WandbModelCheckpoint

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

WandbModelCheckpoint(
 filepath: Union[str, os.PathLike],
 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

Saves 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. monitor (str): The metric name to monitor. 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. 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_weights_only (bool): if True, then only the model's weights will be saved 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
)