tf.keras.callbacks.LambdaCallback

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
)

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 two positional
  arguments: `batch`, `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:

    ```python
    # 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']}) + '
'),
        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])
    ```

on_batch_begin(
    batch, logs=None
)

on_batch_end(
    batch, logs=None
)

on_epoch_begin(
    epoch, logs=None
)

on_epoch_end(
    epoch, logs=None
)

on_predict_batch_begin(
    batch, logs=None
)

on_predict_batch_end(
    batch, logs=None
)

on_predict_begin(
    logs=None
)

on_predict_end(
    logs=None
)

on_test_batch_begin(
    batch, logs=None
)

on_test_batch_end(
    batch, logs=None
)

on_test_begin(
    logs=None
)

on_test_end(
    logs=None
)

on_train_batch_begin(
    batch, logs=None
)

on_train_batch_end(
    batch, logs=None
)

on_train_begin(
    logs=None
)

on_train_end(
    logs=None
)

set_model(
    model
)

set_params(
    params
)