Decoder(n_input, n_output[, n_cat_list, …])
|
Decodes data from latent space to data space |
DecoderSCVI(n_input, n_output[, n_cat_list, …])
|
Decodes data from latent space of n_input dimensions n_output dimensions using a fully-connected neural network of n_hidden layers. |
DecoderTOTALVI(n_input, n_output_genes, …)
|
Decodes data from latent space of n_input dimensions n_output dimensions using a linear decoder |
Encoder(n_input, n_output[, n_cat_list, …])
|
Encodes data of n_input dimensions into a latent space of n_output dimensions using a fully-connected neural network of n_hidden layers. |
EncoderTOTALVI(n_input, n_output[, …])
|
Encodes data of n_input dimensions into a latent space of n_output dimensions using a fully-connected neural network of n_hidden layers. |
FCLayers(n_in, n_out[, n_cat_list, …])
|
A helper class to build fully-connected layers for a neural network. |
LinearDecoderSCVI(n_input, n_output[, …])
|
|
ModuleList([modules])
|
Holds submodules in a list. |
MultiDecoder(n_input, n_output[, …])
|
|
MultiEncoder(n_heads, n_input_list, n_output)
|
|
Normal(loc, scale[, validate_args])
|
Creates a normal (also called Gaussian) distribution parameterized by loc and scale. |