R/optimizers.R

optimizer_nadam

Nesterov Adam optimizer

Description

Much like Adam is essentially RMSprop with momentum, Nadam is Adam RMSprop with Nesterov momentum.

Usage

 
optimizer_nadam( 
  learning_rate = 0.002, 
  beta_1 = 0.9, 
  beta_2 = 0.999, 
  epsilon = NULL, 
  schedule_decay = 0.004, 
  clipnorm = NULL, 
  clipvalue = NULL, 
  ... 
) 

Arguments

Arguments Description
learning_rate float >= 0. Learning rate.
beta_1 The exponential decay rate for the 1st moment estimates. float, 0 < beta < 1. Generally close to 1.
beta_2 The exponential decay rate for the 2nd moment estimates. float, 0 < beta < 1. Generally close to 1.
epsilon float >= 0. Fuzz factor. If NULL, defaults to k_epsilon().
schedule_decay Schedule deacy.
clipnorm Gradients will be clipped when their L2 norm exceeds this value.
clipvalue Gradients will be clipped when their absolute value exceeds this value.
Unused, present only for backwards compatability

Details

Default parameters follow those provided in the paper. It is recommended to leave the parameters of this optimizer at their default values.

See Also

On the importance of initialization and momentum in deep learning. Other optimizers: optimizer_adadelta(), optimizer_adagrad(), optimizer_adamax(), optimizer_adam(), optimizer_rmsprop(), optimizer_sgd()