`mx.symbol.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

mx.symbol.signum_update(...)

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.
`name`	string, optional. Name of the resulting symbol.

out The result mx.symbol