learning_rate_scheduler

allennlp.training.learning_rate_schedulers.learning_rate_scheduler

LearningRateScheduler¶

class LearningRateScheduler(Scheduler,  Registrable):
 | def __init__(
 |     self,
 |     optimizer: torch.optim.Optimizer,
 |     last_epoch: int = -1
 | ) -> None

get_values¶

class LearningRateScheduler(Scheduler,  Registrable):
 | ...
 | def get_values(self)

ConstantLearningRateScheduler¶

@LearningRateScheduler.register("constant")
class ConstantLearningRateScheduler(_PyTorchLearningRateSchedulerWrapper):
 | def __init__(self, optimizer: Optimizer, last_epoch: int = -1) -> None

Registered as a LearningRateScheduler with name "constant". The "optimizer" argument does not get an entry in a configuration file for the object.

Example¶

Config for using the ConstantLearningRateScheduler Learning Rate Scheduler.

{
    ...
   "trainer":{
        ...
        "learning_rate_scheduler": "constant",
        ...
   }
}

Note that you do NOT pass a optimizer key to the Learning rate scheduler.

ConstantWithWarmupLearningRateScheduler¶

@LearningRateScheduler.register("constant_with_warmup")
class ConstantWithWarmupLearningRateScheduler(_PyTorchLearningRateSchedulerWrapper):
 | def __init__(
 |     self,
 |     optimizer: Optimizer,
 |     num_warmup_steps: int,
 |     last_epoch: int = -1
 | ) -> None

Registered as a LearningRateScheduler with name "constant_with_warmup". The "optimizer" argument does not get an entry in a configuration file for the object.

Parameters¶

optimizer : torch.optim.Optimizer
This argument does not get an entry in a configuration file for the object.
num_warmup_steps : int
The number of steps to linearly increase the learning rate.

Example¶

Config for using the ConstantWithWarmupLearningRateScheduler Learning Rate Scheduler with num_warmup_steps set 100.

{
    ...
   "trainer":{
        ...
        "learning_rate_scheduler": {
            "type": "constant_with_warmup",
            "num_warmup_steps": 100
        },
        ...
   }
}

<a id="allennlp.training.learning_rate_schedulers.learning_rate_scheduler.CosineWithWarmupLearningRateScheduler"></a>

## CosineWithWarmupLearningRateScheduler

```python
@LearningRateScheduler.register("cosine_with_warmup")
class CosineWithWarmupLearningRateScheduler(_PyTorchLearningRateSchedulerWrapper):
 | def __init__(
 |     self,
 |     optimizer: Optimizer,
 |     num_warmup_steps: int,
 |     num_training_steps: int,
 |     num_cycles: float = 0.5,
 |     last_epoch: int = -1
 | ) -> None

Registered as a LearningRateScheduler with name "cosine_with_warmup". The "optimizer" argument does not get an entry in a configuration file for the object.

Parameters¶

optimizer : torch.optim.Optimizer
This argument does not get an entry in a configuration file for the object.
num_warmup_steps : int
The number of steps to linearly increase the learning rate.

Example¶

Config for using the CosineWithWarmupLearningRateScheduler Learning Rate Scheduler with num_warmup_steps set 100.

{
    ...
   "trainer":{
        ...
        "learning_rate_scheduler": {
            "type": "cosine_with_warmup",
            "num_warmup_steps": 100
        },
        ...
   }
}

<a id="allennlp.training.learning_rate_schedulers.learning_rate_scheduler.CosineHardRestartsWithWarmupLearningRateScheduler"></a>

## CosineHardRestartsWithWarmupLearningRateScheduler

```python
@LearningRateScheduler.register("cosine_hard_restarts_with_warmup")
class CosineHardRestartsWithWarmupLearningRateScheduler(_PyTorchLearningRateSchedulerWrapper):
 | def __init__(
 |     self,
 |     optimizer: Optimizer,
 |     num_warmup_steps: int,
 |     num_training_steps: int,
 |     num_cycles: int = 1,
 |     last_epoch: int = -1
 | ) -> None

Registered as a LearningRateScheduler with name "cosine_hard_restarts_with_warmup". The "optimizer" argument does not get an entry in a configuration file for the object.

Example¶

Config for using the CosineHardRestartsWithWarmupLearningRateScheduler Learning Rate Scheduler with num_warmup_steps set 100.

```json { ... "trainer":{ ... "learning_rate_scheduler": { "type": "cosine_hard_restarts_with_warmup", "num_warmup_steps": 100 }, ... } }