scvi.train.SemiSupervisedTrainingPlan#
- class scvi.train.SemiSupervisedTrainingPlan(module, classification_ratio=50, lr=0.001, weight_decay=1e-06, n_steps_kl_warmup=None, n_epochs_kl_warmup=400, reduce_lr_on_plateau=False, lr_factor=0.6, lr_patience=30, lr_threshold=0.0, lr_scheduler_metric='elbo_validation', **loss_kwargs)[source]#
Bases:
TrainingPlan
Lightning module task for SemiSupervised Training.
- Parameters:
module (
BaseModuleClass
) – A module instance from classBaseModuleClass
.classification_ratio (
int
(default:50
)) – Weight of the classification_loss in loss functionlr – Learning rate used for optimization
Adam
.weight_decay – Weight decay used in
Adam
.n_steps_kl_warmup (
Optional
[int
] (default:None
)) – 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 (
Optional
[int
] (default:400
)) – 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 (
bool
(default:False
)) – Whether to monitor validation loss and reduce learning rate when validation set lr_scheduler_metric plateaus.lr_factor (
float
(default:0.6
)) – Factor to reduce learning rate.lr_patience (
int
(default:30
)) – Number of epochs with no improvement after which learning rate will be reduced.lr_threshold (
float
(default:0.0
)) – Threshold for measuring the new optimum.lr_scheduler_metric (
Literal
[‘elbo_validation’, ‘reconstruction_loss_validation’, ‘kl_local_validation’] (default:'elbo_validation'
)) – Which metric to track for learning rate reduction.**loss_kwargs – Keyword args to pass to the loss method of the module. kl_weight should not be passed here and is handled automatically.
Attributes table#
Methods table#
|
Training step for semi-supervised training. |
|
Validation step for semi-supervised training. |
Attributes#
training
Methods#
training_step
- SemiSupervisedTrainingPlan.training_step(batch, batch_idx, optimizer_idx=0)[source]#
Training step for semi-supervised training.
validation_step