python/mxnet/optimizer/sgld.py - master - githubmirror/incubator-mxnet

python/mxnet/optimizer/sgld.py
88 строк4 KB
Shuai Zheng
[MXNET-#16167] Refactor Optimizer (#17400)
29 фев 2020, 20:25
29 фев 2020, 20:25f70c7b7
# coding: utf-8
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#   http://www.apache.org/licenses/LICENSE-2.0
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# pylint: disable=W0223
"""SGLD optimizer."""
from __future__ import absolute_import
import math
from ..ndarray import clip
from ..random import normal
from .optimizer import Optimizer, register

__all__ = ['SGLD']


@register
class SGLD(Optimizer):
    """Stochastic Gradient Riemannian Langevin Dynamics.

    This class implements the optimizer described in the paper *Stochastic Gradient
    Riemannian Langevin Dynamics on the Probability Simplex*, available at
    https://papers.nips.cc/paper/4883-stochastic-gradient-riemannian-langevin-dynamics-on-the-probability-simplex.pdf.

    Parameters
    ----------
    learning_rate : float, default 0.001
        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.
    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, use_fused_step=False, **kwargs):
        super(SGLD, self).__init__(learning_rate=learning_rate,
                                   use_fused_step=use_fused_step,
                                   **kwargs)

    def create_state(self, index, weight):
        return None

    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 in zip(indices, weights, grads):
            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 weight
            weight[:] -= lr / 2 * grad
            weight[:] += normal(0, math.sqrt(lr), shape=weight.shape,
                                dtype=weight.dtype, ctx=weight.context)