tf.keras.callbacks.ModelCheckpoint | TensorFlow v2.16.1 (original) (raw)

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

Inherits From: Callback

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

Used in the notebooks

Used in the guide	Used in the tutorials
Migrate checkpoint saving Migrate evaluation Multi-GPU and distributed training	Save and load models Generate music with an RNN Distributed training with Keras Parameter server training with ParameterServerStrategy Human Pose Classification with MoveNet and TensorFlow Lite

ModelCheckpoint callback is used in conjunction with training usingmodel.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.

Example:

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

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

# Model is 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 (that are considered the best) can be loaded as -
keras.models.load_model(checkpoint_filepath)

# Alternatively, one could checkpoint just the model weights as -
checkpoint_filepath = '/tmp/ckpt/checkpoint.weights.h5'
model_checkpoint_callback = 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) can be loaded as -
model.load_weights(checkpoint_filepath)

Args
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). The filepath name needs to end with ".weights.h5" whensave_weights_only=True or should end with ".keras" when checkpoint saving the whole model (default). For example: if filepath is "{epoch:02d}-{val_loss:.2f}.keras", then the model checkpoints will be saved with the epoch number and the validation loss in the filename. The directory of the filepath should not be reused by any other callbacks to avoid conflicts.
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 tometric.name If you're not sure about the metric names you can check the contents of the history.history dictionary returned byhistory = model.fit() Multi-output models set additional prefixes on the metric names.
verbose	Verbosity mode, 0 or 1. Mode 0 is silent, and mode 1 displays messages when the callback takes an action.
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 mode is set to "max" if the quantities monitored are "acc" or start with "fmeasure" and are set to "min" for the rest of the quantities.
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".
initial_value_threshold	Floating point initial "best" value of the metric to be monitored. Only applies if save_best_value=True. Only overwrites the model weights already saved if the performance of current model is better than this value.

| Attributes | | | ---------- | | | model | |

Methods

`on_batch_begin`

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on_batch_begin(
    batch, logs=None
)

A backwards compatibility alias for on_train_batch_begin.

`on_batch_end`

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on_batch_end(
    batch, logs=None
)

A backwards compatibility alias for on_train_batch_end.

`on_epoch_begin`

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on_epoch_begin(
    epoch, logs=None
)

Called at the start of an epoch.

Subclasses should override for any actions to run. This function should only be called during TRAIN mode.

Args
epoch	Integer, index of epoch.
logs	Dict. Currently no data is passed to this argument for this method but that may change in the future.

`on_epoch_end`

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on_epoch_end(
    epoch, logs=None
)

Called at the end of an epoch.

Subclasses should override for any actions to run. This function should only be called during TRAIN mode.

Args
epoch	Integer, index of epoch.
logs	Dict, metric results for this training epoch, and for the validation epoch if validation is performed. Validation result keys are prefixed with val_. For training epoch, the values of the Model's metrics are returned. Example:{'loss': 0.2, 'accuracy': 0.7}.

`on_predict_batch_begin`

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on_predict_batch_begin(
    batch, logs=None
)

Called at the beginning of a batch in predict methods.

Subclasses should override for any actions to run.

Note that if the steps_per_execution argument to compile inModel is set to N, this method will only be called everyN batches.

Args
batch	Integer, index of batch within the current epoch.
logs	Dict. Currently no data is passed to this argument for this method but that may change in the future.

`on_predict_batch_end`

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on_predict_batch_end(
    batch, logs=None
)

Called at the end of a batch in predict methods.

Subclasses should override for any actions to run.

Note that if the steps_per_execution argument to compile inModel is set to N, this method will only be called everyN batches.

Args
batch	Integer, index of batch within the current epoch.
logs	Dict. Aggregated metric results up until this batch.

`on_predict_begin`

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on_predict_begin(
    logs=None
)

Called at the beginning of prediction.

Subclasses should override for any actions to run.

Args
logs	Dict. Currently no data is passed to this argument for this method but that may change in the future.

`on_predict_end`

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on_predict_end(
    logs=None
)

Called at the end of prediction.

Subclasses should override for any actions to run.

Args
logs	Dict. Currently no data is passed to this argument for this method but that may change in the future.

`on_test_batch_begin`

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on_test_batch_begin(
    batch, logs=None
)

Called at the beginning of a batch in evaluate methods.

Also called at the beginning of a validation batch in the fitmethods, if validation data is provided.

Subclasses should override for any actions to run.

Note that if the steps_per_execution argument to compile inModel is set to N, this method will only be called everyN batches.

Args
batch	Integer, index of batch within the current epoch.
logs	Dict. Currently no data is passed to this argument for this method but that may change in the future.

`on_test_batch_end`

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on_test_batch_end(
    batch, logs=None
)

Called at the end of a batch in evaluate methods.

Also called at the end of a validation batch in the fitmethods, if validation data is provided.

Subclasses should override for any actions to run.

Note that if the steps_per_execution argument to compile inModel is set to N, this method will only be called everyN batches.

Args
batch	Integer, index of batch within the current epoch.
logs	Dict. Aggregated metric results up until this batch.

`on_test_begin`

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on_test_begin(
    logs=None
)

Called at the beginning of evaluation or validation.

Subclasses should override for any actions to run.

Args
logs	Dict. Currently no data is passed to this argument for this method but that may change in the future.

`on_test_end`

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on_test_end(
    logs=None
)

Called at the end of evaluation or validation.

Subclasses should override for any actions to run.

Args
logs	Dict. Currently the output of the last call toon_test_batch_end() is passed to this argument for this method but that may change in the future.

`on_train_batch_begin`

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on_train_batch_begin(
    batch, logs=None
)

Called at the beginning of a training batch in fit methods.

Subclasses should override for any actions to run.

Note that if the steps_per_execution argument to compile inModel is set to N, this method will only be called everyN batches.

Args
batch	Integer, index of batch within the current epoch.
logs	Dict. Currently no data is passed to this argument for this method but that may change in the future.

`on_train_batch_end`

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on_train_batch_end(
    batch, logs=None
)

Called at the end of a training batch in fit methods.

Subclasses should override for any actions to run.

Note that if the steps_per_execution argument to compile inModel is set to N, this method will only be called everyN batches.

Args
batch	Integer, index of batch within the current epoch.
logs	Dict. Aggregated metric results up until this batch.

`on_train_begin`

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on_train_begin(
    logs=None
)

Called at the beginning of training.

Subclasses should override for any actions to run.

Args
logs	Dict. Currently no data is passed to this argument for this method but that may change in the future.

`on_train_end`

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on_train_end(
    logs=None
)

Called at the end of training.

Subclasses should override for any actions to run.

Args
logs	Dict. Currently the output of the last call toon_epoch_end() is passed to this argument for this method but that may change in the future.

`set_model`

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set_model(
    model
)

`set_params`

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set_params(
    params
)