# scvi.nn.Encoder#

class scvi.nn.Encoder(n_input, n_output, n_cat_list=None, n_layers=1, n_hidden=128, dropout_rate=0.1, distribution='normal', var_eps=0.0001, var_activation=None, return_dist=False, **kwargs)[source]#

Bases: Module

Encode data of n_input dimensions into a latent space of n_output dimensions.

Uses a fully-connected neural network of n_hidden layers.

Parameters:
• n_input (int) – The dimensionality of the input (data space)

• n_output (int) – The dimensionality of the output (latent space)

• n_cat_list (Iterable[int]) – A list containing the number of categories for each category of interest. Each category will be included using a one-hot encoding

• n_layers (int) – The number of fully-connected hidden layers

• n_hidden (int) – The number of nodes per hidden layer

• dropout_rate (float) – Dropout rate to apply to each of the hidden layers

• distribution (str) – Distribution of z

• var_eps (float) – Minimum value for the variance; used for numerical stability

• var_activation (Optional[Callable]) – Callable used to ensure positivity of the variance. Defaults to torch.exp().

• return_dist (bool) – Return directly the distribution of z instead of its parameters.

• **kwargs – Keyword args for FCLayers

## Methods table#

 forward(x, *cat_list) The forward computation for a single sample.

## Attributes#

training

Encoder.training: bool#

## Methods#

forward

Encoder.forward(x, *cat_list)[source]#

The forward computation for a single sample.

1. Encodes the data into latent space using the encoder network

2. Generates a mean $$q_m$$ and variance $$q_v$$

3. Samples a new value from an i.i.d. multivariate normal $$\sim Ne(q_m, \mathbf{I}q_v)$$

Parameters:
• x (Tensor) – tensor with shape (n_input,)

• cat_list (int) – list of category membership(s) for this sample

Returns:

3-tuple of torch.Tensor tensors of shape (n_latent,) for mean and var, and sample