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.

lr_step(optimizer, step_size, gamma = 0.1, last_epoch = -1)

Arguments

optimizer

(Optimizer): Wrapped optimizer.

step_size

(int): Period of learning rate decay.

gamma

(float): Multiplicative factor of learning rate decay. Default: 0.1.

last_epoch

(int): The index of last epoch. Default: -1.

Examples

if (torch_is_installed()) { if (FALSE) { # 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() } } }