scvi.nn.FCLayers

class scvi.nn.FCLayers(n_in, n_out, n_cat_list=None, n_layers=1, n_hidden=128, dropout_rate=0.1, use_batch_norm=True, use_layer_norm=False, use_activation=True, bias=True, inject_covariates=True, activation_fn=<class 'torch.nn.modules.activation.ReLU'>)

Bases: Module

A helper class to build fully-connected layers for a neural network.

Parameters:

n_in : int

The dimensionality of the input

n_out : int

The dimensionality of the output

n_cat_list : Iterable[int] | NoneOptional[Iterable[int]] (default: None)

A list containing, for each category of interest, the number of categories. Each category will be included using a one-hot encoding.

n_layers : int (default: 1)

The number of fully-connected hidden layers

n_hidden : int (default: 128)

The number of nodes per hidden layer

dropout_rate : float (default: 0.1)

Dropout rate to apply to each of the hidden layers

use_batch_norm : bool (default: True)

Whether to have BatchNorm layers or not

use_layer_norm : bool (default: False)

Whether to have LayerNorm layers or not

use_activation : bool (default: True)

Whether to have layer activation or not

bias : bool (default: True)

Whether to learn bias in linear layers or not

inject_covariates : bool (default: True)

Whether to inject covariates in each layer, or just the first (default).

activation_fn : Module (default: <class 'torch.nn.modules.activation.ReLU'>)

Which activation function to use

Attributes table

Methods table

forward(x, *cat_list)	Forward computation on x.
inject_into_layer(layer_num)	Helper to determine if covariates should be injected.
set_online_update_hooks([hook_first_layer])

Attributes

T_destination

FCLayers.T_destination

alias of TypeVar(‘T_destination’, bound=Mapping[str, Tensor])

alias of TypeVar(‘T_destination’, bound=Mapping[str, Tensor]) .. autoattribute:: FCLayers.T_destination dump_patches ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

FCLayers.dump_patches: bool = False

This allows better BC support for load_state_dict(). In state_dict(), the version number will be saved as in the attribute _metadata of the returned state dict, and thus pickled. _metadata is a dictionary with keys that follow the naming convention of state dict. See _load_from_state_dict on how to use this information in loading.

If new parameters/buffers are added/removed from a module, this number shall be bumped, and the module’s _load_from_state_dict method can compare the version number and do appropriate changes if the state dict is from before the change.

training

FCLayers.training: bool

Methods

forward

FCLayers.forward(x, *cat_list)

Forward computation on x.

Parameters:

x : torch.Tensor

tensor of values with shape (n_in,)

cat_list : int

list of category membership(s) for this sample

x

Returns:

py:class:torch.Tensor tensor of shape (n_out,)

inject_into_layer

FCLayers.inject_into_layer(layer_num)

Helper to determine if covariates should be injected.

Return type:

bool

set_online_update_hooks

FCLayers.set_online_update_hooks(hook_first_layer=True)