keras 2.11.0

• Default TensorFlow version installed by install_keras() is now 2.11.

• All optimizers have been updated for Keras/TensorFlow version 2.11.
  Arguments to all the optimizers have changed. To access the previous
  optimizer implementations, use the constructors available at
  keras$optimizers$legacy. For example, use keras$optimizers$legacy$Adam()
  for the previous implementation of optimizer_adam().

• New optimizer optimizer_frtl().

• updates to layers:

  • layer_attention() gains score_mode and dropout arguments.
  • layer_discretization() gains output_mode and sparse arguments.
  • layer_gaussian_dropout() and layer_gaussian_noise() gain a seed argument.
  • layer_hashing() gains output_mode and sparse arguments.
  • layer_integer_lookup() gains vocabulary_dtype and idf_weights arguments.
  • layer_normalization() gains an invert argument.
  • layer_string_lookup() gains an idf_weights argument.

• Fixed issue where input_shape supplied to custom layers defined with new_layer_class()
  would result in an error (#1338)

• New callback_backup_and_restore(), for resuming an interrupted fit() call.

• The merging family of layers (layer_add, layer_concatenate, etc.) gain the ability
  to accept layers in ..., allowing for easier composition of residual blocks with the pipe %>%.
  e.g. something like this now works:

  block_1_output <- ...
  block_2_output <- block_1_output %>% 
    layer_conv_2d(64, 3, activation = "relu", padding = "same") %>% 
    layer_add(block_1_output)

• model$get_config() method now returns an R object that can be safely serialized
  to rds.

• keras_array() now reflects unconverted Python objects. This enables passing
  objects like pandas.Series() to fit() and evaluate() methods. (#1341)