LinearSchedule¶
A linear parameter schedule.
This schedule holds the parameter at the initial value until the current step count reaches the value set via transition_begin, and then uses linear interpolation to the final value over a number of steps set via transition_steps; after that, the parameter is held at the final value. The interpolation polynomial is evaluated over the rang 0.0 (at the beginning) to 1.0 (at the end). This is a special case of the "Polynomial Schedule" node. 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.
value¶
Schedule value at current step count.
init_value¶
Initial parameter value. This is the value at the beginning of the schedule. The parameter is held at this value until the current step count reaches the value set via transition_begin.
final_value¶
Final parameter value. This is the value at the end of the schedule.
transition_begin¶
Step count at which to begin the transition from the initial value to the final value. The parameter is held at the initial value until this step count is reached.
transition_steps¶
Step count at which to end the transition from the initial value to the final value. The parameter is held at the final value after this step count is reached.
set_breakpoint¶
Set a breakpoint on this node. If this is enabled, your debugger (if one is attached) will trigger a breakpoint.
metadata¶
User-definable meta-data associated with the node. Usually reserved for technical purposes.
step_multiplier¶
Multiplier for the step count. This value is multiplied with each of the step counts to uniformly speed up or slow down the schedule through a single parameter. When used to define an optimizer used by the DeepModel node, this can also be set to 0.0, in which case the multiplier is chosen such that the schedule reaches its final value at the end of the training process, but note that this is not always possible, namely for schedules that are never reach a final value. Otherwise, to make a schedule dependent on the number of steps done by a node, you may normalize your schedule to eg 1000 steps and then wire a formula that calculates the steps done by some process divided by 1000 into this node.