class_docstr: Stop training when the loss is at its min, i.e. the loss stops decreasing.
  
  Arguments:
      patience: Number of epochs to wait after min has been hit. After this
      number of no improvement, training stops.
class_docstr_tok: ["Stop","training","when","the","loss","is","at","its","min",",","i.e",".","the","loss","stops","decreasing",".","Arguments",":","patience",":","Number","of","epochs","to","wait","after","min","has","been","hit",".","After","this","number","of","no","improvement",",","training","stops","."]
code: def on_train_end(self, logs=None):
      if self.stopped_epoch > 0:
          print(f"Epoch {self.stopped_epoch + 1}: early stopping")
  
imports: import numpy
  import keras
label_desc: Use image node_id 4 for calling the EarlyStoppingAtMinLoss obj's underlying member method code with example usage: obj.on_train_end(logs) without return types