Table Of Contents
Table Of Contents

RMSProp

class mxnet.optimizer.RMSProp(learning_rate=0.001, gamma1=0.9, gamma2=0.9, epsilon=1e-08, centered=False, clip_weights=None, **kwargs)[source]

The RMSProp optimizer.

Two versions of RMSProp are implemented:

If centered=False, we follow http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf by Tieleman & Hinton, 2012. For details of the update algorithm see rmsprop_update.

If centered=True, we follow http://arxiv.org/pdf/1308.0850v5.pdf (38)-(45) by Alex Graves, 2013. For details of the update algorithm see rmspropalex_update.

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

Parameters:
  • gamma1 (float, optional) – A decay factor of moving average over past squared gradient.
  • gamma2 (float, optional) – A “momentum” factor. Only used if centered`=``True`.
  • epsilon (float, optional) – Small value to avoid division by 0.
  • centered (bool, optional) – Flag to control which version of RMSProp to use. True will use Graves’s version of RMSProp, False will use Tieleman & Hinton’s version of RMSProp.
  • clip_weights (float, optional) – Clips weights into range [-clip_weights, clip_weights].
__init__(learning_rate=0.001, gamma1=0.9, gamma2=0.9, epsilon=1e-08, centered=False, clip_weights=None, **kwargs)[source]

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

Methods

__init__([learning_rate, gamma1, gamma2, …]) 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