scvi.module.VAEC#
- class scvi.module.VAEC(n_input, n_labels=0, n_hidden=128, n_latent=5, n_layers=2, log_variational=True, ct_weight=None, dropout_rate=0.05, **module_kwargs)[source]#
Bases:
BaseModuleClass
Conditional Variational auto-encoder model.
This is an implementation of the CondSCVI model
- Parameters:
n_input (int) – Number of input genes
n_labels (int) – Number of labels
n_hidden (Tunable_[int]) – Number of nodes per hidden layer
n_latent (Tunable_[int]) – Dimensionality of the latent space
n_layers (Tunable_[int]) – Number of hidden layers used for encoder and decoder NNs
dropout_rate (Tunable_[float]) – Dropout rate for the encoder and decoder neural network
log_variational (bool) – Log(data+1) prior to encoding for numerical stability. Not normalization.
ct_weight (ndarray) –
Attributes table#
Methods table#
|
Runs the generative model. |
|
High level inference method. |
|
Loss computation. |
|
Generate observation samples from the posterior predictive distribution. |
Attributes#
training
Methods#
generative
inference
- VAEC.inference(x, y, n_samples=1)[source]#
High level inference method.
Runs the inference (encoder) model.
loss
- VAEC.loss(tensors, inference_outputs, generative_outputs, kl_weight=1.0)[source]#
Loss computation.
- Parameters:
kl_weight (float) –
sample
- VAEC.sample(tensors, n_samples=1)[source]#
Generate observation samples from the posterior predictive distribution.
The posterior predictive distribution is written as \(p(\hat{x} \mid x)\).
- Parameters:
tensors – Tensors dict
n_samples – Number of required samples for each cell
- Returns:
x_new :
torch.Tensor
tensor with shape (n_cells, n_genes, n_samples)- Return type: