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optimizer

learning.optimizer

building torch optimizers with simple configs

logger module-attribute 

logger = getLogger('SaigeResearch')

_types module-attribute 

_types = {(__name__): _type for _type in _types}

build_optimizer 

build_optimizer(model_params: Iterator[Parameter], _target_: str = 'SGD', **params) -> Optimizer

build optimizer object

Parameters:

  • model_params (Iterator[Parameter]) –

    parameters of model to be trained

  • _target_ (str, default: 'SGD' ) –

    optimizer name. Defaults to "SGD".

Raises:

  • NotImplementedError –

    description

Returns:

  • Optimizer ( Optimizer ) –

    optimizer object

Source code in SaigeToolkit/learning/optimizer.py 
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def build_optimizer(model_params: Iterator[Parameter], _target_: str = "SGD", **params) -> Optimizer:
    """build optimizer object

    Args:
        model_params (Iterator[Parameter]): parameters of model to be trained
        _target_ (str, optional): optimizer name. Defaults to "SGD".

    Raises:
        NotImplementedError: _description_

    Returns:
        Optimizer: optimizer object
    """
    if _target_ not in _types:
        raise NotImplementedError(f"OPTIMIZER {_target_} not implemented")
    logger.info(f"[{'OPTIMIZER'.center(9)}] {_target_} [params] {params}")
    return _types[_target_](model_params, **params)