# MultiStepLR

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

# MultiStepLR

class tensorplay.optim.lr_scheduler.MultiStepLR(optimizer: [Optimizer](tensorplay.optim.optimizer.Optimizer.html#tensorplay.optim.optimizer.Optimizer), milestones: Iterable[[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#MultiStepLR)

Decays the learning rate of each parameter group by gamma once the number of epoch reaches one of the milestones.

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.

- milestones ([list](https://docs.python.org/3/library/stdtypes.html#list)) – List of epoch indices. Must be increasing.

- 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 < 80
>>> # lr = 0.0005   if epoch >= 80
>>> scheduler = MultiStepLR(optimizer, milestones=[30, 80], 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#MultiStepLR.get_lr)

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

If the current epoch is in milestones, decays the
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.MultiStepLR.get_last_lr) instead.

Note

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

Note

If the current epoch appears in milestones n times, we
scale by gamma to the power of n

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.MultiStepLR.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.MultiStepLR.step) without arguments
instead.

Note

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