scvi.module.VAEC

class scvi.module.VAEC(n_input, n_labels=0, n_hidden=128, n_latent=5, n_layers=2, dropout_rate=0.1, log_variational=True, ct_weight=None, **module_kwargs)[source]

Bases: scvi.module.base._base_module.BaseModuleClass

Conditional Variational auto-encoder model.

This is an implementation of the CondSCVI model

Parameters
n_input : intint

Number of input genes

n_labels : intint (default: 0)

Number of labels

n_hidden : intint (default: 128)

Number of nodes per hidden layer

n_latent : intint (default: 5)

Dimensionality of the latent space

n_layers : intint (default: 2)

Number of hidden layers used for encoder and decoder NNs

dropout_rate : floatfloat (default: 0.1)

Dropout rate for the encoder neural network

log_variational : boolbool (default: True)

Log(data+1) prior to encoding for numerical stability. Not normalization.

Methods

generative(z, library, y)

Runs the generative model.

inference(x, y[, n_samples])

High level inference method.

loss(tensors, inference_outputs, …[, …])

Compute the loss for a minibatch of data.

sample(tensors[, n_samples])

Generate observation samples from the posterior predictive distribution.