Callbacks API - Keras
keras.io › api › callbacksCallbacks API. A callback is an object that can perform actions at various stages of training (e.g. at the start or end of an epoch, before or after a single batch, etc). You can use callbacks to: Write TensorBoard logs after every batch of training to monitor your metrics. Periodically save your model to disk.
ModelCheckpoint - Keras
https://keras.io/api/callbacks/model_checkpointCallback to save the Keras model or model weights at some frequency. ModelCheckpoint callback is used in conjunction with training using model.fit() to save a model or weights (in a checkpoint file) at some interval, so the model or weights can be loaded later to continue the training from the state saved.. A few options this callback provides include: ...
Callbacks API - Keras
https://keras.io/api/callbacksCallbacks API. A callback is an object that can perform actions at various stages of training (e.g. at the start or end of an epoch, before or after a single batch, etc). You can use callbacks to: Write TensorBoard logs after every batch of training to monitor your …
EarlyStopping - Keras
https://keras.io/api/callbacks/early_stoppingtf.keras.callbacks.EarlyStopping( monitor="val_loss", min_delta=0, patience=0, verbose=0, mode="auto", baseline=None, restore_best_weights=False, ) Stop training when a monitored metric has stopped improving. Assuming the goal of a training is to minimize the loss. With this, the metric to be monitored would be 'loss', and mode would be 'min'.