Hyperparameter Definition Format

When specifying optimizer_kwargs or loss_kwargs, each value can be written in one of two ways.

Scalar Constant

The value is fixed and not searched by AutoMAX:

optimizer_kwargs:
  lr: 0.01

Search Space Dict

The value is sampled by AutoMAX during the search:

optimizer_kwargs:
  lr:
    val: [0.0001, 0.1]   # tuple → uniform range; list → categorical; scalar → constant
    default: 0.001        # starting/default value
    log: true             # sample on log scale (numeric ranges only)

Sub-fields

Sub-field	Type	Description
val	scalar / tuple / list	Scalar: constant value, never searched. Tuple (low, high): uniform range. List: categorical choices.
default	scalar	Default/starting value. Falls back to lower bound for ranges, first element for lists.
log	bool	Sample the range on a log scale. Ignored for categorical values.

Examples

Fix a learning rate and narrow the margin search space:

training:
  optimizer_kwargs:
    lr: 0.05                   # fixed — not searched

  loss_kwargs:
    margin:
      val: [0.8, 1.0]          # categorical: try 0.8 or 1.0
      default: 1.0

Search learning rate on a log scale:

training:
  optimizer_kwargs:
    lr:
      val: [0.0001, 0.1]       # uniform in log space
      default: 0.001
      log: true
    momentum:
      val: [0.0, 0.9, 0.99]    # categorical: try 3 values
      default: 0.9