Optimizer¶

Optimizer wrapper that resolves PyTorch optimizers by name.

This module provides a thin wrapper around torch.optim that allows optimizers to be instantiated from CLI strings while exposing a uniform interface for learning-rate adjustments.

Example:¶

>>> from experiments import Optimizer, Model
>>> model = Model("lenet", num_classes=10)
>>> optim = Optimizer("adam", model, lr=0.001)
>>> optim.set_lr(0.0001)

class experiments.optimizer.Optimizer(name_build, model, *args, **kwargs)¶

Bases: object

Optimizer wrapper with name resolution and LR control.

Parameters:

name_build (str or callable) – Optimizer name (e.g. "adam", "sgd") or a constructor function. Names are resolved against torch.optim.
model (experiments.Model) – Model whose parameters will be optimized.
*args (object) – Additional positional arguments forwarded to the optimizer constructor.
**kwargs (object) – Additional keyword arguments forwarded to the optimizer constructor.
Raises
------
tools.UnavailableException – If name_build is a string that does not match any known optimizer.

set_lr(lr)¶

Set the learning rate for all parameter groups.

Parameters:: lr (float) – New learning rate.