tf.keras.callbacks.LambdaCallback

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_train_begin=None,
    on_train_end=None,
    on_train_batch_begin=None,
    on_train_batch_end=None,
    **kwargs
)

Used in the notebooks

Used in the tutorials
Displaying image data in TensorBoard

This callback is constructed with anonymous functions that will be called at the appropriate time (during Model.{fit | evaluate | predict}). Note that the callbacks expects positional arguments, as:

on_epoch_begin and on_epoch_end expect two positional arguments: epoch, logs
on_train_begin and on_train_end expect one positional argument: logs
on_train_batch_begin and on_train_batch_end expect a positional argument batch and a keyword argument logs
See Callback class definition for the full list of functions and their expected arguments.

Args
`on_epoch_begin`	called at the beginning of every epoch.
`on_epoch_end`	called at the end of every epoch.
`on_train_begin`	called at the beginning of model training.
`on_train_end`	called at the end of model training.
`on_train_batch_begin`	called at the beginning of every train batch.
`on_train_batch_end`	called at the end of every train batch.
`kwargs`	Any function in `Callback` that you want to override by passing `function_name=function`. For example, `LambdaCallback(.., on_train_end=train_end_fn)`. The custom function needs to have same arguments as the ones defined in `Callback`.

Example:

# Print the batch number at the beginning of every batch.
batch_print_callback = LambdaCallback(
    on_train_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])

Attributes
`model`

Attributes

model

Methods

`on_batch_begin`

View source

on_batch_begin(
    batch, logs=None
)

A backwards compatibility alias for on_train_batch_begin.

`on_batch_end`

View source

on_batch_end(
    batch, logs=None
)

A backwards compatibility alias for on_train_batch_end.

`on_epoch_begin`

View source

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`

View source

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`

View source

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`

View source

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`

View source

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`

View source

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`

View source

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`

View source

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`

View source

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`

View source

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`

View source

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`

View source

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`

View source

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`

View source

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`

View source

set_model(
    model
)

`set_params`

View source

set_params(
    params
)

tf.keras.callbacks.LambdaCallback Stay organized with collections Save and categorize content based on your preferences.

Used in the notebooks

Args

Example:

Attributes

Methods

on_batch_begin

on_batch_end

on_epoch_begin

on_epoch_end

on_predict_batch_begin

on_predict_batch_end

on_predict_begin

on_predict_end

on_test_batch_begin

on_test_batch_end

on_test_begin

on_test_end

on_train_batch_begin

on_train_batch_end

on_train_begin

on_train_end

set_model

set_params

tf.keras.callbacks.LambdaCallback

`on_batch_begin`

`on_batch_end`

`on_epoch_begin`

`on_epoch_end`

`on_predict_batch_begin`

`on_predict_batch_end`

`on_predict_begin`

`on_predict_end`

`on_test_batch_begin`

`on_test_batch_end`

`on_test_begin`

`on_test_end`

`on_train_batch_begin`

`on_train_batch_end`

`on_train_begin`

`on_train_end`

`set_model`

`set_params`