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 ofn_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
Attributes table#
Methods table#
|
The forward computation for a single sample. |
Attributes#
training
Methods#
forward
- Encoder.forward(x, *cat_list)[source]#
The forward computation for a single sample.
Encodes the data into latent space using the encoder network
Generates a mean \( q_m \) and variance \( q_v \)
Samples a new value from an i.i.d. multivariate normal \( \sim Ne(q_m, \mathbf{I}q_v) \)
- Parameters:
- Returns:
3-tuple of
torch.Tensor
tensors of shape(n_latent,)
for mean and var, and sample