Table Of Contents
Table Of Contents

mxnet.optimizer.DCASGD

class mxnet.optimizer.DCASGD(momentum=0.0, lamda=0.04, **kwargs)[source]

The DCASGD optimizer.

This class implements the optimizer described in Asynchronous Stochastic Gradient Descent with Delay Compensation for Distributed Deep Learning, available at https://arxiv.org/abs/1609.08326.

This optimizer accepts the following parameters in addition to those accepted by Optimizer.

Parameters
  • momentum (float, optional) – The momentum value.

  • lamda (float, optional) – Scale DC value.

__init__(momentum=0.0, lamda=0.04, **kwargs)[source]

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__([momentum, lamda])

Initialize self.

create_optimizer(name, **kwargs)

Instantiates an optimizer with a given name and kwargs.

create_state(index, weight)

Creates auxiliary state for a given weight.

create_state_multi_precision(index, weight)

Creates auxiliary state for a given weight, including FP32 high precision copy if original weight is FP16.

register(klass)

Registers a new optimizer.

set_learning_rate(lr)

Sets a new learning rate of the optimizer.

set_lr_mult(args_lr_mult)

Sets an individual learning rate multiplier for each parameter.

set_lr_scale(args_lrscale)

[DEPRECATED] Sets lr scale.

set_wd_mult(args_wd_mult)

Sets an individual weight decay multiplier for each parameter.

update(index, weight, grad, state)

Updates the given parameter using the corresponding gradient and state.

update_multi_precision(index, weight, grad, …)

Updates the given parameter using the corresponding gradient and state.

Attributes

learning_rate

opt_registry