Decays the learning rate of each parameter group by gamma every step_size epochs. Notice that such decay can happen simultaneously with other changes to the learning rate from outside this scheduler. When last_epoch=-1, sets initial lr as lr.
Examples
if (torch_is_installed()) {
if (FALSE) { # \dontrun{
# Assuming optimizer uses lr = 0.05 for all groups
# lr = 0.05 if epoch < 30
# lr = 0.005 if 30 <= epoch < 60
# lr = 0.0005 if 60 <= epoch < 90
# ...
scheduler <- lr_step(optimizer, step_size = 30, gamma = 0.1)
for (epoch in 1:100) {
train(...)
validate(...)
scheduler$step()
}
} # }
}