Table Of Contents
Table Of Contents

Nadam

class mxnet.optimizer.Nadam(learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08, schedule_decay=0.004, **kwargs)[source]

The Nesterov Adam optimizer.

Much like Adam is essentially RMSprop with momentum, Nadam is Adam RMSprop with Nesterov momentum available at http://cs229.stanford.edu/proj2015/054_report.pdf.

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

Parameters:
  • beta1 (float, optional) – Exponential decay rate for the first moment estimates.
  • beta2 (float, optional) – Exponential decay rate for the second moment estimates.
  • epsilon (float, optional) – Small value to avoid division by 0.
  • schedule_decay (float, optional) – Exponential decay rate for the momentum schedule
__init__(learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08, schedule_decay=0.004, **kwargs)[source]

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

Methods

__init__([learning_rate, beta1, beta2, …]) 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.

Attributes

learning_rate
opt_registry