我们如何定义 Keras 的 Early Stopping 以在每批（而不是整个 epoch）之后检查

Question

通常可以定义 Keras 的早期停止以检查它是否在每个 epoch 后达到限制（损失、准确性）。但是在小批量方法中，每个时期都包含多个损失，用于训练和验证阶段。我们如何告诉 Keras 检查一个 epoch 中的每个 acc 或 loss 以便提前停止？

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)

Answer 1

从source 复制EarlyStopping 的代码并将on_epoch_end 更改为on_batch_end。更新一些关于epoch 的其他内容，它已经准备好了：

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