mxnet.optimizer.adadelta

AdaDelta optimizer.

Classes

AdaDelta([learning_rate, rho, epsilon, ...])

The AdaDelta optimizer.
class mxnet.optimizer.adadelta.AdaDelta(learning_rate=1.0, rho=0.9, epsilon=1e-06, use_fused_step=False, **kwargs)[source]

Bases: Optimizer

The AdaDelta optimizer.

This class implements AdaDelta, an optimizer described in ADADELTA: An adaptive learning rate method, available at https://arxiv.org/abs/1212.5701.

This optimizer updates each weight by:

grad = clip(grad * rescale_grad, clip_gradient) + wd * weight
acc_grad = rho * acc_grad + (1. - rho) * grad * grad
delta = sqrt(acc_delta + epsilon) / sqrt(acc_grad + epsilon) * grad
acc_delta = rho * acc_delta + (1. - rho) * delta * delta
weight -= learning_rate * delta

This optimizer accepts the following parameters in addition to those accepted by Optimizer.

Parameters:

  • learning_rate (float, default 1.0) – The initial learning rate. If None, the optimization will use the learning rate from lr_scheduler. If not None, it will overwrite the learning rate in lr_scheduler. If None and lr_scheduler is also None, then it will be set to 0.01 by default.

  • rho (float, default 0.9) – Decay rate for both squared gradients and delta.

  • epsilon (float, default 1e-6) – Small value to avoid division by 0.

  • use_fused_step (bool, default False) – Whether or not to use fused kernels for optimizer. When use_fused_step=False, step is called, otherwise, fused_step is called.

create_state(index, weight)[source]

Creates auxiliary state for a given weight.

Some optimizers require additional states, e.g. as momentum, in addition to gradients in order to update weights. This function creates state for a given weight which will be used in update. This function is called only once for each weight.

Parameters:

  • index (int) – An unique index to identify the weight.

  • weight (NDArray) – The weight.

Returns:

state – The state associated with the weight.

Return type:

any obj

step(indices, weights, grads, states)[source]

Perform an optimization step using gradients and states.

Parameters:

  • indices (list of int) – List of unique indices of the parameters into the individual learning rates and weight decays. Learning rates and weight decay may be set via set_lr_mult() and set_wd_mult(), respectively.

  • weights (list of NDArray) – List of parameters to be updated.

  • grads (list of NDArray) – List of gradients of the objective with respect to this parameter.

  • states (List of any obj) – List of state returned by create_state().