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 TRUE, compute the centered RMSProp, the gradient is normalized by an estimation of its variance weight_decay (float, optional): weight decay (L2 penalty) (default: 0)

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:

$$\theta_{t+1} = \theta_{t} - \frac{\eta }{\sqrt{{E[g^2]}_{t} + \epsilon}} * g_{t}$$

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.