TensorFlow 1 version | View source on GitHub |
Save the model after every epoch.
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
)
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.
Arguments | |
---|---|
filepath
|
string, path to save the model file. |
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 a batch at which this many samples have been seen since
last saving. 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
)