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TensorFlow 1 version
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View source on GitHub
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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',
options=None, **kwargs
)
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/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.
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monitor
|
The metric name to monitor. Typically the metrics are set by the
Model.compile method. Note:
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verbose
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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)).
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save_freq
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'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'.
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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.
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**kwargs
|
Additional arguments for backwards compatibility. Possible key
is period.
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Methods
set_model
set_model(
model
)
set_params
set_params(
params
)