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.

__init__

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

Methods

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_begin

on_train_begin(logs=None)

on_train_end

on_train_end(logs=None)

set_model

set_model(model)

set_params

set_params(params)