noam

allennlp.training.learning_rate_schedulers.noam

NoamLR¶

@LearningRateScheduler.register("noam")
class NoamLR(LearningRateScheduler):
 | def __init__(
 |     self,
 |     optimizer: torch.optim.Optimizer,
 |     model_size: int,
 |     warmup_steps: int,
 |     factor: float = 1.0,
 |     last_epoch: int = -1
 | ) -> None

Implements the Noam Learning rate schedule. This corresponds to increasing the learning rate linearly for the first warmup_steps training steps, and decreasing it thereafter proportionally to the inverse square root of the step number, scaled by the inverse square root of the dimensionality of the model. Time will tell if this is just madness or it's actually important.

The formula for learning rate when using NoamLR is:

lr= factor * ( model_size ** (-0.5) * min(step** (-0.5), step * warmup_steps ** (-1.5)) )

Registered as a LearningRateScheduler with name "noam".

Parameters¶

optimizer : torch.optim.Optimizer
This argument does not get an entry in a configuration file for the object.
model_size : int
The hidden size parameter which dominates the number of parameters in your model.
warmup_steps : int
The number of steps to linearly increase the learning rate.
factor : float, optional (default = 1.0)
The overall scale factor for the learning rate decay.

Example¶

Config for using NoamLR with a model size of 1024, warmup steps of 5, and factor of .25.

{
    ...
   "trainer":{
        ...
        "learning_rate_scheduler": {
            "type": "noam",
            "model_size": 1024,
            "warmup_steps":5,
            "factor":0.25
        },
        ...
   }
}

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

step¶

class NoamLR(LearningRateScheduler):
 | ...
 | def step(self, metric: float = None) -> None

step_batch¶

class NoamLR(LearningRateScheduler):
 | ...
 | def step_batch(self, batch_num_total: int = None) -> None

get_values¶

class NoamLR(LearningRateScheduler):
 | ...
 | def get_values(self)