unitorch.cli.optims

SGDOptimizer

Tip

core/optim/sgd is the section for configuration of SGDOptimizer.

Bases: SGD, CheckpointMixin

Source code in src/unitorch/cli/optims/__init__.py

def __init__(
    self,
    params,
    learning_rate: Optional[float] = 0.00001,
):
    super().__init__(
        params=params,
        lr=learning_rate,
    )

from_config classmethod

from_config(config, **kwargs)

Source code in src/unitorch/cli/optims/__init__.py

@classmethod
@config_defaults_init("core/optim/sgd")
def from_config(cls, config, **kwargs):
    pass

AdamOptimizer

Tip

core/optim/adam is the section for configuration of AdamOptimizer.

Bases: Adam, CheckpointMixin

Source code in src/unitorch/cli/optims/__init__.py

def __init__(
    self,
    params,
    learning_rate: Optional[float] = 0.00001,
):
    super().__init__(
        params=params,
        lr=learning_rate,
    )

from_config classmethod

from_config(config, **kwargs)

Source code in src/unitorch/cli/optims/__init__.py

@classmethod
@config_defaults_init("core/optim/adam")
def from_config(cls, config, **kwargs):
    pass

AdamWOptimizer

Tip

core/optim/adamw is the section for configuration of AdamWOptimizer.

Bases: AdamW, CheckpointMixin

Source code in src/unitorch/cli/optims/__init__.py

def __init__(
    self,
    params,
    learning_rate: Optional[float] = 0.00001,
):
    super().__init__(
        params=params,
        lr=learning_rate,
    )

from_config classmethod

from_config(config, **kwargs)

Source code in src/unitorch/cli/optims/__init__.py

@classmethod
@config_defaults_init("core/optim/adamw")
def from_config(cls, config, **kwargs):
    pass

AdafactorOptimizer

Tip

core/optim/adafactor is the section for configuration of AdafactorOptimizer.

Bases: Adafactor, CheckpointMixin

Source code in src/unitorch/cli/optims/__init__.py

def __init__(
    self,
    params,
    learning_rate: Optional[float] = 0.00001,
    scale_parameter: bool = False,
    relative_step: bool = False,
    warmup_init: bool = False,
):
    super().__init__(
        params=params,
        lr=learning_rate,
        scale_parameter=scale_parameter,
        relative_step=relative_step,
        warmup_init=warmup_init,
    )

from_config classmethod

from_config(config, **kwargs)

Source code in src/unitorch/cli/optims/__init__.py

@classmethod
@config_defaults_init("core/optim/adafactor")
def from_config(cls, config, **kwargs):
    pass

LionOptimizer

Tip

core/optim/lion is the section for configuration of LionOptimizer.

Bases: Lion, CheckpointMixin

Source code in src/unitorch/cli/optims/lion.py

def __init__(
    self,
    params,
    learning_rate: Optional[float] = 0.00001,
):
    super().__init__(
        params=params,
        lr=learning_rate,
    )

from_config classmethod

from_config(config, **kwargs)

Source code in src/unitorch/cli/optims/lion.py

@classmethod
@config_defaults_init("core/optim/lion")
def from_config(cls, config, **kwargs):
    pass