TensorFlow 2 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.
|
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
on_batch_begin
on_batch_begin(
batch, logs=None
)
A backwards compatibility alias for on_train_batch_begin
.
on_batch_end
on_batch_end(
batch, logs=None
)
A backwards compatibility alias for on_train_batch_end
.
on_epoch_begin
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.
Arguments | |
---|---|
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
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.
Arguments | |
---|---|
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_ .
|
on_predict_batch_begin
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.
Arguments | |
---|---|
batch
|
integer, index of batch within the current epoch. |
logs
|
dict. Has keys batch and size representing the current batch
number and the size of the batch.
|
on_predict_batch_end
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.
Arguments | |
---|---|
batch
|
integer, index of batch within the current epoch. |
logs
|
dict. Metric results for this batch. |
on_predict_begin
on_predict_begin(
logs=None
)
Called at the beginning of prediction.
Subclasses should override for any actions to run.
Arguments | |
---|---|
logs
|
dict. Currently no data is passed to this argument for this method but that may change in the future. |
on_predict_end
on_predict_end(
logs=None
)
Called at the end of prediction.
Subclasses should override for any actions to run.
Arguments | |
---|---|
logs
|
dict. Currently no data is passed to this argument for this method but that may change in the future. |
on_test_batch_begin
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.
Arguments | |
---|---|
batch
|
integer, index of batch within the current epoch. |
logs
|
dict. Has keys batch and size representing the current batch
number and the size of the batch.
|
on_test_batch_end
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.
Arguments | |
---|---|
batch
|
integer, index of batch within the current epoch. |
logs
|
dict. Metric results for this batch. |
on_test_begin
on_test_begin(
logs=None
)
Called at the beginning of evaluation or validation.
Subclasses should override for any actions to run.
Arguments | |
---|---|
logs
|
dict. Currently no data is passed to this argument for this method but that may change in the future. |
on_test_end
on_test_end(
logs=None
)
Called at the end of evaluation or validation.
Subclasses should override for any actions to run.
Arguments | |
---|---|
logs
|
dict. Currently no data is passed to this argument for this method but that may change in the future. |
on_train_batch_begin
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.
Arguments | |
---|---|
batch
|
integer, index of batch within the current epoch. |
logs
|
dict. Has keys batch and size representing the current batch
number and the size of the batch.
|
on_train_batch_end
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.
Arguments | |
---|---|
batch
|
integer, index of batch within the current epoch. |
logs
|
dict. Metric results for this batch. |
on_train_begin
on_train_begin(
logs=None
)
Called at the beginning of training.
Subclasses should override for any actions to run.
Arguments | |
---|---|
logs
|
dict. Currently no data is passed to this argument for this method but that may change in the future. |
on_train_end
on_train_end(
logs=None
)
Called at the end of training.
Subclasses should override for any actions to run.
Arguments | |
---|---|
logs
|
dict. Currently no data is passed to this argument for this method but that may change in the future. |
set_model
set_model(
model
)
set_params
set_params(
params
)