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OptimisticGDStep

The Optimistic gradient descent optimizer step.

Based on Mokhtari et al, 2019, this is an advanced optimizer that was originally proposed in the context of saddle-point problems, and has strong convergence for min-max games, where standard gradient descent can oscillate or diverge. Note that this optimizer can be used with schedulers for not only the learning rate but also the alpha and beta parameters, by wiring in the appropriate schedule nodes into the respective ports, without having to use a CustomStep node. 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

alpha_schedule

Optional alpha schedule.

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

beta_schedule

Optional beta schedule.

  • verbose name: Beta 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)

alpha

Alpha coefficient for generalized OGD.

  • verbose name: Alpha
  • default value: 1
  • port type: FloatPort
  • value type: float (can be None)

beta

Beta coefficient for generalized OGD negative momentum.

  • verbose name: Beta
  • default value: 1
  • 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)