Sequence):
for idx, lr in enumerate(self.lr):
parameters[idx]['lr'] = lr
default_lr = self.lr[0]
else:
default_lr = float(self.lr)
if isinstance(self.weight_decay, collections.Sequence):
for idx, weight_decay in enumerate(self.weight_decay):
parameters[idx]['weight_decay'] = weight_decay
default_weight_decay = self.weight_decay[0]
else:
default_weight_decay = self.weight_decay
return torch.optim.Adam(parameters,
lr=default_lr,
betas=self.betas,
eps=self.eps,
weight_decay=default_weight_decay,
amsgrad=self.amsgrad)
else:
parameters = filter(lambda p: p.requires_grad, model.parameters())
return torch.optim.Adam(parameters,
lr=self.lr,
betas=self.betas,
eps=self.eps,
weight_decay=self.weight_decay,
amsgrad=self.amsgrad)
def instantiate(self, model: Model) -> RMSpropTF:
return RMSpropTF(filter(lambda p: p.requires_grad, model.parameters()),
lr=self.lr,
alpha=self.alpha,
eps=self.eps,
weight_decay=self.weight_decay,
momentum=self.momentum,
centered=self.centered)
def instantiate(self, model: Model) -> torch.optim.SGD:
if self.layer_groups:
parameters = mu.to_parameter_groups(model.get_layer_groups())
else:
parameters = filter(lambda p: p.requires_grad, model.parameters())
return torch.optim.SGD(
parameters,
lr=self.lr, momentum=self.momentum, dampening=self.dampening, weight_decay=self.weight_decay,
nesterov=self.nesterov
)