`mx.nd.multi.mp.sgd.update`¶

Description¶

Update function for multi-precision Stochastic Gradient Descent (SDG) optimizer.

It updates the weights using:

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

Argument	Description
`data`	NDArray-or-Symbol[]. Weights
`lrs`	tuple of <float>, required. Learning rates.
`wds`	tuple of <float>, required. 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).
`num.weights`	int, optional, default=’1’. Number of updated weights.

out The result mx.ndarray