CyclicCosineDecaySchedule¶
A cyclic linear warmup followed by cosine decay schedule.
This is essentially a repeated application of the "Linear warmup cosine decay" schedule, where each parameter is a list of values, one for each cycle. See also the "Linear Warmup Cosine Decay Schedule" schedule node for how a single cycle behaves. This schedule is more commonly known as the SGDR (SGD with Restarts) schedule, following a paper by Lochilov and Hutter, 2017 (see also URL). The basic idea is that the optimization can get stuck in a local optimum, and the subsequent cycle can "shake out" the current solution from that optimum and find a better one. 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. More Info... Version 0.2.0
Ports/Properties¶
step¶
Current step (iteration) count.
value¶
Schedule value at current step count.
init_values¶
Initial parameter values. This is the initial value at the beginning of each successive cycle.
peak_values¶
Peak parameter values. This is the value at the peak following the initial warmup, before it is lowered again following a cosine function, for each cycle.
final_values¶
Final parameter values. The cosine decay reduces the value from the peak down to the final value; given for each cycle.
warmup_steps¶
Number of steps over which to ramp up from the initial value to the peak value. After this, the parameter is lowered again following the shape of a cosine function down to the desired final value value; given for each cycle.
decay_steps¶
The number of steps over which the cosine decay takes place. This is a soft transition following a raised- cosine function from the peak value down to the final value; given for each cycle.
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.