gluonts.torch.model.tft.lightning_module module

class gluonts.torch.model.tft.lightning_module.TemporalFusionTransformerLightningModule(model_kwargs: dict, lr: float = 0.001, patience: int = 10, weight_decay: float = 0.0)

Bases: LightningModule

A pl.LightningModule class that can be used to train a TemporalFusionTransformerModel with PyTorch Lightning.

This is a thin layer around a (wrapped) TemporalFusionTransformerModel object, that exposes the methods to evaluate training and validation loss.

Parameters:

• model_kwargs – Keyword arguments to construct the TemporalFusionTransformerModel to be trained.

• lr – Learning rate.

• weight_decay – Weight decay regularization parameter.

• patience – Patience parameter for learning rate scheduler.

configure_optimizers()

Returns the optimizer to use.

forward(*args, **kwargs)

Same as torch.nn.Module.forward().

Parameters:

• *args – Whatever you decide to pass into the forward method.

• **kwargs – Keyword arguments are also possible.

Returns:

Your model’s output

training_step(batch, batch_idx: int)

Execute training step.

validation_step(batch, batch_idx: int)

Execute validation step.