TrainingPlan

A Pytorch lightning wrappper, defines the training/validation step, optimizers, and data loaders.

class DeltaTopic.nn.TrainingPlan.TrainingPlan(*args: Any, **kwargs: Any)

Bases: LightningModule

Lightning module task to train deltaTopic modules.

Parameters:

• module – A module instance from class BaseModuleClass.

• lr – Learning rate used for optimization.

• weight_decay – Weight decay used in optimizatoin.

• eps – eps used for optimization.

• optimizer – One of “Adam” (Adam), “AdamW” (AdamW).

• n_steps_kl_warmup – Number of training steps (minibatches) to scale weight on KL divergences from 0 to 1. Only activated when n_epochs_kl_warmup is set to None.

• n_epochs_kl_warmup – Number of epochs to scale weight on KL divergences from 0 to 1. Overrides n_steps_kl_warmup when both are not None.

• reduce_lr_on_plateau – Whether to monitor validation loss and reduce learning rate when validation set lr_scheduler_metric plateaus.

• lr_factor – Factor to reduce learning rate.

• lr_patience – Number of epochs with no improvement after which learning rate will be reduced.

• lr_threshold – Threshold for measuring the new optimum.

• lr_scheduler_metric – Which metric to track for learning rate reduction.

• lr_min – Minimum learning rate allowed

• **loss_kwargs – Keyword args to pass to the loss method of the module. kl_weight should not be passed here and is handled automatically.

forward(*args, **kwargs)

Passthrough to model.forward().

property kl_weight

Scaling factor on KL divergence during training.

property n_obs_training

Number of observations in the training set.

This will update the loss kwargs for loss rescaling.

validation_epoch_end(outputs)

Aggregate validation step information.