mxnet.optimizer.Adamax¶

class mxnet.optimizer.Adamax(learning_rate=0.002, beta1=0.9, beta2=0.999, **kwargs)[source]¶

The AdaMax optimizer.

It is a variant of Adam based on the infinity norm available at http://arxiv.org/abs/1412.6980 Section 7.

The optimizer updates the weight by:

grad = clip(grad * rescale_grad + wd * weight, clip_gradient)
m = beta1 * m_t + (1 - beta1) * grad
u = maximum(beta2 * u, abs(grad))
weight -= lr / (1 - beta1**t) * m / u

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.

__init__(learning_rate=0.002, beta1=0.9, beta2=0.999, **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`