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AdamaxStep

The Adamax optimizer step.

Based on Kingma and Ba, 2014, Adamax is a variant of the popular Adam optimizer that uses the infinity norm (max norm) for scaling, and which is therefore more conservative at controlling the gradients for unstable subsets of the weights. See also documentation on Adam for some additional considerations when using this family of optimizers. Like all step nodes, this node only processes gradients, and the resulting updates must be applied manually to the weights (this can be accomplished using the Add node). However, you can also pass it to the StepSolver node which implements the full optimization loop. The learning rate can instead be given as a schedule, by wiring one of the Schedule nodes into the learning_rate_schedule port. More Info... Version 0.2.0

Ports/Properties

gradients

Gradients to be transformed.

  • verbose name: Gradients
  • default value: None
  • port type: DataPort
  • value type: object (can be None)
  • data direction: INOUT

weights

Optional current weights.

  • verbose name: Weights
  • default value: None
  • port type: DataPort
  • value type: object (can be None)
  • data direction: IN

state

Explicit state of the node.

  • verbose name: State
  • default value: None
  • port type: DataPort
  • value type: object (can be None)
  • data direction: INOUT

learning_rate_schedule

Optional learning rate schedule.

  • verbose name: Learning Rate Schedule
  • default value: None
  • port type: DataPort
  • value type: BaseNode (can be None)
  • data direction: IN

learning_rate

Learning rate. A typical choice may be 0.001 here, but this is problem dependent. If a learning rate schedule is provided, this value should be left unspecified.

  • verbose name: Learning Rate
  • default value: None
  • port type: FloatPort
  • value type: float (can be None)

beta1

Exponential decay rate for the first moment estimates.

  • verbose name: Beta1
  • default value: 0.9
  • port type: FloatPort
  • value type: float (can be None)

beta2

Exponential decay rate for the second moment estimates.

  • verbose name: Beta2
  • default value: 0.999
  • port type: FloatPort
  • value type: float (can be None)

epsilon

Small value applied to the denominator outside the square root to avoid dividing by zero when rescaling. Note that larger epsilon values have been explored in the literature.

  • verbose name: Epsilon
  • default value: 1e-08
  • port type: FloatPort
  • value type: float (can be None)

epsilon_inroot

Small value applied to the denominator inside the square root to avoid dividing by zero when rescaling. A case where this is needed is when differentiating the optimizer itself, eg for bilevel optimization.

  • verbose name: Epsilon (Inside Root)
  • default value: 0
  • port type: FloatPort
  • value type: float (can be None)

mu_precision

Numeric precision for the first-order accumulator. Keep resolves to the precision of the inputs.

  • verbose name: Mu Precision
  • default value: keep
  • port type: EnumPort
  • value type: str (can be None)

set_breakpoint

Set a breakpoint on this node. If this is enabled, your debugger (if one is attached) will trigger a breakpoint.

  • verbose name: Set Breakpoint (Debug Only)
  • default value: False
  • port type: BoolPort
  • value type: bool (can be None)

metadata

User-definable meta-data associated with the node. Usually reserved for technical purposes.

  • verbose name: Metadata
  • default value: {}
  • port type: DictPort
  • value type: dict (can be None)