scvi.train.TrainingPlanConfig
scvi.train.TrainingPlanConfig
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class scvi.train.TrainingPlanConfig(optimizer='Adam', optimizer_creator=None, lr=0.001, update_only_decoder=False, weight_decay=1e-06, eps=0.01, 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', lr_min=0.0, max_kl_weight=1.0, min_kl_weight=0.0, compile=False, compile_kwargs=None, on_step=False, on_epoch=True, loss_kwargs=<factory>)[source]
Config for TrainingPlan.
Attributes
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TrainingPlanConfig.compile:
bool = False
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TrainingPlanConfig.compile_kwargs:
dict | None = None
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TrainingPlanConfig.eps:
float = 0.01
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TrainingPlanConfig.lr:
float = 0.001
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TrainingPlanConfig.lr_factor:
float = 0.6
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TrainingPlanConfig.lr_min:
float = 0.0
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TrainingPlanConfig.lr_patience:
int = 30
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TrainingPlanConfig.lr_scheduler_metric:
Literal['elbo_validation', 'reconstruction_loss_validation', 'kl_local_validation'] = 'elbo_validation'
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TrainingPlanConfig.lr_threshold:
float = 0.0
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TrainingPlanConfig.max_kl_weight:
float = 1.0
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TrainingPlanConfig.min_kl_weight:
float = 0.0
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TrainingPlanConfig.n_epochs_kl_warmup:
int | None = 400
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TrainingPlanConfig.n_steps_kl_warmup:
int | None = None
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TrainingPlanConfig.on_epoch:
bool | None = True
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TrainingPlanConfig.on_step:
bool | None = False
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TrainingPlanConfig.optimizer:
Literal['Adam', 'AdamW', 'Custom'] = 'Adam'
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TrainingPlanConfig.optimizer_creator:
Callable[[Iterable[Tensor]], Optimizer] | None = None
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TrainingPlanConfig.reduce_lr_on_plateau:
bool = False
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TrainingPlanConfig.update_only_decoder:
bool = False
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TrainingPlanConfig.weight_decay:
float = 1e-06
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TrainingPlanConfig.loss_kwargs:
dict[str, Any]
