GIMVI.train(max_epochs=200, use_gpu=None, kappa=5, train_size=0.9, validation_size=None, batch_size=128, plan_kwargs=None, **kwargs)[source]

Train the model.

max_epochs : intint (default: 200)

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

use_gpu : str | int | bool | NoneUnion[str, int, bool, None] (default: 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).

kappa : intint (default: 5)

Scaling parameter for the discriminator loss.

train_size : floatfloat (default: 0.9)

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

validation_size : float | NoneOptional[float] (default: 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 : intint (default: 128)

Minibatch size to use during training.

plan_kwargs : dict | NoneOptional[dict] (default: None)

Keyword args for model-specific Pytorch Lightning task. Keyword arguments passed to train() will overwrite values present in plan_kwargs, when appropriate.


Other keyword args for Trainer.