new_callback_class: Callback
In rstudio/keras: R Interface to 'Keras'

new_callback_class

R Documentation

Callback

Description

new_callback_class() is an alias for Callback(). See ⁠?⁠Callback() for the full documentation.

Usage

new_callback_class(
  classname,
  on_epoch_begin = NULL,
  on_epoch_end = NULL,
  on_train_begin = NULL,
  on_train_end = NULL,
  on_train_batch_begin = NULL,
  on_train_batch_end = NULL,
  on_test_begin = NULL,
  on_test_end = NULL,
  on_test_batch_begin = NULL,
  on_test_batch_end = NULL,
  on_predict_begin = NULL,
  on_predict_end = NULL,
  on_predict_batch_begin = NULL,
  on_predict_batch_end = NULL,
  ...,
  public = list(),
  private = list(),
  inherit = NULL,
  parent_env = parent.frame()
)

Arguments

`classname`	String, the name of the custom class. (Conventionally, CamelCase).
`on_epoch_begin`	\(epoch, logs = NULL) 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`: Named List. Currently no data is passed to this argument for this method but that may change in the future.
`on_epoch_end`	\(epoch, logs = NULL) 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`: Named List, 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: `list(loss = 0.2, accuracy = 0.7)`.
`on_train_begin`	\(logs = NULL) Called at the beginning of training. Subclasses should override for any actions to run. Args: `logs`: Named list. Currently no data is passed to this argument for this method but that may change in the future.
`on_train_end`	\(logs = NULL) Called at the end of training. Subclasses should override for any actions to run. Args: `logs`: Named list. 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.
`on_train_batch_begin`	\(batch, logs = NULL) 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`: Named list. Currently no data is passed to this argument for this method but that may change in the future.
`on_train_batch_end`	\(batch, logs=NULL) 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`: Named list. Aggregated metric results up until this batch.
`on_test_begin`	\(logs = NULL) Called at the beginning of evaluation or validation. Subclasses should override for any actions to run. Args: `logs`: Named list. Currently no data is passed to this argument for this method but that may change in the future.
`on_test_end`	\(logs = NULL) Called at the end of evaluation or validation. Subclasses should override for any actions to run. Args: `logs`: Named list. 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_test_batch_begin`	\(batch, logs = NULL) 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`: Named list. Currently no data is passed to this argument for this method but that may change in the future.
`on_test_batch_end`	\(batch, logs = NULL) 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`: Named list. Aggregated metric results up until this batch.
`on_predict_begin`	\(logs = NULL) Called at the beginning of prediction. Subclasses should override for any actions to run. Args: `logs`: Named list. Currently no data is passed to this argument for this method but that may change in the future.
`on_predict_end`	\(logs = NULL) Called at the end of prediction. Subclasses should override for any actions to run. Args: `logs`: Named list. Currently no data is passed to this argument for this method but that may change in the future.
`on_predict_batch_begin`	\(batch, logs = NULL) 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`: Named list. Currently no data is passed to this argument for this method but that may change in the future.
`on_predict_batch_end`	\(batch, logs = NULL) 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`: Named list. Aggregated metric results up until this batch.
`...`, `public`	Additional methods or public members of the custom class.
`private`	Named list of R objects (typically, functions) to include in instance private environments. `private` methods will have all the same symbols in scope as public methods (See section "Symbols in Scope"). Each instance will have it's own `private` environment. Any objects in `private` will be invisible from the Keras framework and the Python runtime.
`inherit`	What the custom class will subclass. By default, the base keras class.
`parent_env`	The R environment that all class methods will have as a grandparent.