mx.nd.sgd.mom.update

Description

Momentum update function for Stochastic Gradient Descent (SGD) optimizer.

Momentum update has better convergence rates on neural networks. Mathematically it looks like below:

\[\begin{split}v_1 = \alpha * \nabla J(W_0)\\ v_t = \gamma v_{t-1} - \alpha * \nabla J(W_{t-1})\\ W_t = W_{t-1} + v_t\end{split}\]

It updates the weights using:

v = momentum * v - learning_rate * gradient
weight += v

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

However, if grad's storage type is ``row_sparse``, ``lazy_update`` is True and weight's storage
type is the same as momentum's storage type,

only the row slices whose indices appear in grad.indices are updated (for both weight and momentum):

for row in gradient.indices:
v[row] = momentum[row] * v[row] - learning_rate * gradient[row]
weight[row] += v[row]

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).

lazy.update

boolean, optional, default=1.

If true, lazy updates are applied if gradient’s stype is row_sparse and both weight and momentum have the same stype