scvelo.tl.VELOVI.train

VELOVI.train(max_epochs=500, lr=0.01, weight_decay=0.01, use_gpu=None, accelerator='auto', devices='auto', train_size=0.9, validation_size=None, batch_size=256, early_stopping=True, gradient_clip_val=10, plan_kwargs=None, **trainer_kwargs)

Train the model.

Parameters

max_epochs : int, None

Number of passes through the dataset. If None, defaults to np.min([round((20000 / n_cells) * 400), 400])

lr : float

Learning rate for optimization

weight_decay : float

Weight decay for optimization

use_gpu : str, int, bool, None

Use default GPU if available (if None or True), or index of GPU to use (if int), or name of GPU (if str, e.g., ‘cuda:0’), or use CPU (if False).

train_size : float

Size of training set in the range [0.0, 1.0].

validation_size : float, None

Size of the test set. If None, defaults to 1 - train_size. If train_size + validation_size < 1, the remaining cells belong to a test set.

batch_size : int

Minibatch size to use during training.

early_stopping : bool

Perform early stopping. Additional arguments can be passed in **kwargs. See Trainer for further options.

gradient_clip_val : float

Val for gradient clipping

plan_kwargs : dict, None

Keyword args for TrainingPlan. Keyword arguments passed to train() will overwrite values present in plan_kwargs, when appropriate.

**trainer_kwargs

Other keyword args for Trainer.