def _get_callbacks(self):
callbacks = [
*self._get_extra_callbacks(),
ModelCheckpoint(
get_weights_path(self.output().path),
save_best_only=True,
monitor=self.monitor_metric,
mode=self.monitor_mode,
),
EarlyStopping(
patience=self.early_stopping_patience,
min_delta=self.early_stopping_min_delta,
monitor=self.monitor_metric,
mode=self.monitor_mode,
),
CSVLogger(get_history_path(self.output().path)),
TensorBoard(get_tensorboard_logdir(self.task_id), write_graph=False),
]
if self.gradient_norm_clipping:
callbacks.append(
GradientNormClipping(
self.gradient_norm_clipping, self.gradient_norm_clipping_type
)
)
return callbacks
def test_not_best_only(self):
self.assertTrue(
isinstance(ModelCheckpoint(save_best_only=False), Interval))
def test_best_only(self):
self.assertTrue(isinstance(ModelCheckpoint(save_best_only=True), Best))