torch (version 0.2.1)

optim_rprop: Implements the resilient backpropagation algorithm.

Description

Proposed first in RPROP - A Fast Adaptive Learning Algorithm

Usage

optim_rprop(params, lr = 0.01, etas = c(0.5, 1.2), step_sizes = c(1e-06, 50))

Arguments

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))

Examples

# NOT RUN {
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()
# }
# NOT RUN {
}
# }