How we can define Early Stopping for Keras to check after each batch (not entire epoch)

Question

Normally Early stopping for Keras can be defined to check if it reaches a limit (loss, accuracy) after each epoch. But in mini batch method each epoch contains multiple loss, acc for training and validation phase. How we can tell Keras to check every acc or loss in an epoch for early stopping?

filepath="weights.best.hdf5"

checkpoint = ModelCheckpoint(filepath, monitor='acc', verbose=1, save_best_only=True)

early_stop = EarlyStopping(monitor='acc', patience=5) 

callbacks_list = [checkpoint, early_stop]

history = model.fit(x, y, validation_data=(x_test, y_test), epochs=1, callbacks=callbacks_list)

Daniel Möller Daniel Möller · Accepted Answer · 2019-08-23T00:04:06

Copy the code for EarlyStopping from the source and change on_epoch_end with on_batch_end. Update a few other things that talks about epoch and it's ready:

class BatchEarlyStopping(Callback):
    """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 batches 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 to reach.
            Training will stop if the model doesn't show improvement
            over the baseline.
        restore_best_weights: whether to restore model weights from
            the batch with the best value of the monitored quantity.
            If False, the model weights obtained at the last step of
            training are used.
    """

    def __init__(self,
                 monitor='val_loss',
                 min_delta=0,
                 patience=0,
                 verbose=0,
                 mode='auto',
                 baseline=None,
                 restore_best_weights=False):
        super(BatchEarlyStopping, self).__init__()

        self.monitor = monitor
        self.baseline = baseline
        self.patience = patience
        self.verbose = verbose
        self.min_delta = min_delta
        self.wait = 0
        self.stopped_batch = 0
        self.restore_best_weights = restore_best_weights
        self.best_weights = None

        if mode not in ['auto', 'min', 'max']:
            warnings.warn('BatchEarlyStopping mode %s is unknown, '
                          'fallback to auto mode.' % mode,
                          RuntimeWarning)
            mode = 'auto'

        if mode == 'min':
            self.monitor_op = np.less
        elif mode == 'max':
            self.monitor_op = np.greater
        else:
            if 'acc' in self.monitor:
                self.monitor_op = np.greater
            else:
                self.monitor_op = np.less

        if self.monitor_op == np.greater:
            self.min_delta *= 1
        else:
            self.min_delta *= -1

    def on_train_begin(self, logs=None):
        # Allow instances to be re-used
        self.wait = 0
        self.stopped_batch = 0
        if self.baseline is not None:
            self.best = self.baseline
        else:
            self.best = np.Inf if self.monitor_op == np.less else -np.Inf

    def on_batch_end(self, batch, logs=None):
        current = self.get_monitor_value(logs)
        if current is None:
            return

        if self.monitor_op(current - self.min_delta, self.best):
            self.best = current
            self.wait = 0
            if self.restore_best_weights:
                self.best_weights = self.model.get_weights()
        else:
            self.wait += 1
            if self.wait >= self.patience:
                self.stopped_batch = batch
                self.model.stop_training = True
                if self.restore_best_weights:
                    if self.verbose > 0:
                        print('Restoring model weights from the end of '
                              'the best batch')
                    self.model.set_weights(self.best_weights)

    def on_train_end(self, logs=None):
        if self.stopped_batch > 0 and self.verbose > 0:
            print('Batch %05d: early stopping' % (self.stopped_batch + 1))

    def get_monitor_value(self, logs):
        monitor_value = logs.get(self.monitor)
        if monitor_value is None:
            warnings.warn(
                'Early stopping conditioned on metric `%s` '
                'which is not available. Available metrics are: %s' %
                (self.monitor, ','.join(list(logs.keys()))), RuntimeWarning
            )
        return monitor_value

How we can define Early Stopping for Keras to check after each batch (not entire epoch)

1 Answers