LinearLR

class torch.optim.lr_scheduler.LinearLR(optimizer, start_factor=0.3333333333333333, end_factor=1.0, total_iters=5, last_epoch=-1)

Decays the learning rate of each parameter group by linearly changing small multiplicative factor.

The multiplication is done until the number of epoch reaches a pre-defined milestone: total_iters. 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.

Parameters

• optimizer (Optimizer) – Wrapped optimizer.

• start_factor (float) – The number we multiply learning rate in the first epoch. The multiplication factor changes towards end_factor in the following epochs. Default: 1./3.

• end_factor (float) – The number we multiply learning rate at the end of linear changing process. Default: 1.0.

• total_iters (int) – The number of iterations that multiplicative factor reaches to 1. Default: 5.

• last_epoch (int) – The index of the last epoch. Default: -1.

Example

>>> # Assuming optimizer uses lr = 0.05 for all groups
>>> # lr = 0.003687  if epoch == 0
>>> # lr = 0.004875  if epoch == 1
>>> # lr = 0.006062  if epoch == 2
>>> # lr = 0.00725   if epoch == 3
>>> # ...
>>> # lr = 0.05      if epoch >= 40
>>> scheduler = LinearLR(optimizer, start_factor=0.05, total_iters=40)
>>> for epoch in range(100):
>>>     train(...)
>>>     validate(...)
>>>     scheduler.step()

get_last_lr()

Get the most recent learning rates computed by this scheduler.

Returns

A list of learning rates with entries for each of the optimizer’s param_groups, with the same types as their group["lr"]s.

Return type

list[float | Tensor]

Note

The returned Tensors are copies, and never alias the optimizer’s group["lr"]s.

get_lr()

Compute the next learning rate for each of the optimizer’s param_groups.

Scales the group["lr"]s in the optimizer’s param_groups such that successive steps interpolate linearly from start_factor up to end_factor across total_iters steps.

Returns

A list of learning rates for each of the optimizer’s param_groups with the same types as their current group["lr"]s.

Return type

list[float | Tensor]

Note

If you’re trying to inspect the most recent learning rate, use get_last_lr() instead.

Note

The returned Tensors are copies, and never alias the optimizer’s group["lr"]s.

load_state_dict(state_dict)

Load the scheduler’s state.

Parameters

state_dict (dict) – scheduler state. Should be an object returned from a call to state_dict().

state_dict()

Return the state of the scheduler as a dict.

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

Return type

dict[str, Any]

step(epoch=None)

Step the scheduler.

Parameters

epoch (int, optional) –

Deprecated since version 1.4: If provided, sets last_epoch to epoch and uses _get_closed_form_lr() if it is available. This is not universally supported. Use step() without arguments instead.

Note

Call this method after calling the optimizer’s step().