optimizer_novograd: NovoGrad
In tfaddons: Interface to 'TensorFlow SIG Addons'

Description Usage Arguments Value Examples

NovoGrad

optimizer_novograd(
  learning_rate = 0.001,
  beta_1 = 0.9,
  beta_2 = 0.999,
  epsilon = 1e-07,
  weight_decay = 0,
  grad_averaging = FALSE,
  amsgrad = FALSE,
  name = "NovoGrad",
  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`	A floating point value. Weight decay for each param.
`grad_averaging`	determines whether to use Adam style exponential moving averaging for the first order moments.
`amsgrad`	boolean. Whether to apply AMSGrad variant of this algorithm from the paper "On the Convergence of Adam and beyond"
`name`	Optional name for the operations created when applying gradients. Defaults to "NovoGrad".
`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_novograd(),
    loss='binary_crossentropy',
    metrics='accuracy'
  )

## End(Not run)