def from_config(cls, config: Dict[str, Any]) -> "Adam":
"""Instantiates a Adam from a configuration.
Args:
config: A configuration for a Adam.
See :func:`__init__` for parameters expected in the config.
Returns:
A Adam instance.
"""
# Default params
config.setdefault("eps", 1e-8)
config.setdefault("amsgrad", False)
# Check if betas is a list and convert it to a tuple
# since a JSON config can only have lists
if "betas" in config and type(config["betas"]) == list:
config["betas"] = tuple(config["betas"])
assert (
"lr" in config
), "Config must contain a learning rate 'lr' section for Adam optimizer"
assert (
"betas" in config and type(config["betas"]) == tuple
and len(config["betas"]) == 2 and type(config["betas"][0]) == float
and type(config["betas"][1]) == float and config["betas"][0] >= 0.0
and config["betas"][0] < 1.0 and config["betas"][1] >= 0.0
and config["betas"][1] < 1.0
), "Config must contain a tuple 'betas' in [0, 1) for Adam optimizer"
assert "weight_decay" in config and is_pos_float(
config["weight_decay"]
), "Config must contain a positive 'weight_decay' for Adam optimizer"
lr_config = config["lr"]
if not isinstance(lr_config, dict):
lr_config = {"name": "constant", "value": lr_config}
lr_config["num_epochs"] = config["num_epochs"]
lr_scheduler = build_param_scheduler(lr_config)
return cls(
lr_scheduler=lr_scheduler,
betas=config["betas"],
eps=config["eps"],
weight_decay=config["weight_decay"],
amsgrad=config["amsgrad"],
)
def from_config(cls, config: Dict[str, Any]) -> "RMSProp":
"""Instantiates a RMSProp from a configuration.
Args:
config: A configuration for a RMSProp.
See :func:`__init__` for parameters expected in the config.
Returns:
A RMSProp instance.
"""
# Default params
config.setdefault("eps", 1e-8)
config.setdefault("centered", False)
assert (
"lr" in config
), "Config must contain a learning rate 'lr' section for RMSProp optimizer"
for key in ["momentum", "alpha"]:
assert (
key in config and config[key] >= 0.0 and config[key] < 1.0
and type(config[key]) == float
), f"Config must contain a '{key}' in [0, 1) for RMSProp optimizer"
for key in ["weight_decay", "eps"]:
assert key in config and is_pos_float(
config[key]
), f"Config must contain a positive '{key}' for RMSProp optimizer"
assert "centered" in config and isinstance(
config["centered"], bool
), "Config must contain a boolean 'centered' param for RMSProp optimizer"
lr_config = config["lr"]
if not isinstance(lr_config, dict):
lr_config = {"name": "constant", "value": lr_config}
lr_config["num_epochs"] = config["num_epochs"]
lr_scheduler = build_param_scheduler(lr_config)
return cls(
lr_scheduler=lr_scheduler,
momentum=config["momentum"],
weight_decay=config["weight_decay"],
alpha=config["alpha"],
eps=config["eps"],
centered=config["centered"],
)
def from_config(cls, config: Dict[str, Any]) -> "SGD":
"""Instantiates a SGD from a configuration.
Args:
config: A configuration for a SGD.
See :func:`__init__` for parameters expected in the config.
Returns:
A SGD instance.
"""
# Default params
config["nesterov"] = config.get("nesterov", False)
assert (
"lr" in config
), "Config must contain a learning rate 'lr' section for SGD optimizer"
assert (
"momentum" in config and config["momentum"] >= 0.0
and config["momentum"] < 1.0 and type(config["momentum"]) == float
), "Config must contain a 'momentum' in [0, 1) for SGD optimizer"
assert "nesterov" in config and isinstance(
config["nesterov"], bool
), "Config must contain a boolean 'nesterov' param for SGD optimizer"
assert "weight_decay" in config and is_pos_float(
config["weight_decay"]
), "Config must contain a positive 'weight_decay' for SGD optimizer"
lr_config = config["lr"]
if not isinstance(lr_config, dict):
lr_config = {"name": "constant", "value": lr_config}
lr_config["num_epochs"] = config["num_epochs"]
lr_scheduler = build_param_scheduler(lr_config)
return cls(
lr_scheduler=lr_scheduler,
momentum=config["momentum"],
weight_decay=config["weight_decay"],
nesterov=config["nesterov"],
)
def from_config(cls, config: Dict[str, Any]) -> "RMSPropTF":
"""Instantiates a RMSPropTF from a configuration.
Args:
config: A configuration for a RMSPropTF.
See :func:`__init__` for parameters expected in the config.
Returns:
A RMSPropTF instance.
"""
# Default params
config.setdefault("lr", 0.1)
config.setdefault("momentum", 0.0)
config.setdefault("weight_decay", 0.0)
config.setdefault("alpha", 0.99)
config.setdefault("eps", 1e-8)
config.setdefault("centered", False)
for key in ["momentum", "alpha"]:
assert (
config[key] >= 0.0 and config[key] < 1.0
and type(config[key]) == float
), f"Config must contain a '{key}' in [0, 1) for RMSPropTF optimizer"
assert is_pos_float(
config["eps"]
), f"Config must contain a positive 'eps' for RMSPropTF optimizer"
assert isinstance(
config["centered"], bool
), "Config must contain a boolean 'centered' param for RMSPropTF optimizer"
return cls(
lr=config["lr"],
momentum=config["momentum"],
weight_decay=config["weight_decay"],
alpha=config["alpha"],
eps=config["eps"],
centered=config["centered"],
)