class_docstr: A `TrackerCallback` that terminates training when monitored quantity stops improving.
class_docstr_tok: ["A","`","TrackerCallback","`","that","terminates","training","when","monitored","quantity","stops","improving","."]
code: def __init__(
      self,
      monitor="valid_loss",  # value (usually loss or metric) being monitored.
      comp=None,  # numpy comparison operator; np.less if monitor is loss, np.greater if monitor is metric.
      min_delta=0.0,  # minimum delta between the last monitor value and the best monitor value.
      patience=1,  # number of epochs to wait when training has not improved model.
      reset_on_fit=True,  # before model fitting, reset value being monitored to -infinity (if monitor is metric) or +infinity (if monitor is loss).
  ):
      super().__init__(
          monitor=monitor,
          comp=comp,
          min_delta=min_delta,
          reset_on_fit=reset_on_fit,
      )
      self.patience = patience
  
imports: from __future__ import annotations
  from ..basics import *
  from .progress import *
  from .fp16 import MixedPrecision
label_desc: Use image node_id 1 to create a new EarlyStoppingCallback object from inherited base classes: TrackerCallback with example: obj = EarlyStoppingCallback(monitor, comp, min_delta, patience, reset_on_fit)