TensorFlow 1 version | View source on GitHub |
Callback for creating simple, custom callbacks on-the-fly.
Inherits From: Callback
tf.keras.callbacks.LambdaCallback(
on_epoch_begin=None, on_epoch_end=None, on_batch_begin=None, on_batch_end=None,
on_train_begin=None, on_train_end=None, **kwargs
)
This callback is constructed with anonymous functions that will be called at the appropriate time. Note that the callbacks expects positional arguments, as:
on_epoch_begin
andon_epoch_end
expect two positional arguments:epoch
,logs
on_batch_begin
andon_batch_end
expect two positional arguments:batch
,logs
on_train_begin
andon_train_end
expect one positional argument:logs
Arguments | |
---|---|
on_epoch_begin
|
called at the beginning of every epoch. |
on_epoch_end
|
called at the end of every epoch. |
on_batch_begin
|
called at the beginning of every batch. |
on_batch_end
|
called at the end of every batch. |
on_train_begin
|
called at the beginning of model training. |
on_train_end
|
called at the end of model training. |
Example:
# Print the batch number at the beginning of every batch.
batch_print_callback = LambdaCallback(
on_batch_begin=lambda batch,logs: print(batch))
# Stream the epoch loss to a file in JSON format. The file content
# is not well-formed JSON but rather has a JSON object per line.
import json
json_log = open('loss_log.json', mode='wt', buffering=1)
json_logging_callback = LambdaCallback(
on_epoch_end=lambda epoch, logs: json_log.write(
json.dumps({'epoch': epoch, 'loss': logs['loss']}) + '\n'),
on_train_end=lambda logs: json_log.close()
)
# Terminate some processes after having finished model training.
processes = ...
cleanup_callback = LambdaCallback(
on_train_end=lambda logs: [
p.terminate() for p in processes if p.is_alive()])
model.fit(...,
callbacks=[batch_print_callback,
json_logging_callback,
cleanup_callback])
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
)