optimizer_lamb: Layer-wise Adaptive Moments
In tfaddons: Interface to 'TensorFlow SIG Addons'

Description Usage Arguments Value Examples

Layer-wise Adaptive Moments

optimizer_lamb(
  learning_rate = 0.001,
  beta_1 = 0.9,
  beta_2 = 0.999,
  epsilon = 1e-06,
  weight_decay_rate = 0,
  exclude_from_weight_decay = NULL,
  exclude_from_layer_adaptation = NULL,
  name = "LAMB",
  clipnorm = NULL,
  clipvalue = NULL,
  decay = NULL,
  lr = NULL
)

`learning_rate`	A 'Tensor' or a floating point value. or a schedule that is a 'tf$keras$optimizers$schedules$LearningRateSchedule' The learning rate.
`beta_1`	A 'float' value or a constant 'float' tensor. The exponential decay rate for the 1st moment estimates.
`beta_2`	A 'float' value or a constant 'float' tensor. The exponential decay rate for the 2nd moment estimates.
`epsilon`	A small constant for numerical stability.
`weight_decay_rate`	weight decay rate.
`exclude_from_weight_decay`	List of regex patterns of variables excluded from weight decay. Variables whose name contain a substring matching the pattern will be excluded.
`exclude_from_layer_adaptation`	List of regex patterns of variables excluded from layer adaptation. Variables whose name contain a substring matching the pattern will be excluded.
`name`	Optional name for the operations created when applying gradients. Defaults to "LAMB".
`clipnorm`	is clip gradients by norm.
`clipvalue`	is clip gradients by value.
`decay`	is included for backward compatibility to allow time inverse decay of learning rate.
`lr`	is included for backward compatibility, recommended to use learning_rate instead.

Optimizer for use with 'keras::compile()'

## Not run: 
keras_model_sequential() %>%
  layer_dense(32, input_shape = c(784)) %>%
  compile(
    optimizer = optimizer_lamb(),
    loss='binary_crossentropy',
    metrics='accuracy'
  )

## End(Not run)