def test_does_model_improve(
previous_best: Dict[Text, float],
current_values: Dict[Text, float],
improved: bool,
tmpdir: Path,
):
checkpoint = RasaModelCheckpoint(tmpdir)
checkpoint.best_metrics_so_far = previous_best
# true iff all values are equal or better and at least one is better
assert checkpoint._does_model_improve(current_values) == improved
def create_common_callbacks(
epochs: int,
tensorboard_log_dir: Optional[Text] = None,
tensorboard_log_level: Optional[Text] = None,
checkpoint_dir: Optional[Path] = None,
) -> List["Callback"]:
"""Create common callbacks.
The following callbacks are created:
- RasaTrainingLogger callback
- Optional TensorBoard callback
- Optional RasaModelCheckpoint callback
Args:
epochs: the number of epochs to train
tensorboard_log_dir: optional directory that should be used for tensorboard
tensorboard_log_level: defines when training metrics for tensorboard should be
logged. Valid values: 'epoch' and 'batch'.
checkpoint_dir: optional directory that should be used for model checkpointing
Returns:
A list of callbacks.
"""
import tensorflow as tf
callbacks = [RasaTrainingLogger(epochs, silent=False)]
if tensorboard_log_dir:
if tensorboard_log_level == "minibatch":
tensorboard_log_level = "batch"
rasa.shared.utils.io.raise_deprecation_warning(
"You set 'tensorboard_log_level' to 'minibatch'. This value should not "
"be used anymore. Please use 'batch' instead."
)
callbacks.append(
tf.keras.callbacks.TensorBoard(
log_dir=tensorboard_log_dir,
update_freq=tensorboard_log_level,
write_graph=True,
write_images=True,
histogram_freq=10,
)
)
if checkpoint_dir:
callbacks.append(RasaModelCheckpoint(checkpoint_dir))
return callbacks
def test_on_epoch_end_saves_checkpoints_file(
previous_best: Dict[Text, float],
current_values: Dict[Text, float],
improved: bool,
tmp_path: Path,
trained_ted: TEDPolicy,
):
model_name = "checkpoint"
best_model_file = tmp_path / model_name
assert not best_model_file.exists()
checkpoint = RasaModelCheckpoint(tmp_path)
checkpoint.best_metrics_so_far = previous_best
checkpoint.model = trained_ted.model
checkpoint.on_epoch_end(1, current_values)
if improved:
assert best_model_file.exists()
else:
assert not best_model_file.exists()
