# LinearLR

Source: https://www.tensorplay.cn/docs/generated/tensorplay.optim.lr_scheduler.LinearLR.html

# LinearLR

class tensorplay.optim.lr_scheduler.LinearLR(optimizer: [Optimizer](tensorplay.optim.optimizer.Optimizer.html#tensorplay.optim.optimizer.Optimizer), start_factor: [float](https://docs.python.org/3/library/functions.html#float) = 0.3333333333333333, end_factor: [float](https://docs.python.org/3/library/functions.html#float) = 1.0, total_iters: [int](https://docs.python.org/3/library/functions.html#int) = 5, last_epoch: [int](https://docs.python.org/3/library/functions.html#int) = -1)[[source]](../_modules/tensorplay/optim/lr_scheduler.html#LinearLR)

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](tensorplay.optim.optimizer.Optimizer.html#tensorplay.optim.optimizer.Optimizer)) – Wrapped optimizer.

- start_factor ([float](https://docs.python.org/3/library/functions.html#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](https://docs.python.org/3/library/functions.html#float)) – The number we multiply learning rate at the end of linear changing process. Default: 1.0.

- total_iters ([int](https://docs.python.org/3/library/functions.html#int)) – The number of iterations that multiplicative factor reaches to 1. Default: 5.

- last_epoch ([int](https://docs.python.org/3/library/functions.html#int)) – The index of the last epoch. Default: -1.

Example

```
>>> # xdoctest: +SKIP
>>> # 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() → [list](https://docs.python.org/3/library/stdtypes.html#list)[[float](https://docs.python.org/3/library/functions.html#float) | TensorBase]

Get the most recent learning rates computed by this scheduler.

Returns:

A [list](https://docs.python.org/3/library/stdtypes.html#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](https://docs.python.org/3/library/stdtypes.html#list)[[float](https://docs.python.org/3/library/functions.html#float) | Tensor]

Note

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

get_lr() → [list](https://docs.python.org/3/library/stdtypes.html#list)[[float](https://docs.python.org/3/library/functions.html#float) | TensorBase][[source]](../_modules/tensorplay/optim/lr_scheduler.html#LinearLR.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](https://docs.python.org/3/library/stdtypes.html#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](https://docs.python.org/3/library/stdtypes.html#list)[[float](https://docs.python.org/3/library/functions.html#float) | Tensor]

Note

If you’re trying to inspect the most recent learning rate, use
[get_last_lr()](#tensorplay.optim.lr_scheduler.LinearLR.get_last_lr) instead.

Note

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

load_state_dict(state_dict: [dict](https://docs.python.org/3/library/stdtypes.html#dict)[[str](https://docs.python.org/3/library/stdtypes.html#str), [Any](https://docs.python.org/3/library/typing.html#typing.Any)]) → [None](https://docs.python.org/3/library/constants.html#None)

Load the scheduler’s state.

Parameters:

state_dict ([dict](https://docs.python.org/3/library/stdtypes.html#dict)) – scheduler state. Should be an object returned
from a call to [state_dict()](#tensorplay.optim.lr_scheduler.LinearLR.state_dict).

state_dict() → [dict](https://docs.python.org/3/library/stdtypes.html#dict)[[str](https://docs.python.org/3/library/stdtypes.html#str), [Any](https://docs.python.org/3/library/typing.html#typing.Any)]

Return the state of the scheduler as a [dict](https://docs.python.org/3/library/stdtypes.html#dict).

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

step(epoch: [int](https://docs.python.org/3/library/functions.html#int) | [None](https://docs.python.org/3/library/constants.html#None) = None) → [None](https://docs.python.org/3/library/constants.html#None)

Step the scheduler.

Parameters:

epoch ([int](https://docs.python.org/3/library/functions.html#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()](#tensorplay.optim.lr_scheduler.LinearLR.step) without arguments
instead.

Note

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