torchmin.Minimizer

class torchmin.Minimizer(params, method='bfgs', **minimize_kwargs)

A general-purpose PyTorch optimizer for unconstrained function minimization.

Warning

This optimizer doesn’t support per-parameter options and parameter groups (there can be only one).

Warning

Right now all parameters have to be on a single device. This will be improved in the future.

Parameters

• params (iterable) – An iterable of torch.Tensor s. Specifies what Tensors should be optimized.

• method (str) – Minimization method (algorithm) to use. Must be one of the methods offered in torchmin.minimize(). Defaults to ‘bfgs’.

• **minimize_kwargs (dict) – Additional keyword arguments that will be passed to torchmin.minimize().

__init__(params, method='bfgs', **minimize_kwargs)

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(params[, method])	Initialize self.
add_param_group(param_group)	Add a param group to the Optimizer s param_groups.
closure(x)
dir_evaluate(x, t, d)
load_state_dict(state_dict)	Loads the optimizer state.
profile_hook_step(func)
register_load_state_dict_post_hook(hook[, …])	Register a load_state_dict post-hook which will be called after load_state_dict() is called. It should have the following signature::.
register_load_state_dict_pre_hook(hook[, …])	Register a load_state_dict pre-hook which will be called before load_state_dict() is called. It should have the following signature::.
register_state_dict_post_hook(hook[, prepend])	Register a state dict post-hook which will be called after state_dict() is called. It should have the following signature::.
register_state_dict_pre_hook(hook[, prepend])	Register a state dict pre-hook which will be called before state_dict() is called. It should have the following signature::.
register_step_post_hook(hook)	Register an optimizer step post hook which will be called after optimizer step. It should have the following signature::.
register_step_pre_hook(hook)	Register an optimizer step pre hook which will be called before optimizer step. It should have the following signature::.
state_dict()	Returns the state of the optimizer as a dict.
step(closure)	Perform an optimization step.
zero_grad([set_to_none])	Resets the gradients of all optimized torch.Tensor s.

Attributes

OptimizerPostHook	alias of Callable[[typing_extensions.Self, Tuple[Any, …], Dict[str, Any]], None]
OptimizerPreHook	alias of Callable[[typing_extensions.Self, Tuple[Any, …], Dict[str, Any]], Optional[Tuple[Tuple[Any, …], Dict[str, Any]]]]
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