Table Of Contents
Table Of Contents


class mxnet.optimizer.AdaDelta(rho=0.9, epsilon=1e-05, **kwargs)[source]

The AdaDelta optimizer.

This class implements AdaDelta, an optimizer described in ADADELTA: An adaptive learning rate method, available at

This optimizer updates each weight by:

grad = clip(grad * rescale_grad + wd * weight, clip_gradient)
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 -= (delta + wd * weight)

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

  • rho (float) – Decay rate for both squared gradients and delta.
  • epsilon (float) – Small value to avoid division by 0.
__init__(rho=0.9, epsilon=1e-05, **kwargs)[source]

Initialize self. See help(type(self)) for accurate signature.


__init__([rho, epsilon]) Initialize self.
create_optimizer(name, **kwargs) Instantiates an optimizer with a given name and kwargs.
create_state(index, weight) Creates auxiliary state for a given weight.
create_state_multi_precision(index, weight) Creates auxiliary state for a given weight, including FP32 high precision copy if original weight is FP16.
register(klass) Registers a new optimizer.
set_learning_rate(lr) Sets a new learning rate of the optimizer.
set_lr_mult(args_lr_mult) Sets an individual learning rate multiplier for each parameter.
set_lr_scale(args_lrscale) [DEPRECATED] Sets lr scale.
set_wd_mult(args_wd_mult) Sets an individual weight decay multiplier for each parameter.
update(index, weight, grad, state) Updates the given parameter using the corresponding gradient and state.
update_multi_precision(index, weight, grad, …) Updates the given parameter using the corresponding gradient and state.