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 __init__(self, patience=0):
      super().__init__()
      self.patience = patience
      # best_weights to store the weights at which the minimum loss occurs.
      self.best_weights = None
  
imports: import numpy
  import keras
label_desc: Use image node_id 1 to create a new EarlyStoppingAtMinLoss object from inherited base classes: keras.callbacks with example: obj = EarlyStoppingAtMinLoss(patience)