Table Of Contents
Table Of Contents

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