Table Of Contents
Table Of Contents

mxnet.optimizer

Optimizer API of MXNet.

Optimization methods

AdaDelta([rho, epsilon])

The AdaDelta optimizer.

AdaGrad([eps])

AdaGrad optimizer.

Adam([learning_rate, beta1, beta2, epsilon, …])

The Adam optimizer.

Adamax([learning_rate, beta1, beta2])

The AdaMax optimizer.

DCASGD([momentum, lamda])

The DCASGD optimizer.

FTML([beta1, beta2, epsilon])

The FTML optimizer.

Ftrl([lamda1, learning_rate, beta])

The Ftrl optimizer.

LBSGD([momentum, multi_precision, …])

The Large Batch SGD optimizer with momentum and weight decay.

NAG([momentum])

Nesterov accelerated SGD.

Nadam([learning_rate, beta1, beta2, …])

The Nesterov Adam optimizer.

Optimizer([rescale_grad, param_idx2name, …])

The base class inherited by all optimizers.

RMSProp([learning_rate, gamma1, gamma2, …])

The RMSProp optimizer.

SGD([momentum, lazy_update])

The SGD optimizer with momentum and weight decay.

SGLD(**kwargs)

Stochastic Gradient Riemannian Langevin Dynamics.

Signum([learning_rate, momentum, wd_lh])

The Signum optimizer that takes the sign of gradient or momentum.

Updater(optimizer)

Updater for kvstore.

Helper functions

create(name, **kwargs)

Instantiates an optimizer with a given name and kwargs.

get_updater(optimizer)

Returns a closure of the updater needed for kvstore.

register(klass)

Registers a new optimizer.