optim_rmsprop {torch} | R Documentation |
RMSprop optimizer
Description
Proposed by G. Hinton in his course.
Usage
optim_rmsprop(
params,
lr = 0.01,
alpha = 0.99,
eps = 1e-08,
weight_decay = 0,
momentum = 0,
centered = FALSE
)
Arguments
params |
(iterable): iterable of parameters to optimize or list defining parameter groups |
lr |
(float, optional): learning rate (default: 1e-2) |
alpha |
(float, optional): smoothing constant (default: 0.99) |
eps |
(float, optional): term added to the denominator to improve numerical stability (default: 1e-8) |
weight_decay |
optional weight decay penalty. (default: 0) |
momentum |
(float, optional): momentum factor (default: 0) |
centered |
(bool, optional) : if |
Warning
If you need to move a model to GPU via $cuda()
, please do so before
constructing optimizers for it. Parameters of a model after $cuda()
will be different objects from those before the call. In general, you
should make sure that the objects pointed to by model parameters subject
to optimization remain the same over the whole lifecycle of optimizer
creation and usage.
Note
The centered version first appears in
Generating Sequences With Recurrent Neural Networks.
The implementation here takes the square root of the gradient average before
adding epsilon (note that TensorFlow interchanges these two operations). The effective
learning rate is thus \alpha/(\sqrt{v} + \epsilon)
where \alpha
is the scheduled learning rate and v
is the weighted moving average
of the squared gradient.
Update rule:
\theta_{t+1} = \theta_{t} - \frac{\eta }{\sqrt{{E[g^2]}_{t} + \epsilon}} * g_{t}