### Usage

optim_adadelta(params, lr = 1, rho = 0.9, eps = 1e-06, weight_decay = 0)


### Arguments

 params (iterable): list of parameters to optimize or list defining parameter groups lr (float, optional): learning rate (default: 1e-3) rho (float, optional): coefficient used for computing a running average of squared gradients (default: 0.9) eps (float, optional): term added to the denominator to improve numerical stability (default: 1e-6) weight_decay (float, optional): weight decay (L2 penalty) (default: 0)

### 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

According to the original paper, decaying average of the squared gradients is computed as follows:

E[g^2]_{t} = ρ E[g^2]_{t- 1} + (1 - ρ){g_{t}}^2

RMS of previous squared gradients up to time t:

RMS[g_{t}] = √{E[g^2]_{t} + ε }

\begin{array}{ll} Δ θ_{t} = - \frac{RMS [Δ θ]_{t - 1} }{RMS[g]_{t}} θ_{t+1} = θ_{t} + Δ θ_{t} \end{array}

### Examples

if (torch_is_installed()) {
## Not run:
optimizer <- optim_adadelta(model$parameters, lr = 0.1) optimizer$zero_grad()
loss_fn(model(input), target)$backward() optimizer$step()

## End(Not run)
}


torch documentation built on Oct. 24, 2022, 5:08 p.m.