scvi.external.mrvi.MRVAE

scvi.external.mrvi.MRVAE#

scvi.external.mrvi.MRVAE[source]#: alias of TorchMRVAE

Attributes table#

training

Methods table#

`compute_h_from_x_eps`(x, sample_index, ...[, ...])	Compute normalized gene expression from observations using predefined eps
`generative`(z, library, batch_index, label_index)	Generative model.
`inference`(x, sample_index[, mc_samples, ...])	Run the recognition model.
`loss`(tensors, inference_outputs, ...[, ...])	Compute the loss function value.

Attributes#

MRVAE.training: bool#

Methods#

MRVAE.compute_h_from_x_eps(x, sample_index, batch_index, extra_eps, cf_sample=None, mc_samples=10)[source]#: Compute normalized gene expression from observations using predefined eps

MRVAE.generative(z, library, batch_index, label_index, transform_batch=None)[source]#

Generative model.

Return type:: dict[str, Tensor | Distribution]

MRVAE.inference(x, sample_index, mc_samples=None, cf_sample=None, use_mean=False)[source]#

Run the recognition model.

In the case of variational inference, this function will perform steps related to computing variational distribution parameters. In a VAE, this will involve running data through encoder networks.

This function should return a dictionary with str keys and Tensor values.

Return type:: dict[str, Tensor]

MRVAE.loss(tensors, inference_outputs, generative_outputs, kl_weight=1.0)[source]#

Compute the loss function value.

Return type:: LossOutput