o mib!@sddlZddlZddlmZmZmZmZmZmZddl Z ddl m Z ddl Z ddlmZddlmZddlmZedZed ZeGd d d e jZdS) N)AnyDictListLiteralOptionalUnion) callbacks) telemetry)StrPath)patch_tf_keras)autominmaxepochcseZdZdZ       d!ded ed ed ed ed ede e efde e de ddffdd Z d"dede eee fddfddZd"dede eee fddffdd Z d"dede eeddfddZd#ddZede efdd ZZS)$WandbModelCheckpointah A checkpoint that periodically saves a Keras model or model weights. 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. Args: 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. initial_value_threshold: (Optional[float]) Floating point initial "best" value of the metric to be monitored. val_lossrFr rNfilepathmonitorverbosesave_best_onlysave_weights_onlymode save_freqinitial_value_thresholdkwargsreturnc stjd||||||||d| tjdurtdtjtjd } d| j_Wdn1s2wY||_ |j rA| d|_ dS)N)rrrrrrrrzs