Source code for mxnet.optimizer.dcasgd
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# pylint: disable=W0223
"""DCASGD optimizer."""
from __future__ import absolute_import
from ..ndarray import (zeros, clip, square)
from .optimizer import Optimizer, register
__all__ = ['DCASGD']
[docs]@register
class DCASGD(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 :class:`.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.
"""
def __init__(self, learning_rate=0.1, momentum=0.0, lamda=0.04,
use_fused_step=False, **kwargs):
super(DCASGD, self).__init__(learning_rate=learning_rate,
use_fused_step=use_fused_step,
**kwargs)
self.momentum = momentum
self.weight_previous = {}
self.lamda = lamda
[docs] def create_state(self, index, weight):
if self.momentum == 0.0:
return None, weight.copy() # previous weight
else:
return (zeros(weight.shape, weight.context, dtype=weight.dtype), # momentum
weight.copy()) # previous weight
[docs] def step(self, indices, weights, grads, states):
"""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()`.
"""
for index, weight, grad, state in zip(indices, weights, grads, states):
self._update_count(index)
lr = self._get_lr(index)
wd = self._get_wd(index)
# preprocess grad
grad *= self.rescale_grad
if self.clip_gradient is not None:
grad = clip(grad, -self.clip_gradient, self.clip_gradient)
grad += wd * weight
# update mom, previous_weight
mom, previous_weight = state
d = square(grad)
d *= weight - previous_weight
d *= self.lamda
d += grad
if mom is not None:
mom[:] *= self.momentum
mom[:] -= lr * d
else:
assert (self.momentum == 0.0)
mom = d
mom *= -lr
previous_weight[:] = weight
# update weight
weight[:] += mom
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