TensorFlow 1 version | View source on GitHub |
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', **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.
Example:
EPOCHS = 10
checkpoint_filepath = '/tmp/checkpoint'
model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
filepath=checkpoint_filepath,
save_weights_only=True,
monitor='val_acc',
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, 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
|
quantity to monitor. |
verbose
|
verbosity mode, 0 or 1. |
save_best_only
|
if save_best_only=True , 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. 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'
|
**kwargs
|
Additional arguments for backwards compatibility. Possible key
is period .
|
Methods
set_model
set_model(
model
)
set_params
set_params(
params
)