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Abstract base class used to build new callbacks.
tf.keras.callbacks.Callback()
Callbacks can be passed to keras methods such as fit
, evaluate
, and
predict
in order to hook into the various stages of the model training and
inference lifecycle.
To create a custom callback, subclass keras.callbacks.Callback
and
override the method associated with the stage of interest. See
https://www.tensorflow.org/guide/keras/custom_callback for more information.
Example:
training_finished = False
class MyCallback(tf.keras.callbacks.Callback):
def on_train_end(self, logs=None):
global training_finished
training_finished = True
model = tf.keras.Sequential([
tf.keras.layers.Dense(1, input_shape=(1,))])
model.compile(loss='mean_squared_error')
model.fit(tf.constant([[1.0]]), tf.constant([[1.0]]),
callbacks=[MyCallback()])
assert training_finished == True
If you want to use Callback
objects in a custom training loop:
- You should pack all your callbacks into a single
callbacks.CallbackList
so they can all be called together. - You will need to manually call all the
on_*
methods at the appropriate locations in your loop. Like this:
Example:
callbacks = tf.keras.callbacks.CallbackList([...])
callbacks.append(...)
callbacks.on_train_begin(...)
for epoch in range(EPOCHS):
callbacks.on_epoch_begin(epoch)
for i, data in dataset.enumerate():
callbacks.on_train_batch_begin(i)
batch_logs = model.train_step(data)
callbacks.on_train_batch_end(i, batch_logs)
epoch_logs = ...
callbacks.on_epoch_end(epoch, epoch_logs)
final_logs=...
callbacks.on_train_end(final_logs)
The logs
dictionary that callback methods
take as argument will contain keys for quantities relevant to
the current batch or epoch (see method-specific docstrings).
Attributes | |
---|---|
params
|
Dict. Training parameters (eg. verbosity, batch size, number of epochs...). |
model
|
Instance of keras.models.Model .
Reference of the model being trained.
|
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
tf.keras.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
tf.keras.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
tf.keras.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
tf.keras.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
tf.keras.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
tf.keras.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
)