mxnet.optimizer.sgld¶
SGLD optimizer.
Classes
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Stochastic Gradient Riemannian Langevin Dynamics. |
- class mxnet.optimizer.sgld.SGLD(learning_rate=0.1, use_fused_step=False, **kwargs)[source]¶
Bases:
OptimizerStochastic Gradient Riemannian Langevin Dynamics.
This class implements the optimizer described in the paper Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex, available at https://papers.nips.cc/paper/4883-stochastic-gradient-riemannian-langevin-dynamics-on-the-probability-simplex.pdf.
- 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 inlr_scheduler. If None andlr_scheduleris also None, then it will be set to 0.01 by default.use_fused_step (bool, default False) – 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.
- 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().