mxnet.optimizer.rmsprop

RMSProp optimizer.

Classes

RMSProp([learning_rate, rho, momentum, ...])

The RMSProp optimizer.
class mxnet.optimizer.rmsprop.RMSProp(learning_rate=0.001, rho=0.9, momentum=0.9, epsilon=1e-08, centered=False, clip_weights=None, use_fused_step=True, **kwargs)[source]

Bases: Optimizer

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:

  • learning_rate (float, default 0.001) – 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) – A decay factor of moving average over past squared gradient.

  • momentum (float, default 0.9) – Heavy ball momentum factor. Only used if centered`=``True`.

  • epsilon (float, default 1e-8) – Small value to avoid division by 0.

  • centered (bool, default False) –

    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].

  • use_fused_step (bool, default True) – 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

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

Perform a fused optimization step using gradients and states. Fused kernel is used for update.

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().

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().