Table Of Contents
Table Of Contents

mx.nd.signum.update

Description

SIGN momentUM (Signum) optimizer.

\[\begin{split} g_t = \nabla J(W_{t-1})\\ m_t = \beta m_{t-1} + (1 - \beta) g_t\\ W_t = W_{t-1} - \eta_t \text{sign}(m_t)\end{split}\]
It updates the weights using::

state = momentum * state + (1-momentum) * gradient weight = weight - learning_rate * sign(state)

Where the parameter momentum is the decay rate of momentum estimates at each epoch.

Note

  • sparse ndarray not supported for this optimizer yet.

Arguments

Argument

Description

weight

NDArray-or-Symbol.

Weight

grad

NDArray-or-Symbol.

Gradient

mom

NDArray-or-Symbol.

Momentum

lr

float, required.

Learning rate

momentum

float, optional, default=0.

The decay rate of momentum estimates at each epoch.

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).

wd.lh

float, optional, default=0.

The amount of weight decay that does not go into gradient/momentum calculationsotherwise do weight decay algorithmically only.