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Subclass the Keras LearningRateSchedule base class.

You can use a learning rate schedule to modulate how the learning rate of your optimizer changes over time.

Several built-in learning rate schedules are available, such as learning_rate_schedule_exponential_decay() or learning_rate_schedule_piecewise_constant_decay():

lr_schedule <- learning_rate_schedule_exponential_decay(
  initial_learning_rate = 1e-2,
  decay_steps = 10000,
  decay_rate = 0.9
optimizer <- optimizer_sgd(learning_rate = lr_schedule)

A LearningRateSchedule() instance can be passed in as the learning_rate argument of any optimizer.

To implement your own schedule object, you should implement the call method, which takes a step argument (a scalar integer backend tensor, the current training step count). Note that step is 0-based (i.e., the first step is 0). Like for any other Keras object, you can also optionally make your object serializable by implementing the get_config() and from_config() methods.


  call = NULL,
  initialize = NULL,
  get_config = NULL,
  public = list(),
  private = list(),
  inherit = NULL,
  parent_env = parent.frame()



String, the name of the custom class. (Conventionally, CamelCase).

call, initialize, get_config

Recommended methods to implement. See description and details sections.

..., public

Additional methods or public members of the custom class.


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.


What the custom class will subclass. By default, the base keras class.


The R environment that all class methods will have as a grandparent.


A function that returns LearningRateSchedule instances, similar to the built-in learning_rate_schedule_* family of functions.


my_custom_learning_rate_schedule <- LearningRateSchedule(
  classname = "MyLRSchedule",

  initialize = function(initial_learning_rate) {
    self$initial_learning_rate <- initial_learning_rate

  call = function(step) {
    # note that `step` is a tensor
    # and call() will be traced via tf_function() or similar.

    str(step) # <KerasVariable shape=(), dtype=int64, path=SGD/iteration>

    # print 'step' every 1000 steps
    op_cond((step %% 1000) == 0,
            \() {tensorflow::tf$print(step); NULL},
            \() {NULL})
    self$initial_learning_rate / (step + 1)

optimizer <- optimizer_sgd(
  learning_rate = my_custom_learning_rate_schedule(0.1)

# You can also call schedule instances directly
# (e.g., for interactive testing, or if implementing a custom optimizer)
schedule <- my_custom_learning_rate_schedule(0.1)
step <- keras$Variable(initializer = op_ones,
                       shape = shape(),
                       dtype = "int64")

## <KerasVariable shape=(), dtype=int64, path=variable>

## tf.Tensor(0.0, shape=(), dtype=float64)

Methods available:

  • get_config()

Symbols in scope

All R function custom methods (public and private) will have the following symbols in scope:

  • self: The custom class instance.

  • super: The custom class superclass.

  • private: An R environment specific to the class instance. Any objects assigned here are invisible to the Keras framework.

  • __class__ and as.symbol(classname): the custom class type object.