WandbModelCheckpoint

A checkpoint that periodically saves a Keras model or model weights.

WandbModelCheckpoint(
    filepath: StrPath,
    monitor: str = "val_loss",
    verbose: int = 0,
    save_best_only: bool = (False),
    save_weights_only: bool = (False),
    mode: Mode = "auto",
    save_freq: Union[SaveStrategy, int] = "epoch",
    options: Optional[str] = None,
    initial_value_threshold: Optional[float] = None,
    **kwargs
) -> None

Saved weights are uploaded to W&B as a wandb.Artifact.

Since this callback is subclassed from tf.keras.callbacks.ModelCheckpoint, the checkpointing logic is taken care of by the parent callback. You can learn more here: https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/ModelCheckpoint

This callback is to be used in conjunction with training using model.fit() to save a model or weights (in a checkpoint file) at some interval. The model checkpoints will be logged as W&B Artifacts. You can learn more here: https://docs.wandb.ai/guides/artifacts

This callback provides the following features:

• Save the model that has achieved "best performance" based on "monitor".
• Save the model at the end of every epoch regardless of the performance.
• Save the model at the end of epoch or after a fixed number of training batches.
• Save only model weights, or save the whole model.
• Save the model either in SavedModel format or in .h5 format.

Arguments
filepath	(Union[str, os.PathLike]) path to save the model file. filepath can contain named formatting options, which will be filled by the value of epoch and keys in logs (passed in on_epoch_end). For example: if filepath is model-{epoch:02d}-{val_loss:.2f}, then the model checkpoints will be saved with the epoch number and the validation loss in the filename.
monitor	(str) The metric name to monitor. Default to "val_loss".
verbose	(int) Verbosity mode, 0 or 1. Mode 0 is silent, and mode 1 displays messages when the callback takes an action.
save_best_only	(bool) if save_best_only=True, it only saves when the model is considered the "best" and the latest best model according to the quantity monitored will not be overwritten. If filepath doesn't contain formatting options like {epoch} then filepath will be overwritten by each new better model locally. The model logged as an artifact will still be associated with the correct monitor. Artifacts will be uploaded continuously and versioned separately as a new best model is found.
save_weights_only	(bool) if True, then only the model's weights will be saved.
mode	(Mode) one of {'auto', 'min', 'max'}. For val_acc, this should be max, for val_loss this should be min, etc.
save_freq	(Union[SaveStrategy, int]) epoch or integer. When using 'epoch', the callback saves the model after each epoch. When using an integer, the callback saves the model at end of this many batches. Note that when monitoring validation metrics such as val_acc or val_loss, save_freq must be set to "epoch" as those metrics are only available at the end of an epoch.
options	(Optional[str]) Optional tf.train.CheckpointOptions object if save_weights_only is true or optional tf.saved_model.SaveOptions object if save_weights_only is false.
initial_value_threshold	(Optional[float]) Floating point initial "best" value of the metric to be monitored.

Attributes

Methods

set_model

set_model(
    model
)

set_params

set_params(
    params
)