tf.keras.callbacks.ModelCheckpoint

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

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.

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)

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" when save_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 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. 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.

model

Methods

on_batch_begin

View source

on_batch_begin(
    batch, logs=None
)

A backwards compatibility alias for on_train_batch_begin.

on_batch_end

View source

on_batch_end(
    batch, logs=None
)

A backwards compatibility alias for on_train_batch_end.

on_epoch_begin

View source

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

View source

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

View source

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 in Model is set to N, this method will only be called every N 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

View source

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 in Model is set to N, this method will only be called every N batches.

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

on_predict_begin

View source

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

View source

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 fit methods, if validation data is provided.

Subclasses should override for any actions to run.

Note that if the steps_per_execution argument to compile in Model is set to N, this method will only be called every N 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

View source

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 fit methods, if validation data is provided.

Subclasses should override for any actions to run.

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

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

on_test_begin

View source

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

View source

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 to on_test_batch_end() is passed to this argument for this method but that may change in the future.

on_train_batch_begin

View source

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 in Model is set to N, this method will only be called every N 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

View source

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 in Model is set to N, this method will only be called every N batches.

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

on_train_begin

View source

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

View source

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 to on_epoch_end() is passed to this argument for this method but that may change in the future.

set_model

View source

set_model(
    model
)

set_params

View source

set_params(
    params
)