optimizer_nadam: Optimizer that implements the Nadam algorithm
In keras: R Interface to 'Keras'

optimizer_nadam

R Documentation

Optimizer that implements the Nadam algorithm

Description

Optimizer that implements the Nadam algorithm

Usage

optimizer_nadam(
  learning_rate = 0.001,
  beta_1 = 0.9,
  beta_2 = 0.999,
  epsilon = 1e-07,
  weight_decay = NULL,
  clipnorm = NULL,
  clipvalue = NULL,
  global_clipnorm = NULL,
  use_ema = FALSE,
  ema_momentum = 0.99,
  ema_overwrite_frequency = NULL,
  jit_compile = TRUE,
  name = "Nadam",
  ...
)

Arguments

`learning_rate`	A `tf.Tensor`, floating point value, a schedule that is a `tf.keras.optimizers.schedules.LearningRateSchedule`, or a callable that takes no arguments and returns the actual value to use. The learning rate. Defaults to 0.001.
`beta_1`	A float value or a constant float tensor, or a callable that takes no arguments and returns the actual value to use. The exponential decay rate for the 1st moment estimates. Defaults to 0.9.
`beta_2`	A float value or a constant float tensor, or a callable that takes no arguments and returns the actual value to use. The exponential decay rate for the 2nd moment estimates. Defaults to 0.999.
`epsilon`	A small constant for numerical stability. This epsilon is "epsilon hat" in the Kingma and Ba paper (in the formula just before Section 2.1), not the epsilon in Algorithm 1 of the paper. Defaults to 1e-7.
`weight_decay`	Float, defaults to NULL. If set, weight decay is applied.
`clipnorm`	Float. If set, the gradient of each weight is individually clipped so that its norm is no higher than this value.
`clipvalue`	Float. If set, the gradient of each weight is clipped to be no higher than this value.
`global_clipnorm`	Float. If set, the gradient of all weights is clipped so that their global norm is no higher than this value.
`use_ema`	Boolean, defaults to FALSE. If TRUE, exponential moving average (EMA) is applied. EMA consists of computing an exponential moving average of the weights of the model (as the weight values change after each training batch), and periodically overwriting the weights with their moving average.
`ema_momentum`	Float, defaults to 0.99. Only used if `use_ema=TRUE`. This is # noqa: E501 the momentum to use when computing the EMA of the model's weights: `new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value`.
`ema_overwrite_frequency`	Int or NULL, defaults to NULL. Only used if `use_ema=TRUE`. Every `ema_overwrite_frequency` steps of iterations, we overwrite the model variable by its moving average. If NULL, the optimizer # noqa: E501 does not overwrite model variables in the middle of training, and you need to explicitly overwrite the variables at the end of training by calling `optimizer.finalize_variable_values()` (which updates the model # noqa: E501 variables in-place). When using the built-in `fit()` training loop, this happens automatically after the last epoch, and you don't need to do anything.
`jit_compile`	Boolean, defaults to TRUE. If TRUE, the optimizer will use XLA # noqa: E501 compilation. If no GPU device is found, this flag will be ignored.
`name`	String. The name to use for momentum accumulator weights created by the optimizer.
`...`	Used for backward and forward compatibility