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)
optimizer | (Optimizer): Wrapped optimizer. |
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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. |
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() } } }