NovoGrad

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
)

Arguments

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.

Value

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

Examples

if (FALSE) {
keras_model_sequential() %>%
  layer_dense(32, input_shape = c(784)) %>%
  compile(
    optimizer = optimizer_novograd(),
    loss='binary_crossentropy',
    metrics='accuracy'
  )
}