class_docstr: Early Stopping to terminate training early under certain conditions
  
  EarlyStopping callback to exit the training loop if training or
      validation loss does not improve by a certain amount for a certain
      number of epochs
  
  :param monitor: (string in {'val_loss', 'loss'}):
      whether to monitor train or val loss
  :param min_delta: (float):
      minimum change in monitored value to qualify as improvement.
      This number should be positive.
  :param patience: (int):
      number of epochs to wait for improvment before terminating.
      the counter be reset after each improvment
code: def __init__(self, monitor="val_loss", min_delta=0, patience=5):
      self.monitor = monitor
      self.min_delta = min_delta
      self.patience = patience
      self.wait = 0
      self.best_loss = 1e-15
      self.stopped_epoch = 0
      super(EarlyStopping, self).__init__()
  
imports: from .None import Callback
label_desc: Use image node_id 1 to create a new EarlyStopping object from inherited base classes: Callback with example: obj = EarlyStopping(monitor, min_delta, patience)