Nesterov Adam optimizer

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

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

Arguments

lr

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.

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