def __init__(
self,
model_params: List[Tuple[str, torch.nn.Parameter]],
param_groups: Optional[Union[Dict[str, ParamGroup],
List[ParamGroup]]] = None,
lr: float = 1,
max_iter: int = 20,
max_eval: Optional[int] = None,
tolerance_grad: float = 1e-07,
tolerance_change: float = 1e-09,
history_size: int = 100,
line_search_fn: Optional[str] = None,
):
param_groups = normalize_param_groups(param_groups)
super().__init__(
make_params_groups(model_params, param_groups),
lr,
max_iter,
max_eval,
tolerance_grad,
tolerance_change,
history_size,
line_search_fn,
)
self.param_groups_names = list(param_groups.keys())
def __init__(
self,
model_params: List[Tuple[str, torch.nn.Parameter]],
param_groups: Optional[Union[Dict[str, ParamGroup],
List[ParamGroup]]] = None,
lr: float = 0.01,
etas: Tuple[float, float] = (0.5, 1.2),
step_sizes: Tuple[float, float] = (1e-06, 50),
):
param_groups = normalize_param_groups(param_groups)
super().__init__(make_params_groups(model_params, param_groups), lr,
etas, step_sizes)
self.param_groups_names = list(param_groups.keys())
def __init__(
self,
model_params: List[Tuple[str, torch.nn.Parameter]],
param_groups: Optional[Union[Dict[str, ParamGroup],
List[ParamGroup]]] = None,
alpha: float = 0.01,
):
param_groups = normalize_param_groups(param_groups)
super().__init__(
make_params_groups(model_params, param_groups),
{"alpha": alpha},
list(param_groups.keys()),
)
def apply(self, parameters: List[Tuple[str, torch.nn.Parameter]]):
param_groups = make_params_groups(parameters, self.initializers,
self.exclude_regexes)
if isinstance(param_groups[0], torch.nn.Parameter):
param_groups = [{"params": param_groups}]
param_groups = check_param_groups(param_groups)
for param_group in param_groups:
params = param_group["params"]
initializer = param_group.get("init", None)
if initializer:
assert isinstance(initializer, Callable)
initializer(params)
def __init__(
self,
model_params: List[Tuple[str, torch.nn.Parameter]],
param_groups: Optional[Union[Dict[str, ParamGroup],
List[ParamGroup]]] = None,
lr: float = 1.0,
rho: float = 0.9,
eps: float = 1e-06,
weight_decay: float = 0,
):
param_groups = normalize_param_groups(param_groups)
super().__init__(make_params_groups(model_params, param_groups), lr,
rho, eps, weight_decay)
self.param_groups_names = list(param_groups.keys())
def __init__(
self,
model_params: List[Tuple[str, torch.nn.Parameter]],
param_groups: Optional[Union[Dict[str, ParamGroup],
List[ParamGroup]]] = None,
lr: float = 0.001,
betas: Tuple[float, float] = (0.9, 0.999),
eps: float = 1e-08,
):
param_groups = normalize_param_groups(param_groups)
super().__init__(
make_params_groups(model_params, param_groups),
lr,
betas,
eps,
)
self.param_groups_names = list(param_groups.keys())
def __init__(
self,
model_params: List[Tuple[str, torch.nn.Parameter]],
param_groups: Optional[Union[Dict[str, ParamGroup],
List[ParamGroup]]] = None,
lr: float = 0.01,
lr_decay: float = 0,
weight_decay: float = 0,
initial_accumulator_value: float = 0,
eps: float = 1e-10,
):
param_groups = normalize_param_groups(param_groups)
super().__init__(
make_params_groups(model_params, param_groups),
lr,
lr_decay,
weight_decay,
initial_accumulator_value,
eps,
)
self.param_groups_names = list(param_groups.keys())
def __init__(
self,
model_params: List[Tuple[str, torch.nn.Parameter]],
param_groups: Optional[Union[Dict[str, ParamGroup],
List[ParamGroup]]] = None,
lr: float = 0.001,
momentum: float = 0,
dampening: float = 0,
weight_decay: float = 0,
nesterov: bool = False,
):
param_groups = normalize_param_groups(param_groups)
super().__init__(
make_params_groups(model_params, param_groups),
lr,
momentum,
dampening,
weight_decay,
nesterov,
)
self.param_groups_names = list(param_groups.keys())
def __init__(
self,
model_params: List[Tuple[str, torch.nn.Parameter]],
param_groups: Optional[Union[Dict[str, ParamGroup],
List[ParamGroup]]] = None,
lr: float = 0.01,
lambd: float = 0.0001,
alpha: float = 0.75,
t0: float = 1000000.0,
weight_decay: float = 0,
):
param_groups = normalize_param_groups(param_groups)
super().__init__(
make_params_groups(model_params, param_groups),
lr,
lambd,
alpha,
t0,
weight_decay,
)
self.param_groups_names = list(param_groups.keys())
def __init__(
self,
model_params: List[Tuple[str, torch.nn.Parameter]],
param_groups: Optional[Union[Dict[str, ParamGroup],
List[ParamGroup]]] = None,
lr: float = 0.01,
alpha: float = 0.99,
eps: float = 1e-08,
weight_decay: float = 0,
momentum: float = 0,
centered: bool = False,
):
param_groups = normalize_param_groups(param_groups)
super().__init__(
make_params_groups(model_params, param_groups),
lr,
alpha,
eps,
weight_decay,
momentum,
centered,
)
self.param_groups_names = list(param_groups.keys())
