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RAdamStep

The Rectified Adam optimizer step.

Based on Liu et al., 2020, Rectified Adam addresses a shortcoming in the popular Adam optimizer, where during initial stages of training, the gradients exhibit a large variance due to the limited number of training samples used to estimate the optimizer's statistics, which typically is addressed using warm-up schedules. 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. 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.

  • 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)

threshold

Threshold for variance tractability.

  • verbose name: Threshold
  • default value: 5
  • port type: FloatPort
  • value type: float (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)