ConstantSchedule¶
A constant parameter schedule.
This is the simplest of all schedule node in NeuroPype as it simply emits a constant value. This can be used as a simple drop-in alternative when testing a more complicated schedule. Schedule nodes in NeuroPype are used for fine-grained control over how parameters, like the learning rate, should change over time during optimization. Most Step nodes offer a learning_rate_schedule port, into which a Schedule node can be wired to override the otherwise default constant learning rate. However, any other optimizer step parameter can be controlled by a schedule, simply by wiring the schedule node's output into the respective parameter of the Step nodes, and passing the schedule the current iteration (step) count of the optimization process. Version 0.2.0
Ports/Properties¶
step¶
Current step (iteration) count.
- verbose name: Step
- default value: None
- port type: DataPort
- value type: object (can be None)
- data direction: IN
value¶
Constant value to output.
- verbose name: Constant Value
- default value: 1.0
- port type: FloatPort
- value type: float
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)
step_multiplier¶
Multiplier for the step count. All schedule nodes have this parameter, but in case of a constant schedule it does nothing.
- verbose name: Step Multiplier (Ignored)
- default value: 1.0
- port type: FloatPort
- value type: float (can be None)