tf.keras.callbacks.EarlyStopping

Class EarlyStopping

Inherits From: Callback

Defined in tensorflow/python/keras/callbacks.py.

Stop training when a monitored quantity has stopped improving.

Arguments:

  • monitor: Quantity to be monitored.
  • min_delta: Minimum change in the monitored quantity to qualify as an improvement, i.e. an absolute change of less than min_delta, will count as no improvement.
  • patience: Number of epochs with no improvement after which training will be stopped.
  • verbose: verbosity mode.
  • mode: One of {"auto", "min", "max"}. In min mode, training will stop when the quantity monitored has stopped decreasing; in max mode it will stop when the quantity monitored has stopped increasing; in auto mode, the direction is automatically inferred from the name of the monitored quantity.
  • baseline: Baseline value for the monitored quantity. Training will stop if the model doesn't show improvement over the baseline.
  • restore_best_weights: Whether to restore model weights from the epoch with the best value of the monitored quantity. If False, the model weights obtained at the last step of training are used.

__init__

__init__(
    monitor='val_loss',
    min_delta=0,
    patience=0,
    verbose=0,
    mode='auto',
    baseline=None,
    restore_best_weights=False
)

Initialize self. See help(type(self)) for accurate signature.

Methods

get_monitor_value

get_monitor_value(logs)

on_batch_begin

on_batch_begin(
    batch,
    logs=None
)

on_batch_end

on_batch_end(
    batch,
    logs=None
)

on_epoch_begin

on_epoch_begin(
    epoch,
    logs=None
)

on_epoch_end

on_epoch_end(
    epoch,
    logs=None
)

on_train_batch_begin

on_train_batch_begin(
    batch,
    logs=None
)

on_train_batch_end

on_train_batch_end(
    batch,
    logs=None
)

on_train_begin

on_train_begin(logs=None)

on_train_end

on_train_end(logs=None)

set_model

set_model(model)

set_params

set_params(params)