tf.keras.callbacks.ModelCheckpoint

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Callback to save the Keras model or model weights at some frequency.

Inherits From: Callback

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Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.callbacks.ModelCheckpoint

tf.keras.callbacks.ModelCheckpoint(
    filepath, monitor='val_loss', verbose=0, save_best_only=False,
    save_weights_only=False, mode='auto', save_freq='epoch',
    options=None, **kwargs
)

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:

• Whether to only keep the model that has achieved the "best performance" so far, or whether to save the model at the end of every epoch regardless of performance.
• Definition of 'best'; which quantity to monitor and whether it should be maximized or minimized.
• The frequency it should save at. Currently, the callback supports saving at the end of every epoch, or after a fixed number of training batches.
• Whether only weights are saved, or the whole model is saved.

Note: If you get WARNING:tensorflow:Can save best model only with <name> available, skipping see the description of the monitor argument for details on how to get this right.

Example:

model.compile(loss=..., optimizer=...,
              metrics=['accuracy'])

EPOCHS = 10
checkpoint_filepath = '/tmp/checkpoint'
model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
    filepath=checkpoint_filepath,
    save_weights_only=True,
    monitor='val_accuracy',
    mode='max',
    save_best_only=True)

# Model weights are saved at the end of every epoch, if it's the best seen
# so far.
model.fit(epochs=EPOCHS, callbacks=[model_checkpoint_callback])

# The model weights (that are considered the best) are loaded into the model.
model.load_weights(checkpoint_filepath)

Arguments
filepath	string or PathLike, path to save the model file. filepath can contain named formatting options, which will be filled the value of epoch and keys in logs (passed in on_epoch_end). For example: if filepath is weights.{epoch:02d}-{val_loss:.2f}.hdf5, then the model checkpoints will be saved with the epoch number and the validation loss in the filename.
monitor	The metric name to monitor. Typically the metrics are set by the Model.compile method. Note: Prefix the name with "val_" to monitor validation metrics. Use "loss" or "val_loss" to monitor the model's total loss. If you specify metrics as strings, like "accuracy", pass the same string (with or without the "val_" prefix). If you pass metrics.Metric objects, monitor should be set to metric.name If you're not sure about the metric names you can check the contents of the history.history dictionary returned by history = model.fit() Multi-output models set additional prefixes on the metric names.
verbose	verbosity mode, 0 or 1.
save_best_only	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.
mode	one of {'auto', 'min', 'max'}. If save_best_only=True, the decision to overwrite the current save file is made based on either the maximization or the minimization of the monitored quantity. For val_acc, this should be max, for val_loss this should be min, etc. In auto mode, the direction is automatically inferred from the name of the monitored quantity.
save_weights_only	if True, then only the model's weights will be saved (model.save_weights(filepath)), else the full model is saved (model.save(filepath)).
save_freq	'epoch' or integer. When using 'epoch', the callback saves the model after each epoch. When using integer, the callback saves the model at end of this many batches. If the Model is compiled with steps_per_execution=N, then the saving criteria will be checked every Nth batch. Note that if the saving isn't aligned to epochs, the monitored metric may potentially be less reliable (it could reflect as little as 1 batch, since the metrics get reset every epoch). Defaults to 'epoch'.
options	Optional tf.train.CheckpointOptions object if save_weights_only is true or optional tf.saved_model.SaveOptions object if save_weights_only is false.
**kwargs	Additional arguments for backwards compatibility. Possible key is period.

Methods

set_model

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set_model(
    model
)

set_params

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set_params(
    params
)