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',
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)
Args | |
---|---|
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:
|
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.
|
Attributes | |
---|---|
model
|
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.
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
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
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
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
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
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
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
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
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
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
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
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
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
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
set_model(
model
)
set_params
set_params(
params
)