Implements the resilient backpropagation algorithm.
Proposed first in RPROP - A Fast Adaptive Learning Algorithm
optim_rprop(params, lr = 0.01, etas = c(0.5, 1.2), step_sizes = c(1e-06, 50))
params |
(iterable): iterable of parameters to optimize or lists defining parameter groups |
lr |
(float, optional): learning rate (default: 1e-2) |
etas |
(Tuple(float, float), optional): pair of (etaminus, etaplis), that are multiplicative increase and decrease factors (default: (0.5, 1.2)) |
step_sizes |
(vector(float, float), optional): a pair of minimal and maximal allowed step sizes (default: (1e-6, 50)) |
if (torch_is_installed()) {
## Not run:
optimizer <- optim_rprop(model$parameters(), lr=0.1)
optimizer$zero_grad()
loss_fn(model(input), target)$backward()
optimizer$step()
## End(Not run)
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