mxnet.optimizer.dcasgd

DCASGD optimizer.

Classes

DCASGD([learning_rate, momentum, lamda, ...])

The DCASGD optimizer.
class mxnet.optimizer.dcasgd.DCASGD(learning_rate=0.1, momentum=0.0, lamda=0.04, use_fused_step=False, **kwargs)[source]

Bases: Optimizer

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:

  • learning_rate (float, default 0.1) – The initial learning rate. If None, the optimization will use the learning rate from lr_scheduler. If not None, it will overwrite the learning rate in lr_scheduler. If None and lr_scheduler is also None, then it will be set to 0.01 by default.

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

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

  • use_fused_step (bool, default False) – Whether or not to use fused kernels for optimizer. When use_fused_step=False, step is called, otherwise, fused_step is called.

create_state(index, weight)[source]

Creates auxiliary state for a given weight.

Some optimizers require additional states, e.g. as momentum, in addition to gradients in order to update weights. This function creates state for a given weight which will be used in update. This function is called only once for each weight.

Parameters:

  • index (int) – An unique index to identify the weight.

  • weight (NDArray) – The weight.

Returns:

state – The state associated with the weight.

Return type:

any obj

step(indices, weights, grads, states)[source]

Perform an optimization step using gradients and states.

Parameters:

  • indices (list of int) – List of unique indices of the parameters into the individual learning rates and weight decays. Learning rates and weight decay may be set via set_lr_mult() and set_wd_mult(), respectively.

  • weights (list of NDArray) – List of parameters to be updated.

  • grads (list of NDArray) – List of gradients of the objective with respect to this parameter.

  • states (List of any obj) – List of state returned by create_state().