keras 2.9.0

• New functions for constructing custom keras subclasses:

  • new_model_class()
  • new_layer_class()
  • new_callback_class()
  • new_metric_class()
  • new_loss_class()
  • new_learning_rate_schedule_class().

  Also provided is mark_active(), a decorator for indicating a class method
  should be an active binding (i.e., decorated with Python's @property).
  mark_active() can be used in the new_*_class family of class constructors
  as well as %py_class%.

• r_to_py() method for R6 classes and %py_class% gain support for
  private fields and methods. Any R objects stored in private will only be
  available to methods, and will not be converted to Python.

• New family of functions for controlling optimizer learning rates during training:

  • learning_rate_schedule_cosine_decay()
  • learning_rate_schedule_cosine_decay_restarts()
  • learning_rate_schedule_exponential_decay()
  • learning_rate_schedule_inverse_time_decay()
  • learning_rate_schedule_piecewise_constant_decay()
  • learning_rate_schedule_polynomial_decay()

  Also, a function for constructing custom learning rate schedules:
  new_learning_rate_schedule_class().

• New L2 unit normilization layer: layer_unit_normalization().

• New regularizer_orthogonal, a regularizer that encourages
  orthogonality between the rows (or columns) or a weight matrix.

• New zip_lists() function for transposing lists, optionally matching by name.

• New plot() S3 method for models.

• pydot is now included in the packages installed by install_keras().

• The png package is now listed under Suggests.

• The %<>% assignment pipe from magrittr is exported.

• format() method for keras models (and derivative methods print(), summary(),
  str(), and py_str()):

  • gain a new arg compact. If TRUE (the default) white-space only
    lines are stripped out of model.summary().
  • If any layers are marked non-trainable or frozen, the model summary
    now includes a "Trainable" column, indicating if a layer is frozen.

• freeze_weights() and unfreeze_weights():

  • gain a flexible which argument that can accept layer names (as character strings),
    an integer vector, a boolean vector, or a function that returns a boolean
    when called with a layer. (see updated examples in ?freeze_weights
  • from and to arguments gain the ability to accept negative integers,
    to specify layers counting from the end of the layers list.

• get_weights() gains a trainable argument that can accept TRUE or FALSE,
  allowing for returning only the unfrozen or frozen weights, respectively.

• timeseries_dataset_from_array():

  • R arrays are now cast to the floatx dtype ("float32" by default)
  • start_index and end_index now are 1-based.

• image_dataset_from_directory() gains a crop_to_aspect_ratio argument which
  can be used to prevent distorting images when resizing to a new aspect ratio.

• Layer is deprecated, superseded by new_layer_class().

• load_model_tf() argument custom_objects gains the ability to accept an
  unnamed list (e.g, of objects returned by new_layer_class() or similar).
  Appropriate names for the supplied objects are automatically inferred.

• Fixed an issue where negative values less than -1 supplied to axis
  arguments were selecting the wrong axis.

• get_layer() gains the ability to accept negative values for the index argument.

• Fixed warning from create_layer_wrapper() when the custom layer didn't have
  an overridden initialize or __init__ method.

• Backend functions:

  • k_clip() min_value and max_value gain default values of NULL,
    can be omitted. NULL is taken as -Inf or Inf, respectively.
  • k_squeeze(): axis argument can be omitted, in which case all axes of size 1 are dropped.
  • k_tile(): n argument can now be supplied as a tensor.
  • New function k_unstack().

• KerasTensor objects (e.g, returned by layer_input()) now inherit S3 methods
  for "tensorflow.tensor".

• plot.keras_training_history() no longer issues message
  `geom_smooth()` using formula 'y ~ x' when method = "ggplot2".

• print and related methods for models (format, summary) now accept
  a width argument.

• evaluate(), fit(), and predict() methods for keras Models now default
  to verbose = "auto", with verbosity adjusted appropriately based on calls to
  keras$utils$disable_interactive_logging(), and contexts like
  ParameterServerStrategy.

• install_keras() now accepts version = "release-cpu" as a valid specification.