for each update. Default: "False".
-[ Example ]-



data_loader = torch.utils.data.DataLoader(...)
optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9)
scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr=0.01, steps_per_epoch=len(data_loader), epochs=10)
for epoch in range(10):
    for batch in data_loader:
        train_batch(...)
        scheduler.step()



get_last_lr()
  Return last computed learning rate by current scheduler.

load_state_dict(state_dict)
  Loads the schedulers state.

  Parameters:
     **state_dict** (*dict*) -- scheduler state. Should be an
     object returned from a call to "state_dict()".

print_lr(is_verbose, group, lr, epoch=None)
  Display the current learning rate.

state_dict()
  Returns the state of the scheduler as a "dict".

  It contains an entry for every variable in self.__dict__ which
  is not the optimizer.
