Table Of Contents
Table Of Contents

mx.symbol.sgd_update

Description

Update function for Stochastic Gradient Descent (SDG) optimizer.

It updates the weights using:

weight = weight - learning_rate * (gradient + wd * weight)

However, if gradient is of ``row_sparse`` storage type and ``lazy_update`` is True,

only the row slices whose indices appear in grad.indices are updated:

for row in gradient.indices:
weight[row] = weight[row] - learning_rate * (gradient[row] + wd * weight[row])

Usage

mx.symbol.sgd_update(...)

Arguments

Argument

Description

weight

NDArray-or-Symbol.

Weight

grad

NDArray-or-Symbol.

Gradient

lr

float, required.

Learning rate

wd

float, optional, default=0.

Weight decay augments the objective function with a regularization term that penalizes large weights. The penalty scales with the square of the magnitude of each weight.

rescale.grad

float, optional, default=1.

Rescale gradient to grad = rescale_grad*grad.

clip.gradient

float, optional, default=-1.

Clip gradient to the range of [-clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), -clip_gradient).

lazy.update

boolean, optional, default=1.

If true, lazy updates are applied if gradient’s stype is row_sparse.

name

string, optional.

Name of the resulting symbol.