# StepLR

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

# StepLR

class tensorplay.optim.lr_scheduler.StepLR(optimizer: [Optimizer](tensorplay.optim.optimizer.Optimizer.html#tensorplay.optim.optimizer.Optimizer), step_size: [int](https://docs.python.org/3/library/functions.html#int), gamma: [float](https://docs.python.org/3/library/functions.html#float) = 0.1, last_epoch: [int](https://docs.python.org/3/library/functions.html#int) = -1)[[source]](../_modules/tensorplay/optim/lr_scheduler.html#StepLR)

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.

Parameters:

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

- step_size ([int](https://docs.python.org/3/library/functions.html#int)) – Period of learning rate decay.

- gamma ([float](https://docs.python.org/3/library/functions.html#float)) – Multiplicative factor of learning rate decay. Default: 0.1.

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

Example

```
>>> # xdoctest: +SKIP
>>> # 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 = StepLR(optimizer, step_size=30, gamma=0.1)
>>> for epoch in range(100):
>>>     train(...)
>>>     validate(...)
>>>     scheduler.step()
```

get_last_lr() &#x2192; [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() &#x2192; [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#StepLR.get_lr)

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

If the current epoch is a non-zero multiple of step_size, we
scale the current group["lr"]s in the optimizer’s
param_groups by gamma.

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.StepLR.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)]) &#x2192; [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.StepLR.state_dict).

state_dict() &#x2192; [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) &#x2192; [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.StepLR.step) without arguments
instead.

Note

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